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检索条件"任意字段=24th International Conference on Control Systems and Computer Science, CSCS 2023"

共 928 条记录，以下是481-490 订阅

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Towards a Metacognitive Accuracy Training in Military Education 24th

Towards a Metacognitive Accuracy Training in Military Educat...

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24th international conference on Human-computer Interaction, HCI international, HCII 2022

作者： Juozapavičius, Aušrius Sütterlin, Stefan Lugo, Ricardo G. General Jonas Žemaitis Military Academy of Lithuania Šilo 5A Vilnius10322 Lithuania Faculty of Computer Science Albstadt-Sigmaringen University Albstadt Germany Faculty of Health Welfare and Organisation Østfold University College Halden Norway Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway

ISBN: (纸本)9783031063879

Over- and under-confidence in uncertain and volatile conditions often encountered by military personnel may lead to suboptimal high impact decitions. We investigated the relation between the metacognition accuracy of cadets‘ performance in a technical IT subject and their observed and measured behaviour characteristics: learning outcomes, knowledge of meta-cognitive strategies, emotional states, time spent for studies, and overall satisfaction with the course and their own efforts. Growth of IT knowledge of a group of 19 cadets who received metacognition training during a three-week long IT course significantly exceeded the results of a control group. their metacognitive accuracy changed from over-confidence to a moderate under-confidence towards the end of the course, although the relation between the accuracy and the final grade did not differ significantly from random data. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cognitive systems

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Pick & Mix Isolation Levels: Mixed Serialization Graph Testing 1

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14th TPC Technology conference on Performance Evaluation and Benchmarking, TPCTC 2022, held in conjunction with the 45th international conference on Very Large Data Bases, VLDB 2022

作者： Waudby, Jack Ezhilchelvan, Paul Webber, Jim Newcastle University School of Computing Newcastle upon Tyne United Kingdom Neo4j London United Kingdom

ISBN: (数字)9783031295768

ISBN: (纸本)9783031295751

Concurrency control is an integral component in achieving high performance in many-core databases. Implementing serializable transaction processing efficiently is challenging. One approach, serialization graph testing (SGT) faithfully implements the conflict graph theorem by aborting only those transactions that would actually violate serializability (introduce a cycle), thus maintaining the required acyclic invariant. Alternative approaches, such as two-phase locking, disallow certain valid schedules to increase throughput, whereas SGT has the theoretically optimal property of accepting all and only conflict serializable schedules. Historically, SGT was deemed unviable in practice due to the high computational costs of maintaining an acyclic graph. Research has however overturned this historical view by utilising the increased computational power available due to modern hardware. Furthermore, a survey of 24 databases suggests that not all transactions demand conflict serializability but different transactions can perfectly settle for different, weaker isolation levels which typically require relatively lower overheads. thus, in such a mixed environment, providing only the isolation level required of each transaction should, in theory, increase throughput and reduce aborts. the aim of this paper is to extend SGT for mixed environments subject to Adya’s mixing-correct theorem and demonstrate the resulting performance improvement. We augment the YCSB benchmark to generate transactions with different isolation requirements. For certain workloads, mixed serialization graph testing can achieve up to a 28% increase in throughput and a 19% decrease in aborts over SGT. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Database systems

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Transitioning to Efficient Technologies in Power systems: the Paradigm of Energy Internet survey

Transitioning to Efficient Technologies in Power Systems: Th...

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international Middle-East Power System conference, MEPCON

作者： Dina Emad Omar Abdel-Rahim Tanemasa Asano Sobhy M. Abdelkader Egypt-Japan University of Science and Technology New Borg ElArab Alexandria Egypt Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka

the Energy Internet (EI) has emerged as a ground-breaking energy system framework within the context of future smart grids, bringing in a new era where all distributed electrical devices are connected through energy routers (ERs). It combines advanced power electronics, information technology, and smart control technology. In addition to involving many distributed energy sources, storage devices, and a new power network. this allows energy to flow in both directions and be shared among various types of users. this document briefly discusses the challenges posed by the Energy Internet, as well as the challenges related to energy routing and protocols within the Energy Internet.

