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A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

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Discover Data 2024年第1期2卷 1-39页

作者： Adewole, Kayode S. Alozie, Emmanuel Olagunju, Hawau Faruk, Nasir Aliyu, Ruqayyah Yusuf Imoize, Agbotiname Lucky Abdulkarim, Abubakar Imam-Fulani, Yusuf Olayinka Garba, Salisu Baba, Bashir Abdullahi Hussaini, Mustapha Oloyede, Abdulkarim A. Abdullahi, Aminu Kanya, Rislan Abdulazeez Usman, Dahiru Jafaru Department of Computer Science University of Ilorin Ilorin Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Department Information and Media Studies Faculty of Communication Bayero University Kano Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunications Engineering Kampala International University Kampala Uganda Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Software Engineering Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Cyber Security Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Economics Faculty of Social and Management Sciences Kafin Hausa Nigeria Department: University Medical Services Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Computer Science and Information Technology Baze University Abuja Nigeria Department: International Law and Jurisprudence Faculty of Law Bayero University Kano Nigeria

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural mod

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory

Malware Detection and Classification for Internet of Things ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sudhakar K Saif O. Husain D. Raja Babu M. Ashok Kumar R. Dineshkumar Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

关键词： Adaptation models Recurrent neural networks Training data Malware computer networks Internet of Things Convolutional neural networks

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On Selection of Optimal Kernel Function for software Defect Prediction

On Selection of Optimal Kernel Function for Software Defect ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Mohammad Azzeh Ali Bou Nassif Shadi Banitaan Department of Data Science Princess Sumaya University for Technology Amman Jordan Department of Computer Engineering University of Sharjah Sharjah UAE Department of Software Engineering University of Detroit Mercy Detroit USA

software defect prediction is used to identify the most likely defect-prone modules. This activity can help the quality assurance team to distribute limited resources efficiently as well as reduce the time and cost of the testing phase. Support Vector Machine (SVM) has been widely used to build software defect prediction models. Nevertheless, the accuracy of such method depends on multiple factors such as the type of SVM method, choice of kernel functions and dimensionality of the dataset. This paper continues along that research line and examines the impact and stability of four kernel functions and feature dimensionality reduction on the performance of SVM for software defect pre- diction. Comprehensive experiments have been conducted using four kernel functions, ten feature selection thresholds based on the Information gain algorithm, and seven public datasets. This has resulted in 280 experiments. The findings demonstrate that there is no kernel function that can show stable performance across different experimental settings. However, both Radial basis and Sigmoid kernel functions showed more stability than Polynomial and Linear kernel functions. In addition, changing the feature set from high to low dimensionality for each kernel function did not always improve the accuracy of SVM, which contradicts the findings that say using a small set of features can be as effective as using a large set of features.

关键词： Support vector machines Dimensionality reduction Quality assurance software algorithms Predictive models Feature extraction software

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A Study on Secure Data Access Scheme Based on CP-ABE in Cloud Environments 10th

A Study on Secure Data Access Scheme Based on CP-ABE in Clou...

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10th International Conference on computer science and its Applications, CSA 2018 and the 13th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2018

作者： Hwang, Yong-Woon Lee, Im-Yeong Department of Computer Science Engineering Soonchunhyang University Asan Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

ISBN: (纸本)9789811393402

Recently, with the development of cloud computing, it has become possible to store and share data in cloud environments;however, there are various security threats in these environments. Attacker can leak stored data and, above all, service providers cannot be trusted completely. Therefore, security technologies guaranteeing that data remains secure when stored in cloud environments are required. To this end, researchers are studying techniques based on CP-ABE. However, vulnerabilities to various security threats remain and some schemes are inefficient. In this paper, we apply CP-ABE to address some of the security threats associated with cloud environments, by ensuring that only authorized users can access data stored in the cloud. In addition, we propose an access control system that, in comparison to existing schemes, is more computationally efficient when attributes are revoked. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Access control

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Reconfigurable Intelligent Surface Assisted Semantic Communication Systems

Reconfigurable Intelligent Surface Assisted Semantic Communi...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Jiajia Shi Tse-Tin Chan Haoyuan Pan Tat-Ming Lok Department of Information Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Semantic communication, which focuses on conveying the meaning of information rather than exact bit reconstruction, has gained considerable attention in recent years. Meanwhile, reconfigurable intelligent surface (RIS) is a promising technology that can achieve high spectral and energy efficiency by dynamically reflecting incident signals through programmable passive components. In this paper, we put forth a semantic communication scheme aided by RIS. Using text transmission as an example, experimental results demonstrate that the RIS-assisted semantic communication system outperforms the point-to-point semantic communication system in terms of bilingual evaluation understudy (BLEU) scores in Rayleigh fading channels, especially at low signal-to-noise ratio (SNR) regimes. In addition, the RIS-assisted semantic communication system exhibits superior robustness against channel estimation errors compared to its point-to-point counterpart. RIS can improve performance as it provides extra line-of-sight (LoS) paths and enhances signal propagation conditions compared to point-to-point systems.

