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is-small 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/2408.04514">arXiv:2408.04514</a> <span> [<a href="https://arxiv.org/pdf/2408.04514">pdf</a>, <a href="https://arxiv.org/format/2408.04514">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Emergence in Multi-Agent Systems: A Safety Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Altmann%2C+P">Philipp Altmann</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%B6nberger%2C+J">Julian Sch枚nberger</a>, <a href="/search/cs?searchtype=author&query=Illium%2C+S">Steffen Illium</a>, <a href="/search/cs?searchtype=author&query=Zorn%2C+M">Maximilian Zorn</a>, <a href="/search/cs?searchtype=author&query=Ritz%2C+F">Fabian Ritz</a>, <a href="/search/cs?searchtype=author&query=Haider%2C+T">Tom Haider</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</a>, <a href="/search/cs?searchtype=author&query=Gabor%2C+T">Thomas Gabor</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="2408.04514v1-abstract-short" style="display: inline;"> Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior to catastrophic system failures. To formally define these effects, we identify misalignments between the global inherent specification (the true specification) and its local approximation (such as the configuration of d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04514v1-abstract-full').style.display = 'inline'; document.getElementById('2408.04514v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04514v1-abstract-full" style="display: none;"> Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior to catastrophic system failures. To formally define these effects, we identify misalignments between the global inherent specification (the true specification) and its local approximation (such as the configuration of different reward components or observations). Using established safety terminology, we develop a framework to understand these emergent effects. To showcase the resulting implications, we use two broadly configurable exemplary gridworld scenarios, where insufficient specification leads to unintended behavior deviations when derived independently. Recognizing that a global adaptation might not always be feasible, we propose adjusting the underlying parameterizations to mitigate these issues, thereby improving the system's alignment and reducing the risk of emergent failures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04514v1-abstract-full').style.display = 'none'; document.getElementById('2408.04514v1-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> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">18 pages, 3 figures, accepted for publication at the International Symposium on Leveraging Applications of Formal Methods (ISoLA 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.12114">arXiv:2403.12114</a> <span> [<a href="https://arxiv.org/pdf/2403.12114">pdf</a>, <a href="https://arxiv.org/format/2403.12114">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Safety Analysis of Autonomous Railway Systems: An Introduction to the SACRED Methodology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hunter%2C+J">Josh Hunter</a>, <a href="/search/cs?searchtype=author&query=McDermid%2C+J">John McDermid</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</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="2403.12114v1-abstract-short" style="display: inline;"> As the railway industry increasingly seeks to introduce autonomy and machine learning (ML), several questions arise. How can safety be assured for such systems and technologies? What is the applicability of current safety standards within this new technological landscape? What are the key metrics to classify a system as safe? Currently, safety analysis for the railway reflects the failure modes of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12114v1-abstract-full').style.display = 'inline'; document.getElementById('2403.12114v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.12114v1-abstract-full" style="display: none;"> As the railway industry increasingly seeks to introduce autonomy and machine learning (ML), several questions arise. How can safety be assured for such systems and technologies? What is the applicability of current safety standards within this new technological landscape? What are the key metrics to classify a system as safe? Currently, safety analysis for the railway reflects the failure modes of existing technology; in contrast, the primary concern of analysis of automation is typically average performance. Such purely statistical approaches to measuring ML performance are limited, as they may overlook classes of situations that may occur rarely but in which the function performs consistently poorly. To combat these difficulties we introduce SACRED, a safety methodology for producing an initial safety case and determining important safety metrics for autonomous systems. The development of SACRED is motivated by the proposed GoA-4 light-rail system in Berlin. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12114v1-abstract-full').style.display = 'none'; document.getElementById('2403.12114v1-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> 18 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">S. Bernardi, T. Zoppi (Editors), "Fast Abstracts and Student Forum Proceedings - EDCC 2024 - 19th European Dependable Computing Conference, Leuven, Belgium, 8-11 April 2024"</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.06069">arXiv:2308.06069</a> <span> [<a href="https://arxiv.org/pdf/2308.06069">pdf</a>, <a href="https://arxiv.org/format/2308.06069">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cheng%2C+C">Chih-Hong Cheng</a>, <a href="/search/cs?searchtype=author&query=Venkataramanan%2C+V+P">Venkatesh Prasad Venkataramanan</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+P+K">Pragya Kirti Gupta</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+Y">Yun-Fei Hsu</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</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="2308.06069v1-abstract-short" style="display: inline;"> We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfacti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.06069v1-abstract-full').style.display = 'inline'; document.getElementById('2308.06069v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.06069v1-abstract-full" style="display: