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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

SSRN

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SSRN 2022年

作者： Onile, Abiodun Emmanuel Belikov, Juri Levron, Yoash Petlenkov, Eduard Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Electricity consumers are often faced with challenges relating the choice of optimal energy saving plan, increasing integration of transient’s renewable sources of energy promises tantalizing solution which also constitutes emerging stability challenges for the electricity Grid. Demand side management using Battery storage as grid balancing measures are crucial towards extending physical limits of existing electricity grid. On the one hand, battery storage is assuming a critical role in decarbonization efforts partly due to its tendencies to store excess electricity obtained via renewable energy sources thus enabling unlimited potentials for seasonal storage. On the other hand, while solving the identified problem, other problems relating consumer behavior towards adoption of energy/battery efficiency technologies, consumers comfort-feedback and control loop integration exist. In this study, we presented electricity energy storage (EES) technologies which are embedded into the grid and further enhanced with the use of Reinforcement learning control and recommendation system technologies for improving the grid management, the grid reliability and sustainability, mitigated operational issues, improved BESS utilization and attain self-consumption goals. The result of the study shows that operating the BESS using the multi-agent RL control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Short-time simulation of quantum dynamics by Pauli measurements

arXiv

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arXiv 2024年

作者： Faehrmann, Paul K. Eisert, Jens Kieferová, Mária Kueng, Richard Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Integrated Circuits and Quantum Computing Johannes Kepler University Linz Austria Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz 1 Berlin14109 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia

Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here, we propose leveraging the power of measurements to simulate short-time quantum dynamics of physically prepared quantum states in classical post-processing using a truncated Taylor series approach. While limited to short simulation times, our hybrid quantum-classical method is equipped with rigorous error bounds. It is extendable to estimate low-order Taylor approximations of smooth, time-dependent functions of tractable linear combinations of measurable operators. These insights can be made use of in the context of Hamiltonian learning and device verification, short-time imaginary time evolution, or the application of intractable operations to sub-universal quantum simulators in classical post-processing. © 2024, CC BY.

关键词： Hamiltonians

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Information Security Culture for Guiding Employee's Security Behaviour: A Pilot Study 6

Information Security Culture for Guiding Employee's Security...

引用

6th IEEE International Conference on Information Management, ICIM 2020

作者： Nasir, Akhyari Arshah, Ruzaini Abdullah Hamid, Mohd Rashid Ab TATI University College Faculty of Computer Media and Tech. Management Teluk Kalong Kemaman Terengganu 24000 Malaysia Universiti Malaysia Pahang Lebuhraya Tun Razak Faculty of Computer Systems and Software Engineering Kuantan Pahang26300 Malaysia Universiti Malaysia Pahang Lebuhraya Tun Razak Faculty of Industrial Management Kuantan Pahang26300 Malaysia

ISBN: (纸本)9781728157702

Experts and scholars have suggested that cultivation of a positive Information Security Culture (ISC) could improve employee's security behaviour in organization. However, specific ISC model for employee's security behaviour is limited. This paper discusses a pilot study of our research-in-progress that proposes a holistic ISC model to be used as guidance for employee's security behaviour in organization. ISC concept model developed in the study is represented by seven comprehensive dimensions formulated based on widely accepted concepts of Organizational Culture and ISC. These dimensions embody various aspects of ISC cultivation. The model was tested in a Malaysian public university. This study employed Partial Least Square Structural Equation Modelling (PLS SEM) using Smart PLS 3 software to analyze and validate the model. The findings proved that the ISC model is significant in influencing security compliance behaviour. Hence, this study contributes to ISC literature in terms of conceptualization and empirical validation of a new ISC model based on seven comprehensive dimensions in relation with ISP compliance behaviour. © 2020 IEEE.

关键词： Security of data

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Time-intervals-related pattern selection for continuous event prediction

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Pattern Recognition 2025年 168卷

作者： Nevo Itzhak Szymon Jaroszewicz Robert Moskovitch Software and Information Systems Engineering Ben-Gurion University of the Negev Beer Sheva Israel Institute of Computer Science Polish Academy of Sciences Warsaw Poland Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland

Symbolic time intervals (STIs) are used in different domains to represent real-life events with varying durations, like traffic light timing or medical treatments. Multivariate temporal data may include STIs and event-driven or manual measurements, such as traffic accidents or blood tests. This study proposes temporal abstraction to uniformly represent such heterogeneous multivariate temporal data (time point values, instantaneous events, or time intervals) using STIs and to develop a model to continuously predict events of interest. We introduce the use of multiple TIRPs that end with an event of interest for continuous prediction while using multiple TIRP instance completion predictors simultaneously. Since often there are dozens of discovered patterns, in this paper, we introduce novel discriminative pattern-selection metrics, such as the differences in the frequencies or duration between the entities ( e.g. , patients) having or not having the event of interest. The proposed methods achieved an average improvement of 5% AUROC over LSTM-FCN, the best-performing baseline, out of the evaluated baseline models (RawXGB, Resnet, LSTM-FCN, and ROCKET) that were applied to the raw medical and non-medical datasets. The proposed continuous event prediction method has the potential for broad real-world and real-time applicability in diverse domains with heterogeneous multivariate temporal data, such as early panic attack prediction using wearable devices or early complication prediction in intensive care unit patients.

