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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Liu, Lian Xu, Zhaohui He, Yintao Wang, Ying Li, Huawei Li, Xiaowei Han, Yinhe Cics Institute of Computing Technology Chinese Academy of Sciences China Sklp Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Zhongguancun National Laboratory China School of Information Science and Technology ShanghaiTech University China Shanghai Innovation Center for Processor Technologies China

ISBN: (纸本)9798400706011

Quantization is one of the most hardware-efficient ways to reduce inference costs for deep neural network (DNN) models. Nevertheless, with the continuous increase of DNN model sizes (240× in two years) and the emergence of large language models, existing static quantization methods fail to utilize the sparsity and redundancy of models sufficiently. Motivated by the pervasive dynamism in data tensors across DNN models, we propose a dynamic precision quantization algorithm to further reduce computational costs beyond statically quantized DNN models. Furthermore, we find that existing precision-flexible accelerators cannot support the DNN models with dynamic precision. To this end, we design a novel accelerator, Drift, and achieve online scheduling to efficiently support dynamic precision execution. We conduct experiments with various DNN models, including CNN-based and Transformer-based models. Evaluation results show that Drift achieves 2.85× speedup and 3.12× energy saving compared to existing precision-flexible accelerators with statically quantized models. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep neural networks

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Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

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Navigating Learning Pathways: A Multifaceted Approach to Understanding Student Engagement and Progress in Online Programming Courses 12

Navigating Learning Pathways: A Multifaceted Approach to Und...

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12th International Conference on information and Education technology, ICIET 2024

作者： Wang, Xiaonan Su, Yancong Sun, Yi Shigyo, Kento Ohtsuki, Kazuhiro Kiyomitsu, Hidenari Graduate School Of Intercultural Studies Kobe University Kobe Japan School Of Design And Art Xiamen University Of Technology Xiamen China Graduate School Of Information Technology Kobe Institute Of Computing Kobe Japan Hong Kong University Of Science And Technology Academy Of Interdisciplinary Studies Hong Kong Hong Kong

ISBN: (纸本)9798350371772

In an era when online education is becoming increasingly prevalent, and many institutions are transitioning onto online platforms, it has become crucial to understand online learning behaviors. The user data kept by online learning platforms has supported relevant research that has made great strides in analyzing both engagement and dropout rates in online courses. In online programming courses, learners struggle to understand the code and even code by themselves, while instructors barely have access to check everyone's code and give advice in real-time. It becomes critical for instructors to quickly grasp how learners are progressing and encountering difficulties in their online programming learning. This study combines learners' programming activity data, click stream data from video learning, and textual data such as accompanying notes and questions. A visual representation that combines multiple learning data is proposed, and learners' Learning Trajectories Map in an online programming course is created from experimentally collected data. The results show a strong correlation between learners' programming activities and their engagement with the learning material. In addition, by comparing the learners' learning trajectories, it was possible to observe their different learning strategies, which provided more detailed clues for studying behavioral patterns and learner characteristics in online learning. This study introduces a new tool for educational evaluation, provides a more comprehensive perspective, and addresses the limitations of relying only on a single indicator for evaluation. It is of great significance to educational technology and computer-assisted education. © 2024 IEEE.

关键词： Data visualization

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Fog computing Security Challenges and Open Issues: A Short Survey 7

Fog Computing Security Challenges and Open Issues: A Short S...

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7th IET Smart Cities Symposium, SCS 2023

作者： Altamimi, Shahad Al-Haija, Qasem Abu Al-Fayoumi, Mustafa Dept. of Cybersecurity King Hussein School of Computing Sciences Prince Sumaya University for Technology Jordan Dept. of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology Jordan

ISBN: (纸本)9781839539831

Fog computing (FC) is viewed as an advancement of Cloud computing (CC), with different properties such as location sensitivity, wireless connection, and geographical accessibility to produce new security concerns and issues in cloud security that have not been thoroughly investigated. It also extends CC to forward this message to the network's edge. It defines a decentralized processing model architecture between data-generating hardware and the cloud. FC systems can generate massive amounts of data. Also, it provides CC toward the network's edge. FC technology already poses a threat to data and service privacy and security. Due to its geo-distribution on a grand scale, mobility, and heterogeneity, the current privacy and security mechanisms of CC cannot apply automatically to FC. This paper will discuss and concentrate on the security challenges facing FC and thus will compare surveys focusing on security challenges. © The Institution of Engineering & technology 2023.

