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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Wahshat, Hassan Al Al-Rousan, Riyad Al-Haithami, Wafa Abdulkarem Ahmad Saany, Syarilla Iryani Mohamad, Fatma Susilawati Kumar, Rohit Ajloun National University Faculty of Business Department of Management Information System Ajloun Jordan Department of Virtual and Augmented Reality Jordan Mediu Faculty of Finance and Administrative Sciences Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid growth of electronic commerce (e-commerce) has revolutionized the global business landscape, transforming the way consumers and businesses interact, purchase, and sell goods and services. However, this sector faces a myriad of challenges that impede its sustainable growth and development. This research paper critically examines the current problems encountered by the electronic commerce sector and proposes viable solutions to address these issues. The study identifies three major problem areas affecting the sector: (1) Security and Privacy Concerns, (2) Logistics and Supply Chain Bottlenecks, and (3) Customer Experience and Trust. Regarding security and privacy, the proliferation of cyber-attacks, data breaches, and identity theft has eroded consumer confidence, hindering e-commerce adoption. The paper aims to investigate the latest technologies and best practices, such as blockchain, advanced encryption techniques, and robust authentication methods, to fortify e-commerce platforms against cyber threats and safeguard user data. The second problem area, logistics and supply chain bottleneck, poses challenges related to order fulfilment, inventory management, and last-mile delivery. Through an in-depth analysis, the research proposes innovative approaches like utilizing machine learning algorithms for predictive inventory management, integrating Internet of Things (IoT) devices for real-time tracking, and exploring drone and autonomous vehicle deliveries to enhance overall logistics efficiency. The third issue addressed in this paper pertains to customer experience and trust. The inability to tangibly experience products before purchase, coupled with ambiguous return policies, often deters potential customers from engaging in e-commerce transactions. The study delves into augmented reality (AR) and virtual reality (VR) applications to simulate product experiences and advocates for transparent and customer centric return policies. This research paper sheds li

关键词： Supply chains

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An Efficient Broadcast Encryption Supporting Designation and Revocation Mechanisms

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Chinese Journal of Electronics 2019年第3期28卷 445-456页

作者： ZHU Yan YU Ruyun CHEN E HUANG Dijiang School of Computer and Communication Engineering University of Science and Technology Beijing School of Computing Informatics and Decision System Engineering Arizona State University

In this paper our objective is to explore approaches of secure group-oriented communication with designation and revocation mechanisms *** present a new scheme of Revocation-based broadcast encryption(RBBE) which is designed on Dan Boneh et al.'s scheme with the designation mechanism proposed in 2005. We combine two above-mentioned schemes into a new cryptosystem, called Dual-mode broadcast encryption(DMBE). Based on these work, we reach the following conclusions. We use the DMBE scheme as an example to show that it is feasible to construct a broadcast encryption scheme that supports designation and revocation mechanisms simultaneously. The cryptosystem with dual modes is more efficient than that with single mode over computational costs, and the performance is improved to at most O(「N/2」), where N is the total number of users in the system. We prove completely that both the RBBE scheme and the DMBE scheme are semantically secure against chosen plaintext attack with full collusion under the decisional bilinear Diffie-Hellman exponent assumption.

关键词： Cryptography Broadcast encryption Provable security Revocation Designation Dual modes

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Unleashing the Power of Visuals: A Captivating Exploration of Scientific Data Visualization Methods and Techniques 8th

Unleashing the Power of Visuals: A Captivating Exploration o...

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8th International Visual informatics Conference, IVIC 2023

作者： Baharum, Aslina Ismail, Rozita Ismail, Ismassabah Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Halamy, Suhaida Computing and Information System School of Engineering and Technology Sunway University Jalan Universiti Selangor Bandar Sunway Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Kajang Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Selangor Dengkil Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9789819973385

