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45th European Conference on Optical Communication (ECOC 2019)

作者： Bin Chen Chigo Okonkwo Hartmut Hafermann Alex Alvarado Department of Electrical Engineering Eindhoven University of Technology The Netherlands School of Computer and Information Hefei University of Technology China Mathematical and Algorithmic Sciences Lab Paris Research Center Huawei Technologies France SASU France

The recently introduced 4D 64-ary polarisation-ring-switching format is investigated in dispersion-managed systems. Numerical simulations show a reach increase of 25% with respect to PM-8QAM. This gain is achieved fro... 详细信息

ISBN: (纸本)9781839531859

关键词： CODED MODULATION GENERALISED MUTUAL information DISPERSION MANAGED SYSTEM

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Undersampling Near Decision Boundary for Imbalance Problems

Undersampling Near Decision Boundary for Imbalance Problems

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jianjun Zhang Ting Wang Wing W. Y. Ng Shuai Zhang Chris D. Nugent Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computing Ulster University Jordanstown Belfast United Kingdom

ISBN: (纸本)9781728128177

Undersampling the dataset to rebalance the class distribution is effective to handle class imbalance problems. However, randomly removing majority examples via a uniform distribution may lead to unnecessary information loss. This would result in performance deterioration of classifiers trained using this rebalanced dataset. On the other hand, examples have different sensitivities with respect to class imbalance. Higher sensitivity means that this example is more easily to be affected by class imbalance, which can be used to guide the selection of examples to rebalance the class distribution and to boost the classifier performance. Therefore, in this paper, we propose a novel undersampling method, the UnderSampling using Sensitivity (USS), based on sensitivity of each majority example. Examples with low sensitivities are noisy or safe examples while examples with high sensitivities are borderline examples. In USS, majority examples with higher sensitivities are more likely to be selected. Experiments on 20 datasets confirm the superiority of the USS against one baseline method and five resampling methods.

关键词： Sensitivity Undersampling Imbalance learning USS Undersampling HIGH SENSITIVITY information loss Balance impairment IMBALANCE rebalance Classifiers antimicrobial susceptibility NATIONAL EMERGENCY ALARM REPEATER Dataset

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A cyber-enabled mission-critical system for post-flood response: Exploiting TV white space as network backhaul links

A cyber-enabled mission-critical system for post-flood respo...

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作者： Rahman, M.D. Arafatur Asyhari, A. Taufiq Azad, Saiful Hasan, M.D. Munirul Munaiseche, Cindy P.C. Krisnanda, Made Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan26300 Malaysia IBM Center of Excellence and Earth Resources and Sustainability Center Universiti Malaysia Pahang Kuantan26300 Malaysia School of Computing Electronics and Mathematics Coventry University CoventryCV1 5FB United Kingdom Telkom University Jalan Telekomunikasi Terusan Buah Batu Bandung40257 Indonesia Faculty of Engineering Department of Informatics Universitas Negeri Manado Tondano95618 Indonesia Faculty of Engineering Department of Information and Communication Technology Education Universitas Negeri Manado Tondano95618 Indonesia

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm. © 2017 Oxford University Press. All rights reserved.

关键词： Cyber Physical System

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MUSEFood: Multi-Sensor-Based Food Volume Estimation on Smartphones

MUSEFood: Multi-Sensor-Based Food Volume Estimation on Smart...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Junyi Gao Weihao Tan Liantao Ma Yasha Wang Wen Tang Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Peking University Information Technology Institute (Tianjin Binhai) Tianjin China School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Department of Nephrology Peking University Third Hospital Beijing China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Researches have shown that diet recording can help people increase awareness of food intake and improve nutrition management, and thereby maintain a healthier life. Recently, researchers have been working on smartphone-based diet recording methods and applications that help users accomplish two tasks: record what they eat and how much they eat. Although the former task has made great progress through adopting image recognition technology, it is still a challenge to estimate the volume of foods accurately and conveniently. In this paper, we propose a novel method, named MUSEFood, for food volume estimation. MUSEFood uses the camera to capture photos of the food, but unlike existing volume measurement methods, MUSEFood requires neither training images with volume information nor placing a reference object of known size while taking photos. In addition, considering the impact of different containers on the contour shape of foods, MUSEFood uses a multi-task learning framework to improve the accuracy of food segmentation, and uses a differential model applicable for various containers to further reduce the negative impact of container differences on volume estimation accuracy. Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size. The experiments on real foods indicate that MUSEFood outperforms state-of-the-art approaches, and highly improves the speed of food volume estimation.

