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Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

Journal of Social Computing 2023年第2期4卷 112-124页

作者： Nengjun Zhu Lingdan Sun Jian Cao Xinjiang Lu Runtong Li the School of Computer Engineering and Science Shanghai UniversityShanghai 200444China the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China the Business Intelligence Lab Baidu ResearchBeijing 100085China the Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Session-based recommender systems are increasingly applied to next-item ***,existing approaches encode the session information of each user independently and do not consider the interrelationship between *** work is based on the intuition that dynamic groups of like-minded users exist over *** considering the impact of latent user groups,we can learn a user’s preference in a better *** this end,we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user ***,we utilize two network units to learn users’long and short-term sessions,***,we employ two additional units to determine the affiliation of users with specific latent groups,followed by an aggregation of these latent group ***,user preference representations are shaped comprehensively by considering all these four aspects,based on an attention ***,to avoid setting the number of groups manually,we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups *** experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall,mean average precision(mAP),and area under curve(AUC)metrics.

关键词： session-based recommender user group modeling attention mechanism adaptive learning

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Natural Image Matting with Attended Global Context

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Journal of computer Science & Technology 2023年第3期38卷 659-673页

作者：张億一牛力 Yasushi Makihara 张健夫赵维杰 Yasushi Yagi 张丽清 MoE Key Lab of Artificial Intelligence Department of Computer Science and EngineeringShanghai Jiao Tong University Shanghai 200240China Department of Intelligent Media Institute of Scientific and Industrial ResearchOsaka UniversityOsaka 565-0871Japan

Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the *** benchmark quantitatively and qualitatively.

关键词： image matting global context deformable sampling

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CRLB-based Data-driven Covariance Tuning for 5G KF Vehicular Tracking

CRLB-based Data-driven Covariance Tuning for 5G KF Vehicular...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Bader, Qamar Saleh, Sharief Noureldin, Aboelmagd Chalmers University of Technology Department of Electrical Engineering Gothenburg Sweden Queen's University Department of Electrical and Computer Engineering KingstonON Canada Research Lab Gothenburg Sweden Royal Military College of Canada Department of Electrical and Computer Engineering KingstonON Canada

ISBN: (纸本)9798350351255

5G mmWave offers a high-precision positioning solution, functioning effectively in both line-of-sight (LoS) and operable non-line-of-sight (NLoS) conditions. However, in scenarios with complete signal blockage, integrating with motion-based models becomes crucial. This integration is achieved through Bayesian-based estimators, which entail a prediction and a correction stage weighted by their respective covariance matrices. Although covariance matrices of different prediction models have been extensively studied in the literature, the measurement covariance matrix derived from 5G-based position computations remains largely unexplored. In this paper, we propose a measurement covariance matrix tuning scheme based on a data-driven Cramér-Rao lower bound CRLB model. We validate the proposed algorithm within a simple linear Kalman filter (LKF) positioning framework. The methodology was tested in a controlled simulation scenario using a real 24-minute-long vehicular trajectory in a deep-urban environment. The results demonstrate that the developed data-driven model is reliable, maintaining a standard deviation error of less than 7 cm for 95% of the time and less than 0.5 m for 100% of the time relative to the true computed CRLB. The proposed adaptive KF sustains a position error below 30 cm for 99.3% of the time. © 2024 IEEE.

关键词： Kalman filters

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An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

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computers, Materials & Continua 2024年第10期81卷 1789-1808页

作者： Umar Zaman Junaid Khan Eunkyu Lee Sajjad Hussain Awatef Salim Balobaid Rua Yahya Aburasain Kyungsup Kim Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Environmental IT Engineering Chungnam National UniversityDaejeon34134Republic of Korea Autonomous Ship Research Center Samsung Heavy IndustriesDaejeon34051Republic of Korea ICAROS Center(PRISMA Lab) University of Naples Federico IINaples80131Italy Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazan45142Saudi Arabia

Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic *** study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System(AIS)data and advanced deep learning models,including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Bidirectional LSTM(DBLSTM),Simple Recurrent Neural Network(SimpleRNN),and Kalman *** research implemented rigorous AIS data preprocessing,encompassing record deduplication,noise elimination,stationary simplification,and removal of insignificant *** were trained using key navigational parameters:latitude,longitude,speed,and *** aware processing through trajectory segmentation and topological data analysis(TDA)was employed to capture dynamic *** using a three-month AIS dataset demonstrated significant improvements in prediction *** GRU model exhibited superior performance,achieving training losses of 0.0020(Mean Squared Error,MSE)and 0.0334(Mean Absolute Error,MAE),with validation losses of 0.0708(MSE)and 0.1720(MAE).The LSTM model showed comparable efficacy,with training losses of 0.0011(MSE)and 0.0258(MAE),and validation losses of 0.2290(MSE)and 0.2652(MAE).Both models demonstrated reductions in training and validation losses,measured by MAE,MSE,Average Displacement Error(ADE),and Final Displacement Error(FDE).This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions,contributing significantly to the development of robust,intelligent navigation systems for the maritime industry.

