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2022

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Articles 481 - 510 of 3613

Full-Text Articles in Computer Sciences

Mining Product Textual Data For Recommendation Explanations, Le Trung Hoang Nov 2022

Mining Product Textual Data For Recommendation Explanations, Le Trung Hoang

Dissertations and Theses Collection (Open Access)

Recommendation explanations help to make sense of recommendations, increasing the likelihood of adoption. Here, we are interested in mining product textual data, an unstructured data type, coming from manufacturers, sellers, or consumers, appearing in many places including title, summary, description, review, question and answers, etc., can be a rich source of information to explain the recommendation. As the explanation task could be decoupled from that of recommendation objective, we can categorize recommendation explanation into integrated approach, that uses a single interpretable model to produce both recommendation and explanation, or pipeline approach, that uses a post-hoc explanation model to produce explanation …

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Reinforcement Learning Approach To Coordinate Real-World Multi-Agent Dynamic Routing And Scheduling, Joe Waldy Nov 2022

Reinforcement Learning Approach To Coordinate Real-World Multi-Agent Dynamic Routing And Scheduling, Joe Waldy

Dissertations and Theses Collection (Open Access)

In this thesis, we study new variants of routing and scheduling problems motivated by real-world problems from the urban logistics and law enforcement domains. In particular, we focus on two key aspects: dynamic and multi-agent. While routing problems such as the Vehicle Routing Problem (VRP) is well-studied in the Operations Research (OR) community, we know that in real-world route planning today, initially-planned route plans and schedules may be disrupted by dynamically-occurring events. In addition, routing and scheduling plans cannot be done in silos due to the presence of other agents which may be independent and self-interested. These requirements create …

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Generating Realistic Cyber Data For Training And Evaluating Machine Learning Classifiers For Network Intrusion Detection Systems, Marc W. Chalé, Nathaniel D. Bastian Nov 2022

Generating Realistic Cyber Data For Training And Evaluating Machine Learning Classifiers For Network Intrusion Detection Systems, Marc W. Chalé, Nathaniel D. Bastian

Faculty Publications

No abstract provided.

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In War And Peace: The Impact Of World Politics On Software Ecosystems, Raula Kula, Christoph Treude Nov 2022

In War And Peace: The Impact Of World Politics On Software Ecosystems, Raula Kula, Christoph Treude

Research Collection School Of Computing and Information Systems

Reliance on third-party libraries is now commonplace in contemporary software engineering. Being open source in nature, these libraries should advocate for a world where the freedoms and opportunities of open source software can be enjoyed by all. Yet, there is a growing concern related to maintainers using their influence to make political stances (i.e., referred to as protestware). In this paper, we reflect on the impact of world politics on software ecosystems, especially in the context of the ongoing War in Ukraine. We show three cases where world politics has had an impact on a software ecosystem, and how these …

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Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang Nov 2022

Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts are increasingly used with blockchain systems for high-value applications. It is highly desired to ensure the quality of smart contract source code before they are deployed. This paper proposes a new deep learning-based tool, MANDO-GURU, that aims to accurately detect vulnerabilities in smart contracts at both coarse-grained contract-level and fine-grained line-level. Using a combination of control-flow graphs and call graphs of Solidity code, we design new heterogeneous graph attention neural networks to encode more structural and potentially semantic relations among different types of nodes and edges of such graphs and use the encoded embeddings of the graphs and …

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Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang Nov 2022

Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang

Research Collection School Of Computing and Information Systems

We propose VetSC, a novel UI-driven, program analysis guided model checking technique that can automatically extract contract semantics in DApps so as to enable targeted safety vetting. To facilitate model checking, we extract business model graphs from contract code that capture its intrinsic business and safety logic. To automatically determine what safety specifications to check, we retrieve textual semantics from DApp user interfaces. To exclude untrusted UI text, we also validate the UI-logic consistency and detect any discrepancies. We have implemented VetSC and applied it to 34 real-world DApps. Experiments have demonstrated that VetSC can accurately interpret smart contract code, …

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Fully Deformable Network For Multiview Face Image Synthesis, Cheng Xu, Keke Li, Xuandi Luo, Xuemiao Xu, Shengfeng He, Kun Zhang Nov 2022

