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International Conference on Network Protocols

作者： Shuxin Zhong Yu-Xuan Qiu Rukhsana Ruby Lu Wang Kaishun Wu College of Computer Science and Software Engineering Shenzhen University

Recently, we have seen the unprecedented development in unmanned aerial vehicles (UAVs) from different aspects. Accordingly, an increasing number of applications have emerged based on UAVs. Among which, placing UAVs as Aerial Base Stations (ABSs) has received considerable interest in both the industrial and academic community. Existing solutions focus on the optimization of the UAV deployment problem for static user topology using the control information obtained from the Terrestrial Base Station (TBS), that makes hard for the controller to make real-time decisions. To break this stalemate, we propose a SemI-DistributEd system, named SIDE, for the UAV self-deployment. In SIDE, we introduce a mechanical equilibrium based approach, named EMech, via which the UAV positions are self-adapted according to users' attraction (e.g., user distance and traffic demand) within their transmission range. To facilitate the EMech, we propose a fine-grained area splitting strategy, termed KDivision, that partitions the service area in accordance with the user density. Finally, an area merging technique, namely RMerge, is exploited to approximately optimize the positions of the UAVs assisted by an Utility Function that strikes a balance amid the network performance and economic cost. We conduct field experiments to validate the feasibility of EMech. Extensive simulation results show that the proposed SIDE finds the optimal number of assigned UAVs, which not only reduces the cost of the system significantly, but also improves the achievable rate up to 74.6% compared to the existing solutions while consuming almost the same energy level.

关键词： Topology Unmanned aerial vehicles Network topology Base stations Cellular networks Signal to noise ratio Interference

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PHD: A Deep Learning Based Human Detection Framework for Panoramic Videos

PHD: A Deep Learning Based Human Detection Framework for Pan...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jinting Tang Zhenhui Chen Yongkai Huo Peichang Zhang National Engineering Laboratory for Big Data System Computing Technology Research Institute for Future Media Computing School of Computer Science and Software Engineering Shenzhen University Shenzhen China The College of Information Engineering Shenzhen University Shenzhen China

Panoramic video has attracted substantial research attention as the coming video format. It is capable of providing 360 degree immersive experience of omnidirectional visual information. State-of-the-art detection networks may fail to detect humans on spherical images, which are normally represented in deformed rectangular shapes. In this paper, we propose a socalled Panoramic Human Detection (PHD) scheme to address the task of human detection in panoramic videos. Moreover, the PHD method is designed to detect humans by extracting multiple overlapping sub-images from each integral spherical image, where three-dimensional rotation of spherical images is employed to ensure consistency of sub-images. Two detection box filters are designed for removing redundant boxes. Our PHD method is capable of accomplishing the task of human detection in various panoramic video types. Experiments prove that our PHD method outperforms the baseline by 35% and 48.6% in terms of precision and recall, respectively.

关键词： Videos Task analysis Feature extraction Matrices Visualization Strain Transmission line matrix methods

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Passivity of Reaction-Diffusion Neural Networks Via Sampled-Data Control

Passivity of Reaction-Diffusion Neural Networks Via Sampled-...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zi-Peng Wang Huai-Ning Wu Wu-Hua Chen Jin-Liang Wang School of Electrical Engineering University of Jinan Jinan China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China College of Mathematics and Information Science Guangxi University Nanning China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

A sampled-data (SD) control is introduced for passivity of reaction-diffusion neural networks (RDNNs). With an appropriate Lyapunov functional (LF), a SD control design is developed based on linear matrix inequalities to guarantee the passivity of closed-loop RDNNs. Lastly, we provide a numerical example to support the presented method.

