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Student Academic Advisement Augmented by Artificial Intelligence in the school of computing and information technology, University of technology, Jamaica

Student Academic Advisement Augmented by Artificial Intellig...

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Computer Science, Computer Engineering, & Applied computing (CSCE), Congress in

作者： Tanya Graham Horrett Scarlett Shaula Edwards Braham School of Computing and Information Technology (SCIT) University of Technology (UTech) Kingston Jamaica

In this paper we examine the possibility of using artificial intelligence (AI) to improve academic advisement of students within the school of computing and information technology (SCIT) at the University of technology, Jamaica (Utech). Described as one of the important challenges facing academics [1], academic advisement plays a vital role in student completion. All students at Utech are assigned academic advisors and encouraged to access advisors for advisement. Each faculty manages the process internally. Students are not mandated to seek advisement but are strongly encouraged to do so to allow them to make informed choices related to module selection, academic probation, grade forgiveness, etc. Within SCIT the rate of take up is less than desired resulting in some students going on academic probation, having to switch programs in some cases or failing out of their program. We will explore the automation of the academic advisement process by using AI to push relevant information to students related to their performance. The system will be coded to recognize common situations and contact the students providing information relevant to the situation and schedule an advisement session with the academic advisor (AA).

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Robust federated contrastive recommender system against targeted model poisoning attack

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Science China(information Sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua Science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Exploring & exploiting high-order graph structure for sparse knowledge graph completion

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Frontiers of Computer Science 2025年第2期19卷 31-42页

作者： Tao HE Ming LIU Yixin CAO Zekun WANG Zihao ZHENG Bing QIN Research Center for Social Computing and Information Retrieval Harbin Institute of TechnologyHarbin 150001China Peng Cheng Laboratory Shenzhen 518000China SMU School of Computing and Information Systems Singapore Management UniversitySingapore 178902Singapore

Sparse Knowledge Graph(KG)scenarios pose a challenge for previous Knowledge Graph Completion(KGC)methods,that is,the completion performance decreases rapidly with the increase of graph *** problem is also exacerbated because of the widespread existence of sparse KGs in practical *** alleviate this challenge,we present a novel framework,LR-GCN,that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse *** proposed approach comprises two main components:a GNN-based predictor and a reasoning path *** reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges,explicitly compositing long-range dependencies into the *** step also plays an essential role in densifying KGs,effectively alleviating the sparse ***,the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the *** two components are jointly optimized using a well-designed variational EM *** experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.

关键词： knowledge graph completion graph neural networks reinforcement learning

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Computational techniques, classification, datasets review and way forward with modern analysis of epileptic seizure – a study

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Multimedia Tools and Applications 2024年第38期83卷 85685-85701页

作者： Fathima, Syeda Noor Rekha, K Bhanu Safinaz, S. Ahmed, Syed Thouheed School of Engineering Presidency University Bengaluru India School of Computing and Information Technology REVA University Bengaluru India

According to World Health Organization (WHO), it is estimated that approximately 50 million people have Epilepsy worldwide. 10 million people are effected in India by Epilepsy hardly very few come out with the disorder and undertake proper treatment rest risk their life without recognition of disease. Epilepsy is a chronic disease of the brain which is non-communicable. An epileptic seizure is the second most commonly occurring neurological disorder of the brain. It can be detected using Electroencephalography (EEG) signals, an effective diagnostic tool used to study brain anatomy. Technological advancements and modernization of analytical tools have improved the scope of validation and decision-making. It is important to predict and evaluate epilepsy in its initial stages to avoid the risk and complication. This review paper focuses on the methodology, techniques, and dataset used from different sources to classify and categorize epileptic seizures. The paper includes a detailed review of dataset sources, challenges, and ways forward in understanding epileptic seizures for futuristic learning and decision making. It also concentrates on the comparison of machine learning and deep learning methodology used with different datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Multi-view stereo algorithms based on deep learning: a survey

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Multimedia Tools and Applications 2025年第6期84卷 2877-2908页

作者： Huang, Hongbo Yan, Xiaoxu Zheng, Yaolin He, Jiayu Xu, Longfei Qin, Dechun Computer School Beijing Information Science & Technology University Beijing China Institute of Computing Intelligence Beijing Information Science & Technology University Beijing China Foreign Languages School Beijing Information Science & Technology University Beijing China

