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1-1s-0.448-1-1-1h-18c-0.552 0-1 0.448-1 1s0.448 1 1 1zM3 7h18c0.552 0 1-0.448 1-1s-0.448-1-1-1h-18c-0.552 0-1 0.448-1 1s0.448 1 1 1zM3 19h18c0.552 0 1-0.448 1-1s-0.448-1-1-1h-18c-0.552 0-1 0.448-1 1s0.448 1 1 1z"></path> </svg></span></span> </button> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </header> <div id="inner-wrap" class="wrap hfeed kt-clear"> <section role="banner" class="entry-hero page-hero-section entry-hero-layout-standard"> <div class="entry-hero-container-inner"> <div class="hero-section-overlay"></div> <div class="hero-container site-container"> <header class="entry-header page-title title-align-inherit title-tablet-align-inherit title-mobile-align-inherit"> <h1 class="entry-title">Demonstrations Track</h1> </header> </div> </div> </section> <div id="primary" class="content-area"> <div class="content-container site-container"> <main id="main" class="site-main" role="main"> <div class="content-wrap"> <article id="post-1412" class="entry content-bg single-entry post-1412 page type-page status-publish hentry"> <div class="entry-content-wrap"> <div class="entry-content single-content"> <h2 class="wp-block-heading">Accepted Papers List</h2> <div class="paper_list"><div class="article" primary-keywords="354" all-keywords="354,355,356,357"><div class="id">DM5680</div><div class="title">IMPsys: An Intelligent Mold Processing System for Smart Factory</div><div class="authors">Xueyi Zhou, Yohan Na, Minju Bang, Dong-Kyu Chae</div><div class="more" id="more-DM5680" onclick="document.getElementById('abs-DM5680').style.display='initial'; document.getElementById('less-DM5680').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5680" onclick="document.getElementById('abs-DM5680').style.display='none'; document.getElementById('more-DM5680').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5680">The explosive popularity of smart manufacturing has caught the attention of researchers in terms of intelligent mold processing and management. Machining mold components is a crucial step in the mold production process for many industries, which creates (e.g., cutting, drilling, and shaping a metal) the individual parts (e.g., core pins, ejector pins, cavities, slides, and lifters) that make up a mold used in manufacturing. We present IMPsys, an AI-based system that automatically explores machining jobs, infers their processing time and schedules them on machines, given numerous 3D modelling files of mold components. Our demo video can be found at: http://bit.ly/3EeKnyL.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Humans and AI -> HAI: Intelligent user interfaces</b> <br/>Computer Vision -> CV: 3D computer vision <br/>Humans and AI -> HAI: Human-AI collaboration <br/>Humans and AI -> HAI: Human-computer interaction <br/></div></div></div><div class="article" primary-keywords="358" all-keywords="358,359,360"><div class="id">DM5681</div><div class="title">Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery</div><div class="authors">Yanhao Zhang, Fanyi Wang, Weixuan Sun, Jingwen Su, Peng Liu, Yaqian Li, Xinjie Feng, Zhengxia Zou</div><div class="more" id="more-DM5681" onclick="document.getElementById('abs-DM5681').style.display='initial'; document.getElementById('less-DM5681').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5681" onclick="document.getElementById('abs-DM5681').style.display='none'; document.getElementById('more-DM5681').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5681">Image matting is a fundamental technique in visual understanding and has become one of the most significant capabilities in mobile phones. Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called "Matting Moments" that enables automatic image editing based on matting models in different scenarios. Coupled with accurate and refined matting subjects, our system provides visual element editing abilities and backend services for distribution and recommendation that respond to emotional expressions. Our system comprises three components: 1) photo content structuring, 2) data-driven matting engine, and 3) AIGC functions for generation, which automatically achieve diverse photo beautification in the gallery. This system offers a unified framework that guides consumers to obtain intelligent recommendations with beautifully generated contents, helping them enjoy the moments and memories of their present life.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Computer Vision -> CV: Applications</b> <br/>Computer Vision -> CV: Other <br/>Computer Vision -> CV: Segmentation <br/></div></div></div><div class="article" primary-keywords="361" all-keywords="361,358,360,362,363,364,365,366,367"><div class="id">DM5686</div><div class="title">VideoMaster: A Multimodal Micro Game Video Recreator</div><div class="authors">Yipeng Yu, Xiao Chen, Hui Zhan</div><div class="more" id="more-DM5686" onclick="document.getElementById('abs-DM5686').style.display='initial'; document.getElementById('less-DM5686').