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arXiv 2021年

作者： Wagle, Ankit Khatri, Sunil Vrudhula, Sarma School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Dept. of Electrical and Computer Engineering Texas A&M University College StationTX United States

This paper presents TULIP, a new architecture for a binary neural network (BNN) that uses an optimal schedule for executing the operations of an arbitrary BNN. It was constructed with the goal of maximizing energy efficiency per classification. At the top-level, TULIP consists of a collection of unique processing elements (TULIP-PEs) that are organized in a SIMD fashion. Each TULIP-PE consists of a small network of binary neurons, and a small amount of local memory per neuron. The unique aspect of the binary neuron is that it is implemented as a mixed-signal circuit that natively performs the inner-product and thresholding operation of an artificial binary neuron. Moreover, the binary neuron, which is implemented as a single CMOS standard cell, is reconfigurable, and with a change in a single parameter, can implement all standard operations involved in a BNN. We present novel algorithms for mapping arbitrary nodes of a BNN onto the TULIP-PEs. TULIP was implemented as an ASIC in TSMC 40nm-LP technology. To provide a fair comparison, a recently reported BNN that employs a conventional MAC-based arithmetic processor was also implemented in the same technology. The results show that TULIP is consistently 3X more energy-efficient than the conventional design, without any penalty in performance, area, or accuracy. © 2021, CC BY.

关键词： Energy efficiency

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Smart Homes: Security challenges and privacy concerns

arXiv

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arXiv 2020年

作者： Hall, Fraser Maglaras, Leandros Aivaliotis, Theodoros Xagoraris, Loukas Kantzavelou, Ioanna School of Computer Science and Informatics De Montfort University Leicester United Kingdom Dept. of Informatics and Computer Engineering University of West of Attika Athens Greece

Development and growth of Internet of Things (IoT) technology has exponentially increased over the course of the last 10 years since its inception, and as a result has directly influenced the popularity and size of smart homes. In this article we present the main technologies and applications that constitute a smart home, we identify the main security and privacy challenges that smart home face and we provide good practices to mitigate those threats. Copyright © 2020, The Authors. All rights reserved.

关键词： Automation

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Predicting upper limb disability progression in primary progressive multiple sclerosis using machine learning and statistical methods

Predicting upper limb disability progression in primary prog...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Mostafa, Sally Song, Isabel Hyo Jung Metwally, Ahmed A. Strauli, Nicolas Sewde, Nehemiah Friesenhahn, Michel Usdin, Maxime Jia, Xiaoming San Francisco State University Department of Computer Science San FranciscoCA United States Cairo University Biomedical Engineering Dept Giza Egypt OMNI Biomarker Development Genentech Inc South San FranciscoCA United States DevSci Informatics Genentech Inc South San FranciscoCA United States PHC Biostatistics Genentech Inc South San FranciscoCA United States DevSci Informatics Genentech Inc South San FranciscoCA United States

ISBN: (纸本)9781665401265

Multiple sclerosis (MS) is a heterogeneous immune-mediated disorder of the central nervous system, and primary progressive MS (PPMS) is characterized by insidious injury independent of clinical relapses from disease onset. Predicting disease progression remains an unmet need across the MS disease spectrum. We evaluated statistical and machine learning (ML) models for predicting progression in 596 patients with PPMS from ORATORIO, a randomized, placebo (PBO)-controlled, phase 3 clinical study of ocrelizumab (OCR). Using baseline clinical, magnetic resonance imaging, and biomarker (neurofilament light chain [NfL]) data, we trained 6 ML models to predict a progression event (logistic regression, support vector machines, random forest, neural network, gradient boosting, and extreme gradient boosting) and the time to progression (Cox proportional hazards [PH]) on upper limb function, as measured by 20% confirmed worsening on the 9-hole peg test (9HPT). Predictive performance was similar across the six ML models (area under the curve = 0.63 to 0.67) and was modest using the Cox PH model (C-index = 0.76, an improvement over previous studies). In the Cox PH model, treatment arm, baseline 9HPT, neurologic functional system scores, and serum NfL levels were identified as factors associated with progression;model performance was similar across the OCR, PBO, and combined OCR + PBO treatment arms. Accounting for potential interactions with treatment arm resulted in similar predictive performance. To our knowledge, this is the first study to systematically evaluate ML models and statistical methods for predicting progression in PPMS using a well-characterized clinical trial cohort. © 2021 IEEE.

关键词： Support vector machines

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Opening user model data for motivation and learning: The case of an adaptive multiplication game 11

Opening user model data for motivation and learning: The cas...

