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Automatic analysis of cognitive presence in online discussions: An approach using deep learning and explainable artificial intelligence

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Computers and Education: Artificial Intelligence 2021年 2卷

作者： Hu, Yuanyuan Ferreira Mello, Rafael Gašević, Dragan Faculty of Engineering University of Auckland Auckland New Zealand Departamento de Computação Universidade Federal Rural de Pernambuco Recife Brazil Cesar School Recife Brazil Faculty of Information Technology Monash University Melbourne Australia School of Informatics University of Edinburgh United Kingdom Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

This paper proposes the adoption of a deep learning method to automate the categorisation of online discussion messages according to the phases of cognitive presence, a fundamental construct from the widely used Community of Inquiry (CoI) framework of online learning. We investigated not only the performance of a deep learning classifier but also its generalisability and interpretability, using explainable artificial intelligence algorithms. In the study, we compared a Convolution Neural Network (CNN) model with the previous approaches reported on the literature based on random forest classifiers and linguistics features of psychological processes and cohesion. The CNN classifier trained and tested on the individual data set reached results up to Cohen's κ of 0.528, demonstrating a similar performance to those of the random forest classifiers. Also, the generalisability outcomes of the CNN classifiers across two disciplinary courses were similar to the results of the random forest approach. Finally, the visualisations of explainable artificial intelligence provide novel insights into identifying the phases of cognitive presence by word-level relevant indicators, as a complement to the feature importance analysis from the random forest. Thus, we envisage combining the deep learning method and the conventional machine learning algorithms (e.g. random forest) as complementary approaches to classify the phases of cognitive presence. © 2021 The Authors

关键词： Social networking (online)

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Security Analysis on an Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks

Security Analysis on an Efficient and Provably Secure Authen...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Salman Shamshad Mohammad S. Obaidat Minahil Muhammad Asad Saleem Usman Shamshad Khalid Mahmood COMSATS University Islamabad Sahiwal Pakistan College of Computing and Informatics University of Sharjah Sharjah King Abdullah II School of Information Technology University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China Govt. College and University Lahore Pakistan

ISBN: (数字)9781728195377

ISBN: (纸本)9781728195384

The maturity of intelligent transportation system, cloud computing and Internet of Things (IoT) technology has encouraged the rapid growth of vehicular ad-hoc networks (VANETs). Currently, vehicles are supposed to carry relatively more storage, on board computing facilities, increased sensing power and communication systems. In order to cope with real world demands such as low latency, low storage cost, mobility, etc., for the deployment of VANETs, numerous attempts have been taken to integrate fog-computing with VANETs. In the recent past, Ma et al. (IEEE Internet of Things, pp 2327-4662, 10. 1109/JIOT.2019.2902840) designed “An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks”. Ma et al. claimed that their protocol offers secure communication in fog-based VANETs and is resilient against several security attacks. However, this comment demonstrates that their scheme is defenseless against vehicle-user impersonation attack and reveals secret keys of vehicle-user and fog-node. Moreover, it fails to offer vehicle-user anonymity and has inefficient login phase. This paper also gives some essential suggestions on strengthening resilience of the scheme, which are overlooked by Ma et al.

关键词： Protocols Ad hoc networks Sensors Security Internet of Things Intelligent transportation systems Resilience

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Quantum uncertainty as classical uncertainty of real-deterministic variables constructed from complex weak values and a global random variable

arXiv

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arXiv 2021年

作者： Budiyono, Agung Dipojono, Hermawan K. Research Center for Nanoscience and Nanotechnology Bandung Institute of Technology Bandung40132 Indonesia Department of Engineering Physics Bandung Institute of Technology Bandung40132 Indonesia Laboratory for Quantum Technology School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Kubus Computing and Research Juwana Pati 59185 Indonesia