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5686" onclick="document.getElementById('abs-DM5686').style.display='none'; document.getElementById('more-DM5686').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5686">To free human from laborious video production, this paper proposes the building of VideoMaster, a multimodal system equipped with four capabilities: highlight extraction, video describing, video dubbing and video editing. It extracts interesting episodes from long game videos, generates subtitles for each episode, reads the subtitles through synthesized speech, and finally re-creates a better short video through video editing. Notably, VideoMaster takes a combination of deep learning and traditional computer vision techniques to extract highlights with fine-to-coarse labels, utilizes a novel framework named PCSG-v (probabilistic context sensitive grammar for video) for video description generation, and imitates a target speaker’s voice to read the description. To the best of our knowledge, VideoMaster is the first multimedia system that can automatically produce product-level micro-videos without heavy human annotation.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Multidisciplinary Topics and Applications -> MDA: Arts and creativity</b> <br/>Computer Vision -> CV: Applications <br/>Computer Vision -> CV: Segmentation <br/>Computer Vision -> CV: Video analysis and understanding 聽聽 <br/>Computer Vision -> CV: Vision and language聽 <br/>Machine Learning -> ML: Multi-modal learning <br/>Natural Language Processing -> NLP: Applications <br/>Natural Language Processing -> NLP: Language generation <br/>Natural Language Processing -> NLP: Speech <br/></div></div></div><div class="article" primary-keywords="368" all-keywords="368,369,370,371,357,354,372,365,373"><div class="id">DM5691</div><div class="title">SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning</div><div class="authors">Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf</div><div class="more" id="more-DM5691" onclick="document.getElementById('abs-DM5691').style.display='initial'; document.getElementById('less-DM5691').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5691" onclick="document.getElementById('abs-DM5691').style.display='none'; document.getElementById('more-DM5691').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5691">We present a novel recommendation system designed to provide real-time treatment strategies to therapists during psychotherapy sessions. Our system utilizes a turn-level rating mechanism that forecasts the therapeutic outcome by calculating a similarity score between the profound representation of a scoring inventory and the patient’s current spoken sentence. By transcribing and segmenting the continuous audio stream into patient and therapist turns, our system conducts immediate evaluation of their therapeutic working alliance. The resulting dialogue pairs, along with their computed working alliance ratings, are then utilized in a deep reinforcement learning recommendation system. In this system, the sessions are treated as users, while the topics are treated as items. To showcase the system’s effectiveness, we not only evaluate its performance using an existing dataset of psychotherapy sessions but also demonstrate its practicality through a web app. Through this demo, we aim to provide a tangible and engaging experience of our recommendation system in action.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Humans and AI -> HAI: Computational sustainability and human wellbeing</b> <br/>Data Mining -> DM: Recommender systems <br/>Humans and AI -> HAI: Applications <br/>Humans and AI -> HAI: Brain sciences <br/>Humans and AI -> HAI: Human-computer interaction <br/>Humans and AI -> HAI: Intelligent user interfaces <br/>Multidisciplinary Topics and Applications -> MDA: Health and medicine <br/>Natural Language Processing -> NLP: Applications <br/>Natural Language Processing -> NLP: Dialogue and interactive systems <br/></div></div></div><div class="article" primary-keywords="374" all-keywords="374,375,376,377,357,378"><div class="id">DM5695</div><div class="title">Latent Inspector: An Interactive Tool for Probing Neural Network Behaviors Through Arbitrary Latent Activation</div><div class="authors">Daniel Gei脽ler, Bo Zhou, Paul Lukowicz</div><div class="more" id="more-DM5695" onclick="document.getElementById('abs-DM5695').style.display='initial'; document.getElementById('less-DM5695').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5695" onclick="document.getElementById('abs-DM5695').style.display='none'; document.getElementById('more-DM5695').