Scientific visualizations are the building blocks for conveying results and ideas to a wide range of audiences. It is one of the steps in the data science process, and it should be done before the data presentation phase. Because some data is not easy to read for humans, visualization can help transform the data into visual content such as graphs or plots, as the requirement for scientific visualization techniques and methods is increasing greatly. Reviewing various visualization methods is necessary to increase knowledge about it. In this paper, an overview of scientific data visualization methods and techniques has been reviewed. To conduct the experiment, a total of five peer-reviewed articles relating to scientific visualization techniques were chosen. The problem with this study is that people still do not have a clear idea of how to use or what is the importance of scientific visualization. The findings of this paper show that the occurrence of visualization techniques improves the effectiveness of reading data and helps people to understand data in a more vivid way. A systematic literature review has been adopted in this study to assist the author in doing a study without bias. Finally, this paper can help readers increase their knowledge of the scientific visualization technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data visualization

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A Spark-Based Open Source Framework for Large-Scale Parallel Processing of Rich Text Documents 8

A Spark-Based Open Source Framework for Large-Scale Parallel...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Chen, Qiang Chen, Yinong Wu, Sheng Zhang, Zili College of Computer and Information Science Southwest University Chongqing China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781665425742

The large amount of unstructured educational data is an important data source of educational big data analysis. However, there is a lack of efficient and simple distributed solutions to process these unstructured documents. To address this problem, this study proposes a Spark-based parallel processing framework of large-scale rich text documents (mainly as MS Word documents), which can seamlessly to extend the serial open-source algorithm to distributed computing algorithm to accomplish parallel processing of large-scale rich text documents. We focus on how to achieve non-intrusive deployment of clusters and how Python codes access to HDFS files. The experimental results show that by using 8 physical nodes (with 128 cores all together), the speedup reaches 48 times compared to the standalone Python-docx in the experiments of extracting 232 GB docx files. The experiments also analyze the Spark parameters and explore how to optimize the parameter settings. © 2021 IEEE.

关键词： Big data

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Graph Learning: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第2期2卷 109-127页

作者： Xia, Feng Sun, Ke Yu, Shuo Aziz, Abdul Wan, Liangtian Pan, Shirui Liu, Huan School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia School of Software Dalian University of Technology Dalian116620 China Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in differe

关键词： Surveys

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Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous decisions

Partitioning and Gaussian Processes for Accelerating Samplin...

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2021 Winter Simulation Conference, WSC 2021

作者： Liu, Menghan Pedrielli, Giulia Cao, Yumeng Arizona State University School of Computing Informatics Decision Systems Engineering Tempe85281 United States Arizona State University School of Mathematical and Statistical Sciences Tempe85281 United States

ISBN: (纸本)9781665433112

We propose Part-MCTS for sampling continuous decisions at each stage of a Monte Carlo Tree Search algorithm. At each MCTS stage, Part-MCTS sequentially partitions the decision space and keeps a collection of Gaussian processes to describe the landscape of the objective function. A classification criteria based on the estimation of the minimum allows us to focus the attention on regions with better predicted behavior, reducing the evaluation effort elsewhere. Within each subregion, we can use any sampling distribution, and we propose to sample using Bayesian optimization. We compare our approach to KR-UCT (Yee et al. 2016) as state of the art competitor. Part-MCTS achieves better accuracy over a set of nonlinear test functions, and it has the ability to identify multiple promising solutions in a single run. This can be important when multiple solutions from a stage can be preserved and expanded at subsequent stages. © 2021 IEEE.

关键词： Gaussian noise (electronic)

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection

Towards Effective and Efficient Error Handling Code Fuzzing ...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kang Chen Ming Wen Haoxiang Jia Rongxin Wu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology (HRUST) Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Jin YinHu Laboratory Wuhan China School of Informatics Xiamen University Xiamen China School of Computer Science and Technology HUST Wuhan China Cluster and Grid Computing Lab

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL- F I are effective that contribute significantly to its overall performance.

