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SSRN 2023年

作者： Wang, Jinshui Chen, Shuguang Tang, Zhengyi Lin, Pengchen School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

Online Judge Systems (OJSs) are widely used in higher education to assess students’ SQL programming skills and provide feedback in computer education. However, OJSs may not accurately evaluate students’ queries due to the error-detection capabilities of test sets are insufficient, resulting in false positives that can mislead students and hinder their learning. This study analyzes a large-scale OJS’s evaluation dataset and identifies more than 120,000 false positive queries. It also validates existing SQL error categorization and reveals a new type of logical error called deceptive error, which occurs when students construct queries that pass specific test cases but fail to solve the actual problem. This type of error has been overlooked in previous research and can provide new insights into how to improve OJSs by enhancing test cases and feedback. This study contributes to the understanding of assessment and evaluation practices and processes in programming education, particularly the contribution that OJSs make to student learning and to course, staff and institutional development. It also suggests error prevention and detection techniques that can improve the effectiveness and fairness of OJSs in programming education and competitions. © 2023, The Authors. All rights reserved.

关键词： Students

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Processing of ECG Signal Using Optimized Feature Selection for the Prediction of Irregular Heart Beat

Processing of ECG Signal Using Optimized Feature Selection f...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： K. Rema C.P. Maheswaran Department of Computer Applications Noorul Islam Centre for Higher Education Nagercoil Tamilnadu India Department of Artificial Intelligence and Data Science Sri Krishna College of Technology Coimbatore

The categorization of Electro Cardio Gram (ECG) trials into significant arrhythmia programs to identify abnormal heartbeats is presented in this study as an effective hybridized method. The most frequently used and recognized detection technology to display heart fitness is the physiological detection utilizing ECG data. Arrhythmia beat categorization also plays a significant part in ECG analysis that is intended to clarify cardiac well-being position when investigating heartbeat. Artificial Neural Networks (ANN) is deployed for the segmentation of input ECG images. The proposed work uses Fourier transform to remove the intrinsic sound of ECG signals during the preprocessing step in order to properly categorize ECG samples into main arrhythmia classifications. Chicken Swarm Optimization (CSO) is employed for the effective feature selection which is followed by CNN based classification. This demonstrates how the suggested ECG classification model is successful in correctly classifying ECG signals for abnormality detection along with IoT based

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Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

arXiv

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arXiv 2024年

作者： Yao, Quanming Zhang, Yongqi Wang, Yaqing Yin, Nan Kwok, James Yang, Qiang Department of Electronic Engineering the Tsinghua University China Department of Data Science and Analytics Thrust the Hong Kong University of Science and Technology Guangzhou China Beijing Institute of Mathematical Sciences and Applications China Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong

The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to address this issue in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup. This approach allows us to build a framework that uses this knowledge as "building blocks" to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models. © 2024, CC BY.

关键词： Contrastive Learning

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Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

Evaluating the Effectiveness of Object Detection Algorithms ...

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computer, Electronics & Electrical Engineering & their applications (IC2E3), International Conference on

作者： Utkarsh Sharma Krishnan Subramonian Aaron Dsouza Sughosh P Savitha G Girisha S Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the ‘PKLot’ dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development.

关键词： Training Accuracy Urban areas Training data Object detection Manuals Object recognition Traffic congestion Testing Strain

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Graph Embedding of Chronic Myeloid Leukaemia K562 Cells Gene Network Reveals a Hyperbolic Latent Geometry 8th

Graph Embedding of Chronic Myeloid Leukaemia K562 Cells Gen...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Lecca, Paola Re, Angela Lombardi, Giulia Latorre, Roberta Valeria Sorio, Claudio Faculty of Computer Science Smart Data Factory Laboratory Free University of Bozen-Bolzano Bolzano Italy Member of National Group for Mathematical Analysis Probability and their Applications Francesco Severi’ National Institute of High Mathematics Rome Italy Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Mathematics University of Trento Trento Italy Department of Medicine University of Verona Verona Italy

