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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks

Line Graph is the Key: An Exploration of Line Graph on Link ...

引用

International Conference on Behavior, Economic and Social Computing (BESC)

作者： Chen Xing Masoud Makrehchi Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods.

关键词： Analytical models Social computing Social networking (online) Network topology Navigation Network analyzers Predictive models Vectors Robustness Topology

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Decision Support System: Analysis and Design Methodology 2

Decision Support System: Analysis and Design Methodology

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Darbi, Huweida A. Saleh, Eiman M. University of Derna Dept. Computer Science Derna Libya Benghazi University Dept. Software Engineering Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The success of any organization depends on the quality of the decisions made, and good decisions can be made by analyzing all the available data and knowledge of the organization. Decision Support Systems (DSSs) are used to analyze this data and aid in decision-making in the formulation of well-studied decisions based on knowledge. However, the analysis and design of decision support systems (DSS) is a complex process that involves the use of appropriate methodologies, methods, and tools to model decision-making processes. This paper presents an architectural design of DSS framework. The proposed approach helps developers develop decision-making systems through models that detail the analysis and design. © 2022 IEEE.

关键词： Decision support systems

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Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer

Enhancing Brain Tumor Classification Using Data-Efficient Im...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Jiafeng Yang Alena Rusak Alexander Belozubov Technology Dept. Software Engineering and Computer ITMO University Saint-Petersburg Russia

Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tumor MRI images. We use a combination of three publicly available brain tumor datasets classified into four categories and appropriately modify existing transformer variants, including adjustments to patch size, number of categories, dropout, etc. The results indicate that, due to the condition of insufficient data volume, most transformer variants are still inferior to the benchmark model VGG-16. The DeiT model with knowledge distillation has significantly improved various indices on the test set, verifying its effectiveness in the classification of brain tumor MRI images under limited data. The article supports the application of transformer-based models to medical image analysis. Follow-up work will verify the generalization performance of the model on other datasets and explore methods to reduce the dependence of the transformer on the data, making it more applicable to tasks such as medical images classification.

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XAI for Understanding the Success in Curbing COVID-19 6

XAI for Understanding the Success in Curbing COVID-19

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Kunnath, Nimmi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec École de Technologie Supérieure MontrealQC Canada

ISBN: (纸本)9798350350180

The Covid-19 pandemic has prompted governments worldwide to implement various non-pharmaceutical interventions (NPIs) in an effort to curb the pandemic to attenuate the harmness of the pandemic. However, there is a debate about how to assess the effectiveness of these interventions. A set of indicators has been used to monitor the outbreak such as case counts, infection rates, death rate, hospitalization. In this study, we contribute to the debate by firstly proposing a definition of the success in managing the pandemic in terms of mortality rate, socio-demographic and epidemiological factors. Secondly, we propose a modeling framework based on (1) analysing the time series pandemic data and (2) employing eXplainable Artificial Intelligence (XAI) to provide interpretability of the successfulness of countries in responding to the pandemic. By using a rich dataset collected by Oxford COVID-19 Government Response Tracker (OxCGRT), we built a success model based on Random Forest Regressor for time series analysis and we identified the major factors/practices/measures/protocols. Our findings indicate a significant relationship between policy compliance levels and death counts, highlighting the importance of considering mortality outcomes in evaluating the efficacy of interventions. Additionally, we discovered that socio-demographic and epidemiological factors such as elderly populations aged (65+), prevalent cardiovascular disease, high diabetes prevalence, increased smoking rates, and reduced life expectancy based on the person correlation coefficient are key determinants of success in managing the pandemic. © 2024 IEEE.

关键词： COVID-19

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A software Framework Based on JUnit for Automated software Testing in computer Science Courses

A Software Framework Based on JUnit for Automated Software T...

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Frontiers in Education (FIE) Conference

作者： Michael A. Zmuda Dept. of Computer Science and Software Engineering Miami University Oxford OH USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= “remove”, points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing computer science Java Annotations Education Life estimation software Libraries software development management

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Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks using Reinforcement Learning

Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks usi...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tarana Ara Ramiro Liscano Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper presents an innovative MAC scheduling algorithm to achieve energy savings in IEEE 802.15.4e TSCH sensor networks leveraging reinforcement learning to determine an optimal number of slots to keep active in order to maintain a specific packet delivery ratio for the network. The goal is to turn off slots in the 802.15.4e TSCH frame that are not highly utilized. Each node determines the slots that should be deactivated based on a threshold $\mathbf{Q}$ value. This scheduling strategy allows the nodes to conserve energy effectively by finding the optimal active timeslots for both transmission and reception. This algorithm we name it as RAST Reinforcement-based Slot utilization Technique) algorithm. Through extensive simulations and evaluations across various network configurations, the RAST algorithm reveals a significant packet delivery ratio improvement as compared to Orchestra utilizing less active slots.

关键词： IEEE 802.15 Standard Scheduling algorithms Energy conservation Reinforcement learning Land mobile radio

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Adaptive and Responsive Traffic Signal Control Using Reinforcement Learning and Fog Computing

Adaptive and Responsive Traffic Signal Control Using Reinfor...

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IEEE Cloud Summit

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798350370065

ISBN: (纸本)9798350370072

As urban traffic becomes increasingly complex, conventional actuated traffic signal control methods are showing their limitations in mitigating congestion, and Deep Reinforcement Learning (DRL) is considered a promising solution. Existing studies mainly focus on using distributed agents, each intersection being controlled by a separate agent that can communicate with its neighbors to facilitate local coordination, which subsequently increases network complexity and usage. This article presents a DRL-based intelligent traffic signal control framework that leverages the fog computing paradigm. Traffic signals are divided into clusters, each controlled by a shared DRL agent in a fog node based on the collective information of the regional traffic situation. We have conducted simulation experiments in multiple scenarios, and the experimental results show that the proposed method yields lower average waiting times compared to existing methods.

关键词： Deep reinforcement learning Complexity theory Edge computing

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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Intelligent Cybersecurity Conference (ICSC)

作者： Simon Bertrand Pascal Germain Nadia Tawbi dept. of Computer Science & Software Engineering Laval University Québec Canada

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%.

关键词： Neural networks Manuals Feature extraction Vectors Threat assessment Data models Real-time systems Bayes methods Optimization Tuning

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DC Fault Detection Using Time Derivative of High-Pass Filter Response in MTDC Systems

DC Fault Detection Using Time Derivative of High-Pass Filter...

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Annual Conference of Industrial Electronics Society

作者： Saad Ahmed Khan Abhisek Ukil Nirmal-Kumar C. Nair Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation.

关键词： Fault diagnosis Accuracy Fault detection Load management Electrical fault detection Topology Power systems High frequency Passband Protection

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions

Automated Hematological Analysis:Advances in Segmentation Ap...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Saleha Eram Muhammad Usman Akram Maham Nayab Dept. Computer & Software Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75–80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes.

关键词： Deep learning Instance segmentation Accuracy Scalability Computational modeling Semantic segmentation Semantics Robustness Blood Systematic literature review

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