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5th International Conference on Advancement in Data Science, E-Learning and information Systems, ICADEIS 2023

作者： Yasirandi, Rahmat Thanasopon, Bundit King Mongkut's Institute of Technology Ladkrabang School of Information Technology Bangkok Thailand Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350303414

The food service industry is one of the most promising businesses today, and the availability of food delivery is one of the digital transformations that has occurred in this industry. The continuous development of innovation in this business promises to increase success and revenue. This research examines how food delivery is one of the innovations that plays a crucial role in the sustainability of restaurants/food merchants. The exploratory scope in this survey study is based on research questions, starting with the process evolution in food delivery over time. This includes the influence of three technological eras: cellular telephones, websites, and mobile operating systems, which have an evolutionary impact on the business concept point of view, from restaurant-based delivery to the emergence of e-commerce. The research also explores the benefits and challenges that arise during adoption, with 5 benefits identified, including improved customer experience, increased order efficiency, increased order flexibility, improved food control, and crisis management. The challenges that arise during adoption include inconsistent delivery and quality, technology dependency, partnership dissatisfaction, and health and environmental issues. Based on the findings of this study, there is an opportunity to design a technology adoption model for online food delivery in Indonesia in the future. © 2023 IEEE.

关键词： Developing countries

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Adaptive Stabilization Based on Machine Learning for Column Generation 41

Adaptive Stabilization Based on Machine Learning for Column ...

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41st International Conference on Machine Learning, ICML 2024

作者： Shen, Yunzhuang Sun, Yuan Li, Xiaodong Cao, Zhiguang Eberhard, Andrew Zhang, Guangquan Australian Artificial Intelligence Institute University of Technology Sydney Australia La Trobe Business School La Trobe University Australia School of Computing Technologies Royal Melbourne Institute of Technology Australia School of Computing and Information Systems Singapore Management University Singapore

Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. However, there remains a significant gap in obtaining more accurate dual values at an earlier stage. To further narrow this gap, this paper introduces a novel approach consisting of 1) a machine learning approach for accurate prediction of optimal dual solutions and 2) an adaptive stabilization technique that effectively capitalizes on accurate predictions. On the graph coloring problem, we show that our method achieves a significantly improved convergence rate compared to traditional methods. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Identifying Completeness Issues in Unstructured Social Media Data for Business Insights: Data Usability Perspective

Identifying Completeness Issues in Unstructured Social Media...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Jamalud-Din Adnan, Kiran Goh, Hock Guan Akbar, Rehan Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia School of Computing and Information Sciences Florida International University FL United States

ISBN: (纸本)9798331530303

Social media data has become a vital source of information for businesses, offering diverse insights through various media formats. Text, in particular, holds significant potential for providing valuable information. However, the large volume of text data often exists in an unstructured form, posing challenges for extracting maximum value due to usability issues. Improving the usability of unstructured social media data is essential for organisations to gain deeper market insights and make more informed decisions. The existing literature identifies different types of usability dimensions from unstructured big data such as availability, completeness and relevance. To the best of our knowledge, existing literature lacks a comprehensive investigation of challenges for one particular usability dimension. This research aims to address this gap by conducting a systematic literature review to identify the issues of completeness dimension for unstructured social media data in the business domain. The findings of this study will assist researchers in overcoming these challenges and optimising the extraction of meaningful insights from unstructured social media data. ©2024 IEEE.

