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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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Author Correction: A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

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Scientific data 2023年第1期10卷 928页

作者： Yanxin Xi Yu Liu Tong Li Jingtao Ding Yunke Zhang Sasu Tarkoma Yong Li Pan Hui Department of Computer Science University of Helsinki Helsinki Finland. Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China. Department of Electronic Engineering Tsinghua University Beijing P. R. China. Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China. liyong07@***. Department of Electronic Engineering Tsinghua University Beijing P. R. China. liyong07@***. Department of Computer Science University of Helsinki Helsinki Finland. panhui@ust.hk. Computational Media and Arts Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou P. R. China. panhui@ust.hk. Division of Emerging Interdisciplinary Areas Hong Kong University of Science and Technology Hong Kong P. R. China. panhui@ust.hk.

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Mapping digital forensic application requirement specification to an international standard

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Forensic Science International: Reports 2020年 2卷 100137-100137页

作者： Kebande, Victor R. Baror, Stacey O. Parizi, Reza M. Choo, Kim-Kwang Raymond Venter, H.S. Department of Computer Science & Media Technology Malmö Universitete Malmö Sweden Department of Computer Science University of Pretoria South Africa College of Computing and Software Engineering Kennesaw State University Marietta GA United States Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio 78249-0631 TX United States

A potential security incident may go unsolved if standardized forensic approaches are not applied during lawful investigations. This paper highlights the importance of mapping the digital forensic application requirement specification to an international standard, precisely ISO/IEC 27043. The outcome of this work is projected to contribute to the problem of secure DF tool creation, and in the process address Software Requirements Specification (SRS) as a process of digital evidence admissibility. © 2020 The Author(s)

关键词： Applications design Digital forensics ISO/IEC 27043 Software requirements specification (SRS)

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Artifact Removal Using GAN Network for Limited-Angle CT Reconstruction

Artifact Removal Using GAN Network for Limited-Angle CT Reco...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Shipeng Xie Hui Xu Haibo Li College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Media Technology and Interaction Design School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockolm Sweden

Computed tomography (CT) plays an increasingly important role in clinical diagnosis. However, in practical applications of CT, physical limitations on acquisition lead to some blind regions where data cannot be sampled. CT image reconstruction from limited-angle would enable a rapid scanning with a reduced x-ray dose delivered to the patient. As it is known, Generative Adversarial Networks (GAN) can keep the original information and details of the sample very well. In this paper, we propose an end-to-end Generative Adversarial Networks model used for removing artifacts from limited-angle CT reconstruction images. The proposed GAN is based on the conditional GAN with the joint loss function, which .can remove the artifacts while retaining the complete details and sharp edges. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. Compared to several other classic methods, our GAN model shows better consequent, in terms of artifact reduction, feature preservation, and computational efficiency for limited-angle CT reconstruction.

关键词： Image reconstruction Computed tomography Gallium nitride Training Generative adversarial networks Subspace constraints Generators

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Conceptual Compression via Deep Structure and Texture Synthesis

arXiv

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arXiv 2020年

作者： Chang, Jianhui Zhao, Zhenghui Jia, Chuanmin Wang, Shiqi Yang, Lingbo Mao, Qi Zhang, Jian Ma, Siwei National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China The Information Technology Research and Development Innovation Center Peking University Shaoxing312000 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen China The Department of Computer Science City University of Hong Kong Hong Kong LMAM School of Mathematical Sciences Peking University Beijing100871 China School of Information and Communication Engineering The State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

Existing compression methods typically focus on the removal of signal-level redundancies, while the potential and versatility of decomposing visual data into compact conceptual components still lack further study. To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks. In particular, we propose to compress images by a dual-layered model consisting of two complementary visual features: 1) structure layer represented by structural maps and 2) texture layer characterized by low-dimensional deep representations. At the encoder side, the structural maps and texture representations are individually extracted and compressed, generating the compact, interpretable, inter-operable bitstreams. During the decoding stage, a hierarchical fusion GAN (HF-GAN) is proposed to learn the synthesis paradigm where the textures are rendered into the decoded structural maps, leading to high-quality reconstruction with remarkable visual realism. Extensive experiments on diverse images have demonstrated the superiority of our framework with lower bitrates, higher reconstruction quality, and increased versatility towards visual analysis and content manipulation tasks. © 2020, CC BY-NC-ND.

关键词： Textures

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Forecasting energy consumption from smart home sensor network by deep learning 1

引用

2nd International Conference on Smart Trends for information technology and computer Communications, SmartCom 2017

作者： Dey, Nilanjan Fong, Simon Song, Wei Cho, Kyungeun Department of Information Technology Techno India College of Technology Kolkata India Department of Computer and Information Science University of Macau Taipa China Department of Digital Media Technology North China University of Technology Beijing China Department of Multimedia Engineering Dongguk University Seoul Korea Republic of

ISBN: (数字)9789811314230

ISBN: (纸本)9789811314223

Modern smart homes would be equipped with ZigBee sensors that connect home appliances via IoT network. Forecasting the future use of energy for the home appliances would be useful and practical for the home users. Since IoT sensors are designed to collect information in real-time from the home appliances, that include energy usage, indoor/outdoor temperatures and relative humidity measures, the data for harvesting insights should be abundant. Computationally a challenge is to seek for a most appropriate time-series forecasting algorithm that can produce the most accurate results. The difference between the traditional time-series forecasting algorithms and the one that involves IoT data is the ability to learn from the sheer volume of IoT data, which is known as big data nowadays. The sensor data can amount to a huge volume, and the energy drawn from an appliance, for example, air-conditioner can depend on multiple factors – the temperature/humidity of surrounding regions as well as the current weather at the time of the day. In this paper, such forecasting is tested with a range of time-series algorithms including the classical ones in comparison with deep learning which is acclaimed as a suitable prediction tool for learning over very non-linear and complex patterns. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Domestic appliances

