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K. RAMAKRISHNAN</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/21</dd></dl><dl><dt><dd>INVITED PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024NTI0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024NTI0001/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (10.1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024NTP0004/_advpub_f"><span class="TEXT-TITLE">DGA-based Malware Communication Detection from DoH Traffic Using Hierarchical Machine Learning Analysis</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Rikima MITSUHASHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yong JIN</span>  <span id="skip_info" class="TEXT-AUTHOR">Katsuyoshi IIDA</span>  <span id="skip_info" class="TEXT-AUTHOR">Yoshiaki TAKAI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/21</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024NTP0004/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024NTP0004/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (3.8MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8059/_advpub_f"><span class="TEXT-TITLE">Exploiting Multi-Level Data Uncertainty for Japanese-Chinese Neural Machine Translation</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Zezhong LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Jianjun MA</span>  <span id="skip_info" class="TEXT-AUTHOR">Fuji REN</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/19</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8059/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8059/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (275.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0002/_advpub_f"><span class="TEXT-TITLE">Improving Sentiment Analysis with an Ensemble Transformers Model on Health Pandemic based on Twitter data</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Lorenzo Mamelona</span>  <span id="skip_info" class="TEXT-AUTHOR">TingHuai Ma</span>  <span id="skip_info" class="TEXT-AUTHOR">Jia Li</span>  <span id="skip_info" class="TEXT-AUTHOR">Bright Bediako-Kyeremeh</span>  <span id="skip_info" class="TEXT-AUTHOR">Benjamin Kwapong Osibo</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/19</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0002/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0002/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8061/_advpub_f"><span class="TEXT-TITLE">APW: Asymmetric Padded Winograd to reduce thread divergence for computational efficiency on SIMT architecture</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Wonho LEE</span>  <span id="skip_info" class="TEXT-AUTHOR">Jong Wook KWAK</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/14</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8061/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8061/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7022/_advpub_f"><span class="TEXT-TITLE">Effects of Numerical Method Selection on Fully-Pipelined FPGA Accelerators for Neural Simulations</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Xiaoxiao ZHOU</span>  <span id="skip_info" class="TEXT-AUTHOR">Yukinori SATO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/13</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7022/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7022/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.5MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7104/_advpub_f"><span class="TEXT-TITLE">A Text-to-Lyrics Generation Method Leveraging Image-based Semantics and Reducing Plagiarism Risk</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Kento WATANABE</span>  <span id="skip_info" class="TEXT-AUTHOR">Masataka GOTO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/13</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7104/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7104/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.6MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024HCP0002/_advpub_f"><span class="TEXT-TITLE">Multimodal Voice Activity Projection for Turn-taking and Effects on Speaker Adaptation</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Kazuyo ONISHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Hiroki TANAKA</span>  <span id="skip_info" class="TEXT-AUTHOR">Satoshi NAKAMURA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/13</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024HCP0002/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024HCP0002/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.6MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8073/_advpub_f"><span class="TEXT-TITLE">On a Perturbation Concept in Regular Interconnection Networks</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Takashi YOKOTA</span>  <span id="skip_info" class="TEXT-AUTHOR">Kanemitsu OOTSU</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/12</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8073/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8073/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (217.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0010/_advpub_f"><span class="TEXT-TITLE">Bayesian-Optimization-based auto Optical Mark array recognition for flexible paper answer sheet</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Chenbo SHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Wenxin SUN</span>  <span id="skip_info" class="TEXT-AUTHOR">Jie ZHANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Junsheng ZHANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Chun ZHANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Changsheng ZHU</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/12</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0010/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0010/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (3.9MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIL0001/_advpub_f"><span class="TEXT-TITLE">Selecting Source Code Generation Tools Based on Bandit Algorithms</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Masateru TSUNODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Ryoto SHIMA</span>  <span id="skip_info" class="TEXT-AUTHOR">Amjed TAHIR</span>  <span id="skip_info" class="TEXT-AUTHOR">Kwabena Ebo BENNIN</span>  <span id="skip_info" class="TEXT-AUTHOR">Akito MONDEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Koji TODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Keitaro NAKASAI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/11</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIL0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIL0001/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (130.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIL0002/_advpub_f"><span class="TEXT-TITLE">On the Application of Bandit Algorithm for Selecting Clone Detection Methods</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Masateru TSUNODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Takuto KUDO</span>  <span id="skip_info" class="TEXT-AUTHOR">Akito MONDEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Amjed TAHIR</span>  <span id="skip_info" class="TEXT-AUTHOR">Kwabena Ebo BENNIN</span>  <span id="skip_info" class="TEXT-AUTHOR">Koji TODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Keitaro NAKASAI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kenichi MATSUMOTO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/11</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIL0002/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIL0002/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7074/_advpub_f"><span class="TEXT-TITLE">Lightweight Neural Data Sequence Modeling by Scale Causal Blocks</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Hiroaki AKUTSU</span>  <span id="skip_info" class="TEXT-AUTHOR">Ko ARAI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/08</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7074/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7074/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.6MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7166/_advpub_f"><span