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An Exploratory Study on the Introduction and Removal of Different Types of Technical Debt

arXiv

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arXiv 2021年

作者： Liu, Jiakun Huang, Qiao Xia, Xin Shihab, Emad Lo, David Li, Shanping College of Computer Science and Technology Zhejiang University China PengCheng Laboratory China College of Computer Science and Technology Zhejiang University China Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Software Engineering Concordia University Canada School of Information Systems Singapore Management University Singapore

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of different deep learning frameworks is the same, which enables us to find common behaviors on the removal of different types of technical debt across projects. By mining the file history of these frameworks, we find that design debt is introduced the most along the development process. As for the removal of technical debt, we find that requirement debt is removed the most, and design debt is removed the fastest. Most of test debt, design debt, and requirement debt are removed by the developers who introduced them. Based on the introduction and removal of different types of technical debt, we discuss the evolution of the frequencies of different types of technical debt to depict the unresolved sub-optimal trade-offs or decisions that are confronted by developers along the development process. We also discuss the removal patterns of different types of technical debt, highlight future research directions, and provide recommendations for practitioners. © 2021, CC BY.

关键词： Economic and social effects

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BigData: Issues, Challenges, Technologies and Methods 2nd

BigData: Issues, Challenges, Technologies and Methods

引用

2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Adam, Khalid Fakharaldien, Mohammed Adam Ibrahim Zain, Jasni Mohamed Majid, Mazlina Abdul Noraziah, Ahmad Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gambang KuantanPahang26300 Malaysia

ISBN: (纸本)9789811317972

With dramatically increase of emerging applications such as social media, sensors, videos and digital pictures data is become generating from everywhere. Due to characteristic of big data it becomes very difficult to manage, analysis, store, transport, and process the data using the existing traditional techniques. This paper introduces several Big Data, technologies and methods. First, we introduce the Big Data and its problem, important of Big Data, technologies and methods of Big Data management, and analyzed includes the Distributed File System, NOSQL Database, Hadoop/MapReduce. Next, we present the Big Data model;and discuss the Big Data security challenges. Finally, we present the future work and the conclusion. © Springer Nature Singapore Pte Ltd. 2019.

关键词： MapReduce

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Modeling of adsorptive removal of azithromycin from aquatic media by CoFe₂O₄_/NiO anchored microalgae-derived nitrogen-doped porous activated carbon adsorbent and colorimetric quantifying of azithromycin in pharmaceutical products

引用

Chemosphere 2023年第1期329卷 138635页

作者： Ameen, Fuad Mostafazadeh, Reza Hamidian, Yasamin Erk, Nevin Sanati, Afsaneh L. Karaman, Ceren Ayati, Ali Department of Botany and Microbiology College of Science King Saud University Riyadh 11451 Saudi Arabia Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara 06560 Turkey Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra 3030-290 Portugal Akdeniz University Vocational School of Technical Sciences Department of Electricity and Energy Antalya 07070 Turkey ChemBio Cluster ITMO University Lomonosova Street 9 Saint Petersburg 191002 Russian Federation

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe2O4/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC)... 详细信息

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe₂O₄_/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC), besides developing a colorimetric method for the swift monitoring of Azi in pharmaceutical products. In this study, the Spirulina platensis (Sp) was used as a biomass resource for fabricating CoFe₂O₄/NiO@N-PAC adsorbent. The pores of N-PAC mainly entail mesoporous structures with a mean pore diameter of 21.546 nm and total cavity volume (V_total) of 0.033578 cm³. g⁻¹. The adsorption studies offered that 98.5% of Azi in aqueous media could remove by CoFe₂O₄/NiO@N-PAC. For the cyclic stability analysis, the adsorbent was separated magnetically and assessed at the end of five adsorption-desorption cycles with a negligible decrease in adsorption. The kinetic modeling revealed that the adsorption of Azi onto the CoFe₂O₄/NiO@N-PAC was well-fitted to the second-order reaction kinetics, and the highest adsorption capacity was found as 2000 mg. g⁻¹ at 25 °C based on the Langmuir adsorption isotherm model at 0.8 g. L⁻¹ adsorbent concentration. The Freundlich isotherm model had the best agreement with the experimental data. Thermodynamic modeling indicated the spontaneous and exothermic nature of the adsorption process. Moreover, the effects of pH, temperature, and operating time were also optimized in the colorimetric Azi detection. The blue ion-pair complexes between Azi and Coomassie Brilliant Blue G-250 (CBBG-250) reagent followed Beer's law at wavelengths of 640 nm in the concentration range of 1.0 μM to 1.0 mM with a 0.94 μM limit of detection (LOD). In addition, the selectivity of Azi determination was verified in presence of various species. Furthermore, the applicability of CBBG-250 dye for quantifying

关键词： Adsorption Adsorption modeling Azithromycin Biomass-derived activated carbon Spectrophotometry Spirulina platensis

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A framework for managing enterprise architecture debts - Outline and research directions 10

A framework for managing enterprise architecture debts - Out...

引用

10th International Workshop on Enterprise Modeling and Information systems Architectures, EMISA 2020

作者： Alexander, Peter Hacks, Simon Jung, Jürgen Lichter, Horst Steffens, Ulrike Uludağ, Ömer RWTH Aachen University Research Group Software Construction Aachen Germany KTH Royal Institute of Technology Network and Systems Engineering Stockholm Sweden Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt Germany Hamburg University of Applied Sciences Department of Computer Science Hamburg Germany Technical University of Munich Department of Informatics Munich Germany

Even though enterprise architecture management (EAM) offers a wide range of methods and tools for aligning business with IT, an architect’s work is challenged by reality. The evolution of enterprise architecture (EA) and given constraints (e.g. legacy systems and processes) lead to debts which may complicate and hinder opportunities;however, the management of such debts has not been considered in EAM research. This paper presents a framework for strategically managing EA-debt-related issues and propose open questions as well as future research directions in this field. Copyright © 2020 for this paper by its authors.

关键词： Legacy systems

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