Fully Deformable Network For Multiview Face Image Synthesis, Cheng Xu, Keke Li, Xuandi Luo, Xuemiao Xu, Shengfeng He, Kun Zhang

Research Collection School Of Computing and Information Systems

Photorealistic multiview face synthesis from a single image is a challenging problem. Existing works mainly learn a texture mapping model from the source to the target faces. However, they rarely consider the geometric constraints on the internal deformation arising from pose variations, which causes a high level of uncertainty in face pose modeling, and hence, produces inferior results for large pose variations. Moreover, current methods typically suffer from undesired facial details loss due to the adoption of the de-facto standard encoder-decoder architecture without any skip connections (SCs). In this article, we directly learn and exploit geometric constraints and propose a …

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Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun Nov 2022

Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and postprocessing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the …

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Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li Nov 2022

Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li

Research Collection School Of Computing and Information Systems

Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing wellestablished benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study …

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Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw Nov 2022

Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Topic models infer latent topic distributions based on observed word co-occurrences in a text corpus. While typically a corpus contains documents of variable lengths, most previous topic models treat documents of different lengths uniformly, assuming that each document is sufficiently informative. However, shorter documents may have only a few word co-occurrences, resulting in inferior topic quality. Some other previous works assume that all documents are short, and leverage external auxiliary data, e.g., pretrained word embeddings and document connectivity. Orthogonal to existing works, we remedy this problem within the corpus itself by proposing a Meta-Complement Topic Model, which improves topic quality …

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Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang Nov 2022

Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper we study how to measure stereotypical bias in pre-trained vision-language models. We leverage a recently released text-only dataset, StereoSet, which covers a wide range of stereotypical bias, and extend it into a vision-language probing dataset called VLStereoSet to measure stereotypical bias in vision-language models. We analyze the differences between text and image and propose a probing task that detects bias by evaluating a model’s tendency to pick stereotypical statements as captions for anti-stereotypical images. We further define several metrics to measure both a vision-language model’s overall stereotypical bias and its intra-modal and inter-modal bias. Experiments on six …

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An Empirical Study Of Blockchain System Vulnerabilities: Modules, Types, And Patterns, Xiao Yi, Daoyuan Wu, Lingxiao Jiang, Yuzhou Fang, Kehuan Zhang, Wei Zhang Nov 2022

An Empirical Study Of Blockchain System Vulnerabilities: Modules, Types, And Patterns, Xiao Yi, Daoyuan Wu, Lingxiao Jiang, Yuzhou Fang, Kehuan Zhang, Wei Zhang

Research Collection School Of Computing and Information Systems

Blockchain, as a distributed ledger technology, becomes increasingly popular, especially for enabling valuable cryptocurrencies and smart contracts. However, the blockchain software systems inevitably have many bugs. Although bugs in smart contracts have been extensively investigated, security bugs of the underlying blockchain systems are much less explored. In this paper, we conduct an empirical study on blockchain’s system vulnerabilities from four representative blockchains, Bitcoin, Ethereum, Monero, and Stellar. Specifically, we first design a systematic filtering process to effectively identify 1,037 vulnerabilities and their 2,317 patches from 34,245 issues/PRs (pull requests) and 85,164 commits on GitHub. We thus build the first blockchain …

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Vfirm: Verifiable Fine-Grained Encrypted Image Retrieval In Multi-Owner Multi-User Settings, Qiuyun Tong, Yinbin Miao, Lei Chen, Jian Weng, Kim-Kwang Raymond Choo, Ximeng Liu, Robert H. Deng Nov 2022

Vfirm: Verifiable Fine-Grained Encrypted Image Retrieval In Multi-Owner Multi-User Settings, Qiuyun Tong, Yinbin Miao, Lei Chen, Jian Weng, Kim-Kwang Raymond Choo, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of supporting efficient fine-grained access control and result verification simultaneously. Therefore, in this article, we propose a Verifiable Fine-grained encrypted Image Retrieval scheme in the Multi-owner multi-user settings (VFIRM). VFIRM first utilizes a novel polynomial-based access strategy to provide efficient fine-grained access control. Then, it employs the dual secure kk-nearest neighbor technique to distribute distinct keys to …