none;"> We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfaction of the original safety requirements. The discretization enables advanced engineering methods such as synthesizing shields for safe reinforcement learning as well as formal verification, where for statistical model checking, the probabilistic guarantee acquired by LTL model checking forms a lower bound for the satisfaction of the original real-time safety requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.06069v1-abstract-full').style.display = 'none'; document.getElementById('2308.06069v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.11632">arXiv:2209.11632</a> <span> [<a href="https://arxiv.org/pdf/2209.11632">pdf</a>, <a href="https://arxiv.org/format/2209.11632">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Facilitating Change Implementation for Continuous ML-Safety Assurance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cheng%2C+C">Chih-Hong Cheng</a>, <a href="/search/cs?searchtype=author&query=Doan%2C+N+A+V">Nguyen Anh Vu Doan</a>, <a href="/search/cs?searchtype=author&query=Balu%2C+B">Balahari Balu</a>, <a href="/search/cs?searchtype=author&query=Schwaiger%2C+F">Franziska Schwaiger</a>, <a href="/search/cs?searchtype=author&query=Seferis%2C+E">Emmanouil Seferis</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</a>, <a href="/search/cs?searchtype=author&query=Qamsane%2C+Y">Yassine Qamsane</a>, <a href="/search/cs?searchtype=author&query=Shukla%2C+A">Ankit Shukla</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yinchong Yang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Z">Zhiliang Wu</a>, <a href="/search/cs?searchtype=author&query=Hapfelmeier%2C+A">Andreas Hapfelmeier</a>, <a href="/search/cs?searchtype=author&query=Thon%2C+I">Ingo Thon</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="2209.11632v1-abstract-short" style="display: inline;"> We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering process is desired. We associate semantic tags with the safety case argumentation and turn each piece of evidence into a quantitative metric or a logic formula. With proper tool support, the impact can be characterized by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11632v1-abstract-full').style.display = 'inline'; document.getElementById('2209.11632v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.11632v1-abstract-full" style="display: none;"> We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering process is desired. We associate semantic tags with the safety case argumentation and turn each piece of evidence into a quantitative metric or a logic formula. With proper tool support, the impact can be characterized by a query over the safety argumentation tree to highlight evidence turning invalid. The concept is exemplified using a vision-based emergency braking system of an autonomous guided vehicle for factory automation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11632v1-abstract-full').style.display = 'none'; document.getElementById('2209.11632v1-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> 23 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.05123">arXiv:2202.05123</a> <span> [<a href="https://arxiv.org/pdf/2202.05123">pdf</a>, <a href="https://arxiv.org/format/2202.05123">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">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schuster%2C+T">Tobias Schuster</a>, <a href="/search/cs?searchtype=author&query=Seferis%2C+E">Emmanouil Seferis</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+C">Chih-Hong Cheng</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="2202.05123v1-abstract-short" style="display: inline;"> In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: the prediction bounding box cannot be perfectly aligned with the ground truth, but the computed Intersection-over-Union metric is always larger than a given threshold. Under such type of performance limitation, we formally… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05123v1-abstract-full').style.display = 'inline'; document.getElementById('2202.05123v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.05123v1-abstract-full" style="display: none;"> In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: the prediction bounding box cannot be perfectly aligned with the ground truth, but the computed Intersection-over-Union metric is always larger than a given threshold. Under such type of performance limitation, we formally prove the minimum required bounding box enlargement factor to cover the ground truth. We then demonstrate that the factor can be mathematically adjusted to a smaller value, provided that the motion planner takes a fixed-length buffer in making its decisions. Finally, observing the difference between an empirically measured enlargement factor and our formally derived worst-case enlargement factor offers an interesting connection between the quantitative evidence (demonstrated by statistics) and the qualitative evidence (demonstrated by worst-case analysis). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05123v1-abstract-full').style.display = 'none'; document.getElementById('2202.05123v1-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> 10 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.10436">arXiv:2201.10436</a> <span> [<a href="https://arxiv.org/pdf/2201.10436">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Safe AI -- How is this Possible? