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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vineet Kumar Chauhan Awadhesh Kumar Suresh Kumar Trilok Singh Rakesh Kumar Dwivedi Dept. of Information Technology Galgotias College of Engg. & Tech. Greater Noida Dept. of Computer Science & Engg. Kamla Nehru Institute of Technology Sultanpur School of Computing Graphic Era Hill University Haldwani CCSIT Teerthanker Mahaveer University Moradabad

In this paper, we present two interconnected security methodologies for enhancing database and web application protection. The first method focuses on safeguarding sensitive data, including identity information, passwords, and financial records in databases. It utilizes an ensemble classification algorithm trained on 22 features from queries containing malicious code to perform realtime analysis of SQL queries, enabling the detection and classification of SQL injection attacks with an accuracy exceeding 98%. These attacks are further categorized as simple, unified, or lateral, with tailored responses such as query blocking and source IP address banning. The second approach addresses the prevalent challenge of Cross-Site Scripting (XSS) attacks, notorious for their impact on web security. Diverging from traditional XSS detection methods, our innovative approach employs ensemble learning through Bayesian networks enriched with domain knowledge and threat intelligence. The results are presented in a user-friendly format through a node sorting technique, offering valuable insights into the nature of the attack. Experimental validation with real-world data demonstrates the method's effectiveness, particularly as attack volumes increase. Furthermore, the sorted node results empower security teams with actionable information to proactively manage the evolving XSS threat landscape. Together, these strategies reinforce cybersecurity defenses, providing robust protection against a diverse and ever-evolving range of cyber threats.

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Experimenting Deep Convolutional Visual Feature Learning using Compositional Subspace Representation and Fashion-MNIST

Experimenting Deep Convolutional Visual Feature Learning usi...

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2020 IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2020

作者： Teow, Matthew Y. W. School of Science and Technology Sunway University Department of Computing and Information Systems Bandar Sunway Selangor Malaysia

ISBN: (纸本)9781728169460

This paper introduces a formal framework to model the convolutional visual feature learning in a convolutional neural network, which is called compositional subspace representation. The objective is to explain the convolutional visual feature learning computation using a rigid and structural method. The theoretical basis of the proposed framework is, the best way for representation to model a complex learning function is by using a composition of simple two-dimensional piecewise-linear functions to form a multilayers successive cascaded projection function for complex representation. Under the same hypothesis, the proposed framework also explains the hierarchical feature learning representation in a convolutional neural network, the well-acknowledged significant advantage of convolutional neural networks in visual computing. The proposed framework has experimented with image classification using the Fashion-MNIST dataset. Experimental assessments using learning curves analysis, confusion matrix, and visual assessment are presented and discussed. The experimental results were consistent with the theoretical expectation. © 2020 IEEE.

关键词： Image classification

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A Novel Maximum Distance Separable Code Based RIS-OFDM: Design and Optimization

A Novel Maximum Distance Separable Code Based RIS-OFDM: Desi...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yiqian Huang Ping Yang Yue Xiao Ming Xiao Shaoqian Li Wei Xiang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Sichuan China information science and engineering (ISE) department School of Electrical Engineering KTH Sweden School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne VIC Australia

ISBN: (纸本)9781665435413

In this paper, we propose a novel maximum distance separable (MDS) code based and reconfigurable intelligent surface (RIS) assisted wireless communication system with orthogonal frequency division multiplexing (OFDM). Specifically, input bits are firstly divided into groups and their MDS codes are utilized to decide the amplitudes and phases of subcarriers. The introduction of the MDS code helps to increase the minimum Hamming distance between symbols and improve on the capability of error detection. Besides, the RIS is adopted to create additional paths between the radio frequency (RF) and the receiver as well as alter the signal phases with derived optimal solution. Benefiting from the strength of the RIS, the proposed system can better overcome multipath fading compared with conventional systems. Simulation results are presented to demonstrate the efficacy of the proposed system in terms of reducing bit error rate (BER) through multipath channels.

