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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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A Decentralized Vehicle-to-Vehicle Energy Trading System Based on Efficient Sharding Services 21

A Decentralized Vehicle-to-Vehicle Energy Trading System Bas...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Meng, Kun Sun, Lijun Chen, Xiao Wu, Haiqin Li, Shuaiyong Qingdao University of Science and Technology College of Information Science and Technology Shandong China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China

ISBN: (纸本)9798350329223

Vehicle-to-vehicle (V2V) energy trading stands as a significant technology, allowing electric vehicles (EVs) to share energy. This balances energy demand and supply, reducing pressure on the power grid. However, two substantial challenges require attention: 1) Creating a standardized protocol universally adoptable by EV manufacturers and energy trading participants to establish a common secure and reliable platform for real-time energy trading between EVs. 2) Determining a fair and transparent pricing and transaction management mechanism, accounting for factors such as energy demand and supply, location, and user preferences. To tackle these hurdles, this paper proposes a decentralized V2V energy trading system (VETS) based on a novel high-performance sharding blockchain framework. VETS incorporates a standard energy trading management protocol, achieving trustless and transparent transaction processing. It employs a blockchain sharding and parallel architecture for high transaction throughput, catering to a large number of EVs. Furthermore, an efficient consensus algorithm is developed to minimize service delays and adapt to real-time transaction demands. A practical testbed operationalizes VETS, facilitating experimental analysis within a real-world cloud environment. © 2023 IEEE.

关键词： Blockchain

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A Taxonomy for Requirements engineering and software Test Alignment

arXiv

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arXiv 2023年

作者： Unterkalmsteiner, Michael Feldt, Robert Gorschek, Tony Software Engineering Research Lab School of Computing Blekinge Institute of Technology Sweden Department of Computer Science and Engineering Chalmers University of Technology Sweden

Requirements engineering and software Testing are mature areas and have seen a lot of research. Nevertheless, their interactions have been sparsely explored beyond the concept of traceability. To fill this gap we propose a definition of requirements engineering and software test (REST) alignment, a taxonomy that characterizes the methods linking the respective areas, and a process to assess alignment. The taxonomy can support researchers to identify new opportunities for investigation, as well as practitioners to compare alignment methods and evaluate alignment, or lack thereof. We constructed the REST taxonomy by analyzing alignment methods published in literature, iteratively validating the emerging dimensions. The resulting concept of an information dyad characterizes the exchange of information required for any alignment to take place. We demonstrate use of the taxonomy by applying it on five in-depth cases and illustrate angles of analysis on a set of thirteen alignment methods. In addition we developed an assessment framework (REST-bench), applied it in an industrial assessment, and showed that it, with a low effort, can identify opportunities to improve REST alignment. Although we expect that the taxonomy can be further refined, we believe that the information dyad is a valid and useful construct to understand alignment. Categories and Subject Descriptors: D.2.1 [software engineering]: Requirements/Specifications;D.2.4 [software engineering]: software/Program Verification;D.2.9 [software engineering]: Management—software quality assurance © 2023, CC BY-NC-SA.

关键词： Requirements engineering

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Conceptual Modelling of Explanation Experiences Through the iSeeOnto Ontology 30

Conceptual Modelling of Explanation Experiences Through the ...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Caro-Martínez, Marta Wijekoon, Anjana Recio-García, Juan A. Corsar, David Nkisi-Orji, Ikechukwu Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain School of Computing Robert Gordon University Aberdeen United Kingdom

Explainable Artificial Intelligence is a big research field required in many situations where we need to understand Artificial Intelligence behaviour. However, each explanation need is unique which makes it difficult to apply explanation techniques and solutions that are already implemented when faced with a new problem. Therefore, the task to implement an explanation system can be very challenging because we need to take the AI model into account, user's needs and goals, available data, suitable explainers, etc. In this work, we propose a formal model to define and orchestrate all the elements involved in an explanation system, and make a novel contribution regarding the formalisation of this model as the iSeeOnto ontology. This ontology not only enables the conceptualisation of a wide range of explanation systems, but also supports the application of Case-Based Reasoning as a knowledge transfer approach that reuses previous explanation experiences from unrelated domains. To demonstrate the suitability of the proposed model, we present an exhaustive validation by classifying reference explanation systems found in the literature into the iSeeOnto ontology. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Ontology