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rue%C3%9F%2C+H">Harald Rue脽</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</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="2201.10436v2-abstract-short" style="display: inline;"> Ttraditional safety engineering is coming to a turning point moving from deterministic, non-evolving systems operating in well-defined contexts to increasingly autonomous and learning-enabled AI systems which are acting in largely unpredictable operating contexts. We outline some of underlying challenges of safe AI and suggest a rigorous engineering framework for minimizing uncertainty, thereby in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.10436v2-abstract-full').style.display = 'inline'; document.getElementById('2201.10436v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.10436v2-abstract-full" style="display: none;"> Ttraditional safety engineering is coming to a turning point moving from deterministic, non-evolving systems operating in well-defined contexts to increasingly autonomous and learning-enabled AI systems which are acting in largely unpredictable operating contexts. We outline some of underlying challenges of safe AI and suggest a rigorous engineering framework for minimizing uncertainty, thereby increasing confidence, up to tolerable levels, in the safe behavior of AI systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.10436v2-abstract-full').style.display = 'none'; document.getElementById('2201.10436v2-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </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">42 pages, 4 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.05451">arXiv:2201.05451</a> <span> [<a href="https://arxiv.org/pdf/2201.05451">pdf</a>, <a href="https://arxiv.org/format/2201.05451">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> A causal model of safety assurance for machine learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</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="2201.05451v3-abstract-short" style="display: inline;"> This paper proposes a framework based on a causal model of safety upon which effective safety assurance cases for ML-based applications can be built. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper defines four categories of safety case evidence and a structured analysis approach within which thes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.05451v3-abstract-full').style.display = 'inline'; document.getElementById('2201.05451v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.05451v3-abstract-full" style="display: none;"> This paper proposes a framework based on a causal model of safety upon which effective safety assurance cases for ML-based applications can be built. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper defines four categories of safety case evidence and a structured analysis approach within which these evidences can be effectively combined. Where appropriate, abstract formalisations of these contributions are used to illustrate the causalities they evaluate, their contributions to the safety argument and desirable properties of the evidences. Based on the proposed framework, progress in this area is re-evaluated and a set of future research directions proposed in order for tangible progress in this field to be made. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.05451v3-abstract-full').style.display = 'none'; document.getElementById('2201.05451v3-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> 9 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.02649">arXiv:2111.02649</a> <span> [<a href="https://arxiv.org/pdf/2111.02649">pdf</a>, <a href="https://arxiv.org/format/2111.02649">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</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"> Logically Sound Arguments for the Effectiveness of ML Safety Measures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cheng%2C+C">Chih-Hong Cheng</a>, <a href="/search/cs?searchtype=author&query=Schuster%2C+T">Tobias Schuster</a>, <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</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="2111.02649v2-abstract-short" style="display: inline;"> We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening leads to introducing a conservative post-processor after the standard non-max-su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.02649v2-abstract-full').style.display = 'inline'; document.getElementById('2111.02649v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.02649v2-abstract-full" style="display: none;"> We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening leads to introducing a conservative post-processor after the standard non-max-suppression as a counter-measure. We then propose a semi-formal assurance case for arguing the effectiveness of the post-processor, which is further translated into formal proof obligations for demonstrating the soundness of the arguments. Applying theorem proving not only discovers the need to introduce missing claims and mathematical concepts but also reveals the limitation of Dempster-Shafer's rules used in semi-formal argumentation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.02649v2-abstract-full').style.display = 'none'; document.getElementById('2111.02649v2-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> 10 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </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">v2: fix typos and change some phrases to make the context clear</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.11208">arXiv:2107.11208</a> <span> [<a href="https://arxiv.org/pdf/2107.11208">pdf</a>, <a href="https://arxiv.org/format/2107.11208">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Entropy, Derivation Operators and Huffman Trees </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Burton%2C+S">Simon Burton</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="2107.11208v1-abstract-short" style="display: inline;"> We build a theory of binary trees on finite multisets that categorifies, or operationalizes, the entropy of a finite probability distribution. Multisets operationalize probabilities as the event outcomes of an experiment. Huffman trees operationalize the entropy of the distribution of these events. We show how the derivation property of the entropy of a joint distribution lifts to Huffman trees. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.11208v1-abstract-full" style="display: none;"> We build a theory of binary trees on finite multisets that categorifies, or operationalizes, the entropy of a finite probability distribution. Multisets operationalize probabilities as the event outcomes of an experiment. Huffman trees operationalize the entropy of the distribution of these events. We show how the derivation property of the entropy of a joint distribution lifts to Huffman trees. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.11208v1-abstract-full').style.display = 'none'; document.getElementById('2107.11208v1-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> 23 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </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"> <li><a 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