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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Simulating the Social Influence in Transport Mode Choices

Simulating the Social Influence in Transport Mode Choices

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Simulation Winter Conference

作者： Kathleen Salazar-Serna Lynnette Hui Xian Ng Kathleen Carley Lorena Cadavid Carlos Jaime Franco Department of Civil and Industrial Engineering Pontificia Universidad Javeriana Cali Valle del Cauca Colombia Software and Societal Systems Carnegie Mellon University Pittsburgh PA USA Department of Computer and Decision Sciences Universidad Nacional de Colombia Medellín Antioquia Colombia

Agent-based simulations have been used in modeling transportation systems for traffic management and passenger flows. In this work, we hope to shed light on the complex factors that influence transportation mode decisions within developing countries, using Colombia as a case study. We model an ecosystem of human agents that decide at each time step on the mode of transportation they would take to work. Their decision is based on a combination of their personal satisfaction with the journey they had just taken, which is evaluated across a personal vector of needs, the information they crowdsource from their prevailing social network, and their personal uncertainty about the experience of trying a new transport solution. We simulate different network structures to analyze the social influence for different decision-makers. We find that in low/medium connected groups inquisitive people actively change modes cyclically over the years while imitators cluster rapidly and change less frequently.

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Information Technology for Contact Tracking in the COVID-19 Pandemic 8

Information Technology for Contact Tracking in the COVID-19 ...

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8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Kuznetsov, Alexandr Gorbenko, Yuriy Kiian, Anastasiia Kuznetsova, Tetiana Ulianovska, Yuliia Rudyanova, Tatyana V. N. Karazin Kharkiv National University Department of Information Systems And Technologies Security Kharkiv Ukraine Jsc 'Institute of Information Technologies' Kharkiv Ukraine University of Customs And Finance Department of Computer Science And Software Engineering Dnipro Ukraine

ISBN: (纸本)9781665406826

The development and implementation of the latest information technologies for contact tracking in the context of the COVID-19 pandemic is an extremely important and urgent task, which is directly related to the possible adjustment and mitigation of quarantine restrictions without increasing the risks and threats of infection. In this article, we analyze, research, and substantiate various information technologies (such as Bluetooth, Wi-Fi, GPS, etc.) in contact tracking tools to prevent the spread of infectious diseases. We explore various aspects such as centralization;the need to use a central server (data warehouse);used cryptographic protection algorithms;acceptable actions when establishing contact, etc. The research results make it possible to formulate practical recommendations on the choice of information technologies for building global contact tracing systems. © 2021 IEEE.

关键词： Data warehouses

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DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning

arXiv

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arXiv 2023年

作者： Chen, Wei Wang, Qiushi Long, Zefei Zhang, Xianyin Lu, Zhongtian Li, Bingxuan Wang, Siyuan Xu, Jiarong Bai, Xiang Huang, Xuanjing Wei, Zhongyu School of Data Science Fudan University China School of Software Engineering Huazhong University of Science and Technology China School of Management Fudan University China School of Computer Science Fudan University China Research Institute of Intelligent Complex Systems Fudan University China

We propose Multiple Experts Fine-tuning Framework to build a financial largelanguage model (LLM), DISC-FinLLM. Our methodology improves general LLMs byendowing them with multi-turn question answering abilities, domain textprocessing capabilities, mathematical computation skills, andretrieval-enhanced generation capabilities. We build a financialinstruction-tuning dataset named DISC-FIN-SFT, including instruction samples offour categories (consulting, NLP tasks, computing and retrieval-augmentedgeneration). Evaluations conducted on multiple benchmarks demonstrate that ourmodel performs better than baseline models in various financial *** resources can be found at https://***/FudanDISC/DISC-FinLLM. Copyright © 2023, The Authors. All rights reserved.

关键词： Finance

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

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IEEE International Conference on Data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

arXiv

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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

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From Controlled to Chaotic: Disparities in Laboratory vs Real-World Stress Detection

From Controlled to Chaotic: Disparities in Laboratory vs Rea...

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International Workshop on Content-Based Multimedia Indexing, CBMI

作者： Simão Ferreira Fátima Rodrigues Johanna Kallio Filipe Coelho Vesa Kyllonen Nuno Rocha Matilde A. Rodrigues Elena Vildjiounaite Center for Translational Health and Medical Biotechnology Research TBIO School of Health Polytechnic Institute of Porto INESC Institute for Systems and Computer Engineering Technology and Science ISEP Institute of Engineering – Polytechnic of Porto VTT Technical Research Centre of Finland Linovt Linovt – Engenharia Software Lda Center for Translational Health and Medical Biotechnology Research TBIO@RISE School of Health of Polytechnic Institute of Porto

ISBN: (数字)9798350378443

ISBN: (纸本)9798350378450

This paper explores the discrepancies between laboratory and real-world stress detection, emphasizing the pronounced differences in data loss, data preprocessing, feature design, and classifier selection. Laboratory studies offer a controlled environment that optimizes data quality, whereas real-world settings introduce chaotic and unpredictable elements, coupled with a diverse range of human behaviours, resulting in substantial data loss and compromised data quality. We discuss the development of stress detectors for two distinct types of data: physiological and behavioural. We also address the specific challenges associated with designing effective stress detection systems for each data type and compare the features and classifiers used in both laboratory and real-world contexts. Additionally, this paper proposes future research directions aimed at crafting stress detectors that are robust and effective in real-life scenarios.

关键词： Privacy Accuracy Protocols Reviews Data integrity Anxiety disorders Refining Detectors Feature extraction Physiology

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Dynamic Management of Multi-level-simulation Workflows in the Cloud 2nd

Dynamic Management of Multi-level-simulation Workflows in th...

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2nd International Workshop on Simulation Science, SimScience 2019

作者： Erbel, Johannes Wittek, Stefan Grabowski, Jens Rausch, Andreas Institute of Computer Science University of Goettingen Goettingen Germany Institute of Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany

ISBN: (纸本)9783030457174

Executing dynamic simulations in a distributed environment allows saving resources and time which is a desired goal in research and industry. One example dynamic simulation is the multi-level-simulation. Here, specific parts of the simulation can be inspected on different levels of detail at runtime. To cope with the changing simulation requirements an elastic and scalable infrastructure is required, as well as an approach adjusting the infrastructure to the simulation needs. In this paper, we enhance a former approach coupling workflows with architectural needs to utilize monitored runtime information and support decision making. Moreover, we demonstrate the concept of executing dynamic simulations over a workflow based approach by dynamically choosing the levels of detail within a supply chain multi-level-simulation. © Springer Nature Switzerland AG 2020.

关键词： Supply chains

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A Modified Symbiotic Organism Search Algorithm with Lévy Flight for software Module Clustering Problem 1

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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Zainal, Nurul Asyikin Zamli, Kamal Z. Din, Fakhrud Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Gambang KuantanPahang26300 Malaysia Department of Computer Science & IT University of Malakand Khyber Pakhtunkhwa Pakistan

ISBN: (数字)9789811523175

ISBN: (纸本)9789811523168

To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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