Multi-View Stereo (MVS) is a long-standing and fundamental task in computer vision, which aims to reconstruct the 3D geometry of a scene from a set of overlapping images. With known camera parameters, MVS matches pixels across images to compute dense correspondences and recover 3D points. Traditional MVS methods often encounter difficulties in addressing complex scenes due to limitations in robustness. However, recent advancements in deep-learning-based MVS have demonstrated remarkable performance gains, attributed to their enhanced feature extraction and cost volume processing capabilities. This comprehensive survey delves into deep-learning-based MVS algorithms, highlighting key challenges and avenues for future exploration. Our focus is on studies aimed at mitigating algorithmic memory requirements and elevating processing speeds. To better understand the developments in this field, we propose a new taxonomy. The deep-learning-based MVS algorithms are categorized into two categories: scene-oriented and view-oriented methods. Scene-oriented methods typically employ voxels or neural implicit representations as the representative form of the scene and infer the complete scene directly. View-oriented methods focus on estimating the depth map of a single view. Detailed classifications and reviews have been conducted for each category. Furthermore, we introduce several widely used datasets and metrics and empirical evaluation of representative algorithms across three popular datasets. This article concludes by discussing prevalent challenges in the MVS domain and promising research directions for the future. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Stereo image processing

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Performance Assessment of Optimized Link State Routing Protocol on Vehicular Ad Hoc Network Simulation

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 289-302页

作者： Xian, Yap Yu Yogarayan, Sumendra Razak, Siti Fatimah Abdul Sayeed, Shohel Abdullah, Fikri Azli Kannan, Subarmaniam Azman, Afizan Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia School of Computing Faculty of Information and Technology Taylors University Selangor Subang Jaya Malaysia

Vehicular Ad-hoc Networks (VANETs) are dedicated forms of wireless communication networks designed to handle the challenges of vehicular environments, including high mobility, varying traffic densities, and constantly changing topologies. These factors necessitate the development and evaluation of routing protocols to ensure reliable data communication between vehicles. This study evaluates the performance of the Optimized Link State Routing (OLSR) protocol within Vehicular Ad-hoc Networks (VANETs), focusing on its capability to handle different traffic densities and dynamic environments. Reliable data communication in VANETs is critical due to the high mobility and constantly changing topologies, especially in urban and highway settings. Using NS-3 for network simulation and Simulation of Urban MObility (SUMO) for realistic vehicular mobility modelling, we conducted a series of simulations to assess OLSR’s performance in low-density and high-density scenarios across highway and urban environments. Key performance metrics, including packet delivery ratio (PDR), end-to-end delay (E2ED) and throughput were analyzed to capture OLSR’s strengths and weaknesses in each setting. The analysis showed that OLSR excels in low-density highway scenarios, achieving a PDR of 100% and low E2ED. However, in high-density urban settings, the protocol encounters performance challenges, with a reduced PDR of 81.40% and a high E2ED of 85.52 seconds, indicating delays in data transmission. These findings emphasize the limitations of OLSR in dense urban environments, highlighting the necessity for adaptive routing protocols that can improve performance in complex, high-density vehicular networks. © 2025, Ital Publication. All rights reserved.

关键词： Vehicular ad hoc networks

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Intelligent Traffic Scheduling for Mobile Edge computing in IoT via Deep Learning

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 1815-1835页

作者： Shaoxuan Yun Ying Chen School of Computing Science Beijing Information Science and Technology UniversityBeijing100101China

Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our *** data generated by mobile devices has reached a massive *** traditional centralized processing is not suitable for processing the data due to limited computing power and transmission *** Edge computing(MEC)has been proposed to solve these *** of limited computation ability and battery capacity,tasks can be executed in the MEC ***,how to schedule those tasks becomes a challenge,and is the main topic of this *** this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in *** view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification *** demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.

关键词： Mobile Edge computing(MEC) traffic scheduling deep learning Internet of Things(IoT)

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Software Measurements Using Machine Learning Techniques-A Review

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Recent Advances in Computer Science and Communications 2023年第1期16卷 38-55页

作者： Goyal, Somya School of Computing and Information Technology Manipal University Jaipur Jaipur India

Background: Software Measurement (SM) is pivotal for efficient planning, scheduling, tracking, and controlling software projects, which significantly affects the success or failure of a project. Machine Learning (ML) techniques have been applied for software measurements for the past three decades. Objective: This review aims to synthesize the studies conducted from the years 1990 to 2020 to provide a broad picture of the role of machine learning in the world of software measurement. Methods: The Systematic Literature Review (SLR) approach is adopted to conduct this review. Inclusion/exclusion criteria are defined to select the most relevant studies. The researcher searched the prominent databases and archives and obtained around 2310 studies, from which 108 studies were selected as primary studies, which were then summarized to accomplish the goals of this review. Results: The distinguished contribution of this review is that it covers all aspects of software measurements from the perspective of the application of machine learning techniques. It guides the software practitioners regarding the journey of software measurements to date using machine learning techniques in a single synthesized study. It further provides future guidelines for the researchers working in this field. Conclusion: Machine learning techniques have extensive applications for software measurements. Software fault prediction and software effort estimation are the most prevailing SM tasks harnessing the ML techniques. The most popular ML technique is the artificial neural network for SM. For empirical studies, NASA and promise datasets are extensively used. Over the last decade (2011-2020), SM paradigm has been shifting towards ensembles of individual ML models and deep learning models. © 2023 Bentham Science Publishers.

关键词： Decision trees

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