关键词： Artificial neural networks Linear matrix inequalities Control design Closed loop systems Lyapunov methods Microcontrollers Sampled data systems

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Portable Blended MOOC Laboratory

Portable Blended MOOC Laboratory

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Learning With MOOCS (LWMOOCS)

作者： Pedro Plaza Elio Sancristobal German Carro Manuel Blazquez Antonio Menacho Felix García-Loro Clara Perez Javier Muñoz Edmundo Tovar JIm Sluss Russ Meier Manuel Castro Electrical and Computer Engineering Department (DIEECTQAI) UNED Madrid Spain Plaza Robotica Madrid Spain Languages Computer Systems and Software Engineering Department UPM Madrid Spain Dean Tuksa Graduate College School of Electrical and Computer Engineering Tulsa OK USA Electrical Engineering and Computer Science Department Milwaukee School of Engineering Milwaukee WI USA Director Electrical and Computer Engineering Department (DIEECTQAI) UNED Madrid Spain

Two types of teaching coexist today: traditional face-to-face methods and teaching based on self-learning where the student has ubiquitous access to course contents. This paper shows how tests have been made of a type of learning that is becoming more common in educational environments. This hybrid teaching method combines the Massively Open Online Course (MOOC) and students' face-to-face work. On the one hand, the advantages of face-to-face learning are taken advantage of: teacher in the classroom, social bonds and daily work sessions. On the other hand, the advantages of distance learning are integrated: flexibility, accessible material and wide variety. Also included in the paper is a description of the equipment and the way in which it has been used with students. Finally, the paper details the educational experience carried out with students and the outcomes obtained. These pilot tests are framed within the education of STEM topics and robotics.

关键词： computer aided instruction Robots Electronic learning Tools Light emitting diodes Training

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Graph-augmented convolutional networks on drug-drug interactions prediction

arXiv

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arXiv 2019年

作者： Zhong, Yi Chen, Xueyu Zhao, Yu Chen, Xiaoming Gao, Tingfang Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian province China Centre for Big Data Research in Burns and Trauma College of Mathematics and Computer Science Fuzhou University Fujian province China School of Software Shandong University Shandong Province China

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph convolutional neural network with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Drug interactions

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Guardians of the Mitochondria: Space Mitochondria 2.0 Systemic Analysis Reveals Bioenergetic Dysregulation Across Species

SSRN

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SSRN 2025年

作者： Guarnieri, Joseph W. Maghsoudi, Zeynab Kim, JangKeun Bya, Phi Widjaja, Gabrielle A. Barker, Richard Burke, Marissa Cen, Zimu Fazelinia, Hossein Tsoy, Sergey Tiersky, Rachel Peczak, Anna Kim, Jihan Kim, Ye-Ah Haltom, Jeffrey Almeida, Michael Garris, Alex Day, Sarah Sanchez-Hodge, Rebekah Zilberman, Aleeza H. Allen, Noah G. Kubik, Angela J. Blaber, Elizabeth A. Mathyk, Begum Harris, Frederick C. Singh, Kanhaiya Sen, Chandan K. Innes, Lucinda Ali, Nilufar Berliner, Aaron J. Kar, Upendra K. Overbey, Eliah Giunta, Simona Podrabsky, Jason E. Neal, Matthew D. Billiar, Timothy R. Headley, Colwyn Meydan, Cem Tasoula, Alexia Szewczyk, Nathaniel Ikeda, Yasutaka Gotoh-Katoh, Aina Schisler, Jonathan C. Kim, Man S. Schwartz, Robert E. Wallace, Douglas C. Mason, Christopher E. Nguyen, Tin Beheshti, Afshin Children’s Hospital of Philadelphia PhiladelphiaPA United States Computer Science and Engineering University of Nevada Reno RenoNV United States Department of Physiology and Biophysics and WorldQuant Initiative for Quantitative Prediction Weill Cornell Medicine New YorkNY United States Computer Science and Software Engineering University of Auburn AL United States Purdue University Department of Agricultural and Biological engineering IN United States Department of Neurosurgery Houston Methodist Research Institute HoustonTX United States Department of Medicine Division of Gastroenterology and Hepatology Weill Cornell Medicine New YorkNY United States Kyung Hee University Hospital at Gangdong Kyung Hee University Seoul Korea Republic of McAllister Heart Institute Department of Pharmacology The University of North Carolina at Chapel Hill Chapel HillNC United States Shirley Ann Jackson Ph.D. Center for Biotechnology and Interdisciplinary Studies Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY United States Division of Reproductive Endocrinology and Infertility Department of Obstetrics and Gynecology University of South Florida Morsani College of Medicine TampaFL United States Center for Space Biomedicine McGowan Institute for Regenerative Medicine Department of Surgery University of Pittsburgh School of Medicine PittsburghPA United States Department of Biological Science Boise State University BoiseID United States Department of Radiation Oncology Weill Cornell Medicine New YorkNY United States BerkeleyCA United States University of Pittsburgh School of Medicine PittsburghPA United States The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New YorkNY United States BioAstra Inc New YorkNY United States Center for STEM University of Austin AustinTX78701 United States Laboratory of Genome Evolution Department of Biology and Biotechnology Charle

