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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Pathmanathan, P. Ravindran Baker El-Ebiary, Yousef A. Yusoff, M. Hafiz Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Pande, Bishwajeet Abu-Ulbeh, Waheeb Al Moaiad, Yazeed Bamansoor, Samer International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia Faculty of Computer and Information Technology MEDIU Malaysia

ISBN: (纸本)9781665432221

It is generally accepted that the advancement of information and communication technology (ICT) has contributed to the exponential growth of the electronic marketplace. ICT, especially the internet, is believed to be the most cost-effective tool to help brick and mortar companies gain greater markets and the opportunity to compete with other competitive organizations in attracting consumers to their goods, services and knowledge with the strong waves of globalization and liberalization around the world. Nowadays, technology is developing and making our world borderless and infinite. E-commerce is one of the innovations that is transforming the demand for businesses. Electronic commerce (e-commerce) is considered to be an effective method for promoting, selling and incorporating online services that can play an important role in customer recognition, acquisition and maintenance. Through the e-commerce site, people can also promote services, replacing the conventional process, which is cheaper and more efficient. There is a set of effective organizational and external organizational variables in e-commerce that should be taken into consideration. The aim of this study is to suggest an e-commerce solution that can solve the problem for tourism companies. The paper focuses on Malaysia's tourism industry and focuses only on the angle of e-commerce adoption management. © 2021 IEEE.

关键词： Cost effectiveness

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Efficiently Verifiable Quantum Advantage on Near-Term Analog Quantum Simulators

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PRX Quantum 2025年第1期6卷 010341-010341页

作者： Zhenning Liu Dhruv Devulapalli Dominik Hangleiter Yi-Kai Liu Alicia J. Kollár Alexey V. Gorshkov Andrew M. Childs Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Simons Institute for the Theory of Computing University of California at Berkeley California 94720 USA Applied and Computational Mathematics Division National Institute of Standards and Technology (NIST) Gaithersburg Maryland 20899 USA Department of Physics University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based on single-step Feynman-Kitaev verification of an analog quantum simulation, in which the verifier need only run an O(λ2)-time classical computation, and the prover need only prepare O(1) samples of a history state and perform O(λ2) single-qubit measurements, for a security parameter λ. We also propose a near-term feasible strategy for honest provers and discuss potential experimental realizations.

关键词： Quantum computers

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Remuneration Review: Risk, Planning, and Modelling beyond the Covid Crisis 12

Remuneration Review: Risk, Planning, and Modelling beyond th...

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12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Cherrington, Marianne Airehrour, David Madanian, Samaneh Lu, Joan Xu, Qiang Artiushkina, Polina School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom School of Applied Business Unitec Institute of Technology Auckland New Zealand Auckland University of Technology Department of Computer Science Auckland New Zealand University of Huddersfield Department of Computer Science Huddersfield United Kingdom University of Huddersfield Department of Engineering Technology Huddersfield United Kingdom Otago Polytechnic AIC Department of Applied Management Auckland New Zealand

ISBN: (纸本)9781665400664

Organisations that have survived Covid-19 jolts, uncertainties and lockdowns now require business intelligence and information system integration for decision support. Risks and market challenges have shifted as the pandemic progresses. Crisis management repositioning has left many firms cash-poor. Now, firms are facing labour shortages. Standard remuneration policies and practices need revision to keep core talent, stabilise teams from burn-out and to move forward strategically. This paper revises essential remuneration review considerations for crisis management in a new Covid-19 context. Current trends are discussed, as restrictions and pandemic uncertainties lift, allowing greater accuracy in risk scenario planning. Reliable data, fit for analytics and complex modelling, are making results more meaningful. The unique contribution of this research is the Covid-19 post-survival organisational perspective that can transform remuneration modelling. The scope extends beyond the governance level, and considers semantics and dynamic risk planning. Specific scenarios are updated from lessons learned from financial crisis management. Conclusions take a holistic view of how data can enhance remuneration practices to add value for organisations in the current Covid-19 climate. The paper advocates post crisis remuneration review that includes complex modelling with dynamic risk analysis for strategic planning in mid-term scenarios. © 2021 IEEE.

关键词： Semantics

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An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics

arXiv

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arXiv 2023年

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University School of Computer Science and Informatics Cardiff University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University CardiffCF5 2YB United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2023, CC BY-NC-SA.

关键词： COVID-19

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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A Systematic review of Multi-Mode Fiber based on Dimensional Code in Optical-CDMA 3

A Systematic review of Multi-Mode Fiber based on Dimensional...

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3rd International Conference on applied and Practical sciences, ICAPS 2021

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Rashidi, C.B.M. Al-Dawoodi, Aras Mahmood, Baban A. Fareed, Alaa Zaenal, Mohammed U. Qasim, Nameer Hashim Rafeeq, Ranjdr M. Faculty of Applied and Human Sciences University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq Computer Technical Engineering Al-Qalam University College Iraq School of Computing University Utara Malaysia Malaysia

This paper deals with optical code division multiple access (optical-CDMA) and provides a general analysis of multi-mode fiber (MMF). The purpose of this work is to classify the works in the literature that are related to Optical-CDMA based on MMF and to find the limitation of the researches done in this field. Several challenges that occur in the medium such as multiple-access interference (MAI), pulse dispersion (PD) and nonlinearity in optical fibers deteriorate system performances and become a major performance-limiting factor. © Published under licence by IOP Publishing Ltd.