关键词： Trajectory prediction AIS data smart vessel deep learning LSTM GRU

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Deep Learning and Context-Aware Mobile Computing for Improved Academic Supervisory Processes 5th

Deep Learning and Context-Aware Mobile Computing for Improve...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Ali, Nayem Shorme, Irin Hira, Md. Mahdi Hossain Munim, Md. Abdul Kohinoor, Md. Saidur Rahman Department of Computer Science and Engineering Leading University Sylhet Bangladesh Research Lab Sylhet Bangladesh Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9789819791118

In academic settings, especially in densely populated areas, supervisory roles face multifaceted challenges. Managing communication, discussions, task assignments, attendance tracking, and schedule coordination across varied platforms can be particularly burdensome, also detracting from students’ experiences. Existing mobile applications generally lack the necessary integration for these settings, as they are predominantly designed with industrial project management needs in mind. Our study introduces "Supervision", a comprehensive mobile application that leverages deep learning and context-aware computing to address these challenges. Apart from integrating the latest mobile application development and intuitive UI/UX technologies, we conducted a comparative analysis of popular facial recognition models (FaceNet, OpenFace, DeepFace, and VGGFace) using the LFW dataset to deploy the most ideal model in real-time face identification on resource-limited devices. Evaluating several criteria (accuracy, inference time, and memory utilization), our findings revealed that FaceNet outperformed other models with 94.04% accuracy, the shortest inference time (0.018 sec), and the lowest memory usage. This study also enhances the efficiency of the supervisory process, promoting timely project completion and fostering robust communication between supervisors and students. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Square tessellation for stochastic connected k- coverage in planar wireless sensor networks 28

Square tessellation for stochastic connected k- coverage in ...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Nakka, Kalyan Ammari, Habib M. Research Lab Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350300482

In this paper, we focus on the problem of connected k-coverage in planar wireless sensor networks (PWSNs), where every point in a field of interest (FoI) is covered by at least k sensors simultaneously, while all the participating sensors are mutually connected, where k > 1. To this end, we develop a global framework using a square tessellation that considers both deterministic and stochastic sensing models. Initially, we tessellate a planar FoI into adjacent and congruent square tiles. In each tile of this tessellation, we construct a cusp-square area for sensor placement to achieve k-coverage. Based on this cusp-squared square tile configuration, we compute the minimum sensor density that is required for deterministic and stochastic k-coverage in PWSNs. Then, we establish the necessary relationship that should exist between the sensing and communication ranges of the sensors to maintain network connectivity in k-covered PWSNs. Finally, we propose our stochastic k-coverage protocol for sensor scheduling and substantiate our theoretical analysis with simulation results. © 2023 IEEE.

关键词： Wireless sensor networks

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An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

An Efficient Deep Learning Approach to Detect Neurodegenerat...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Irfanuddin, Chowdhury Mohammad Shafin, Wasique Islam Ahmed, Koushik Khan, Md. Hasib Ashraful Alam, Md. Rahman, Rafeed Dipto, Shakib Mahmud Brac University Department of Computer Science and Engineering Dhaka Bangladesh Research Lab Dept. of Cse Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Neurodegenerative disorders like Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease typically get diagnosed through brain MRI, CT scans, genetic testing, and various laboratory screening tests that are often tedious, time-consuming, and beyond the reach of the financial capability of many people especially, those living in the developing world. Moreover, they often pose potential hazards for patients with certain pre-existing conditions. To remedy this, we propose an efficient deep-learning approach that enables the detection of such neurodegenerative diseases through retinal images rather than brain images. Our proposed system can proactively detect such disorders simply through retinal scans, which are fast and cost-effective compared to traditional diagnostic approaches, which require expensive and sophisticated equipment. We used a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals for our research, and our model achieved 98.85% accuracy while classifying healthy and diseased individuals from retinal scans. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Far-field super-resolution chemical microscopy

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Light(Science & Applications) 2023年第7期12卷 1246-1267页

作者： Mingwei Tang Yubing Han Danchen Jia Qing Yang Ji-Xin Cheng State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Intelligent Perception Research Institute Zhejiang LabHangzhou 311100China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02459USA

Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.

关键词： chemical details optics

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Prototype Development of a Multilevel Solar Dryer for Agriculture

Prototype Development of a Multilevel Solar Dryer for Agricu...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Fernandez, Gian Carlo C. Luis, Jed Matthew A. San Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Basic food processing in the Philippine agricultural sector relies on drying, which makes food products less perishable. A common practice is traditional sun-drying, which is simple, but exposes the produce to contaminants. This study developed a multilevel solar dryer for agriculture to provide better drying conditions while keeping costs down. A temperature and humidity monitoring system was built into the dryer to control its cooling fans and the humidity inside. The dryer demonstrated better drying conditions with an average drying temperature of 47.38 °C compared to the traditional sun-drying's 39.38 °C. In addition, the solar dryer's inside humidity was also better with a recorded average of 31.29% RH compared to the traditional setup's 39.66% RH. The dryer successfully dried a hybrid of Capsicum frutescens and Capsicum annuum or siling tingala, as it is locally known, at a drying rate faster than the traditional sun-drying setup. Overall, the multilevel solar dryer was able to provide better drying conditions, a faster drying rate, and a protected enclosed space for drying food products. © 2024 IEEE.

关键词： Solar dryers

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