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25th International Arab Conference on Information Technology, ACIT 2024

作者： Asassfeh, Mahmoud Al-Mousa, Mohammad Rasmi Amer, Waleed Almomani, Adai Mohammad Al-Shaikh, Ala'a Afaneh, Suha Alrefai, Mohammed Nayef Elrashidi, Ali Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Software Enginereeing Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Nowadays, a big challenge is how to protect information from unwanted accesses. It constitutes a big challenge when we have different ways to access data. That is, a lot of information today travels through networks and our challenge today is to guarantee that traveled information should be secret that is not divulged by intruders. There are many different ways to assure that information being transmitted through a network is secret. In this paper, after we have shown in Section 2 a brief description of the security problems inside a local network, the most well-known network security models are presented. This paper provides a review of the confidentiality in network security. Confidentiality is a property that aims to ensure that only authorized users can access the required information and data while unauthorized users and third parties cannot learn it. Confidentiality components encompass the use of access controls, encryption with cryptographic techniques, and secure data communications. Confidentiality has encountered many threats and security risks during its use and deployment such as eavesdropping, network traffic analysis, passive attacks, deception and impersonation attacks, insecure user authentication, and weak password vulnerabilities. Therefore, strategies and measures should be applied and used to enhance the confidentiality and thus secure the communication of data across the internet and other means. © 2024 IEEE.

关键词： Network intrusion

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Predicting ICU Length of Stay for Patients with Diabetes Using Machine Learning Techniques

Predicting ICU Length of Stay for Patients with Diabetes Usi...

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2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022

作者： Hu, Yuansi Zheng, Ling Wang, Jiacun Computer Science and Software Engineering Department Monmouth University West Long Branch NJ United States

ISBN: (数字)9781665498357

ISBN: (纸本)9781665498357

Diabetes is a prevalent chronic disease that can result in serious damages to various organ systems gradually. Patients with diabetes in the intensive care unit (ICU) have poor health outcomes and require more intensive care with higher healthcare costs. To facilitate resource management of hospitals and to improve health outcomes of patients with diabetes, accurately estimating the length of stay at an early stage of ICU admissions is necessary. This study is aimed to predict the length of stay for patients with diabetes by applying machine learning techniques on clinical data available during the first 8 hours of ICU admissions. Two prediction tasks, the number of days in ICU and whether an ICU stay is long or short distinguished by the threshold 10 days, were explored. The neural network model achieved the best performance in predicting the number of days in ICU with a R2 value 0.3969 and a mean absolute error 1.94 days. The gradient boosting model is the best one to classify long and short ICU stays with an accuracy 0.8214. The results demonstrate that these two models are promising to estimate the length of stay at an early stage of ICU admissions for patients with diabetes. © 2022 IEEE.

关键词： Intensive care units

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Multiple Feature Selection Frameworks Based on Evolutionary Computing and Ensemble Learning for software Defect Prediction 5

Multiple Feature Selection Frameworks Based on Evolutionary ...

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5th International Conference on Computational Intelligence and Networks, CINE 2022

作者： Malhotra, Ruchika Budhiraja, Anmol Singh, Abhinav Kumar Ghoshal, Ishani Meena, Shweta Delhi Technological University Department of Software Engineering Delhi India Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665464659

software defects may cause severe crashes in the system, leading to the software's high maintenance costs. Early identification of these defects would lead to high-quality software, thus saving time and money. This study proposes five feature selection approaches based on evolutionary computing algorithms, each coupled with a majority voting ensemble for software defect prediction. The objective is to improve the existing process by targeting the metric selection stage. The study was conducted on thirty open-source defect datasets. The proposed feature selection techniques were applied on a within-project defect prediction model and a heterogeneous defect prediction model. The Friedman and the Wilcoxon Signed-rank test concluded that the proposed techniques were promising and generated results comparable to some other state-of-the-art feature selection methodologies. © 2022 IEEE.

