Reviewerly/exHarmony huggingface dataset:
A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm

Authors:
raw_author_name: Raissa Souza
raw_affiliation_string: Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
raw_author_name: Emma A. M. Stanley
raw_affiliation_string: Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
raw_author_name: Zahinoor Ismail
raw_affiliation_string: Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom; Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
raw_author_name: Matthias Wilms
raw_affiliation_string: Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
raw_author_name: Nils D. Forkert
raw_affiliation_string: Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

Abstract: Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.
Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

Authors:
raw_author_name: Yuliana Jiménez-Gaona
raw_affiliation_string: Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, Loja, Ecuador; Instituto de Instrumentación para la Imagen Molecular i3M, Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC), Valencia, Spain; Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, Canada
raw_author_name: María José Rodríguez-Álvarez
raw_affiliation_string: Instituto de Instrumentación para la Imagen Molecular i3M, Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC), Valencia, Spain
raw_author_name: Darwin Castillo-Malla
raw_affiliation_string: Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, Loja, Ecuador; Instituto de Instrumentación para la Imagen Molecular i3M, Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC), Valencia, Spain; Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, Canada
raw_author_name: Vasudevan Lakshminarayanan
raw_affiliation_string: Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada; Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, Canada

Abstract: This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.
“Dirclustering”: a semantic clustering approach to optimize website structure discovery during penetration testing

Authors:
raw_author_name: Gaetano Perrone
raw_affiliation_string: Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy
raw_author_name: Simon Pietro Romano
raw_affiliation_string: Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy

Abstract: Dirbusting is a technique used to brute force directories and file names on web servers while monitoring HTTP responses in order to enumerate server contents. Such a technique uses lists of common words to discover the hidden structure of the target website. Dirbusting typically relies on response codes as discovery conditions to find new pages. It is widely used in web application penetration testing, an activity that allows companies to detect website vulnerabilities. Dirbusting techniques are both time and resource-consuming, and innovative approaches have never been explored in this field. We hence propose "Dirclustering", an advanced technique to optimize the dirbusting process by leveraging semantic clustering. Specifically, we use semantic clustering techniques to organize wordlist items in different groups according to their semantic meaning. The created clusters are used in an ad-hoc implemented next-word intelligent strategy. This paper demonstrates that clustering techniques outperform the commonly used brute-force methods. Performance is evaluated by testing eight different web applications. Results show a performance increase that is up to 50% for each of the conducted experiments.
Model of collaborative information retrieval behavior using information systems among academic researchers of medical sciences

Authors:
raw_author_name: Saeed Rezaei Sharif Abadi
raw_affiliation_string: Alzahra University
raw_author_name: Masoumeh Karbala Aghaee Kamran
raw_affiliation_string: Alzahra University

Abstract: Aim The purpose of this study was to analyze the collaborative information retrieval (CIR) behavior among faculty members of research centers based in the University of Medical Sciences to present a structural model. Methodology The type of this experimental study was based on a pretest-posttest with the control group. Data were collected using a questionnaire, and the participants were selected by stratified random sampling. Out of a total of 73 participants, there were 42 people in the intervention group and 31 participants in the control group. Findings The variables of awareness, information sharing, division of labor, and team communication had a significant positive relationship with the team collaboration (p &lt; 0.05), collaboration had effect on the evaluation of retrieved information with a significant positive relationship (p &lt; 0.05). Conclusion Based on the findings, CIR is the set of activities that can include factors such as the ability to share the obtained knowledge, analyze the retrieved information, communicate, and collaborative awareness. The structural model confirmed our findings that developing the interactive and synergies capabilities of research team members to increase collaboration through a network-based structure reduces the loss of stored collective knowledge and also accelerates the process of access to information resources, and required content. Originality This research has carried on as the one of the first studies on collaborative information retrieval behavior with an experimental method in Iran and many others countries. A considerable point in the present study was assessing the nature of CIR behavior from the perspective of human behaviors, which was mentioned as a very important approach in information science. Accordingly, a human behavioral approach to information retrieval was considered and strengthened, regardless of technical aspects. Another strength of this study was the model extracted from our findings which have emphasized specifically CIR as specific and dynamic activities.
Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things