Spaceflight imposes unique stressors that disrupt mitochondrial function, vital for energy production and immune regulation. Our multi-omics analysis (proteomics, bisulfite sequencing, RNA-seq, and single-nuclei RNA/ATAC-seq) on astronauts, rodents, and model organisms (flies, worms, plants) revealed progressive impairment of mitochondrial oxidative phosphorylation (OXPHOS) during spaceflight, with delayed recovery post-return across species. In rodents, radiation ≥10.34 mGy activated persistent mitochondrial stress pathways across multiple organs except in the spleen, with older and male C57BL/6 mice most affected. Astronaut data from the NASA Twins Study, JAXA, and Inspiration4 missions showed prolonged mitochondrial dysfunction, with OXPHOS suppression and TCA cycle inhibition lasting up to 82 days. Bisulfite sequencing confirmed lasting epigenetic changes in OXPHOS genes. Lastly, Kaempferol, an antioxidant and mitochondrial activator, mitigated radiation-induced liver atrophy in mice and preserved liver function in human organoids. This cross-species study underscores the need for targeted therapies to protect mitochondrial biogenesis during long-duration space missions. © 2025, The Authors. All rights reserved.

关键词： Mammals

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Joint long-term cache allocation and short-term content delivery in green cloud small cell networks

arXiv

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arXiv 2019年

作者： Wu, Xiongwei Li, Qiang Li, Xiuhua Leung, Victor C.M. Ching, P.C. Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong School of Info. & Comm. Eng. University of Electronic Science and Technology of China Chengdu China School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. of Electrical & Computer Engineering University of British Columbia Vancouver Canada

Recent years have witnessed an exponential growth of mobile data traffic, which may lead to a serious traffic burn on the wireless networks and considerable power consumption. Network densification and edge caching are effective approaches to addressing these challenges. In this study, we investigate joint long-term cache allocation and short-term content delivery in cloud small cell networks (C-SCNs), where multiple smallcell BSs (SBSs) are connected to the central processor via fronthaul and can store popular contents so as to reduce the duplicated transmissions in networks. Accordingly, a long-term power minimization problem is formulated by jointly optimizing multicast beamforming, BS clustering, and cache allocation under quality of service (QoS) and storage constraints. The resultant mixed timescale design problem is an anticausal problem because the optimal cache allocation depends on the future file requests. To handle it, a two-stage optimization scheme is proposed by utilizing historical knowledge of users' requests and channel state information. Specifically, the online content delivery design is tackled with a penalty-based approach, and the periodic cache updating is optimized with a distributed alternating method. Simulation results indicate that the proposed scheme significantly outperforms conventional schemes and performs extremely close to a genie-aided lower bound in the low caching region. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

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Predicting Electricity Usage Based on Deep Neural Network*

