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SSRN 2023年

作者： Wang, Xuan Sun, Zhaojie Chehri, Abdellah Jeon, Gwanggil Song, Yongchao School of Computer and Control Engineering Yantai University Qingquan Road 30 Yantai264005 China Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Yeonsu-gu Incheon22012 Korea Republic of

Unsupervised pedestrian re-identification (re-ID) is still a challenging task. Although convolutional neural networks (CNN) have had great success in pedestrian re-ID, they face the challenge of dealing with pose changes, occlusions, and lighting changes. To effectively tackle these challenges, it is imperative to prioritize the implementation of efficient sampling strategies. We propose an attention-driven framework with clustering optimization (AFC) to address the above issues. First, we introduce a new attention mechanism that enhances multi-scale spatial attention and reduces the number of trainable parameters. Then, we employed a straightforward and effective method of group sampling. In addition, we apply a clustering consensus approach to estimate pseudo-label similarity in continuous training and use temporal propagation and ensembles to improve pseudo-labels. Extensive experiments on Market-1501, DukeMTMC-reID, and MSMT17 have shown that our approach achieves significant performance gains in unsupervised pedestrian re-ID tasks, providing valuable insights for further research in this area. © 2023, The Authors. All rights reserved.

关键词： Pattern recognition

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Wander 22

Wander

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Proceedings of the SIGGRAPH Asia 2022 Art Gallery

作者： Yuqian Sun Chang Hee Lee Chenhang Cheng Ali Asadipour Ying Xu Yihua Li Computer Science Research Centre Royal College of Art London United Kingdom Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Unaffiliated Shanghai China Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Department of Product Design College of Fashion and Design Donghua University Shanghai China

This is a public art project centred on an AI chatbot called Wander. She can generate interactive fiction based on realworld locations' knowledge graphs. Through public participation, the stories are visualised on...

ISBN: (纸本)9781450394710

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A Prognostic Model to Improve Asthma Prediction Outcomes Using Machine Learning

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Open Bioinformatics Journal 2024年 17卷

作者： M R, Pooja Ravi, Vinayakumar Lokesh, Gururaj Harinahalli Al Mazroa, Alanoud Ravi, Pradeep Department of Computer Science & Engineering Vidyavardhaka College of Engineering Mysuru India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Technology Manipal Academy of Higher Education Manipal 576104 India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information Science and Engineering GSSS Institute of Engineering and Technology for Women Karnataka Mysuru India

Purpose: The utility of predictive models for the prognosis of asthma disease that rely on clinical history and findings has been on the constant rise owing to the attempts to achieve better disease outcomes through improved clinical processes. With the prognostic model, the primary focus is on the search for a combination of features that are as robust as possible in predicting the disease outcome. Clinical decisions concerning obstructive lung diseases such as Chronic obstructive Pulmonary Disease (COPD) have a high chance of leading to results that can be misinterpreted with wrong inferences drawn that may have long-term implications, including the targeted therapy that can be mistakenly beset. Hence, we suggest data-centric approaches that harness learning techniques to facilitate the disease prediction process and augment the inferences through clinical findings. Methods: A dataset containing information on both symptomatic representations and medical history in the form of categorical data along with lung function parameters, which were estimated using a spirometer (with the data basically being quantitative (numerical) in nature) was used. The Naïve Bayes classifier performed comparatively well with the optimized feature set. The adoption of One-Class Support Vector Machines (OCSVM) as an alternative method to sampling data has resulted in the selection of an ideal representation of the data rather than the regular sampling approach that is used for undersampling. Results: The model was able to predict the disease outcome with a precision of 86.1% and recall of 84.7%, accounting for an F1 measure of 84.5%.The Area under Curve(AUC) and Classification Accuracy (CA) were evaluated to be 92.2% and 84.7% respectively. Conclusion: Incorporating domain knowledge into the prediction models involves identifying clinical features that are most relevant to the process of disease classification using prior knowledge about the disease and its contributing factors, which c

关键词： Asthma Machine learning Predictors Pulmonary Spirometry Tiffeneau-pinelli

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Crane Cloud: a resilient multi-cloud service layer for resource constrained settings