class="TEXT-TITLE">Recaptured Image Detection Based on Multi-Scale Residual Features of Discriminative Regions</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Lanxi LIU</span>  <span id="skip_info" class="TEXT-AUTHOR">Pengpeng YANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Suwen DU</span>  <span id="skip_info" class="TEXT-AUTHOR">Sani M. ABDULLAHI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/08</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7166/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7166/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (5.9MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7204/_advpub_f"><span class="TEXT-TITLE">Learn Discriminative Features for Small Object Detection through Multi-scale Image Degradation with Contrastive Learning</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Xiaoguang TU</span>  <span id="skip_info" class="TEXT-AUTHOR">Zhi HE</span>  <span id="skip_info" class="TEXT-AUTHOR">Gui FU</span>  <span id="skip_info" class="TEXT-AUTHOR">Jianhua LIU</span>  <span id="skip_info" class="TEXT-AUTHOR">Mian ZHONG</span>  <span id="skip_info" class="TEXT-AUTHOR">Chao ZHOU</span>  <span id="skip_info" class="TEXT-AUTHOR">Xia LEI</span>  <span id="skip_info" class="TEXT-AUTHOR">Juhang YIN</span>  <span id="skip_info" class="TEXT-AUTHOR">Yi HUANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Yu WANG</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/05</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7204/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7204/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.4MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8054/_advpub_f"><span class="TEXT-TITLE">Joint Distribution-Aligned Dual-Sparse Linear Regression for Cross-Stimulus Speech-Based Depression Detection</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yingying LU</span>  <span id="skip_info" class="TEXT-AUTHOR">Cheng LU</span>  <span id="skip_info" class="TEXT-AUTHOR">Yuan ZONG</span>  <span id="skip_info" class="TEXT-AUTHOR">Feng ZHOU</span>  <span id="skip_info" class="TEXT-AUTHOR">Chuangao TANG</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/11/01</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8054/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8054/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (229.4KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7200/_advpub_f"><span class="TEXT-TITLE">Multi-grained Guaranteeable Requirement Analysis for Iterative Adaptation</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Jialong LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Takuto YAMAUCHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Takanori HIRANO</span>  <span id="skip_info" class="TEXT-AUTHOR">Jinyu CAI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kenji TEI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/31</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7200/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7200/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (567.6KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7276/_advpub_f"><span class="TEXT-TITLE">A fully digital transmitting-receiving platform for MIMO radar waveform diversity experiment</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Wei LEI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yue ZHANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Hanfeng XIE</span>  <span id="skip_info" class="TEXT-AUTHOR">Zebin CHEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Zengping CHEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Weixing LI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/30</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7276/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7276/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (7MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7194/_advpub_f"><span class="TEXT-TITLE">Leveraging Different Boolean Function Decompositions to Reduce T-Count in LUT-based Quantum Circuit Synthesis</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">David CLARINO</span>  <span id="skip_info" class="TEXT-AUTHOR">Naoya ASADA</span>  <span id="skip_info" class="TEXT-AUTHOR">Atsushi MATSUO</span>  <span id="skip_info" class="TEXT-AUTHOR">Shigeru YAMASHITA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/30</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7194/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7194/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.3MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7204/_advpub_f"><span class="TEXT-TITLE">Criticality and Tolerance in Injection Timing in Cup-Stacking Method for Collective Communication</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Takashi YOKOTA</span>  <span id="skip_info" class="TEXT-AUTHOR">Kanemitsu OOTSU</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/28</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7204/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7204/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.5MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7117/_advpub_f"><span class="TEXT-TITLE">An anchor-free Siamese tracker with multi-attention and corner detection mechanism</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Xiaokang Jin</span>  <span id="skip_info" class="TEXT-AUTHOR">Benben Huang</span>  <span id="skip_info" class="TEXT-AUTHOR">Hao Sheng</span>  <span id="skip_info" class="TEXT-AUTHOR">Yao Wu</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/28</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7117/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7117/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (3MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8030/_advpub_f"><span class="TEXT-TITLE">Effect of Politeness on Trust in Re-enter Requests to User by Smart Speaker -Pilot Study-</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Tomoki MIYAMOTO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/23</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8030/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8030/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2.2MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7108/_advpub_f"><span class="TEXT-TITLE">Fine-tuning Models for Final Disagreement Anticipation in Negotiation Mid-Dialogues</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Ken WATANABE</span>  <span id="skip_info" class="TEXT-AUTHOR">Katsuhide FUJITA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/10</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7108/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7108/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (3.8MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MUI0001/_advpub_f"><span class="TEXT-TITLE">Deepfake speech detection: approaches from acoustic features related to auditory perception to deep neural networks</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Masashi UNOKI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kai LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Anuwat CHAIWONGYEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Quoc-Huy NGUYEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Khalid ZAMAN</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/07</dd></dl><dl><dt><dd>INVITED PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MUI0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MUI0001/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (965KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MUP0003/_advpub_f"><span class="TEXT-TITLE">Video Watermarking Method Based on 3D U-Net Robust Against Re-shooting</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Takaharu TSUBOYAMA</span>  <span id="skip_info" class="TEXT-AUTHOR">Ryota TAKAHASHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Motoi IWATA</span>  <span id="skip_info" class="TEXT-AUTHOR">Koichi KISE</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/07</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MUP0003/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MUP0003/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (4MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7036/_advpub_f"><span class="TEXT-TITLE">UTStyleCap4K: Generating Image Captions with Sentimental Styles</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Chi ZHANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Li TAO</span>  <span id="skip_info" class="TEXT-AUTHOR">Toshihiko YAMASAKI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/02</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7036/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7036/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2.5MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8057/_advpub_f"><span class="TEXT-TITLE">FP-GNN: A Graph Neural Network for Hardware Trojan Detection in Gate-Level Netlist</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Ann Jelyn TIEMPO</span>  <span id="skip_info" class="TEXT-AUTHOR">Yong-Jin JEONG</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/10/01</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8057/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8057/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (532.