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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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An Environmentally complex Requirement for Safe Separation Distance between UAVs 32

An Environmentally Complex Requirement for Safe Separation D...

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32nd ieee international Requirements Engineering conference (RE)

作者： Granadeno, Pedro Antonio Alarcon Bernal, Arturo Miguel Russell Al Islam, Md Nafee Cleland-Huang, Jane Univ Notre Dame Comp Sci & Engn South Bend IN 46556 USA

ISBN: (纸本)9798350395525;9798350395518

Cyber-Physical Systems (CPS) interact closely with their surroundings. They are directly impacted by their physical and operational environment, adjacent systems, user interactions, regulatory codes, and the underlying development process. Both the requirements and design are highly dependent upon assumptions made about the surrounding world, and therefore environmental assumptions must be carefully documented, and their correctness validated as part of the iterative requirements and design process. Prior work exploring environmental assumptions has focused on projects adopting formal methods or building safety assurance cases. However, we emphasize the important role of environmental assumptions in a less formal software development process, characterized by natural language requirements, iterative design, and robust testing, where formal methods are either absent or used for only parts of the specification. In this paper, we present a preliminary case study for dynamically computing the safe minimum separation distance between two small Uncrewed Aerial Systems based on drone characteristics and environmental conditions. In contrast to prior community case studies, such as the mine pump problem, patient monitoring system, and train control system, we provide several concrete examples of environmental assumptions, and then show how they are iteratively validated at various stages of the requirements and design process, using a combination of simulations, field-collected data, and runtime monitoring.

关键词： requirements environmental assumptions small unmanned aerial systems UAV case-study

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STS-CCL: SPATIAL-TEMPORAL SYNCHRONOUS CONTEXTUAL CONTRASTIVE LEARNING FOR URBAN TRAFFIC FORECASTING 49

STS-CCL: SPATIAL-TEMPORAL SYNCHRONOUS CONTEXTUAL CONTRASTIVE...

引用

49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Lincan Yang, Kaixiang Bi, Jichao Luo, Fengji Univ New South Wales Data & Knowledge Res Grp Sydney NSW Australia South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Zhejiang Inst Ind & Informat Technol Hangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350344868;9798350344851

Efficiently capturing the complex spatiotemporal representations from large-scale traffic data with uneven data quality remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.

关键词： Spatial-temporal data mining Contrastive learning Dynamic graph neural networks Model Training Schema Urban computing

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Advanced and Secure data Sharing Scheme with Blockchain and IPFS: A Brief Review 15

Advanced and Secure Data Sharing Scheme with Blockchain and ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mukhedkar, Moresh Kote, Prajwal Zonde, Mounesha Jadhav, Om Bhasme, Vaibhav Dawande, Nitin A. D Y Patil University School of Engineering and Technology E&tc Engineering Department Pune India D Y Patil University School of Engineering and Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350370249

In the ever-evolving information age, the challenges posed by the exponential growth of data have led to innovative solutions that leverage the power of technology. This paper introduces a comprehensive data sharing platform that seamlessly integrates blockchain and InterPlanetary File System (IPFS) technologies to address the complex issues of data sharing and storage. The approach begins by establishing an alliance blockchain, employing a consensus mechanism rooted in computing power competition to maintain data integrity within the blockchain. Simultaneously, the IPFS system is harnessed for data storage, utilizing distributed storage, file splitting, and splicing techniques. The resultant data sharing platform consists of distinct modules, including blockchain, IPFS, encryption and decryption, and fast retrieval, providing end-to-end data security and accessibility. Furthermore, the paper delves into the practical implementation of this technology by examining a course sharing project that leverages IPFS and Ethereum blockchain. By utilizing IPFS as an access medium, the paper efficiently reduces storage costs and enhances storage efficiency, effectively addressing the challenge of managing extensive course data. The incorporation of Ethereum blockchain technology ensures the elimination of single points of failure and establishes a robust transaction mechanism that incentivizes course users to engage in sharing and enhances course liquidity. This innovative amalgamation of blockchain and IPFS not only resolves the data sharing conundrum but also guarantees secure, decentralized, and efficient data storage and exchange in an era defined by the relentless growth of digital information. © 2024 ieee.

