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8th international conference on big data, knowledge and control systems engineering, bdkcse 2023

作者： Boneva, Yordanka Institute of Information and Communication Technologies Bulgarian Academy of Sciences Department of Distributed Information and Control Systems Sofia Bulgaria

ISBN: (纸本)9798350313246

the research goal of the presented experiment was to test if the current control plan can be improved through analytical modeling of the traffic by defining a bi-level problem and dynamically simulating the existing control plan and solving the analytical model. the comparison is done by queue lengths in an urban network of crossroads. Two models were developed for this purpose - a python-based package, named PAO and a solver were used for definition and solution of a bi-level optimization model and a microsimulation model was developed to test the results in a simulation environment named Aimsun. the results showed that the bilevel optimization led to better settings for the duration of green light but it also revealed that the originally measured settings provide good performance of the investigated traffic network. © 2023 IEEE.

关键词： Python

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Using LLMs in Cyber-Physical systems for Agriculture - ZEMELA 8

Using LLMs in Cyber-Physical Systems for Agriculture - ZEMEL...

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8th international conference on big data, knowledge and control systems engineering, bdkcse 2023

作者： Stoyanov, Stanimir Kumurdjieva, Milena Tabakova-Komsalova, Veneta Doukovska, Lyubka University of Plovdiv 'Paisii Hilendarski' Plovdiv Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Intelligent Systems Department Sofia Bulgaria

ISBN: (纸本)9798350313246

this paper presents the idea of developing an advisory service using the capabilities of generative artificial intelligence and in particular of Large Language Model. the service will assess the risks for farmers when preparing projects under different programs, taking into account the Bulgarian legislation related to agriculture, as well as the requirements of the relevant program. the results of a feasibility analysis are summarized in the article. Furthermore, two architectural approaches are discussed. the service will be integrated in the platform for smart agriculture named ZEMELA. A brief overview of this platform is also given in the article. © 2023 IEEE.

关键词： Agriculture

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knowledge and data Management: the Cornerstone of Effective Organizational Strategy 8

Knowledge and Data Management: The Cornerstone of Effective ...

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8th international conference on big data, knowledge and control systems engineering, bdkcse 2023

作者： Rajic, Milena Milosavljevic, Peda Kostic, Zorana University of Niš Niš Faculty of Mechanical Engineering Department of Management in Mechanical Engineering Serbia

ISBN: (纸本)9798350313246

In the modern business landscape, organizations operate in highly complex and dynamic environments, facing constant challenges in maximizing efficiency, sustainability, and profitability. To achieve success, businesses must harness and leverage knowledge and data effectively. knowledge and data Management are important in shaping an organization's strategy. By maintaining a knowledge repository and implementing collaborative platforms, businesses can avoid duplicating efforts, reduce errors, and develop strategies with a long-term vision. the seamless transfer of knowledge from experienced employees to new hires also ensures the preservation of critical expertise, further improving an organization's competitive advantage. the effective utilization of data is crucial to optimize energy consumption, reduce costs, make processes more sustainable, and consequently minimize environmental impact. Energy-intensive industries must carefully monitor and analyse data related to energy consumption, production processes, and equipment efficiency to identify opportunities for improvement, energy management, and industrial management, serving as the base for informed decision-making and sustainable growth. this paper explores the interconnectedness of these key areas and how they can be improved through effective knowledge and data management practices. © 2023 IEEE.

关键词： Energy management

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big data Processing for Bulgarian Healthcare - Smart Cards and Some Simulating Decisions 8

Big Data Processing for Bulgarian Healthcare - Smart Cards a...

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8th international conference on big data, knowledge and control systems engineering, bdkcse 2023

作者： Boneva, Ani Ivanova, Veronika Vasilev, Plamen Ivanov, Stoyan Ivanova, Tsvetelina Institute of Information and Communication Technologies Bulgarian Academy of Sciences Department of Communication Systems and Services Sofia Bulgaria Institute of Robotics Bulgaria Academy of Sciences Department of Robotized and Mechatronics Intelligent Systems Sofia Bulgaria University of Chemical Technologies and Metallurgy Department of Industrial Automation Sofia Bulgaria

ISBN: (纸本)9798350313246

this publication presents a review of the application of large databases (big data) in medicine. the aim of the work is to provide some positive guidelines for the optimization of health care and improvement of patient care in Bulgaria. therefore, the article discusses designed applications that can find their place in various fields of medicine. Our proposed optimization is the introduction of electronic chip cards. the development assumes that each patient will be provided with a processor card carrying information about basic data related to the condition and its personalization and possible access to this data when necessary. thus, the proposed solution provides an opportunity to upgrade (for the future) and build an information system with a connection to the health fund. In addition, an early-stage project is described to integrate data from personal portable devices with the electronic health record, thereby providing information to medical personnel about the individual's health status. Portable personal devices and other non-medical equipment generate a huge, almost continuous flow of data that can be useful in making decisions regarding the treatment of patients with various health conditions. the applications described are a continuation of the team's previous developments. © 2023 IEEE.