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5695">This work presents an active software instrument allowing deep learning architects to interactively inspect neural network models’ output behavior from user-manipulated values in any latent layer. Latent Inspector offers multiple dimension reduction techniques to visualize the model’s high dimensional latent layer output in human-perceptible, two-dimensional plots. The system is implemented with Node.js front end for interactive user input and Python back end for interacting with the model. By utilizing a general and modular architecture, our proposed solution dynamically adapts to a versatile range of models and data structures. Compared to already existing tools, our asynchronous approach of separating the training process from the inspection offers additional possibilities, such as interactive data generation, by actively working with the model instead of visualizing training logs. Overall, Latent Inspector demonstrates the possibilities as well as the appearing limits for providing a generalized, tool-based concept for enhancing model insight in terms of explainable and transparent AI.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Machine Learning -> ML: Explainable/Interpretable machine learning</b> <br/>AI Ethics, Trust, Fairness -> ETF: Trustworthy AI <br/>Data Mining -> DM: Data visualization <br/>Data Mining -> DM: Exploratory data mining <br/>Humans and AI -> HAI: Human-computer interaction <br/>Machine Learning -> ML: Feature extraction, selection and dimensionality reduction <br/></div></div></div><div class="article" primary-keywords="379" all-keywords="379,380"><div class="id">DM5696</div><div class="title">Automated Planning for Generating and Simulating Traffic Signal Strategies</div><div class="authors">Saumya Bhatnagar, Rongge Guo, Keith McCabe, Thomas McCluskey, Francesco Percassi, Mauro Vallati</div><div class="more" id="more-DM5696" onclick="document.getElementById('abs-DM5696').style.display='initial'; document.getElementById('less-DM5696').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5696" onclick="document.getElementById('abs-DM5696').style.display='none'; document.getElementById('more-DM5696').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5696">There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches. In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Planning and Scheduling -> PS: Applications</b> <br/>Planning and Scheduling -> PS: Mixed discrete/continuous planning <br/></div></div></div><div class="article" primary-keywords="381" all-keywords="381,382,383,384"><div class="id">DM5703</div><div class="title">Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion</div><div class="authors">Enrique Tom谩s Mart铆nez Beltr谩n, Pedro Miguel S谩nchez S谩nchez, Sergio L贸pez Bernal, G茅r么me Bovet, Manuel Gil P茅rez, Gregorio Mart铆nez P茅rez, Alberto Huertas Celdr谩n</div><div class="more" id="more-DM5703" onclick="document.getElementById('abs-DM5703').style.display='initial'; document.getElementById('less-DM5703').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5703" onclick="document.getElementById('abs-DM5703').style.display='none'; document.getElementById('more-DM5703').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5703">This paper presents Fedstellar, a platform for training decentralized Federated Learning (FL) models in heterogeneous topologies in terms of the number of federation participants and their connections. Fedstellar allows users to build custom topologies, enabling them to control the aggregation of model parameters in a decentralized manner. The platform offers a Web application for creating, managing, and connecting nodes to ensure data privacy and provides tools to measure, monitor, and analyze the performance of the nodes. The paper describes the functionalities of Fedstellar and its potential applications. To demonstrate the applicability of the platform, different use cases are presented in which decentralized, semi-decentralized, and centralized architectures are compared in terms of model performance, convergence time, and network overhead when collaboratively classifying hand-written digits using the MNIST dataset.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Machine Learning -> ML: Federated learning</b> <br/>Machine Learning -> ML: Applications <br/>Machine Learning -> ML: Classification <br/>Machine Learning -> ML: Evaluation <br/></div></div></div><div class="article" primary-keywords="385" all-keywords="385"><div class="id">DM5705</div><div class="title">SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code</div><div class="authors">Ibrahim Abdelaziz, Julian Dolby, Udayan Khurana, Horst Samulowitz, Kavitha Srinivas</div><div class="more" id="more-DM5705" onclick="document.getElementById('abs-DM5705').style.display='initial'; document.getElementById('less-DM5705').