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KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-school Recommendation System 19th

KNN-Based Collaborative Filtering for Fine-Grained Intellig...

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Xu, Jinfeng Liu, Jiyi Ma, Zixiao Wang, Yuyang Wang, Wei Ngai, Edith Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Software Engineering Sun Yat-Sen University Guangdong Zhuhai528406 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Computer Science Cornell University IthacaNY14853 United States Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BITUniversity Guangdong Shenzhen518172 China

ISBN: (纸本)9783031651250

The development of the Internet has led to information overload, and how to filter and sift information is a rigorous requirement in all fields. In response to this challenge, recommendation systems have emerged as a valuable tool, offering personalized content and services by efficiently searching and processing dynamically generated information. For students applying to grad schools, finding relevant information can be time-consuming and unreliable from official websites or forums. In light of these challenges, we present a novel solution in the form of an application recommendation platform. Our proposed platform leverages specific open-source datasets and real-time information from platform users using KNN (K-Nearest Neighbor) and CF (Collaborative Filtering) techniques to provide recommendations based on users’ individual backgrounds, we aim to reduce the complexity inherent in information retrieval while simultaneously enhancing the relevance of the recommendations delivered to users. Specifically, we first collect user behavior data, then we will construct the data model and perform some preprocessing on it. Calculate the user similarity, and find out the K-nearest neighbors and rate based on K-nearest neighbors, finally, the recommendation engine is used to calculate the highest-rated items to be recommended to the users. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Collaborative filtering

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Outgoing Editorial

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IEEE Transactions on Circuits and Systems I: Regular Papers 2023年第12期70卷 4675-4677页

作者： Zhao, Weisheng Li, Hai Helen Zito, Domenico Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China Fert Beijing Institute Beihang University Beijing100191 China Beihang University Miit Key Laboratory for Spintronics Beijing100191 China Duke University Department of Electrical and Computer Engineering DurhamNC27708 United States Agh University of Science and Technology Department of Measurements and Electronics Krakow30-059 Poland

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Multi-Objective Optimized Generative Adversarial Networks For Video Anomaly Detection

Multi-Objective Optimized Generative Adversarial Networks Fo...

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2023 China Automation Congress, CAC 2023

作者： Cao, Zhexiao Fu, Yao Wang, Tian Liu, Deyuan Wang, Jian Snoussi, Hichem Institute of Artificial Intelligence Beihang University Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Institute of Artificial Intelligence Sklsde Beihang University Zhongguancun Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Wuhan University Wuhan China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303759

The objective of video anomaly detection is to distinguish events within videos that deviate from expected normal behavior. An effective anomaly detection model requires strong spatio-temporal feature extraction capabilities to capture both appearance and motion information from the video. We utilize a generative adversarial networks model to perform anomaly detection by predicting future frame. The model is trained with multi-objective loss function for appearance and motion constraints. We introduce multi-objective optimization algorithm to guarantee the convergence of training objectives, including intensity loss, gradient loss for appearance constraints, optical flow loss for motion constraints and adversarial loss for adversarial training. By ensuring corresponding generative outcome for both normal events to confirm to expectation and anomaly events not to do so, we obtain an anomaly detection model with satisfying experimental result on video anomaly detection datasets, showing the success of the proposed training strategy. © 2023 IEEE.

关键词： Multiobjective optimization

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Open-Vocabulary Audio-Visual Semantic Segmentation 24

Open-Vocabulary Audio-Visual Semantic Segmentation

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Ruohao Qu, Liao Niu, Dantong Qi, Yanyu Yue, Wenzhen Shi, Ji Xing, Bowei Ying, Xianghua National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Berkeley Ai Research University of California Berkeley BerkeleyCA United States College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9798400706868

Audio-visual semantic segmentation (AVSS) aims to segment and classify sounding objects in videos with acoustic cues. However, most approaches operate on the close-set assumption and only identify pre-defined categories from training data, lacking the generalization ability to detect novel categories in practical applications. In this paper, we introduce a new task: open-vocabulary audio-visual semantic segmentation, extending AVSS task to open-world scenarios beyond the annotated label space. This is a more challenging task that requires recognizing all categories, even those that have never been seen nor heard during training. Moreover, we propose the first open-vocabulary AVSS framework, OV-AVSS, which mainly consists of two parts: 1) a universal sound source localization module to perform audio-visual fusion and locate all potential sounding objects and 2) an open-vocabulary classification module to predict categories with the help of the prior knowledge from large-scale pre-trained vision-language models. To properly evaluate the open-vocabulary AVSS, we split zero-shot training and testing subsets based on the AVSBench-semantic benchmark, namely AVSBench-OV. Extensive experiments demonstrate the strong segmentation and zero-shot generalization ability of our model on all categories. On the AVSBench-OV dataset, OV-AVSS achieves 55.43% mIoU on base categories and 29.14% mIoU on novel categories, exceeding the state-of-the-art zero-shot method by 41.88%/20.61% and open-vocabulary method by 10.2%/11.6%. The code is available at https://***/ruohaoguo/ovavss. © 2024 ACM.

