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Utilizing the Blockchain Technology for Higher Education in the Era of Pandemics: A Systematic Review 14

Utilizing the Blockchain Technology for Higher Education in ...

引用

14th International Conference on Information and Communication Systems, ICICS 2023

作者： Al-Tawara, Feda'a Qasaimeh, Malik Jarad, Dania Al-Qassas, Raad S. Princess Sumaya University for Technology Department of Software Engineering Amman Jordan Jordan University of Science and Technology Department of Software Engineering Department of Cyber Security Irbid Jordan Princess Sumaya University for Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798350307870

The availability of important characteristics such as decentralization, permanence, anonymity, and audacity has driven interest in blockchain technology more recently than ever before. This technology has been employed in a variety of applications, including education. Therefore, this research is a systematic review of research that investigates the applications of blockchain technology in higher education institutions in addition to those that present the role of this technology in combating the Corona pandemic, as it focuses on the following main topics: (1) blockchain-based applications and systems that can be used in higher education institutions;and (2) the current state of blockchain challenges in this area that need to be addressed in the future. A detailed analysis of the results of each topic as well as an extensive discussion were conducted based on the results. This review also provides insight into other areas that could benefit from blockchain technology during and after the pandemic. © 2023 IEEE.

关键词： Blockchain

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UAV Enabled Sustainable IoT Network with OTPDRL 26

UAV Enabled Sustainable IoT Network with OTPDRL

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Tiantian Liu, Lei Sun, Hongbo Hu, Jia Shandong University School of Software Jinan China YanTai University School of Computer and Control Engineering YanTai China Exeter University Computer Science United Kingdom

ISBN: (纸本)9798350331684

Integrating large-scale sensors into the network has become a research hotspot for its promising flexibility in monitoring vitally critical wild areas. However, the existing Internet of Things (IoT) systems are limited due to the lack of a stable power supply, which seriously affects the system's sustainability. The combination of sensors equipped with cordless power batteries and long-distance power transmission has ushered in a new era. Using the unmanned aerial vehicles (UAVs) to charge the battery ensures the flexibility and sustainability of the sensor in environmental detection. In this work, we aim to provide a solution for maintaining the sustainability of the sensors while optimizing UAV trajectory to minimize the overall energy consumption of UAV. Since deep reinforcement learning successfully solves the NP-hard combinatorial optimization problem, deep reinforcement learning is introduced in this work to obtain a feasible solution. We formulate the trajectory planning of UAV as a Markov decision problem and employ a deep reinforcement learning (DRL) model based on an attention mechanism to find the optimal policy efficiently, named the optimal trajectory planning algorithm based on DRL (OTPDRL). The experimental results suggest the OTPDRL obtains a good trade-off between performance gain and computational time. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Development of a Soft Hand and Wrist Rehabilitation Robot Utilizing Lattice Structure Actuators

Development of a Soft Hand and Wrist Rehabilitation Robot Ut...

引用

2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Lai, Jianwei Song, Aiguo Zhang, Wenbin Lu, Ye Wang, Jiajin Li, Ye School of Instrument Science and Engineering Southeast University State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory of Remote Measurement and Control Nanjing210096 China College of Computer Science and Software Engineering Hohai University Nanjing210024 China

ISBN: (纸本)9798350355123

Soft robots offer promising solutions for rehabilitation due to their inherent attributes such as suppleness, lightweight construction, and safety features. However, prevalent soft actuators face limitations in output force and bending angle, impeding their effective application in rehabilitation. This paper proposes a novel design approach employing a two-layer lattice structure actuator, comprising flexion, guiding, and extension layers. This innovative design facilitates supple and efficient bi-directional rehabilitation training for fingers and wrists. Experimental results demonstrate the efficacy of the proposed dual-layer lattice structure actuator. At an air pressure of 150 kPa, it achieves an end force of 14.88 N, marking a remarkable 906% increase compared to its single-layer counterpart. Additionally, the dual-layer structure exhibits a minimum bending radius of 2.77 cm under 60 kPa, representing a 38.5% reduction relative to the single-layer actuator. Leveraging these actuators, a hand and wrist rehabilitation robot was developed and subjected to a clinical trial involving two healthy subjects. The trial outcomes reveal significant improvements in finger grasping ability and wrist range of motion with the assistance of the rehabilitation robot. These results underscore the practical usability and effectiveness of the soft hand and wrist rehabilitation robot, thereby holding promise for enhancing patient rehabilitation outcomes. © 2024 IEEE.

