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International Conference on Systems and Informatics (ICSAI)

作者： Jiehuang Zhu Xianfeng Liu Jin Zhang Sheng Wu College of Information and Computer Engineering Hunan Normal University Changsha China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

Drug response prediction plays a crucial role in precision medicine, such as cancer analysis and treatment. Due to the uncertainty of drug efficacy and the heterogeneity of cancer, predicting drug response in vitro is expected to assist in guiding anticancer drug design and understanding cancer biology. Based on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), we propose a deep learning method for predicting drug sensitivity. Specifically, we first construct graphs of drugs and cell lines in the benchmark dataset. And we design a drug feature extraction module based on Graph Transformer and a cell line feature extraction module based on Graph Attention Networks (GAT) to capture the embeddings of drugs and cell lines. Finally, we feed the drug embeddings and cell line embeddings into the module for prediction. Experiment results show that our method outperforms several advanced models in drug response prediction, demonstrating the good performance and enormous application potential in precision medicine. In addition, the results of ablation experiments demonstrate the effectiveness of the modules in our model.

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Node influence-based label propagation for community detection using both topology and attributes

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Expert Systems with Applications 2025年 287卷

作者： Zhao, Zhili Xie, Jiquan Zhang, Nana Hu, Ahui Yan, Ruiyi Tang, Jianxin School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

Community detection can partition nodes in complex networks into several tightly connected communities, and is helpful for understanding relationships between different communities and inferring the functions of communities and their members. However, traditional community detection methods often rely on topological structure and neglect the contribution of node semantic attributes, resulting in an unstable performance, especially in assigning isolated nodes to right communities. Although there have been some methods that combine network topology and node semantic attributes, they are often incomplete in flexibly balancing their contributions;moreover, they typically have high complexity and are not suitable for large-scale complex networks. Therefore, this study introduces a novel node influence-based label propagation algorithm (NILPA) for community detection to improve its accuracy. Different from classical label propagation algorithm (LPA), NILPA flexibly considers different contributions of network topology and node semantic attributes. Moreover, NILPA introduces a novel node centrality metric that considers both network topology and node semantic attributes to measure node influence. During the iterative process of label propagation, NILPA not only updates node labels with a fixed order but also updates node labels by comprehensively considering the influence of all its first- and second-order neighbors until all node labels are stable. NILPA further improves the accuracy by checking fuzzy nodes that may be assigned wrong labels after the process of label propagation. The experimental results show that the average improvements of NILPA are 23.16 % and 22.13 % in terms of normalized mutual information (NMI) and adjusted rand index (ARI), respectively. © 2025 Elsevier Ltd

关键词： Semantic Web

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Application of Evolutionary Computing for Optimal Design of Base Station Antenna

Application of Evolutionary Computing for Optimal Design of ...

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IEEE International Conference on Electronic Information and communication technology (ICEICT)

作者： Naveed Hamid Nawaz Khan Botao Feng Xiao Ding Li Deng Wenzhe Gu Kwok L. Chung College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering Huizhou University Huizhou P. R. China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This study outlines the development and refinement of an innovative base station antenna for contemporary wireless communication systems, which operates within the frequency range of 1.9 to 4.9 GHz. The antenna is made of copper and has a radiating patch thickness of 0.2 mm. The ground patch measures 38×38 mm and the total height of the antenna is 16 mm. The performance of the system relies on four reflecting walls that have been carefully carved to reduce out-of-band radiations and minimize interference. The antenna attains a peak gain of 6 dBi throughout the operational range, while keeping the |S 11 | parameters below -10 dBi, ensuring effective signal transmission. The process of optimization involves the utilization of evolutionary computers, more specifically a Genetic Algorithm, to meticulously adjust the design parameters. This approach guarantees that the antenna fulfills the demanding criteria of high gain and wide bandwidth, offering a strong and reliable solution for modern communication requirements.

关键词： Wireless communication Antenna measurements Base stations Transmitting antennas Interference Bandwidth Reliability

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CL-TAP: An Efficient Certificateless Based Trusted Access Protocol for WLAN

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Chinese Journal of Electronics 2025年第1期23卷 142-146页

作者： Zhuo Ma Fenghua Li Jianfeng Ma Wenjiang Ji School of Computer Science and Technology Xidian University Xi'an Shannxi China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China

Based on the certificateless public key cryptography and the trusted computing technologies, a certificateless based trusted access protocol for WLAN (Wireless local area networks) is proposed. Such protocol realizes the mutual authentication and unicast session key agreement between STA and AP within 3 protocol rounds. In particular, the platform authentication and integrity verification are achieved during the authentication procedure. The security properties of the new protocol are examined using the Extended Canetti-Krawczyk security model. The analytic comparisons show that the new protocol is very efficient in both computing and communications.