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5705" onclick="document.getElementById('abs-DM5705').style.display='none'; document.getElementById('more-DM5705').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5705">Careful choice of feature transformations in a dataset can help predictive model performance, data understanding and data exploration. However, finding useful features is a challenge, and while recent Automated Machine Learning (AutoML) systems provide some limited automation for feature engineering or data exploration, it is still mostly done by humans. We demonstrate a system called SemFORMS (Semantic Transforms), which attempts to mine useful expressions for a dataset from access to a repository of code that may target the same dataset/similar dataset. In many enterprises, numerous data scientists often work on the same or similar datasets, but are largely unaware of each other’s work. SemFORMS finds appropriate code from such a repository, and normalizes the code to be an actionable transform that can prepended into any AutoML pipeline. We demonstrate SemFORMS operating over example datasets from the OpenML benchmarks where it sometimes leads to significant improvements in AutoML performance.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Machine Learning -> ML: Automated machine learning</b> <br/></div></div></div><div class="article" primary-keywords="361" all-keywords="361,386"><div class="id">DM5712</div><div class="title">LingGe: An Automatic Ancient Chinese Poem-to-Song Generation System</div><div class="authors">Yong Shan, Jinchao Zhang, Huiying Ren, Yao Qiu, Jie Zhou</div><div class="more" id="more-DM5712" onclick="document.getElementById('abs-DM5712').style.display='initial'; document.getElementById('less-DM5712').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5712" onclick="document.getElementById('abs-DM5712').style.display='none'; document.getElementById('more-DM5712').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5712">This paper presents a novel system, named LingGe ("浼舵瓕" in Chinese), to generate songs for ancient Chinese poems automatically. LingGe takes the poem as the lyric, composes music conditioned on the lyric, and finally outputs a full song including the singing and the accompaniment. It consists of four modules: rhythm recognition, melody generation, accompaniment generation, and audio synthesis. Firstly, the rhythm recognition module analyzes the song structure and rhythm according to the poem. Secondly, the melody generation module assembles the rhythm into the template and then generates the melody. Thirdly, the accompaniment generation module predicts the accompaniment in harmony with the melody. Finally, the audio synthesis module generates singing and accompaniment audio and then mixes them to obtain songs. The results show that LingGe can generate high-quality and expressive songs for ancient Chinese poems, both in harmony and rhythm.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Multidisciplinary Topics and Applications -> MDA: Arts and creativity</b> <br/>Multidisciplinary Topics and Applications -> MDA: Other <br/></div></div></div><div class="article" primary-keywords="387" all-keywords="387,388,374,389,390"><div class="id">DM5718</div><div class="title">Modeling the Impact of Policy Interventions for Sustainable Development</div><div class="authors">Sowmith Nandan Rachuri, Arpitha Malavalli, Niharika Sri Parasa, Pooja Bassin, Srinath Srinivasa</div><div class="more" id="more-DM5718" onclick="document.getElementById('abs-DM5718').style.display='initial'; document.getElementById('less-DM5718').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5718" onclick="document.getElementById('abs-DM5718').style.display='none'; document.getElementById('more-DM5718').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5718">There is an increasing demand to design policy interventions to achieve various targets specified by the UN Sustainable Development Goals by 2030. Designing interventions is a complex task given that the system may often respond in unexpected ways to a given intervention. This could be due to interventions towards a given target, affecting other unrelated variables, and/or interventions leading to acute disparities in nearby geographic areas. In order to address such issues, we propose a novel concept called Stress Modeling that analyzes the holistic impact of a policy intervention by taking into account the interactions within a system, after the intervention. The simulation is based on the postulate that complex systems of interacting entities tend to settle down into "low energy” configurations by minimizing differentials in capabilities of neighbouring entities. The simulation shows how policy impact percolates through geospatial boundaries over time and can be applied at any granularity. The theory and the corresponding package have been explained along with a case study analyzing a fertilizer policy in the Agro-climatic Zones of the state of Karnataka, India.