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7056/_advpub_f"><span class="TEXT-TITLE">Adaptive Merge Candidate Selection based on Geometric Partitioning Mode beyond Versatile Video Coding</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Haruhisa KATO</span>  <span id="skip_info" class="TEXT-AUTHOR">Yoshitaka KIDANI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kei KAWAMURA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/24</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7056/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7056/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (4MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0005/_advpub_f"><span class="TEXT-TITLE">A Multi-Agent Deep Reinforcement Learning Algorithm for Task offloading in future 6G V2X Network</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Jiakun LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Jiajian LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yanjun SHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Hui LIAN</span>  <span id="skip_info" class="TEXT-AUTHOR">Haifan WU</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/24</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0005/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024IIP0005/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.2MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8060/_advpub_f"><span class="TEXT-TITLE">Dalio: In-Kernel Centralized Replication for Key-Value Stores</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Gyuyeong KIM</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/20</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8060/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8060/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (138.7KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7160/_advpub_f"><span class="TEXT-TITLE">Detecting Textual Backdoor Attacks via Class Difference for Text Classification System</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Hyun KWON</span>  <span id="skip_info" class="TEXT-AUTHOR">Jun LEE</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/19</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7160/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7160/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (680.5KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8067/_advpub_f"><span class="TEXT-TITLE">D2PT: Density to Point Transformer with Knowledge Distillation for Crowd Counting and Localization</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Fan LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Enze YANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Chao LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Shuoyan LIU</span>  <span id="skip_info" class="TEXT-AUTHOR">Haodong WANG</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/17</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8067/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8067/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.8MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7129/_advpub_f"><span class="TEXT-TITLE">Incremental learning for network traffic classification using generative adversarial networks</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Guangjin Ouyang</span>  <span id="skip_info" class="TEXT-AUTHOR">Yong Guo</span>  <span id="skip_info" class="TEXT-AUTHOR">Yu Lu</span>  <span id="skip_info" class="TEXT-AUTHOR">Fang He</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/13</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7129/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7129/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.3MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7049/_advpub_f"><span class="TEXT-TITLE">Multi-Scale Rail Surface Anomaly Detection Based on Weighted Multivariate Gaussian Distribution</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yuyao LIU</span>  <span id="skip_info" class="TEXT-AUTHOR">Qingyong LI</span>  <span id="skip_info" class="TEXT-AUTHOR">Shi BAO</span>  <span id="skip_info" class="TEXT-AUTHOR">Wen WANG</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/12</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7049/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7049/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (7.8MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8042/_advpub_f"><span class="TEXT-TITLE">BP-CRN: A Lightweight Two-Stage Convolutional Recurrent Network For Multi-channel Speech Enhancement</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Cong PANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Ye NI</span>  <span id="skip_info" class="TEXT-AUTHOR">Jia Ming CHENG</span>  <span id="skip_info" class="TEXT-AUTHOR">Lin ZHOU</span>  <span id="skip_info" class="TEXT-AUTHOR">Li ZHAO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/10</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8042/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8042/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2.6MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MPL0001/_advpub_f"><span class="TEXT-TITLE">Building Defect Prediction Models by Online Learning Considering Defect Overlooking</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Nikolay FEDOROV</span>  <span id="skip_info" class="TEXT-AUTHOR">Yuta YAMASAKI</span>  <span id="skip_info" class="TEXT-AUTHOR">Masateru TSUNODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Akito MONDEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Amjed TAHIR</span>  <span id="skip_info" class="TEXT-AUTHOR">Kwabena Ebo BENNIN</span>  <span id="skip_info" class="TEXT-AUTHOR">Koji TODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Keitaro NAKASAI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/09</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MPL0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MPL0001/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (96.5KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MPL0002/_advpub_f"><span class="TEXT-TITLE">The Impact of Defect (Re) Prediction on Software Testing</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yukasa MURAKAMI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yuta YAMASAKI</span>  <span id="skip_info" class="TEXT-AUTHOR">Masateru TSUNODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Akito MONDEN</span>  <span id="skip_info" class="TEXT-AUTHOR">Amjed TAHIR</span>  <span id="skip_info" class="TEXT-AUTHOR">Kwabena Ebo BENNIN</span>  <span id="skip_info" class="TEXT-AUTHOR">Koji TODA</span>  <span id="skip_info" class="TEXT-AUTHOR">Keitaro NAKASAI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/09</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MPL0002/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024MPL0002/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (148.4KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7229/_advpub_f"><span