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Information technology (OCIT), OITS International Conference on

作者： N. Senthamarai Jafar Ali Ibrahim Syed Masood M. Sathya N. S. Kalyan Chakravarthy Bharati Rathore M Mohamed Kalith Oli Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Pontypridd UK School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. Research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. Data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy.

关键词： Support vector machines Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Diabetes Classification algorithms Random forests Diseases

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK 23

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Zhou, Weidong Xia, Chunhe Feng, Nan Pan, Xinyi Wang, Tianbo Li, Xiaojian Beihang University Beijing Key Lab. of Network Technology Beijing China Guangxi Normal University Guangxi Collab. Innovation Center of Multi-Source Information Integration and Intelligent Processing Guilin China Peking University School of Software & Microelectronics Beijing China Beihang University School of Cyber Science and Technology Beijing China Guangxi Normal University School of Computer Science and Engineering Guilin China

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing"and "complex associations missing."The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing"problem. Second, we employ Relation Graph Convolution Neural networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing"problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics. © 2024 IEEE.

关键词： attack inference heterogeneous dependency graph MITRE ATT&CK RGCN

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Enhancing source code summarization from structure and semantics

Enhancing source code summarization from structure and seman...

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International Joint Conference on Neural networks (IJCNN)

作者： Xurong Lu Jun Niu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China Zhejiang Engineering Research Center of Advcanced Mass Spectrometry and Clinical Application Ningbo China

Source code summarization aims to generate concise and high-quality natural language descriptions for code snippets. High-quality code summaries can help developers better understand source codes. Researchers have made great efforts to generate more accurate summaries; however, due to the lack of preservation of source code structure and semantics, previous approaches have struggled to generate summaries that accurately describe the functionality or other major characteristics of codes. In this paper, we propose a novel approach called Code Structure and Semantic Fusion (CSSF) for automatically generating summaries for source code. CSSF can utilize both the structural and semantic information of source codes. To achieve this, we extract the overall structure of the Abstract Syntax Tree (AST) by expanding the AST and using a heterogeneous graph attention network. Furthermore, we use an additional sequence model to obtain the semantic information of the code fragment. Finally, we fuse the two kinds of information through a novel modality fusion method. We evaluate our approach on a widely used Java dataset; experimental results confirm that our approach outperforms existing methods.

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Peak-Controlled Logits Poisoning Attack in Federated Distillation

arXiv

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arXiv 2024年

作者： Tang, Yuhan Zhang, Aoxu Wu, Zhiyuan Gao, Bo Wen, Tian Wang, Yuwei Sun, Sheng Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education School of Computer Science and Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to a central server. While FD has gained popularity, its vulnerability to poisoning attacks remains underexplored. To address this gap, we previously introduced FDLA (Federated Distillation Logits Attack), a method that manipulates logits communication to mislead and degrade the performance of client models. However, the impact of FDLA on participants with different identities and the effects of malicious modifications at various stages of knowledge transfer remain unexplored. To this end, we present PCFDLA (Peak-Controlled Federated Distillation Logits Attack), an advanced and more stealthy logits poisoning attack method for FD. PCFDLA enhances the effectiveness of FDLA by carefully controlling the peak values of logits to create highly misleading yet inconspicuous modifications. Furthermore, we introduce a novel metric for better evaluating attack efficacy, demonstrating that PCFDLA maintains stealth while being significantly more disruptive to victim models compared to its predecessors. Experimental results across various datasets confirm the superior impact of PCFDLA on model accuracy, solidifying its potential threat in federated distillation systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Sequence-to-Sequence Knowledge Graph Completion Based On Gated Attention Unit

Sequence-to-Sequence Knowledge Graph Completion Based On Gat...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fengge Yi Xiumei Wei Xiaojing Liu Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.