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Multidisciplinary Topics and Applications -> MDA: Computational sustainability</b> <br/>AI Ethics, Trust, Fairness -> ETF: AI and law, governance, regulation <br/>Machine Learning -> ML: Explainable/Interpretable machine learning <br/>Multidisciplinary Topics and Applications -> MDA: Energy, environment and sustainability <br/>Uncertainty in AI -> UAI: Bayesian networks <br/></div></div></div><div class="article" primary-keywords="389" all-keywords="389,391,392,378"><div class="id">DM5719</div><div class="title">Optimized Crystallographic Graph Generation for Material Science</div><div class="authors">Astrid Klipfel, Ya毛l Fr茅gier, Adlane Sayede, Zied Bouraoui</div><div class="more" id="more-DM5719" onclick="document.getElementById('abs-DM5719').style.display='initial'; document.getElementById('less-DM5719').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5719" onclick="document.getElementById('abs-DM5719').style.display='none'; document.getElementById('more-DM5719').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5719">Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Multidisciplinary Topics and Applications -> MDA: Energy, environment and sustainability</b> <br/>Multidisciplinary Topics and Applications -> MDA: Life sciences <br/>Multidisciplinary Topics and Applications -> MDA: Physical sciences <br/>Machine Learning -> ML: Feature extraction, selection and dimensionality reduction <br/></div></div></div><div class="article" primary-keywords="393" all-keywords="393,365,395,397,396,398,394"><div class="id">DM5722</div><div class="title">mahaNLP: A Marathi Natural Language Processing Library</div><div class="authors">Vidula Magdum, Omkar Dhekane, Sharayu Hiwarkhedkar, Saloni Mittal, Raviraj Joshi</div><div class="more" id="more-DM5722" onclick="document.getElementById('abs-DM5722').style.display='initial'; document.getElementById('less-DM5722').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5722" onclick="document.getElementById('abs-DM5722').style.display='none'; document.getElementById('more-DM5722').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5722">We present mahaNLP, an open-source natural language processing (NLP) library specifically built for the Marathi language. It aims to enhance the support for the low-resource Indian language Marathi in the field of NLP. It is an easy-to-use, extensible and modular toolkit for Marathi text analysis built on state-of-the-art transformer models. In comparison to other existing Indic NLP libraries that support basic Marathi processing, this toolkit houses an extensive set of NLP tasks ranging from basic preprocessing tasks to advanced NLP tasks. Additionally, it provides functionality to load datasets for supervised tasks like Marathi sentiment analysis, NER, and Hate speech detection as data frames. This paper focuses on the overview of the mahaNLP framework, its features, and its usage. This work is a part of the L3Cube MahaNLP initiative, more information about it can be found at https://github.com/l3cube-pune/MarathiNLP and the demonstration video and file of mahaNLP are available at https://youtu.be/KxExcwCrTO0 and https://cutt.ly/f1FYQak respectively.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Natural Language Processing -> NLP: Tools</b> <br/>Natural Language Processing -> NLP: Applications <br/>Natural Language Processing -> NLP: Language models <br/>Natural Language Processing -> NLP: Sentiment analysis, stylistic analysis, and argument mining <br/>Natural Language Processing -> NLP: Named entities <br/>Natural Language Processing -> NLP: Text classification <br/>Natural Language Processing -> NLP: Information retrieval and text mining <br/></div></div></div><div class="article" primary-keywords="399" all-keywords="399,400,365,402"><div class="id">DM5728</div><div class="title">SiWare: Contextual Understanding of Industrial Data for Situational Awareness</div><div class="authors">Anuradha Bhamidipaty, Elham Khabiri, Bhavna Agrawal, Yingjie Li</div><div class="more" id="more-DM5728" onclick="document.getElementById('abs-DM5728').style.display='initial'; document.getElementById('less-DM5728').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5728" onclick="document.getElementById('abs-DM5728').style.display='none'; document.getElementById('more-DM5728').