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11th International Conference on computer Supported Education, CSEDU 2019

作者： Leonardou, Angeliki Rigou, Maria Garofalakis, John Dept. of Computer Engineering and Informatics University of Patras Rio Campus26500 Greece

ISBN: (纸本)9789897583674

Multiplication table fluency is of core importance, as it consists a fundamental stage of mathematics education. It is a common phenomenon that pupils face difficulties in perceiving this knowledge and achieving multiplication skills. This paper presents an adaptive multiplication game for assessing and gradually improving multiplication skills. The game also incorporates Open Learner Model elements which expose parts of the learner model to the user through easily perceivable visualizations for improving self-reflection, fostering self-regulated learning and increasing user motivation. The game has been tested with a representative sample of primary school students and based on the data collected the game and the Open Learner Model’s features have been received positively. © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Motivation

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Implementation of depth-HOG based human upper body detection on a mini pc using a low cost stereo camera 1

Implementation of depth-HOG based human upper body detection...

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1st International Conference of Artificial Intelligence and Information Technology, ICAIIT 2019

作者： Dewantara, Bima Sena Bayu Ardilla, Fernando Thoriqy, Ardiansyah At Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9781538684481

In this paper, we propose a human upper body detection using a depth image that is implemented on a mini PC using a low cost stereo camera. The camera, named Minoru 3D webcam, produces depth image from its two parallel cameras. A pyramid-based region of interest (RoI) is applied on the depth-image frames to scan the possibility of human upper body existence. Simultaneously, a histogram of oriented gradient (HOG) method is performed in the ROI to extract the HOG feature. The results of the HOG feature extraction are then classified using linear support vector machine (SVM). Our system has been experimentally tested publicly in the campus environment. The detection speed obtained from a computer is 8.53 fps and the Mini PC is 4.26 fps under non-Threaded programming. The result of object detection using HOG and SVM Classification method on static image reaches an average accuracy of 78.71%. Testing the system for real implementation has average error accuracy 4.60%. The results of detection for two human objects can achieve an accuracy 71.00%. Moving image testing has an average error of 4.60%. © 2019 IEEE.

关键词： Support vector machines

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A Study of Multilayer Omega Networks Operating with Cut-Through Mechanism under Uniform Traffic

A Study of Multilayer Omega Networks Operating with Cut-Thro...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Eleftherios Stergiou John Garofalakis Dimitrios Liarokapis Spiridoula V. Margariti Dept. Informatics & Telecommunications University of Ioannina Arta Greece Dept. of Computer Engineering and Informatics University of Patras and Computer Technology Institute and Press—“DIOPHANTUS” (CTI)) Patras Greece

ISBN: (数字)9781728164458

ISBN: (纸本)9781728164465

There is an enduring interest in developing more efficient multistage interconnection networks (MINs), as they are a key aspect of various switching sectors and the demands for higher data transfer rates. Here, semi-layer Omega type networks operating under the cut-through forwarding policy are selected as a test-bed subject for performance evaluation. This special shuffle-exchange network was modelled, analysed, and simulated. Moreover, this type of fabric is compared with corresponding single-layer Omega networks operating via cut-through or a `store and forward' forwarding technique. The results show, in a quantitative manner, how superior is the under study fabric compared to the corresponding single-layer constructions, in terms of network size, showing the advantages that are provided by both usages: fan-out and cut-through mechanisms. Moreover, the roles of the switch element's size and the reliability metric were investigated. The findings that are derived can be valuable assets for MIN designers, in order to achieve more efficient delivering networks with low overall deployment costs.

关键词： Omega network banyan networks performance evaluation cut-through semi-layer multistage interconnection networks uniform traffic multistage interconnection networks cut-through Performance Evaluation Materials

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical & Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e... 详细信息

关键词： Timing circuits

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical &Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

ISBN: (纸本)9798350350012

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HPTMT parallel operators for high performance data science & data engineering

arXiv

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arXiv 2021年

作者： Abeykoon, Vibhatha Kamburugamuve, Supun Widanage, Chathura Perera, Niranda Uyar, Ahmet Kanewala, Thejaka Amila Von Laszewski, Gregor Fox, Geoffrey Luddy School of Informatics Computing and Engineering BloomingtonIN47408 United States Biocomplexity Institute Initiative and Computer Science Dept. University of Virginia Indiana University Alumni IN47408 United States

Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning. These applications are built around efficient data abstractions and operators that suit the applications of different domains. Often lack of a clear definition of data structures and operators in the field has led to other implementations that do not work well together. The HPTMT architecture that we proposed recently, identifies a set of data structures, operators, and an execution model for creating rich data applications that links all aspects of data engineering and data science together efficiently. This paper elaborates and illustrates this architecture using an end-to-end application with deep learning and data engineering parts working together. © 2021, CC BY.

关键词： Data structures

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Fitness dependent optimizer with neural networks for COVID-19 patients

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computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer Machine learning Swarm intelligence

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