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Intelligent Computing and Control (IC&C), International Conference on

作者： Heng Zhao Yixing Chen College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Time series prediction has been a hot research topic for decades. Temperature data is typical time series data, so temperature prediction is an important applications. In recent years, Machine Learning has been applied to time series prediction in many fields. Besides, the development of Deep Learning has made many new breakthroughs in these predictions. Generally, Deep Learning is considered to be superior to traditional Machine Learning in temperature prediction. But in fact, the traditional methods can also perform well for short-term forecast in this regard. This research compares the performance of Linear Regression, SVR, CART, KNN and LSTM models in short-term temperature prediction through three evaluation indicators: MAE, MSE and R2-score. The results explicate that the traditional model can also be better than the depth model in short-term temperature prediction.

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Advancements in Cybersecurity Using Deep Learning Techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Hosanna Princye, P. Ahila, A. Lakshmi, A. Anna Ragavendran, N. Purushothaman, K.E. Uma Maheswari, G. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India R.M.K. Engineering College Department of Information Technology Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Software Analyst Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. Due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. Deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study. © 2024 IEEE.

关键词： Cyber attacks

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2D Animation - Q Version of the AIDS Prevention Short Video 10

2D Animation - Q Version of the AIDS Prevention Short Video

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10th International Conference on Orange Technology, ICOT 2022

作者： Wei, Chun-Cheng Hsieh, Hui-Ming Hung, Long-Chen Chen, Ru-Yi Huang, Jiang-Fang Lu, Xin-Chun Zhong, Yu-Shan School of Software and Big Data Changzhou College of Information Technology Changzhou China Taiwan Shoufu University Department of Computer Science and Multimedia Design Tainan Taiwan Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan

ISBN: (纸本)9781665454193

AIDS is a sexually transmitted disease. The medical term is called Acquired Immune Deficiency Syndrome. Medical methods such as cocktail therapy and anticancer drugs can kill AIDS. Although there are currently cured cases, prevention is still required to prevent the infection from expanding;prevention Ways, like the AIDS vaccine or preventive safe sex, can reduce the risk of infection. We hope that through the production of animated short films for viewing, we can help reduce the HIV infection rate. This short film is presented as a story similar to a micro-film by ourselves. And using the modern scene and the character design of the Q version as the material, the story content is made into 2D animation with Adobe Flash CS5. © 2022 IEEE.

关键词： Animation

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Birth Mode Prediction Using Bagging Ensemble Classifier: A C...

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2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021

作者： Bin Alam, Md. Sakib Patwary, Muhammed J. A. Hassan, Maruf Asian University for Women Department of Computer Science Bangladesh Big Data Institute College of Computer Science and Software Engineering Shenzhen University China International Islamic University Chittagong Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665414609

Maternal mortality and childbirth complications are major delivery issues in most developing countries, especially in rural areas. The proper identification of risks associated with the delivery of an expecting woman at an earlier stage can substantially reduce the mortality rate. A few studies have been conducted on using Machine Learning (ML) techniques for predicting birth mode i.e. caesarean section or normal delivery. The most commonly used methods are Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). In this study we have implemented Bagging Ensemble Classifiers based on these traditional machine learning algorithms, which is a novel approach in the area of birth mode prediction. This paper examines the performance of four ML algorithms, with bagging ensemble classifiers (DT-Bagging, KNN-Bagging, NB-Bagging, SVM-Bagging). The result shows that bagging ensemble models outperformed the traditional models in this domain. Besides, we have identified the association between important factors and caesarean section. This study may later be used to create a decision support system by extracting knowledge from the hidden patterns in data to reduce the rate of caesarean delivery in Bangladesh. © 2021 IEEE.

关键词： Decision trees

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Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition

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Expert Systems with Applications 2025年 283卷

作者： Chen, Jiale Xu, Feng Lyu, Xin Zeng, Tao Li, Xin Chen, Shangjing College of Computer Science and Software Engineering Hohai University Nanjing210024 China School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211106 China

Few-shot fine-grained recognition (FS-FGR) aims to recognize nuanced categories with a limited number of labeled samples that were not encountered during training. Previous work has made significant progress by enhancing the learning of foreground refined regions and the alignment of consistent semantics. However, the detrimental impact of insufficient background diversity on constructing representative category prototypes has been overlooked. Meanwhile, the alignment of semantically consistent features has been hampered by the reliance on singular metrics, resulting in suboptimal feature extraction. To address the limitations above, a novel framework with multi-view aggregation and multi-relation alignment (MVRA) is proposed. In this framework, we strive to refine category prototypes by generating and consolidating multiple views from limited learnable samples. Specifically, we generate foreground-refined views, pinpointing discriminative regions, and background-obfuscated views, broadening the landscape of background diversity. Further, without relying on the entire prior, a global label assignment module is designed to automatically assign reliable labels to the query set samples. Finally, armed with these credible labels, the multi-relation alignment module harnesses the enriched views and their semantic congruencies, facilitating robust feature extraction. The effectiveness and outstanding performance of MVRA are evaluated through extensive experiments conducted on three fine-grained benchmark datasets. © 2025

关键词： Feature extraction

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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An Efficient Blockchain-Based Certificateless Anonymous Authentication Scheme for VANETs

An Efficient Blockchain-Based Certificateless Anonymous Auth...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Bi, Changbing Tian, Youliang Li, Ta Wu, Shihong Zhou, Hua Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou University Guizhou Province Key Laboratory of Cryptography and Block Chain Technology Guiyang550025 China Institute of Cryptography & Data Security Guizhou University Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9798350327380

Vehicle ad-hoc networks (VANETs) play a critical role in providing security and privacy protection for intelligent transportation systems. However, the limited network bandwidth and computing capacity within VANETs have resulted in existing authentication schemes having excessive computational overhead, which cannot meet the low-latency communication requirements of VANETs. To tackle these problems, we propose an efficient blockchain-based certificateless anonymous authentication scheme for VANETs. Firstly, we propose a novel signature scheme without bilinear pairing to reduce the computational overhead in VANETs. Secondly, we utilize blockchain to facilitate trusted information sharing. Thirdly, we provide privacy-preserving through the use of anonymous keys, and enable tracing of vehicle identities in the event of malicious behavior. Finally, security analysis and simulation experiments demonstrate that our proposed scheme has lower communication and computational overhead, while satisfying various security requirements for VANETs, such as anonymity, unlinkability, and traceability. © 2023 IEEE.

关键词： Authentication

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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