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5728">SiWare is an AI-powered Knowledge Discovery system, that helps unlock new insights and accelerates data-driven decisions with contextualized Industrial data. SiWare links and fuses heterogeneous data sources with an industry semantic model leveraging multiple AI capabilities to provide system-wide visibility into operational characteristics. As part of this demo paper, we describe the requirements for such a system, and deployment aspects, and demonstrate the benefits in two industrial scenarios.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Data Mining -> DM: Mining heterogenous data</b> <br/>Data Mining -> DM: Knowledge graphs and knowledge base completion <br/>Natural Language Processing -> NLP: Applications <br/>Knowledge Representation and Reasoning -> KRR: Applications <br/></div></div></div><div class="article" primary-keywords="401" all-keywords="401,402,403,404"><div class="id">DM5729</div><div class="title">NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs</div><div class="authors">Victor David, Raphael Fournier-S’niehotta, Nicolas Travers</div><div class="more" id="more-DM5729" onclick="document.getElementById('abs-DM5729').style.display='initial'; document.getElementById('less-DM5729').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5729" onclick="document.getElementById('abs-DM5729').style.display='none'; document.getElementById('more-DM5729').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5729">Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Knowledge Representation and Reasoning -> KRR: Reasoning about knowledge and belief</b> <br/>Knowledge Representation and Reasoning -> KRR: Applications <br/>Multidisciplinary Topics and Applications -> MDA: Databases <br/>Planning and Scheduling -> PS: Markov decisions processes <br/></div></div></div><div class="article" primary-keywords="376" all-keywords="376,405,406"><div class="id">DM5731</div><div class="title">Understanding the Night-Sky? Developing AI-Enabled System for Exploring Night-Light Usage Patterns</div><div class="authors">Jakob Hederich, Shreya Ghosh, Zeyu He, Prasenjit Mitra</div><div class="more" id="more-DM5731" onclick="document.getElementById('abs-DM5731').style.display='initial'; document.getElementById('less-DM5731').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5731" onclick="document.getElementById('abs-DM5731').style.display='none'; document.getElementById('more-DM5731').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5731">We present a demonstration of nighttime light pattern (NTL) analysis system. Our tool named NightVIEW is powered by an efficient system architecture to easily export and analyse a huge volume of spatial data (NTL), image segmentation and clustering algorithms to find unusual NTL patterns and identify hotspots of excess night light usage as well as finding semantics of cities.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Data Mining -> DM: Data visualization</b> <br/>Data Mining -> DM: Applications <br/>Data Mining -> DM: Mining spatial and/or temporal data <br/></div></div></div><div class="article" primary-keywords="361" all-keywords="361,386"><div class="id">DM5732</div><div class="title">Humming2Music: Being A Composer As Long As You Can Humming</div><div class="authors">Yao Qiu, Jinchao Zhang, Huiying Ren, Yong Shan, Jie Zhou</div><div class="more" id="more-DM5732" onclick="document.getElementById('abs-DM5732').style.display='initial'; document.getElementById('less-DM5732').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5732" onclick="document.getElementById('abs-DM5732').style.display='none'; document.getElementById('more-DM5732').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5732">Creating a piece of music is difficult for people who have never been trained to compose. We present an automatic music generation system to lower the threshold of creating music. The system takes the user’s humming as input and creates full music based on the humming melody. The system consists of five modules: 1) humming transcription, 2) melody generation, 3) broken chord generation, 4) accompaniment generation, and 5) audio synthesis. The first module transcribes the user’s humming audio to a score, and then the melody generation module composes a complete melody based on the user’s humming melody. After that, the third module will generate a broken chord track to accompany the full melody, and the fourth module will create more accompanying tracks. Finally, the audio synthesis module mixes all the tracks to generate the music. Through the user experiment, our system can generate high-quality music with natural expression based on the user’s humming input.