class="TEXT-TITLE">Deterministic and Probabilistic Certified Defenses for Content-Based Image Retrieval</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Kazuya KAKIZAKI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kazuto FUKUCHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Jun SAKUMA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/05</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7229/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7229/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (3.4MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7076/_advpub_f"><span class="TEXT-TITLE">Fault-tolerant Routing in Bicubes</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yitong WANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Htoo Htoo Sandi KYAW</span>  <span id="skip_info" class="TEXT-AUTHOR">Kunihiro FUJIYOSHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Keiichi KANEKO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/09/05</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7076/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7076/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (759.2KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7020/_advpub_f"><span class="TEXT-TITLE">Integrating Cyber-Physical Modeling for Pandemic Surveillance: A Graph-Based Approach for Disease Hotspot Prediction and Public Awareness</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Waqas NAWAZ</span>  <span id="skip_info" class="TEXT-AUTHOR">Muhammad UZAIR</span>  <span id="skip_info" class="TEXT-AUTHOR">Kifayat ULLAH KHAN</span>  <span id="skip_info" class="TEXT-AUTHOR">Iram FATIMA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/29</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7020/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7020/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2.2MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8048/_advpub_f"><span class="TEXT-TITLE">Real-time Interactions with Photos and Texts in Large Classrooms</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Haeyoung Lee</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/28</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8048/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8048/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (372.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8014/_advpub_f"><span class="TEXT-TITLE">CNN-based feature integration network for speech enhancement in microphone arrays</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Ji XI</span>  <span id="skip_info" class="TEXT-AUTHOR">Pengxu JIANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Yue XIE</span>  <span id="skip_info" class="TEXT-AUTHOR">Wei JIANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Hao DING</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/26</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8014/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8014/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7087/_advpub_f"><span class="TEXT-TITLE">Partial Enhancement and Channel Aggregation for Visible-Infrared Person Re-Identification</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Weiwei JING</span>  <span id="skip_info" class="TEXT-AUTHOR">Zhonghua LI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/26</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7087/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7087/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.4MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8046/_advpub_f"><span class="TEXT-TITLE">Practical APT Group Hash Unit Profiling Framework Using TTPs</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Sena LEE</span>  <span id="skip_info" class="TEXT-AUTHOR">Chaeyoung KIM</span>  <span id="skip_info" class="TEXT-AUTHOR">Hoorin PARK</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/20</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8046/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8046/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (716.5KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0008/_advpub_f"><span class="TEXT-TITLE">Bilaterally Colored Finite Automata and Bilaterally Colored Regular Expressions</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Akira ITO</span>  <span id="skip_info" class="TEXT-AUTHOR">Yoshiaki TAKAHASHI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/20</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0008/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0008/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (652.7KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0010/_advpub_f"><span class="TEXT-TITLE">Strategies and Equilibria on Indistinguishability of Winning Objectives and Related Decision Problems</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Rindo NAKANISHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yoshiaki TAKATA</span>  <span id="skip_info" class="TEXT-AUTHOR">Hiroyuki SEKI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/20</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0010/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0010/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.5MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0004/_advpub_f"><span class="TEXT-TITLE">Computational Complexity of Yajisan-Kazusan and Stained Glass</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Chuzo IWAMOTO</span>  <span id="skip_info" class="TEXT-AUTHOR">Ryo TAKAISHI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/16</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0004/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0004/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (682.2KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8047/_advpub_f"><span class="TEXT-TITLE">A clustering-based deep learning method for water level prediction</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Chih-Ping Wang</span>  <span id="skip_info" class="TEXT-AUTHOR">Duen-Ren Liu</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/14</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8047/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8047/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (778.9KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7139/_advpub_f"><span class="TEXT-TITLE">Stochastic Dual Coordinate Ascent for Learning Sign Constrained Linear Predictors</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yuya TAKADA</span>  <span id="skip_info" class="TEXT-AUTHOR">Rikuto MOCHIDA</span>  <span id="skip_info" class="TEXT-AUTHOR">Miya NAKAJIMA</span>  <span id="skip_info" class="TEXT-AUTHOR">Syun-suke KADOYA</span>  <span id="skip_info" class="TEXT-AUTHOR">Daisuke SANO</span>  <span id="skip_info" class="TEXT-AUTHOR">Tsuyoshi KATO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/08</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7139/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7139/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (483.5KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8020/_advpub_f"><span class="TEXT-TITLE">Multi-dimensional and Multi-task Facial Expression Recognition for Academic Outcomes Prediction</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yi Huo</span>  <span id="skip_info" class="TEXT-AUTHOR">Yun Ge</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/08</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8020/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8020/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (458.2KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8040/_advpub_f"><span class="TEXT-TITLE">Mixup SVM Learning for Compound Toxicity Prediction Using Human Pluripotent Stem Cells</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Rikuto MOCHIDA</span>  <span id="skip_info" class="TEXT-AUTHOR">Miya NAKAJIMA</span>  <span id="skip_info" class="TEXT-AUTHOR">Haruki ONO</span>  <span id="skip_info" class="TEXT-AUTHOR">Takahiro ANDO</span>  <span id="skip_info" class="TEXT-AUTHOR">Tsuyoshi