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the effect of a two-dimensional optical illusion pattern on the three-dimensional interpretation of objects using café wall "Illusion Blocks" 18th

The effect of a two-dimensional optical illusion pattern on ...

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18th international conference on Geometry and Graphics, ICGG 2018

作者： Ohtani, Tomoko Amanai, Daiki Maruya, Kazushi Art Media Center Tokyo University of the Arts 12-8 Ueno Park Taito-kuTokyo110-8714 Japan Shizuoka University of Art and Culture 2-1-1 Chuo Naka-ku Hamamatsu CityShizuoka430-8533 Japan Science and Core Technology Laboratory Group Nippon Telegraph and Telephone Corporation 3-1 Morinosato Wakamiya Atsugi-shiKanagawa243-0198 Japan

ISBN: (纸本)9783319955872

In this paper, we present a new method to utilize the workshops (WS) interpreting café wall illusions. As an example in which our proposed method worked effectively, we show that in a 3-dimensional (3D) scene, when a perspective projection consisting of a solid object with a staircase-like 3D pattern is present, the projected image has a similar structure to café wall patterns, resembling shadows of vertical pillars cast upon a broken surface. Additionally, we showed that the café wall illusion can be explained by our brain trying to correct perspective projection distortions. To the best of our knowledge, this is the first study that relates the café wall illusion to visual 3D-scene processing. We analyze example items created in past WSs, and show that the basic conditions required to create a new expert idea are fulfilled by the entire group as the idea is presented. thus, the expert idea emerges from collective knowledge accumulation;it does not emerge suddenly from one expert. this is a clue to clarifying how discussions and knowledge level increases are triggered during WSs. © 2019, Springer international Publishing AG, part of Springer Nature.

关键词： Artificial intelligence

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Exoskeleton Knee Extension Assist Suspension Brace for Reducing Joint Pain in Elderly Aged Population

Exoskeleton Knee Extension Assist Suspension Brace for Reduc...

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international conference on ent computing and Control Systems (ICICCS)

作者： K Nandhini V Nivetha R Sabari M.G. Sumithra S. Velmurugan Department of Biomedical Engineering Dr.N.G.P.Institute of Technology Coimbatore India

Knee joint is a complex joint which plays an important role in day-to-day life activities. A person with a healthy knee can able to walk freely without any pain in the knee joint but factors like ageing, osteoarthritis can damage the meniscus, articular cartilage and can cause increased compression between the bone which results in knee pain. this compression between the bones can be reduced by extending the knee joint which can provide pain relief. In this proposed method, an exoskeleton knee extension system for transferring the whole-body weight to shoe and an actuating mechanism is designed to extend the knee joint. NodeMCU ESP8266 controller is used to receive feedback from the load cell to drive the motor which provides extension. Only a prototype of extension device is proposed. the necessary extension has been achieved with this prototype and it lifted up to 17 kg. the placements of cuff in the thigh and shank region provided proper grip such that the upper body weight is transferred to the shoe. this is a prototype model and it can be further developed into a clinical device and the extension in the knee joint can be tested by proper clinical trials in future.

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A selective and biased choice of techniques for building a distributed data store 23

A selective and biased choice of techniques for building a d...

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Proceedings of the 17th ACM international conference on distributed and Event-based Systems

作者： Paweł Tomasz Wojciechowski Faculty of Computing and Telecommunications Institute of Computing Science Poznan University of Technology Poznań Poland

ISBN: (纸本)9798400701221

Single-machine data stores cannot support the scale and ubiquity of data today. the Internet applications and services must process a huge number of concurrent requests and events per second. So, they use distributed (or replicated) data stores which store and process data on multiple machines, offering key advantages in performance, scalability, and *** purpose of the talk is to present a selective and biased choice of techniques and results which can be used for building an efficient distributed data store. Biased, because I only present solutions and results developed within a research project that I did with my PhD students. Selective, because an exhaustive description would be too exhausting to fit into a single talk. therefore I will be discussing in detail just the design of our novel database index for key-value data store systems, and only skim our other contributions that are directly related to distributed *** index, called Jiffy, has been designed with performance and scalability in mind. therefore it has been designed as a lock-free concurrent data structure, which can dynamically adapt to the changing workload. It achieves superior performance despite built-in atomic operations (batch updates, snapshots, and range scans). During the talk I will be presenting Jiffy's architecture, the algorithms for inserting and looking up the key-value pairs, and the operations used for resizing the data structure *** other contributions of our project include: efficient support for replica state recovery after failures, either by extending the classic Paxos consensus algorithm, or through the use of persistent memory, and some surprising theoretical results which are applicable to distributed data store systems that compromise consistency in favour of high availability and speed, but also support operations ensuring strong consistency (which requires consensus among replicas).

