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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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ClipComb: Global-Local Composition Network based on CLIP for Composed Image Retrieval

ClipComb: Global-Local Composition Network based on CLIP for...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yingying Zhu Dafeng Li Zhihang Liu Hong Zhou College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we focus on the task of composed image retrieval. To develop a comprehensive understanding of image and text, we propose a novel global-local composition network (ClipComb) based on the vision-language pretraining CLIP model. The two main phases of ClipComb are the fine-tuning training stage and the composition training stage. First, in the fine-tuning training step, we fine-tune the text and image encoders of the CLIP model to transfer CLIP to this task. We also perform contrastive loss alignment on both global and local features. Second, during the composition training phase, we devise a global-local composition module (GLC) based on the fine-tuned composition learning framework. The GLC module make full use of the CLIP’s pretraining knowledge to generate a composite representation aligned with the target representation. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on two benchmark datasets.

关键词： Training Image retrieval Footwear Benchmark testing

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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

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computer Systems science & engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach 9th

Lake Water Level Forecasting Using LSTM and GRU: A Deep Lear...

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9th Future Technologies Conference, FTC 2024

作者： Du, Yuxin Fan, Jing Happonen, Ari Paulraj, Dassan Tuape, Micheal Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Mukkulankatu 19 Lahti15210 Finland Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Yliopistonkatu 34 Lappeenranta53850 Finland Department of Computer Science and Engineering R.M.K. Engineering College Chennai India

ISBN: (纸本)9783031731242

Artificial Intelligence and Deep Learning-based methods show constant promise in addressing time series forecasting challenges. Lake water level forecasting is an essential & significant environmental and societal impact related time series forecasting problem, with climate change connections. Lakes’ diverse hydrological characteristics make high-performance forecasting models to be lake-specific. Thus, comprehensive experiments are necessary to develop accurate deep learning-based forecasting models. We propose an approach for effective and efficient systematic search conduction for high-performance Deep Learning forecasting models. The method is applicable across various time series forecasting challenges. The research was structured around three experimental groups, each focusing on predicting the water levels of Lake Vesijärvi in Lahti, Finland, over periods of 1 day, 3 days, and 7 days, respectively with Long Short-Term Memory and Gated Recurrent Unit. The results are highly promising. All models achieved a Nash-Sutcliffe Efficiency above 0.95 and a Root Mean Squared Error below 0.025. The best-performing model achieved a Nash-Sutcliffe Efficiency above 0.99 and a Root Mean Squared Error below 0.0011. All evaluation metrics were calculated from testing data without signs of overfitting. This research provides insights into Deep Learning-based time series forecasting and a replicable method to conduct such studies effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lakes

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NAS-SW: Efficient Neural Architecture Search with Stage-Wise Search Strategy

NAS-SW: Efficient Neural Architecture Search with Stage-Wise...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jikun Nie Yeming Yang Qingling Zhu Qiuzhen Lin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

A number of Neural Architecture Search (NAS) methods have been proposed to automate the design of Convolutional Neural Networks (CNNs). However, they typically require significant computational resources and exhibit search instability. To save computational costs, many NAS methods design only a single group of cell architectures (i.e., a small part of the total network architecture), which are simply stacked to form the final network. This leads to a lack of diversity in network architecture design, thereby reducing network performance. To alleviate this problem, we propose a Particle Swarm Optimization (PSO)-based Stage-Wise NAS algorithm (named NAS-SW). Initially, we introduce a stage-wise architecture search strategy, which can find different cell architectures at various search stages. Then, we propose a grouping architecture update strategy, which can realize effective information transfer between cell architectures rapidly. Importantly, these strategies do not add extra computational costs. Experiments demonstrate that NAS-SW has superior performance and better efficiency. NAS-SW only needs 0.37 GPU days to design a CNN on CIFAR-10. Additionally, on the CIFAR-10 and CIFAR-100 datasets, the network architectures searched by NAS-SW achieve top-1 validation accuracies of 97.55% and 83.78%, respectively.

关键词： Microprocessors Design methodology Neural networks Graphics processing units computer architecture Network architecture Search problems

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BCLB: A Scalable and Cooperative Layer-4 Load Balancer for Data Centers

BCLB: A Scalable and Cooperative Layer-4 Load Balancer for D...

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International Conference on Distributed Computing Systems

作者： Shu Yang Xinze Wu Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Nowadays, load balancing is more and more important for huge data centers. Most data centers usually adopt a software-based load balancer (LB), which consumes too many server resources and does not scale with the increasing traffic volume. However, there is an increased demand for layer-4 load balancers, which need to check more bits in packet headers. Although hardware-based load balancers can meet the requirements, they are much more expensive. During the past years, data centers frequently update their devices, including kinds of switches. Thus, there exists a huge number of obsolete switches, some of which are in good condition and equipped with high-performance storage like SRAM (Static Random Access Memory). In this paper, we proposed BCLB (Bit-based Collaborative Load Balancer), a new cooperative LB, which builds more powerful load balancing based on existing switches. Different with previous cooperative mechanisms that distribute rules to different LBs, BCLB lets switches cooperate based on bits. Many switches along the data path check different bits, and cooperatively check all bits in 5-tuple (which would be 296 bits in IPv6) for layer-4 packet header. In this way, rule updating will not influence the load balancing in BCLB. To optimize the performance of BCLB, we formulate the problem as an optimization problem, then we propose a dynamic programming algorithm to solve it. Finally, we conduct comprehensive simulations using both real-world traffic datasets and a P4-based prototype, the results show that BCLB performs much better than previous rule-based cooperative schemes.