关键词： Patient rehabilitation

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Prediction of Advertising Click-Through Rate Based on Dual Attention Network 4

Prediction of Advertising Click-Through Rate Based on Dual A...

引用

4th International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Zhang, Chao Zhang, Chunna University of Science and Technology LiaoNing School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350333947

Click-through rate (CTR) prediction refers to whether users will click on the content distributed by manufacturers. In the field of computing advertising and recommendation systems, click-through rate prediction has always been a mainstream hot topic. The relationship between features is often hidden in the data, detailed enumeration will lead to a waste of resources and manpower, and meaningless features will also interfere with and influence the final prediction results. Therefore, the key challenge in CTR task is how to effectively model feature interaction. In this paper, we continue the framework of the traditional deep click rate prediction model, considering the sparsity of the effective feature interaction, we add a feature generation module in the feature input part, and a dual attention network is proposed to further interact and filter the input features. The experiments are conducted on Criteo and Avazu datasets, and the results are improved compared with other models. © 2022 IEEE.

关键词： Marketing

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How (Un)Faithful is Attention? 5

How (Un)Faithful is Attention?

引用

5th Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Amini, Hessam Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9781959429050

Although attention weights have been commonly used as a means to provide explanations for deep learning models, the approach has been widely criticized due to its lack of faithfulness. In this work, we present a simple approach to compute the newly proposed metric AtteFa, which can quantitatively represent the degree of faithfulness of the attention weights. Using this metric, we further validate the effect of the frequency of informative input elements and the use of contextual vs. non-contextual encoders on the faithfulness of the attention mechanism. Finally, we apply the approach on several real-life binary classification datasets to measure the faithfulness of attention weights in real-life settings. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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Interactive Machine Learning Framework for Predicting Asthma Health Conditions Using XGBoost 4

Interactive Machine Learning Framework for Predicting Asthma...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Edward, Chimba S. Ekpo, Raphael H. Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Crawford University Department of Computer and Mathematical Sciences Ogun StateFaith Igbesa Nigeria

ISBN: (纸本)9798350353266

Asthma is a far reaching non-communicable sickness that affects individuals of any age and is a significant danger to human wellbeing worldwide, especially in low and middle-income countries. Its prevalence is substantial, and it remains one of the major contributors to global death rate. A substantial number of asthma development prediction models, and machine learning based approaches, exist. The problem associated with conventional methods of asthma prediction is low predictive accuracy of the model. The objective of this study is to develop an interactive Machine Learning (iML) framework for predicting Asthma Health conditions in selected African countries. The model was tested using dataset obtained from National Survey of Children's Health (NSCH). The machine learning technique used was XGBoost, and model development including preprocessing, training, and tested were carried out using Python programming language on Google Collaboratory (Collab) environment. The results of benchmarking of the performance accuracy of XGBoost shows 90.6% which is higher than the existing results in literature for Decision Tree (90.49%), KNN (90.28%), Naïve Bayes (82.7%). The study found out that the model developed by the researchers performed comparatively well in predicting asthma when compared with some existing state-of-the-art machine learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Breast Microcalcification detection in digital mammograms using Deep Transfer learning approaches 23

Breast Microcalcification detection in digital mammograms us...

引用

9th International Conference on Computing and Data engineering, ICCDE 2023

作者： Rasool, Ehtsham Anwar, Muhammad Junaid Shaker, Bilawal Hashmi, Muhammad Harris Rehman, Khalil Ur Seed, Yousaf Department of Computer Science The University of Alabama Birmingham United States School of Software Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781450398022

Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women's at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies. © 2023 ACM.

