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Framework for Optimizing Distribution of Deep Neural Network Inference Tasks across Computing Networks

Framework for Optimizing Distribution of Deep Neural Network...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Preetha Evangeline, D. Sahu, Atma R. Anandhakumar, P. Siva Subramaniya Nadar College of Engineering Dept. of Information Technology Tamil Nadu Kalavakkam India Coppin State University Dept. of Mathematics and Computer Science BaltimoreMD United States Madras Institute of Technology Anna University Dept. Of Information Technology Tamil Nadu India

ISBN: (纸本)9798350364910

Ubiquitous artificial intelligence (AI) is considered one of the key services in 6G systems. AI services typically rely on deep neural networks (DNN) requiring heavy computation. Hence, to support ubiquitous AI, it is crucial to provide a solution for offloading or distributing computational burden due to DNN, especially at end devices with limited resources. This paper proposes an optimization framework for assigning the tasks of DNN layer computations to computing resources in the network, to reduce the inference latency. To this end, we propose a layered graph model with which simple conventional routing jointly solves the problem of selecting nodes for computation and paths for data transfer between nodes. The proposed framework is applied to derive algorithms for minimizing the end-to-end inference latency. The numerical evaluations show that our new formulation can find a solution for DNN inference job distribution much faster than the existing formulation and that our algorithms can select computing nodes and data paths adaptively to the computational attributes of given DNN inference jobs. © 2024 IEEE.

关键词： Computation offloading

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Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects 12

Tangispeak: A Digital Game Based Language Learning System by...

引用

12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ongoro, Catherine Akoth Fanjiang, Yong-Yi Hung, Chi-Huang Lin, Bing-Jun Lee, Mu-En Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350340181

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills. © 2023 IEEE.

关键词： Augmented reality

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

引用

8th International Conference on computer Technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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Photonic Neuromorphic Accelerator Combined with an Event-Based Neuromorphic Camera for High-Speed Object Classification

Photonic Neuromorphic Accelerator Combined with an Event-Bas...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsirigotis, Aris Tsilikas, Ioannis Sozos, Kostas Bogris, Adonis Mesaritakis, Charis University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece National Technical University of Athens Dept. Applied Physics and Mathematics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

ISBN: (纸本)9798350345995

In this work, we demonstrate the impact of an unconventional convolutional photonic accelerator, based on an optical spectrum slicing (OSS) [1], on the classification accuracy of objects, generated through a high-frame rate neuromorphic event-based camera [2]. The experimental setup is depicted in Fig.1a. Ιt consists of a 5 mW-632 nm-LED source, two objective lenses with NA=0.65 that concentrate the light beam into a 100 μmX100 μm channel. In our experiments, the targeted objects consist of test spheres, of different diameters (12, 16, and 20 μm), used in aqua solutions. A pump regulated the sphere's speed to 0.8 m/s. The objects were recorded by a 10 kframe/s capable neuromorphic camera, with a temporal resolution of 1 μs. The camera detects pixel's contrast changes (events), similar to biological systems [2]. The recorded events were exported into 1 kframes/sec videos through a synthetic frame generator software, resulting to 2988 images per particle [2]. Images were post-processed using only noise reduction by frame subtraction and image cropping so as to reduce data volume to 100 x 100 pixels. © 2023 IEEE.

关键词： Cameras

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Ethical and Practical Implications of Implementing Facial Recognition Technology for Attendance Control in Educational Institutions and Businesses 1

Ethical and Practical Implications of Implementing Facial Re...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kurup, Ravindra Muzalda, Devendra Sharma, Vinod Hudda, Yashika Singh, Vijay Vir Princy, Benila Madhav University Dept. of Humanities and S.Science Pindwara India SLP College Dept of CSE Gwalior India Chandigarh University Dept of Computer Science Engineering Chandigarh India Lingayas Vidyapeeth Dept. of Mathematics Faridabad India Meenakshi A.H.E.R Dept. of English Chennai India