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Reliable Policy Updating Under Efficient Policy Hidden Fine-Grained Access Control Framework For Cloud Data Sharing, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu, Robert H. Deng Nov 2022

Reliable Policy Updating Under Efficient Policy Hidden Fine-Grained Access Control Framework For Cloud Data Sharing, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as …

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M-Edese: Multi-Domain, Easily Deployable, And Efficiently Searchable Encryption, Jiaming Yuan, Yingjiu Li, Jianting Ning, Robert H. Deng Nov 2022

M-Edese: Multi-Domain, Easily Deployable, And Efficiently Searchable Encryption, Jiaming Yuan, Yingjiu Li, Jianting Ning, Robert H. Deng

Research Collection School Of Computing and Information Systems

Searchable encryption is an essential component of cryptography, which allows users to search for keywords and retrieve records from an encrypted database at cloud storage while ensuring the confidentiality of users’ queries. While most existing research on searchable encryption focuses on the single domain setting, we propose the first Multi-Domain, Easily-Deployable, Efficiently-Searchable Encryption (M-EDESE) system that allows users to query keywords cross domains with high efficiency and preserved privacy without additional cooperation from the cloud storage. In the multi-domain setting, a user who belongs to a domain can query keywords from another domain under an inter-domain partnership. Any party can …

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What Motivates Software Practitioners To Contribute To Inner Source?, Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, Ahmed E. Hassan Nov 2022

What Motivates Software Practitioners To Contribute To Inner Source?, Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Software development organizations have adopted open source development practices to support or augment their software development processes, a phenomenon referred to as inner source. Given the rapid adoption of inner source, we wonder what motivates software practitioners to contribute to inner source projects. We followed a mixed-methods approach--a qualitative phase of interviews with 20 interviewees, followed by a quantitative phase of an exploratory survey with 124 respondents from 13 countries across four continents. Our study uncovers practitioners' motivation to contribute to inner source projects, as well as how the motivation differs from what motivates practitioners to participate in open source …

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Autopruner: Transformer-Based Call Graph Pruning, Cong Thanh Le, Hong Jin Kang, Truong Giang Nguyen, Stefanus Agus Haryono, David Lo, Xuan-Bach D. Le, Huynh Quyet Thang Nov 2022

Autopruner: Transformer-Based Call Graph Pruning, Cong Thanh Le, Hong Jin Kang, Truong Giang Nguyen, Stefanus Agus Haryono, David Lo, Xuan-Bach D. Le, Huynh Quyet Thang

Research Collection School Of Computing and Information Systems

Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise. To address this problem, researchers have recently proposed call graph pruning empowered by machine learning to post-process call graphs constructed by static analysis. A machine learning model is built to capture information from the call graph by extracting structural features for use in a random forest classifier. It then removes edges that are predicted to be false positives. Despite the improvements shown by machine learning models, they are still limited as they do not consider the source code …

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Vulcurator: A Vulnerability-Fixing Commit Detector, Truong Giang Nguyen, Cong Thanh Le, Hong Jin Kang, Xuan-Bach D. Le, David Lo Nov 2022

Vulcurator: A Vulnerability-Fixing Commit Detector, Truong Giang Nguyen, Cong Thanh Le, Hong Jin Kang, Xuan-Bach D. Le, David Lo

Research Collection School Of Computing and Information Systems

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time-consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, …

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Blockchain And Federated Learning-Based Security Solutions For Telesurgery System: A Comprehensive Review, Sachi Chaudjary, Riya Kakkar, Rajesh Gupta, Sudeep Tanwar, Smita Agrawal, Ravi Sharma Nov 2022

Blockchain And Federated Learning-Based Security Solutions For Telesurgery System: A Comprehensive Review, Sachi Chaudjary, Riya Kakkar, Rajesh Gupta, Sudeep Tanwar, Smita Agrawal, Ravi Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

The advent of telemedicine with its remote surgical procedures has effectively transformed the working of healthcare professionals. The evolution of telemedicine facilitates the remote monitoring of patients that lead to the advent of telesurgery systems, i.e. one of the most critical applications in telemedicine systems. Apart from gaining popularity, the telesurgery system may encounter security and trust issues of patients? data while communicating with the surgeon for their remote treatment. Motivated by this, we have presented a comprehensive survey on secure telesurgery systems comprising healthcare, surgical robots, traditional telesurgery systems, and the role of artificial intelligence to deal with the …