关键词： mixed consistency paxos concurrent data structures lock-free skip list replica state recovery ordered index

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New Hybrid Algorithm Implementation on spread Wireless Sensor to determine the point of fire in the building

New Hybrid Algorithm Implementation on spread Wireless Senso...

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international conference on Information technology, Computer and Electrical Engineering (ICITACEE)

作者： Irawan Dwi Wahyono Khoirudin Asfani Mohd Murtadha Mohamad HA Rosyid AN Afandi Aripriharta Universitas Negeri Malang Malang Indonesia

ISBN: (数字)9781728172262

ISBN: (纸本)9781728172279

this research focused on developing accuracy to determine the fire point of a building using Wireless Sensor Network (WSN) which had been spread unevenly and in motion. At present, the constraints faced by WSN that are distributed unevenly are the weak level of accuracy of the data obtained, which can be caused by the weakness of the WSN itself, namely resource limitations in the form of bandwidth, power, delay, latency, and computing. therefore, an intelligent algorithm that functions to process data based on the unevenly distributed WSN was needed to get better accuracy in building fire determination information. this study used 2 types of algorithms, namely K-Nearest Neighbor(k-NN) for the grouping of positions from the scattered WSN, and the Decision Tree algorithm to determine the outcome of a building fire decision based on each of the grouping results. From testing of 20 WSNs that implemented the 2 algorithms mentioned earlier, the results obtained were in the form of an accuracy rate of 70% and an average error of 30%.

关键词： Wireless communication Wireless sensor networks Buildings Clustering algorithms distributed databases Decision trees Testing

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A Healthcare Data Sharing Scheme Based on Homomorphic Encryption and Federated Metric Learning

A Healthcare Data Sharing Scheme Based on Homomorphic Encryp...

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intelligent computing and Human-Computer Interaction (ICHCI), international conference on

作者： Haoming Zhang Zhentao Liu Lang Wu Han Li School of Applied Science Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Medical data is highly sensitive, often leading to “data silos” in sharing processes. To address this issue, this paper combines blockchain and federated metric learning technologies, and innovatively integrates them with homomorphic encryption technology to propose a privacy protection system suitable for application in medical data sharing scenarios. the system employs federated learning to train a global model across distributed client nodes without sharing raw data. Metric learning classifies clients as benign or malicious based on data similarity, only aggregating models from benign clients to improve efficiency and mitigate data distribution issues. Additionally, differential privacy and homomorphic encryption are used to further protect data privacy by preventing the original data from being reverse deduced and model parameter leakage during aggregation. Experimental results show that the system can achieve a prediction accuracy of 89.102564% under different data distributions, and its performance on medical datasets has been significantly improved compared to traditional federated learning.

关键词： Measurement Differential privacy Accuracy Federated learning Information sharing Data models Regulation Blockchains Protection Homomorphic encryption

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Researching of HLA-based Collaborative Simulation intelligent Evaluation System

Researching of HLA-based Collaborative Simulation Intelligen...

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Asian Simulation conference/the 6th international conference on system Simulation and Scientific computing

作者： GUO Bin, FAN Wenhui, XIONG Guangleng, SHI Hongen, CHANG Xiaomeng (CIMS/ERC, Tsinghua University, Beijing, China, 100084)

distributed simulation technology is evolving continually from early 1980 to the present day, and a series of specifications and protocols have been made. there are a number of research issues which are raised by HLA-based collaborative simulation. As there is the lack of architecture of collaborative simulation evaluation method which serves during the whole process of simulation execution, this paper presents a framework of HLA-based collaborative simulation intelligent evaluation system. Firstly, scenario evaluation method based on Expert System is present. Secondly, run-time evaluation technology are proposed to watch simulation execution and help to optimize mode of data traffic. thirdly, this paper presents a approach to flexibly collect data, and flexibly realize various evaluation algorithms. Finally, this paper illuminates the prototype of the intelligent evaluation system and its application in an warship simulation system.