关键词： Wireless LAN Trusted computing Unicast Computational modeling Authentication Access protocols Public key cryptography Delays Security Research and development

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Beyond Precision and Recall: Measuring Search Engine Consistency Using Rank Stability

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IEEE Access 2025年 13卷 92242-92259页

作者： Lakshmi, S. Thinakaran, Rajermani Batri, Krishnan Sowrirajan, R. Murugan, Sivaram Jain University Department of Electronics and Communication Engineering Karnataka Bengaluru India INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Jain University School of Computer Science and Engineering Karnataka Bengaluru India Dr. N.G.P. Arts and Science College Department of Mathematics Tamil Nadu Coimbatore India Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas58000 Turkey

Traditional information retrieval metrics such as precision, recall, and F-measure assess document relevance but fail to capture the stability of search rankings. Search engines frequently update their algorithms, leading to variations in retrieved documents and their rankings across identical queries. This study introduces a novel rank correlation measure that quantifies search result consistency by analyzing rank fluctuations in repeated queries, serving as a proxy for temporally separated searches. The proposed approach distinguishes between overlapping and non-overlapping documents, providing a detailed measure of ranking stability. Empirical evaluations comparing Google and Bing reveal that Google exhibits greater rank fluctuations for non-overlapping documents, whereas both search engines maintain relatively stable rankings for overlapping documents. These findings highlight the importance of rank-based evaluation frameworks in search engine analysis, extending beyond conventional retrieval metrics. By incorporating stability assessments, this research enhances the methodology for evaluating search consistency in dynamic web environments, contributing to process innovation in information retrieval (SDG 9: Industry, Innovation, and Infrastructure) and promoting fair, reliable access to information (SDG 16: Peace, Justice, and Strong Institutions). © 2013 IEEE.

关键词： Online searching

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SHFuzz:A Hybrid Fuzzing Method Assisted by Static Analysis for Binary Programs

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China communications 2021年第8期18卷 1-16页

作者： Wenjie Wang Donghai Tian Rui Ma Hang Wei Qianjin Ying Xiaoqi Jia Lei Zuo School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of SciencesBeijing 100049China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China NSFOCUS Inc. Beijing 100089China

Fuzzing is an effective technique to find security bugs in programs by quickly exploring the input space of *** further discover vulnerabilities hidden in deep execution paths,the hybrid fuzzing combines fuzzing and concolic execution for going through complex branch *** general,we observe that the execution path which comes across more and complex basic blocks may have a higher chance of containing a security *** on this observation,we propose a hybrid fuzzing method assisted by static analysis for binary *** basic idea of our method is to prioritize seed inputs according to the complexity of their associated execution *** this purpose,we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs’*** key advantage of our method is that our system can test binary programs efficiently by using the hardware trace and hybrid *** evaluate the effectiveness of our method,we design and implement a prototype system,namely *** evaluation results show SHFuzz discovers more unique crashes on several real-world applications and the LAVA-M dataset when compared to the previous solutions.

关键词： hybrid fuzzing static analysis concolic execution binary programs

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Adaptive Constraint Partition Based Optimization Framework for Large-Scale Integer Linear Programming 37

Adaptive Constraint Partition Based Optimization Framework f...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Ye, Huigen Wang, Hongyan Xu, Hua Wang, Chengming Jiang, Yu State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Meituan Inc. Block F&G Wangjing International R&D Park No.6 Wang Jing East Rd Chaoyang District Beijing100102 China

ISBN: (纸本)9781577358800

Integer programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs. To solve this type of IPs, Large neighborhood search (LNS) uses an initial feasible solution and iteratively improves it by searching a large neighborhood around the current solution. However, LNS easily steps into local optima and ignores the correlation between variables to be optimized, leading to compromised performance. This paper presents a general adaptive constraint partition-based optimization framework (ACP) for large-scale IPs that can efficiently use any existing optimization solver as a subroutine. Specifically, ACP first randomly partitions the constraints into blocks, where the number of blocks is adaptively adjusted to avoid local optima. Then, ACP uses a subroutine solver to optimize the decision variables in a randomly selected block of constraints to enhance the variable correlation. ACP is compared with LNS framework with different subroutine solvers on four IPs and a real-world IP. The experimental results demonstrate that in specified wall-clock time ACP shows better performance than SCIP and Gurobi. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Integer programming

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Encryption Traffic Classification Based on Mining Traffic Context and Transport Relationship

Encryption Traffic Classification Based on Mining Traffic Co...

