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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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Explainable Unsupervised Multi-Sensor Industrial Anomaly Detection and Categorization

Explainable Unsupervised Multi-Sensor Industrial Anomaly Det...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Mina Ameli Philipp Aaron Becker Katharina Lankers Markus van Ackeren Holger Bähring Wolfgang Maaß Cognitive Assistants Systems Department DFKI GmbH Saarbrücken Germany Lab Department deZem GmbH Berlin Germany Research & Development – Data Science Department Schott AG Mainz Germany Development Department SEITEC GmbH Erfurt Germany Smart Service Engineering Department DFKI GmbH Saarbrücken Germany

Real-time Anomaly Detection is of great importance in industrial applications in order to have high-quality production and avoid downtime or failure of the system. In this paper, we study the application of anomaly detection over the multivariate data collected from Glass Production Industry. Our experiments utilize and compare different Unsupervised multivariate time series Anomaly Detection and Localization algorithms that have already demonstrated significant results on the state-of-the-art data sets. We propose a two-level multivariate anomaly detection approach that not only detects anomalous events in the production line but also categorize the different type of anomalies based on statistical pattern recognition. Furthermore, we localize the anomalous sensors by utilizing Explainable-AI approaches to help better decision-making in glass production monitoring. In this work, we propose an efficient pipeline for Anomaly Detection, Categorization and Localization which the experiments show promising results.

关键词： Location awareness Machine learning algorithms Time series analysis Pipelines Production Glass Real-time systems

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Miniature UAV Empowered Reconfigurable Energy Harvesting Holographic Surfaces in THz Cooperative Networks

arXiv

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arXiv 2024年

作者： Song, Yifei Jalali, Jalal Lemic, Filip Devroye, Natasha Famaey, Jeroen Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States IDLab University of Antwerp - imec Sint-Pietersvliet 7 Antwerp2000 Belgium Wireless Communication Research Group JuliaSpace Inc. ChicagoIL United States AI-Driven Systems Lab i2Cat Foundation Spain University of Illinois Chicago ChicagoIL United States

This paper focuses on enhancing the energy efficiency (EE) of a cooperative network featuring a ‘miniature’ unmanned aerial vehicle (UAV) that operates at terahertz (THz) frequencies, utilizing holographic surfaces to improve the network’s performance. Unlike traditional reconfigurable intelligent surfaces (RIS) that are typically used as passive relays to adjust signal reflections, this work introduces a novel concept: Energy harvesting (EH) using reconfigurable holographic surfaces (RHS) mounted on the miniature UAV. In this system, a source node facilitates the simultaneous reception of information and energy signals by the UAV, with the harvested energy from the RHS being used by the UAV to transmit data to a specific destination. The EE optimization involves adjusting non-orthogonal multiple access (NOMA) power coefficients and the UAV’s flight path, considering the peculiarities of the THz channel. The optimization problem is solved in two steps. Initially, the trajectory is refined using a successive convex approximation (SCA) method, followed by the adjustment of NOMA power coefficients through a quadratic transform technique. The effectiveness of the proposed algorithm is demonstrated through simulations, showing superior results when compared to baseline methods. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design

Reinforcement-Learning Based Method for Accelerating Functio...

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International Conference on computer Theory and Applications (ICCTA)

作者： Youstina M. Halim Khaled A. Ismail Mohamed A. Abd El Ghany Sameh A. Ibrahim Youssef M. Halim Mixel Egypt. Electronics and Communications Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt GSI Helmholtz Center for Heavy-Ion Research GmbH (GSI) Germany Electronics Department German University in Cairo Egypt. Integrated Electronic Systems Lab TU Darmstadt Germany Electronics and Communications Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Engineering and Software Systems Dept. Faculty of Engineering Ain Shams University Cairo Egypt

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases.

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Analysis of the research Status of Information Technology Education Literature in China and Abroad

Analysis of the Research Status of Information Technology Ed...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yulin Zhao Kai Liu Junke Li School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Machine learning and Unstructured Data Processing of Qiannan Duyun China School of Information Engineering Suqian University Suqian Jiangsu China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu China

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.

