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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Wang, Meifeng Wang, Yuanyuan Li, Yazhou Gu, Dingyun Zhu, Yemeng Huaiyin Institute of Technology Institute of Computer and Software Engineering Jiangsu Province Internet of Things Mobile Interconnection Technology Engineering Laboratory Huai'an City Jiangsu223001 China

ISBN: (纸本)9798350306293

In response to the issue of inadequate public datasets concerning imbalances in Internet recruitment scams, this paper constructs an imbalanced experimental dataset, labels it, and proposes an ensemble learning method based on model stacking. This method enhances the prediction accuracy of the original model built with Random Forest. Initially, the method employs label encoding and TF-IDF (Term Frequency- Inverse Document Frequency) to preprocess structured and unstructured data respectively. Then, it utilizes Truncated SVD (Singular Value Decomposition) to reduce the dimensionality of the sparse matrix generated by feature extraction from TF-IDF. Subsequently, decision trees, random forests, and Light Gradient Boosting Machine are utilized as base models, with the random forest employed as the meta-model to integrate the predictions of the base models. This paper presents a comparison of the experimental results from seven machine learning models based on the self-constructed experimental dataset, with model stacking implemented on the random forest that yielded the best experimental result. Through model stacking enhancement of the machine learning models, compared to the random forest model that performed best on the self-constructed experimental dataset, an accuracy of 94.85% was achieved, marking a 7.33% improvement. The experimental results hold significant value and practical implications in understanding and resolving the probability risk issue of recruitment fraud. © 2023 IEEE.

关键词： Learning systems

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Camouflage pattern adversarial generation model using color and semantic constraints 3

Camouflage pattern adversarial generation model using color ...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Zhang, Hao Xie, Zhenping School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Jiangsu Key Laboratory of Media Design and Software Technology Jiangsu Wuxi214122 China

ISBN: (纸本)9781510681316

In response to the challenge that traditional camouflage design methods struggle to evade detection by modern unmanned aerial reconnaissance, we propose a camouflage pattern generation adversarial network model using color-semantic constraints. A reference image generation model using color-semantic constraints has been established, which through sentence encoding model, generates reference images with fundamental texture and color features. This is achieved by incorporating adversarial loss, texture loss, and pixel-level loss. We design a color standardization processing strategy based on the SimCLR framework. This model generates semantic camouflage images in batches by designing data augmentation strategies, positive-negative sample similarity measurement strategies, and sample structural similarity algorithms, regard to reference images. Qualitative and quantitative experimental results demonstrate that our proposed method exhibits strong camouflage performance in different environmental settings. © 2024 SPIE.

关键词： Design

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data 10

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Huang, Tao Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis. © 2023 IEEE.

关键词： Entropy

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ScIAMC:Single-Cell Imputation via adaptive matrix completion 10

ScIAMC:Single-Cell Imputation via adaptive matrix completion

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Shuai Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts. © 2023 IEEE.

关键词： Cell proliferation

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of computer Science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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Automated Functionality and Security Evaluation of Large Language Models

Automated Functionality and Security Evaluation of Large Lan...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Minjie Ding Ying Shen Mingang Chen Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

Natural language processing (NLP) is rapidly developing. A series of Large Language Models (LLMs) have emerged, represented by ChatGPT, which have made significant breakthroughs in natural language understanding and generation, enabling fluent dialogue with humans, understanding human intentions, and completing complex tasks. However, in addition to the fairness and toxicity of traditional language models, some new problems, including hallucination, have also emerged in LLMs, making them hard to use. Evaluating LLMs manually is challenging due to subjectivity and inefficiency. In this paper, we focused on the fuzzy matching, toxicity detection, and hallucination detection in the evaluation of LLMs automatically, and fine-tune the Mixtral-8x7B Model, which can be deployed in private cloud environment, and prove the effectiveness of our method through experiments.

关键词： Cloud computing Toxicology Accuracy Graphics processing units Chatbots Security Task analysis

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts 41

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Shi, Jiang-Xin Wei, Tong Zhou, Zhi Shao, Jie-Jing Han, Xin-Yan Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Hierarchical Sketch: An Efficient Solution for Threshold-t Flows Measurement in High-Speed Networks 21

Hierarchical Sketch: An Efficient Solution for Threshold-t F...

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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： Han, Yifan Huang, He Du, Yang Sun, Yu-E Liu, Jia Xu, Hongli Soochow University School of Computer Science and Technology China Soochow University School of Rail Transportation China Nanjing University State Key Laboratory for Novel Software Technology China University of Science and Technology of China School of Computer Science and Technology China

ISBN: (纸本)9798350329223

Per-flow size measurement has wide applications in data center networks, such as flow scheduling, anomaly detection, and traffic engineering. To fit the module into limited on-chip memory and catch up with the line speed, existing works either use the bit sharing scheme to compress the flow information or require complex calculations to screen out large flows, resulting in substantial accuracy loss or heavy memory accesses, especially failing to measure medium-sized flows accurately, which are meaningful for practical applications such as usage accounting and billing. In light of this, we propose a new Hierarchical Sketch that adopts a hybrid on-chip/off-chip architecture to detect and estimate the flows whose size exceeds the threshold t (threshold-t flows for short) to cover both large and medium-sized flows. In Hierarchical Sketch, we design an on-chip bucket array together with an adaptive update scheme to record potential candidates for threshold-t flows within one memory access as well as filter out unwanted flows. Besides, the flow records are downloaded to off-chip space for high memory efficiency and supporting online queries. We also implement a prototype on the NetFPGA development platform. Experimental results show that Hierarchical Sketch is up to 72.61% more accurate and 245% faster than the best baseline. © 2023 IEEE.

关键词： HIgh speed networks

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Collaborative Video Caching, Transcoding and Transmission Joint Optimization Strategy in Multi-Access Edge Computing 5

Collaborative Video Caching, Transcoding and Transmission Jo...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Liu, Pingshan You, Jun Huang, Fu Business School Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China Computer and Information Security School Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350386943

Current multi-access edge computing (MEC) joint caching, transcoding, and transmission optimization strategies do not effectively balance the conflict between rental cost of resources with the user's QoE affected by transmission. In order to maximize QoE and minimize resource rental costs, a new strategy for cooperative joint optimizing of video caching, transcoding, and transmission is proposed. This paper establishes the cooperative joint caching, transcoding, and transmission problem as a optimization model, aiming at maximizing QoE and minimizing resource rental costs, while jointly optimizing video caching, transcoding, and transmission. To solve this problem, a multi-objective differential evolution algorithm is proposed to find optimal solutions to balance the conflict between resource rental costs and the impact of transmission on user QoE. Simulation experiments show that compared with the existing strategy, this strategy can improve cache hit ratio, reduce resource rental costs for content providers, and ensure high QoE. © 2024 IEEE.

关键词： Edge computing

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach 40

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Xu, Yin Zhang, Xichong Xiao, Mingjun Wu, Jie Liu, An Zhang, Sheng School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Temple University Department of Computer and Information Sciences Philadelphia United States School of Computer Science and Technology Soochow University Suzhou China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350317152

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines. © 2024 IEEE.

关键词： Game theory

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