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NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields

arXiv

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arXiv 2023年

作者： Liu, Xiaoliang Shen, Furao Han, Feng Zhao, Jian Nie, Changhai State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns. Existing research primarily focuses on transferability to different FR models, overlooking the direct transferability to victim’s face images, which is a practical threat in real-world scenarios. In this study, we propose a novel adversarial attack method that considers both the transferability to the FR model and the victim’s face image, called NeRFTAP. Leveraging NeRF-based 3D-GAN, we generate new view face images for the source and target subjects to enhance transferability of adversarial patches. We introduce a style consistency loss to ensure the visual similarity between the adversarial UV map and the target UV map under a 0-1 mask, enhancing the effectiveness and naturalness of the generated adversarial face images. Extensive experiments and evaluations on various FR models demonstrate the superiority of our approach over existing attack techniques. Our work provides valuable insights for enhancing the robustness of FR systems in practical adversarial settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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MetaSlicing: A Novel Resource Allocation Framework for Metaverse

arXiv

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arXiv 2022年

作者： Chu, Nam H. Hoang, Dinh Thai Nguyen, Diep N. Phan, Khoa T. Dutkiewicz, Eryk Niyato, Dusit Shu, Tao The School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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A Topology Independent Fast Rerouting Scheme with Hop-by-Hop Forwarding Can Achieve a Full Protection

SSRN

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SSRN 2022年

作者： Geng, Haijun Meng, Zhuo Zhang, Han Shi, Xingang Wang, Zhiliang Yin, Xia Chen, Meng Guo, Yingya The School of Automation and Software Engineering Shanxi University School of Computer and Information Technology China The Institute for Network Sciences and Cyberspace Tsinghua University China The Department of Computer Science and Technology Tsinghua University China The School of Big Data and Software Engineering Chongqing University China The College of Computer and Data Science Fuzhou University China

To support critical applications or guarantee a high quality of service, Internet Service Providers (ISPs) generally deploy a technique called routing protection algorithm for local fast rerouting. Alternate next hops are stored alongside with the default next hops in a router’s forwarding table, and can be immediately activated to invoke a loop free repair path in face of link or node failures, or used for multipath transmission. However, the existing routing protection methods either cannot protect all possible single-failure scenarios in the network or needs the assistance of an auxiliary address. In response to the above problems, the present study proposes a single-failure routing protection algorithm based on hop by hop routing (SPA). The algorithm has the following three advantages: (1) supports hop-by-hop forwarding, (2) supports incremental deployment, and (3) can protect all possible singlefailure scenarios in the network as long as network keep connected. Extensive simulations show that, compared to the state-of-the-art schemes which are adopted by the industry, SPA not only can handle arbitrary single-failure situations in the network but also has a small path stretch. These advantages make SPA a good candidate for practical solution for IP fast reroute when network failure occurs. © 2022, The Authors. All rights reserved.

关键词： Internet service providers

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A New Deep Learning Model for Semi-supervised Soft-sensing of an Industrial Production Process

A New Deep Learning Model for Semi-supervised Soft-sensing o...

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IEEE International Conference on Automation science and engineering (CASE)

作者： XuDong Shi ChenYu Tian Qi Kang MengChu Zhou HanQiu Bao Jing An Department of Control Science and Engineering Tongji University Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China School of Internet of Things Engineering Jiangnan University Wuxi China School of Software Dalian University of Technology Liaoning China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Soft sensing offers a promising solution for predicting key quality variables in various production industries. One of the major challenges in developing an effective data-driven soft sensor is the scarcity of labeled data and the obstacle of extracting useful information from unlabeled samples. To address this issue, this work proposes a new deep learning-based soft sensor model called a spatiotemporal deep learning network. It leverages an encoder-decoder structure to explicitly exploit the spatial and temporal information in both labeled and unlabeled data, enabling efficient utilization of the latter to facilitate prediction performance. The encoder realizes more detailed spatiotemporal dependencies extraction by the proposed gated recurrent unit-based attention mechanism and the channel-calibration attention-based one. Finally, the extracted spatiotemporal features are fed to a multi-layer perceptron-based prediction head for soft sensor modeling. A mixed form loss is employed in the decoder to train our proposed model which incorporates both labeled and unlabeled data. Experiments are conducted on a real-life industrial process, demonstrating the feasibility and effectiveness of the proposed method.

关键词： Deep learning Attention mechanisms Accuracy Soft sensors Production Predictive models Feature extraction Data models Spatiotemporal phenomena Data mining

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Effective Entry-Wise Flow for Molecule Generation

Effective Entry-Wise Flow for Molecule Generation

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International Conference on Data engineering

作者： Qifan Zhang Junjie Yao Yuquan Yang Yizhou Shi Wei Gao Xiaoling Wang School of Computer Science and Technology East China Normal University Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Department of Chemistry University of Massachusetts Boston University of Michigan Ann Arbor.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts.

关键词： Training Couplings Materials science and technology Interpolation Data engineering Drug discovery Task analysis

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