KATO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/08</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8040/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8040/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (170.9KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0003/_advpub_f"><span class="TEXT-TITLE">A Bigram Based ILP Formulation for Break Minimization in Sports Scheduling Problems</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Koichi FUJII</span>  <span id="skip_info" class="TEXT-AUTHOR">Tomomi MATSUI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/08</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0003/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0003/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8021/_advpub_f"><span class="TEXT-TITLE">Dendritic Learning-based Feature Fusion for Deep Networks</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yaotong SONG</span>  <span id="skip_info" class="TEXT-AUTHOR">Zhipeng LIU</span>  <span id="skip_info" class="TEXT-AUTHOR">Zhiming ZHANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Jun TANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Zhenyu LEI</span>  <span id="skip_info" class="TEXT-AUTHOR">Shangce GAO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/07</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8021/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8021/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (289.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7033/_advpub_f"><span class="TEXT-TITLE">Applying Run-Length Compression to the Configuration Data of SLM Fine-Grained Reconfigurable Logic</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Souhei TAKAGI</span>  <span id="skip_info" class="TEXT-AUTHOR">Takuya KOJIMA</span>  <span id="skip_info" class="TEXT-AUTHOR">Hideharu AMANO</span>  <span id="skip_info" class="TEXT-AUTHOR">Morihiro KUGA</span>  <span id="skip_info" class="TEXT-AUTHOR">Masahiro IIDA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/07</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7033/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7033/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2.6MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0006/_advpub_f"><span class="TEXT-TITLE">Imperceptible Trojan Attacks to the Graph-based Big Data Processing in Smart Society</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Jun ZHOU</span>  <span id="skip_info" class="TEXT-AUTHOR">Masaaki KONDO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/07</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0006/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0006/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (825.5KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7092/_advpub_f"><span class="TEXT-TITLE">Feasibility Study of Applying Spatial Crowd Smoothing Without Economic Incentives on Ticket Reservation System that Applies Nudges</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Tetsuya MANABE</span>  <span id="skip_info" class="TEXT-AUTHOR">Wataru UNUMA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/05</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7092/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7092/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.3MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCL0002/_advpub_f"><span class="TEXT-TITLE">(15/14)<i>n</i> Flips are (almost) Sufficient to Sort Heydari and Sudborough's Pancake Stack</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Kazuyuki AMANO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/05</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCL0002/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCL0002/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (288.7KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0011/_advpub_f"><span class="TEXT-TITLE">Overlapping of Lattice Unfolding for Cuboids</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Takumi SHIOTA</span>  <span id="skip_info" class="TEXT-AUTHOR">Tonan KAMATA</span>  <span id="skip_info" class="TEXT-AUTHOR">Ryuhei UEHARA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/05</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0011/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0011/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (722.9KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0009/_advpub_f"><span class="TEXT-TITLE">An FPT Algorithm for the Exact Matching Problem and NP-hardness of Related Problems</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Hitoshi MURAKAMI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yutaro YAMAGUCHI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/08/01</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0009/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0009/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (704.5KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7015/_advpub_f"><span class="TEXT-TITLE">Recognition of Vibration Dampers Based on Deep Learning Method in UAV Images</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Jingjing Liu</span>  <span id="skip_info" class="TEXT-AUTHOR">Chuanyang Liu</span>  <span id="skip_info" class="TEXT-AUTHOR">Yiquan Wu</span>  <span id="skip_info" class="TEXT-AUTHOR">Zuo Sun</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/30</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7015/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7015/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (3.2MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8041/_advpub_f"><span class="TEXT-TITLE">Temporal correlation-based end-to-end rate control in DCVC</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Zhenglong YANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Weihao DENG</span>  <span id="skip_info" class="TEXT-AUTHOR">Guozhong WANG</span>  <span id="skip_info" class="TEXT-AUTHOR">Tao FAN</span>  <span id="skip_info" class="TEXT-AUTHOR">Yixi LUO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/29</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8041/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8041/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (574.4KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8058/_advpub_f"><span class="TEXT-TITLE">A Subclass of Mu-Calculus with the Freeze Quantifier Equivalent to Büchi Register Automata</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yoshiaki TAKATA</span>  <span id="skip_info" class="TEXT-AUTHOR">Akira ONISHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Ryoma SENDA</span>  <span id="skip_info" class="TEXT-AUTHOR">Hiroyuki SEKI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/26</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8058/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8058/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (137.6KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7016/_advpub_f"><span class="TEXT-TITLE">Degraded image classification using knowledge distillation and robust data augmentations</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Dinesh DAULTANI</span>  <span id="skip_info" class="TEXT-AUTHOR">Masayuki TANAKA</span>  <span id="skip_info" class="TEXT-AUTHOR">Masatoshi OKUTOMI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kazuki ENDO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/26</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7016/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7016/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (4.4MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0002/_advpub_f"><span class="TEXT-TITLE">Escape from the Room</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Kento KIMURA</span>  <span id="skip_info" class="TEXT-AUTHOR">Tomohiro HARAMIISHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kazuyuki AMANO</span>  <span id="skip_info" class="TEXT-AUTHOR">Shin-ichi NAKANO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/11</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0002/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0002/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0006/_advpub_f"><span