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2020 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2020

作者： Jhong, Sin-Ye Tseng, Po-Yen Siriphockpirom, Natnuntnita Hsia, Chih-Hsien Huang, Ming-Shih Hua, Kai-Lung Chen, Yung-Yao National Cheng Kung University Dept. of Engineering Science Tainan Taiwan Grad. Inst. of Automation Technology National Taipei University of Technology Taipei Taiwan Dept. of Computer Science and Information Engineering National Ilan University Ilan Taiwan Open AI Fab Inc. Taipei Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781728198231

Recently, automated biometric identification system (ABIS) has wide applications involving automatic identification and data capture (AIDC), which includes automatic security checking, verifying personal identity to prevent information disclosure or identity fraud, and so on. With the advancement of biotechnology, identification systems based on biometrics have emerged in the market. These systems require high accuracy and ease of use. Palm vein identification is a type of biometric that identifies palm vein features. Compared with other features, palm vein recognition provides accurate results and has received considerable attention. We developed a novel high-performance and noncontact palm vein recognition system by using high-performance adaptive background filtering to obtain palm vein images of the region of interest. We then used a modified convolutional neural network to determine the best recognition model through training and testing. Finally, the developed system was implemented on the low-level embedded Raspberry Pi platform with cloud computing technology. The results showed that the system can achieve an accuracy of 96.54%. © 2020 IEEE.

关键词： Biometrics

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Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

Expert-driven Rule-based Refinement of Semantic Segmentation...

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IEEE Symposium on Intelligent Vehicle

作者： Eric L. Manibardo Ibai Laña Javier Del Ser Alexander Carballo Kazuya Takeda Tecnalia Basque Research and Technology Alliance (BRTA) Derio Spain University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute of Innovation for Future Society Nagoya University Nagoya Japan Tier IV Inc Nagoya University Open Innovation Center Nagoya Japan Graduate School of Informatics Nagoya University Nagoya Japan

Semantic segmentation aims at assigning labels to every pixel of a given image. In the context of autonomous vehicles, semantic segmentation models should be trained with data collected from the traffic network through which vehicles are expected to circulate. Road regulation, weather conditions, and other context features may differ between regions, making local semantic segmentation datasets extremely valuable. However, the high ground truth annotation costs represent a hindrance to the development of such models. The upsurge of powerful feature learning architectures leaves room for semantic segmentation models trained on an unsupervised fashion. This observation vertebrates the purpose of this work: to produce coarse segmentation maps for scene understanding without the need of annotated data. We depart from an unsupervised model that yields low-quality results. The proposed methodology establishes a set of guidelines for the enhancement of segmentation maps. Obtained results expose an improvement of the segmentation quality thanks to the application of our devised guidelines, paving the way for the automatic generation of semantic segmentation datasets.

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Live multi-streaming and donation recommendations via coupled donation-response tensor factorization

arXiv

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arXiv 2021年

作者： Lai, Hsu-Chao Huang, Jiun-Long Tsai, Jui-Yi Lee, Wang-Chien Shuai, Hong-Han Yang, De-Nian Dept. of Computer Science National Chiao Tung Univ. Inst. of Information Science Academia Sinica Taiwan Dept. of Computer Science National Chiao Tung Univ. Taiwan Dept. of Electrical and Computer Engineering National Chiao Tung Univ. Inst. of Information Science Academia Sinica Taiwan Dept. of Computer Science and Engineering Pennsylvania State Univ. United States Dept. of Electrical and Computer Engineering National Chiao Tung Univ. Taiwan Inst. of Information Science Research Center Information Technology Innovation Academia Sinica Taiwan

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision. Copyright © 2021, The Authors. All rights reserved.

关键词： Factorization

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Automated Bell Inequality Violation using Uncalibrated Fiber Squeezing Polarization Controllers

Automated Bell Inequality Violation using Uncalibrated Fiber...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Evan Dowling Mark Morris Gerald Baumgartner Rajarshi Roy Thomas E. Murphy Inst. for Research in Electronics & Applied Physics University of Maryland College Park Maryland USA Dept. of Physics University of Maryland College Park Maryland USA Laboratory for Telecommunication Sciences College Park Maryland USA Inst. for Physical Science & Technology University of Maryland College Park Maryland USA Dept. of Electrical & Computer Engineering University of Maryland College Park Maryland USA

We experimentally report on a real-time self-guided method to search for maximal CHSH violations between two observers sharing polarization entangled photon pairs using uncalibrated piezoelectric fiber squeezers as po... 详细信息

ISBN: (纸本)9781957171258

关键词： Photon polarization Polarization Polarization control Quantum communications Quantum key distribution Single photon detectors

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Deep learning integration to inject intelligence in network routing mechanism

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AIP Conference Proceedings 2023年第1期2587卷

作者： Vivek Veeraiah Mohammed Rahmath Karunendra Verma Umesh Pratap Singh Ankur Gupta Malik Bader Alazzam Dept. of R & D Computer Science Adichunchanagiri University Mandya Karnataka India Dept. of Computer Science Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Dept. of Computer Science and Engineering Shri Ramswaroop Memorial Group of Professional Colleges Lucknow India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Mubis Jordan