ISBN: (纸本)9798350356816

Facial recognition technology has garnered significant attention recently as a potential solution for attendance management in educational institutions and companies. This study proposes a facial recognition attendance system designed to address limitations of existing systems and navigate the ethical and legal considerations associated with the technology. To ensure accuracy, security, and user acceptance, the proposed system integrates advanced facial recognition algorithms, strict data protection protocols, and intuitive interfaces. The methodology encompasses a thorough literature review, primary data collection through surveys and interviews, system design, testing, and data analysis to evaluate the feasibility, efficacy, and ethical implications of deploying facial recognition for attendance tracking. The experimental setup includes cameras, laptops, servers, facial recognition algorithms, attendance management software, and a diverse collection of facial images. Analysis of the collected data aims to assess the system's accuracy and effectiveness. The study's findings will offer insights into the potential benefits and challenges of adopting facial recognition technology for attendance management in educational and corporate settings, along with recommendations for future research and implementation. © 2024 IEEE.

关键词： Accuracy Attendance Management Data Analysis Diverse Facial Image Facial Recognition

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Analyzing the Potential of ReXNet-150: A Novel Architecture for Automobile Parts Classification 3

Analyzing the Potential of ReXNet-150: A Novel Architecture ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ranjith Kumar, M. Adithiyan, Pv Sendur, G Jeevan Kumar, S Praveen Kumar, S Mahendira Nikhil, V. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engg Chennai India

ISBN: (纸本)9798350372120

Excellent automotive inspection is one of the key application areas for getting enhanced quality at a low cost, and it may be obtained using advanced computer vision technology. Automated car component assembly and quality control depend on autonomous automotive part recognition. ReXNet's efficient and lightweight architecture enables the automation of auto component assembly and the improvement of quality control processes with an advanced solution. With the help of ReXNet, autonomous cars can effectively recognize and inspect a wide range of parts, including electrical modules, actuators, connectors, and sensors. The dataset comprises 16,600 RGB photos depicting 50 distinct types of auto parts. It encompasses a train set, test set, and validation set. Each of the 50 classes in both the validation and test sets has five images. In the identification of individual parts, the maximum testing accuracy of 98.6% and the maximum training accuracy of 98.4% were reached. Efficient car inspection systems, model verification, and vehicle manufacturing are just a few of the areas that stand to benefit significantly from such precise car part classification. Classifying car parts is helpful for several purposes, including model verification, automotive inspecting systems, and automobile manufacturing. © 2024 IEEE.

关键词： computer vision

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Thermal Performance Prediction of Metal-Foam Heat Sink Using Genetic Algorithm Driven Neural Network 2

Thermal Performance Prediction of Metal-Foam Heat Sink Using...

引用

2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Chandan, K. Singh, Rimjhim Padam Nagaraja, K.V. Kumar, Priyanka Amrita School of Engineering Bengaluru Dept. of Mathematics India Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Texas at San Antonio Department of Computer Science United States

ISBN: (纸本)9798350314168

Electronic device temperature management is crucial for maximum performance as well as its longer life and metal foam heat sinks (MFHS) are widely employed in electronic cooling due to their large specific heat transfer surface area and fluid mixing capabilities. However, due to the complicated and random geometrical character of MFHSs, precisely predicting their performance is difficult and costly. Several deep learning approaches have been used to address this difficulty, combining input features such as metal foam shape, heat sink dimensions, and coolant flow conditions to forecast thermal-hydraulic performance. This paper offers a unique approach for parameter estimation that combines Genetic Algorithm with an Artificial Neural Network (ANN) model. The Genetic Algorithm is used to optimize the ANN model's parameters thereby, increasing its accuracy in forecasting the friction rate and Nusselt number of MFHSs. The Genetic Algorithm seeks for the best set of parameters that minimizes prediction errors by iteratively evolving a population of candidate solutions using methods influenced by natural selection and genetic variation. The outperforming findings show that Genetic Algorithm can be used to estimate parameters in complex systems, specifically in predicting the thermal-hydraulic performance of MFHSs. © 2023 IEEE.

