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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ehsan Rokhsatyazdi Shahryar Rahnamayan Sevil Zanjani Miyandoab Azam Asilian Bidgoli H.R. Tizhoosh Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada Department of Artificial Intelligence and Informatics Mayo Clinic Rhazes Lab Rochester MN USA

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously; for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

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ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions

arXiv

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arXiv 2023年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Spitale, Micol Ge, Zongyuan Shen, Linlin Gunes, Hatice Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Computer Sciences University of Exeter ExeterEX4 4PY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Airdoc-Monash Research Centre Monash University Faculty of IT Monash University Melbourne Australia

In dyadic interaction, predicting the listener’s facial reactions is challenging as different reactions could be appropriate in response to the same speaker’s behaviour. Previous approaches predominantly treated this task as an interpolation or fitting problem, emphasizing deterministic outcomes but ignoring the diversity and uncertainty of human facial reactions. Furthermore, these methods often failed to model short-range and long-range dependencies within the interaction context, leading to issues in the synchrony and appropriateness of the generated facial reactions. To address these limitations, this paper reformulates the task as an extrapolation or prediction problem, and proposes an novel framework (called ReactFace) to generate multiple different but appropriate facial reactions from a speaker behaviour rather than merely replicating the corresponding listener facial behaviours. Our ReactFace generates multiple different but appropriate photo-realistic human facial reactions by: (i) learning an appropriate facial reaction distribution representing multiple different but appropriate facial reactions;and (ii) synchronizing the generated facial reactions with the speaker verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences. Experimental results demonstrate the effectiveness of our approach in generating multiple diverse, synchronized, and appropriate facial reactions from each speaker’s behaviour. The quality of the generated facial reactions is intimately tied to the speaker’s speech and facial expressions, achieved through our novel speaker-listener interaction modules. Our code is made publicly available at https://***/lingjivoo/ReactFace. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Limited quantum advantage for stellar interferometry via continuous-variable teleportation

arXiv

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arXiv 2023年

作者： Huang, Zixin Baragiola, Ben Q. Menicucci, Nicolas C. Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and IT University of Technology Sydney Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University MelbourneVIC3000 Australia School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

We consider stellar interferometry in the continuous-variable (CV) quantum information formalism and use the quantum Fisher information (QFI) to characterize the performance of three key strategies: direct interferometry (DI), local heterodyne measurement, and a CV teleportation-based strategy. In the lossless regime, we show that a squeezing parameter of r ≈ 2 (18 dB) is required to reach ≈ 95% of the QFI achievable with DI;such a squeezing level is beyond what has been achieved experimentally. In the low-loss regime, the CV teleportation strategy becomes inferior to DI, and the performance gap widens as loss increases. Curiously, in the high-loss regime, a small region of loss exists where the CV teleportation strategy slightly outperforms both DI and local heterodyne, representing a transition in the optimal strategy. We describe this advantage as limited because it occurs for a small region of loss, and the magnitude of the advantage is also small. We argue that practical difficulties further impede achieving any quantum advantage, limiting the merits of a CV teleportation-based strategy for stellar interferometry. Copyright © 2023, The Authors. All rights reserved.

关键词： Interferometry

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The maintenance scheduling and location choice problem for railway rolling stock

arXiv

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arXiv 2021年

作者： Zomer, Jordi Bešinović, Nikola de Weerdt, Mathijs M. Goverde, Rob M.P. Faculty of Civil Engineering and Geosciences Department of Transport & Planning Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Department of Software and Computer Technology Delft University of Technology Delft Netherlands

Due to increasing railway use, the capacity at railway yards and maintenance locations is becoming limiting. Therefore, the scheduling of rolling stock maintenance and the choice regarding optimal locations to perform maintenance is increasingly complicated. This research introduces a Maintenance Scheduling and Location Choice Problem (MSLCP). It simultaneously determines maintenance locations and maintenance schedules of rolling stock, while it also considers the available capacity of maintenance locations, measured in the number of available teams. To solve the MSLCP, an optimization framework based on Logic-Based Benders' Decomposition (LBBD) is proposed by combining two models, the Maintenance Location Choice Problem (MLCP) and the Activity Planning Problem (APP), to assess the capacity of a MLCP solution. Within the LBBD, four cut generation procedures are introduced to improve the computational performance: a naive procedure, two heuristic procedures and the so-called min-cut procedure that aims to exploit the specific characteristics of the problem at hand. The framework is demonstrated on a realistic scenarios from the Dutch railways. It is shown that the best choice for cut generation procedure depends on the objective: when aiming to find a good but not necessarily optimal solution, the min-cut procedure performs best, whereas when aiming for the optimal solution, one of the heuristic procedures is the preferred option. The techniques used in the current research are new to the current field and offer interesting next research opportunities. © 2021, CC BY.

