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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Comparative analysis of FSO, OFC and diffused channel links in photonics using artificial intelligence based S-band, C-band and L-band of the attenuation metrics

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Optical and Quantum Electronics 2022年第7期54卷 420-420页

作者： Karpagarajesh, G. Santhana Krishnan, R. Harold Robinson, Y. Vimal, S. Thamizharasan, S. Subbulakshmi, P. Kaliappan, M. Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Tirunelveli India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of ECE Sri Manakula Vinayagar Engineering College Puducherry Madagadipet 605107 India School of computer science and Engineering VIT Chennai India

Optical communication system is preferred over microwave and radio frequency communication system because of license free operation. Simulative analysis of 10gbps Optical Communication using different optical bands of L-Band S-Band, E-Band and C-Band under different attenuation condition has been performed in this paper. The Free Space Optics system has been analyzed with transmission distance of 1 km using Optical Wireless Communication channel and the effect of variation in attenuation value in the scenario of different weather conditions (clear, haze, rain and fog) were also examined. The Optical Fiber Communication is also examined under conditions of attenuations due to physical imperfections. The cross-layer artificial intelligence Framework is implemented to perform the prediction of different optical communication. The diffused channel is studied by varying the parameters of transmitter half angle and the distance of separation between the transmitter and the receiver. The attenuation effects of the links are also taken into consideration. Bit Error Rate and Quality factor performance of the different optical links are carried over at different wavelengths;thus the best link for the particular wavelength and the distance is identified. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Attenuation C-band FSO L-band Mach–Zehnder modulator OFC Quality factor S-band

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A Novel Mode Selection-Based Fast Intra Prediction Algorithm for Spatial SHVC

A Novel Mode Selection-Based Fast Intra Prediction Algorithm...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yu Sun Weisheng Li Lele Xie Xin Lu Frederic Dufaux Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science University of Central Arkansas Computing Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University CNRS CentraleSupélec Laboratoire des Signaux et systèmes Université Paris-Saclay School of Information and Communication Engineering University of Electronic Science and Technology of China

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.

关键词： Machine learning algorithms Costs Signal processing algorithms Machine learning Gaussian distribution Prediction algorithms Encoding

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Platinum clusters Pt n +/0/- (n = 3-21): from triangles to remarkable tubular architectures

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RSC advances 2025年第24期15卷 19273-19286页

作者： Bao-Ngan Nguyen-Ha My-Phuong Pham-Ho Nguyen Minh Tam Minh Tho Nguyen Laboratory for Chemical Computation and Modeling Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Vietnam ngan.nguyenhabao@vlu.edu.vn. Faculty of Applied Technology School of Technology Van Lang University Ho Chi Minh City Vietnam. Faculty of Chemical Engineering Ho Chi Minh City University of Technology (HCMUT) 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Vietnam. Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City Vietnam. Faculty of Basic Sciences University of Phan Thiet 225 Nguyen Thong Phan Thiet City Binh Thuan Vietnam nmtam@upt.edu.vn.

This paper reports on a theoretical investigation of Pt clusters with = 3-21 in three-charge states using density functional theory with the B3PW91 functional in conjunction with the aug-cc-pVTZ-PP and Def2-TZVP basis sets. Geometric structures of Pt in the small-to-medium size range are primarily derived from Pt ; whereas Pt , Pt and Pt also emerge as building blocks. The sizes Pt, Pt, Pt and Pt emerge as magic clusters, in part in agreement with experimental mass spectrometry. Tubular Pt structures are identified at the sizes of Pt, Pt and Pt clusters that can be constructed by either stacking planar Pt or assembling prismatic Pt units. These tubular configurations are energetically favorable and present an effective pathway for the design of more complex Pt-based nanostructures. The Pt triangle features six σ bonds, six conjugated π-bonds of (2c-1e) delocalization and six (6c-1e) bonds reflecting a fully SP hybridization. The Pt prism contains three (6c-1e) bonds and nine σ-bonds from (2c-1e) delocalization, spanning its edges and faces. These delocalized bonds facilitate the structural integrity and connectivity of tubular Pt, Pt and Pt isomers. In these tubular clusters, increased bond coordination and redistribution of electron contributions among s, p and d orbitals enhance bonding interactions and promote stabilized structural assemblies.

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Efficient Planar Pose Estimation via UWB Measurements

Efficient Planar Pose Estimation via UWB Measurements

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haodong Jiang Wentao Wang Yuan Shen Xinghan Li Xiaoqiang Ren Biqiang Mu Junfeng Wu Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) School of Data Science the Chinese University of HongKong Shenzhen P. R. China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou P. R. China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing P. R. China School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation.

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IoT Based Fish Pond Monitoring System to Enhance Its Productivity

IoT Based Fish Pond Monitoring System to Enhance Its Product...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： S M Ravi Kumar Thayyaba Khatoon Mohammed Seeram Srinivasa Rao Dinesh Anton Raja P Ruhi Bakhare Ashok Kumar Swagata Sarkar Department of Mechanical G. Pullaiah College of Engineering and Technology Andhra Pradesh India School of Engineering Malla Reddy University Telangana India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Electronics & Communication R.M.K. Engineering College Chennai India Department of Master of Business Administration Dr. Ambedkar Institute of Management Studies & Research Maharashtra India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Artificial Intelligence & Data Science Sri Sai Ram Engineering College Chennai India

Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location.

关键词： Temperature sensors Temperature measurement Productivity Cloud computing Microcontrollers Fish Real-time systems

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Teacher-Student Complementary Sample Contrastive Distillation

SSRN

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SSRN 2023年

作者： Bao, Zhiqiang Huang, Zhenhua Gou, Jianping Du, Lan Liu, Kang Chen, Yunwen School of Computer Science South China Normal University South China Normal University Guangdong Guangzhou510631 China School of Artificial Intelligence School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China Faculty of Information Technology Monash University MelbourneVIC3800 Australia Research and Development Department DataGrand Inc. Shanghai Shanghai201203 China

Knowledge distillation (KD) is a widely adopted model compression technique for improving theperformance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that addresses these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher’s supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques. © 2023, The Authors. All rights reserved.

关键词： Distillation

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Algorithmic Fairness: A Tolerance Perspective

arXiv

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arXiv 2024年

作者： Luo, Renqiang Tang, Tao Xia, Feng Liu, Jiaying Xu, Chengpei Zhang, Leo Yu Xiang, Wei Zhang, Chengqi School of Software Dalian University of Technology Dalian116620 China Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC3353 Australia School of Computing Technologies RMIT University MelbourneVIC3000 Australia School of Economics and Management Dalian University of Technology Dalian116024 China MIoT IPIN Lab School of Minerals and Energy Resources Engineering University of New South Wales SydneyNSW2052 Australia School of Information and Communication Technology Griffith University Gold CoastQLD4222 Australia School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia

Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In response, this survey delves into the existing literature on algorithmic fairness, specifically highlighting its multifaceted social consequences. We introduce a novel taxonomy based on'tolerance', a term we define as the degree to which variations in fairness outcomes are acceptable, providing a structured approach to understanding the subtleties of fairness within algorithmic decisions. Our systematic review covers diverse industries, revealing critical insights into the balance between algorithmic decision making and social equity. By synthesizing these insights, we outline a series of emerging challenges and propose strategic directions for future research and policy making, with the goal of advancing the field towards more equitable algorithmic *** Codes 68T01, 68W40 © 2024, CC BY.

关键词： Decision making

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Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Liu, Jiangchuan Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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