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Multidisciplinary Topics and Applications -> MDA: Arts and creativity</b> <br/>Multidisciplinary Topics and Applications -> MDA: Other <br/></div></div></div><div class="article" primary-keywords="407" all-keywords="407,375"><div class="id">DM5735</div><div class="title">Bias On Demand: Investigating Bias with a Synthetic Data Generator</div><div class="authors">Joachim Baumann, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, Daniele Regoli</div><div class="more" id="more-DM5735" onclick="document.getElementById('abs-DM5735').style.display='initial'; document.getElementById('less-DM5735').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5735" onclick="document.getElementById('abs-DM5735').style.display='none'; document.getElementById('more-DM5735').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5735">Machine Learning (ML) systems are increasingly being adopted to make decisions that might have a significant impact on people’s lives. Because these decision-making systems rely on data-driven learning, the risk is that they will systematically propagate the bias embedded in the data. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced and possibly how to mitigate it. We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. We include a demo of our proposed synthetic data generator, in which we illustrate experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. We encourage readers to explore the full paper for a more detailed analysis.<div class="keywords"><div class="keywords-header">List of keywords </div><b>AI Ethics, Trust, Fairness -> ETF: Bias</b> <br/>AI Ethics, Trust, Fairness -> ETF: Trustworthy AI <br/></div></div></div><div class="article" primary-keywords="408" all-keywords="408,409,410"><div class="id">DM5739</div><div class="title">Practical Model Reductions for Verification of Multi-Agent Systems</div><div class="authors">Wojciech Jamroga, Yan Kim</div><div class="more" id="more-DM5739" onclick="document.getElementById('abs-DM5739').style.display='initial'; document.getElementById('less-DM5739').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5739" onclick="document.getElementById('abs-DM5739').style.display='none'; document.getElementById('more-DM5739').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5739">Formal verification of intelligent agents is often computationally infeasible due to state-space explosion. We present a tool for reducing the impact of the explosion by means of state abstraction that is (a) easy to use and understand by non-experts, and (b) agent-based in the sense that it operates on a modular representation of the system, rather than on its huge explicit state model.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Agent-based and Multi-agent Systems -> MAS: Engineering methods, platforms, languages and tools</b> <br/>Agent-based and Multi-agent Systems -> MAS: Applications <br/>Agent-based and Multi-agent Systems -> MAS: Formal verification, validation and synthesis <br/></div></div></div><div class="article" primary-keywords="378" all-keywords="378,389,412,413,414,383,411"><div class="id">DM5740</div><div class="title">A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets</div><div class="authors">Hannes Kath, Thiago S. Gouv锚a, Daniel Sonntag</div><div class="more" id="more-DM5740" onclick="document.getElementById('abs-DM5740').style.display='initial'; document.getElementById('less-DM5740').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5740" onclick="document.getElementById('abs-DM5740').style.display='none'; document.getElementById('more-DM5740').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5740">Deep learning methods are well suited for data analysis in several domains, but application is often limited by technical entry barriers and the availability of large annotated datasets. We present an interactive machine learning tool for annotating passive acoustic monitoring datasets created for wildlife monitoring, which are time-consuming and costly to annotate manually. The tool, designed as a web application, consists of an interactive user interface implementing a human-in-the-loop workflow. Class label annotations provided manually as bounding boxes drawn over a spectrogram are consumed by a deep generative model (DGM) that learns a low-dimensional representation of the input data, as well as the available class labels. The learned low-dimensional representation is displayed as an interactive interface element, where new bounding boxes can be efficiently generated by the user with lasso-selection; alternatively, the DGM can propose new, automatically generated bounding boxes on demand. The user can accept, edit, or reject annotations suggested by the model, thus owning final judgement. Generated annotations can be used to fine-tune the underlying model, thus closing the loop. Investigations of the prediction accuracy and first empirical experiments show promising results on an artificial data set, laying the ground for application to a real life scenario.