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Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

作者： Pythagoras Karampiperis Giannis Mouchakis George Paliouras Vangelis Karkaletsis Software and Knowledge Engineering Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Agia Paraskevi Attikis Athens Greece

ISBN: (纸本)9781450307727

Currently there exist several tools for Complex Event Recognition, varying from design platforms for business process modeling (BPM) to advanced Complex Event Processing (CEP) engines. Several efforts have been reported in literature aiming to support domain experts in the process of defining event recognition (ER) rules. However, few of them offer graphical design environments for the definition of such rules, limiting the broad adoption of ER systems. In this paper, we present a graphical Event Definition Authoring Tool, referred to as the Event Recognition Designer Toolkit (ERDT) with which, a domain expert can easily design event recognition rules on temporal data and produce standalone Event Recognizers.

关键词： complex event processing authoring tools event recognition

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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A Comprehensive Part-of-Speech Tagging to Standardize Central-Kurdish Language: A Research Guide for Kurdish Natural Language Processing Tasks

arXiv

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arXiv 2025年

作者： Sabr, Shadan Shukr Mustafa, Nazira Sabr Omar, Talar Sabah Rasool, Salah Hwayyiz Omer, Nawzad Anwer Hamad, Darya Sabir Shams, Hemin Abdulhameed Kareem, Omer Mahmood Abdullah, Rozhan Noori Abdullah, Khabat Atar Mohammad, Mahabad Azad Al-Raghefy, Haneen Asaad, Safar M. Mohammed, Sara Jamal Ali, Twana Saeed Shawrow, Fazil Maghdid, Halgurd S. Department of Kurdish Language Faculty of Education Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq Department of Computer Science Faculty of Health and Science Koya University Koya Kurdistan Region KOY45 Iraq Department of Software Engineering Faculty of Engineering Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of English language Faculty of Education Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering University of Sulaimani Region Kurdistan Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq

The field of natural language processing (NLP) has dramatically expanded within the last decade. Many human-being applications are conducted daily via NLP tasks, starting from machine translation, speech recognition, text generation and recommendations, Part-of-Speech tagging (POS), and Named-Entity Recognition (NER). However, low-resourced languages, such as the Central-Kurdish language (CKL), mainly remain unexamined due to shortage of necessary resources to support their development. The POS tagging task is the base of other NLP tasks;for example, the POS tag set has been used to standardized languages to provide the relationship between words among the sentences, followed by machine translation and text recommendation. Specifically, for the CKL, most of the utilized or provided POS tagsets are neither standardized nor comprehensive. To this end, this study presented an accurate and comprehensive POS tagset for the CKL to provide better performance of the Kurdish NLP tasks. The article also collected most of the POS tags from different studies as well as from Kurdish linguistic experts to standardized part-of-speech tags. The proposed POS tagset is designed to annotate a large CKL corpus and support Kurdish NLP tasks. The initial investigations of this study via comparison with the Universal Dependencies framework for standard languages, show that the proposed POS tagset can streamline or correct sentences more accurately for Kurdish NLP tasks. © 2025, CC BY.

关键词： Calibration

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Towards enriching linked open data via open information extraction

Towards enriching linked open data via open information extr...

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1st International Workshop on knowledge Discovery and Data Mining Meets Linked Open Data, KNOW@LOD 2012

作者： Koukourikos, Antonis Karkaletsis, Vangelis Vouros, George University of Piraeus Department of Digital Systems 80 Karaoli and Dimitriou Str. Piraeus 18534 Greece Software and Knowledge Engineering Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Agia Paraskevi Attikis P.O. Box 60228 15310 Athens Greece

The descriptions of various entities on Linked Data repositories are subject to constant renewals and modifications, with respect to both the descriptions of concepts and relations and entities realizing their instantiations. Thus, the underlying ontologies have to be updated accordingly in order to reflect these changes. This paper presents a system for examining the possibilities of discovering new relations and updating/verifying existing ones for entities described in Linked Data repositories by using Open Information Extraction techniques. These are applied to web content. The process aims to the enrichment of the examined datasets and the expansion of the ontologies with newly-discovered concepts and relations. Towards this target, the paper discusses the intricacies, pitfalls and challenges present.

关键词： Linked data

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Fuzzy Control For Improving Energy Management Within Indoor Building Environments

Fuzzy Control For Improving Energy Management Within Indoor ...

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Electronics, Robotics and Automotive Mechanics Conference

作者： Enrique Sierra Alejandro Hossian Paola Britos Dario Rodriguez Ramon Garcia-Martinez Electrotecnic Department School of Engineering National University of Comahue Argentina Software & Knowledge Engineering Center Graduate School ITBA Intelligent Systems Laboratory School of Engineering University of Buenos Aires Argentina

Research on energy efficiency in buildings has proved that the design and even the sophisticated facilities such as building energy management systems (BEMS), aiming to improve the indoor environmental conditions while minimizing the energy needs, are not sufficient enough due to users interference. Users are a dynamic part of the building, therefore they should be taken into account in the control strategy. Latest trends in designing intelligent building energy management systems (IBEMS) integrate a man machine interface that could store the user's preferences and adapt the control strategy accordingly. The objective of the present paper is to present the advantages of the fuzzy control techniques together with a man machine interface based on a smart card terminal in satisfying the user preferences. A fuzzy controller is developed and the minimization of energy consumption is achieved by the use of a suitable cost function for the whole system. The overall control system including the cost function has been modeled and tested by using the software tool MATLAB/SIMULINK.