关键词： blockchain encryption ethereum IPFS security

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Continual Adaptation for Unsupervised Time Series Anomaly Detection 9

Continual Adaptation for Unsupervised Time Series Anomaly De...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Yu, Ting Ding, Bo Wang, Xu College of Computer National University of Defense Technology State Key Laboratory of Complex & Critical Software Environment Changsha China

ISBN: (纸本)9798350376548

The application of time series anomaly detection has been widespread across various industrial scenarios. Although current algorithms for time series anomaly detection have shown promising results, they primarily focus on the characteristics of the data distribution within the current training set. This often leads to a lack of adaptability to changes in data distribution while retaining the ability to accommodate new data and preserve existing knowledge. Given the dynamic nature of real-world industrial production systems, the issue of continuous updating of time series anomaly detection algorithms warrants significant attention. However, existing time series anomaly detection algorithms predominantly rely on unsupervised methods, thereby resulting in sparse availability of relevant update strategies. Recognizing this challenge, this paper proposes an unsupervised anomaly detection algorithm update mechanism that incorporates continual learning methods. This approach aims to address the issue in time series anomaly detection algorithms stemming from the absence of label guidance, which leads to a lack of continuous updating capability in models. The method employs a strategy of retaining key information about the past data's feature distribution to ensure memory of past information, thus fulfilling the need for updating and adapting to new data features with minimal storage overhead. Compared to other potential improvement algorithms for continual updating, our approach achieves comparable performance with significantly reduced overhead. In particular, it addresses the issue in the current domain of unsupervised time series anomaly detection, where the lack of labeled data results in a deficiency of continuous updating methods. © 2024 ieee.

关键词： Time series

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A Model for Evaluating the Effectiveness of News Dissemination under the Combination of Big data and Artificial Intelligence 3

A Model for Evaluating the Effectiveness of News Disseminati...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Liu, Chenjun Xu, Yan College of Liberal Arts Journalism and Communication Ocean University of China Shandong Qingdao266000 China School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang414006 China

ISBN: (纸本)9798331519032

In today's era of information explosion, with the rapid development of big data technology and artificial intelligence, the form of news dissemination and the assessment mode of dissemination effect are undergoing profound changes. The traditional method of evaluating the effect of news dissemination relies on limited indicators and a single data source, which is difficult to accurately reflect the multidimensional response of the audience and its dissemination path in modern news dissemination. In contrast, this paper proposes a new news communication effect assessment model by combining big data analysis and artificial intelligence algorithms, which can effectively process massive data, deeply explore the audience's behavioral characteristics, emotional changes and information dissemination path, and then provide a more accurate assessment of the communication effect. Although AI reduces the demand for labor in conventional occupations, it increases the demand for labor in non-conventional occupations, which mainly comes from the increase in the demand for non-conventional cognitive occupations such as management and technology. The job creation effect of AI is not only reflected in the connotation, i.e., the increase in the number of demand for the labor force of the already existing non-conventional occupations. The results of the study show that the deep combination of big data and artificial intelligence not only significantly improves the depth and breadth of the analysis of news communication effects, but also provides a more intelligent and automated solution for the future evaluation of communication effects. © 2024 ieee.

关键词： artificial intelligence Big data effectiveness evaluation model building news dissemination

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data Integrity and Cost Efficiency in Smart Grids: Block-chain Integration Approach 1

Data Integrity and Cost Efficiency in Smart Grids: Block-cha...

