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Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Wei Liu Benhui Zou Zicong Han Wenke Kuang Wenzhuo Xie Long Wang Lei Zhang Xiaona Zhang Hebei Communications Investment Group Zhangshi Expressway Baoding Section Co. Ltd. China China-Road Transportation Verification & Inspection Hi-Tech Co. Ltd China China University of Petroleum China School of Civil Engineering and Architecture Wuhan Institute of Technology China

ISBN: (纸本)9798400716478

highway tunnels are more prone to traffic accidents than open highways, and the analysis of the spatial and temporal distribution characteristics of traffic accidents in long downhill tunnel sections of mountain highways can provide a clearer understanding of the changing characteristics of different accidents, so as to enhance the safety management of the long downhill section of mountain road. In this paper, the accident data information is processed by SPSS. According to the processed data, we analyze the time and place where traffic accidents are prone to occur in two dimensions, analyze the reasons and put forward suggestions for improvement, which is of great significance to improve the safety of the tunnel section of mountain highway.

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Multi-Level Social Network Alignment Via Adversarial Learning and Graphlet Modeling

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SSRN 2024年

作者： Duan, Jingyuan Kang, Zhao Tian, Ling Xin, Yichen School of Computer Science and Engineering University of Electronic Science and Technology of China No.2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu611731 China Kash Institute of Electronics and Information Industry Silkroad Talent Plaza Shenka Ave Kashagar City Xinjiang844099 China

Aiming to identify corresponding users in diﬀerent networks, social network alignment is signiﬁcant for numerous subsequent applications. Most existing models apply consistency assumptions on undirected networks, ignoring platform disparity caused by diverse functionalities and universal directed relations like follower-followee. Consisting of indiscriminative nodes and edges, subgraph isomorphism is unavoidable in neighborhoods. To alleviate platform disparity and subgraph isomorphism issues simultaneously, the Multi-level Adversarial and Graphlet-based Social Network Alignment (MAGSNA) for directed networks with both individual-level and partition-level is proposed. For original networks at individual-level, random walk with restart alleviates topology disparity, and attribute disparity is mitigated by directed weight-sharing embeddings and a bidirectional optimizer using Wasserstein distance and adversarial learning. With overlapped partitions extracted from graphlet orbits, subgraph isomorphism is alleviated by directed weight-sharing partition representations and a hubness-aware reﬁnement, which also mitigate the platform disparity at partition-level. Integrating similarities, an accurate and comprehensive alignment is further obtained. Experiments on four real-world datasets show that MAGSNA outperforms state-of-the-art social network alignment algorithms. © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Associations between levels of Internet Gaming Disorder symptoms and striatal morphology–replication and associations with social anxiety

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Psychoradiology 2022年第4期2卷 207-215页

作者： Benjamin Klugah-Brown Xinqi Zhou LanWang Xianyang Gan Ran Zhang Xiqin Liu Xinwei Song Weihua Zhao Bharat B.Biswal Fangwen Yu Christian Montag Benjamin Becker The Center of Psychosomatic Medicine Sichuan Provincial Center for Mental HealthSichuan Provincial People’s HospitalMOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaNo.2006Xiyuan AveWest Hi-Tech Zone611731ChengduSichuanP.R.China Institute of Brain and Psychological Sciences Sichuan Normal UniversityChengdu610101China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNew Jersey 07102USA Department of Molecular Psychology Institute of Psychology and EducationUlm University89069 UlmGermany

Background:Brain structural alterations of the striatum have been frequently observed in internet gaming disorder(IGD);however,the replicability of the results and the associations with social-affective dysregulations such as social anxiety remain to be ***:The present study combined a dimensional neuroimaging approach with both voxel-wise and data-driven multivariate approaches to(i)replicate our previous results on a negative association between IGD symptom load(assessed by the Internet Gaming Disorder Scale-Short Form)and striatal volume,(ii)extend these findings to female individuals,and(iii)employ multivariate and mediation models to determine common brain structural representations of IGD and social anxiety(assessed by the Liebowitz Social Anxiety Scale).Results:In line with the original study,the voxel-wise analyses revealed a negative association between IGD and volumes of the bilateral *** beyond the earlier study investigating only male participants,the present study demonstrates that the association in the right caudate was comparable in both the male and the female *** examination using themultivariate approach revealed regionally different associations between IGD and social anxiety with striatal density representations in the dorsal striatum(caudate)and ventral striatum(nucleus accumbens).higher levels of IGD were associated with higher social anxiety and the association was critically mediated by the multivariate neurostructural density variations of the ***:Altered striatal volumes may represent a replicable and generalizable marker of IGD ***,exploratory multivariate analyses revealed more complex and regional specific associations between striatal density and IGD as well as social anxiety *** in both tendencies may share common structural brain representations,which mediate the association between increased IGD and social anxiety.