关键词： Defects

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Decentralized Property Registration and Management Platform 4

Decentralized Property Registration and Management Platform

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Yasas, R.M. Bandara, M.H.M.N.D.T. Praveena, T. Abeywardena, K. Kasthurirathna, D. Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The existing property registry management does not have a well-defined protocol for verifying and validating transactions that occur within the domain. These transactions rely on handwritten signatures, an unreliable methodology for determining an asset's ownership. The legal system governs this process. However, several disputes have occurred due to improper validation and verification when registering properties, changing custody, and maintaining the chain of ownership. Trades have been made by including a lower value than the actual asset value, which will reduce the tax owed to the government and will lead to the failure of these departments. There are no appropriate mechanisms to resolve common disputes that arise within the domain. The courts must resolve these disputes using the same recurring traditional procedure, which will take years or decades to conclude. The main objective of this research is to develop a secure property registration mechanism by creating a digital protocol using a decentralized blockchain network. In addition, the research will focus on developing a minimum asset value calculator using machine learning and geographic information system, verifying the authenticity of the generated digital documents, and creating digital deeds for new and old paper-based records. © 2022 IEEE.

关键词： Blockchain

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Identification of selection criteria for blockchain platforms

Identification of selection criteria for blockchain platform...

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2022 International Conference on Frontiers of Communications, Information System and Data science, CISDS 2022

作者： Muradi, Shahr Banoo Kia Chiam, Yin Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665464468

Blockchain technology is widely used to develop software systems in different industries such as finance, healthcare, supply chain management, data management, Internet of Things (IoT). To adopt blockchain, some critical challenges such as performance, interoperability, availability, security attacks, sustainability, and latency need to be taken into consideration. If the blockchain platform selected is not appropriate, the functionality and quality of the blockchain-based systems will be affected. Additionally, numerous blockchain platforms with unique properties and specifications are available in the market, and the task of selecting the right platform that best meets the project needs is getting complicated. It is crucial to define significant criteria that can help to shortlist the blockchain platforms for further comparison and selection using multicriteria decision-making methods. This research proposes a set of selection criteria that cover both features and quality attributes of the blockchain platforms. A survey was conducted to get the opinions of blockchain practitioners on the suitability of the selection criteria for the evaluation and selection of blockchain platforms. © 2022 IEEE.

关键词： Blockchain

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification

arXiv

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arXiv 2024年

作者： Ghose, Shuvozit Wang, Yang Department of Computer Science University of Manitoba Winnipeg Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Point cloud classification refers to the process of assigning semantic labels or categories to individual points within a point cloud data structure. Recent works have explored the extension of pre-trained CLIP to 3D recognition. In this direction, CLIP-based point cloud models like PointCLIP, CLIP2Point have become state-of-the-art methods in the few-shot setup. Although these methods show promising performance for some classes like airplanes, desks, guitars, etc, the performance for some classes like the cup, flower pot, sink, nightstand, etc is still far from satisfactory. This is due to the fact that the adapter of CLIP-based models is trained using randomly sampled N-way K-shot data in the standard supervised learning setup. In this paper, we propose a novel meta-episodic learning framework for CLIP-based point cloud classification, addressing the challenges of limited training examples and sampling unknown classes. Additionally, we introduce dynamic task sampling within the episode based on performance memory. This sampling strategy effectively addresses the challenge of sampling unknown classes, ensuring that the model learns from a diverse range of classes and promotes the exploration of underrepresented categories. By dynamically updating the performance memory, we adaptively prioritize the sampling of classes based on their performance, enhancing the model’s ability to handle challenging and real-world scenarios. Experiments show an average performance gain of 3-6% on ModelNet40 and ScanobjectNN datasets in a few-shot setup. © 2024, CC BY.

