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Meaning of the Clusters on Dimensionality Reduction by Word Clustering 12

Meaning of the Clusters on Dimensionality Reduction by Word ...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Deguchi, Toshinori Seo, Sinsuke Ishii, Naohiro Dept. of Electrical and Computer Engineering Gifu College Gifu Japan Interdisciplinary Technology Development Gifu College Gifu Japan Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not *** proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the *** this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset. © 2022 IEEE.

关键词： Classification (of information)

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Intelligent Resource Allocation in Power Grids: Leveraging Variable Neighborhood Search for Optimal Switch Allocation

Intelligent Resource Allocation in Power Grids: Leveraging V...

引用

IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Henrique O. Caetano Matheus de S. S. Fogliatto N. Luiz Desuó Benvindo R. Pereira J Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Within the paradigm of Evolving and Adaptive Intelligent systems, the Electrical Power System (EPS) represents a Critical Infrastructure demanding robust reliability metrics. This study pioneers the application of Variable Neighborhood Search (VNS) heuristic to the Optimal Switch Allocation (OSA) problem in the EPS. Adapting neighborhood structures and local search methods, we consider the joint allocation of Manual Switches and Remote-Controlled switches, addressing interdependencies. Results highlight VNS efficacy in navigating OSA challenges, showcasing its adaptability in an evolving system. Comparative analyses endorse VNS as a valuable tool for addressing EPS reliability within the dynamic landscape of evolving and adaptive intelligent systems.

关键词： Adaptive systems Uncertainty Switches Search problems Robustness Power grids Critical infrastructure

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Quantifying uncertainty in Bayesian Networks structural learning

Quantifying uncertainty in Bayesian Networks structural lear...

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IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Vitor O. Barth Henrique O. Caetano Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships through Joint Probability Distributions. Most research relies on score-based or conditional test methods for model selection. However, when using real-world data, it can be challenging to identify whether the learned DAG represents the underlying relations inherent in the limited datasets, particularly when evaluating data obtained from multiple independent sources. This study presents a methodology to assess the credible interval for both the existence and direction of each edge within Bayesian networks derived from data. Furthermore, it explores the fusion of models acquired from distinct and independent datasets. This approach enables the Bayesian learning of Bayesian Networks (BNs) from data by treating the uncertainty associated with the existence and orientation of each edge as a random variable. By evaluating the probability of the orientation of each edge, it is possible to suggest the existence of a potential latent variable within the dataset. If an edge exhibits equiprobable directions and is verified to exist, it becomes a plausible hypothesis for a latent variable. The Fast Causal Algorithm, originally introduced by [1], is the foundation of this approach. Finally, by employing a maximum a posteriori estimation, the most prominent edges and their respective orientations are identified and employed to create the leading DAG. We present our findings in simulated datasets with different length sizes. By comparing the structure of the learned DAGs with existing structures and evaluating the inference capabilities of the final BN, we establish that our approach achieves results comparable to the most recent studies in the field, while offering insights into the model’s reliability and improving the use of the data.

关键词： Maximum a posteriori estimation Uncertainty Scalability Data models Probability distribution Bayes methods Random variables

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Development of an Ontology for Bulgarian Soil Types

Development of an Ontology for Bulgarian Soil Types

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Automatics and Informatics (ICAI), International Conference

作者： Asya Stoyanova-Doycheva Sebiha Madanska Sheban Bilyanov Dept. of Computer Systems University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria IICT Bulgarian Academy of Sciences Sofia Bulgaria Dept. of Residential Buildings University of Architecture Civil Engineering and Geodesy Sofia Bulgaria

The article presents the development of an ontology for the Taxonomic List of Soils in Bulgaria (according to the FAO World System). In addition to soil types, the ontology includes knowledge about the different agro-climatic regions in Bulgaria that are suitable for cultivating various agricultural crops. The ontology is part of the knowledge base of the ZEMELA platform for smart agriculture.

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BC4ECO - Using Visual Tools for a Shared Understanding and Pedagogical Approach across an Interdisciplinary Consortium

BC4ECO - Using Visual Tools for a Shared Understanding and P...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Andy Peruccon Roisin Lyons Amalia de Gotzen Tiziana Margaria Luca Simeone dept. of Architecture Design and Media Technology Aalborg University Copenhagen Denmark Kemmy Business School University of Limerick Limerick Ireland Dept. of Computer Science and Information Systems University of Limerick Limerick Ireland

Effective communication is crucial when working on complex or wicked problems in interdisciplinary, international teams. Developing a shared understanding of critical concepts and aligning knowledge, focus, and resources can be problematic when stakeholders speak different languages, span several disciplines and work within different national or industry-based norms. Using visual tools to represent critical concepts and their relations, and map out our strategic goals has greatly helped combat such issues. This paper recounts the use and impact of adopting visual tools within the transnational and interdisciplinary collaborative environment in the BC4ECO project's international consortium. The Miro Board and the canvas approach greatly helped represent, visualise, and thus concretise the structure and contents of our collaboration, which takes place within a 3 years ERASMUS+ framework that builds and delivers Summer School, MOOCS as well as teacher and multiplier events about harnessing the potential of Distributed Ledger Technologies (DLTs) for environmental sustainability. As the materials and MOOCS are expected to become Open Education Resources and have a wide impact, it is particularly crucial to get the contents as well as the pedagogy right, in spite of the team's differences in almost any dimension of background and culture.

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Stealthy Attack Detection of controlled ramp meters in freeway networks

Stealthy Attack Detection of controlled ramp meters in freew...