class="TEXT-TITLE">Online combinatorial linear optimization via a Frank-Wolfe-based metarounding algorithm</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Ryotaro MITSUBOSHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Kohei HATANO</span>  <span id="skip_info" class="TEXT-AUTHOR">Eiji TAKIMOTO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/11</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0006/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0006/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.1MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0007/_advpub_f"><span class="TEXT-TITLE">A Flip-count-based Dynamic Temperature Control Method for Constrained Combinatorial Optimization by Parallel Annealing Algorithms</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Genta INOUE</span>  <span id="skip_info" class="TEXT-AUTHOR">Daiki OKONOGI</span>  <span id="skip_info" class="TEXT-AUTHOR">Satoru JIMBO</span>  <span id="skip_info" class="TEXT-AUTHOR">Thiem Van CHU</span>  <span id="skip_info" class="TEXT-AUTHOR">Masato MOTOMURA</span>  <span id="skip_info" class="TEXT-AUTHOR">Kazushi KAWAMURA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/11</dd></dl><dl><dt><dd>PAPER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0007/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0007/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.7MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PCL0001/_advpub_f"><span class="TEXT-TITLE">Performance evaluation of CAIN model frame interpolation using training data limited by fixed camera scene detection</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Hikaru USAMI</span>  <span id="skip_info" class="TEXT-AUTHOR">Yusuke KAMEDA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/07/11</dd></dl><dl><dt><dd>LETTER</dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PCL0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PCL0001/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (708.9KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0002/_advpub_f"><span class="TEXT-TITLE">Towards Superior Pruning Performance in Federated Learning with Discriminative Data</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Yinan YANG</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/06/27</dd></dl><dl><dt><dd></dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0002/_advpub_f" ><span 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href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0003/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PAP0003/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (2.5MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PCP0004/_advpub_f"><span class="TEXT-TITLE">HDR-VDA: A Full Stage Data Augmentation Method for HDR Video Reconstruction</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Fengshan ZHAO</span>  <span id="skip_info" class="TEXT-AUTHOR">Qin LIU</span>  <span id="skip_info" class="TEXT-AUTHOR">Takeshi IKENAGA</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/06/17</dd></dl><dl><dt><dd></dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PCP0004/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024PCP0004/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (1.2MB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0005/_advpub_f"><span class="TEXT-TITLE">Space-efficient FPT Algorithms for Degeneracy</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Naohito MATSUMOTO</span>  <span id="skip_info" class="TEXT-AUTHOR">Kazuhiro KURITA</span>  <span id="skip_info" class="TEXT-AUTHOR">Masashi KIYOMI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/05/31</dd></dl><dl><dt><dd></dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0005/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0005/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (101.3KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCL0001/_advpub_f"><span class="TEXT-TITLE">The Least Core of Routing Game Without Triangle Inequality</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Tomohiro KOBAYASHI</span>  <span id="skip_info" class="TEXT-AUTHOR">Tomomi MATSUI</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/05/30</dd></dl><dl><dt><dd></dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCL0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCL0001/_pdf_advpub" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info">Free </span><span id="skip_info">PDF (232.9KB) </span></a></li></ul></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0001/_advpub_f"><span class="TEXT-TITLE">Enumerating floorplans with Aligned Columns</span></a></h4><p><span id="skip_info" class="TEXT-AUTHOR">Shin-ichi NAKANO</span>  <br> <div class="data"> <dl><dt>Pubricized:</dt><dd>2024/05/30</dd></dl><dl><dt><dd></dd></dt></dl></div><div class="action"><ul><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024FCP0001/_advpub_f" ><span id="skip_info">Summary</span></a></li><li class="pdf"><a 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<h3>Volume E107-D No.11  (Publication Date:2024/11/01)</h3> <div class="list"> <ul> <span style="padding: 0.2em 0.2em; margin: 2em 0; background: #f5f5dc;" class="TEXT-SPL">Regular Section</span><hr noshade align="right" width="97%" size="1" color="#cccccc"><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7025/_f" ><span class="TEXT-TITLE">BiConvNet: Integrating Spatial Details and Deep Semantic Features in a Bilateral-Branch Image Segmentation Network</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Zhigang%20WU"><span id="skip_info" class="TEXT-AUTHOR">Zhigang WU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yaohui%20ZHU"><span id="skip_info" class="TEXT-AUTHOR">Yaohui ZHU</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>PAPER-Fundamentals of Information Systems</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/16</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1385-1395</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1385" id="hidden0"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7025/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7025/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (7.6MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7279/_f" ><span class="TEXT-TITLE">Aggregated to Pipelined Structure Based Streaming SSN for 1-ms Superpixel Segmentation System in Factory Automation</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Yuan%20LI"><span id="skip_info" class="TEXT-AUTHOR">Yuan LI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Tingting%20HU"><span id="skip_info" class="TEXT-AUTHOR">Tingting HU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Ryuji%20FUCHIKAMI"><span id="skip_info" class="TEXT-AUTHOR">Ryuji FUCHIKAMI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Takeshi%20IKENAGA"><span id="skip_info" class="TEXT-AUTHOR">Takeshi IKENAGA</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>PAPER-Computer System</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/23</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1396-1407</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1396" id="hidden1"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7279/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7279/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (10.3MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer’s aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-low latency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7019/_f" ><span class="TEXT-TITLE">Runtime Tests for Memory Error Handlers of In-Memory Key Value Stores Using MemFI</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Naoya%20NEZU"><span id="skip_info" class="TEXT-AUTHOR">Naoya NEZU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Hiroshi%20YAMADA"><span id="skip_info" class="TEXT-AUTHOR">Hiroshi YAMADA</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>PAPER-Software