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arXiv 2023年

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level *** Codes 68T07 © 2023, CC BY-NC-ND.

关键词： eHealth

What if?.. Fabulating African HCI Futures within the Veil of HCI 23

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What if?.. Fabulating African HCI Futures within the Veil of...

Proceedings of the 4th African Human Computer Interaction Conference

作者： Shaimaa Lazem Muhammad Sadi Adamu Yasmeen Abdrabou Chris Muashekele Sheena Erete Jakita Thomas Hafeni Mthoko Heike Winschiers-Teophilus Nicola Bidwell Aderonke Sakpere Anicia Peters City of Scientific Research and Technology Applications (SRTA-City) Egypt Imagination Lancaster Lancaster University United Kingdom School of Computing and Communications Lancaster University United Kingdom Aalborg University Denmark College of Information Studeies University of Maryland USA Department of Computer science and software Engineering (CSSE) Auburn University USA NurtureID8 Namibia Department of Software Engineering Namibia University of Science and Technology Namibia Intenational Uiversity of Management Namibia and University of Melbourne Australia University of Ibadan Nigeria National Commission of Research Sicence and Technology (NCRST) Namibia

ISBN: (纸本)9798400708879

This workshop aims to bring together researchers and practitioners (African and otherwise) engaging with diverse communities and industries to discuss the geopolitics of knowledge production in computing research and design. Our proposal builds on existing works that have explored the modalities of the knowledge economy - particularly as knowledge is created, legitimized, and circulated as objective truth via the exercise of power. For example, engaging indigenous communities in conferences and knowledge fairs have sought to challenge disciplinary silos and boundaries imposed on non-Western academics and practitioners. This workshop, as a platform for interaction and discussion with no “sage on the stage”, is the first of its kind within AfriCHI and it seeks to explore and promote more subtle discussions about HCI knowledge production and dissemination practices.

关键词： African HCI Critical fabulation Decolonizing HCI Indigenous knowledge alt.HCI

An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

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arXiv 2022年

作者： Li, Lingjie Xuan, Manlin Lin, Qiuzhen Jiang, Min Ming, Zhong Tan, Kay Chen The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Informatics Xiamen University Xiamen361005 China The Department of Computing The Hong Kong Polytechnic University Hong Kong

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature Selection

Asynchronous Federated Clustering with Unknown Number of Clusters

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arXiv 2024年

作者： Zhang, Yunfan Zhang, Yiqun Lu, Yang Li, Mengke Chen, Xi Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong

Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the server for aggregation. However, some tricky but common FC problems are still relatively unexplored, including the heterogeneity in terms of clients’ communication capacity and the unknown number of proper clusters k∗. To further bridge the gap between FC and real application scenarios, this paper first shows that the clients’ communication asynchrony and unknown k∗ are complex coupling problems, and then proposes an Asynchronous Federated Cluster Learning (AFCL) method accordingly. It spreads the excessive number of seed points to the clients as a learning medium and coordinates them across the clients to form a consensus. To alleviate the distribution imbalance cumulated due to the unforeseen asynchronous uploading from the heterogeneous clients, we also design a balancing mechanism for seeds updating. As a result, the seeds gradually adapt to each other to reveal a proper number of clusters. Extensive experiments demonstrate the efficacy of AFCL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Coplane-Constrained Sparse Depth Sampling and Local Depth Propagation for Depth Estimation

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SSRN

SSRN 2023年

作者： Zhang, Jiehua Yang, Zhiwen Li, Liang Chen, Chuqiao Wang, Hongkui Wang, Tingyu Yan, Chenggang Gong, Yihong School of Software Engineering Xi’an Jiaotong University Xi’an710000 China Hangzhou Dianzi University Hangzhou310018 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Lishui Institute of Hangzhou Dianzi University Hangzhou310018 China Department of Automation

Depth estimation with sparse reference has emerged recently, and predicts depth map from a monocular image and a set of depth reference samples. Previous works randomly select reference samples by sensors, leading to severe depth bias as this sampling is independent to image semantic and neglects the unbalance of depth distribution in regions. This paper proposes a Coplane-Constrained sparse Depth (CCD) sampling to explore representative reference samples, and design a Local Depth Propagation (LDP) network for complete the sparse point cloud map. This can capture diverse depth information and diffuse the valid points to neighbors with geometry prior. Specifically, we first construct the surface normal map and detect coplane pixels by superpixel segmenting for sampling references, whose depth can be represented by that of superpixel centroid. Then, we introduce local depth propagation to obtain coarse-level depth map with geometric information, which dynamically diffuses the depth from the reference to neighbors based on local planar assumption. Further, we generate the fine-level depth map by devising a pixel-wise focal loss, which imposes the semantic and geometry calibration on pixels with low confidence in coarse-level prediction. Extensive experiments on public datasets demonstrate that our model outperforms SOTA depth estimation and completion methods. © 2023, The Authors. All rights reserved.

