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Automatic Decomposition of a Sequential Algorithm for MapReduce Frameworks

Automatic Decomposition of a Sequential Algorithm for MapRed...

IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics engineering, SIBIRCON

作者： Victor S. Simonov Marat S. Khairetdinov Dept. Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation Dept. Geophysical Informatics Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russian Federation

ISBN: (纸本)9781665464819

Effective programming of parallel architectures has always been a difficult t ask. T o d ate, programming languages and technologies have been developed that simplify the programmer’s work, but do not make parallelization automatic. MapReduce is a model of programming for the development of large-scale computations with intensive use of data. There are many frameworks where the implementation of this paradigm has been recently developed. There is a need to rewrite existing serial code to use the frameworks listed. The researcher must be familiar with the problems of parallelization, the API of the framework, and also have considerable experience. This prompted us to develop a new tool that automatically translates sequential programs into ready-made versions suitable for execution in the MapReduce paradigm. The code fragment from the serial version is converted in two stages. At the first stage, the synthesis of the program, the functional specification, was made. It was necessary to find information about the calculation structure for each block of code. The result was stored as a high-level intermediate language, reminiscent of the program format for MapReduce frameworks. Checking for semantic equivalence to the original has done using the proof of the theorem. At the second stage, executable code is created, which was the result of generation from a sequential program using the Hadoop or Spark instruction set. Creating a parallelizing compiler is one way to solve this problem. This will allow you to translate code written in a different paradigm (for example, imperative code) into a parallel version for the framework. Classical compilers, such as logical plan-to-physical compilers, use pattern matching rules. The compiler contains a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code.

关键词： Codes Instruction sets Semantics Transforms Manuals Programming Data models

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Local Binary Pattern Histogram for Face Recognition in Student Attendance System 3

Local Binary Pattern Histogram for Face Recognition in Stude...

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3rd International Conference on computer and informatics engineering, IC2IE 2020

作者： Alexander, Allan D Salkiawati, Ratna Lubis, Hendarman Rahman, Fathur Herlawati, Herlawati Handayanto, Rahmadya Trias Universitas Bhayangkara Jakarta Raya Bekasi Dept. of Informatics Engineering Bekasi Indonesia Universitas Islam 45 Bekasi Dept. of Computer Engineering Bekasi Indonesia

ISBN: (纸本)9781728182476

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy. © 2020 IEEE.

关键词： Face recognition

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SafeSCHEMA: Multi-domain Orchestration of Slices based on SafeRL for B5G Networks

SafeSCHEMA: Multi-domain Orchestration of Slices based on Sa...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Anestis Dalgkitsis Ashima Chawla Anne-Marie Bosneag Christos Verikoukis Iquadrat Informatica S.L. Barcelona Spain Network Management Research Lab LM Ericsson Athlone Ireland Computer Engineering & Informatics Dept. University of Patras Greece

ISBN: (纸本)9781665435413

The success of Software-Defined Networking and Network Function Virtualization enabled providers to partially automate the decision-making of various network operating workflows. Network slicing is an innate feature of the Fifth Generation (5G) and Beyond-5G (B5G) networks, bringing many advantages but also increasing the complexity of managing such networks. In this context, automation tools based on Artificial Intelligence (AI) have become a preeminent instrument of automation used by the industry, as it would be impossible to manually provision and manage such a vast and dynamic infrastructure. Safe interaction of the AI agents with the network is one of the predominant challenges, especially when Reinforcement Learning (RL) is used in critical environments. This is particularly important when the RL agent actions have a high or irreversible impact on the network or service. Slice management is one of the major features that operators want to automate, to offer future services at large scales with manageable complexity to the operator. However, during the exploration phase, RL agents can cause significant performance degradation during operation and possibly introduce irreversible damage to the service being offered. To address this major challenge, we propose a multi-agent, modular, SafeRL architecture for distributed slice orchestration. We study the problem of zero-touch slice management and orchestration, in the context of Ultra-Reliable Low Latency Communication services for B5G networks. Our results demonstrate improved performance over competing solutions, while ensuring the safety of the performed actions during real-time slice orchestration.

关键词： Automation Scalability Reinforcement learning Ultra reliable low latency communication Real-time systems Space exploration Safety

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FoodX, a System to Reduce Food Waste 3

FoodX, a System to Reduce Food Waste

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3rd International Conference on computer and informatics engineering, IC2IE 2020

作者： Shinta Oktaviana, R. Febriani, Diana Ambarwati Yoshana, Intan Payanta, L.R. Politeknik Negeri Jakarta Dept. Computer and Informatics Engineering Indonesia Instituto Politecnico de Braganca Dept. Technology and Management Portugal

ISBN: (纸本)9781728182476

Food waste is a serious problem that occurs in various countries. Indonesia is a country that produces food waste, the second largest after Saudi Arabia. Currently, there are several communities who care about the issue of food waste and hunger in Indonesia. The Community collects excess food from eligible donor consumption to be distributed to people in need. They have the aim to reduce the problem of food waste and numbers starving in Indonesia. However, the process of channeling food to donors and the community is still practically a manual where the community contacts the donors one by one, so it is considered less effective. This research aims to create a system to connect the community with individuals or organizations that want to donate excess food. In order for users to give faster feedback, this system was made using the prototype methodology. At the final stage of the development, testing was carried out by involving several volunteers and 3 communities to see the completeness of the features system. FoodX system made already accommodate the needs of 2 types of food communities (with and without volunteer). © 2020 IEEE.