Research Square

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Research Square 2021年

作者： Bainomugisha, Engineer Mwotil, Alex Department of Computer Science School of Computing and Informatics Technology College of Computing and Information Sciences Makerere University Kampala Uganda

Whereas the main cloud providers have set up cloud services on stable infrastructure, developers and users situated in low-resource settings access cloud services and platforms using low-end computing devices that often connect to the Internet via slow mobile connections. These settings require custom software abstraction layers that consider such bandwidth constraints and intermittent connections as a rule rather than the exception. In this paper, we identify key challenges for developing for and accessing cloud services in resource constrained settings, namely, (1) Frequent Internet partitions and bandwidth constraints, (2) Data jurisdiction restrictions, (3) Vendor lock-in, and (4) Poor quality of service. To address these challenges, we propose a set of design considerations and properties for a resilient multi-cloud service layer, that includes: (1) Containerisation and orchestration of applications, (2) Service scheduling and replication, (3) Portability and multi-cloud migration, (4) Resilience to network partitions and bandwidth constraints, (5) Automated service discovery and load balancing, (6) Localised image registry, and (7) Support for platform monitoring and management. We present a prototype validation case study, Crane Cloud, an open source multi-cloud service abstraction layer built on-top of Kubernetes that is designed with inherent support for resilience to network partitions, microservice orchestration (deployment, scaling and management of containerized applications)a localized image registry, support for migration of services between private and public clouds to avoid vendor lock-in issues and platform monitoring. We evaluate the performance and user experience of Crane Cloud by implementing and deploying a computational and bandwidth intensive machine learning system shows lower response time compared when hosted on other public clouds. © 2021, CC BY.

关键词： Containers

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Incorporating Transfer Learning in Engine Health Assessment for Accurate Remaining Useful Life Prediction

Incorporating Transfer Learning in Engine Health Assessment ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： C. Karpagavalli Nelson A R. Raja Saranya E Manzoore Elahi M. Soudagar Christu Paul Ramaian Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering College Sivagangai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective; it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft.

关键词： Training Accuracy Atmospheric modeling Computational modeling Transfer learning Predictive models Maintenance

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Linecept: An Early Prototype of a Timeline-Based Design Coordination Tool 42

Linecept: An Early Prototype of a Timeline-Based Design Coor...

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42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020

作者： Kutas, David Tamas Nair, Aditya Singh, Prerna Kan, Emily Burge, Janet Van Der Hoek, André Department of Informatics University of California IrvineCA United States Department of Mathematics and Computer Science Colorado College Colorado SpringsCO United States

ISBN: (纸本)9781450379632

In software design, the various stakeholders generate large numbers of heterogeneous artifacts. These artifacts are often developed in, and managed by, different tools. In this paper, we present our initial prototype of Linecept, a tool that helps stakeholders organize, find, and view disparate design artifacts by organizing them on a timeline that presents a single unified view of the artifacts and who created them. We have used Linecept to retrospectively capture design artifacts for its own creation and in a software design class. © 2020 ACM.

关键词： Software design

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Deep Learning and Multi-Modal Fusion for Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, and Comparative Study

SSRN

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SSRN 2023年

Real-time multi-object tracking (MOT) is a complex task that involves the detection and tracking of multiple objects. After the objects are detected, they are assigned markers, and their trajectories are tracked in real-time. The scientific community is intrigued bythe possibilities of utilizing MOT technology in the context of smart cities. Their primary focus lies in the domains of intelligent transportation, detection of vehicles and pedestrians, crowd surveillance, and public safety. Deep learning techniques have been developed in recent years to effectively tackle the challenges of real-time MOT tasks and enhance tracking performance. Environmental perception within smart traffic applications relies heavily on sensor data fusion. In traffic scenarios, a combination of sensors and cameras can be used to detect and track targets, as well as collect data. However, it faces challenges when it comesto detecting and tracking objects that are in motion, have complex changes in appearance, or are in crowded scenes. This paper investigates the baseline standard for real-time Multiple Object Tracking tasks. Focusing on quantitative measures, we conduct a thorough and exhaustive analysis of commonly used benchmark datasets and metrics. In this study, we also examine established embedding techniques and real-time multi-object tracking algorithms. Each strategy will be categorized and evaluated based on a set of predetermined principles. Several MOT strategies are analyzed and represented visually. Finally, this paper provides an overview of the MOT mission’s present challenges and prospective objectives. © 2023, The Authors. All rights reserved.