关键词： Intelligent systems

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AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios 8

AIOps-Driven Enhancement of Log Anomaly Detection in Unsuper...

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8th international conference on big data, knowledge and control systems engineering, bdkcse 2023

作者： Dave, Daksh Sawhney, Gauransh Khut, Dhruv Nawale, Sahil Aggrawal, Pushkar Bhavathankar, Prasenjit Bits Pilani Dept. of Electrical Electronics Pilani India Sardar Patel Institute of Technology Information Technology Mumbai India Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350313246

Artificial intelligence operations (AIOps) play a pivotal role in identifying, mitigating, and analyzing anomalous system behaviors and alerts. However, the research landscape in this field remains limited, leaving significant gaps unexplored. this study introduces a novel hybrid framework through an innovative algorithm that incorporates an unsupervised strategy. this strategy integrates Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs) and uses a custom loss function to substantially enhance the effectiveness of log anomaly detection. the proposed approach encompasses the utilization of both simulated and real-world datasets, including logs from SockShop and Hadoop Distributed File System (HDFS). the experimental results are highly promising, demonstrating significant reductions in pseudo-positives. Moreover, this strategy offers notable advantages, such as the ability to process logs in their raw, unprocessed form, and the potential for further enhancements. the successful implementation of this approach showcases a remarkable reduction in anomalous logs, thus un-equivocally establishing the efficacy of the proposed methodology. Ultimately, this study makes a substantial contribution to the advancement of log anomaly detection within AIOps platforms, addressing the critical need for effective and efficient log analysis in modern and complex systems. © 2023 IEEE.

关键词： Anomaly detection

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Exploring Public Opinions Toward the Use of Generative Artificial Intelligence Chatbot in Higher Education: An Insight from Topic Modelling and Sentiment Analysis 8

Exploring Public Opinions Toward the Use of Generative Artif...

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8th international conference on big data, knowledge and control systems engineering, bdkcse 2023

作者： Sarsam, Samer Muthana Alias, Abd Aziz Mon, Chit Su Al-Samarraie, Hosam Al-Hatem, Ahmed Isam School of Strategy and Leadership Coventry University Coventry United Kingdom Sunway University Business School Sunway University Department of Business Analytics Selangor Petaling Jaya Malaysia School of Mathematical and Computer Sciences Heriot-Watt University Putrajaya Malaysia School of Design University of Leeds Leeds United Kingdom Universiti Sains Centre for Instructional Technology and Multimedia Penang Malaysia School of Computer Engineering Al-Nukhba University College Baghdad Iraq

ISBN: (纸本)9798350313246

the Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education, so this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. English tweets were collected from Twitter's free streaming Application Programming Interface (API). the Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the tweets. User sentiments were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. this study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. the findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journey. © 2023 IEEE.

关键词： Sentiment analysis

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big data and knowledge Management: How to Implement Conceptual Models in NoSQL systems? 8

Big Data and Knowledge Management: How to Implement Conceptu...

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8th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management (KDIR)

作者： Abdelhedi, Fatma Brahim, Amal Ait Atigui, Faten Zurfluh, Gilles Toulouse Capitole Univ Toulouse Inst Comp Sci Res IRIT Toulouse France CBI2 TRIMANE Paris France CEDRIC CNAM Paris France

ISBN: (纸本)9789897582035

In 2014, big data has passed the top of the Gartner Hype Cycle, proving that big data technologies and application start to be mature, becoming more realistic about how big data can be useful for organizations. NoSQL data stores are becoming widely used to handle big data;these databases operate on schema-less data model enabling users to incorporate new data into their applications without using a predefined schema. But, there is still a need for a conceptual model to define how data will be structured in the database. In this paper, we show how to store big data within NoSQL systems. For this, we use the Model Driven Architecture (MDA) that provides a framework for models automatic transformation. Starting from a conceptual model that describes a set of complex objects, we propose transformation rules formalized with QVT to generate a column-oriented NoSQL model. To ensure efficient automatic transformation, we use a logical model that limits the impacts related to technical aspects of column-oriented platforms. We provide experiments of our approach using a case study example taken from the health care domain. the results of our experiments show that the proposed logical model can be effectively implemented in different column-oriented systems independently of their specific technical details.