关键词： Business Intelligence Completeness issues Customer Sentiment Social Media Unstructured Data

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An indoor delivery robot based on YOLOv8 and ROS 7

An indoor delivery robot based on YOLOv8 and ROS

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2024 7th International Conference on Intelligent Manufacturing and Automation, IMA 2024

作者： Sun, Zhijin Fan, Jianan Lu, Jialin Zhang, Lei Li, Xiaoyang Deng, Baoru School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China School of Mechanical Engineering Zhuhai College of Science and Technology Zhuhai China School of Computing Zhuhai College of Science and Technology Zhuhai China

Aiming at the problems of inflexible movement, poor operation efficiency, low space utilization in the storage area and poor compatibility of placed items, intelligent route planning ability and weak intelligent obstacle avoidance ability in the current indoor delivery robot operation process, this paper takes ROS combined with YOLOv8 as the intelligent driving core, uses the A∗ algorithm to make the robot go to the target position, plan a reasonable route, take STM32 as the intelligent control core, and use the Mecanum wheel as the power take-off wheel. Based on this, an indoor delivery robot based on ROS and the McNum wheel motion scheme is designed. This paper designs the robot as a whole, designs the mechanical structure and functional modules of the robot, and simulates and analyzes the obstacle avoidance scheme and key functions of intelligent driving. Experiments have proved that the design of the delivery robot can not only achieve efficient and accurate distribution, but also reduce energy consumption to a certain extent, and provide new ideas for the intelligent transformation and development of the logistics industry. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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An Agent-based Adaptive Mechanism for Efficient Job Scheduling in Open and Large-scale Environments

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Journal of Systems Science and Systems Engineering 2021年第4期30卷 400-416页

作者： Yikun Yang Fenghui Ren Minjie Zhang School of Computing and Information Technology University of WollongongWollongongNSW2522Australia

Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to *** agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are *** new jobs and resources and unexpected events,decentralized agents can respond adaptively and ***,decentralized approaches are easy to be extended because there is no central control agent that limits the ***,decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive,due to their local views during decision *** interacting with a large number of agents,each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other *** improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments,and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling,this paper provides a new agent-based adaptive mechanism for efficient job scheduling.A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job *** proposed mechanism was developed in JADE and tested in open and large-scale *** experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.

关键词： Agent-based scheduling multi-agent system resource allocation

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Parallel Software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of Computer Science & technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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SMS-Shield: A Lightweight Approach for Smishing Detection using Machine Learning 1

SMS-Shield: A Lightweight Approach for Smishing Detection us...

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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Samad, Saleem Raja Abdul Ganesan, Pradeepa Thangam, S. Balasubramaniyan, Sundaravadivazhagan Rajiakodi, Saranya Al Kaabi, Amna Salim Rashid University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences Information Technology Department Shinas Oman Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India University of Technology and Applied Sciences-Al-Musannah College of Computing and Information Sciences Information Technology Department Al-Musannah Oman Central University of Tamil Nadu Department of Computer Science Tamil Nadu Thiruvarur India

ISBN: (纸本)9798350348637

SMS continues to be an indispensable component of contemporary digital communication. Because it is so widely available, cost-effective, reliable, and delivers information instantly. Its versatility enables its utilization for real-time chats, alerts, and verification purposes. Nevertheless, the ubiquitous nature of SMS renders it an easy prey for smishing, a deceitful form of cyberattack. Cybercriminals exploit the trust associated with SMS messages to deceive recipients. Smishing, an increasingly prevalent deceptive SMS fraud, poses a threat to both businesses and individuals. These messages include websites or phone numbers and purport to be from banks, governmental bodies, or well-known businesses. These misleading text messages lure their users into disclosing private information or clicking on links that could be harmful in many ways. Early detection of such malicious messages prevents users from falling victim to them. Various techniques were presented by researchers to accurately detect smishing attacks. Machine learning models exhibit great potential as a strategy to counter the increasingly dangerous SMS phishing attacks. Machine learning models can discern legitimate from fraudulent SMS messages by analyzing datasets. This paper introduces a novel method (SMS-SHIELD), which combines Machine Learning and Natural Language Processing techniques to enhance smishing detection. This approach yields outstanding results, with random forest and extreme gradient boosting algorithms achieving remarkable accuracy rates of 99.05% and 98.03%, coupled with impressive F1-scores of 98.95% and 97.86%, respectively. © 2024 IEEE.

关键词： Phishing

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