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International Journal of Interactive Mobile Technologies 2012年第3期6卷 34-41页

作者： Baloch, Hasnain Zafar Abdulrhaman, Aziziah Ihad, Noorminshah A. University of Technology Malaysia Malaysia Faculty of Computer Science and Information system Department of Information system University of Technology Malaysia Malaysia

Smart Mobile Devices (SMD) are there for many years but using them as learning tools started to emerge as new research area. The trend to merge collaborative learning methodology by using mobile devices in informal context is important for implementation of Learner Centric Learning (LCL). Survey and numerous studies show that more than 95% of students in colleges are users of these smart mobile devices in developed world. Developing counties are also catching up and we can see this percentage is almost same in university level in these countries. Students are using SMDs for learning in some form. Higher education Institutions also try to embark their E-learning to Mobile learning (ML). The aim of this paper is to do propose operational framework for designing Mobile Collaborative Informal learning activities using SMDs. Show results of experimental and case study done to study the Mobile Collaborative Informal learning using Activity Theory (AT). Core Components of framework are Mobile Learning Activities/Objects, Wireless/Mobile Smart devices, Collaborative knowledge and Collaborative learning. The research mention here is its infancy stage. © 2012, International Federation of Engineering Education Societies (IFEES). All rights reserved.

关键词： Students

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

arXiv

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arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Vehicle trajectory discovery for vehicular wireless networks

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International Journal of Smart Home 2014年第1期8卷 157-164页

作者： Kim, Jeong-Sig Kim, Jin-Hong Department of Computer Information System Gyeonggi College of Science and Technology Department of Computer Engineering Hansung University Korea Republic of

This paper introduces vehicle trajectory discovery for vehicular wireless networks. Nowadays, GPS-based navigation systems are popular used for providing efficient driving paths for drivers, as well as it's still a major challenge to process such information in order to explain moving object interactions, which could help in deriving trajectory patterns. To that end, we consider a vehicular wireless networks-based representation of trajectory data. This paper shows the inefficient elements during the route discovery in well-known reactive protocols such as ERS (Expanding Ring Search) Method. © 2014 SERSC.

关键词： Trajectories

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Open source software adaptation in Africa: Is a matter of inferior or cheap is not quality?

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International Journal of Open Source Software and Processes 2014年第1期5卷 1-15页

作者： Bakar, Abubakar Diwani Md Sultan, Abu Bakar Zulzalil, Hazura Din, Jamilah Department of Information System Faculty of Computer Science and Information Technology University Putra Malaysia Serdang Selangor Malaysia

The African continent has long benefited from adopting OSS in its private and public organizations that have changed their way of development, the use and how to acquire proprietary software. This frequency of adaptation does not appear to be in balance with the contribution to the OSS community. Using views from experienced software practitioners working in different organizations across two African countries it has been observed that neglected infrastructure, a wide availability of proprietary software and misconceptions of a clear meaning of Open Source Software across Africa have been an obstacle towards the participation in the OSS technology in the global network. Copyright © 2014, IGI Global.

关键词： Open source software

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Corrections to “Effective Gait Abnormality Detection in Parkinson Patients for Multi-Sensors Surveillance system”

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IEEE Access 2025年 13卷 62892-62892页

作者： Abdulwahab Alazeb Mouazma Batool Naif Al Mudawi Mohammed S. Alshehri Sultan Almakdi Nouf Abdullah Almujally Asaad Algarni Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia Department of Computer Science Air University Islamabad Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Presents corrections to the paper, (Corrections to “Effective Gait Abnormality Detection in Parkinson Patients for Multi-Sensors Surveillance system”).

关键词： Surveillance

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Comprehensive investigation of rooftop photovoltaic power plants with monocrystalline polycrystalline and thin-film technologies for exergy economic and environmental assessments

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Scientific Reports 2025年第1期15卷 1-15页

作者： Lekbir, Abdelhak Hassani, Samir Mohamad Radzi, Putri Nor Liyana Mekhilef, Saad Tey, Kok Soon Samatar, Abdullahi Mohamed Alshammari, Obaid Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Universiti Malaya Kuala Lumpur 50603 Malaysia Unité de Recherche Appliquée en Energies Renouvelables CDER Ghardaia 47133 Algeria School of Engineering Swinburne University of Technology Hawthorn 3122 VIC Australia Department of Electrical Engineering Faculty of Engineering Hormuud University Mogadishu Somalia Department of Electrical Engineering College of Engineering University of Hail Hail 2240 Saudi Arabia

This research aims to conduct an exergy, economic, and environmental analysis of a 6.57 kWp rooftop photovoltaic (PV) power plant that combines different PV technologies, comprising 2 kWp of poly-crystalline (p-Si), 1... 详细信息

This research aims to conduct an exergy, economic, and environmental analysis of a 6.57 kW_p rooftop photovoltaic (PV) power plant that combines different PV technologies, comprising 2 kW_p of poly-crystalline (p-Si), 1.87 kW_p of mono-crystalline (m-Si), and 2.7 kW_p of thin-film amorphous silicon (a-Si) technologies. A comprehensive assessment was conducted to evaluate the environmental and techno-economic parameters of a PV plant system. This study is based on the performance data obtained over four years of energy production under the weather conditions of Kuala Lumpur, Malaysia. The embodied energy required for the manufacturing of the PV power plant was estimated using embodied energy indices available in the literature. Additionally, a detailed economic evaluation was conducted based on the electricity costs in Malaysia. Moreover, the environmental impact was assessed over the plant’s life cycle, considering the emission factors of coal power plants. Results indicate that the exergy payback time for the different technologies i.e., m-Si, p-Si, a-Si, when operated individually, and when combined within a single PV system, are found to be ~, and years, respectively. Over its life cycle, it was found that the PV plant emits about. Emission breakdown analysis has revealed that the manufacturing process of the m-Si, p-Si, a-Si, and the monitoring systems contribute to,,, and of emissions, respectively. However, the PV power plant could offset about annually, equivalent to the emission of a car over approximately 3 years. This study offers critical insights into the exergy efficiency, environmental impact, and economic viability of a grid-connected rooftop PV power plant that integrates multiple PV technologies under tropical climate conditions. © The Author(s) 2025.

关键词： CO2 emission Economic assessment Environmental benefits Life cycle PV material

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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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