What does it take for real-deterministic c-valued (i.e., classical, commuting) variables to comply with the Heisenberg uncertainty principle? Here, we construct a class of real-deterministic c-valued variables out of the weak values obtained via a non-perturbing weak measurement of quantum operators with a post-selection over a complete set of state vectors basis, which always satisfies the Kennard-Robertson-Schrödinger uncertainty relation. First, we introduce an auxiliary global random variable and couple it to the imaginary part of the weak value to transform the incompatibility between the quantum operator and the basis into the fluctuation of an 'error term', and then superimpose it onto the real-part of the weak value. We show that this class of "c-valued physical quantities" provides a real-deterministic contextual hidden variable model for the quantum expectation value of a certain class of operators. We then show that the Schrödinger and the Kennard-Robertson lower bounds can be obtained separately by imposing the classical uncertainty relation to the c-valued physical quantities associated with a pair of Hermitian operators. Within the representation, the complementarity between two incompatible quantum observables manifests the absence of a basis wherein the error terms of the associated two c-valued physical quantities simultaneously vanish. Furthermore, quantum uncertainty relation is captured by a specific irreducible epistemic restriction, foreign in classical mechanics, constraining the allowed form of the joint distribution of the two c-valued physical quantities. We then suggest an epistemic interpretation of the two terms decomposing the c-valued physical quantity as the optimal estimate under the epistemic restriction and the associated estimation error, and discuss the classical limit. © 2021, CC BY-NC-ND.

关键词： Random variables

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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GADSA: Persuasive Gamified Antimicrobial Stewardship Decision Support App for Antibiotics Prescribing Behaviour Change in Nigeria

GADSA: Persuasive Gamified Antimicrobial Stewardship Decisio...

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IEEE International Conference on Serious Games and Applications for Health (SeGAH)

作者： Andreea Molnar Georgiana Birjovanu Susanne Luedtke Carmen E. Lefevre Fortune Ncube Caroline Wood Patrick Okonji Laura Shallcross Andrew Hayward Sue Wiseman Olajumoke Olufemi Eneyi Kpokiri Folasade Ogunsola Patty Kostkova Department of Computing Technology Swinburne University of Technology Melbourne Australia UCL dPHE University College London London UK Centre for Behaviour Change University College London London UK College of Medicine University of Lagos Lagos Nigeria Inst of Health Informatics University College London London UK Inst of Epidemiology and Health University College London London UK College of Medicine Lagos University Teaching Hospital Lagos Nigeria Faculty of Infectious and Tropical Diseases London School of Hygiene and Tropical Medicine London UK

Antimicrobial Resistance (AMR) is a prominent global threat. The spread of AMR is further accelerated by the inappropriate use of antibiotics. While misuse and overuse of antibiotics are particular global challenges regardless of resources and settings, there is evidence these are amplified in low-to-middle income countries (LMIC) such as Nigeria. While successful antimicrobial stewardship (AMS) programmes have been implemented in LMIC, more research is required to better understand how to overcome some of the underlying reasons and behaviour obstacles, such as lack of training, limited resources, culture and inadequate infection prevention and control practice. This is of particular importance for prophylactic antibiotic prescribing to prevent surgical site infections (SSI). Evidence-based guidelines published by the World Health Organization (WHO) and other public health agencies advise on best practice for prescribing and administering prophylactic surgical antibiotics. However, compliance with such guidelines amongst surgical teams is often limited. The development of engaging decision support tools, accessible at the point of care, at low cost for use in LMICs, is required to help surgeons apply AMS guidelines to their daily practice. Serious mobile games, developed with an educational or training purpose rather than for entertainment, are a powerful tool for persuasive behaviour change but little interest has been given to research for AMR to change prescription behaviour in LMIC. In this research we present the co-development of a decision support tool for the “Gamified Antimicrobial Stewardship Decision Support App” (GADSA) in Nigeria with surgeons from three participating hospital sites. GADSA is the first gamified decision support app using persuasive games techniques for prescribing behaviour change demonstrated to deliver a prescription behaviour change increasing compliance with SSI guidelines at the point of care. GADSA is also is novel at providing de

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Sneakernet: A Technological Overview and Improvement

Sneakernet: A Technological Overview and Improvement

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International Conference on Sustainable Engineering and Creative computing (ICSECC)

作者： Andika Candra Jaya Cutifa Safitri Rila Mandala Faculty of Computing President University Cikarang Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175898

This paper is carried out to critically examine and evaluate the implementation of offline data transfer, most commonly known as sneakernet. This work reviews the details of the available information of the current and proposes possible future deployment of sneakernet. The idea of the concept theory is based on the available and future advancement of the technology. The expensive data transfer for delay-tolerant applications is solved in this work by employing massive sneakernet in various range utilization. This concept is able to reduce the burden of backbone networks and still be able to bring the information from source to destination securely. Another advantages of the proposed concept are the introduction of the security layer during information in transit. The viability of the concept is assessed based on the use-cases and the standard sneakernet procedure.

