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International Joint Conference on Neural Networks (IJCNN)

作者： Lei Shen Shuai Yu Xiaoyu Shen GEB Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Informatics Eastern Institute of Technology Ningbo China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cross-lingual transfer is important for developing high-quality chatbots in multiple languages to address the imbalanced distribution of language resources. A typical approach of cross-lingual transfer, which has been proved effective on classification tasks, is to leverage machine translation (MT) systems to utilize either the training corpus or models from high-resource languages. In this work, we investigate whether it is helpful to utilize MT for cross-lingual transfer in dialog generation tasks. We collect a benchmark dataset for the low-resource scenario, assuming access to limited Chinese dialog data in the movie domain and large amounts of English dialog from multiple domains. Experiments show that leveraging English dialog corpora can improve the naturalness, relevance, and cross-domain transferability in Chinese. However, directly using English corpora in their original form is better than translating them into Chinese. As the topics and wording habits in dialogs are strongly culture-dependent, translating them can reinforce the bias from high-resource languages. To avoid this issue and also reduce the embedding mismatch, we propose to use embedding freezing and post-alignment, which align words from different languages as translation would, but without introducing translation biases. Experiments show that embedding freezing and post-alignment can further improve generation performance. The results of the analysis together with the collected benchmark dataset are presented to draw attention to this area and support future research.

关键词： Training Metalearning Neural networks Benchmark testing Motion pictures Chatbots Machine translation

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Modeling of Complex Electrodynamic Objects Based on a Combined Approach

Modeling of Complex Electrodynamic Objects Based on a Combin...

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2022 International Siberian Conference on Control and Communications, SIBCON 2022

作者： Lvovich, Igor Lvovich, Yakov Preobrazhenskiy, Andrey Preobrazhenskiy, Yuriy Avetisyan, Tatyana Faculty of Informatics Voronezh Institute of High Technologies Voronezh Russia Voronezh State Technical University Faculty of Information Technology and Computer Security Voronezh Russia

ISBN: (纸本)9781665476287

The paper deals with the problem of assessing the characteristics of electromagnetic wave scattering on electrodynamic objects with a complex shape. A combined approach, which is based on the use of statistical methods, methods of fuzzy estimation, and expert analysis, is proposed. A numerical estimate was determined for the index characterizing the level of scattered electromagnetic fields. Linguistic values of the indicators based on expert evaluations were compared. Numerical evaluations of the characteristics of the electrodynamic objects surveyed were given. Based on the formed information base, the frequency of occurrence of different responses was calculated for the analyzed groups, which included objects with over reflections (which was considered as the main group, and objects without over reflections (which was considered as the control group). The values of the electromagnetic wave scattering angle limits under the analyzed scattering conditions were demonstrated. © 2022 IEEE.

关键词： Electrodynamics

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Approximate EFX and Exact tEFX Allocations for Indivisible Chores: Improved Algorithms

arXiv

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arXiv 2024年

作者： Afshinmehr, Mahyar Ansaripour, Matin Danaei, Alireza Mehlhorn, Kurt Department of Computer Engineering Sharif University of Technology Iran Computer and Communications Sciences Department EPFL Switzerland Max Planck Institute for Informatics SIC Germany

We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX allocation exists and is computable in_polynomial time for three agents with subadditive cost functions, improving upon the previous (2 + √6) approximation for additive cost functions. This result requires extensive case analysis. Christoforidis and Santorinaios (2024) independently claim the same approximation for additive cost functions;however, we provide a counter-example to their algorithm. We expand the number of agents to any number to get the same approximation guarantee with the assumption of partially identical ordering (IDO) for the cost functions. Additionally, we establish that a tEFX allocation is achievable for three agents if one has an additive 2-ratio bounded cost function, while the others may have general monotone cost functions. This is an improvement from the prior requirement of two agents with additive 2-ratio bounded cost functions. This allocation can also be extended to agent groups with identical valuations. Further, we show various analyses of EFX allocations for chores, such as the relaxations for additive α-ratio-bounded cost functions. © 2024, CC BY-NC-SA.

关键词： Cost functions

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Effective Parametric Optimization of Heating-Cooling Process with Optimum near the Domain Border 11

Effective Parametric Optimization of Heating-Cooling Process...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Guzowski, Hubert Smolka, MacIej Byrski, Aleksander Pekar, Libor Oplatkova, Zuzana Kominkova Senkerik, Roman Matusu, Radek Gazdos, Frantisek Agh University of Science and Technology Institute of Computer Science Krakow Poland Tomas Bata University in Zlín Faculty of Applied Informatics Czech Republic

ISBN: (纸本)9781665456562

Heating-cooling processes are widely used in industry and are often subject to latencies and delays. Although the parameters of such processes can be identified using mathematical and physical modeling, this technique is highly demanding both when it comes to the amount of a priori knowledge required and the time it takes to find a solution. In this work, we provide a robust formulation of a time-delay control problem and propose a numerical approach to finding its parameters in a form of an optimization task. Consequently, we apply various techniques and metaheuristics to obtain a high quality solution and discuss key observations made in the process. © 2022 IEEE.