System</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/11</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1408-1421</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1408" id="hidden2"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7019/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7019/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (5.9MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">Modern memory devices such as DRAM are prone to errors that occur because of unintended bit flips during their operation. Since memory errors severely impact in-memory key-value stores (KVSes), software mechanisms for hardening them against memory errors are being explored. However, it is hard to efficiently test the memory error handling code due to its characteristics: the code is event-driven, the handlers depend on the memory object, and in-memory KVSes manage various objects in huge memory space. This paper presents <I>MemFI</I> that supports runtime tests for the memory error handlers of in-memory KVSes. Our approach performs the software fault injection of memory errors at the memory object level to trigger the target handler while smoothly carrying out tests on the same running state. To show the effectiveness of MemFI, we integrate error handling mechanisms into a real-world in-memory KVS, memcached 1.6.9 and Redis 6.2.7, and check their behavior using the MemFI prototypes. The results show that the MemFI-based runtime test allows us to check the behavior of the error handling mechanisms. We also show its efficiency by comparing it to other fault injection approaches based on a trial model.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7046/_f" ><span class="TEXT-TITLE">Multi-Focus Image Fusion Algorithm Based on Multi-Task Learning and PS-ViT</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Qinghua%20WU"><span id="skip_info" class="TEXT-AUTHOR">Qinghua WU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Weitong%20LI"><span id="skip_info" class="TEXT-AUTHOR">Weitong LI</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>PAPER-Image Recognition, Computer Vision</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/11</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1422-1432</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1422" id="hidden3"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7046/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7046/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (8.2MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7028/_f" ><span class="TEXT-TITLE">Ontology Matching and Repair Based on Semantic Association and Probabilistic Logic</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Nan%20WU"><span id="skip_info" class="TEXT-AUTHOR">Nan WU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Xiaocong%20LAI"><span id="skip_info" class="TEXT-AUTHOR">Xiaocong LAI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Mei%20CHEN"><span id="skip_info" class="TEXT-AUTHOR">Mei CHEN</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Ying%20PAN"><span id="skip_info" class="TEXT-AUTHOR">Ying PAN</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>PAPER-Natural Language Processing</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/11</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1433-1443</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1433" id="hidden4"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7028/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDP7028/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (935.8KB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8002/_f" ><span class="TEXT-TITLE">Measuring Mental Workload of Software Developers Based on Nasal Skin Temperature</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Keitaro%20NAKASAI"><span id="skip_info" class="TEXT-AUTHOR">Keitaro NAKASAI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Shin%20KOMEDA"><span id="skip_info" class="TEXT-AUTHOR">Shin KOMEDA</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Masateru%20TSUNODA"><span id="skip_info" class="TEXT-AUTHOR">Masateru TSUNODA</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Masayuki%20KASHIMA"><span id="skip_info" class="TEXT-AUTHOR">Masayuki KASHIMA</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Software Engineering</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/11</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1444-1448</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1444" id="hidden5"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8002/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8002/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (405.2KB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">To automatically measure the mental workload of developers, existing studies have used biometric measures such as brain waves and the heart rate. However, developers are often required to equip certain devices when measuring them, and can therefore be physically burdened. In this study, we evaluated the feasibility of non-contact biometric measures based on the nasal skin temperature (NST). In the experiment, the proposed biometric measures were more accurate than non-biometric measures.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8005/_f" ><span class="TEXT-TITLE">CLEAR & RETURN: Stopping Run-Time Countermeasures in Cryptographic Primitives</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Myung-Hyun%20KIM"><span id="skip_info" class="TEXT-AUTHOR">Myung-Hyun KIM</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Seungkwang%20LEE"><span id="skip_info" class="TEXT-AUTHOR">Seungkwang LEE</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Information Network</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/06/26</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1449-1452</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1449" id="hidden6"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8005/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8005/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (1.4MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">White-box cryptographic implementations often use masking and shuffling as countermeasures against key extraction attacks. To counter these defenses, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed. These methods aim to breach these countermeasures without needing reverse engineering. However, these non-invasive attacks are expensive and can be thwarted by updating the masking and shuffling techniques. This paper introduces a simple binary injection attack, aptly named <I>clear & return</I>, designed to bypass advanced masking and shuffling defenses employed in white-box cryptography. The attack involves injecting a small amount of assembly code, which effectively disables run-time random sources. This loss of randomness exposes the unprotected lookup value within white-box implementations, making them vulnerable to simple statistical analysis. In experiments targeting open-source white-box cryptographic implementations, the attack strategy of hijacking entries in the Global Offset Table (GOT) or function calls shows effectiveness in circumventing run-time countermeasures.