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36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021

作者： Luo, Yixing Zhang, Xiao-Yi Arcaini, Paolo Jin, Zhi Zhao, Haiyan Ishikawa, Fuyuki Wu, Rongxin Xie, Tao Peking University Ministry of Education Key Lab. of High-Confidence Software Technologies Beijing China Department of Computer Science and Technology School of EECS Peking University Beijing China National Institute of Informatics Tokyo Japan Xiamen University School of Informatics Xiamen China

ISBN: (纸本)9781665403375

Autonomous Driving Systems (ADSs) are complex systems that must satisfy multiple requirements such as safety, compliance to traffic rules, and comfortableness. However, satisfying all these requirements may not always be possible due to emerging environmental conditions. Therefore, the ADSs may have to make trade-offs among multiple requirements during the ongoing operation, resulting in one or more requirements violations. For ADS engineers, it is highly important to know which combinations of requirements violations may occur, as different combinations can expose different types of failures. However, there is currently no testing approach that can generate scenarios to expose different combinations of requirements violations. To address this issue, in this paper, we introduce the notion of requirements violation pattern to characterize a specific combination of requirements violations. Based on this notion, we propose a testing approach named EMOOD that can effectively generate test scenarios to expose as many requirements violation patterns as possible. EMOOD uses a prioritization technique to sort all possible patterns to search for, from the most to the least critical ones. Then, EMOOD iteratively includes an evolutionary many-objective optimization algorithm to find different combinations of requirements violations. In each iteration, the targeted pattern is determined by a dynamic prioritization technique to give preferences to those patterns with higher criticality and higher likelihood to occur. We apply EMOOD to an industrial ADS under two common traffic situations. Evaluation results show that EMOOD outperforms three baseline approaches in generating test scenarios by discovering more requirements violation patterns. © 2021 IEEE.

关键词： Iterative methods

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FC Portugal 3D Simulation Team: Team Description Paper 2020

arXiv

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arXiv 2023年

作者： Lau, Nuno Reis, Luís Paulo Simões, David Kasaei, Mohammadreza Abreu, Miguel Silva, Tiago Resende, Francisco DETI/UA Electronics Telecommunications and Informatics Dep. University of Aveiro Portugal DEI/FEUP Informatics Engineering Dep. Faculty of Engineering The University of Porto Portugal IEETA Institute of Electronics and Telematics Engineering of Aveiro Portugal LIACC Artificial Intelligence and Computer Science Lab. University of Porto Portugal

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. This paper also includes general information related to the design of our agent architecture. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Low-Energy and Reduced-Area Hardware Architecture for the Versatile Video Coding FME

Low-Energy and Reduced-Area Hardware Architecture for the Ve...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Vanio Rodrigues Filho Ismael Seidel Nicole Citadin Marcio Monteiro Mateus Grellert José Luis Güntzel Dept. of Informatics and Statistics (INE) Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

The Versatile Video Coding (VVC) standard incorporates a number of new tools to significantly improve the coding efficiency over its predecessor, the High Efficiency Video Coding (HEVC), at the cost of increased complexity. Hence, to meet real-time and energy efficiency requirements, portable devices must adopt techniques to effectively reduce the VVC complexity, including customized hardware accelerators. This work presents a low-energy hardware architecture design for the Fractional Motion Estimation (FME), which is amongst the most critical VVC and HEVC encoder steps. The proposed architecture reduces the FME complexity by operating over a subset of candidates selected to reduce the energy and area demands without compromising too much the coding efficiency. At the average BD-Rate of only 0.34% and 0.28% for the Low Delay with P slices only (LD-P) and Random Access (RA) configurations, respectively, the proposed architecture achieves a 75.5% energy consumption reduction when compared to a baseline FME architecture while occupying only 36.6 % of the area.

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Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces

Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensiona...

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International Conference on Software Engineering (ICSE)

作者： Haoxiang Jia Ming Wen Zifan Xie Xiaochen Guo Rongxin Wu Maolin Sun Kang Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab. Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System School of Informatics Xiamen University China School of Computer Science and Technology Huazhong University of Science and Technology China

Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex and large-scale Java programs, Just-In-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such nov-elties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.

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A Generalized Hybrid Hoare Logic

arXiv

引用

arXiv 2023年

作者： Zhan, Naijun Jin, Xiangyu Zhan, Bohua Wang, Shuling Guelev, Dimitar School of Computer Science Peking University Institute of Software Chinese Academy of Sciences Beijing China State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences University of CAS Beijing China Huawei Technologies Co. Ltd. Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of CAS Beijing China Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria

Deductive verification of hybrid systems (HSs) increasingly attracts more attention in recent years because of its power and scalab.lity, where a powerful specification logic for HSs is the cornerstone. Often, HSs are naturally modelled by concurrent processes that communicate with each other. However, existing specification logics cannot easily handle such models. In this paper, we present a specification logic and proof system for Hybrid Communicating Sequential Processes (HCSP), that extends CSP with ordinary differential equations (ODE) and interrupts to model interactions between continuous and discrete evolution. Because it includes a rich set of algebraic operators, complicated hybrid systems can be easily modelled in an algebra-like compositional way in HCSP. Our logic can be seen as a generalization and simplification of existing hybrid Hoare logics (HHL) based on duration calculus (DC), as well as a conservative extension of existing Hoare logics for concurrent programs. Its assertion logic is the first-order theory of differential equations (FOD), together with assertions about traces recording communications, readiness, and continuous *** prove continuous relative completeness of the logic w.r.t. FOD, as well as discrete relative completeness in the sense that continuous behaviour can be arbitrarily approximated by discretization. Finally, we implement the above logic in Isabelle/HOL, and apply it to verify two case studies to illustrate the power and scalab.lity of our logic. Copyright © 2023, The Authors. All rights reserved.