关键词： Data centers Heuristic algorithms Random access memory Prototypes Distributed databases Load management Dynamic programming

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FireRobBrain: Planning for a Firefighting Robot using Knowledge Graph and Large Language Model

FireRobBrain: Planning for a Firefighting Robot using Knowle...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Jialei Zhang Shubin Cai Zhiwei Jiang Jian Xiao Zhong Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guang Ming Laboratory Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guang Ming Laboratory Shenzhen Technology University Shenzhen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Firefighting robots play a crucial role in improving fire safety. However, these robots have limitations in understanding their surroundings and adapting to changing situations. In this paper, we propose the “FireRobBrain”, a combination of a knowledge graph and a large language model, to act as the “brain” of firefighting robots. This approach aims to tackle challenges related to planning robots in dynamic environments. The FireRobBrain consists of a KG with a dynamic information base and a relatively static knowledge base. It also includes a prompting module that helps the language model generate suggestions for the robot's reactions. We evaluated the framework using a dataset of 864 samples and discovered that the combination of LLM and KG, facilitated by a well-designed prompt module, significantly improves the quality of answers, particularly for tasks involving specific contexts and structured information. Furthermore, we noted that providing a task scope in the input prefix contributes to a better understanding of the robot's task, resulting in enhanced performance.

关键词： Large language models Prevention and mitigation Knowledge based systems Knowledge graphs Planning Security Reliability

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of computer science & Technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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A Novel Unsupervised Anomaly Detection Approach Using Neural Transformation in Cloud Environment 1

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15th International Conference on Cloud Computing, CLOUD 2022, held as part of the Services Conference Federation, SCF 2022

作者： Zhang, Rui Chen, Juan Song, Yujia Shan, Wenyu Chen, Peng Xia, Yunni School of Computer and Software Engineering Xihua University Chengdu China College of Computer Science Chongqing University Chongqing China

ISBN: (数字)9783031234989

ISBN: (纸本)9783031234972

As a new way of computing on the Internet, cloud computing has completely changed the abstraction and utilization of computing infrastructure and is favored by users of significant enterprises. Therefore, various large-scale cloud computing systems have emerged. Cloud computing systems capture real-time indicators in the form of multivariate time series. Therefore, it is necessary and valuable to do multivariate time series anomaly detection in the cloud computing system. However, because the data monitored in the cloud computing system is massive, redundant, contains noise and missing values, and is accompanied by the randomness and scarcity of anomalies, anomaly detection in the cloud computing system will generate high computational costs and the challenge of uncertain detection results. To solve these problems, we use neural transformation (NT) for anomaly detection, that is, Neural Transformation-Encoding-Auto Regression (NT-E-AR). NT-E-AR uses NT to generate different transformation views from the input data. Convolutional Long-Short Term Memory (ConvLSTM) encoding network and Autoregressive Long-Short Term Memory (LSTM) are combined to extract Spatio-Temporal features of time series data to achieve better anomaly detection capability. Extensive experiments show that NT-E-AR consistently outperforms all baselines on three open datasets and achieves an average F1 (0.772), increasing the average accuracy by 16.52%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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SRE-KGC : A Knowledge Graph Completion Model Based on Hidden Graph Structure and Relational Semantic Enhancement

SRE-KGC : A Knowledge Graph Completion Model Based on Hidden...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Sijun Lu Guoyan Xu Shuangyang Sun College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Knowledge Graph Completion (KGC) endeavors to use existing knowledge graph data for predicting missing elements in triples. Recently, due to the efficiency of graph neural networks (GNNs) in capturing topological structures and the effectiveness of text descriptions in supplementing semantic information, numerous models integrating graph structures and entity descriptions have emerged. However, these approaches typically focus on aggregating neighboring feature information and overlook mining hidden structural information. Furthermore, they often append textual descriptions to entities independently without considering the semantics within specific relations. Hence, we propose the SRE-KGC model to address these challenges. First, while aggregating neighbors, we analyze and mine the hidden structure in the neighborhood from the perspective of entities and relations; then we introduced a dual-layer attention mechanism to extract the most pertinent textual information towards relations from both the relational semantic level and the neighbor semantic level respectively; finally, the two learned features are fused and sent to the decoder for scoring. Experiments demonstrate that our model delivers superior performance.

关键词： Knowledge engineering Attention mechanisms Semantics Knowledge graphs Interference Predictive models Feature extraction Graph neural networks Decoding Data mining

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