关键词： Wireless communication Radio frequency Codes Simulation OFDM System performance Bit error rate

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See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification

arXiv

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arXiv 2024年

作者： Han, Xiyu Zhong, Xian Huang, Wenxin Jia, Xuemei Liu, Wenxuan Yu, Xiaohan Kot, Alex Chichung Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572025 China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Computer Science Wuhan University Wuhan430072 China School of Computer Science Peking University Beijing100091 China School of Computing Macquarie University SydneyNSW2109 Australia Rapid-Rich Object Search Lab School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features. Specifically, we introduce Semantic Separation Enhancement (SSE) module, which uses dual learnable text tokens to separately capture confounding and clothing-related semantic information, effectively isolating ID-relevant features from distracting clothing semantics. Additionally, we develop a Semantic-Guided Interaction Module (SIM) that uses orthogonalized text features to guide visual representations, sharpening the model’s focus on distinctive ID characteristics. This integration enhances the model’s discriminative power and enriches the visual context with high-dimensional semantic insights. Extensive experiments on three CC-ReID datasets demonstrate that our method outperforms state-of-the-art techniques. The code will be released at github. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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An Urban Electric Vehicle Charging System via Hybrid Heterogeneous Modes

An Urban Electric Vehicle Charging System via Hybrid Heterog...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Keyang Zhang Yueheng Liu Shuohan Liu Junqiao Gao Yue Cao Naveed Ahmad Xu Zhang School of Cyber Science and Engineering Wuhan University China Wuhan Cyber Security Association China Qilu University of Technology China College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computing Sciences University of East Anglia (UEA) Norwich U.K.

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Electric Vehicle (EV) is regarded as the optimal alternative to traditional fuel-powered vehicles. However, the exponential surge in EV charging demand poses challenges in charging infrastructure planning and charging behavior management. The efficacy of traditional Grid-to-Vehicle (G2V) charging mode, which obtains power from the grid, is curtailed by the limited number and uneven distribution of charging facilities, inevitably leading to charging congestion. Instead, the concept of Vehicle-to-Vehicle (V2V) charging has emerged as a spatio-temporally flexible charging mode, which expands EV's role from consumer to provider, forming V2V pairs and utilizing urban Parking Lots (PLs) as charging locations. In this paper, we propose a Hybrid Heterogeneous Modes (HHM)-based EV charging optimization scheme in urban settings. Building upon the G2V mode, we introduce synchronous and asynchronous V2V charging modes as optimization strategies, integrating and exploiting the unique advantages of each mode. We also utilize global EV charging scheduling and comprehensively take four dimensions into consideration (energy trading cost, travel cost, waiting time cost and loss cost), thus achieving flexible mode selection, V2V pairing, and designated charging locations. Simulations confirm the effectiveness of the proposed scheme in reducing total charging costs, optimizing user service experiences, and improving charging facility utilization rates.

关键词： Costs Processor scheduling Computational modeling Vehicular ad hoc networks Electric vehicle charging Topology Power systems Quality of experience Surges Optimization

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Applying B and ProB to a Real-world Data Validation Project 16

Applying B and ProB to a Real-world Data Validation Project

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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Peng, Cheng Keming, Wang Southwest Jiaotong University School of Information Science and Technology Chengdu China National-local Joint Engineering Laboratory of System Credibility Automatic Verification Chengdu China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665405539

Data validation is a constraint satisfaction problem that can be modelled rigorously by formal methods like B. This paper presents our experiences on validating a real-world section topology of tram lines using the B language and ProB tool. Based on the section topology, validation rules are designed and implemented by using the ASSERTIONS Clause of B. The Epsilon Generation Language Script is used to build a data conversion schema under automatically deriving the topology data into the B model. Furthermore, the ProB is used to validate whether the data satisfy the rules. In this way, the validated topology improves the functional correctness of the tram control system. © 2021 IEEE.

关键词： Topology

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Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment

arXiv

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arXiv 2023年

作者： Li, Xudong Zheng, Jingyuan Zheng, Xiawu Hu, Runze Zhang, Enwei Gao, Yuting Shen, Yunhang Li, Ke Liu, Yutao Dai, Pingyang Zhang, Yan Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Medicine Xiamen University China Peng Cheng Laboratory Shenzhen China School of Information and Electronics Beijing Institute of Technology China Tencent Youtu Lab China School of Computer Science and Technology Ocean University of China China

Image Quality Assessment (IQA) with reference images have achieved great success by imitating the human vision system, in which the image quality is effectively assessed by comparing the query image with its pristine reference image. However, for the images in the wild, it is quite difficult to access accurate reference images. We argue that it is possible to learn reference knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting, which is effective and efficient empirically. Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images. And then, to achieve fast convergence and avoid overfitting, we further propose an inductive bias regularization. Such a framework not only solves the congenital defects of NR-IQA but also improves the feature extraction framework, enabling it to express more abundant quality information. Surprisingly, our method utilizes less input while obtaining a more significant improvement compared to the teacher models. Extensive experiments on 8 standard NR-IQA datasets demonstrate the superior performance to the state-of-the-art NR-IQA methods, i.e., achieving the PLCC values of 0.917 (↑ 3.3% vs. 0.884 in LIVEC) and 0.686 (↑ 2.5% vs. 0.661 in LIVEFB). Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Performance Evaluation of Supervised Machine Learning Algorithms in Prediction of Heart Disease

Performance Evaluation of Supervised Machine Learning Algori...

