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Compound splitting and lexical unit recombination for improved performance of a speech recognition system for German parliamentary speeches 6

Compound splitting and lexical unit recombination for improv...

引用

6th International Conference on Spoken Language Processing, ICSLP 2000

作者： Larson, Martha Willett, Daniel Köhler, Joachim Rigoll, Gerhard IMK: Institute for Media Communication GMD German National Research Institute for Information Technology Sankt Augustin Germany Department of Computer Science Faculty of Electrical Engineering Duisburg University Duisburg Germany

ISBN: (纸本)7801501144

This paper proposes a novel combined compound splitting and phrase recombination method that optimizes the composition of the speech recognition lexicon for a given domain. Data-driven compound word splitting is followed by iterative recombination of high frequency combinations. Language model perplexity and size are the criteria used to identify a balance between compound decomposition, which reduces OOV, and lexical unit recombination, which packs additional context into a fixed-size vocabulary. The method provides a basis for lexicon design for a LVCSR system on the domain of German parliamentary speeches that is to be used as the foundation of a spoken document information retrieval system. The approach achieves a 35% reduction in OOV without a prohibitively large sacrifice in recognition performance.

关键词： Speech recognition

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

Exploring the Impact of Random Distribution Choices on Parti...

引用

27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Studying the Effects of Prolonged Thermal Stress Aiming to Induce Artificial Aging on DRAM Retention-Based Physical Unclonable Functions

Studying the Effects of Prolonged Thermal Stress Aiming to I...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Anagnostopoulos, Nikolaos Athanasios Mexis, Nico Fan, Yufan Bartsch, Stephanie Senjuty Schaier, Tanja Katzenbeisser, Stefan Arul, Tolga Technical University of Darmstadt Department of Electrical Engineering and Information Technology Darmstadt64283 Germany University of Passau Faculty of Computer Science and Mathematics Passau94032 Germany Technical University of Darmstadt Department of Computer Science Darmstadt64289 Germany

ISBN: (纸本)9798350324136

This work studies the effects of thermal stress on Dynamic Random-Access Memory (DRAM) retention-based Physical Unclonable Functions (PUFs) based on Commercial Off-The-Shelf (COTS) Single-Board computer (SBC) modules. Our results show that prolonged thermal stress, in general, leads to a decrease in the entropy of the responses of this PUF (as the number of bit flips is reduced) and a loss of its reliability (as responses taken before and after the stress has been applied to this PUF do not adequately match each other). Nevertheless, we note that as time passes after we stop applying thermal stress to the relevant PUF devices, their responses start to resemble more and more those that they produced before thermal stress was applied to them. As prolonged thermal stress has been utilised in order to induce artificial aging on electronics, our work not only studies the potential effects of aging on such electronics as DRAMs in general and DRAM PUFs in particular, but also sheds some doubt on the ability of this method to induce permanent effects that would truly resemble the effects of aging. © 2024 IEEE.

关键词： DRAM PUF DRAM retention-based PUF Physical Unclonable Function (PUF) Physical Unique Function silicon aging thermal stress transistor aging

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Energy Consumption Analysis Based on Energy Efficiency Approach: A Case of Suburban Area 9

Energy Consumption Analysis Based on Energy Efficiency Appro...

引用

9th International Unimas Stem engineering Conference, ENCON 2016

作者： Rahman, K.A. Leman, A.M. Faris Mubin, M. Yusof, M.Z.M. Hariri, Azian Salleh, M.N.M. Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

Sufficient data about electricity consumption over large periods of time was accumulated and analysed in order to develop appropriate electricity-saving measures. An important first step was to analyse and identify electrical appliances that had energy saving potential. Different behavioural consumption profiles were analysed using information from two sources: 1) technical data about electricity consumption (electricity bill) and 2) data about household electrical appliance usage and consumer awareness obtained from a questionnaire survey. The questionnaire consisted of four sections of questions which concerned residents' backgrounds, residential information, consumers' awareness about energy efficiency (EE) and the energy usage of each house. The results showed that the electricity consumption profile of an individual household could be most related to the EE approach. It indicated that consumers' behaviour (awareness and practice) resulted in one of the important factors related to high electricity consumption. Another important conclusion of our questionnaire-based analysis was that the implementation of the EE approach could help the consumer to reduce their monthly energy consumption. © The Authors, published by EDP Sciences, 2017.

关键词： Energy efficiency

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A Dual Broadband Antenna for V2X, LTE, and 5G Vehicular Communication Systems 7

A Dual Broadband Antenna for V2X, LTE, and 5G Vehicular Comm...