ISBN: (纸本)9789819930906

Recent research emphasises the significance of identifying the latent geometry—also known as the geometry underlying a complex network—which is determined by the manifold class, curvature, and dimension. Geometry’s explanation of the organisational principles of complex systems and the potential that network nodes might be categorised according to their distances in this geometry are what confers geometry its significance. In this study, we analysed the network of genes resulting differentially expressed as a consequence of the modulation of protein tyrosine phosphatase receptor type G (PTPRG) in chronic myeloid leukaemia (CML) cell model K562 transcriptome. We found that the latent geometry of this network is hyperbolic, and that clustering according to the angular coordinates of its nodes in hyperbolic space classifies genes by functional pathway classes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational geometry

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Lateralizing Seizure Onset Zone Through Interictal Sleep Scalp EEG High-Frequency Oscillations

Lateralizing Seizure Onset Zone Through Interictal Sleep Sca...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Dhruva P Achar Karunakar A Kotegar Kurupath Radhakrishnan Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Neurosciences AVITIS Institute of Medical Sciences Nemmara Palakkad Kerala India

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Epilepsy, a neurological disorder impacting an estimated 50 million people globally, sees about 30% of patients developing drug-resistant epilepsy (DRE). For these patients, surgery emerges as a more promising option for achieving seizure freedom. The precise delineation of the Epileptogenic Zone (EZ) is crucial for considering a patient as a surgical candidate, with the Seizure Onset Zone (SOZ) considered the most reliable estimate of the EZ. In this retrospective study, we analysed interictal sleep electroencephalography (EEG) data from 5 patients to evaluate the rate of High-Frequency Oscillations (HFOs) in the 80-250 Hz range. Our objective was to explore whether regions exhibiting a higher incidence of HFO rates correlated with those diagnosed as the SOZ based on patient reports. Additionally, we computed the asymmetry index to examine whether the heightened HFO rate lateralizes to the hemisphere where the seizure occurs. Our results revealed a precise correlation between elevated HFO rates in a region and the specified SOZ as per the report in 4 out of 5 patients. Furthermore, the asymmetry index consistently lateralized to the same hemisphere causing seizure as specified in the report a cross all 5 patients. This implies that the analysis of HFOs can provide valuable assistance to clinicians in accurately identifying the hemisphere responsible for seizures.

关键词： Correlation Sleep Scalp Epilepsy Surgery Hafnium oxide Electroencephalography

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A Dynamic Parallel Harris Hawks Optimization Based WSN Node Localization Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 688-703页

作者： He, Xiankang Chu, Shu-Chuan Liu, Shi-Jian Pan, Jeng-Shyang Yan, Lijun College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Shenzhen Institute of Information Technology Shenzhen518000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

The localization of wireless sensor network (WSN) is an increasingly promi-nent problem. The goal of this problem is to use the anchor nodes in WSN to esti-mate the geographical location of the unknown nodes. This paper proposes a novel al-gorithm, named dynamic parallel Harris Hawks optimization (DPHHO). It contains the dynamic control strategy of the escaping energy and the parallel communication mecha-nism. DPHHO algorithm significantly improves the global search ability of the original Harris Hawks optimization (HHO) algorithm. Also, a novel localization algorithm based on hop distance correction and DPHHO is proposed. The proposed DPHHO algorithm was tested on the 23 classical test functions and the DPHHO-DV-Hop algorithm is applied to the localization of WSN. The experimental results show that, compared with HHO and other optimization algorithms, the proposed DPHHO algorithm is more effective and efficient. Compared with DV-Hop and other localization algorithms, the proposed DPHHO-DV-Hop is an effective algorithm for the localization of WSN. © 2021, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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Grey Wolf Optimizer with Deep Learning based Short Term Traffic Forecasting in Smart City Environment 5