关键词： Image classification

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Automated Detection of COVID-19 Using Deep Convolutional Neural Network (CNNs) Using Chest Radiograph and CT Scan Images 1st

Automated Detection of COVID-19 Using Deep Convolutional Neu...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Uallah, Naeem Khan, Javed Ali Raza, Asaf Shah, Syed Yaqub Assam, Muhammad Arshad, Hasna Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Software Engineering University of Science and Technology Bannu Bannu Pakistan Department of Computer Science HITEC University Taxila Pakistan

ISBN: (纸本)9783031776199

Many countries are severely affected by COVID-19, and massive efforts are required to mitigate the terrible impacts of the COVID-19 global epidemic. To stop COVID-19 from spreading widely and minimize COVID-19 patient treatment issues, early discovery of the virus in its early stages is essential. Conventional methods for COVID-19 detection are time-consuming, cumbersome, unreliable, and yield an incorrect diagnosis. Most deep learning (DL) methods for COVID-19 identification described in the past only support one data type (chest radiographs or CT scan images). Examining unusual patterns in various data types may yield high classification performance. For this purpose, we use two image datasets: chest radiographs and chest computer tomography (CT). This research study proposed an excellent COVID-19 detection strategy based on the Resnet-18 deep convolutional neural network (CNN) to timely and efficiently identify COVID-19 spread. Also, we used each image data type to conduct extensive Covid-19 detection experiments with the proposed DL model. We performed a trinary classification task (normal, covid-19, and pneumonia) using chest radiographs and a binary classification (COVID-19 and Normal) using CT scan images. We validated the performance of the proposed approach on two standard Kaggle datasets, i.e., the COVID-19 Radiography Database and the SARS-CoV-2 CT scan dataset. The Resnet model's detection accuracy of 99.42% using chest radiographs (CRs) and 100% using CT scans for COVID-19 detection indicates its efficacy in detecting COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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EvoChain: A Framework for Tracking and Visualizing Smart Contract Evolution

EvoChain: A Framework for Tracking and Visualizing Smart Con...

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International Mining software Repositories, MSR

作者： Ilham Qasse Mohammad Hamdaqa Björn Þór Jónsson Department of Computer Science Reykjavik University Reykjavik Iceland Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart contract evolution. Building upon data from our previous empirical study, EvoChain models contract relationships using a Neo4j graph database and provides an interactive web interface for exploration. The framework consists of a data layer, an API layer, and a user interface layer. EvoChain allows stakeholders to analyze contract histories, upgrade paths, and associated vulnerabilities by leveraging these components. Our dataset encompasses approximately 1.3 million upgradeable proxies and nearly 15,000 historical versions, enhancing transparency and trust in blockchain ecosystems by providing an accessible platform for understanding smart contract evolution.

关键词： software maintenance Databases Smart contracts Ecosystems Data visualization Data models Blockchains Stakeholders Security Predictive analytics

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In-Range Farthest Point Queries and Related Problem in High Dimensions 49

In-Range Farthest Point Queries and Related Problem in High ...

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49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022

作者： Huang, Ziyun Xu, Jinhui Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States

ISBN: (纸本)9783959772358

Range-aggregate query is an important type of queries with numerous applications. It aims to obtain some structural information (defined by an aggregate function F(·)) of the points (from a point set P) inside a given query range B. In this paper, we study the range-aggregate query problem in high dimensional space for two aggregate functions: (1) F(P ∩ B) is the farthest point in P ∩ B to a query point q in d and (2) F(P ∩ B) is the minimum enclosing ball (MEB) of P ∩ B. For problem (1), called In-Range Farthest Point (IFP) Query, we develop a bi-criteria approximation scheme: For any ϵ > 0 that specifies the approximation ratio of the farthest distance and any γ > 0 that measures the "fuzziness" of the query range, we show that it is possible to pre-process P into a data structure of size Õϵ,γ(dn1+ρ) in Õϵ,γ(dn1+ρ) time such that given any Rd query ball B and query point q, it outputs in Õϵ,γ(dnρ) time a point p that is a (1 − ϵ)-approximation of the farthest point to q among all points lying in a (1 + γ)-expansion B(1 + γ) of B, where 0 © Ziyun Huang and Jinhui Xu;licensed under Creative Commons License CC-BY 4.0

关键词： Aggregates

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