关键词： Semantics

SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Kai Shen Dayou Zhang Zi Zhu Lei Zhang Fangxin Wang Dan Wang The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen School of Science and Engineering The Chinese University of Hong Kong Shenzhen College of Computer Science and Software Engineering Shenzhen University Peng Cheng Laboratory The Guangdong Provincial Key Laboratory of Future Networks of Intelligence Department of Computing The Hong Kong Polytechnic University

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%.

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Automatic segmentation for meniscus magnetic resonance images of knee joint based on Mask region-based convolution neural network 5

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Automatic segmentation for meniscus magnetic resonance image...

5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhang, Liyan Zhou, Hao Wang, Juan Wang, Lei Xia, Chengyi Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin300384 China Department of Knee Surgery Tianjin Hospital Tianjin300211 China

ISBN: (纸本)9781450384155

Over the past two decades, magnetic resonance imaging (MRI) has been widely applied into the diagnosis of knee joint diseases. Due to the complexity and diversity of MRI data, traditional feature extraction requires manual searching for features to segment meniscus, and the final segmentation results still need to be further filtered. Therefore, it is necessary to design a novel method to automatically extract features directly from images. In this study, we develop a framework to implement this goal by using a mask region-based convolution neural network (Mask R-CNN) without manual intervention. In order to highlight the proportion of meniscus, we first preprocess the original image data so that it is reduced to about 1/8 of the original size, and then input the preprocessed image data into the trained Mask R-CNN. Afterwards, transfer learning is used to generate the weight of our network. By testing 1000 images, the mean intersection over union (IOU) and dice similarity coefficient (DSC) are up to 83.68% and 91.13%, respectively. The current results demonstrate that our approach is feasible and has a potential significance in clinical practice. © 2021 ACM.

关键词： Magnetic resonance imaging

Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

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Comparative Analysis of Machine Learning Models for Students...

International Conference on Advances in Digital Science, ICADS 2021

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030717810

Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

A Low-Cost Smart Office Design Framework Using Arduino

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Advances in Internet of Things 2023年第3期13卷 83-108页

作者： Ssemwogerere Rajab Nabagesera Veronica Kawalya Melese Alemsra Tsehayu Logose Masitula Wamwoyo Faruk Rahman Shiddiqur Ekwaro Dominic Mbalyowere Pasis Maritah Nambobi Mutwalibi Sayo Nakeleh Turay Mugerwa Derrick Kasagga Usama Seguya Asad Department of Computer Science & Information Technology Islamic University in Uganda Kampala Uganda College of Computing & Informatics Technology Makerere University Kampala Uganda School of Information and Software Engineering University of Electronic Science & Technology of China Chengdu China School of Computer Science Neijiang Normal University Neijiang China School of Computer Science and Engineering University of Electronic Science & Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

The main motive of our research work is security enhancement and light energy conservation. This paper describes a study investigating the potential of a controlled office solution by integrating the Internet of Things (IoT) with wireless sensor networks (WSNs). A prototype of a smart office is developed using a global system of mobile Bluetooth and Radio Frequency Identification (RFID) technology. The user can turn on and off the fan remotely at any time. This prototype focuses on security and provides human-friendly assistance when in or out of the Office by integrating a mobile application platform. The innovative automated smart Office is designed with intelligent Security doors, lights, alarms, temperature humidifiers, and bright Liquid Crystal Display (LCD) screens for viewing. Our study has opened up virtual possibilities for producing cheap innovative frameworks in this Generation of IoT and the fifth Generation (5G) technology. Therefore, when implemented, this innovation will ease and improve human quality of life. So, this paper aims to provide a low-cost, effective Internet of the things-based automated smart Office.

关键词： Intelligent Office Automation System Wireless Sensor Networks Internet of Things Ultrasonic Radio Frequency Identification Arduino