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Retraction Note to: Secure storage allocation scheme using fuzzy based heuristic algorithm for cloud

Journal of Ambient Intelligence and Humanized Computing 2022年第1期14卷 325-325页

作者： Sivaram, M. Kaliappan, M. Shobana, S. Jeya Prakash, M. Viju Porkodi, V. Vijayalakshmi, K. Vimal, S. Suresh, A. Department of Computer Networking College of Engineering and Computer Science Lebanese French University Erbil Iraq Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Erbil Iraq Department of Information Technology National Engineering College Kovilpatti India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

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A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis

arXiv

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arXiv 2021年

作者： Gkoumas, Dimitris Wang, Bo Tsakalidis, Adam Wolters, Maria Purver, Matthew Zubiaga, Arkaitz Liakata, Maria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Center for Precision Psychiatry Massachusetts General Hospital Boston United States The Alan Turing Institute London United Kingdom School of informatics University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Slovenia

Dementia affects cognitive functions of adults, including memory, language, and behaviour. Standard diagnostic biomarkers such as MRI are costly, whilst neuropsychological tests suffer from sensitivity issues in detecting dementia onset. The analysis of speech and language has emerged as a promising and non-intrusive technology to diagnose and monitor dementia. Currently, most work in this direction ignores the multi-modal nature of human communication and interactive aspects of everyday conversational interaction. Moreover, most studies ignore changes in cognitive status over time due to the lack of consistent longitudinal data. Here we introduce a novel fine-grained longitudinal multi-modal corpus collected in a natural setting from healthy controls and people with dementia over two phases, each spanning 28 sessions. The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extralinguistic information such as pen strokes and keystrokes. We present the data collection process and describe the corpus in detail. Furthermore, we establish baselines for capturing longitudinal changes in language across different modalities for two cohorts, healthy controls and people with dementia, outlining future research directions enabled by the corpus. © 2021, CC BY.

关键词： Neurodegenerative diseases

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Method for Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma

Method for Preoperative Prediction of Microvascular Invasion...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhao Tian Bai Tongjia Chu Feng Wei Fa Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Hepatobiliary and Pancreas Department General Surgery Center First Hospital of Jilin University Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.

关键词： Deep learning Pathology Correlation Biological system modeling Surgery Predictive models Data mining

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Contrastive Fusion of Generative and Discriminative Lesion Maps for Weakly Supervised Medical Image Segmentation

SSRN

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SSRN 2023年

作者： Du, Wei Huo, Yongkang Tang, Shiyi Chen, Liang Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China College of Engineering Shantou University Shantou515063 China Translational Medical Center for Stem Cell Therapy Institute for Regenerative Medicine Shanghai East Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

Medical image segmentation is a crucial computer-aided diagnosis (CAD) procedure in various clinical applications. However, most existing methods rely on pixel-level annotations, which are costly and time-consuming. We propose a novel contrastive fusion model of generative and discriminative lesion maps for weakly supervised medical image segmentation to address this limitation. The generative lesion map is generated by a lesion region-guided cycle-consistent adversarial network (CCANet), and the discriminative lesion map is produced by a class-aware-based discriminative lesion localization network (DLLNet). CCANet and DLLNet share weights at the encoder layer, which contains skip connections and channel attention modules to enhance feature embedding. In order to fully utilize and integrate the generative and discriminative lesion maps, and improve the segmentation accuracy, we design a fusion method based on contrastive loss to jointly train CCANet and DLLNet. The final lesion segmentation result is obtained by applying dense conditional random fields (Dense CRF) to the fused lesion map. Extensive experiments on three benchmark medical image datasets show that our model outperforms the state-of-the-art methods on weakly supervised medical image segmentation, especially in datasets with few samples, validating the proposed model's good generalization ability and robustness in various medical image segmentation scenarios. The source code and experiment datasets are available on GitHub: https://***/mlcb-jlu/GDC4MedSeg. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Responses to “An Al view of the treatment of uncertainty” by Alessandro Saffiotti

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The knowledge engineering Review 1988年第1期3卷 59-86页