关键词： Neural networks

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Indonesian Finance News Sentiment from Hybrid Deep Learning and Support Vector Machine 22

Indonesian Finance News Sentiment from Hybrid Deep Learning ...

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5th International Conference on mathematics and Statistics, ICoMS 2022

作者： Mukhlash, Imam Gama, Athyah D. S. Iqbal, Mohammad Darmaji, Darmaji Kimura, Masaomi Dept. of Mathematics Institut Teknologi Sepuluh Nopember Indonesia Dept. of Computer Science and Engineering Shibaura Institute of Technology Japan

ISBN: (纸本)9781450396233

One common action to keep aware of the current investment progress is by updating finance news continuously. Indeed, we can read a bunch of news relating to finance from social media, which is often difficult to figure out at a glance. Hence, this work aims to propose hybrid models that can help us to classify whether the finance news is positive to follow. Also, we may sort a few articles containing neutral ones. More specifically, we incorporate deep neural networks: deep convolutional neural networks and long short term memory, to draw diverse word representations, and support vector machines to categorize them as a multi-class classification case. In this work, we evaluated the proposed models on Indonesian finance news that was officially reported from the Bank of Indonesia around 2019 before the pandemic started. In the evaluation results, we showed the DCNN-SVM better accuracy compared to others. © 2022 ACM.

关键词： Support vector machines

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Low-cost Smart Glasses for People with Visual Impairments 1

Low-cost Smart Glasses for People with Visual Impairments

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Nayana, B.R. Raju N R, Yogesh Velidi, Abhav S M S Ramaiah Univesrity of Applied Sciences Dept. of Computer Science Engineering Karnataka Bangalore India Sir M Visvesvaraya Institute of Technology Dept. of Electrical and Electronics Engineering Karnataka Bangalore India National Institute of Technology Dept. of Electronics and Commuincation Engineering Suratkal Karnataka India

ISBN: (纸本)9798350348057

An efficient visual aid is essential for visually impaired candidate. At the same instance it has to be simple, robust and cost effective. However, regardless of being expensive, it is challenging to incorporate high end artificial intelligence into a compact device to serve the purpose. This paper presents a prototype of low-cost smart glasses for visually impaired individuals. Also, a brief survey on the cost and features of the goggles available in the market are included. In this work a prototype is developed integrating Raspberry Pi module, camera, sensors and a goggles. The captured images are processed using object recognition models and its corresponding text is read aloud using google text to translator. Subsequently, a comparative analysis of object recognition model is explored and discussed. Essential experimental results are incorporated along with cost involved for the prototype development. © 2023 IEEE.

关键词： Object recognition

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Multi-Tenancy Architecture for Augmented Security in Cloud Computing 5

Multi-Tenancy Architecture for Augmented Security in Cloud C...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Koley, Santanu Majumdar, Jayeeta Bera, Mihir Baran Acharjya, Pinaki Pratim Haldia Institute of Technology Dept. of Computer Science & Engineering Haldia India School of Applied Science and Humanities Haldia Institute of Technology Haldia India

ISBN: (纸本)9798350321425

Several Virtual Machines (VMs) belonging to various users transparently share physical resources in multi-tenancy cloud milieu. In these situations, implementing an effective access control system can stop unwanted access to cloud resources. In the context of multi-tenancy cloud systems, a right of entry in charge of technique is suggested in this article that offers scalable and safe access management to the cloud. By implementing such a method, malevolent tenants will be prevented from creating and transmitting unlawful traffic to the Cloud network. Although both cloud providers and customers can greatly benefit from multi-tenancy, the main barriers to use are security and privacy issues. Since resource sharing is so important to Multi-Tenancy, numerous experts have proposed several strategies to protect Multi-Tenancy. © 2023 IEEE.

关键词： Access control

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