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A Privacy-Preserving Evolutionary Computation Framework for Feature Selection 24th

A Privacy-Preserving Evolutionary Computation Framework for ...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Sun, Bing Li, Jian-Yu Liu, Xiao-Fang Yang, Qiang Zhan, Zhi-Hui Zhang, Jun Nankai University Tianjin China Hanyang University Ansan Korea Republic of Nanjing University of Information Science and Technology Nanjing China South China University of Technology Guangzhou China

ISBN: (纸本)9789819972531

Feature selection is a crucial process in data science that involves selecting the most effective subset of features. Evolutionary computation (EC) is one of the most commonly-used feature selection techniques and has demonstrated good performance, which can help find the suitable feature subset based on training data and fitness information. However, in real-world scenarios, the exact fitness information and privacy-protected data cannot be directly accessed due to privacy and security issues, which leads to a great optimization challenge. To solve such privacy-preserving feature selection problems efficiently, this paper proposes a novel EC-based feature selection framework that balances data privacy and optimization efficiency, together with three contributions. First, based on the rank-based cryptographic function that returns the rank of solutions rather than the exact fitness information, this paper proposes a new fitness function to guide the EC algorithm to approach the global optimum without knowing the exact fitness information and the dataset, thereby preserving data privacy. Second, by integrating the proposed method and EC algorithms, this paper develops a new differential evolution and particle swarm optimization algorithms for efficient feature selection. Finally, experiments are conducted on public datasets, which demonstrate that the proposed method can maintain feature selection efficiency while preserving data privacy. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Design a Compliant Pivot with Nearly Zero Axial Drift and High Radial Stiffness 24th

Design a Compliant Pivot with Nearly Zero Axial Drift and Hi...

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24th IFToMM China international conference on Mechanism and Machine science and Engineering, IFToMM CCMMS 2024

作者： Bai, Ruiyu Ma, Fulei Li, Bo Chen, Guimin State Key Laboratory for Manufacturing Systems Engineering and Shaanxi Key Lab of Intelligent Robots Xi’an Jiaotong University Shaanxi Xi’an 710049 China School of Electro-Mechanical Engineering Xidian University Shaanxi Xi’an 710071 China

ISBN: (纸本)9789819614639

Compliant pivots are widely used in mechanical systems to provide accurate and repeatable rotations. However, the significant axial drift and low radial stiffness during the rotation of compliant pivots pose great challenges in the design and control of compliant pivot-based mechanisms. To mitigate these difficulties, this paper proposes a novel compliant pivot with nearly zero axial drift and high radial stiffness based on the rigid-body replacement method and symmetric arrangement. By designing a series of symmetric rigid linkages with one degree of freedom and substituting the rigid joints in them with compliant elements, several compliant building blocks are obtained. When arranging these building blocks in a circle, compliant pivots with zeros axial drift are obtained. By discussing the source of compliance in the radial direction, a pivot with high radial stiffness is obtained. the modeling of the pivot using a chained beam constraint model and a nonlinear finite element model demonstrates the kinetostatic properties of the pivot. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Axial drift Chained beam constraint model Compliant mechanism Compliant pivot

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Final version - the Low-Voltage X, $\mathrm{K}_{\mathrm{u}}$ and W-Bands Magnetrons with Two Energy Outputs: New Possibilities

Final version - The Low-Voltage X, $\mathrm{K}_{\mathrm{u}}$...

引用

international conference on Vacuum Electronics

作者： Gennadiy Churyumov Shuang Qiu Nan-nan Wang Vacuum Electronics Department O.Ya. Usikov Institute for Radiophysics and Electronics National Academy of Science of Ukraine Kharkiv Ukraine Applied Physics Department Tsinghua University Beijing China Microwave Engineering Department Harbin Institute of Technology Harbin China

this paper presents the theoretical and experimental results of the studies of the new designs of the magnetrons known as the magnetrons with two energy outputs (or dual-output magnetrons). the prototypes of the two magnetrons for X and Ku-bands are developed. the magnetrons produce the average maximum powers of ~ 18.6 W and ~ 15.5 W, frequency tuning of ~ 220 MHz and ~ 150 MHz, and frequency stability of $(3\ldots 5)10^{-6}$ , respectively. the results of computer modeling of the W-band magnetron are presented. Examples of applying the dual-output magnetrons in radar systems and communication are given.

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Analyzing Hepatotoxicity of Marine Venoms Using Artificial Intelligence: An Ecoinformatics Perspective

Analyzing Hepatotoxicity of Marine Venoms Using Artificial I...

