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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.09175">arXiv:2411.09175</a> <span> [<a href="https://arxiv.org/pdf/2411.09175">pdf</a>, <a href="https://arxiv.org/format/2411.09175">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Hybrid deep additive neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+G+M">Gyu Min Kim</a>, <a href="/search/cs?searchtype=author&query=Jeon%2C+J+M">Jeong Min Jeon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.09175v1-abstract-short" style="display: inline;"> Traditional neural networks (multi-layer perceptrons) have become an important tool in data science due to their success across a wide range of tasks. However, their performance is sometimes unsatisfactory, and they often require a large number of parameters, primarily due to their reliance on the linear combination structure. Meanwhile, additive regression has been a popular alternative to linear… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09175v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09175v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09175v1-abstract-full" style="display: none;"> Traditional neural networks (multi-layer perceptrons) have become an important tool in data science due to their success across a wide range of tasks. However, their performance is sometimes unsatisfactory, and they often require a large number of parameters, primarily due to their reliance on the linear combination structure. Meanwhile, additive regression has been a popular alternative to linear regression in statistics. In this work, we introduce novel deep neural networks that incorporate the idea of additive regression. Our neural networks share architectural similarities with Kolmogorov-Arnold networks but are based on simpler yet flexible activation and basis functions. Additionally, we introduce several hybrid neural networks that combine this architecture with that of traditional neural networks. We derive their universal approximation properties and demonstrate their effectiveness through simulation studies and a real-data application. The numerical results indicate that our neural networks generally achieve better performance than traditional neural networks while using fewer parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09175v1-abstract-full').style.display = 'none'; document.getElementById('2411.09175v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 13 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07; 62J02 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14496">arXiv:2311.14496</a> <span> [<a href="https://arxiv.org/pdf/2311.14496">pdf</a>, <a href="https://arxiv.org/format/2311.14496">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> RTPS Attack Dataset Description </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D+Y">Dong Young Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D">Dongsung Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yuchan Song</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+G+M">Gang Min Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+M+G">Min Geun Song</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.14496v4-abstract-short" style="display: inline;"> This paper explains all about our RTPS datasets. We collect malicious/benign packet data by injecting attack data in an Unmanned Ground Vehicle (UGV) in the normal state. We assembled the testbed, consisting of UGV, Controller, PC, and Router. We collect this dataset in the UGV part of our testbed. We conducted two types of attack "Command Injection" and "Command Injection with ARP Spoofing" on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14496v4-abstract-full').style.display = 'inline'; document.getElementById('2311.14496v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14496v4-abstract-full" style="display: none;"> This paper explains all about our RTPS datasets. We collect malicious/benign packet data by injecting attack data in an Unmanned Ground Vehicle (UGV) in the normal state. We assembled the testbed, consisting of UGV, Controller, PC, and Router. We collect this dataset in the UGV part of our testbed. We conducted two types of attack "Command Injection" and "Command Injection with ARP Spoofing" on our testbed. The data collection time is 180, 300, 600, and 1200. The scenario has 30 each on collection time, 240 total. We expect this dataset to contribute to the development of defense technologies like anomaly detection to address security threat issues in ROS2 networks and Fast-DDS implements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14496v4-abstract-full').style.display = 'none'; document.getElementById('2311.14496v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This manuscript is written in Korean. You can download our dataset through our lab: https://ocslab.hksecurity.net/Datasets/rtps-attack-dataset We welcome your comments or feedback. Contact INFO: Dong Young Kim (klgh1256@korea.ac.kr), Huy Kang Kim (cenda@korea.ac.kr)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.07448">arXiv:2310.07448</a> <span> [<a href="https://arxiv.org/pdf/2310.07448">pdf</a>, <a href="https://arxiv.org/format/2310.07448">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> Faster Location in Combinatorial Interaction Testing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dougherty%2C+R+E">Ryan E. Dougherty</a>, <a href="/search/cs?searchtype=author&query=Green%2C+D+N">Dylan N. Green</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+G+M">Grace M. Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.07448v1-abstract-short" style="display: inline;"> Factors within a large-scale software system that simultaneously interact and strongly impact the system's response under a configuration are often difficult to identify. Although screening such a system for the existence of such interactions is important, determining their location is more useful for system engineers. Combinatorial interaction testing (CIT) concerns creation of test suites that n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07448v1-abstract-full').style.display = 'inline'; document.getElementById('2310.07448v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.07448v1-abstract-full" style="display: none;"> Factors within a large-scale software system that simultaneously interact and strongly impact the system's response under a configuration are often difficult to identify. Although screening such a system for the existence of such interactions is important, determining their location is more useful for system engineers. Combinatorial interaction testing (CIT) concerns creation of test suites that nonadaptively either detect or locate the desired interactions, each of at most a specified size or show that no such set exists. Under the assumption that there are at most a given number of such interactions causing such a response, locating arrays (LAs) guarantee unique location for every such set of interactions and an algorithm to deal with outliers and nondeterministic behavior from real systems, we additionally require the LAs to have a "separation" between these collections. State-of-the-art approaches generate LAs that can locate at most one interaction of size at most three, due to the massive number of interaction combinations for larger parameters if no constraints are given. This paper presents LocAG, a two-stage algorithm that generates (unconstrained) LAs using a simple, but powerful partitioning strategy of these combinations. In particular, we are able to generate LAs with more factors, with any desired separation, and greater interaction size than existing approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07448v1-abstract-full').style.display = 'none'; document.getElementById('2310.07448v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> 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