关键词： Multiple access interference

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Detection of retinal microlesions through YOLOR-CSP architecture and image slicing with the SAHI algorithm

Detection of retinal microlesions through YOLOR-CSP architec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Alejandro Pereira Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Dias Marcelo Ribeiro Reza Ahmadi Computer Science Federal University of Pelotas - UFPel Pelotas Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Alegrete Brazil Postgraduate Program in Computing Federal University of Pelotas - UFPel Pelotas Brazil Department of Applied Computing Federal University of Santa Maria - UFSM Santa Maria Brazil Geobotica Brisbane Australia

Diabetic retinopathy affects millions of working-age people worldwide. Of the countries in Latin America, Brazil has the highest incidence of cases. Diabetic retinopathy is detected through images of the fundus that contain lesions such as hard exudates, soft exudates, microaneurysms, and hemorrhages. Early identification of these lesions prevents the progression of the disease, which leads to a decrease in visual capacity. In addition, the early identification of these lesions allows the screening of patients who need priority care. The detection of these lesions occurs through the processing and analysis of fundus images using deep learning models. In this work, we present a new method that uses the You Only Learn One Representation with Cross Stage Partial Network (YOLOR-CSP) architecture combined with the Slicing Aided Hyper Inference (SAHI) framework to detect lesions. The proposed method was trained, adjusted, and evaluated using the Dataset for Diabetic Retinopathy (DDR) and the Indian Diabetic Retinopathy Image Dataset (IDRiD). The proposed method obtained in the data set DDR mAP equal to 38.08%, in the validation set, and 22.25% in the test set with SGD optimizer. The presented results were superior in the detection of eye fundus lesions in comparison with similar works found in the state-of-the-art literature.

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Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code

Bi-Directional Transformers vs. word2vec: Discovering Vulner...

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Cyber Awareness and Research Symposium (CARS)

作者： Gary A. McCully John D. Hastings Shengjie Xu Adam Fortier The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD USA Department of Cyber Intelligence and Information Operations College of Applied Science & Technology University of Arizona Tucson AZ USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350386417

ISBN: (纸本)9798350386424

Detecting vulnerabilities within compiled binaries is challenging due to lost high-level code structures and other factors such as architectural dependencies, compilers, and optimization options. To address these obstacles, this research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoBERTa to learn semantics from intermediate representation (LLVM IR) code. Long short-term memory (LSTM) neural networks were trained on embeddings from encoders created using approximately 48k LLVM functions from the Juliet dataset. This study is pioneering in its comparison of word2vec models with multiple bidirectional transformers (BERT, RoBERTa) embeddings built using LLVM code to train neural networks to detect vulnerabilities in compiled binaries. Word2vec Skip-Gram models achieved 92% validation accuracy in detecting vulnerabilities, outperforming word2vec Continuous Bag of Words (CBOW), BERT, and RoBERTa. This suggests that complex contextual embeddings may not provide advantages over simpler word2vec models for this task when a limited number (e.g. 48K) of data samples are used to train the bidirectional transformer-based models. The comparative results provide novel insights into selecting optimal embeddings for learning compiler-independent semantic code representations to advance machine learning detection of vulnerabilities in compiled binaries.

关键词： Codes Accuracy Semantics Neural networks Bidirectional control Transformers Encoding Natural language processing Long short term memory Context modeling

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Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Alves, Julio César Mateus, Geraldo Robson Department of Applied Computing Universidade Federal de Lavras MG Lavras Brazil Department of Computer Science Universidade Federal de Minas Gerais MG Belo Horizonte Brazil

We address the problem of production planning and distribution in multi-echelon supply chains. We consider uncertain demands and lead times which makes the problem stochastic and non-linear. A Markov Decision Process formulation and a Non-linear Programming model are presented. As a sequential decision-making problem, Deep Reinforcement Learning (RL) is a possible solution approach. This type of technique has gained a lot of attention from Artificial Intelligence and Optimization communities in recent years. Considering the good results obtained with Deep RL approaches in different areas there is a growing interest in applying them in problems from the Operations Research field. We have used a Deep RL technique, namely Proximal Policy Optimization (PPO2), to solve the problem considering uncertain, regular and seasonal demands and constant or stochastic lead times. Experiments are carried out in different scenarios to better assess the suitability of the algorithm. An agent based on a linearized model is used as a baseline. Experimental results indicate that PPO2 is a competitive and adequate tool for this type of problem. PPO2 agent is better than baseline in all scenarios with stochastic lead times (7.3-11.2%), regardless of whether demands are seasonal or not. In scenarios with constant lead times, the PPO2 agent is better when uncertain demands are non-seasonal (2.2-4.7%). The results show that the greater the uncertainty of the scenario, the greater the viability of this type of approach. © 2022, CC BY.

关键词： Deep learning

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Investigating Cases and Clusters-Based Reuse Policies for Card-Playing Agents

Investigating Cases and Clusters-Based Reuse Policies for Ca...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Gustavo B. Paulus Daniel P. Vargas Joaquim V. C. Assunção Luis A. L. Silva Federal Institute of Education Science and Technology of Rio Grande do Sul Ibirubá Brazil Graduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Applied Computing Department Federal University of Santa Maria Santa Maria Brazil

computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently.

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