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Adaptive Control of Spectral Bias in Untrained Neural Network Priors for Inverse Problems

SSRN

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SSRN 2023年

作者： Zhao, Zunjin Shi, Daming Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This paper addresses the challenge of overfitting in Untrained Neural Network Priors (UNNPs), which has shown success in various image reconstruction tasks. Current approaches to this problem either develop early-stopping method or introduce variants to enhance robustness. One more concurrent work controls the spectral bias of UNNPs by bounding the spectral norms of all convolutional layers ***, we argue that this method overlooks the distinct contributions of each convolutional layer to spectral bias due to differences in depth. In this paper, we propose an adaptive controlling approach to mitigate the overfitting in ***, first, we construct a modular UNNPs network with a decoder. Second, we analyze the relationship between the upper bound of spectral norms and GF. With the finding that, the increase of spectral norm can be bounded by GF and learning rate, we propose to guide GF using attenuated learning rate to upper bound the spectral norms of UNNPs to different values, so as to control the spectral bias of UNNPs network. Experiments demonstrate that this guided GF approach effectively mitigates overfitting in the UNNPs model, improving its robustness compared to other variants. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Competitive and Cooperative Computation Offloading for Intensive Heterogeneous Tasks in Vehicular Edge computing Networks

Competitive and Cooperative Computation Offloading for Inten...

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IEEE International Conference on Communications (ICC)

作者： Yuan Chen Xiuhua Li Guozeng Xu Ling Liu Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Computation offloading is widely used in vehicular edge computing (VEC) networks to satisfy the computational intensity and latency sensitivity requirements. However, many existing offloading algorithms do not comprehensively consider the dynamically changing characteristics of heterogeneous tasks within a roadside unit (RSU), resulting in tasks timeout and being dropped. In this paper, we design a competitive and cooperative computation offloading (C3O) model to reduce task execution latency. Specifically, when intensive heterogeneous tasks are generated, these vehicles jointly compete for the computing resource of a RSU, or they can also offload tasks to the task vehicle (TaV) with free computing resource. Meanwhile, We analyze the latency model of local execution and offloading to RSU or TaV execution and formulate a sequential task offloading decision problem, NP-hard. To address it, we propose a multi-agent reinforcement learning algorithm based on C3O (MARC3O) to intelligently determine the computation offloading policy for each vehicle according to the state of VEC networks. Simulation results demonstrate that the proposed algorithm can significantly reduce task execution latency and improve task completion rates compared with baseline schemes.

关键词： Analytical models Sensitivity Costs Computational modeling Simulation Heuristic algorithms Collaboration

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Research and Implementation of the Generation Method of Point Array Based on Loop and Iteration 3

Research and Implementation of the Generation Method of Poin...

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2022 3rd International Conference on Computer Information and Big Data Applications, CIBDA 2022

作者： Chen, Ruxian Cai, Musheng Zhou, QiXing Dong, Ya Bian, Buxing Li, Jing Software Engineering Institute of Guangzhou Guangzhou China Institute of Computing Science and Technology Guangzhou University Guangzhou China Wushen Banner High School Inner Mongolia Autonomous Region China

ISBN: (纸本)9783800758760

In Mathematics, a point array refers to a graphic composed of regularly arranged points, for example, numbers in the Pascal's Triangle are placed in the form of a special point array. By exploring key features of a point array, such as the total number of the points, the arrangement rule of the points, and the relationship between the ordinal number and position of each point, students can improve their observation ability and logical thinking skills, especially in a dynamic mathematics environment Specifically, Dynamic Geometry software, in which elements can be interactively created and manipulated, is a good choice for learning a point array. However, multi-branch iteration, which is a common method for drawing a point array in the Dynamic Geometry software, has some problems. For instance, the running efficiency is low, many repeated points are generated, and only one point array can be drawn at a time. Hence, a multiloop single branch iteration method is proposed in this paper. On one hand, generation of repeated points is avoided and the running efficiency is improved;and on the other hand, point arrays of various shapes can be easily drawn, due to the adoption of translation and variables. Moreover, the multi-loop single branch iteration method is verified by drawing two common point array cases, i.e., the Pascal's Triangle and the snailshell-shaped point array, and it turns out that the method is effective and can be applied to a teaching scene. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Students