关键词： Statistical analysis Databases Education Bibliometrics Educational technology Market research Internet

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ICSGD-Momentum: SGD Momentum Based on Inter-gradient Collision

ICSGD-Momentum: SGD Momentum Based on Inter-gradient Collisi...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weidong Zou Weipeng Cao Yuanqing Xia Bineng Zhong Dachuan Li School of Automation Beijing Institute of Technology Beijing China Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, Radam, Adabound, and AdaBelief.

关键词： Training computer vision Artificial neural networks Benchmark testing Natural language processing Informatics Physics Optimization Convergence

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Reliability-Aware Network Slicing based on Multi-Objective Optimization

Reliability-Aware Network Slicing based on Multi-Objective O...

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Congress on Evolutionary Computation

作者： Qiqi Xia Jin Wang Chengqiang Huang Xin Yao Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Reliability Technology Lab Huawei Technologies Co. Ltd. Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong SAR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric.

关键词： Measurement 6G mobile communication Costs Network topology Network slicing Evolutionary computation Approximation algorithms

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Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications

arXiv

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arXiv 2023年

作者： Ma, Zhe Wu, Wen Gao, Feifei Shen, Xuemin The Institute for Artificial Intelligence Tsinghua University State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen518055 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the noncoherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance. © 2023, CC BY-NC-SA.

关键词： Message passing

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Striving for Authentic and Sustained Technology Use In the Classroom: Lessons Learned from a Longitudinal Evaluation of a Sensor-based Science Education Platform

arXiv

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arXiv 2023年

作者： Chua, Yvonne Cooray, Sankha Cortes, Juan Pablo Forero Denny, Paul Dupuch, Sonia Garbett, Dawn L. Nassani, Alaeddin Cao, Jiashuo Qiao, Hannah Reis, Andrew Reis, Deviana Scholl, Philipp M. Sridhar, Priyashri Kamlesh Suriyaarachchi, Hussel Taimana, Fiona Tang, Vanessa Weerasinghe, Chamod Wen, Elliott Wu, Michelle Wu, Qin Zhang, Haimo Nanayakkara, Suranga Augmented Human Lab. Auckland Bioengineering Institute The University of Auckland New Zealand Augmented Human Lab. Department of Information Systems and Analytics National University of Singapore Singapore School of Computer Science The University of Auckland New Zealand Faculty of Education The University of Auckland Auckland New Zealand Engineering Product Development Singapore University of Technology and Design Singapore OPPO Research Institute Shanghai China

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in realistic classroom settings. However, sustained and authentic classroom use requires technology platforms to be seen by teachers as both easy to use and of value. We describe our development of a sensor-based platform to support science teaching that followed a 14-month user-centered design process. We share insights from this design and development approach, and report findings from a 6-month large-scale evaluation involving 35 schools and 1245 students. We share lessons learnt, including that technology integration is not an educational goal per se and that technology should be a transparent tool to enable students to achieve their learning goals. © 2023, CC BY-NC-ND.

关键词： Students

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ASSESSING THE ROBUSTNESS OF CRITICAL BEHAVIOR IN STOCHASTIC CELLULAR AUTOMATA

arXiv

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arXiv 2022年

作者： Pontes-Filho, Sidney Lind, Pedro Nichele, Stefano Department of Computer Science Oslo Metropolitan University Oslo Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway AI Lab - OsloMet Artificial Intelligence Lab Oslo Norway NordSTAR Nordic Center for Sustainable and Trustworthy AI Research Oslo Norway Department of Holistic Systems Simula Metropolitan Centre for Digital Engineering Oslo Norway Department of Computer Science and Communication Østfold University College Halden Norway

There is evidence that biological systems, such as the brain, work at a critical regime robust to noise, and are therefore able to remain in it under perturbations. In this work, we address the question of robustness of critical systems to noise. In particular, we investigate the robustness of stochastic cellular automata (CAs) at criticality. A stochastic CA is one of the simplest stochastic models showing criticality. The transition state of stochastic CA is defined through a set of probabilities. We systematically perturb the probabilities of an optimal stochastic CA known to produce critical behavior, and we report that such a CA is able to remain in a critical regime up to a certain degree of noise. We present the results using error metrics of the resulting power-law fitting, such as Kolmogorov-Smirnov statistic and Kullback-Leibler divergence. We discuss the implication of our results in regards to future realization of brain-inspired artificial intelligence *** Codes 68T01 © 2022, CC BY.

关键词： Stochastic models

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