关键词： food waste hunger rate social entrepreneurship. sustainable development goals

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Vehicle Classification in Intelligent Transportation Systems Using Deep Learning and Seismic Data

Vehicle Classification in Intelligent Transportation Systems...

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IEEE International Conference on Intelligence and Security informatics, ISI

作者： Sherief Hashima Mohamed H. Saad Kohei Hatano Hamada Rizk Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Engineering Dept Egyptian Atomic Energy Authority Cairo Egypt Radiation Engineering Dept NCRRT Egyptian Atomic Energy Authority Cairo Egypt Department of Informatics Kyushu University Fukuoka Japan Graduate School of Information Science and Technology Osaka University Suita Japan Computer and Control Engineering Department Tanta University Tanta Egypt

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques.

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High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks

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Communications engineering 2022年第1期1卷 1-10页

作者： Sozos, Kostas Bogris, Adonis Bienstman, Peter Sarantoglou, George Deligiannidis, Stavros Mesaritakis, Charis Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Information Technology Ghent University-imec Gent Belgium Dept. Information and Communication Systems Engineering Engineering School University of the Aegean Samos Greece

Neuromorphic computing using photonic hardware is a promising route towards ultrafast processing while maintaining low power consumption. Here we present and numerically evaluate a hardware concept for realizing photonic recurrent neural networks and reservoir computing architectures. Our method, called Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), uses simple optical filters placed in a loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to the filters’ central frequencies and bandwidths. Numerical application to high baud rate optical signal equalization (>100 Gbaud) reveals that ROSS-NN extends optical signal transmission reach to > 60 km, more than four times that of two state-of-the-art digital equalizers. Furthermore, ROSS-NN relaxes complexity, requiring less than 100 multiplications/bit in the digital domain, offering tenfold reduction in power consumption with respect to these digital counterparts. ROSS-NNs hold promise for efficient photonic hardware accelerators tailored for processing high-bandwidth (>100 GHz) optical signals in optical communication and high-speed imaging applications. © The Author(s) 2022.

关键词： computer science Electrical and electronic engineering Photonic devices

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Real-Time Cyberattack Detection with Offline and Online Learning

Real-Time Cyberattack Detection with Offline and Online Lear...

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IEEE Workshop on Local and Metropolitan Area Networks (LANMAN)

作者： Erol Gelenbe Mert Nakip Polish Academy Sciences Institute of Theoretical and Applied Informatics Gliwice Poland Lab. I3S Université Côte d'Azur Nice France Dept. of Computer Engineering Yaşar University Bornova Izmir Turkey

This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network. Some of these algorithms require offline learning, while others allow the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation of the accuracy of all these methods is carried out with real attack traces. The novel methods presented here are compared with other state-of-the-art approaches, showing that they offer better or equal performance, with lower learning times and shorter detection times, as compared to the existing state-of-the-art approaches.

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis

Development of a Calibration-Free Brain-Computer Interface U...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Korhan Cengiz Shreyas J Udayaprasad P K Gururaj H L Dilip Kumar S M College of Information Technology University of Fujairah UAE Faculty of Organization and Informatics University of Zagreb Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology.

关键词： Training Machine learning algorithms Artificial neural networks Electroencephalography Brain-computer interfaces Calibration Reliability

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Converging for Security: Blockchain, Internet of Things, Artificial Intelligence - Why Not Together?

Converging for Security: Blockchain, Internet of Things, Art...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Ahmad Anwar Zainuddin Dini Handayani Isyraq Haziq Mohd Ridza Siti Husna Abdul Rahman Saidatul Izyanie Kamarudin Khairul Zakwan Ahmad Mirza Darwisy Mahazir Muazzam Hazmi Sukhaimi Krishnan Subramaniam Mohamad Irfan Firdaus Basri Nurul Hanis Mohd Dhuzuki Dept. of Computer Science KICT International Islamic University Malaysia Gombak Malaysia Dept. of Computer Science KICT International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Computing Informatics Multimedia University Cyberjaya Selangor Malaysia Kolej Pengajian Pengkomputeran Informatik dan Matematik (KPPIM) Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Nilai Negeri Sembilan Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Blockchain, renowned for its decentralized and secure nature, and Artificial Intelligence, the pinnacle of machine intelligence, stand as pillars of innovation. However, despite the recognition of their few successful collaborations, the interrelation among these triumvirates - Internet-of-Things (IoT), Blockchain, and Artificial Intelligence (AI) - still remains relatively unexplored terrain. Issues arise regarding their synergy: the challenges of integration, the potential improvements in existing technologies, and the careful consideration required before their unification. While evidence suggests their individual prowess, understanding their convergence and the implications thereof is crucial for shaping a technologically robust future. This paper delves into the complex interplay between IoT, Blockchain, and AI, navigating their relationship and exploring opportunities for enhancement. In addition, this paper will also look into several challenges and complexities hindering seamless integration.

关键词： Ethics Technological innovation Navigation Collaboration Blockchains Complexity theory Stakeholders

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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Automation and Computation (AUTOCOM), International Conference on

作者： Kottala Sri Yogi Avinash Sharma V Dankan Gowda Rini Saxena Tarkeshwar Barua Khalid Mohiuddin Dept of Operations Symbiosis institute of Business Management Hyderabad Symbiosis International University Pune Hyderabad Telangana Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Business Informatics College of Business King Khalid University PO Box-3247 Abha Saudi Arabia

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area.

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