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1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Srinivas, Chodagam Vigna Priya, D.M. Sagar, M.V.S.P. Nagendra Babu, Kakumanu V V Senthilkumar, A. Pavan Kumar, A.V. Madanapalle Institute of Technology & Science Department of Eee Andhra Pradesh India Andhra Pradesh Visakhapatnam India Department of Eee Andhra Pradesh Visakhapatnam India Bonam Venkata Chalamayya Institute of Technology Department of CSE-AIDS & Aim Andhra Pradesh India M.A.M. School of Engineering Department of Eee Trichirappalli India

ISBN: (纸本)9798350367720

This paper presents a novel approach to minimize power generation costs by harnessing the potential of an adaptive BAT algorithm synergistically integrated with block-chain technology, particularly Proof of Work (PoW). The focus is on optimizing power generation in complex electrical grids, exemplified through experimentation on the ieee 14 bus system and the ieee 30 bus system. By leveraging the adaptability of the BAT algorithm and the decentralized ledger capabilities of block-chain technology, the proposed methodology offers a robust solution to the power generation cost minimization conundrum. Through extensive testing and validation, the efficacy of the approach is demonstrated, showcasing significant improvements over existing optimization techniques. Furthermore, the comparative analysis reveals that the proposed approach consistently outperforms conventional methods in terms of efficiency and effectiveness. The results underscore the transformative potential of integrating advanced optimization algorithms with block-chain technology in addressing critical challenges within the energy sector. © 2024 ieee.

关键词： and ieee 30 Bus System BAT Algorithm Block-chain Technology ieee 14 Bus System Power Generation Cost Minimization Proof of Work (PoW)

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Smart Exact String Matching Algorithm Specifically for DNA Sequencing 2

Smart Exact String Matching Algorithm Specifically for DNA S...

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2nd international conference on Cyber Resilience, ICCR 2024

作者： Rehman, Shujah Ur Safdar, Sania Butt, Yasir Aawais Ullah Khan Sumbal, Muhamad Intisar Ali, Atif Khan, Muhammad Adnan Asif, Muhammad Abasyn University Islamabad Pakistan National University Of Science & Technology Pakistan Nutech University Islamabad Pakistan Multimedia University Cyberj 63100 Malaysia School Of Computing Skyline University College Sharjah United Arab Emirates Riphah School Of Computing And Innovation Riphah International University Lahore Pakistan University Of Education Lahore Pakistan

ISBN: (纸本)9798350394962

Various users with different data needs must explore big data, which refers to large data repositories continually being added to the internet in the modern digital landscape. However, when looking for specific data, typical users frequently encounter difficulties such as lengthy search times and complex computational procedures, which can be uncomfortable and time-consuming. Additionally, obtaining the needed information from these vast data warehouses depends critically on the quality of the search results. This research aims to create an effective solution using an exact string-searching algorithm in response to the recognition of this frequent and constant requirement among regular users. Compared to other precise string searching methods, this algorithm's simplicity promises to increase effectiveness and cut down on processing time, especially in terms of complexity at its worst. A comparative analysis is conducted between the proposed algorithm and the Naive Approach, Boyer Moore Hoorspool, Knuth Morris Pratt (KMP), and Back and Forth Matching Algorithms to evaluate the method's performance fully. © 2024 ieee.

关键词： Machine learning

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Design of data-Driven Learning Path Based on Knowledge Graph and Tracing Model 25

Design of Data-Driven Learning Path Based on Knowledge Graph...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on data Science and Systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Juli Xia, Ruheng Wang, Quan Xidian University Academy of Advanced Interdisciplinary Research Xian China College of Computer Science and Technology Xidian University Xian China