关键词： Semantic Segmentation

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Scalable and flexible porous hybrid film as a thermal insulating subambient radiative cooler for energy-saving buildings

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National science Open 2023年第4期2卷 153-167页

作者： Tong Wang Shuhua Tu Yinggang Chen Qian Zhu Yinan Zhang Zhiyi Ding Xue Mei Min Chen Min Gu Limin Wu Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers Fudan UniversityShanghai 200433China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai 200093China

Passive daytime radiative cooling(PDRC)is one of the promising alternatives to electrical cooling and has a significant impact on worldwide energy consumption and carbon *** real-world applications,however,the parasitic heat input and heat leakage pose crucial challenges to commercial and residential buildings *** integrating of radiative cooling and thermal insulation properties represents an attractive direction in renewable energy-efficient building envelope ***,we present a hierarchically porous hybrid film as a scalable and flexible thermal insulating subambient radiative cooler via a simple and inexpensive inverse high internal phase emulsion *** as-prepared porous hybrid film exhibits an intrinsic combination of high solar reflectance(0.95),strong longwave infrared thermal emittance(0.97),and low thermal conductivity(31 mW/(m K)),yielding a subambient cooling temperature of~8.4℃ during the night and~6.5℃ during the hot midday with an average cooling power of~94 W/m^(2) under a solar intensity of~900 W/m^(2).Promisingly,combining the superhydrophobicity,durability,superelasticity,robust mechanical strength,and industrial applicability,the film is favorable for large-scale,sustainable and energy-saving applications in a wide variety of climates and complicated surfaces,enabling a substantial reduction of energy costs,greenhouse gas emission and associated ozone-depleting from traditional cooling systems.

关键词： radiative cooling thermal insulation energy-saving buildings scalability flexibility

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Few-Shot Learning Based on Information Geometry

Few-Shot Learning Based on Information Geometry

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2023 China Automation Congress, CAC 2023

作者： Chen, Jiacheng Fu, Yao Wang, Tian Liu, Deyuan Wang, Jian Snoussi, Hichem Institute of Artificial Intelligence Beihang University Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Institute of Artificial Intelligence Sklsde Beihang University Zhongguancun Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Wuhan University Wuhan China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303759

Few-shot classification, as the name suggests, is a very challenging task where only a few training examples are available for each category. Extensive research has been done to solve the problem of the high cost of labeling a large number of samples. A recent research strategy is to find similarity measures between query sets and support sets for judgment. Recently, information geometry techniques have been applied in deep learning research, which explores the relationships and properties between probability distributions by treating them as geometric objects and using geometric tools. Based on information geometry, this paper characterizes differences in the geometric properties of images as differences in probability distributions. Assume that the data satisfies a multivariate Gaussian distribution and that different images are considered as different points on the manifold according to the parameters that characterize the probability distribution of the images. We measure the similarity between different images by the geodesic and validate three methods for estimating the geodesic on a neural network model with 64 neurons at four levels and improve the model performance by Firth bias. The GM, GMF, KM, and KMF methods are validated in the resnet-12 and resnet-18 frameworks using a meta-learning approach on the miniImageNet and CUB datasets, respectively. The experiments show that the models trained by the above four algorithms have good performance and have advantages over the classical algorithms. © 2023 IEEE.

关键词： Probability distributions

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Complexity based investigation in collaborative assembly scenarios via non intrusive techniques

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Procedia computer science 2023年 217卷 478-485页

作者： Sotirios Panagou Monica Sileo Konstantinos Papoutsakis Fabio Fruggiero Ammar Qammaz Antonis Argyros School of Engineering University of Basilicata 85100 Potenza Italy Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas 70013 Heraklion Greece

Human and robot collaboration in assembly tasks is an integral part in modern manufactories. Robots provide advantages in both process and productivity with their repeatability and usability in different tasks, while human operators provide flexibility and can act as safeguards. However, process complexity increases which can lower the overall quality. Increased complexity can negatively influence decision making due to cognitive load on human operators, which can lead to lower quality, be it product, process or human work. Moreover, it can lead to safety risks, human-system error and accidents. In this work, we present the preliminary results on an experiment performed with student-participants, based on an assembly task. The experiment was set up to emulate an industrial assembly, and data collection was performed through qualitative and non-intrusive quantitative methods. Questionnaires were used to assess perceptual task complexity and cognitive load, while a stereo camera provided recordings for after-task analysis on process errors and human work quality based on a 3D skeleton-based human pose estimation and tracking method. The aim of the study is to investigate causes of errors and implications on quality. Future direction of the work is discussed.