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Information Technology (ACIT)

作者： Heider Wahsheh Mohammad Wahsha Tariq Al-Najjar Maroof Khalaf Haya Al-Tarawneh Department of Information Systems College of Computer Science and Information Technology King Faisal University Al-Ahsa Saudi Arabia Marine Science Station The University of Jordan Aqaba Branch Aqaba Jordan Faculty of Basic and Marine Sciences The University of Jordan Aqaba Branch Aqaba Jordan Control Health Division Aqaba Special Economic Zone Authority Aqaba Jordan

Marine toxins present considerable challenges to public health due to their intricate biochemical profiles that complicate effectual analysis. In addressing this, our study utilizes a pioneering Ecoinformatics method, employing artificial intelligence to meticulously examine the hepatotoxic effects of stonefish venom on murine models. the convergence of biochemical assays, histopathological scrutiny, and cutting-edge machine learning algorithms is strategically designed to unravel the complex modalities of venom-induced toxicity. Our findings offer unprecedented insight into the hepatotoxic dynamics of marine venoms, underscoring the utility of AI in advancing marine toxin research. this multifaceted research not only deepens our comprehension of venom pathology but also forges a pathway toward enhanced antivenom solutions, thereby reinforcing public health measures in marine and coastal ecosystems.

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Machine learning based noise suppression in narrow-band speech communication systems

Machine learning based noise suppression in narrow-band spee...

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international conference on Digital Signal Processing (DSP)

作者： Joyraj Chakraborty Martin Reed Nikolaos thomos Geoff Pratt Nigel Wilson School of Computer Science and Electronic Engineering University of Essex UK CML Microcircuits (UK) Ltd. Maldon UK

Narrow-band digital personal radio systems are used for speech communication in challenging environments where background noise, such as machinery or emergency sirens, can pose significant problems for speech intelligibility. this paper proposes a machine learning based noise suppression approach that utilises a neuro-fuzzy logic-based neural network for noise estimation and reduction. the technique is shown to give significant improvements in noise suppression compared to a non-adaptive noise suppression approach. the choice of a neuro-fuzzy logic neural network is motivated by the need for a low-power implementation suitable for mobile, power constrained, terminals. To validate this, the algorithm has been tested in a real-time system showing that it can be implemented in constrained devices unlike more complex machine learning techniques that are unsuitable for low-power digital personal radio systems.

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TrustGAT: Sparse Trust Data Mining with Graph Attention for Mobile Social Networks

TrustGAT: Sparse Trust Data Mining with Graph Attention for ...

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international conference on Mobile Data Management

作者： Maolan Zhang Di Xiao College of Computer Science Chongqing University Chongqing China

Trust-aware recommendation plays an essential role in alleviating information overload by exploiting social relationships among users to build recommendation systems. However, recommendation systems in mobile social networks suffer from the sparse trust problem, which severely affects the reliability of trust propagation and the accuracy of the recommendation. the flourishing graph neural networks have revitalized Trust-aware Recommendation systems. therefore, we propose a sparse trust data mining method based on graph attention networks (TrustGAT) to mine potential trust information between entities in large-scale mobile social networks. First, the potential relationships between the trustors and the trustees are simulated in sparse data, and thereof an adaptive trust network is built. On this basis, features of trust-related information and items are learned in multi-head attention modules to enable the scheme stability. In addition, the implicit influence of trust is introduced to augment network node representation. Empirical results on three public benchmarks show that TrustGAT can make recommendation for users rapidly and accurately as well as alleviate the sparse trust problem effectively.

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Comparing Ranking Learning Algorithms for Information Retrieval systems 24th

Comparing Ranking Learning Algorithms for Information Retri...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Zilles, J. Borges, E.N. Lucca, G. Marco-Detchart, C. Berri, Rafael A. Dimuro, G.P. Centro de Ciências Computacionais Universidade Federal do Rio Grande Av. Itália km 08 Campus Carreiros Rio Grande96201-900 Brazil Programa de Pós-Graduação em Engenharia Eletrônica e Computação Universidade Católica de Pelotas Gonçalves Chaves Pelotas96015-560 Brazil Camino de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031482311

the growth of machine learning applications in various fields has enabled the advancement of Information Retrieval systems. As a result of this evolution, it has become possible to solve the well-known document classification problem. In the beginning, document positions within a result were given from a score, where each document receives an assigned value based on the terms used in the input query. the use of machine learning in this field is known as Learning to Rank, which allows the classification of documents to better meet user search requirements, taking into account aspects such as document preference, importance, and relevance. this paper presents a comparison of different algorithms for ranking documents using machine learning. It is observed that RankSVM presents relatively satisfactory results in smaller datasets, while algorithms that use Gradient Boosting obtain better results for larger datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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