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Application Of Hierarchical Clustering On Electricity Demand Of Electric Vehicles For Gep Problems, Seyedkazem Afghah, Hati̇ce Teki̇ner Moğulkoç, Bi̇jan Bi̇bak Nov 2022

Application Of Hierarchical Clustering On Electricity Demand Of Electric Vehicles For Gep Problems, Seyedkazem Afghah, Hati̇ce Teki̇ner Moğulkoç, Bi̇jan Bi̇bak

Turkish Journal of Electrical Engineering and Computer Sciences

Increasing fossil fuel consumption and consequently the effects of greenhouse gases (GHGs) on the environment and economy are a major concern for all nations and governments. Electric vehicles (EVs) with plug-in capabilities have the potential to ease such problems. However, the extracted power from the grid for charging the EVs' batteries will significantly impact daily power demand. To satisfy the increasing demand and ensure generation capacity adequacy, the generation expansion planning (GEP) problem is solved to determine the investment decisions for electricity generation sources. Even though there are no centralized utilities for generation planning in most markets, there is still …

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Asking The Right Questions To Solve Algebraic Word Problems, Ege Yi̇ği̇t Çeli̇k, Zeynel Orulluoğlu, Ridvan Mertoğlu, Selma Teki̇r Nov 2022

Asking The Right Questions To Solve Algebraic Word Problems, Ege Yi̇ği̇t Çeli̇k, Zeynel Orulluoğlu, Ridvan Mertoğlu, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Word algebra problems are among challenging AI tasks as they combine natural language understanding with a formal equation system. Traditional approaches to the problem work with equation templates and frame the task as a template selection and number assignment to the selected template. The recent deep learning-based solutions exploit contextual language models like BERT and encode the natural language text to decode the corresponding equation system. The proposed approach is similar to the template-based methods as it works with a template and fills in the number slots. Nevertheless, it has contextual understanding because it adopts a question generation and answering …

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Graph Neural Network With Self-Attention And Multi-Task Learning For Credit Default Risk Prediction, Zihao Li, Xianzhi Wang, Lina Yao, Yakun Chen, Guandong Xu, Ee-Peng Lim Nov 2022

Graph Neural Network With Self-Attention And Multi-Task Learning For Credit Default Risk Prediction, Zihao Li, Xianzhi Wang, Lina Yao, Yakun Chen, Guandong Xu, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We propose a graph neural network with self-attention and multi-task learning (SaM-GNN) to leverage the advantages of deep learning for credit default risk prediction. Our approach incorporates two parallel tasks based on shared intermediate vectors for input vector reconstruction and credit default risk prediction, respectively. To better leverage supervised data, we use self-attention layers for feature representation of categorical and numeric data; we further link raw data into a graph and use a graph convolution module to aggregate similar information and cope with missing values during constructing intermediate vectors. Our method does not heavily rely on feature engineering work and …

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Understanding Deviance And Victimization In Cyber Space Among Diverse Populations, Insun Park Nov 2022

Understanding Deviance And Victimization In Cyber Space Among Diverse Populations, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

Recent years have witnessed a growing academic interest in deviance and victimization in the cyber space. The current issue of the International Journal of Cybersecurity Intelligence and Cybercrime features three empirical research articles on online behavior of traditionally under-researched populations and a review of much waited book on digital forensics and investigation. This paper was prepared to introduce these important scholarly works in the context of newly emerging scholarship that focuses on the experiences of diverse subgroups in cyberspace.

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Aggressive Reality Docuseries And Cyberbullying: A Partial Test Of Glaser’S Differential Identification Theory, J. Ra’Chel Fowler, Darren R. Beneby, Kenethia L. Fuller Nov 2022

Aggressive Reality Docuseries And Cyberbullying: A Partial Test Of Glaser’S Differential Identification Theory, J. Ra’Chel Fowler, Darren R. Beneby, Kenethia L. Fuller