关键词： Fuzzy control Energy management Buildings Man machine systems Control systems Cost function Energy efficiency Interference Intelligent structures Machine intelligence

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Hybrid features and semantic reinforcement network for image forgery detection

Hybrid features and semantic reinforcement network for image...

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作者： Chen, Haipeng Chang, Chaoqun Shi, Zenan Lyu, Yingda College of Computer Science and Technology Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Public Computer Education and Research Center Jilin University Changchun130012 China

Image forgery detection focuses more on tampering regions than image content of semantic segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient semantic information causes low efficiency of forgery detection. To address these issues, we propose a hybrid features and semantic reinforcement network (HFSRNet) for image forgery detection, which is an encoding and decoding based network. Specifically, long-short term memory with resampling features has been applied to capture traces from the image patches for finding manipulating artifacts. Consolidated features extracted from rotating residual units are further leveraged to amplify the discrepancy between un-tampered and tampered regions. We then hybridize features from them through a concatenation to further incorporate spatial co-occurrence of these two modalities. In addition, for achieving the semantic consistency between two same level features associated by across layers, semantic reinforcement is implemented on the decoding stage. HFSRNet is an end-to-end architecture that handles multiple types of image forgery including copy-move, splicing, removal. Experiments on three standard image manipulation datasets (NIST16, COVERAGE and CASIA) demonstrate that HFSRNet obtains state-of-the-art performance compared to existing models and baselines. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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A Multi-agent Intelligent Tutoring System for Learning Computer Programming

A Multi-agent Intelligent Tutoring System for Learning Compu...

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Electronics, Robotics and Automotive Mechanics Conference

作者： Enrique Sierra Alejandro Hossian Paola Britos Dario Rodriguez Ramon Garcia-Martinez Electrotecnic Department School of Engineering National University of Comahue Argentina Software & Knowledge Engineering Center Graduate School. ITBA Intelligent Systems Laboratory School of Engineering University of Buenos Aires Argentina

In this paper, we consider a multi-agent intelligent tutoring system for the learning of computer programming. It provides a multiple domain learning environment for language learners. The aim of the system is to teach the target domain by using a supporting domain(s) to reinforce the learning of it. The system is strongly built upon a cognitive theory that affirms there are successful knowledge transfers in the learning of knowledge-rich domains like computer programming. To implement it, we chose a multi-agent system because of the features of agents, which provide flexibility for future expansion.

关键词： Intelligent systems Programming Computer languages Multiagent systems knowledge transfer knowledge engineering Laboratories Information processing Java Humans

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Extending an agent-based FMS scheduling approach with parallel genetic algorithms

Extending an agent-based FMS scheduling approach with parall...

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Annual Conference of Industrial Electronics Society

作者： Ghada Abaza Iman Badr Peter Goehner Sabina Jeschke Institute of Industrial Automation and Software Engineering University of Stuttgart Germany Institute of Information Management in Mechanical Engineering & Center for Learning and Knowledge Management ZLW/IMA RWTH Aachen University Germany

Flexible manufacturing systems (FMS) aim at efficiently reacting to changing market needs to stand the increasing competitiveness. This imposes efficiency and flexibility requirements on FMS scheduling. Manufacturing scheduling is the process of allocating available manufacturing resources to the set of planned jobs over time. It is an optimization process by which limited manufacturing resources are to be allocated to several jobs of different products efficiently. The agent-based scheduling approach has shown the ability to fulfill the flexibility requirement. Although this approach emphasizes flexibility, it lacks the optimization support. In this paper, an agent-based scheduling approach is extended with parallel genetic algorithms (PGA) to provide the required optimization support. Test results have shown a remarkable enhancement to the optimality of the generated schedules with respect to the predefined set of manufacturing objectives. The extended approach fulfils both flexibility and efficiency requirements on manufacturing scheduling.

关键词： Biological cells Job shop scheduling Electronics packaging Schedules Manufacturing Computer architecture

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Three-phase heuristic for Inventory Routing Problem

Three-phase heuristic for Inventory Routing Problem

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IIE Annual Conference and Expo 2013

作者： Ramkumar, N. Pazhani, Subramanian Narendran, T.T. Ganesh, K. Logistics Division JDA Software India Private Limited India Harold and Inge Marcus Department of Industrial and Manufacturing Engineering Pennsylvania State University University Park United States Department of Management Studies Indian Institute of Technology Madras Chennai Tamilnadu India McKinsey Knowledge Center India Private Limited Supply Chain Management - Center of Competence McKinsey and Company India

ISBN: (纸本)9780983762423

Vendor Managed Inventory systems require integration of distribution and routing which constitute the Inventory Routing Problem (IRP). IRP involves decisions on timing of deliveries, on sizes of shipments and on routing of vehicles. An IRP with a single manufacturer which supplies a product to a set of warehouses is taken for this study. We develop an Integer Linear Program to model the problem. We propose a three-phased approach called CAR, i.e., Clustering of receiver nodes, Allocation of quantities to these nodes and Routing of delivery vehicles through clusters of nodes. We develop two heuristics, based on this approach. Trials conducted over a large number of data sets provide encouraging results.

关键词： Integer programming

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