关键词： Image segmentation Containers Shape Sensors Volume measurement Smart phones Solid modeling

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Joint fronthaul multicast and cooperative beamforming for cache-enabled cloud-based small cell networks: An MDS codes-aided approach

arXiv

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arXiv 2019年

作者： Wu, Xiongwei Li, Qiang Leung, Victor C.M. Ching, P.C. Department of Electronic Engineering Faculty of Engineering Chinese University of Hong Kong Shatin Hong Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

The performance of cloud-based small cell networks (C-SCNs) relies highly on a capacity-limited fronthaul, which degrade quality of service when it is saturated. Coded caching is a promising approach to addressing these challenges, as it provides abundant opportunities for fronthaul multicast and cooperative transmissions. This paper investigates a cache-enabled C-SCNs, in which small-cell base stations (SBSs) are connected to the central processor via fronthaul, and can prefetch popular contents by applying maximum distance separable (MDS) codes. To fully capture the benefits of fronthaul multicast and cooperative transmissions, an MDS codes-aided transmission scheme is first proposed. We formulate the problem to minimize the content delivery latency by jointly optimizing fronthaul bandwidth allocation, SBS clustering, and beamforming. To efficiently solve the resulting nonlinear integer programming problem, we propose a penalty-based design by leveraging variational reformulations of binary constraints. To improve the solution of the penalty-based design, a greedy SBS clustering design is also developed. Furthermore, closed-form characterization of the optimal solution is obtained, through which the benefits of MDS codes can be quantified. Simulation results are given to demonstrate the significant benefits of the proposed MDS codes-aided transmission scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Multicasting

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Feature-Selected and -Preserved Sampling for High-Dimensional Stream Data Summary

Feature-Selected and -Preserved Sampling for High-Dimensiona...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： ling Lin Qian Yu Wen Ji Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Electronics and Information Engineering College Yili Normal University Yining Xinjiang China School of Data and Computer Science Shandong Womens University China

Along with the prosperity of the Mobile Internet, a large amount of stream data has emerged. Stream data cannot be completely stored in memory because of its massive volume and continuous arrival. Moreover, it should be accessed only once and handled in time due to the high cost of multiple accesses. Therefore, the intrinsic nature of stream data calls facilitates the development of a summary in the main memory to enable fast incremental learning and to allow working in limited time and memory. Sampling techniques are one of the commonly used methods for constructing data stream summaries. Given that the traditional random sampling algorithm deviates from the real data distribution and does not consider the true distribution of the stream data attributes, we propose a novel sampling algorithm based on feature-selected and -preserved algorithm. We first use matrix approximation to select important features in stream data. Then, the feature-preserved sampling algorithm is used to generate high-quality representative samples over a sliding window. The sampling quality of our algorithm could guarantee a high degree of consistency between the distribution of attribute values in the population (the entire data) and that in the sample. Experiments on real datasets show that the proposed algorithm can select a representative sample with high efficiency.

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Ontology based global and collective motion patterns for event classification in basketball videos

arXiv

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arXiv 2019年

作者： Wu, Lifang Yang, Zhou He, Jiaoyu Jian, Meng Xu, Yaowen Xu, Dezhong Chen, Chang Wen College of Information and Communication Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Municipal Key Lab of Computation Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Computer Science and Engineering SUNY Buffalo NY United States

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that pre-event and event-occ segments are effective for classification, while post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for basketball event classification. First, a two-stage GCMP based event classification scheme is proposed. The GCMP is extracted using optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize sequential convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF) based algorithms. Finally the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed

关键词： Sports

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Fingerprint-based access to personally controlled health records in emergency situations

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Science China(information Sciences) 2018年第5期61卷 338-340页

作者： Shaopeng GUAN Yongyu WANG Jian SHEN School of Information and Electronic Engineering Shandong Institute of Business and Technology Haian High-Tech Research Institute of Nanjing University School of Computer and Software Nanjing University of Information Science and Technology

Dear editor,Medical records are the original diagnosis and treatment calendar of the patient in medical institutions,including the patient’s personal information,diagnosis results,treatment methods,prescriptions,and medical *** records not only form the basis for the treatment of patients,but also provide a reference for future ***,medical records are handwritten by *** the application of information and network technology in the medical

关键词： Fingerprint-based access to personally controlled health records in emergency situations

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using computer Simulation technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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A Game Theory Approach for Multi-agent System Resources Allocation against Outside Threats

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Journal of Risk Analysis and Crisis Response 2019年第3期9卷 134-144页

作者： Wu, Cheng-Kuang Hu, Xingwei School of Computer Science and Software Engineering Zhaoqing University Zhaoqing Road Guangdong Province Zhaoqing City 526021 China Information Technology Department International Monetary Fund 700 19th St NW Washington 20431 DC United States

This study proposes an integrated model for the deployment of multiagent resources for resisting outside threats. The proposed two-stage model applies the divide-and-conquer strategy to solve the resources allocation problem. First, the interactive actions between an external attack and a response agent are modeled as a non-cooperative game, after which the external threat value is derived from the Nash equilibrium. Second, the threat values of all response agents are utilized to compute each agent’s Shapley value. Then an acceptable resource allocation of agents based on their expected marginal contribution creates a minimum set of resource deployment costs. The experimental results show that our approach is feasible as a means to mobilize search and rescue resources from a non-affected district and to improve relief efforts against earthquake damage. The Shapley value allocation approach proposed in this study;the percentage of resources allocation of districts is closer to death rate of each district than the proportional division of resources. © 2019 The Authors. Published by Atlantis Press SARL.

关键词： external threat value Multi-agent system Nash equilibrium resources allocation Shapley value

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