关键词： collaboration assembly task complexity cognitive load human pose estimation tracking

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Current-induced Néel order switching facilitated by magnetic phase transition

Current-induced Néel order switching facilitated by magneti...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Wu, Hao Zhang, Hantao Wang, Baomin Guo, Yaqin Cheng, Ran Wang, Kang L. Songshan Lake Materials Laboratory Dongguan523808 China University of California Department of Electrical and Computer Engineering Los AngelesCA90095 United States University of California Department of Electrical and Computer Engineering RiversideCA92521 United States Ningbo University School of Physical Science and Technology Ningbo315211 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Cas Key Laboratory of Magnetic Materials and Devices Ningbo315201 China

ISBN: (纸本)9798350338362

Antiferromagnetic materials (AFMs), which have zero-stray field and the THz-range magnetic resonance frequency, are promising candidates for the next-generation magnetic memory technology. Here, we report an electrical writing of antiferromagnetic orders through a record-low current density on the order of 106 A cm-2 facilitated by the unique AFM-ferromagnetic (FM) phase transition in FeRh. The spin-orbit torque can switch the AFM order parameter by 90° with a reduced writing current density by introducing a transient FM state via current-induced Joule heating. This work demonstrates the exciting possibility of writing operations in AFM-based devices with a lower current density, opening a new pathway toward pure AFM memory applications. © 2023 IEEE.

关键词： Magnetic resonance

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Soliton Vortex Comb Generation in an AlGaAs Microresonator for Self-Torque Pulse Synthesis

Soliton Vortex Comb Generation in an AlGaAs Microresonator f...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Y. Chen, B. Zhou, Y. Ye, C. Cao, Q. Huang, P. Kim, C. Zheng, Y. Oxenløwe, L.K. Yvind, K. Li, J. Li, J. Zhang, Y. Dong, C. Fu, S. Zhan, Q. Wang, X. Liu, J. Pu, M. DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark State Key Laboratory of Optoelectronic Materials and Technologies School of Physics School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Zhangjiang Laboratory Shanghai China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China

We demonstrate bright soliton generation at room temperature within a grating-dressed AlGaAs-on-insulator microresonator and the simultaneous emission of phase-locked vortices, enabling the synthesis of self-torque pu... 详细信息

关键词： Microresonators

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Detection of urban fugitive dust emission sources from optical satellite remote sensing images 8

Detection of urban fugitive dust emission sources from optic...

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Remote Sensing Technologies and Applications in Urban Environments VIII 2023

作者： He, Xiaoqing Wang, Zhibao Bai, Lu Wang, Mei Fan, Meng School of Computer and Information Technology Northeast Petroleum University Daqing China Bohai-Rim Energy Research Institute Northeast Petroleum University Qinhuangdao China School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510666993

Urban fugitive dust emission is an open pollution source that enters the atmosphere because of the dust on the ground being lifted by the wind or human activities. Dust pollution is a major contributor to atmospheric particulate matter, making it a focus for pollution control and environmental surveillance stakeholders. The identification and monitoring of dust sources hold profound practical implications. The use of remote sensing detection method facilitates extensive coverage, high accuracy, and non-invasive monitoring of urban fugitive dust emission sources. This approach enables timely alerts about potential air pollution threats, allowing swift interventions to alleviate adverse consequences. This paper mainly studies the semantic segmentation of fugitive dust sources from remote sensing images, employing advanced deep learning algorithms. In this paper, we selected Wuhai City in China as the experimental area and created Wuhai Dust Sources Dataset. This dataset, established through high-resolution satellite remote sensing data from Gaofen-1 satellite, contains 2,648 images, capturing 707 distinct dust sources. This work evaluates four different deep learning models utilising FCN and U-Net architectures as backbones in conjunction with a variety of feature extraction convolutional neural networks. The experimental results exhibit promising detection outcomes for all four models. Among these, the U-Net combined with VGG feature extraction network has the best performance, achieving an MIoU at 81% and a Mean Precision at 92%. © 2023 SPIE.

关键词： Feature extraction

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