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2020 IEEE International Conference for Innovation in technology, INOCON 2020

作者： Sujatha, P. Mahalakshmi, K. Vels Institute of Science Technology Advanced Studies Department of Information Technology Chennai India School of Computing Sciences Vels Institute of Science Technology Advanced Studies Chennai India

ISBN: (纸本)9781728197449

Big challenge in health care industry is to record and analyze the massive amount of information about patients. Innovations in technologies made revolution in the healthcare industries. In recent years the data analytics developed as promising tool for problem solving and decision making in healthcare professions. Data analytics process the data automatically to make healthcare system more dynamic and robust. It systematically uses and analyses the data of health care for better treatment with low costs. The chief applications of Machine learning in healthcare are the detection and diagnosis of diseases. The heart is the chief organ of human body. Heart disease increases the mortality rate in the world. Around 90% of heart diseases are preventable. Machine learning plays a remarkable role in the health care industry in prediction of heart disease. In this research paper, the presence of heart disease is predicted by employing Decision Tree, Naïve Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbor and logistic Regression algorithms. The performance of the algorithms was analyzed using parameters such as Accuracy, Precision, AUC and F1-score. From the experimental result, it is found that the Random Forest is more accurate for predicting the heart disease with accuracy of 83.52% compared with other supervised machine learning algorithms. The F1- Score, AUC and precision score of Random forest classifiers are 84.21%, 88.24% and 88.89% respectively. © 2020 IEEE.

关键词： Data Analytics

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Modeling and performance analysis of moving V2V 5G NR systems

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 9卷

作者： Rjab, Mohamed Chamkhia, Hela Omri, Aymen Bouallegue, Seifeddine Bouallegue, Ridha National Engineering School of Tunis University of Tunis El Manar Tunisia Innovation of Communicant & Cooperative Mobiles (Innov'Com) Lab (SUP'COM) Tunis Tunisia Hela Chamkhia is with Computer Science and Engineering Department Qatar University Doha Qatar Seifeddine Bouallegue is with College of Computing and Information Technology University of Doha for Science and Technology Qatar

Vehicle-to-vehicle (V2V) communication is a promising solution for enhancing network performance. This paper presents the modeling and performance analysis of moving V2V fifth generation new radio (5G NR) systems using Poisson point process (PPP) and Matern cluster process (MCP). A new mode selection scheme, the hybrid communication scheme (HCS), is proposed to improve V2V 5G NR system performance. The HCS employs side link (SL) communications, a predefined average threshold of SL received signal strength (RSS), and a minimum residence time for V2V mode. We derive expressions for the threshold RSS, minimum residence time, and ergodic capacity to evaluate HCS performance. Monte Carlo simulations using Matlab confirm the analytical results and demonstrate the advantages of the proposed scheme over related approaches in the literature. © 2024 The Author(s)

关键词： Hybrid communication scheme Side link V2V

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FF-PRec: A Feature Fusion-Based Paper Recommendation Method on Academic Big Data of Heterogeneous Network

FF-PRec: A Feature Fusion-Based Paper Recommendation Method ...

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Computer information science and Artificial Intelligence (CISAI), International Conference on

作者： Yongkang Du Weilong Ding Ying Liang School of Information Science and Technology North China University of Technology Beijing China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9781665406932

With the development of information technology, academic data has increased dramatically. Nowadays, recommendation algorithm is widely used for scholars to find useful information from massive data. However, due to the discrete data and complex semantic relationship in the academic network, existing recommendation algorithms have limitations in feature extraction and data sparsity, which bring negative impacts on recommendation accuracy and personalization. In order to solve the above problems, we propose FF-PRec, a paper recommendation method based on feature fusion in the academic network. First, FF-PRec implements graph representation learning and a natural language processing tool to extract network features and text features respectively. Two types of features are combined as the representation vectors of scholars and papers. Second, a meta-path is designed based on prior knowledge to guide semantic information extraction. To validate our proposed method, we conducted experiments on the AMiner dataset. The experimental results indicate that FF-PRec performs better than traditional methods in paper recommendation tasks and shows high accuracy and correlation.

关键词： Representation learning information science Correlation Semantics Feature extraction information retrieval Natural language processing

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