作者： Clark, Dominic A. Prade, Henri Imperial Cancer Research Fund Laboratories London WC2A 3PX 44 Lincoln’s Inn Fields United Kingdom Engineering Mathematics Department University of Bristol United Kingdom NASA Ames Research Cenler/Heer Associates Inc. Mail slop: 244-17 Moffell Field CA 94040 United States Experimental Knowledge Systems Laboratory Department of Computer and Information Science University of Massachusetts Amherst Mass 01004 United States Laboratoire Langages et Systèmes Informatiques Université Paul Sabatier 118 route de Narbonne 31062 TOULOUSE Cédex France Imperial Cancer Research Fund Laboratories 44 Lincoln’s Inn Fields London WC2A 3PX United Kingdom Knowledge Systems Concepts Inc. Rome NY 13440 Italy MRC Biostatistics Unit Cambridge CB2 2BW United States IRIDIA Université Libre de Bruxelles 50 av. F. Roosevelt CP 194/6 1050 Bruxelles Belgium Computer Science Division University of California Berkeley CA 94720 United States

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Reports of the AAAI 2012 conference workshops

Reports of the AAAI 2012 conference workshops

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作者： Agrawal, Vikas Baier, Jorge Bekris, Kostas Chen, Yiling D'Avila Garcez, Artur S. Hitzler, Pascal Haslum, Patrik Jannach, Dietmar Law, Edith Lecue, Freddy Lamb, Luis C. Matuszek, Cynthia Palacios, Hector Srivastava, Biplav Shastri, Lokendra Sturtevant, Nathan Stern, Roni Tellex, Stefanie Vassos, Stavros Center for Knowledge Driven Intelligent Systems Enterprise Technology Research Labs Infosys Limited India Pontificia Universidad Católica de Chile Chile Rutgers University NJ United States Harvard University United States City University London United Kingdom Wright State University Dayton OH United States Australian National University Australia TU Dortmund Germany Machine Learning Department Carnegie Mellon University United States IBM Research - Smarter Cities Technology Centre Dublin Ireland Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science and Engineering Department University of Washington United States Universidad Carlos III de Madrid Spain IBM Research - India India University of Denver United States Ben Gurion University Negev Israel Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation Systems, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence.

关键词： Problem solving

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Teachers' views on the use of empathic robotic tutors in the classroom

Teachers' views on the use of empathic robotic tutors in the...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sofia Serholt Wolmet Barendregt Iolanda Leite Helen Hastie Aidan Jones Ana Paiva Asimina Vasalou Ginevra Castellano Department of Applied Information Technology University of Gothenburg Gothenburg Sweden GAIPS Instituto Superior Tecnico Lisboa Portugal Social Robotics Lab Yale University New Haven CT USA School of Mathematical and Computer Science Heriot-Watt University Edinburgh UK School of Electronic Electrical and Computer Engineering University of Birmingham Birmingham UK London Knowledge Lab Institute of Education London UK

ISBN: (纸本)9781479967667

In this paper, we describe the results of an interview study conducted across several European countries on teachers' views on the use of empathic robotic tutors in the classroom. The main goals of the study were to elicit teachers' thoughts on the integration of the robotic tutors in the daily school practice, understanding the main roles that these robots could play and gather teachers' main concerns about this type of technology. Teachers' concerns were much related to the fairness of access to the technology, robustness of the robot in students' hands and disruption of other classroom activities. They saw a role for the tutor in acting as an engaging tool for all, preferably in groups, and gathering information about students' learning progress without taking over the teachers' responsibility for the actual assessment. The implications of these results are discussed in relation to teacher acceptance of ubiquitous technologies in general and robots in particular.

关键词： Educational institutions Interviews Robot sensing systems Service robots Europe

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Correction to: Network Functions Virtualization for Mobile Core and Heterogeneous Cellular Networks

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Wireless Personal Communications 2021年第3期122卷 2561-2561页

作者： Qureshi, Kashif Naseer Ahmad , Ejaz Anwar, Muhammad Ghafoor, Kayhan Zrar Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer Research Faisalabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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On the Stability and Generalization of Triplet Learning

arXiv

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arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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An empirical examination of balancing strategy for counterfactual estimation on time series 24

An empirical examination of balancing strategy for counterfa...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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