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8084/_f" ><span class="TEXT-TITLE">Local Density Estimation Procedure for Autoregressive Modeling of Point Process Data</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Nat%20PAVASANT"><span id="skip_info" class="TEXT-AUTHOR">Nat PAVASANT</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Takashi%20MORITA"><span id="skip_info" class="TEXT-AUTHOR">Takashi MORITA</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Masayuki%20NUMAO"><span id="skip_info" class="TEXT-AUTHOR">Masayuki NUMAO</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Ken-ichi%20FUKUI"><span id="skip_info" class="TEXT-AUTHOR">Ken-ichi FUKUI</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Artificial Intelligence, Data Mining</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/11</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1453-1457</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1453" id="hidden7"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8084/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8084/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (220KB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8064/_f" ><span class="TEXT-TITLE">Loss Function for Deep Learning to Model Dynamical Systems</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Takahito%20YOSHIDA"><span id="skip_info" class="TEXT-AUTHOR">Takahito YOSHIDA</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Takaharu%20YAGUCHI"><span id="skip_info" class="TEXT-AUTHOR">Takaharu YAGUCHI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Takashi%20MATSUBARA"><span id="skip_info" class="TEXT-AUTHOR">Takashi MATSUBARA</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Artificial Intelligence, Data Mining</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/22</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1458-1462</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1458" id="hidden8"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8064/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8064/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (913KB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">Accurately simulating physical systems is essential in various fields. In recent years, deep learning has been used to automatically build models of such systems by learning from data. One such method is the neural ordinary differential equation (neural ODE), which treats the output of a neural network as the time derivative of the system states. However, while this and related methods have shown promise, their training strategies still require further development. Inspired by error analysis techniques in numerical analysis while replacing numerical errors with modeling errors, we propose the error-analytic strategy to address this issue. Therefore, our strategy can capture long-term errors and thus improve the accuracy of long-term predictions.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8034/_f" ><span class="TEXT-TITLE">Multimodal Speech Emotion Recognition Based on Large Language Model</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Congcong%20FANG"><span id="skip_info" class="TEXT-AUTHOR">Congcong FANG</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yun%20JIN"><span id="skip_info" class="TEXT-AUTHOR">Yun JIN</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Guanlin%20CHEN"><span id="skip_info" class="TEXT-AUTHOR">Guanlin CHEN</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yunfan%20ZHANG"><span id="skip_info" class="TEXT-AUTHOR">Yunfan ZHANG</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Shidang%20LI"><span id="skip_info" class="TEXT-AUTHOR">Shidang LI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yong%20MA"><span id="skip_info" class="TEXT-AUTHOR">Yong MA</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yue%20XIE"><span id="skip_info" class="TEXT-AUTHOR">Yue XIE</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Speech and Hearing</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/22</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1463-1467</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1463" id="hidden9"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8034/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8034/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (1.4MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8011/_f" ><span class="TEXT-TITLE">SH-YOLO: Small Target High Performance YOLO for Abnormal Behavior Detection in Escalator Scene</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Shuoyan%20LIU"><span id="skip_info" class="TEXT-AUTHOR">Shuoyan LIU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Chao%20LI"><span id="skip_info" class="TEXT-AUTHOR">Chao LI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yuxin%20LIU"><span id="skip_info" class="TEXT-AUTHOR">Yuxin LIU</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Yanqiu%20WANG"><span id="skip_info" class="TEXT-AUTHOR">Yanqiu WANG</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Image Recognition, Computer Vision</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/06/26</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1468-1471</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1468" id="hidden10"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8011/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8011/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (838.5KB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">Escalators are an indispensable facility in public places. While they can provide convenience to people, abnormal accidents can lead to serious consequences. Yolo is a function that detects human behavior in real time. However, the model exhibits low accuracy and a high miss rate for small targets. To this end, this paper proposes the Small Target High Performance YOLO (SH-YOLO) model to detect abnormal behavior in escalators. The SH-YOLO model first enhances the backbone network through attention mechanisms. Subsequently, a small target detection layer is incorporated in order to enhance detection of key points for small objects. Finally, the conv and the SPPF are replaced with a Region Dynamic Perception Depth Separable Conv (DR-DP-Conv) and Atrous Spatial Pyramid Pooling (ASPP), respectively. The experimental results demonstrate that the proposed model is capable of accurately and robustly detecting anomalies in the real-world escalator scene.</p> </div></div></li><li><h4><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8043/_f" ><span class="TEXT-TITLE">Vision Transformer with Key-Select Routing Attention for Single Image Dehazing</span></a> <span class="open_access">Open Access</span></h4><p><a href="https://global.ieice.org/en_transactions/Author/a_name=Lihan%20TONG"><span id="skip_info" class="TEXT-AUTHOR">Lihan TONG</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Weijia%20LI"><span id="skip_info" class="TEXT-AUTHOR">Weijia LI</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Qingxia%20YANG"><span id="skip_info" class="TEXT-AUTHOR">Qingxia YANG</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Liyuan%20CHEN"><span id="skip_info" class="TEXT-AUTHOR">Liyuan CHEN</a></span>  <a href="https://global.ieice.org/en_transactions/Author/a_name=Peng%20CHEN"><span id="skip_info" class="TEXT-AUTHOR">Peng CHEN</a></span>  <br> <div class="data"> <dl><dt> </dt><dd class='TEXT-COL' id='skip_info'>LETTER-Image Recognition, Computer Vision</dd></dl><br><dl> <dt class="TEXT-COL">  Pubricized:</dt><dd class="TEXT-COL">2024/07/01</dd></dl><dl class="TEXT-COL"><dt class='TEXT-COL'>  Page(s):</dt><dd class='TEXT-COL'>1472-1475</dd></dl></div><div class="action"><ul><li class="summary toggle"><a onClick="ga('send', 'event', 'summary', 'click', 'ED');"><span id="skip_info"><i class="fas fa-angle-down"></i></span></a></li> <input type="hidden" name="pid" value="e107-d_11_1472" id="hidden11"><li class="html"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8043/_f" onClick="ga('send', 'event', 'intro', 'click', 'ED');"><span id="skip_info">HTML</span></a></li><li class="pdf"><a href="https://global.ieice.org/en_transactions/information/10.1587/transinf.2024EDL8043/_pdf" onClick="ga('send', 'event', 'PDF', 'Down Load', 'ED');" target="_blank"><i class="fas fa-file-pdf"></i><span id="skip_info"><span id="skip_info">Free </span>PDF (11.8MB) </span></a></li></ul><div class="summary_txt"> <p class="TEXT-COL">We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.</p> </div></div></li><input type="hidden" name="request_uri" value="/en_transactions/information" id="set_request_uri"><input type="hidden" name="site_url" value="https://global.ieice.org/" id="set_site_url"> </ul> </div> <div class="pagination"> <ul> <li class="prev"><a href="https://global.ieice.org/en_transactions/information/E107-D_10"><span>Previous</span></a> </li> <li class="next"></li> </ul> </div> </section> </div> <div class="right_box">   <section class="latest_issue"> <h4 id="skip_info">Latest Issue</h4> <ul id="skip_info"> <li class="a"><a href="https://global.ieice.org/en_transactions/fundamentals">IEICE Trans. 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