引用

7th IEEE International Conference on Electronic information and Communication technology, ICEICT 2024

作者： Hu, Yiting Feng, Botao Chung, Kwok L. Ding, Xiao Gu, Wenzhe Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

In this paper, we present a novel dual broadband antenna tailored for vehicular applications. The antenna design incorporates circular and square patches, metal cylinders, and conductive vias. Our simulated results de... 详细信息

ISBN: (纸本)9798350384437

关键词： Advanced driver assistance systems

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An XML approach to semantically extract data from HTML tables

引用

16th International Conference on Database and Expert Systems Applications, DEXA 2005

作者： Liu, Jixue Ao, Zhuoyun Park, Ho-Hyun Chen, Yongfeng School of Computer and Information Science University of South Australia Australia School of Electrical and Electronics Engineering Chung-Ang University Korea Republic of Faculty of Management Xian University of Architecture and Technology China

ISBN: (纸本)9783540285663

Data intensive information is often published on the internet in the format of HTML tables. Extracting some of the information that is of users’ interest from the internet, especially when large number of web pages need to be accessed, is time consuming. To automate the processes of information extraction, this paper proposes an XML way of semantically analyzing HTML tables for the data od interest. It firstly introduces a mini language in XML syntax for specifying ontologies that represent the data of interest. Then it defines algorithms that parse HTML tables to a specially defined type of XML trees. The XML trees are then compared with the ontologies to semantically analyze and locate the part of table or nested tables that have the interesting data. Finally, interesting data, once identified, is output as XML documents. © Springer-Verlag Berlin Heidelberg 2005.

关键词： XML

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Maximum margin clustering on evolutionary data 12

Maximum margin clustering on evolutionary data

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21st ACM International Conference on information and Knowledge Management, CIKM 2012

作者： Fan, Xuhui Zhu, Lin Cao, Longbing Cui, Xia Ong, Yew-Soon Advanced Analytics Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China School of Computer Engineering Nanyang Technological University Signapore Singapore

ISBN: (纸本)9781450311564

Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data. © 2012 ACM.

关键词： Clustering algorithms

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Implementation of Industrial Automation Sustainable Training in Savannakhet University, Laos 8

Implementation of Industrial Automation Sustainable Training...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Georgieva, Tsvetelina Bencheva, Nina Daskalov, Plamen Yahoui, Hamed Khemmarath, Sitha Norasane, Khonesavanh Chanthanouvong, Lamnguen Leaunglith, Youlananh Keovorabouth, Vinai University of Ruse Angel Kanchev Automation and Mechatronics Department Ruse Bulgaria University of Ruse Angel Kanchev Telecommunications Department Ruse Bulgaria Automation &processing Claude Bernard University Lyon 1 Department of Mechanical Engineering Lyon France Savannakhet University Faculty of Engineering Savannakhet Laos Savannakhet University Faculty of Information Technology Technology Computer Network Dept. Savannakhet Laos Savannakhet University Faculty of Information Technology Savannakhet Laos

ISBN: (纸本)9798350396546

The paper presents part of the work fulfilled under the Asean Factori 4.0 Erasmus+ project focused on the implementation of industrial automation in the education in 6 universities from 3 countries in South-East Asia: Thailand, Laos and Cambodia. The implementation of the project tasks, related to increasing the capacity and more precisely to the training of the professors from the Asian Universities is presented. According to the planned activities, professors from the University of Ruse conduct training for teachers from two universities: Chulalongkorn University, Thailand and Savannakhet University, Laos. The phases of training included in the project activities and how many of them have already been implemented are briefly presented. The program and the tasks of the teachers practical training for building the necessary teaching capacity in Savannakhet University and implementation of sustainable training in the field of industrial automation were presented and analyzed. Conclusions and future training of the staff of Asian universities planned under the project are presented in the paper. © 2023 IEEE.

关键词： Personnel training

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引用

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016

作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Banacha 2 Warsaw02-097 Poland Electric Power University 235 Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Technology Poland Faculty of Information Technology VNU University of Engineering and Technology 144 Xuan Thuy Hanoi Viet Nam

ISBN: (纸本)9781509018970

We introduce comparisons with respect to information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. As bisimilarity is a natural notion for characterizing indiscernibility in modal and description logics, it is useful for concept learning in description logics also when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons with respect to information in paraconsistent description logics. As consequences, we also obtain invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2016 IEEE.

关键词： Data description

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Attack Detection in IoT using RF and ANN 4

Attack Detection in IoT using RF and ANN

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4th IEEE International Conference on Smart information Systems and Technologies, SIST 2024

作者： Adamova, Aigul Zhukabayeva, Tamara Adamov, Nurgalym Department of Computer Engineering Astana IT University Astana Kazakhstan Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Astana International University Astana Kazakhstan

ISBN: (纸本)9798350374865

Internet of Things (IoT) devices are the weak link in organizing a Wireless Sensor Network. Various Attacks on IoT devices can lead to different complex consequences. Real applications of the IoT generate a large amount of data every second, the confidentiality of which is of very high value. Therefore, detecting attacks in IoT interactions is of paramount interest from both science and industry. Among various attack detection approaches, machine learning methods show great potential due to their early detection ability. The paper presents a methodology for detecting attacks based on two machine learning methods: Random Forest (RF) and Artificial Neural Network (ANN). To conduct the experiment, various data sets were considered, the descriptions of which are given in the work. The experiment was carried out using open data sets obtained from real IoT devices. As a result, RF demonstrated a high accuracy of 98.6%. © 2024 IEEE.

关键词： Adversarial machine learning

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