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RETRACTED ARTICLE: Social media analytics and their applications to evaluate an activity in online health interventions using CRITIC and TOPSIS techniques

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Soft Computing 2023年第2期28卷 481-481页

作者： Yushan Lin Yasser Alshehri Noha Alnazzawi Mohammad Abid Samina A Khan Fouzia Jabeen Ibrahim Elwarfalli School of New Media Art Xi’an Polytechnic University Xi’an Shaanxi Province China School of Marxism Xi’an Jiaotong University Xi’an Shaanxi Province China Computer Science and Engineering Department Yanbu Industrial College Royal Commission for Jubail and Yanbu Yanbu Industrial City Saudi Arabia City University of Science and Information Technology Dalazak Road Peshawar Peshawar Pakistan School of Computer Sciences University Sains Malaysia 11800 Gelugor Pulau Penang Malaysia Department of Computer Science Shaheed Benazir Bhutto Women University Peshawar Pakistan Eye Institute West Virginia University Morgantown USA

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Dispensed transformer network for unsupervised domain adaptation

arXiv

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arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

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Estimation of differences in selected indices of vibroacoustic signals between healthy and osteoarthritic patellofemoral joints as a potential non-invasive diagnostic tool

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Journal of Physics: Conference Series 2021年第1期2130卷

作者： R Karpiński P Krakowski J Jonak A Machrowska M Maciejewski A Nogalski Lublin University of Technology Faculty of Mechanical Engineering Department of Machine Design and Mechatronics Nadbystrzycka 36 20-618 Lublin Poland Medical University of Lublin Chair and Department of Trauma Surgery andEmergency Medicine Staszica 11 20-081 Lublin Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Department of Electronics and Information Technology Nadbystrzycka 36 20-618 Lublin Poland

Osteoarthritis (OA) is currently the most generic form of joint disease. It is a complex process in which degenerative changes occur in the articular cartilage [AC], subchondral bone, and synovial membrane and can lead to permanent joint failure. The primary and most commonly used method of diagnosing degenerative changes is classic radiography. Magnetic resonance imaging (MRI) may be used to assess the extent of damage to joint surfaces, but this method is limited by the availability of specialised equipment and the excessive cost of the examination. Arthroscopy, an invasive procedure, is considered the "gold standard" in joint diagnosis. The occurrence of degenerative changes is closely related to the friction and lubrication processes within the joint. The main causes of osteoarthritis are a change or lack of synovial fluid, deformation of the joint bones, local damage to the articular cartilage, and a change in the mechanical properties of the articular cartilage due to water loss from the damaged superficial layer. An alternative, non-invasive method that allows for a delicate assessment of the condition of moving joints is vibroarthrography (VAG). The analysis of vibroacoustic signals generated by moving joint surfaces has an immense potential in the non-invasive assessment of the degree of damage to articular cartilage, meniscus and ligaments and the general diagnosis of degenerative diseases. The purpose of this study is to analyse and statistically compare the basic characteristics of vibroacoustic signals recorded with a CM-01B contact microphone placed on the patella for motion in the 90°–0°–90° range in a closed kinetic chain (CKC) in a control group (HC) and a group of patients diagnosed with osteoarthritis (OA), qualified for the knee alloplasty.

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Analysis of differences in vibroacoustic signals between healthy and osteoarthritic knees using EMD algorithm and statistical analysis

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Journal of Physics: Conference Series 2021年第1期2130卷

作者： R Karpiński P Krakowski J Jonak A Machrowska M Maciejewski A Nogalski Lublin University of Technology Faculty of Mechanical Engineering Department of Machine Design and Mechatronics Nadbystrzycka 36 20-618 Lublin Poland Medical University of Lublin Chair and Department of Trauma Surgery and Emergency Medicine Staszica 11 20-081 Lublin Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Department of Electronics and Information Technology Nadbystrzycka 36 20-618 Lublin Poland

The knee joint is the largest and one of the most vulnerable and most frequently damaged joints in the human body. It is characterized by a complex structure. All articular surfaces are covered with hyaline cartilage. This cartilage has minimal regenerative capacity. Under the influence of cyclical micro-injuries, inflammatory mediators, prolonged excessive pressure or immobility, and thus disturbance of tissue nutrition, the cartilage becomes susceptible to damage and is easily covered with villi, cracks and abrasion. As a result, this translates into changes in the friction and lubrication processes within the joint and may affect the generated vibroacoustic processes. In this study, the signals recorded in a group of 28 volunteers were analysed, 15 of them were healthy people (HC) and 13 were people diagnosed with osteoarthritis (OA) qualified for surgery. The study aims to check the usefulness of the EMD (Empirical Mode Decomposition) algorithm in the filtration procedures of vibroacoustic signals. This algorithm is most often used in the analysis of signals that are most often nonlinear and non-stationary. Selected statistical indicators, such as RMS, VMS, variance and energy, were determined for the signals constituting the sum of the IMFs (Intrinsic Mode Functions) 1-8, having a normal distribution in the assessment of damage to the articular cartilage of the knee joint. Statistical analysis was performed for the values of individual indicators obtained. The vibroacoustic signals were recorded using CM-01B contact microphones placed in the central part of the medial and lateral joint fissure for movement in the range of 90°–0°–90° in closed kinetic chains (CKC) in the control group (HC) and the group of patients diagnosed with osteoarthritis (OA).

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