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Machine Learning -> ML: Feature extraction, selection and dimensionality reduction</b> <br/>Multidisciplinary Topics and Applications -> MDA: Energy, environment and sustainability <br/>Machine Learning -> ML: Autoencoders <br/>Machine Learning -> ML: Incremental learning <br/>Machine Learning -> ML: Representation learning <br/>Machine Learning -> ML: Classification <br/>Machine Learning -> ML: Active learning <br/></div></div></div><div class="article" primary-keywords="415" all-keywords="415,385"><div class="id">DM5741</div><div class="title">AutoML for Outlier Detection with Optimal Transport Distances</div><div class="authors">Prabhant Singh, Joaquin Vanschoren</div><div class="more" id="more-DM5741" onclick="document.getElementById('abs-DM5741').style.display='initial'; document.getElementById('less-DM5741').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5741" onclick="document.getElementById('abs-DM5741').style.display='none'; document.getElementById('more-DM5741').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5741">Automated machine learning (AutoML) has been widely researched and adopted for supervised problems, but progress in unsupervised settings has been limited. We propose `"LOTUS", a novel framework to automate outlier detection based on meta-learning. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our framework and find that it outperforms all state-of-the-art automated outlier detection tools. This approach can also be easily generalized to automate other unsupervised settings.<div class="keywords"><div class="keywords-header">List of keywords </div><b>Data Mining -> DM: Anomaly/outlier detection</b> <br/>Machine Learning -> ML: Automated machine learning <br/></div></div></div><div class="article" primary-keywords="416" all-keywords="416,366"><div class="id">DM5742</div><div class="title">Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers</div><div class="authors">Vishal Pallagani, Bharath Muppasani, Biplav Srivastava, Francesca Rossi, Lior Horesh, Keerthiram Murugesan, Andrea Loreggia, Francesco Fabiano, Rony Joseph, Yathin Kethepalli</div><div class="more" id="more-DM5742" onclick="document.getElementById('abs-DM5742').style.display='initial'; document.getElementById('less-DM5742').style.display='inherit'; this.style.display='none'">[+] More </div><div class="less" id="less-DM5742" onclick="document.getElementById('abs-DM5742').style.display='none'; document.getElementById('more-DM5742').style.display='inherit'; this.style.display='none'">[-] Less </div><div class="abstract" id="abs-DM5742">Plansformer is a novel tool that utilizes a fine-tuned language model based on transformer architecture to generate symbolic plans. Transformers are a type of neural network architecture that have been shown to be highly effective in a range of natural language processing tasks. Unlike traditional planning systems that use heuristic-based search strategies, Plansformer is fine-tuned on specific classical planning domains to generate high-quality plans that are both fluent and feasible. Plansformer takes the domain and problem files as input (in PDDL) and outputs a sequence of actions that can be executed to solve the problem. We demonstrate the effectiveness of Plansformer on a variety of benchmark problems and provide both qualitative and quantitative results obtained during our evaluation, including its limitations. Plansformer has the potential to significantly improve the efficiency and effectiveness of planning in various domains, from logistics and scheduling to natural language processing and human-computer interaction. In addition, we provide public access to Plansformer via a website as well as an API endpoint; this enables other researchers to utilize our tool for planning and execution. The demo video is available at https://youtu.be/_1rlctCGsrk<div class="keywords"><div class="keywords-header">List of keywords </div><b>Planning and Scheduling -> PS: Learning in planning and scheduling</b> <br/>Natural Language Processing -> NLP: Language generation <br/></div></div></div></div> <script> function area_selected(element) { let allowed_values = element.value.split(","); let els = document.getElementsByClassName("article"); [].forEach.call(els, function (el) { var elid = el.getAttribute("primary-keywords"); if (allowed_values.includes(elid) || allowed_values == "all") { el.style.display = "flex"; } else { el.style.display = "none"; } }); } </script> </div> </div> </article> </div> </main> </div> </div> </div> <footer id="colophon" class="site-footer" role="contentinfo"> <div class="site-footer-wrap"> <div class="site-bottom-footer-wrap site-footer-row-container site-footer-focus-item site-footer-row-layout-standard site-footer-row-tablet-layout-default 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