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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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Algebraic Approach in Molecular Modeling

Algebraic Approach in Molecular Modeling

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Letychevskyi, Oleksandr Peschanenko, Volodymyr Tarasich, Yuliia Volkov, Vladislav Department of Theory Digital Machine No 100 V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine Kyiv Ukraine Private Enterprise LitSoft Kherson Ukraine Department of Computer Science and Software Engineering Kherson State University Kherson Ukraine

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The presence of a number of open problems in the fields of chemistry, physics, and biology suggests the need to find new approaches, tools, and methods for researching the original properties of organic and inorganic substances. computer molecular modeling is the basis of many experimental studies is the use. For this time, a lot of special software systems were created. They were successfully used in such diverse areas as medicine, bioinformatics, pharmacology, materials science, and nanotechnology. But, despite a large amount of existing software, new discoveries and challenges in the field of molecular and, in particular, biomolecular modeling provoke the need to develop new methods and tools. In particular, there is a need to develop new algorithms for the use of a quantum-chemical apparatus for the analysis, first of all, of intermolecular interactions, which are the basis for modeling bioactive molecules and materials. One of these approaches is the algebraic approach proposed by the authors. The algebraic approach is based on behavior algebra and the theory of interaction between agents and environments. The article describes the construction of a tool for the study of molecular interactions, which is based on the formalism of behavior algebra and insertion modeling. Representation of the structure of substances at the level of their electronic structure, modeling of the interaction of organic and inorganic substances at the level of quantum-chemical mechanisms of interaction using an algebraic approach allows for solving open issues in natural sciences. In particular, it allows for studying the properties of substances, cell-virus interaction, radiation therapy, etc. © 2022 IEEE.

关键词： Algebra

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors Data models Medical diagnosis Medical diagnostic imaging

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Empowering Adaptive Endogenous Security Trend Prediction Detection for IoT Sensor Nodes

Empowering Adaptive Endogenous Security Trend Prediction Det...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Lihu Zhou Xiuying Dong Enli Zhang Ting Wang Xiao Zhang Chong Zhang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Massively deployed IoT devices require a lightweight design to reduce costs. However, this architecture inherently limits their security, increasing the risk of data breaches and tampering on a large scale. In this paper, we propose CGAD, a model that leverages one-dimensional convolution (Conv1D), gated recurrent units (GRU), and the attention mechanism to analyze multi-sensor time series data at the data gateway from sensing terminals. To achieve this, we generate time series predictions and detect anomalies by comparing predicted values with real data using a threshold-based approach, thereby enhancing data security. To address the labeling problem of massive data, we employ unsupervised learning and design a dynamic sliding window threshold to improve the judgment of time series with varying characteristics. We also used a number of datasets, such as the KW51 railroad bridge dataset and the self-picked temperature and humidity dataset, to evaluate our system. The results demonstrate that our CGAD model achieves recall rates of 98 % and 99 % for anomaly detection on the two datasets, respectively. Additionally, the precision and F1 scores surpass other methods, ensuring cost-effectiveness for massive terminals and addressing the security issues of sensitive data. These advantages contribute to realizing the Internet of Everything.

关键词： Adaptation models Data security Time series analysis Humidity Predictive models Logic gates Data models Sensors Internet of Things Anomaly detection

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Empowering Adaptive Endogenous Security Trend Prediction Detection for IoT Sensor Nodes 5

Empowering Adaptive Endogenous Security Trend Prediction Det...

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5th International Conference on Advances in Electrical, Electronics and Computing Technology, EECT 2025

作者： Zhou, Lihu Dong, Xiuying Zhang, Enli Wang, Ting Zhang, Xiao Zhang, Chong School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Exploration and Development Research Institute of PetroChina Southwest Oil & Gasfield Company Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541545

Massively deployed IoT devices require a lightweight design to reduce costs. However, this architecture inherently limits their security, increasing the risk of data breaches and tampering on a large scale. In this paper, we propose CGAD, a model that leverages one-dimensional convolution (Conv1D), gated recurrent units (GRU), and the attention mechanism to analyze multi-sensor time series data at the data gateway from sensing terminals. To achieve this, we generate time series predictions and detect anomalies by comparing predicted values with real data using a threshold-based approach, thereby enhancing data security. To address the labeling problem of massive data, we employ unsupervised learning and design a dynamic sliding window threshold to improve the judgment of time series with varying characteristics. We also used a number of datasets, such as the KW51 railroad bridge dataset and the self-picked temperature and humidity dataset, to evaluate our system. The results demonstrate that our CGAD model achieves recall rates of 98% and 99% for anomaly detection on the two datasets, respectively. Additionally, the precision and F1 scores surpass other methods, ensuring cost-effectiveness for massive terminals and addressing the security issues of sensitive data. These advantages contribute to realizing the Internet of Everything. © 2025 IEEE.

