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A prompt-based approach to adversarial example generation and robustness enhancement

Frontiers of computer science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Distributed Game-Theoretical D2D-Enabled Task Offloading in Mobile Edge Computing

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Journal of computer science & technology 2022年第4期37卷 919-941页

作者： En Wang Han Wang Peng-Min Dong Yuan-Bo Xu Yong-Jian Yang Department of Computer Science and Technology Jilin UniversityChangchun 130012China Department of Software Jilin UniversityChangchun 130012China

Mobile Edge Computing(MEC)has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and ***,most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process,while ignoring the impact of offloading among edge *** addition,centralized task offloading strategies result in enormous computation complexity in center *** this line,we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users'offloading strategies and propose the Parallel User Selection Algorithm(PUS)and Single User Selection Algorithm(SUS)to substantially accelerate the *** practically,we extend the users'offloading strategies to take into account idle devices and cloud services,which considers the potential computing resources at the ***,we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency,and find the potential function to prove the existence of Nash equilibrium(NE).Additionally,we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets,and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.

关键词： computation offloading potential game Nash equilibrium device-to-device(D2D) acceptable latency

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Quick Hidden Layer Size Tuning in ELM for Classification Problems

Mendel

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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of technology. All rights reserved.

关键词： Knowledge acquisition

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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Seeking Information with a More Knowledgeable Other

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Interactions 2023年第1期30卷 70-73页

作者： Ekstrand, Michael D. Pera, Maria Soledad Wright, Katherine Landau Department of Computer Science Boise State University United States Web Information Systems Group Delft University of Technology Software Technology Department Netherlands

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. - - Juho Kim and Henriette Cramer, Editors © 2023 ACM.

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED LEARNING 12

ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Yang, Yi-Rui Shi, Chang-Wei Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer a significant drop on model accuracy due to the large variance of stochastic gradients. Increasing batch size is a simple yet effective way to reduce the variance. However, when the total number of gradient computation is fixed, a too-large batch size will lead to a too-small iteration number (update number), which may also degrade the model accuracy. In view of this challenge, we mainly study the effect of batch size when the total number of gradient computation is fixed in this work. In particular, we show that when the total number of gradient computation is fixed, the optimal batch size corresponding to the tightest theoretical upper bound in BRDL increases with the fraction of Byzantine workers. Therefore, compared to the case without attacks, a larger batch size is preferred when under Byzantine attacks. Motivated by the theoretical finding, we propose a novel method called Byzantine-robust stochastic gradient descent with normalized momentum (ByzSGDnm) in order to further increase model accuracy in BRDL. We theoretically prove the convergence of ByzSGDnm for general non-convex cases under Byzantine attacks. Empirical results show that when under Byzantine attacks, using a relatively large batch size can significantly increase the model accuracy, which is consistent with our theoretical results. Moreover, ByzSGDnm can achieve higher model accuracy than existing BRDL methods when under deliberately crafted attacks. In addition, we empirically show that increasing batch size has the bonus of training acceleration. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Stochastic systems

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Reflections on Conducting Online Think-Alouds

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ACM Inroads 2023年第2期14卷 26-34页

作者： Imbulpitiya, Asanthika Whalley, Jacqueline Senapathi, Mali Computer Science and Software Engineering Department Auckland University of Technology St Paul StreetCentral Auckland1010 New Zealand

This article presents a protocol for conducting online think-aloud interviews as well as the reflections of the participants and interviewer on this process. The interviewer and participants commenced the interviews in person but then shifted to an online mode partway through the study. Thus, their reflections provide a comparison of the two different modes. These reflections are situated within a study of novice database designers. This computer science context, to some extent, influences both the protocol and the experiences of the study participants. Recommendations based on these experiences are provided for future computer science education researchers interested in using an online mode for conducting think-alouds, and suggestions for the use of aspects of the protocol in teaching are presented. © 2023 ACM.

关键词： Education computing

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Robust Stability Region of PI Controllers for an Interval Time Delayed Load Frequency Control

Robust Stability Region of PI Controllers for an Interval Ti...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Ghorbani, Majid Nosrati, Komeil Tepljakov, Aleksei Belikov, Juri Petlenkov, Eduard Tallinn University of Technology The Department of Computer Systems Tallinn Estonia Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (纸本)9789531842976

Transmission delays, load fluctuations, and intermittent output power of renewable energy sources will all have a substantial impact on frequency stability in microgrids (MGs). In response, various types of advanced control techniques were employed to improve the performance of the load frequency control (LFC) systems. Notwithstanding this, the fragility of the current techniques makes LFC systems vulnerable to parametric (interval) uncertainties associated with communication time delay. Inspired by recent breakthroughs in robust control, in this study, we device a novel graphical tuning method of the PI controller for tackling this issue. First, we determine the shape of the value set for the characteristic function of uncertain LFC system. Then, the exposed edges are constructed to the boundary of this set under interval uncertainties. To accomplish this step and thanks to the D-decomposition method, the robust stability region in the parameter space of the controllers are determined. Our devised methodology contributes a family of robust stabilizing controllers, instead of just one, where it enables a more flexible choice for the controller parameters. The efficacy of the proposed methodology is indicated on a given single-area MG. © 2024 IEEE.

关键词： Proportional control systems

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