关键词： internet gaming disorder social anxiety source-based morphometry gray matter MRI replication

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Hybrid Models for Effective Adversarial Attack Detection in Cyberspace Using Machine Learning

Hybrid Models for Effective Adversarial Attack Detection in ...

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Disruptive technologies (ICDT), International Conference on

作者： Bhavesh Kumar Sharma Atul Kumar Rai Pawan Kumar Asim Kumar Rai Khushboo Tripathi Dept. of CSA Sharda University Greater Noida U.P India Dept. of CSE Galgotias University Greater Noida U.P India Dept. Of MCA Hi-Tech Institute of Engineering and Technology Ghaziabad U.P India Dept. of CSE IMS Engineering College Ghaziabad U.P India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This With the exponential proliferation of cyberspace, it brought us in to a new area of security challenges that require cutting-edge methods and tools for adversaries to launch attack. The paper describes a novel idea of hybrid model using latest machine learning algorithms in order to handle adversarial attacks on Internet. The hybrid architecture combines a mix of algorithms that cheats the accuracy and ensures robustness against new cyber threats. It achieves latest performance with 97.8% detection rate on the benchmark datasets via extensive analysis and experiments, outperforming standalone models by a significant margin (+12.3 %). On targeted perturbations too, the hybrid approach exhibits 93.5% detection accuracy and proves to be more computationally secure against adversaries with diverse adversarial attacks (vs. standard model). This research should be a framework to measure the performance of hybrid architectures generally and especially for enhancing security in cyberspace using advanced machine learning algorithms.

关键词： Machine learning algorithms Accuracy Computational modeling Perturbation methods Neural networks Cyberspace Machine learning Computer architecture Robustness Standards

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Field Emission Performance of Holey Expanded Graphite

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SSRN 2024年

作者： Zhang, Yihui Ji, Qianyu Zhang, Jiacheng Wang, Chengbao Guo, Wenhua Zhao, Jiyuan Zi, Yanyang School of Mechanical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Innovation Institute of Additive Manufacturing #997 Shanglinyuan 8th Road Hi-tech District Shaanxi Xi’an710000 China School of Automation Beijing Information Science and Technology University Beijing100192 China

Expanded graphite (EG) has promising applications in field emission due to its high conductivity, thermal stability and low cost. In this paper, the field emission performance of EG is modulated by introducing hole defects through partial air oxidation. First, EG was prepared by electrochemical intercalation and microwave-assisted expansion methods. Subsequently, EG was heat treated in air at different temperatures to obtain holey expanded graphite (HEG). Finally, bulk HEG cathodes were prepared by the cold pressing method and the effect of holes on the field emission performance was investigated. The results showed that the conductivity and tensile properties of EG were significantly improved after heat treatment. Optimal heat treatment parameters were obtained at 400°C in an air atmosphere. The turn-on field (at 1 mA/cm2) and threshold field (at 10 mA/cm2) were 3.01 V/μm and 3.42 V/μm, respectively, with a maximum current density of 1359.17 mA/cm2 (at 5.83 V/μm) and a maximum field enhancement factor of 2407. The voltage fluctuation is only 1.1% over 24 h at 3 mA @ 440.33 mA/cm2, indicating good emission stability and lifespan. © 2024, The Authors. All rights reserved.

关键词： Heat treatment

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Hybrid Models for Effective Adversarial Attack Detection in Cyberspace Using Machine Learning 3

Hybrid Models for Effective Adversarial Attack Detection in ...

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3rd International Conference on Disruptive technologies, ICDT 2025

作者： Sharma, Bhavesh Kumar Rai, Atul Kumar Kumar, Pawan Rai, Asim Kumar Tripathi, Khushboo Sharda University Dept. of Csa U.P Greater Noida India Galgotias University Dept. of Cse U.P Greater Noida India Hi-Tech Institute of Engineering and Technology Dept. Of Mca U.P Ghaziabad India Ims Engineering College Dept. of Cse U.P Ghaziabad India

ISBN: (纸本)9798331519582

关键词： Neural networks

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Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning

arXiv

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arXiv 2023年

作者： Hu, Zhihang Yu, Qinze Guo, Yucheng Wang, Taifeng King, Irwin Gao, Xin Song, Le Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Intelligence Technology Limited Beijing China Computational Bioscience Reseach Center King Abdullah University of Science and Technology Thuwal Saudi Arabia The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China

Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves the state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we’re robust against unseen drugs and surpass almost 15% AU ROC compared to the second best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine. © 2023, CC BY-NC-ND.