关键词： Time delay

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Lantian Wei Tadashi Wadayama Kazunori Hayashi Department of Computer Science Nagoya Institute of Technology Nagoya Japan Graduate School of Informatics Kyoto University Kyoto Japan

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

This paper proposes a vector similarity search (VSS) based offline learning approach for the deep-unfolded MIMO detector. The VSS offline learning approach consists of an offline learning phase and a real-time detection phase. In the offline learning phase, trained parameters of the deep-unfolded MIMO detector are stored in a vector database with a feature vector extracted from the channel matrix. In the real-time detection phase, the DU parameters are retrieved from the database with similarity matching of the feature vector. The critical advantage of the proposal is that it can offload the training computational cost from the edge server to the training server. Numerical results indicate that the VSS offline learning provides appropriate convergence acceleration in almost all cases, and that it improves the robustness of the deep-unfolded MIMO detector in dynamic channel environments.

关键词： Training Databases Image edge detection Detectors Feature extraction Vectors Real-time systems

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KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm 1

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Dhanvardini, R. Deepak, Gerard Priyadarshini, J. Sheeba Santhanavijayan, A. Healthcare Informatics Division Optum UnitedHealth Groups Hyderabad India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India Department of Data Science Manipal Institute of Technology Bengaluru Bengaluru India Department of Data Science Manipal Academy of Higher Education Manipal India Bangalore India

ISBN: (数字)9789811997198

ISBN: (纸本)9789811997181

Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers’ preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models’ estimates with an accuracy of 95.94% and average F-measure of 95.93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics 11

Socio-cognitive Optimization of Time-delay Control Problems ...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Kipinski, Piotr Guzowski, Hubert Urbanczyk, Aleksandra Smolka, MacIej Kisiel-Dorohinicki, Marek Byrski, Aleksander Oplatkova, Zuzana Kominkova Senkerik, Roman Pekar, Libor Matusu, Radek Gazdos, Frantisek Agh University of Science and Technology Institute of Computer Science Krakow Poland Tomas Bata University in Zlín Faculty of Applied Informatics Czech Republic

ISBN: (纸本)9781665456562

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background and the details of the proposed algorithms we apply them to optimization of selected variants of the problem and discuss the results. © 2022 IEEE.

关键词： Optimization

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A Projected Subgradient Method for the Computation of Adapted Metrics for Dynamical Systems

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SIAM Journal on Applied Dynamical Systems 2022年第4期21卷 2610-2641页

作者： Louzeiro, Mauricio Kawan, Christoph Hafstein, Sigurdur Giesl, Peter Yuan, Jinyun Dongguan University of Technology School of Computer Science and Technology Guangdong Dongguan523808 China Institute of Informatics LMU Munich München80538 Germany The Science Institute University of Iceland Dunhagi 5 Reykjavik107 Iceland Department of Mathematics University of Sussex FalmerBN1 9QH United Kingdom

In this paper, we extend a recently established subgradient method for the computation of Riemannian metrics that optimizes certain singular value functions associated with dynamical systems. This extension is threefold. First, we introduce a projected subgradient method which results in Riemannian metrics whose parameters are confined to a compact convex set and we can thus prove that a minimizer exists;second, we allow inexact subgradients and study the effect of the errors on the computed metrics;and third, we analyze the subgradient algorithm for three different choices of step sizes: constant, exogenous, and Polyak. The new methods are illustrated by application to dimension and entropy estimation of the Hénon map. © 2022 Society for Industrial and Applied Mathematics.

关键词： Dynamical systems

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Extreme Learning Machine for Parking Slot Recognition in Smart Parking System 8

Extreme Learning Machine for Parking Slot Recognition in Sma...

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8th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Rahmat, Romi Fadillah Faza, Sharfina Syahputra, Ilham Nainggolan, Pauzi Ibrahim Sulaiman, Riza Rahman, Mohd Amiruddin Abd Universitas Sumatera Utara Department of Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Putra Malaysia Faculty of Science Selangor Malaysia

ISBN: (纸本)9798331531249

In the modern era, technology has become an integral part of human life, particularly in image processing. This advanced technology is now applied to parking areas to identify vacant parking spaces. The application is designed to detect empty parking slots in real time using image data captured by a camera as input. The system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. The process involves several key stages, such as image pre-processing, image segmentation, and object detection. The system employs the Extreme Learning Machine (ELM) method to enhance its object detection capabilities. This system achieves a high accuracy rate of 94%under well-lit conditions. This novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time. © 2024 IEEE.

关键词： Adversarial machine learning

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