关键词： high level architecture collaborative simulation expert system data collection

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Malware Classification by Using Deep Learning Framework 5th

Malware Classification by Using Deep Learning Framework

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5th international conference on Green technology and Sustainable Development, GTSD 2020

作者： Toai, Tran Kim Senkerik, Roman Hanh, Vo thi Xuan Zelinka, Ivan VSB-Technical University of Ostrava 17 Listopadu 15/2172 Ostrava-Poruba708 33 Czech Republic Faculty of Applied Informatics Tomas Bata University in Zlin T. G. Masaryka 5555 Zlin760 01 Czech Republic Faculty of Economics HCMC University of Technology and Education No. 1 Vo van Ngan Street Linh Chieu Ward Ho Chi Minh Viet Nam

ISBN: (纸本)9783030623234

In this paper, we propose an original deep learning framework for malware classifying based on the malware behavior data. Currently, machine learning techniques are becoming popular for classifying malware. However, most of the existing machine learning methods for malware classifying use shallow learning algorithms such as Support Vector Machine, decision trees, Random Forest, and Naive Bayes. Recently, a deep learning approach has shown superior performance compared to traditional machine learning algorithms, especially in tasks such as image classification. In this paper we present the approach, in which malware binaries are converted to a grayscale image. Specifically, data in the raw form are converted into a 2D decimal valued matrix to represent an image. We propose here an original DNN architecture with deep denoising Autoencoder for feature compression, since the autoencoder is much more advantageous due to the ability to model complex nonlinear functions compared to principal component analysis (PCA) which is restricted to a linear map. the compressed malware features are then classified with a deep neural network. Preliminary test results are quite promising, with 96% classification accuracy on a malware database of 6000 samples with six different families of malware compared to SVM and Random Forest algorithms. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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New service discovery algorithm based on DHT for mobile application

New service discovery algorithm based on DHT for mobile appl...

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IEEE Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： De-gan Zhang Wen-bin Li Zhen Ma Key Laboratory of Computer Vision and System Ministry of Education China Tianiin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology China

In order to solve how to enhance the discovery efficiency and coverage, based on DHT (distributed Hash Table), we put forward a new algorithm of service discovery for mobile application. In traditional DHT discovery algorithm, each node maintains the finger-table that store node information of adjacent node. By using Small-World theory, we put forward adding a remote node into the finger-table and adding the corresponding remote index. It is different from selecting the remote connection node randomly. We select the remote connection node by calculating local node and it can assure not only the cove range of service discovery but also not increase the length of finger-table, which simplifies the calculation of the finger-table and maintenance work. the simulation proved that the algorithm can reduce the path length of service discovery effectively, improve success rate of service discovery.

关键词： Fingers Peer-to-peer computing Algorithm design and analysis Redundancy Servers Mobile applications conferences

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the Ranking Peer for Hybrid Peer-to-Peer Real Time Video Streaming

The Ranking Peer for Hybrid Peer-to-Peer Real Time Video Str...

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international conference on Advanced Communication technology (ICACT)

作者： Ahmad Tajuddin Samsudin Nanna Suryana Herman Mat Kamil Awang Security Intelligent Application and Multimedia Program Research Division Telekom Research and Development Serdang Selangor Malaysia Faculty of Information Technology and Communication National Technical University College of Malaysia Ayer Keroh Malacca Japan

We present the design of a hybrid P2P system called BisinLUMS. In previous P2P systems, they are commonly recognized as a file sharing system that utilizes a download/upload mechanism to distribute content. In our P2P system, we introduce a streaming concept instead of download. However, unicast streaming requires a certain level of guaranteed quality to achieve good real-time playback. the heterogeneity of peer such as bandwidth, CPU power, packet loss rate, uptimes, memory and storage need to be considered. In this short paper, we address the distributed supervised peer, how to rank the peers, and surface preview on files delegation and active/standby peer. We believe that, selecting the best peer to serve a streaming session is vital in providing good quality video streaming.

关键词： Peer to peer computing Streaming media Network topology Unicast Bandwidth Watches Network servers Central Processing Unit Information technology Communication system security

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A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption

A Medical Pre-diagnosis Scheme Based on Neural Network and I...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Xu Cui Hao Liu Min Tang Yihong Ma School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. this paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads.

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