关键词： Gateways (computer networks)

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ICTDroid: Parameter-Aware Combinatorial Testing for Components of Android Apps

ICTDroid: Parameter-Aware Combinatorial Testing for Componen...

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IEEE International Conference on Automated software engineering (ASE)

作者： Shixin Zhang Shanna Li Xi Deng Jiwei Yan Jun Yan Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Information Technology Center Beijing Jiaotong University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences

Components are the fundamental building blocks of Android applications. Different functional modules represented by components often rely on inter-component communication mechanisms to achieve cross-module data transfer and method invocation. It is necessary to conduct robustness testing on components to prevent component launching crashes and privacy leaks caused by unexpected input parameters. However, as the complexity of the input parameter structure and the diversity of possible inputs, developers may overlook specific inputs that result in exceptions. At the same time, the vast input space also brings challenges to efficient component testing. In this paper, we designed an automated test generation and execution tool for Android application components named ICTDroid, which combines static parameter extraction and adaptive-strength combinatorial testing generation to detect bugs with a compact test suite. Experiments have shown that the tool triggers 205 unique exceptions in 30 open-source applications with 1,919 test cases in 83 minutes, where the developers have confirmed six defects in three issues we reported.

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An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata

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IEEE Transactions on Big Data 2024年

作者： Rizwan, Muhammad Hawbani, Ammar Xingfu, Wang Anjum, Adeel Angin, Pelin Sever, Yigit Chen, Sanchuan Zhao, Liang Al-Dubai, Ahmed University of Science and Technology of China School of Computer Science and Technology China Shenyang Aerospace University School of Computer Science China Quaid i Azam University Institute of Information Technology Islamabad Pakistan Middle East Technical University Department of Computer Engineering Turkey Auburn University Department of Computer Science and Software Engineering United States Computing School of Edinburgh Napier University United Kingdom

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model lc, ls-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack (Vc0) and a Vulnerable sensitive attribute attack (Vsa) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;'INFORMS','YOUTUBE', and 'IMDb' demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets © 2024 IEEE.

关键词： Anonymity

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MCAN: An Efficient Multi-Task Network for Facial Expression Analysis

MCAN: An Efficient Multi-Task Network for Facial Expression ...

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International Conference on computer Supported Cooperative Work in Design

作者： Haitao Wang Rui Wang Peng Zou Qingjian Ni Xiao Sun College of Software Engineering Southeast University Nanjing China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the development of artificial intelligence, artificial intelligence technology is widely used in robots. For example, emotional computing robots need to be able to complete the function of facial expression analysis. The multi-task deep learning model MCAN proposed in this paper is designed to complete facial expression analysis for robots which can predict discrete expressions and dimensional measures. The class center loss function proposed in this paper can increase the inter-class distance while reducing the intra-class distance. In addition, multi-head attention network was improved to focus on different areas of the input image. Finally, feature pyramid network allows information exchange and fusion between feature maps at different levels. Experiments have proven that MCAN achieves excellent results on both the AffectNet dataset and the AFEW-VA dataset, and also speeds up inference.

关键词： Measurement Deep learning Federated learning Computational modeling Predictive models Multitasking Feature extraction

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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Fuzzy-Classical Linguistic Concept Acquisition Approach Based on Attribute Topology 2nd

Fuzzy-Classical Linguistic Concept Acquisition Approach Base...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Pang, Kuo Kang, Ning Zou, Li Lu, Mingyu Information Science and Technology College Dalian Maritime University Dalian116026 China Software Engineering Institute East China Normal University Shanghai200062 China School of Computer Science and Technology Shandong Jianzhu University Jinan250102 China

ISBN: (纸本)9789811975097

There is a large amount of fuzzy linguistic-valued data in real life. For the complexity of fuzzy linguistic concept acquisition, in this paper, we propose a Fuzzy-classical Linguistic Concept Lattice Acquisition (FLCLC) approach based on attribute topology. Specifically, the fuzzy-classical linguistic concept induced operator is proposed according to the fuzzy linguistic formal context. On this basis, the fuzzy linguistic attribute topology is generated by the fuzzy linguistic coupling relationship between attributes. Furthermore, in order to improve the interpretability of the fuzzy-classical linguistic concept acquisition process, we traverse the weight paths of the coarse fuzzy linguistic attribute topology, and the set of attributes obtained by traversing the paths is the intents of the fuzzy-classical linguistic concept, and the weights on the paths are the extents of the fuzzy-classical linguistic concept, resulting in the fuzzy-classical linguistic concept. Finally, examples are used to demonstrate the effectiveness and practicality of our proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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