Grey Wolf Optimizer with Deep Learning based Short Term Traf...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Jegadeesan, R. Rapaka, E. Vijayakrishna Himabindu, K. Behera, Nihar Ranjan Shukla, Arvind Kumar Dangi, Arvind Kumar Jyothishmathi Institute of Technology and Science Karimnagar India Data Science Indian Institute of Technology Madras Tamilnadu Chennai India Kl University Department of Csa Andhrapradesh Guntur India Swiss School of Business and Management Geneva Av. des Morgines 12 Petit-Lancy1213 Switzerland Iftm University Department of Computer Applications Uttar Pradesh Moradabad India Mriirs Haryana Faridabad India

ISBN: (纸本)9781665474672

Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that aids in minimizing traffic congestion and improving traffic quality. ITS provides real-time analysis and very effective traffic management by utilizing big data and communication technology. Traffic Flow Prediction (TFP) becomes a dynamic component in smart city management and was utilized for predicting the future traffic conditions on transportation networks relevant to past data. Machine Learning (ML) and Neural Network (NN) techniques can be broadly used in resolving real-time problems as these techniques are capable of managing adaptive data for some time. Deep Learning (DL) is a sub-divison of ML methods which earns effective performance on prediction and data classification tasks. This article designs a Grey Wolf optimizer with Deep Learning Based Short Term Traffic Forecasting (GWODL-STTF) in smart city environment. The presented GWODL-STTF technique concentrates on the prediction of traffic flow in smart cities. The presented GWODL-STTF technique involves two major processes. At the initial stage, the GWODL-STTF technique employed gated recurrent unit-neural network (GRU-NN) model to forecast traffic flow. Next, in the second stage, the GWODLSTTF technique makes use of GWO algorithm as a hyperparameter optimizer. The simulation values of the GWODL-STTF method can be tested under several metrics and the outcomes show the significant performance of the GWODLSTTF method over recent approaches with minimum MSE of 105.627. © 2023 IEEE.

关键词： Smart city

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Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks 39

Adversarial-Inspired Backdoor Defense via Bridging Backdoor ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yin, Jia-Li Wang, Weijian Lyhwa Lin, Wei Liu, Ximeng Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Lion Rock Labs of Cyberspace Security CTIHE Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adversarial attacks and observe two intriguing phenomena when applying adversarial attacks on an infected model implanted with backdoors: 1) the sample is harder to be turned into an AE when the trigger is presented;2) the AEs generated from backdoor samples are highly likely to be predicted as its true labels. Inspired by these observations, we proposed a novel backdoor defense method, dubbed Adversarial-Inspired Backdoor Defense (AIBD), to isolate the backdoor samples by leveraging a progressive top-q scheme and break the correlation between backdoor samples and their target labels using adversarial labels. Through extensive experiments on various datasets against six state-of-the-art backdoor attacks, the AIBD-trained models on poisoned data demonstrate superior performance over the existing defense methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Cybersecurity Threat Detection using Machine Learning on Network Traffic

Cybersecurity Threat Detection using Machine Learning on Net...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Rithi N. Nithya A. Akila R. Saravanakumar M. Vargheese S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Web phishing is a prevalent online attack in which victims are tricked into revealing their personal information by visiting fraudulent websites. Phishers often use URL shortening tools to disguise malicious links, making it easier to steal sensitive data such as usernames, passwords, and banking details. These phishing websites are designed to mimic legitimate ones, making detection more challenging. As phishing techniques continue to evolve rapidly alongside technological advancements, the need for effective anti-phishing systems has become increasingly crucial. Machine learning has emerged as a powerful tool for recognizing and mitigating phishing scams. This article provides a comprehensive overview of machine learning techniques for phishing detection and their effectiveness in combating online threats.

关键词： Uniform resource locators Accuracy Reviews Phishing Scalability Machine learning Telecommunication traffic Passwords Threat assessment Real-time systems

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