关键词： Codes Computer bugs Fuzzing Software systems Software reliability Testing

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CleanRL: high-quality single-file implementations of deep reinforcement learning algorithms

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 12585-12602页

作者： Shengyi Huang Rousslan Fernand JulienDossa Dossa Chang Ye Jeff Braga Dipam Chakraborty Kinal Mehta João G.M. Araújo College of Computing and Informatics Drexel University Graduate School of System Informatics Kobe University Japan Tandon School of Engineering New York University AIcrowd International Institute of Information Technology Hyderabad Cohere

CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning (DRL) algorithms. These single-file implementations are self-contained algorithm variant files such as ***, ***, and ppo_*** that individually include all algorithm variant's implementation details. Such a paradigm significantly reduces the complexity and the lines of code (LOC) in each implemented variant, which makes them quicker and easier to understand. This paradigm gives the researchers the most fine-grained control over all aspects of the algorithm in a single file, allowing them to prototype novel features quickly. Despite having succinct implementations, CleanRL's codebase is thoroughly documented and benchmarked to ensure performance is on par with reputable sources. As a result, CleanRL produces a repository tailor-fit for two purposes: 1) understanding all implementation details of DRL algorithms and 2) quickly prototyping novel features.

关键词： deep reinforcement learning single-file implementation open-source

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A leader-follower game theoretic approach to arrest cascading failure in smart grid

arXiv

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arXiv 2021年

作者： Roy, Sohini Sen, Arunabha School of Computing Informatics and Decision System Engineering Arizona State University AZTempe-85281 United States

—The Smart Grid system (SGS) is a joint network comprising the power and the communication network. In this paper, the underlying intra-and-interdependencies between entities for a given SGS is captured using a dependency model called Modified Implicative Interdependency Model (MIIM) [1]. Given an integer K, the K-contingency list problem gives the list of K-most critical entities, failure of which maximizes the network damage at the current time. The problem being NP complete [2] and owing to the higher running time of the given Integer Linear Programming (ILP) based solution [3], a much faster heuristic solution to generate an event driven self-updating K-contingency list [4] is also given in this paper. Based on the contingency lists obtained from both the solutions, this paper proposes an adaptive entity hardening technique based on a leader-follower game theoretic approach that arrests the cascading failure of entities in the SGS after an initial failure of entities. The validation of the work is done by comparing the contingency lists using both types of solutions, obtained for different K values using the MIIM model on a smart grid of IEEE 14-Bus system with that obtained by simulating the smart grid using a co-simulation system formed by MATPOWER and Java Network Simulator (JNS). The K-contingency list obtained for a smart grid of IEEE 14-Bus system also indicate that the network damage predicted by both the ILP based solution and heuristic solution using MIIM are more realistic compared to that obtained using another dependency model called Implicative Interdependency Model (IIM) [2]. Advantage of using the MIIM based heuristic solution is also shown in this paper when larger SGS of IEEE 118-Bus is considered. Finally, it is shown how the adaptive hardening helps in improving the network performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Smart power grids

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A self-updating K-contingency list for smart grid system

arXiv

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arXiv 2021年

作者： Roy, Sohini Sen, Arunabha School of Computing Informatics and Decision System Engineering Arizona State University AZTempe-85281 United States

A reliable decision making by the operator in a smart grid is contingent upon correct analysis of intra-and-interdependencies between its entities and also on accurate identification of the most critical entities at a given point of time. A measurement based self-updating contingency list can provide real-time information to the operator about current system condition which can help the operator to take the required action. In this paper, the underlying intra-and-interdependencies between entities for a given power-communication network is captured using a dependency model called Modified Implicative Interdependency Model (MIIM) [1]. Given an integer K, the event-driven self-updating contingency list problem gives the list of K-most critical entities, failure of which maximizes the network damage at the current time. Owing to the problem being NP complete, a fast heuristic method to generate a real-time contingency list using system measurements is provided here. The validation of the work is done by comparing the contingency list obtained for different K values using the MIIM model on a smart grid of IEEE 14-Bus system with that obtained by simulating the smart grid using a co-simulation system formed by MATPOWER and Java Network Simulator (JNS). The results also indicate that the network damage predicted by both the ILP based solution [2] and the proposed heuristic solution using MIIM are more realistic compared to that obtained using another dependency model called Implicative Interdependency Model (IIM) [3]. Copyright © 2021, The Authors. All rights reserved.

关键词： Heuristic methods

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