International Journal of Cybersecurity Intelligence & Cybercrime

Reality docuseries have dominated primetime airwaves for the greater part of three decades. However, little is known about how viewers who are enamored with the genre’s most aggressive characters are influenced. Using Glaser’s (1956) theory of differential identification, this study employs survey data from 210 college students at a historically Black college and university to explore whether identification with characters from aggressive reality docuseries (ARDs) and the frequency of viewing ARD are positively associated with cyberbullying. Results of multivariate analyses revealed that men were more likely than women to publicly shame others and air other’s dirty laundry online. Additionally, the …

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Emerging Trends In Cybercrime Awareness In Nigeria, Ogochukwu Favour Nzeakor, Bonaventure N. Nwokeoma, Ibrahim Hassan, Benjamin Okorie Ajah, John T. Okpa Nov 2022

Emerging Trends In Cybercrime Awareness In Nigeria, Ogochukwu Favour Nzeakor, Bonaventure N. Nwokeoma, Ibrahim Hassan, Benjamin Okorie Ajah, John T. Okpa

International Journal of Cybersecurity Intelligence & Cybercrime

The study examined the current trend in cybercrime awareness and the relationship such trend has with cybercrime vulnerability or victimization. Selecting a sample of 1104 Internet users from Umuahia, Abia State, Nigeria, We found that: 1) awareness of information security was high in that about 2 in every 3 (68%) participants demonstrated a favorable awareness of information security and cybercrime. It was, however, revealed that such a high level of awareness could be partial and weak. 2) most Internet users demonstrated the awareness of fraud-related cybercrime categories (39%), e-theft (15%), hacking (12%), and ATM theft (10%). However, they were rarely …

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Book Review: Digital Forensics And Cyber Investigation Nov 2022

Book Review: Digital Forensics And Cyber Investigation

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

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Hydrological Drought Forecasting Using A Deep Transformer Model, Amobichukwu C. Amanambu, Joann Mossa, Yin-Hsuen Chen Nov 2022

Hydrological Drought Forecasting Using A Deep Transformer Model, Amobichukwu C. Amanambu, Joann Mossa, Yin-Hsuen Chen

University Administration Publications

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were …

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The Impact Of Cdio's Dimensions And Values On It Learner's Attitude And Behavior: A Regression Model Using Partial Least Squares, Ahmed Shuhaiber, Monther Aldwairi Nov 2022

The Impact Of Cdio's Dimensions And Values On It Learner's Attitude And Behavior: A Regression Model Using Partial Least Squares, Ahmed Shuhaiber, Monther Aldwairi

All Works

CDIO (Conceiving-Designing-Implementing-Operating), crowdsourcing and gamification are gaining more popularity in IT education. However, factors that influence learners' attitude toward this method are yet to be discovered. Therefore, this study aims to develop and test a conceptual model of implementing CDIO-based curriculum in IT education. For this purpose, CDIO dimensions were conceptualized and developed into questionnaire items. Then 141 students who experienced the CDIO method in information security course and lab, were sampled through action-research approach to investigate their perceptions and experiences about the learning stages, dimensions and values of this teaching method. Data gathered were analyzed by multiple regression algorithm …

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Towards Effective And Efficient Online Exam Systems Using Deep Learning-Based Cheating Detection Approach, Sanaa Kaddoura, Abdu Gumaei Nov 2022

Towards Effective And Efficient Online Exam Systems Using Deep Learning-Based Cheating Detection Approach, Sanaa Kaddoura, Abdu Gumaei

All Works

With the high growth of digitization and globalization, online exam systems continue to gain popularity and stretch, especially in the case of spreading infections like a pandemic. Cheating detection in online exam systems is a significant and necessary task to maintain the integrity of the exam and give unbiased, fair results. Currently, online exam systems use vision-based traditional machine learning (ML) methods and provide examiners with tools to detect cheating throughout the exam. However, conventional ML methods depend on handcrafted features and cannot learn the hierarchical representations of objects from data itself, affecting the efficiency and effectiveness of such systems. …

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Hill Climbing-Based Efficient Model For Link Prediction In Undirected Graphs, Haji Gul, Feras Al-Obeidat, Adnan Amin, Fernando Moreira, Kaizhu Huang Nov 2022

Hill Climbing-Based Efficient Model For Link Prediction In Undirected Graphs, Haji Gul, Feras Al-Obeidat, Adnan Amin, Fernando Moreira, Kaizhu Huang

All Works

Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest …

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