关键词： Maintenance

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Review and Analysis of Risk Factor of Maternal Health in Remote Area Using the Internet of Things (IoT) 5th

Review and Analysis of Risk Factor of Maternal Health in Rem...

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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Ahmed, Marzia Kashem, Mohammod Abul Rahman, Mostafijur Khatun, Sabira Department of Computer Science and Engineering Dhaka University of Science and Technology Gazipur Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Faculty of Electrical and Electronics Engineering University Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9789811523168

IoT is the greatest ingenious innovation in the modern era, which can exploit also in mission-critical like the healthcare industry. This paper demonstrates effective monitoring of pregnant women mostly in a rural area of a developing country, with the help of wearable sensing enabled technology, which also notifies the pregnant women and her family about the health conditions. There are many researchers have been researched to reduce the maternal and fetal mortality but the mortality rate is not reducing, where it should be in zero tolerance. This research intended to use machine learning algorithms for discovering the risk level on the basis of risk factors in pregnancy. In this research, an existing dataset (Pima-Indian-diabetes dataset) has been used for the analysis of risk factor and comparison of some machine learning algorithm shows that Logistic Model Tree (LMT) gives the highest accuracy in case of classification and prediction of the risk level. Regardless, few selected pregnant women’s data has been collected (through IoT enabled devices) and the same process also applied for this dataset also by using LMT. Comparison results show that the prediction of risks is the same for the existing and real dataset. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

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Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

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Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs

arXiv

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arXiv 2022年

作者： Vale, Guilherme Correia, Filipe Figueiredo Guerra, Eduardo Martins de Oliveira Rosa, Thatiane Fritzsch, Jonas Bogner, Justus Faculty of Engineering University of Porto INESCTEC Porto Portugal Computer Science Faculty Free University of Bozen-Bolzano Alto Adige Bolzano Italy Institute of Mathematics and Statistics University of São Paulo SP São Paulo Brazil Institute of Software Engineering University of Stuttgart Stuttgart Germany

The promise of increased agility, autonomy, scalability, and reusability has made the microservices architecture a de facto standard for the development of large-scale and cloud-native commercial applications. software patterns are an important design tool, and often they are selected and combined with the goal of obtaining a set of desired quality attributes. However, from a research standpoint, many patterns have not been widely validated against industry practice, making them not much more than interesting theories. To address this, we investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. Hence, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes. We found that many of the trade-offs reported in our study matched the documentation of each respective pattern, and identified several gains and pains which have not yet been reported, leading to novel insight about microservice patterns. Copyright © 2022, The Authors. All rights reserved.

关键词： Reusability

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Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI

arXiv

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arXiv 2021年

作者： Kumar, Mohit Moser, Bernhard A. Fischer, Lukas Freudenthaler, Bernhard Software Competence Center Hagenberg Upper Austria Austria Faculty of Computer Science and Electrical Engineering University of Rostock Mecklenburg-Vorpommern Germany

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic trustworthy AI framework is introduced. A unified approach to "privacy-preserving interpretable and transferable learning" is considered for studying and optimizing the tradeoffs between privacy, interpretability, and transferability aspects. A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures via maximizing a lower-bound using variational optimization. The study presents a unified information theoretic approach to study different aspects of trustworthy AI in a rigorous analytical manner. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress on individuals using heart rate variability analysis. © 2021, CC BY.

关键词： Mapping

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Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks 19th

Breast Cancer Classification from Histopathological Images U...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Aloyayri, Abdulrahman Krzyżak, Adam Department of Computer Science and Software Engineering Concordia University MontrealH3G 1M8 Canada Faculty of Computer Science Department Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783030614003

Early diagnosis of breast cancer is the most reliable and practical approach to mitigate cancer. computer-aided detection or computer-aided diagnosis is one of the software technologies designed to assist doctors in detecting or diagnosing cancer and to reduce mortality using medical image analysis. Recently, Convolution Neural Networks became very popular in medical image analysis helping to process vast amount of data to detect and classify cancer in a fast and efficient manner. In this paper, we implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer in histopathological images. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images from the public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 98.73% for ResNet18, followed by 97.65% for ShuffleNet and 97.44% for Inception-V3Net. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Exploring Issues of Story-Based Effort Estimation in Agile software Development

SSRN

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SSRN 2023年

作者： Iqbal, Muhammad Ijaz, Muhammad Mazhar, Tehseen Shahzad, Tariq Abbas, Qamar Ghadi, Yazeed Yasin Ahmed, Waseem Hamam, Habib Institute of Computing and Information Technology Gomal University Dera Ismail Khan KP Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science FCIT International Islamic University Islamabad Pakistan Department of computer science and software engineering Al Ain University Abu Dhabi15322 United Arab Emirates Department of Computer Science and Information Technology University of Malakand Pakistan Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering. Science University of Johannesburg Johannesburg2006 South Africa Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia

Objctives This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy. Methods To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. Results The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition. © 2023, The Authors. All rights reserved.

关键词： software design

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