Authors:
raw_author_name: Mohammad Kamrul Hasan
raw_affiliation_string: Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
raw_author_name: Shayla Islam
raw_affiliation_string: Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Malaysia
raw_author_name: Shailesh Khapre
raw_affiliation_string: Department of Data Science & Artificial Intelligence, Dr. S. P. Mukherjee International Institute of Information Technology, Naya Raipur, Chhattisgarh, India
raw_author_name: Salwani Abdullah
raw_affiliation_string: Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
raw_author_name: Taher M. Ghazal
raw_affiliation_string: Applied Science Research Center, Applied Science Private University, Amman, Jordan; Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia; School of Information Technology, Skyline University College, University City Sharjah, Sharjah, UAE
raw_author_name: Ahmed Ibrahim Alzahrani
raw_affiliation_string: Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Nasser Alalwan
raw_affiliation_string: Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Md Akhtaruzzaman
raw_affiliation_string: Asian University of Bangladesh Computer Science &amp; Engineering Dhaka Bangladesh

Abstract: Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.
MA‐CAT: Misclassification‐Aware Contrastive Adversarial Training

Authors:
raw_author_name: Hongxin Zhi
raw_affiliation_string: Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450000 China; National Digital Switching System Engineering Technology Research Center, Zhengzhou, 450000 China
raw_author_name: Hongtao Yu
raw_affiliation_string: National Digital Switching System Engineering Technology Research Center, Zhengzhou, 450000 China
raw_author_name: Shaomei Li
raw_affiliation_string: Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450000 China; National Digital Switching System Engineering Technology Research Center, Zhengzhou, 450000 China
raw_author_name: Ruiyang Huang
raw_affiliation_string: Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450000 China; National Digital Switching System Engineering Technology Research Center, Zhengzhou, 450000 China

Abstract: Vulnerability to adversarial examples poses a significant challenge to the secure application of deep neural networks. Adversarial training and its variants have shown great potential in addressing this problem. However, such approaches, which directly optimize the decision boundary, often result in overly complex adversarial decision boundaries that are detrimental to generalization. To deal with this issue, a novel plug‐and‐play method known as Misclassification‐Aware Contrastive Adversarial Training (MA‐CAT) from the perspective of data distribution optimization is proposed. MA‐CAT leverages supervised decoupled contrastive learning to cluster nature examples within the same class in the logit space, indirectly increasing the margins of examples. Moreover, by taking into account the varying difficulty levels of adversarial training for different examples, MA‐CAT adaptively customizes the strength of adversarial training for each example using an instance‐wise misclassification‐aware adaptive temperature coefficient. Extensive experiments on the CIFAR‐10, CIFAR‐100, and SVHN datasets demonstrate that MA‐CAT can be easily integrated into existing models and significantly improves robustness with minimal computational cost.
An energy-efficient image filtering interpolation algorithm using domain-specific dynamic reconfigurable array processor

Authors:
raw_author_name: Aiying Guo
raw_affiliation_string: Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle, School of Microelectronics, Shanghai University, Shanghai 200444, China
raw_author_name: Jingjing Liu
raw_affiliation_string: Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle, School of Microelectronics, Shanghai University, Shanghai 200444, China; State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China

Abstract: With the development of micro-LED displays, the bicubic interpolation algorithm has become a critical technology. It is widely utilized in micro-LED displays to enhance the scaled details of video frames. However, the produced image exhibits the blurring effect and boundary artifacts after scaling. To address the above problems, we propose an image filtering interpolation algorithm. The smooth and Laplace filters are pre-filters to enhance image edges and reduce noise. The four corner-aligned bicubic interpolation algorithm has the high-quality effect of scaling. Secondly, to enhance the processing speed and energy-efficient of the image filtering interpolation algorithm, a domain-specific dynamic reconfigurable array processor is also proposed, which contains a 8 × 8 processing element array with two types of heterogeneous processing elements. The processor supports the energy-efficient acceleration of the image filtering interpolation algorithm. To reduce hardware costs, we integrate the attributes of the smooth filter and the Laplace filter into a simplified composite filter. Besides, we utilize the efficient alternating line caching technology to eliminate the power consumption of repeatedly reading data through reorchestrates data cache order and keep reused data. The experiment results show that the peak signal-to-noise ratio of the proposed image filtering interpolation algorithm is 0.32 dB better than the state-of-the-art methods. In the implementation of the HLMC 55 nm process, the energy-efficient of the proposed processor is 23,661 MPixels/J. Moreover, the processing rate achieves 583.5 MPixels/s for three DRAP, enough to 3840 × 2160@60fps. Using alternating line caching technology, the reconfigurable array processor reduces 37.5% of line buffer resources.
Revisiting technology-assisted language learning affordances for language components: a decade survey