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A GAN-based Semantic Communication for Text without CSI

arXiv

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arXiv 2023年

作者： Mao, Jin Xiong, Ke Liu, Ming Qin, Zhijin Chen, Wei Fan, Pingyi Letaief, Khaled Ben The Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The Department of Electronic Engineering Tsinghua University Beijing100084 China The Beijing National Research Center for Information Science and Technology The Department of Electronic Engineering Tsinghua University Beijing100084 China Hong Kong

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn't require CSI. In Ti-GSC, two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM) are included where AEDM first encodes the data in the semantic dimension at the source before transmission, and then GSDSM suppresses the distortion of the received signals in both syntactic and semantic dimensions at the destination. At last, AEDM decodes the distortion-suppressed signal at the destination. As SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, r

关键词： Semantics

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Effect of the Mixing Structure Parameters of a Self-reflux Burner on Combustion Characteristics and NO_(x)Emission

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Journal of Thermal Science 2021年第4期30卷 1224-1236页

作者： XU Qian SHEN Meng SHI Kejian LIU Zhihui AKKURT Nevzat XIONG Yaxuan LIU Lin FENG Junxiao WANG Jiulong School of energy and environmental engineering Shunde Graduate SchoolUniversity of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Key Laboratory of HVAC Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

To solve the problem of low efficiency of NO_(x)emission reduction in self-reflux burners,this study is based on the concept of coordinated control of self-reflux burner structural and thermal *** completing the structural design and optimization of thermal parameters,we continue to adjust the two key structural parameters:the nozzle axis distance and the length of the cylindrical section,to minimize NO_(x)*** are the two parameters that chiefly affect the mixing of flue gas and fuel *** results show that increasing nozzle axis distance can delay the mixing of gas and air and create a more uniform oxygen concentration field for the combustion *** maximum combustion temperature is reduced from 1973.65 K to 1935.88 K and the volume fraction of NO_(x)in the flue gas is reduced from 188.08×10^(–6)to 143.47×10^(–6).However,compared with the nozzle axis distance,the length of the cylindrical section of the burner has little effect on the mixing of the flow *** different cylindrical section lengths,the maximum combustion temperature does not change more than 3 K,and the volume fraction of NO_(x)in the flue gas changes within 5×10~(–6).

关键词： self-reflux structure structural parameter control thermal parameter combustion characteristic NO_(x)emission

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Rate Control for VVC Intra Coding with Simplified Cubic Rate-Distortion Model

Rate Control for VVC Intra Coding with Simplified Cubic Rate...

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IEEE Workshop on Multimedia Signal Processing

作者： Yizhao Wang Jiaqi Zhang Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

In this paper, we propose a simplified cubic polynomial R-D model with corresponding rate control methods for Versatile Video Coding (VVC) intra frame coding. First, we explore the rate-distortion (R-D) characteristics of VVC intra coding. By comparing several potential R-D modeling approaches, a new intra coding R-D model has been proposed based on the simplified cubic polynomial function. Subsequently, we derive the corresponding $R-\lambda$ model and introduce a complexity measurement to improve the performance of intra frame rate control. Furthermore, we propose a Coding Tree Unit (CTU)-level rate control method based on the newly proposed R-D model and further develop a pre-compression-based approach on this basis. Experimental results show that the proposed method can achieve 1.87% and 0.55% bit rate reduction for All-Intra (AI) and Random-Access (RA) configurations over the original rate control in VVC Test Model (VTM), while the computational complexity increment is negligible. Meanwhile, the enhanced bit rate accuracy from rate control has been observed in the proposed methods.

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

arXiv

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arXiv 2024年

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2024, CC BY-NC-SA.

关键词： Speech recognition

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Research and Innovation In Next Generation Security and Privacy In Industry 5.0 Iot

Research and Innovation In Next Generation Security and Priv...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Geeta S. Navale Ravikiran Madala Manjunath Managuli N. Jayalakshmi G. Kadiravan Ruchira Rawat Department of Computer Engineering Sinhgad Institute of Technology and Science Pune Maharashtra India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belegavi Karnataka India Master of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Computer Science and Information Technology KL Deemed to be University Andhra Pradsh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

The objective of the newest manufacturing paradigm, known as “Industry 5.0,” is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers.

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