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IEEE Conference on Decision and Control

作者： Charalambos Menelaou Kangkang Zhang Stelios Timotheou Christos G. Panayiotou Thomas Parisini Dept. of Electrical and Computer Engineering KIOS CoE University of Cyprus Dept. of Electrical and Electronic Engineering Imperial College London London U.K. Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The increasing reliance on intelligent transportation systems (ITS) for traffic management has simultaneously heightened the potential for cybersecurity threats. Malicious cyber attacks on such systems can lead to operational inefficiencies, heightened congestion, and compromised safety. This work introduces a stealthy integrity attack tailored for ramp metered freeway systems. The attack model is conceptualized as a closed-loop dynamical system, formulated as an optimization problem based on the Cell Transmission Model. This work further proposes a distributed detection mechanism designed to identify attack residuals, which, by incorporating an adaptive threshold scheme, can detect the presence of an attack. To demonstrate the efficacy of our detection scheme, we present a scenario illustrating its application in freeway road networks.

关键词： Meters Adaptation models Roads Traffic control Safety Dynamical systems Cyberattack Optimization Intelligent transportation systems Resilience

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Navigating Green Transportation in Urban Agglomerations: A Refined Approach for Carbon Emissions Assessment 24

Navigating Green Transportation in Urban Agglomerations: A R...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Boyou Wang, Yuanqing Abolarin, Emmanuel Zhang, Shuangyan Liu, Xihao Guo, Hanlin Ren, Baolong Dept. of Built Environment Bartlett School of Planning Univ. College London London United Kingdom Dept. of Transportation Engineering College of Transportation Engineering Chang’an Univ. Xi’an China Dept. of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science Univ. of Michigan–Dearborn Dearborn United States School of Civil and Transportation Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China Sino-French Engineer School Beihang Univ. Beijing China Dept. of Instrument Science and Technology College of Mechanical Engineering Nanjing Univ. of Science and Technology Nanjing China Sino-Danish College Univ. of Chinese Academy of Sciences Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon footprints and eco-friendly transportation evolution is essential for sustainability. However, research in this domain is limited. This research refines emissions estimation methods, introducing models tailored for urban agglomerations. An evaluation framework for green transportation detailing a computational method for future policies is proposed. The research highlights primary transportation structures and energy consumption. Using a "bottom-up" approach, carbon estimation models for inter- and intra-city transportation are drafted. Assessment indicators are redefined by merging the DPSIR model with urban metrics, forming five main modules with 19 indicators. Using the Regression Random Forest method and focusing on the Yangtze River Delta in China, the link between annual emissions and these indicators is examined, providing a theoretical analysis approach for general cases. © ASCE.

关键词： Carbon sequestration

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Dynamic Adaptation Gain Design and Tuning for Threat Discrimination

Dynamic Adaptation Gain Design and Tuning for Threat Discrim...

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IEEE Conference on Decision and Control

作者： Kangkang Zhang Kaiwen Chen Marios M. Polycarpou Thomas Parisini Dept. of Electrical and Electronic Engineering Imperial College London London U.K. Dept. of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat type. Typically, stealthy attacks have only weak effects easily obscured by disturbances on the system outputs. To solve this problem, a parameter adaptation algorithm based on a newly designed dynamic adaptive gain generator is proposed, aiming at improving the sensitivity of the adaptive threat discrimination scheme to potential threats. Only the strictly positive real condition of the proposed gain generator sufficiently ensures the stability of the adaptive observer error system. A moment-matching method is then developed to determine the proper parameters of the gain generator, allowing for the improvement of the sensitivity of the threat discriminators. A numerical example to demonstrate the effectiveness of the proposed methodology is presented.

关键词： Fault diagnosis Sensitivity Adaptive systems Heuristic algorithms Observers Cyber-physical systems Generators Cyberattack Tuning Numerical stability

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Modified ARQ-based cooperative relaying scheme for a delay tolerant network

Modified ARQ-based cooperative relaying scheme for a delay t...

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2021

作者： Otaru, Musa Adinoyi, Abdulkareem Ajiya, Mohammed Aljlayl, Mohammed Yanikomeroglu, Halim Bayero University Kano Department of Electrical Engineering Nigeria Technology Strategy Architecture Saudi Telecommunications Company Saudi Arabia Carleton University Dept. of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665412629

We use fixed relays deployed by network operators to reduce re-transmission (thereby reducing network power requirements) in addition to providing excellent end-to-end error performance in a revisit to automatic repeat request-based cooperative relaying. The new relaying scheme facilitates the creation of a connectivity suitable for delay tolerant internet-of-things (IoT)-type services. For IoT devices, link reliability and power efficiency are a major system design consideration. The proposed scheme operates in the following fashion. The source (e.g., a sensor) transmits for a certain time window. During this period, the source and the relays do not require per transmission acknowledgment (ACK) from the destination. At the end of a transmission window, the destination sends a 'group' ACK. If negative, relays are invited to help. Depending on the cooperation strategy, each relay transmits in either one transmission time slot or a few time slots, using a suitably chosen higher-order modulation constellation. If no error occurs, the source continues its transmission. Furthermore, we devise a novel strategy for selecting the best relay for this new way of exploiting the benefits of multiple relays in a network. In the process, we reclaim the bandwidth expansion that comes with multi-relay system but still maintaining E2E bit error rate that is superior to that of a single relay cooperation. Numerical results that reveal the benefits of the new and modified ARQ cooperative relaying scheme are presented. © 2021 IEEE.

关键词： Energy efficiency

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