Predicting Electricity Usage Based on Deep Neural Network*

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Ran Wei Jinhai Wang Qirui Gan Xin Dang Huiquan Wang College of Life Sciences Tianjin Polytechnic University Tianjin China School of Electronics and Information Engineering Tianjin Polytechnic University Tianjin China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

This paper describes a deep neural network (DNN) based method for forecasting short-term hospital electricity usage. In Experiment One, a 4-layer DNN stack auto-encoder (SAE) based model is constructed to verify the accuracy of the method. Kilowatt-hours (kwh), capacitance (pf), power factor (phi), voltage (v), electricity reactive power (var), and electricity active power (w) are the main input variables. After training the model, the prediction accuracy can reach 77.60%. In the improvement phase, the model is altered to use more common variables; specifically, kilowatt-hours (kwh), electric charge (charg), average active power (avg-w), and maximum active power (max-w) are used as input variables. In order to optimize the training of the model, Experiment Two improves on the basis of the original DNN model. As a result, the prediction accuracy can be increased to 85.17%. Finally, the four power data with the best measurement are used, namely current(I), voltage(V), reactive power(Var) and active power(W), and the predicted result is 98.14%. This method indicates that the planning and scheduling of the hospital' s electricity usage will also be improved.

关键词： Training Hospitals Predictive models Neural networks Forecasting Reactive power Mathematical model

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Personalized group of readers' emotion traits modeling based on sentimental analysis of novels

Personalized group of readers' emotion traits modeling based...

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IEEE International Conference on Cognitive Informatics

作者： Yuya Hatakeyama Runhe Huang Bowen Du Neil Yen Haiquan Wang Graduate School of C.I.S Hosei University Japan State Key Lab of Software Development Environment Beihang University China School of Computer Science and Engineering University of Aizu Japan College of Software Beihang University China

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

Studies concerning sentiment analysis haven drawn attentions from a wide spectrum of researchers in the past few years. One of the most eye-catching topics within this emerging field is how emotion can be dectected and extracted properly from the given contexts. This study chooses content from novel to stimulate above mentioned long contexts, and expects analysis results can be projected to a three-dimensions space model. Two major tasks are conducted. First, specific content selected from novel is proceeded through morpheme analysis to estimate emotion status. Second, obtained emotion status is calculated through existing emotion dictionary which was built based on data from the novel. Results are then demonstrated on the three-dimensions space model. At last, two experiments, a systematic way to compare emotion status and an empirical questionnaire, are conducted to verify the performance of proposed method.

关键词： Dictionaries Analytical models Estimation Mathematical model Computational modeling software Sentiment analysis

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Data sharing and privacy for patient IoT devices using blockchain 7th

Data sharing and privacy for patient IoT devices using block...

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7th International Conference on Smart City and Informatization, iSCI 2019

作者： Srivastava, Gautam Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Mathematics and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Center for Interneural Computing China Medical University Taichung40402 Taiwan College of Computing and Software Engineering Kennesaw State University KennesawGA30144 United States Cyber Science Lab School of Computer Science University of Guelph GuelphONN1G 2W1 Canada Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249 United States

ISBN: (纸本)9789811513008

Once a fitness fad, wearable and other related Internet of Things (IoT) devices are fast becoming common place in many different smart city applications such as healthcare. However, IoT devices, particularly inexpensive devices, often trade security and privacy for usability. One solution to protect privacy in the healthcare domain which has begun to be explored is blockchain-based technology. However, there are a number of limitations underpinning the use of blockchain, which limits its adoption particularly in applications that require low energy and computational footprints. In this paper, we present a transactional protocol for remote patient monitoring using directed acyclic graphs. We use a newer blockchain protocol called GHOSTDAG in both a public blockchain and a private blockchain. Our novel proposed solution aims to resolve known security issues for healthcare, without affecting scalability (a feature of classic blockchain architecture). © Springer Nature Singapore Pte Ltd. 2019.

关键词： Internet of things

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