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CMCache: An Adaptive Cross-Level Data Placement Method for Multi-Level Cache

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zeng, Zhaoyang Tan, Yujuan Ma, Zhulin Li, Jiali Zhao, Sanle Liu, Duo Chen, Xianzhang Ren, Ao Chongqing University College of Computer Science Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing Universityof Posts and Telecommunications College of Software Engineering China Chongqing University College of Big Data and Software Engineering Chongqing China

Multi-level cache systems enhance I/O performance by optimizing data placement across various cache levels from a global perspective. However, existing methods often struggle to place data at the optimal cache level promptly due to their reliance on historical access patterns and inflexible placement strategies. These methods face two main challenges: (1) For already cached data with sufficient access history, existing approaches only optimize movement between adjacent cache levels, potentially delaying data arrival at its globally optimal cache level and leading to unnecessary bandwidth consumption and increased latency. (2) For newly entered data without access history, current methods cannot accurately predict their future hotness and simply place them at a fixed cache level (i.e. first or last level), overlooking future accesses of new data and potentially resulting in high cache miss rates or cache pollution. To address these issues, we propose CMCache, an adaptive cross-level data placement method for multi-level cache. CMCache applies distinct placement strategies for cached and new data to reach the optimal level timely, considering their different characteristics. It also logically divides cache space into two sections to manage cached and new data separately, dynamically adjusting section sizes based on access patterns. This approach significantly improves data placement efficiency, achieving up to an 89% reduction in miss rates and a 79% decrease in average response times compared to existing methods. © 2025 IEEE.

关键词： Cache memory

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An Application of Localized Model Explainability: Identifying Key Disparities in Social Determinants of Health in Food Deserts

An Application of Localized Model Explainability: Identifyin...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Alam, Md Shafiul Ishmakhametov, Namazbai Xie, Ying Chakravarty, Sumit Kennesaw State University Analytics and Data Science Kennesaw United States Kennesaw State University College of Computing and Software Engineering Kennesaw United States Kennesaw State University Department of Electronic Engineering Kennesaw United States

ISBN: (纸本)9781665480451

Food deserts are geographic areas where people have limited access to healthy and affordable food. Millions of people in the US are experiencing adverse effects stemming from living in such areas and it becomes an important challenge for federal and local authorities to improve the quality of life of people living in food deserts. The majority of studies in this field explicitly consider income level when analyzing problems of food deserts, thus shadowing other attributes that might provide deeper insights into the problems of the food desert's population. The current paper assesses the significance of subtler, non-income related characteristics of census tracts by using a new variable attribution technique Appley which approximates Shapley values in linear time. The results show that attributes of census tracts that fall into the set of the so called "social determinants of health"have similar classification power compared to models that explicitly use income variables. The results suggest that each food desert has its own unique set of problems with social determinants of health with varying levels of intensities of each problem and this allows to clearly distinguish not only food deserts from non-food desert areas but also distinguish food deserts from each other. Appley attributes localized importance scores to food deserts' social determinants of health, thus showing which issues are more severe in a given food desert compared to another one. This allows for devising food desert-specific solutions to the health issues of desert residents thus letting authorities improve the quality of life of people in a targeted and budget-efficient manner. © 2022 IEEE.

关键词： Budget control

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing

Performance Evaluation of Clustering Algorithms for Enhancin...

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Advanced Research in computing (ICARC), International Conference on

作者： S. Prabath Puviskar W.V.S.K. Wasalthilaka B.T.G.S Kumara Department of Computing & Information Systems Sabaragamuwa University of Sri Lanka Sri Lanka Department of Software Engineering Sabaragamuwa University of Sri Lanka Sri Lanka

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes.

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