Deep learning is a type of machine learning that imitates the way humans gain certain types of knowledge. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. Intelligent Routing (or Skills-based Routing), is a technology contact centers use to gather customer inquiries through voice, digital, or social channels, and then applies rules to route it to the agent best fit to resolve the issue. Research aims to inject intelligence in network routing mechanism using deep learning. Research work is working on routing mechanism to reduce the problems of previous researches. Proposed work increases the speed of routing, and less time consumption. Proposed work is based on deep learning technology for intelligent routing mechanism. The weight of a node route is determined in the first phase by taking into account these parameters. Rewards and penalties are assigned to each node based on its route once it has successfully sent data. Six scenarios and ten associated nodes were used in the route selection simulation. Nodes 1, 2, 3, 4, and 5 form a network in this architecture. This opportunistic network has nodes 6, 7, 8, 9, and 10. Black and blue nodes in the opportunistic network are shown in simulations, respectively. Using the node route table, a node’s connectivity is discovered. The system has become sophisticated enough to make decisions thanks to the usage of neural fuzzy logic. The optimization approaches choose the shortest route to pick a network for a real-world application while taking into account the best solution. The sensing mechanism, as well as animal and crop scouting mechanisms have all been examined in this study. The information would be sent in the form of analogue signals via the various sensor nodes that are linked to the Neuro fuzzy controller. The proposed work would provide flexible and scalable solution to implement opportunistic routing in precision

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A Power Effective DLA for PBs in Opto-Electrical Neural Network Architecture

A Power Effective DLA for PBs in Opto-Electrical Neural Netw...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Ralph Gerard B. Sangalang Shih-Heng Luo Hsin-Che Wu Bao-Qi He Shen-Fu Hsiao Chua-Chin Wang Chewnpu Jou Harry Hsia Douglas C.-H. Yu Dept. of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Dept. of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Inst. of Undersea Technology National Sun Yat-Sen University Taiwan Taiwan Semiconductor Manufacturing Company Hsinchu Taiwan

Deep neural networks (DNN) have been widely used in many real-time artificial intelligent (AI) applications because of effective hardware accelerators. However, most present designs either suffer from high area cost or low hardware usage. This paper presents a design of a digital logic accelerator (DLA) for use in PBs (processing block) of an opto-electrical neural network (OENN). The proposed DLA uses processing elements that detects underflow and overflow. Besides, it also increased the processing time to resolve the timing problems. The details of the design together with post-layout simulations are presented in this paper. The DLA is implemented using a typical 40-nm CMOS process. It showed a performance result of 51.2 GOPS and the power consumption is 91.3 mW at 125 MHz.

关键词： Deep learning Semiconductor device modeling Energy consumption Power demand Neural networks Lead CMOS process

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. Research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. Research work has proposed modified algorithm enhances the security of big data in cloud application against SQL injection, cross script.

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning

Constructing a Cryptocurrency-Price Prediction Model Using D...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Toai Kim Tran Thanh Thi Tuyet Le Thinh Tien Bui Vang Quang Dang Roman Senkerik Faculty of Economics Ho Chi Minh City University of Technology and Education No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Vietnam Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. Listopadu 2172/15 Ostrava-Poruba Czech Repulic Faculty of Applied Informatics Tomas Bata University in Zlin T. G. Masaryka 5555 Zlin Czech Republic

ISBN: (纸本)9781665491075

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies.

关键词： Deep learning Recurrent neural networks Bitcoin Predictive models Prediction algorithms Market research Convolutional neural networks

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Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization

arXiv

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arXiv 2022年

作者： Amin, Askandar H. Ahmed, Botan K. Maaroof, Bestan B. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University KRG Sulaimani Iraq Administration and Economics University of Sulaimani KRG Sulaimani Iraq IT Department College of Commerce University of Sulaimani KRG Sulaimani Iraq School of Information Technology Fanshawe College London Canada Computer Science and Engineering Dept. University of Kurdistan Hewler KRG Erbil Iraq

The prior detection of a heart attack could lead to the saving of one’s life. Putting specific criteria into a system that provides an early warning of an imminent attack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient from getting such a disease. In this paper, Neural Network trained with Particle Swarm Optimization (PSONN) is used to analyze the input criteria and enhance heart attack anticipation. A real and novel dataset that has been recorded on the disease is used. After preprocessing the data, the features are fed into the system. As a result, the outcomes from PSONN have been evaluated against those from other algorithms. Decision Tree, Random Forest, Neural network trained with Backpropagation (BPNN), and Naive Bayes were among those employed. Then the results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced concerning the mentioned algorithms, which show that PSONN has recorded the highest accuracy rate among all other tested algorithms. © 2022, CC BY.

关键词： Swarm intelligence

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Optical Fiber Sensor for Micro Displacement Monitoring Based on a Balloon-Like Interferometer with a Spring-Shaped Structure

Optical Fiber Sensor for Micro Displacement Monitoring Based...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Victor H. R. Cardoso Paulo Caldas Maria Thereza M. Rocco Giraldi Orlando Frazão João Weyl Costal José L. Santos Applied Electromagnetism Laboratory Federal University of Para Belem Brazil Inst. for Systems and Computer Engineering Technology and Science Porto Portugal Polytech. Inst. of Viana do Castelo R. Escola Industrial e Comercial de Nun 'Alvares Viana do Castelo Portugal Laboratory of Photonics Military Institute of Engineering Rio de Janeiro Brazil Dept. of Physics and Astronomy Faculty of Sciences of U.Porto Porto Portugal

Ahstract-A sensor based on a balloon-like interferometer and a spring-shaped structure for micro curvature measurement is proposed and experimentally demonstrated. The sensor is composed by singlemode fiber inserted into a capillary tube. The experimental results show micro-curvature sensitivities of -35.04 $\text{pm}/\mu \mathrm{m}$ , -28.07 $\text{pm}/\mu \mathrm{m} \mathrm{e}-18.7 \text{pn}/\mu \mathrm{m}$ in the range from 0 to 200 $\mu \mathrm{m}$ for three resonants dips $\lambda_{1}, \lambda_{2}$ and $\lambda_{3}$ , respectively. In addition, the sensor has advantages of easy fabrication, low cost, and satisfactory sensitivity, which shows good results of sensing of micro curvature in some applications.

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