关键词： Diseases

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Money Laundering Detection on Ethereum: Applying Traditional Approaches to New Scene 29

Money Laundering Detection on Ethereum: Applying Traditional...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yu, Yunmei Wu, Jiajing Lin, Dan Fu, Qishuang Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Guangzhou China

ISBN: (纸本)9798350330717

With the continuous evolution of blockchain technology, cryptocurrency platforms such as Ethereum have emerged as centers for digital asset transactions and smart contracts deployment. However, this nascent financial ecosystem also introduces potential money laundering risks. The traditional financial industry has accumulated significant antimoney laundering (AML) experience and technical means for monitoring and detecting money laundering activities. Yet, the adaptability of these established AML algorithms in the context of blockchain remains unclear. This paper aims to investigate the practical adaptability of traditional AML algorithms on Ethereum data through empirical experiments. We gather eight real-world money laundering case datasets collected from Ethereum and conduct experiments using three traditional AML algorithms on these datasets. We evaluate the performance of these algorithms from various angles, including precision, recall, and the distribution of detected accounts' labels in comparison to the original datasets. It turns out algorithms demonstrate distinct performance in diverse money laundering cases, indicating that the adaptability of traditional AML algorithms on Ethereum data presents certain adaptability and limitations. Holoscope's accuracy demonstrates the value of dense subgraph properties in Ethereum money laundering detection, and further research can be conducted based on this model framework combined with the money laundering characteristics of Ethereum. Our study provides valuable insights for strengthening AML mechanisms on blockchain platforms and offers guidance for further research on detecting money laundering accounts in blockchain environments. © 2023 IEEE.

关键词： Anti-money laundering Blockchain Security Ethereum Money Laundering Detection

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Modeling and Discovering Data Race with Concurrent Code Property Graphs 23

Modeling and Discovering Data Race with Concurrent Code Prop...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Cao, Yang Dong, Yunwei School of Software Northwestern Polytechnical University Xi'an China School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350359398

Concurrent programs have brought about higher resource utilization and faster response times. However, the non-determinism in concurrent programs introduces concurrency bugs that are harder to detect than traditional program bugs. These concurrency bugs pose serious threats to the safety and security of software systems. Data races are the most common type of concurrency bug. Previous static analysis techniques either lack precision or face difficulties when applied to large-scale programs. The vulnerability detection tool Joern provides a new static approach that uses a novel source code representation, called code property graph, and effectively mines various types of source code vulnerabilities in large-scale programs through graph traversal. However, because the code property graph does not model the concurrency relationships between threads, Joern does not support the detection of concurrency bugs. Therefore, this paper proposes a property graph-based data race detection method called RaceQuery. This method introduces the concept of property graphs to represent concurrent programs. It constructs the concurrent code property graph based on the control flow graph and enhances it with synchronization operations analysis to improve detection precision. Subsequently, RaceQuery generates a set of concurrent thread pairs for the program based on the concurrent code property graph and models data races through graph traversal. This enables RaceQuery to effectively locate data races in the concurrent code property graph. The method is implemented as a prototype system using the popular graph database Neo4j and successfully detects 2 data races in an open-source project. © 2023 IEEE.

关键词： Static analysis

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A Digital Twin Based Framework for Real-Time Machine Condition Monitoring 19

A Digital Twin Based Framework for Real-Time Machine Conditi...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Chen, Zixiao Choudhury, Madhurjya Dev Blincoe, Kelly Dhupia, Jaspreet Singh University of Auckland Department of Mechanical and Mechatronics Engineering New Zealand School of Engineering and Computer Science Victoria University of Wellington New Zealand University of Auckland Department of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350320695

Condition Monitoring (CM) is an important approach to extending the life of complex equipment by forecasting the outcome of an event before catastrophic failure occurs. Recent advancements in digital twins (DT) offer additional benefits to machine condition monitoring. In this study, a framework based on DT for real-time condition monitoring of industrial machines is proposed. The multi-layer DT framework consists of a physical entity (PE), virtual equipment (VE), edge device, fidelity service and digital twin services. The virtual equipment is a replica of the physical entity or the monitored machine. It also contains a cloud platform to store data online and an application to interface with the cloud enabling users to check the data remotely. The fidelity service ensures conformity between the PE and the VE. The digital service provides optimal operation and maintenance schedules based on the data from both physical and virtual spaces. The integration of the edge layer enables real-time handling of high-frequency machine data for effective health monitoring. The validity of the proposed framework is demonstrated with a case study based on monitoring a critical component of an industrial drivetrain test rig. The features of the framework allow end-users to visualize the component's real-time health status remotely. © 2023 IEEE.

关键词： Condition monitoring

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