关键词： big data NoSQL knowledge MDA QVT Transformation

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Analysis of Semantic-based knowledge Graph Embedding Models in Uncertainty knowledge Graphs 24

Analysis of Semantic-based Knowledge Graph Embedding Models ...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Jianjing Li, Guanfeng School of Information Engineering Ningxia University Yinchuan China

ISBN: (纸本)9798350319934

Recent years have witnessed rapid growth in knowledge graph embedding (KGE). KGE aims to improve knowledge acquisition, integration and reasoning skills, but there is less research on this aspect of uncertain knowledge graphs (UKG). While some existing research on uncertain knowledge graph embedding uses human labor and domain knowledge to enhance performance, it ignores that semantic-based modeling approaches are capable of modeling knowledge graphs for multiple relational patterns, including equivalence, symmetry, antisymmetry, composition, etc. therefore, we propose some uncertain knowledge graph representation learning models based on classical semantic matching methods, and conduct experimental analysis on them. In order to ensure the fairness and scientificity of the experiments, we propose and design a unified model training framework to control variables. We use three public uncertain knowledge graph datasets and repaired the unreasonable ones. the experiment was conducted through three tasks, i.e. link prediction, confidence prediction, and relational fact ranking. We find that our proposed U ANALOGY model can combine the advantages of other models and can model the uncertainty knowledge graph well. Likewise, the proposed approach achieves the best tradeoff between performance and accuracy of uncertain knowledge graph embedding and inferring. Our dataset and code will be published at https://***/Chienking1997/AnalyzeUKGE. © 2022 IEEE.

关键词： Semantics

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HybridRTS: A Hybrid Congestion control Framework with Rule and Reinforcement Learning for Low-Latency WebRTC Live Video Streaming 24

HybridRTS: A Hybrid Congestion Control Framework with Rule a...

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作者： Zhang, Kaizhe Wang, Zhiwen Ma, Hansen Du, Haipeng Zhang, Weizhan School of Computer Science and Technology Bdke Lab China School of Continuing Education Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350319934

Web Real-Time Communication (WebRTC) technology meets real-time communication standards and is currently supported by mainstream browsers to meet low-latency live streaming requirements. the WebRTC Google Congestion control (GCC) algorithm utilizes a one-way delay gradient signal for conservative bandwidth estimation. However, this makes it sensitive to jitter, resulting in a decrease in the estimated bandwidth and low video quality. In this paper, we propose HybridRTS, a framework for congestion control aiming to strategically switch between proposed rule-based and reinforcement learning (RL) based methods. HybridRTS is evaluated via trace-driven simulations and real-world experiments, outperforming GCC with improvements in average Quality of Service (QoS) scores of 11.57% - 18.57%. Additionally, HybridRTS outperforms standalone rule-based and RL-based methods with decreasing in average latency by 11.46% - 29.66%. According to extensive experiments, HybridRTS yields solid results on various network conditions. © 2022 IEEE.

关键词： Reinforcement learning

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the 2013 CLSR-LSPI seminar on electronic identity: the global challenge - Presented at the 8th international conference on Legal, Security and Privacy issues in IT Law (LSPI) November 11-15, 2013, Tilleke & Gibbins international Ltd., Bangkok, thailand

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COMPUTER LAW & SECURITY REVIEW 2014年第2期30卷 112-125页

作者： Saxby, Steve

We are the middle of a global identity crisis. New notions of identity are made possible in the online world where people eagerly share their personal data and leave 'digital footprints'. Multiple, partial identities emerge distributed across cyberspace divorced from the physical person. the representation of personal characteristics in data sets, together with developing technologies and systems for identity management, in turn change how we are identified. Trustworthy means of electronic identification is now a key issue for business, governments and individuals in the fight against online identity crime. Yet, along with the increasing economic value of digital identity, there are also risks of identity misuse by organisations that mine large data sets for commercial purposes and in some cases by governments. data proliferation and the non-transparency of processing practices make it impossible for the individual to track and police their use. Potential risks encompass not only threats to our privacy, but also knowledge-engineering that can falsify digital profiles attributed to us with harmful consequences. this panel session will address some of the big challenges around identity in the digital age and what they mean for policy and law (its regulation and protection). Questions for discussion include: What does identity mean today? What types of legal solutions are fit for purpose to protect modem identity interests? What rights, obligations and responsibilities should be associated with our digital identities? Should identity management be regulated and who should be held liable and for what? What should be the role of private and public sectors in identity assurance schemes? What are the global drivers of identity policies? How can due process be ensured where automated technologies affect the rights and concerns of citizens? How can individuals be more empowered to control their identity data and give informed consent to its use? How are biometrics and locati

关键词： Digital identity Electronic identity Identity crime Managing online identity big data Mobile identity Automated identification Identity surveillance Biometrics

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