Authors:
raw_author_name: Akbar Bahari
raw_affiliation_string: English Language Teaching Department, Urmia University, Urmia, Iran
raw_author_name: Rui Li
raw_affiliation_string: School of Foreign Languages, Hunan University, Changsha, People’s Republic of China

Abstract: Given the important technology-assisted language learning (TALL)-empowered affordances, a comprehensive understanding of how these TALL tools facilitate learning across different language components, e.g., phonology, morphology, syntax, semantics, and pragmatics, could not only present the panoramic scenery of the subject matter but also inform pedagogy for applied linguistics researchers and practitioners. Despite the increasing attention being paid to the affordances of a particular TALL platform, there is still a lack of more holistic research practice to the affordances for the five language components. To bridge the gap, this study aims to systematically and quantitively revisit the TALL-empowered affordances with respect to these language components. To this end, a total of valid 201 articles published from 2010 to 2023 were selected and coded based on the proposed inclusion/exclusion criteria. Results revealed that different TALL platforms (viz. computer, mobile, tablet, virtual reality, and robot) help improve students' language learning performance and efficacy among the five language components. The implications of the study, in perfect tune with the distance language learning experiences in the post COVID-19 pandemic era, justify the need to take full advantage of the TALL affordances and accelerate the move toward a more technology-friendly field of study.
Bidirectional Obstacle Avoidance Enhancement‐Deep Deterministic Policy Gradient: A Novel Algorithm for Mobile‐Robot Path Planning in Unknown Dynamic Environments

Authors:
raw_author_name: Junxiao Xue
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310000 China; Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, 310000 China; School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450002 China; School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study of UCAS, Hangzhou, 310000 China
raw_author_name: Yafei Lu
raw_affiliation_string: Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, 310000 China
raw_author_name: Xiaoran Yan
raw_affiliation_string: Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, 310000 China
raw_author_name: Yuanxun Zheng
raw_affiliation_string: Yellow River Laboratory, Zhengzhou University, Zhengzhou, 450001 China

Abstract: Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle‐avoidance reactions from the state and action. The cross‐attention mechanism is incorporated to enhance the attention to valuable obstacle‐avoidance information. Meanwhile, the obstacle‐avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. The results show that the proposed method is suitable for path planning tasks in unknown environments and has an excellent obstacle‐avoidance learning capability.
Alternative method to reveal encoded images via Gaussian distribution functions

Authors:
raw_author_name: J. Tuxpan
raw_affiliation_string: Division de Geociencias Aplicadas, Instituto Potosino de Investigacion Cientifica y Tecnologica, Camino a la Presa San José 2055. Col. Lomas 4 sección, 78216, San Luis Potosi, Mexico
raw_author_name: E. Campos-Cantón
raw_affiliation_string: Division de Control y Sistemas Dinamicos, Mexico

Abstract: In this work, we present an image processing approach that proved to be effective to reveal an image that was codified by different substitution boxes. The image was hidden by applying a different substitution box on each line producing an image with uniform distribution. Once the original image was codified through this procedure, the codified picture presented a shapeless uniform grey tone due to encoding. In order to reveal the image several assumptions were made, the most important of which was to consider that the gray tones of the original image could be reproduced by a normal o Gaussian distribution or by a superposition of several Gaussian functions. This assumption was implemented at the level of each line of the image by replacing the encoded pixels with those generated by a Gaussian distribution. Among the main findings of this work, the following are worth highlighted: (i) According to our research, for the first time Gaussian distribution functions are used to reveal encoded images, without the need to decode them; (ii) a general technique was implemented to reveal an encoded image, which is applicable to encoded images in which a different encoding rule was applied to each row of the original image; (iii) the values generated by the Gaussian functions were used to replace the pixel values of the encoded image, but their original positions were maintained; (iv) Image enhancement, including contrast, gray tone balance and feature definition, was achieved either by varying the number of Gaussian functions, or dividing the image in zones, or by adjusting pixels 0 and 255, among other resources used. Finally, it is also worth noting that this is a general procedure applicable to decode any image encoded using substitution boxes, as long as the original positions of the encoded pixels are preserved. This method has considerable simplicity and the best results were obtained when applied to high-resolution images.