ISBN: (纸本)9798350330014

With the explosive growth of online courses, per-sonalized learning path recommendations have gained much attention in E-learning area. Studies aim to provide customized guidance that cater to the needs of each learner. However, generating an appropriate learning path remains a challenge due to the complex cognitive relationships among learning items and learners' different abilities. Existing methods often overlook the impacts of cognitive structure and mastery level on the outcomes of customized instructions, leading to cognitive confusion problem. To address this issue, we propose a model named data-Driven Learning Path based on Knowledge Graph and Tracing Model (LP-KAT), which combines knowledge graph (KG) and knowledge tracing model (KT) to store the relationships among learning items and reflect learners' abilities through the abundant data. The guidance is separated into concept and practice identification;besides, through reinforcement learning, our model can suggest a sequence of practices for different learners. Specifically, we first utilize a knowledge tracing model to track learners' evolving mastery levels at each learning step. Then we design a concept selector that employs knowledge graph navigation algorithm and reinforcement learning to determine which concept to learn next. Finally, we utilize a practice selector to identify practice question based on the relationship between mastery level and difficulty of practice. We evaluate the proposed model against KNN, GRU4Rec, MCS and DQN using a real-world dataset, and the results show an improvement of more than 30 % in the effectiveness of concept mastery level and nearly 20% better than GRU4Rec across various learning path lengths. © 2023 ieee.

关键词： Reinforcement learning

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Exploring the Drivers of University Students' Engagement of Online Learning Platforms Among Non-Native Arabic Speakers: A Case of Thailand's Southern Border Provinces

Exploring the Drivers of University Students' Engagement of ...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tawalbeh, Anas Elsharqawy, Moustafa Soliman, Mohamed School of Computing Universiti Utara Malaysia Sintok Malaysia Faculty of Education Department of Arabic Language Education Pattani Thailand Islamic Business Innovation Faculty of Islamic Sciences Pattani Thailand

ISBN: (纸本)9798350372229

The current study aims to explore university students' engagement in the use of online learning with a theoretical foundation beyond the pandemic. This technology was recently introduced to higher education, but few studies have examined its effects. A combination of the technology acceptance model (TAM) and social cognitive theory (SCT) is used in this study to examine the factors that impact university students' engagement and their academic performance in using online learning to teach Arabic language among non-native speakers. A questionnaire will be given to university students to acquire data for the suggested model. The effect of students' engagement using the online learning platform will be investigated using a hybrid approach consisting of partial least squares structural equation modelling (PLS-SEM) and an artificial neural network (ANN) to capture linear and nonlinear relationships within a non-compensatory model. The study will be interesting for scholars, policymakers, and practitioners from the higher education context. © 2024 ieee.

关键词： ANN higher education Online learning PLS-SEM social cognitive theory students' engagement TAM Thailand universities

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Active Learning Framework for Anomaly Detection in Astronomical data 15

Active Learning Framework for Anomaly Detection in Astronomi...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dixit, Ghadiyaram Hanumath Srinivasa Parmar, Jitendra Verma, Atul Kumar Mishra, Raghavendra Behera, Gopal School of Computing Science & AI VIT Bhopal University Madhya Pradesh Kothri Kalan India Dept. of Computer Sc. & Engineering Manipal University Jaipur Rajasthan Jaipur India Dept. of Computer Sc. & Engineering Government College of Engineering Kalahandi Odisha Kalahandi India

ISBN: (纸本)9798350370249

When properly analyzed, the large astronomical datasets likely yield anomalies, which is meant to be a new phenomenon in the dataset. However, the challenge lies in the scale of the datasets, making it complex for humans to find anomalies by themselves using traditional techniques. The most reliable method for detecting noteworthy irregularities is through anomaly detection algorithms. Even though machine learning proves to be a valuable tool in this context, it faces difficulty in finding out the exact anomalies which are needed to confidently conclude the nature of a point in the data;as such, many broad points in output may not be extremely necessary for getting into a direct conclusion. This is where active learning comes into play;active learning combines human techniques with machine learning's algorithm, strategically selecting specific data for expert labelling to minimize the workload for maximum efficiency. Here, we present a novel anomaly detection framework called Anomaly detection. In astronomical data, which employs an active learning approach to give an output of probability of finding an anomaly. Furthermore, anomaly detection in astronomical data adaptability allows for integrating new feature extraction methods, anomaly detection algorithms, and alternative active learning techniques. © 2024 ieee.

关键词： Active learning

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