关键词： Graphic methods

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Cellulose nanocrystal-modified hydrogel wearable sensor with self-healing function from dynamic bond and controllable dual hydrogen-bond crosslinking

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Carbohydrate Polymers 2025年 363卷 123760页

作者： Gu, Ruihuan Chen, Weiwei Zou, Yanrui Yang, Tingting Liu, Zirui Yu, Ning Huang, Jin Gan, Lin School of Chemistry and Chemical Engineering and Chongqing Key Laboratory of Soft-Matter Materials Manufacturing Chongqing Sports Medicine Industry Technology Innovation Center Southwest University No. 2 Tiansheng Road Beibei Chongqing400715 China School of Chemistry and Chemical Engineering Engineering Research Center of Materials-Oriented Chemical Engineering of Xinjiang Bintuan Shihezi University North Fourth Road Shihezi832003 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences No.266 Fangzheng Avenue Shuitu Hi-tech Industrial Park Shuitu Town Beibei District Chongqing400714 China

Multi‑hydrogen bond networks have been introduced into hydrogels to enhance responsiveness, self-healing, and processability, but low interaction density and weak bond force limit their strength and tear resistance. In this study, we construct a multiple-crosslinked interpenetrating network system of dynamic covalent bond, dual‑hydrogen bond, and percolation around cellulose nanocrystals (CNCs) to resolve the function-plasticity trade-off. This design enhances strength and toughness without sacrificing the hydrogel's electromechanical sensing and self-healing performance. The resulting hydrogel demonstrates improved tensile strength (25.54 kPa), toughness (246.09 kJ/m3), and self-healing (93.3 %). Meanwhile, based on ionic conductivity and excellent mechanical plasticity, the hydrogels achieve quick linear response sensing under a wide strain region and high sensitivity (4.36). The mechanism study for the multiple-crosslinked interpenetrating network system reveals that although dual‑hydrogen bonds are weak, they dissipate impact energy over long distances, reducing stress concentration. The percolation network, enabled by the rod-like filler particles of CNCs, enhances strength at a low filler concentration, allowing the dynamic covalent bond crosslinking to maintain flexibility and function. Since the CNCs also present high biocompatibility, the hydrogel must be able to offer promising solutions for applications in wearable electronics and wound dressings. © 2025

关键词： Fracture toughness

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Internet of Things based Smart Water Leakage Monitoring and Alert System

Internet of Things based Smart Water Leakage Monitoring and ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Keerthana S Elango S Judgi T Vivek K S Ponmurugan P Neelam Sanjeev Kumar Department of Electrical and Electronics Engineering J K K Nataraja College of Engineering and Technology Komarapalayam India Department of Electrical and Electronics Engineering Nandha Engineering College Erode Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology India Department of Electrical and Electronics Engineering Shree Venkateshwara Hi-Tech Engineering College Gobichettipalayam Department of Research Meenakshi Academy of Higher Education and Research Chennai Department of Biomedical Engineering Saveetha School of Engineering SIMATS Chennai India

It is critical to manage water supply in a safe and efficient manner. Significant amounts of water are lost each year as a result of leaks in water distribution systems (WDN). As a result, locating leaks in a reliable manner is critical. Water leaks can be detected using monitoring software that analyses the flow of water. Apps that track the user’s location can be extremely useful. If this technology is widely adopted, it will aid in the resolution of water management issues such as human error and waste of limited water resources. Water leaks can be located predictably using the Internet of Things (Internet of Things). Many countries struggle to get enough water, but the situation is not hopeless. It is critical to accurately locate water leaks in order to reduce system resistance.

关键词： Wires Inspection Software Software reliability Internet of Things Electrical resistance measurement Water resources

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Smart Attendance Monitoring System Using IoT

Smart Attendance Monitoring System Using IoT

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Ramani G Pradeepkumar G Palanisamy P N Ashika Preethi S Velappagari Sekhar Neelam Sanjeev Kumar Department of Electrical and Electronics Engineering Nandha Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Mahendra College of Engineering Salem Tamilnadu Department of Electrical and Electronics Engineering Shree Venkateshwara Hi-Tech Engineering College Gobichettipalayam India Department of Electrical and Electronics Engineering Kuppam Engineering College Kuppam Andhra Pradesh Department of Biomedical Engineering SIMATS Saveetha School of Engineering Chennai India

Currently, it is difficult to keep maintaining the student’s attendance at a single university. This paper proposes an intelligent method for tracking student attendance to make keeping track of students' presence easier. It has largely replaced the traditional roll call procedure in businesses and educational institutions. By automatically marking attendance in the database, it reduces the amount of time staff spends manually entering attendance information. RFID tags and readers, a component of the Internet of Things, can be used to identify event attendees instead of time-consuming paper logs. They are simple to use for the average person. Unauthorized students would be barred from entering the school using this RFID-based access control system.

关键词： Databases Organizations Cameras Liquid crystal displays Recording Internet of Things IP networks

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