关键词： Fog computing

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The Role of Exploratory Data Analysis and Pre-processing in the Machine Learning Predictive Model for Heart Disease 2

The Role of Exploratory Data Analysis and Pre-processing in ...

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Mahalakshmi, K. Sujatha, P. Vels Institute of Science Technology & Advanced Studies School of Computing Sciences Chennai India Women's Christian College Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai India

ISBN: (纸本)9798350315905

The heart is the organ which pumps blood to the human body. There is an important need for early disease prediction systems for heart related problems. The disease prediction system supports the doctors to diagnose critical diseases as early as possible. The disease prediction system is built using field knowledge of the healthcare profession and machine learning (ML), artificial intelligence. This research paper aims to explore the role of Exploratory Data Analysis [EDA] and pre-processing of heart disease (HD) data for the prediction of HD. This research explores three ML classifiers, namely Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT) with missing value imputers, feature scaling techniques, data analytics, and visualization tools using four benchmark HD datasets from the UCI repository. Imputation methods mean, median, most frequent, constant, KNN imputer and iterative imputer with scaling of features were analysed with the help of three classifiers RF, SVM and DT. Random Forest with an iterative imputer achieved 86.41% of accuracy. © 2023 IEEE.

关键词： Diseases

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The influence of culture on creativity in ideation: a review

The influence of culture on creativity in ideation: a review

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2024 International Design Society Conference, Design 2024

作者： Gong, Zhengya Gonçalves, Milene Nanjappan, Vijayakumar Georgiev, Georgi V. Center for Ubiquitous Computing University of Oulu Finland Department of Design Organisation and Strategy Faculty of Industrial Design Engineering Delft University of Technology Netherlands School of Computer Science and Information Technology University College Cork Ireland

The relationship between culture and creativity has sparked the interest of researchers for decades. Although researchers have attempted to establish a connection between culture and creativity, the precise relationship between the two remains ambiguous. The current paper examined extant literature on the subject matter and synthesized the relations between culture and creativity in ideation over the past twenty years. The present study expounds upon the utilized samples, measures implemented to assess creativity and culture, and the study results. © 2024 Proceedings of the Design Society. All rights reserved.

关键词： Behavioral research

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Career Track Based Capstone Course Design for information Systems Curriculum 14

Career Track Based Capstone Course Design for Information Sy...

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14th IEEE Global Engineering Education Conference, EDUCON 2023

作者： Gottipati, Swapna Shim, Kyong Jin Datta, Subhajit School of Computing and Information Systems Singapore Management University Singapore Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

The aim of technical education is to design a career-based curriculum that imparts a set of skills to students and enable them to contribute to a nation's economic progress. With their multifarious uses, information systems (IS) lie at the heart of modern economies. IS curricula offer several tracks for students to pursue career paths based on their interests and opportunities. To offer students an opportunity to apply the knowledge they glean from classroom instructions on real-world problems, capstone projects are essential elements in most, if not all, IS programs. As IS programs increasingly offer a wider range of career paths for students, the design and delivery of such capstone projects present a set of unique challenges. In this paper, we examine the set of challenges and share our experience from designing and delivering a career track-based capstone course for the first time at a large university. Feedback from students and faculty supervisors indicates that the course was able to meet its learning objectives to a notable extent and offered important pointers to future improvements in the course design. © 2023 IEEE.

关键词： information systems

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Large-Scale Network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah Md.Siraj Bander Ali Saleh Al-rimy Sultan Noman Qasem Tawfik Al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious *** threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware *** security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying *** can lead to missed attacks or an abundance of false alarms,impacting overall security *** address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-*** novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and ***-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud *** evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark *** results demonstrate that ACIDS-PELT outperf

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

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A connectivity index based on adjacent vertices in cubic fuzzy graph with an application

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11025-11040页

作者： Guan, Hao Sadati, Seyed Hossein Talebi, Ali Asghar Shafi, Jana Khan, Aysha Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Duyun China Department of Mathematics University of Mazandaran Babolsar Iran Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Wadi Alddawasir Saudi Arabia Department of Mathematics Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank's data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index. © 2024-IOS Press. All rights reserved.

关键词： Graph theory

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