关键词： Conferences Data transfer Security Standards

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Enabling Deep Residual Networks for Weakly Supervised Object Detection 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Shen, Yunhang Ji, Rongrong Wang, Yan Chen, Zhiwei Zheng, Feng Huang, Feiyue Wu, Yunsheng Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China Pinterest San Francisco United States CSE Southern University of Science and Technology Shenzhen China Co. Ltd. Shanghai China

ISBN: (数字)9783030585983

ISBN: (纸本)9783030585976

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale image-level annotation for detector training. Whilst deep residual networks such as ResNet and DenseNet have become the standard backbones for many computer vision tasks, the cutting-edge WSOD methods still rely on plain networks, e.g., VGG, as backbones. It is indeed not trivial to employ deep residual networks for WSOD, which even shows significant deterioration of detection accuracy and non-convergence. In this paper, we discover the intrinsic root with sophisticated analysis and propose a sequence of design principles to take full advantages of deep residual learning for WSOD from the perspectives of adding redundancy, improving robustness and aligning features. First, a redundant adaptation neck is key for effective object instance localization and discriminative feature learning. Second, small-kernel convolutions and MaxPool down-samplings help improve the robustness of information flow, which gives finer object boundaries and make the detector more sensitivity to small objects. Third, dilated convolution is essential to align the proposal features and exploit diverse local information by extracting high-resolution feature maps. Extensive experiments show that the proposed principles enable deep residual networks to establishes new state-of-the-arts on PASCAL VOC and MS COCO. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Using Machine Learning for Intrusion Detection System in Wireless Body Area Network 1

Using Machine Learning for Intrusion Detection System in Wir...

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1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020

作者： Alsubaie, Fheed Al-Akhras, Mousa Alzahrani, Hamdan A. Cyber Security Division Communication and It Department Riyadh1495-22463 Saudi Arabia King Abdullah Ii School of 3King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan College of Computing and Informatics Saudi Electronic University Riyadh11673 Saudi Arabia

ISBN: (纸本)9781728174075

This paper introduces a technique that enhances the capabilities of an intrusion detection system (IDS) in a wireless body area network (WBAN). This technique involves adopting two known machine-learning algorithms: artificial neural network (ANN) and the J48 form of decision trees. The enhanced technique reduces the security threats to a WBAN, such as denial-of-service (DoS) attacks. It is essential to manage noise, which might affect the data gathered by the sensors. In this paper, noise in data is measured because it can affect the accuracy of the machine learning algorithms and demonstrate the level of noise at which the machine-learning model can be trusted. The results show that J48 is the best model when there is no noise, with an accuracy reaching 99.66%, as compared to the ANN algorithm. However, with noisy datasets, ANN shows more tolerance to noise. © 2020 IEEE.

关键词： Neural networks

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Collaborative wi-fi fingerprinting indoor positioning using near relation 24

Collaborative wi-fi fingerprinting indoor positioning using ...

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2020 24th ISPRS Congress - Technical Commission IV on Spatial Information Science

作者： Wang, Y. Wang, W. Li, X. Zhang, W. Guo, R. National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Urban Informatics Shenzhen Key Laboratory of Spatial Smart Sensing and Services Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

Indoor positioning is of great importance to the era of mobile computing. Currently, much attention has been paid to RSS-based location for that it can provide position information without additional equipment. However, this method suffers from many challenges: (1) fingerprint ambiguity;(2) labor-intensive of fingerprint collection;(3) low efficiency of fingerprint matching. To get over these drawbacks, we provide a collaborative WiFi fingerprinting indoor positioning method using near relation. The base idea of this method is that interpolation method is used to enrich sparse Wi-Fi fingerprint. Near relation boundary is provided and Wi-Fi fingerprints is constrained to this region to reduce fingerprint ambiguity, which also can improve the efficiency of fingerprint matching. Extensive experiments show that a positioning accuracy of 3.8m can be achieved with the near relation under 1m interpolation density. © 2020 Authors.

关键词： Efficiency

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