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Fine-grained temporal orientation and its relationship with psycho-demographic correlates

Fine-grained temporal orientation and its relationship with ...

2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Kamila, Sabyasachi Hasanuzzaman, Mohammed Ekbal, Asif Bhattacharyya, Pushpak Way, Andy Department of Computer Science and Engineering Indian Institute of Technology Patna India ADAPT Centre School of Computing Dublin City University Dublin Ireland

ISBN: (纸本)9781948087278

Temporal orientation refers to an individual's tendency to connect to the psychological concepts of past, present or future, and it affects personality, motivation, emotion, decision making and stress coping processes. The study of the social media users' psychodemographic attributes from the perspective of human temporal orientation can be of utmost interest and importance to the business and administrative decision makers as it can provide an extra precious information for them to make informed decisions. In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets. We first create a temporal orientation classifier in a minimally supervised way which classifies each tweet of the users in one of the three temporal categories, namely past, present, and future. A deep Bi-directional Long Short Term Memory (BLSTM) is used for the tweet classification task. Our tweet classifier achieves an accuracy of 78.27% when tested on a manually created test set. We then determine the users' overall temporal orientation based on their tweets on the social media. The sentiment is added to the tweets at the fine-grained level where each temporal tweet is given a sentiment with either of the positive, negative or neutral. Our experiment reveals that depending upon the sentiment view of temporal orientation, a user's attributes vary. We finally measure the correlation between the users' sentiment view of temporal orientation and their different psychodemographic factors using regression. © 2018 The Association for Computational Linguistics.

关键词： Decision making

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Slip and thermal effects on MHD fractional Jeffrey nanofluid flow

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Journal of Radiation Research and Applied sciences 2025年第3期18卷

作者： Shajar Abbas S. Saleem Maryam Fatima Ioan-Lucian Popa Mudassar Nazar Ghada Ragheb Elnaggar Rashid Jan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan AS (Ayub Shah) Institute of Mathematics and Research Centre Multan Pakistan Department of Mathematics College of Science King Khalid University Abha 61413 Saudi Arabia Center for Artificial Intelligence (CAI) King Khalid University Abha 61421 Saudi Arabia Department of Computing Mathematics and Electronics “1 Decembrie 1918” University of Alba Iulia 15 510009 Alba Iulia Romania Faculty of Mathematics and Computer Science Transilvania University of Brasov Iuliu Maniu Street 50 500091 Brasov Romania Industrial and Systems Engineering Department College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mathematics Saveetha School of Engineering (SIMATS) Thandalam 600124 Chennai Tamil Nadu India Department of Mathematics Khazar University AZ1096 Baku Azerbaijan

Nanofluids with superior thermal characteristics are increasingly important in radiation-influenced environments across industrial and biomedical domains. This work develops a fractional-order model describing the mixed convection flow of human blood-based nanofluids embedded with single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs) along an infinite vertical surface. To capture memory and hereditary effects, the constant proportional Caputo (CPC) operator is utilized, while the Laplace technique yields semi-analytical solutions for temperature, velocity, and concentration profiles, and its inverse Laplace transform is obtained using the Stehfest and Tzou algorithm. The results show that thermal radiation significantly enhances heat transfer, with a 12 % increase in temperature for blood-SWCNT nanofluid due to superior thermal conductivity, while blood-MWCNT nanofluid exhibits higher velocity due to lower density. These findings contribute to the development and optimization of nanofluid systems for use in industrial engineering applications, including high-performance heat exchangers, solar collectors, electronic cooling systems, and biomedical thermal therapies where precise thermal diffusion control over is essential.

关键词： Thermal flow Laplace transform Carbon nanotubes MHD Jeffrey fluid

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Hybrid WDM and Optical-CDMA over Multi-Mode Fiber Transmission System based on Optical Vortex

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Journal of Physics: Conference Series 2021年第1期1755卷 012001-012001页

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut C. B. M. Rashidi N. Ali Aras Al-dawoodi Ahmed M. Fakhrudeen Alaa Fareed Teena Sharma School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Alkitab University Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq School of Computing University Utara Malaysia Malaysia Department of Electronics and Communication Engineering Malviya National Institute of Technology Jaipur India