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IEEE Conference on Wireless communications and Networking

作者： Weilin Gai Runqing Zhang Huiyuan Zhang Yu Guo Jun Yin Peng Zhang TCA Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response TechnicalTeam/Coordination Center of China Beijing China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper proposes a novel ETC-MTCTR, which is designed to enable more accurate, versatile and efficient traffic classification in the context of multi-scenario, low-resource encrypted traffic. Through three modules of Datagram Token conversion, pretraining and fine-tuning, the method uses large-scale unlabeled encrypted traffic for pretraining, mining and learning the traffic context and transmission relationship of encrypted traffic classification tasks, so that a small number of labeled data samples can be effectively used in the fine-tuning stage. Significantly improve the performance of the model on specific downstream classification tasks, enhance the accuracy, adaptability and robustness of the model in diverse environments, limited resources and new encryption security protocols, and realize efficient encryption traffic classification in multi-scenario and low-resource background. The results show that ETC-MTCTR achieves the best performance on three tasks: encryption malware classification, VPN encrypted traffic classification, and TLS 1.3 encryption application classification. Its F1 score is improved by 0.22% in the classification task of encrypted malware, 1.4% in the classification task of VPN encrypted traffic App, 4.56% in the classification task of VPN encrypted traffic Service, and 9.89% in the classification task of TLS 1.3 encrypted application, which is significantly better than other comparison methods.

关键词： Adaptation models Accuracy Protocols Malware Robustness Encryption Virtual private networks Data mining

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Facial Instance Learning for Video-based ASD Diagnosis

Facial Instance Learning for Video-based ASD Diagnosis

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Wenhao Duan Jing Li Gaoxiang Ouyang School of Computer Science and Engineering Tianjin University of Technology China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder marked by social interaction difficulties and repetitive, stereotyped behaviors. Previous studies have shown that facial expression recognition has been successfully applied to ASD classification. However, they typically designed a specific framework to explore predefined facial patterns in video data, which usually ignore subtle but potentially vital information embedded in facial movements. To address this limitation and fully leverage the richness of facial data, we design the facial instance learning (FIL) network, a video classification network based on weakly supervised learning. Firstly, the FIL network leverages multi-instance learning to efficiently analyze video sequences without relying on frame-by-frame annotations. Secondly, we employ the R3D18 backbone network to capture both short-term facial dynamics and spatial details of video sequences. Finally, we design the spatiotemporal aggregation module to learn long-term temporal relationships and spatial features. The experiments on the DERW dataset show that the performance of the FIL network is competitive in dynamic facial expression recognition and shows a surprising effect on ASD diagnosis, achieving the accuracy of 98.72%.

关键词： Autism Accuracy Mechatronics Face recognition Machine vision Video sequences Supervised learning Feature extraction Data models Spatiotemporal phenomena

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AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer

arXiv

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arXiv 2024年

作者： Wu, Zhuguanyu Chen, Jiaxin Zhong, Hanwen Huang, Di Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks in the computer vision community. Despite the high accuracy, deploying it in real applications raises critical challenges including the high computational cost and inference latency. Recently, the post-training quantization (PTQ) technique has emerged as a promising way to enhance ViT's efficiency. Nevertheless, existing PTQ approaches for ViT suffer from the inflexible quantization on the post-Softmax and post-GELU activations that obey the power-law-like distributions. To address these issues, we propose a novel non-uniform quantizer, dubbed the Adaptive Logarithm AdaLog (AdaLog) quantizer. It optimizes the logarithmic base to accommodate the power-law-like distribution of activations, while simultaneously allowing for hardware-friendly quantization and de-quantization. By employing the bias reparameterization, the AdaLog quantizer is applicable to both the post-Softmax and post-GELU activations. Moreover, we develop an efficient Fast Progressive Combining Search (FPCS) strategy to determine the optimal logarithm base for AdaLog, as well as the scaling factors and zero points for the uniform quantizers. Extensive experimental results on public benchmarks demonstrate the effectiveness of our approach for various ViT-based architectures and vision tasks including classification, object detection, and instance segmentation. Code is available at https://***/GoatWu/AdaLog. Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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