关键词： Scalab.lity

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Quantifying the reproducibility of graph neural networks using multigraph brain data

arXiv

引用

arXiv 2021年

作者： Gharsallaoui, Mohammed Amine Rekik, Islem BASIRA Lab. Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom

Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis and related fields. While prior studies have focused on boosting the model accuracy, quantifying the reproducibility of the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular. Specifically, the reproducibility of biological markers across clinical datasets and distribution shifts across classes (e.g., healthy and disordered brains) is of paramount importance in revealing the underpinning mechanisms of diseases as well as propelling the development of personalized treatment. Motivated by these issues, we propose, for the first time, reproducibility-based GNN selection (RG-Select), a framework for GNN reproducibility assessment via the quantification of the most discriminative features (i.e., biomarkers) shared between different models. To ascertain the soundness of our framework, the reproducibility assessment embraces variations of different factors such as training strategies and data perturbations. Despite these challenges, our framework successfully yielded replicable conclusions across different training strategies and various clinical datasets. Our findings could thus pave the way for the development of biomarker trustworthiness and reliability assessment methods for computer-aided diagnosis and prognosis tasks. RG-Select code is availab.e on GitHub at https://***/basiralab.RG-Select. © 2021, CC BY-NC-ND.

关键词： Biomarkers

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"Chatty Devices" and edge-based activity classification

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Discover Internet of Things 2021年第1期1卷 1-15页

作者： Lakoju, Mike Javed, Amir Rana, Omer Burnap, Pete Atiba, Samuelson T. Cherkaoui, Soumaya Cardiff School of Technologies Cardiff Metropolitan University Western Avenue CardiffCF5 2YB United Kingdom School of Computer Science & amp Informatics Cardiff University 5 The Parade CardiffCF24 3AA United Kingdom Interlab Lab. Dept. of Electical & ampComp. Engineering Université de Sherbrooke Sherbrooke QuebecJ1K 2R1 Canada

With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a "chatty" factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availab.lity of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use;(ii) associate a lab.l with product use using unsupervised learning—making use of edge-based analytics;(iii) transmission of this data to a cloud environment where lab.ls can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy). © The Author(s) 2021.

关键词： Edge computing

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eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre

eShadow+: Mixed Reality Storytelling Inspired by Traditional...

引用

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Nektarios Moumoutzis Marios Christoulakis Chara Xanthaki Yiannis Maragoudakis Stavros Christodoulakis Desislava Paneva-Marinova Lilia Pavlova Bulgarian Academy of Sciences Institute of Mathematics and Informatics Sofia Bulgaria Lab. of Distr. Multimedia Inf. Systems &#x0026 Applications School of Electrical &#x0026 Computer Eng. Technical University of Crete Chania Greece Computer Science Teacher Directorate of Secondary Education Chania Greece Laboratory of Telematics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.

关键词： Conferences Mixed reality Market research Software Recording

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Opponent Modelling using Policy Reconstruction for Multi-Objective Normal Form Games

Opponent Modelling using Policy Reconstruction for Multi-Obj...

引用

Adaptive and Learning Agents Workshop, ALA 2020 at AAMAS 2020

作者： Zhang, Yijie Rădulescu, Roxana Mannion, Patrick Roijers, Diederik M. Nowé, Ann Informatics Institute Universiteit van Amsterdam Netherlands Artificial Intelligence Lab. Vrije Universiteit Brussel Belgium School of Computer Science National University of Ireland Galway Ireland Microsystems Technology HU Univ. of Appl. Sci. Utrecht Netherlands

In many multi-agent interactions in the real world, agents receive payoffs over multiple distinct criteria;i.e. the payoffs are multiobjective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent modelling on multi-objective multiagent interactions with non-linear utilities. Specifically, we consider multi-objective normal form games with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute a novel actor-critic formulation to allow reinforcement learning of mixed strategies in this setting, along with an extension that incorporates opponent policy reconstruction using conditional action frequencies. Empirical results in five different MONFGs demonstrate that opponent modelling can drastically alter the learning dynamics in this setting. When equilibria are present opponent modelling can confer significant benefits on agents that implement it. However, when there are no Nash equilibria, opponent modelling can have adverse effects on utility, and has a neutral effect at best (after extensive hyper-parameter optimisation). © 2020 ALA 2020 - Adaptive and Learning Agents Workshop at AAMAS 2020. All rights reserved.

关键词： Reinforcement learning

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Pore Detection from Human Skin Image using U-Net 9

Pore Detection from Human Skin Image using U-Net

引用

9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Kachi, Keisuke Iwahori, Yuji Usami, Hiroyasu Fukui, Shinji Wang, Aili Bhuyan, M.K. Chubu University Dept. of Computer Science Kasugai487-8501 Japan Aichi University of Education Informatics and Communication Tech. Kariya448-8542 Japan Harbin University of Science and Technology Higher Education Key Lab. Harbin150006 China Indian Institute of Technology Guwahati Dept. of Electronics and Electrical Eng. Guwahati781039 India

ISBN: (纸本)9781728173979

There are medical hair removal and esthetic's hair removal in the current hair removal approach. Hair loss damages the tissues from which hair grows (hair cells, dermal papilla). Esthetic's hair removal has more constraints than the medical hair removal without a resident medical doctor and the effectiveness of the hair removal is lower. Therefore, pore detection is important to monitor the effect of hair removal. This paper proposes pore detection approach using U-Net [1] which is used in medical image segmentation and shows that proposed method detects pores from human skin images with high accuracy. © 2020 IEEE.

关键词： Image segmentation

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