The optical vortex has recently attracted scholars to implement it in optical tweezers, microscopy, optical communications, quantum information processing, optical trapping, and laser machining. Optical vortex beam applied classically that can be transferred to the transverse amplitude of a heralded single-photon, and optical vortex possesses a helical wavefront and carries orbital angular momentum. In this study, Optical vortex is applied in optical-CDMA (optical code-division multiple-access) in conjunction with WDM (wavelength division multiplexing). This mechanism aims to increase the capacity and security in optical communication significantly. The implementation of Laguerre-Gaussian (LG) modes with optical vortex based on one dimension zero cross-correlation (ZCC) code shows that mode coupling reduces effectively. Consequently, a positive increase in channel performance and response. Accordingly, the LG modes based on the 1D-ZCC code are investigated and propagated over multi-mode fiber (MMF) based on an optical vortex, which also substantially reduces channel effects. Consequently, all these attributes combined will result in a hybrid WDM-Optical-CDMA with an optical vortex system over MMF.

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Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers

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Cleaner engineering and Technology 2025年 27卷

作者： Ma, Shuaiyin Cao, Yichun Liu, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an 710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Department of Management and Engineering Linköping University Linköping SE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu 90570 Finland

Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R2 reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations. © 2025 The Authors

关键词： Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN

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Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

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Cybersecurity 2018年第1期1卷 767-787页

作者： Petar Radanliev David De Roure Kevin Page Jason R.C.Nurse Rafael Mantilla Montalvo Omar Santos La’Treall Maddox Pete Burnap Oxford e-Research Centre Engineering Sciences DepartmentUniversity of OxfordOxfordEngland School of Computing University of KentKentEnglandUK Cisco Research Centre Research Triangle ParkDurhamNorth CarolinaUSA School of Computer Science and Informatics Cardiff UniversityCardiffWalesUK

Digital technologies have changed the way supply chain operations are *** this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber *** analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new *** paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk *** system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the *** enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.

关键词： Industry 4.0 Supply chain design Transformational design roadmap IIoT supply chain model Decision support for information management artificial intelligence and machine learning(AI/ML) dynamic self-adapting system cognition engine predictive cyber risk analytics

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RETRACTED: Affirmative data analytics based data processing method for 6G wireless network applications

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Transactions on Emerging Telecommunications Technologies 2022年第10期35卷

作者： Muath Jarrah Ahmed Abu-Khadrah Hamza Alrababah Abdul Syukor Bin Mohamad Jaya Zakaria N. M. Alqattan School of Information Technology Skyline University College Sharjah UAE Department of Computer Science University Malaysia of Computer Science & Engineering (UNIMY) Cyberjaya Selangor Malaysia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Fakulti Teknologi Maklumat dan Komunikasi University Teknikal Malaysia Melaka Melaka Malaysia National Advanced IPv6 Centre of Excellence (NAv6) University Sains Malaysia Penang Malaysia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Sixth generation (6G) wireless network infrastructure makes use of terahertz communication interfaces and latency service. The growth of real-time applications and service support increases the data handling capacity and processing requirements. The data processing rate must be comparatively high with the available resources to meet the users' quality of service (QoS) requirements. This article proposes an affirmative data analytics (ADA) method to improve data processing consistency in 6G wireless networks. The consistency of the ADA method relies on the 6G service features in the service-rendering environment. The affirmation process is provided using support vector machine (SVM) learning to achieve consistency in handling diverse-attributed data. Then the attribute and association between data and services are achieved with best-fit processing time and minimum complexity. The performance of the proposed ADA is verified for heterogeneous service applications in a wireless network using the metrics analysis time (723.629 ms), complexity (11.311%), and response time (1.034 s).

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Reduction theorem for secrecy over linear network code for active attacks

arXiv

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arXiv 2020年

作者： Hayashi, Masahito Owari, Masaki Kato, Go Cai, Ning Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518000 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore Department of Computer Science Faculty of Informatics Shizuoka University Japan NTT Communication Science Laboratories NTT Corporation Japan School of Information Science and Technology ShanghaiTech University Middle Huaxia Road no 393 Pudong Shanghai201210 China

We discuss the effect of sequential error injection on information leakage under a network code. We formulate a network code for the single transmission setting and the multiple transmission setting. Under this formulation, we show that the eavesdropper cannot improve the power of eavesdropping by sequential error injection when the operations in the network are linear *** demonstrate the usefulness of this reduction theorem by applying a concrete example of network. Copyright © 2020, The Authors. All rights reserved.

关键词： Network coding

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

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arXiv 2023年

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics Agh University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine Mevis Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan Forxai Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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