关键词： Smart city

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Neurological Disorder Detection Using OCT Scan Image of Eye

Neurological Disorder Detection Using OCT Scan Image of Eye

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Cyber Resilience (ICCR), International Conference on

作者： Muhammad Arslan Aslam Muhammad Zonain Salman Muneer Omar Sattar Mohammad Salahat Muhammad Saleem Department of Computer Systems Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan School of Computer Science NCBA&E Lahore Pakistan General Education Department Skyline University College University City Sharjah Sharjah UAE College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE School of Information Technology Skyline University College University City Sharjah UAE

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Extracting useful features from medical images (in Radiology) helps to improve the early precautions and treatment as well as reducing the risk of performing major surgeries. There is an immense need of a methodology which can automatically extract retinal features and classify it into desired class. The proposed automated technique is based on Deep Convolutional Neural Networks of four different types of Convolutional Neural Networks (CNN) models i.e. Baseline 5 layer model, AlexNet and ResNET. These three CNN techniques are used for image segmentations, features extraction and classification of normal and abnormal images in three different image formats: Choroidal Neovascularization (CNV), DRUSEN (small yellow colored deposits of debris of eyes, and Diabetic Macular Edema (DME). Furthermore, the optimizers used are adaptive moment estimation (ADM) and stochastic gradient descent (SGD) which provide different accuracy under same iterations. ADM optimizer gives the best accuracy in all the CNN networks while SGD gives the least accuracy. Various type of abnormalities are detected in retina images are CNV, DME, and DRUSEN. The method proposed in this research is implemented on the Optical coherence tomography (OCT) Retinal Image dataset. For the sake of validation of proposed methodology, some known performance parameters like accuracy, F1_score, recall, precision, support and loss function are used. The observed and visualized results of the proposed method Compared to previous techniques, the findings of the approach are encouraging.

关键词： Deep learning Training Surgery Stochastic processes Feature extraction Retina Stability analysis

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Many-Problem Surrogates for Transfer Evolutionary Multiobjective Optimization With Sparse Transfer Stacking

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IEEE Transactions on Evolutionary Computation 2025年

作者： Li, Hao Wan, Fanggao Gong, Maoguo Qin, A.K. Wu, Yue Xing, Lining Xidian University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Shaanxi Province Xi'an710071 China Inner Mongolia Normal University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an China and Academy of Artificial Intelligence College of Mathematics Science Hohhot China Swinburne University of Technology Department of Computing Technologies HawthornVIC3122 Australia Xidian University School of Computer Science and Technology Xi'an710071 China

For expensive multiobjective optimization problems, there exists useful knowledge, e.g., the trained surrogate models, can be transferred to assist the optimization of a target optimization problem, which is termed as multi-problem surrogates. Stacking transfer is able to combine the pretrained source surrogate models and the preliminary target model with a meta-regression algorithm to transfer knowledge from source to target. However, when large-scale source models are involved in the many-problem scenarios, the less correlated sources may hurt the target performance, which is known as negative transfer. In this paper, sparse representation of the coefficients of meta-regression is considered to automatically select the most relevant source models for largely avoiding negative transfer. In the proposed many-problem surrogates, the coefficients of the source and target models are assumed to be sparse under the non-negativity and sum-to-one constraints. Then a sparse transfer stacking model is established with l1-norm of the coefficients. Next, the alternating direction method of multipliers is employed to solve the resulting constrained optimization problem by converting it into several much simpler problems. Most of the previous works assume that the costs for evaluation have no much difference and this assumption rarely holds in the real-world applications. In order to further reduce the total costs, an improved surrogate model with a cost-sensitive measure is designed to estimate the cost and select new solutions for real evaluation based on their estimated fitness, uncertainty and cost. Experimental results on synthetic and practical problems have demonstrated the superiority of the proposed many-problem surrogates. © 1997-2012 IEEE.

关键词： Multiobjective optimization sparse representation surrogate-assisted evolutionary algorithm transfer stacking

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