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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Emil Agusalim Habi Talib Paulus Insap Santosa Sunu Wibirama Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Signal Processing Laboratory Universitas Gadjah Mada Indonesia

The use of a learning management system (LMS) in the world of education is encouraged to support and to optimize the teaching and learning process. Using the LMS, the learning process that has previously been performed in schools can be performed anywhere and anytime. A good LMS provides convenience and comfort to its users. However, to the best of our knowledge, there is no review study that focusses on the usability and user experience of LMS. Therefore, in this literature study, we surveyed several methods used to measure usability and user experience in the LMS. We managed to collect 40 papers discussing the evaluation of usability and user experience in LMS. Furthermore, we successfully answered four formulated research questions. Based on our study, we found that there are 9 methods used to measure usability and user experience, 5 stages carried out in evaluating usability and user experience, 25 characteristics measured, and there are 13 data analysis methods that can be used to analyze usability and user experience in LMS. These findings are important for academics and practitioners working on information technology to support the educational process.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling 39

WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Su, Huai-An Geng, Jiaxiang Li, Liang Qin, Xiaoqi Hou, Yanzhao Wang, Hao Fu, Xin Pan, Miao Department of Electrical and Computer Engineering University of Houston United States School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Frontier Research Center Peng Cheng Laboratory China Department of Electrical and Computer Engineering Stevens Institute of Technology United States

ISBN: (纸本)157735897X

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices’ computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices’ communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Federated learning

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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Chinese Automation Congress (CAC)

作者： Haoran Zhang Zhezhuang Xu Yuhang Zhu Ye Lin Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface.

关键词： Accuracy Automation Shape Image edge detection Wood industry Feature extraction Encoding Character recognition Faces Defect detection

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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Chinese Automation Congress (CAC)

作者： Ying Zhang Kang Chen Zhezhuang Xu Ye Lin Qingbo Zeng Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Education College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement.

关键词： Correlation Automation Statistical analysis Education Linear regression Process control Data collection Software Hybrid learning Interviews

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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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SW-VAE: Weakly Supervised Learn Disentangled Representation via Latent Factor Swapping 17th

SW-VAE: Weakly Supervised Learn Disentangled Representation ...

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17th European Conference on computer Vision, ECCV 2022

作者： Zhu, Jiageng Xie, Hanchen Abd-Almageed, Wael USC Ming Hsieh Department of Electrical and Computer Engineering Los Angeles United States USC Information Sciences Institute Marina del Rey United States Visual Intelligence and Multimedia Analytics Laboratory Los Angeles United States

ISBN: (纸本)9783031250620

Representation disentanglement is an important goal of the representation learning that benefits various of downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signal by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Online distributed tracking of generalized Nash equilibrium on physical networks——Closing the Loop via Measurement Feedback

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Autonomous Intelligent Systems 2021年第1期1卷 69-80页

作者： Yifan Su Feng Liu Zhaojian Wang Shengwei Mei Qiang Lu The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina The Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghaiChina

In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational *** online algorithms is recognized as a promising method to cope with this challenge,where the task of computing system states is replaced by directly using measured values from the physical *** this paper,we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing *** that some system states are not measurable and measurement noise always exists,a dynamic state estimator is incorporated based on a Kalman filter,rendering a closed-loop dynamics of measurement-feedback driven online *** prove that,with a fixed step size,this online algorithm converges to a neighborhood of the GNE in *** simulations validate the theoretical results.

关键词： Generalized Nash equilibrium Distributed optimization Online optimization Feedback-based optimization State estimation Sharing market

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Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

arXiv

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arXiv 2024年

作者： Murshed, Rafid Umayer Ullah, Md Saheed Saquib, Mohammad Win, Moe Z. Department of Electrical and Computer Engineering The University of Texas DallasTX United States Department of Electrical and Computer Engineering University of Delaware Delaware United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts United States

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods. © 2024, CC BY.

关键词： Self-supervised learning

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Tellurium intervened Fe-N codoped carbon for improved oxygen reduction reaction and high-performance Zn-air batteries

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Journal of Materials Science & Technology 2023年第6期137卷 215-222页

作者： Rui Wang Zihan Meng Xuemin Yan Tian Tian Ming Lei Rami Adel Pashameah Hala MAbo-Dief Hassan Algadi Nina Huang Zhanhu Guo Haolin Tang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Xianhu Hydrogen ValleyFoshan 528200China College of Chemistry and Environmental Engineering Yangtze UniversityJingzhou 434023China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Chemistry Faculty of Applied ScienceUmm Al-Qura UniversityMakkah 24230Saudi Arabia Department of Science and Technology University College-RanyahTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering Faculty of EngineeringNajran UniversityP.O.Box 1988Najran 11001Saudi Arabia College of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China Department of Chemical and Bimolecular Engineering Integrated Composites Laboratory(ICL)University of TennesseeKnoxvilleTN 37996USA Guangdong Hydrogen Energy Institute of WHUT Xianhu Hydrogen ValleyFoshan 528200China

Pyrolysis-acquired iron and nitrogen codoped carbon(Fe-N-C)has been comprehensively investigated for its promising oxygen reduction reaction(ORR)catalytic performance and structural *** modification of non-metal elements with larger atomic radius and the corresponding intrinsic microstructure-property relations are rarely *** this study,tellurium(Te)intervened Fe-N-C was prepared by micelles-induced polymerization with Te nanowires as an in-situ intervening *** out-plane bonding of Te with Fe induced the increase of both N content and proportion of pyridinic N on the material surface,thus improving the ORR catalytic *** assembled Zn-air battery demonstrated a maximum power density of 250 mW/cm^(2)and excellent rate capability under various discharge current densities,which was much better than the Pt/***,the current work demonstrates a novel Te/Fe-N-C material and reveals an original Te intervened Fe-N-C strategy and N reconfiguration mechanism,which is of great significance for the design of key materials in energy-related fields.

关键词： Te/Fe-N-C Assembly ORR Electrocatalysis Zn-air battery

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