Reviewerly/exHarmony huggingface dataset:
A lightweight and personalized edge federated learning model

Authors:
raw_author_name: Peiyan Yuan
raw_affiliation_string: School of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
raw_author_name: Ling Shi
raw_affiliation_string: School of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
raw_author_name: Xiaoyan Zhao
raw_affiliation_string: School of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
raw_author_name: Junna Zhang
raw_affiliation_string: School of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

Abstract: Abstract As a new distributed machine learning paradigm, federated learning has gained increasing attention in the industry and research community. However, federated learning is challenging to implement on edge devices with limited resources and heterogeneous data. This study aims to realize a lightweight and personalized model through pruning and masking with insufficient resources and heterogeneous data. Particularly, the server first downloads the subnetwork to the client according to the mask, and client prunes the subnetwork with the alternating direction method of multipliers (ADMM), so as to remove the unimportant parameters and reduce the cost of training and communication. At the same time, mask is used to mark the pruning condition of the model. Then, the unpruned parts and masks of local models are transmitted to the server for aggregation. The experimental results showed that the accuracy of the proposed model was improved by 9.36%, and the communication cost was reduced by 1.45 times compared with state-of-the-art models. Last but not least, we deploy flower identification models in Android Studio to illustrate the practicality of the proposed method.
Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis

Authors:
raw_author_name: Khadijeh Moulaei
raw_affiliation_string: Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
raw_author_name: Kambiz Bahaadinbeigy
raw_affiliation_string: Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Abstract: Cognitive impairments present challenges for patients, impacting memory, attention, and problem-solving abilities. Virtual reality (VR) offers innovative ways to enhance cognitive function and well-being. This study explores the effects of VR-based training programs and games on improving cognitive disorders.PubMed, Scopus, and Web of Science were systematically searched until May 20, 2023. Two researchers selected and extracted data based on inclusion and exclusion criteria, resolving disagreements through consultation with two other authors. Inclusion criteria required studies of individuals with any cognitive disorder engaged in at least one VR-based training session, reporting cognitive impairment data via scales like the MMSE. Only English-published RCTs were considered, while exclusion criteria included materials not primarily focused on the intersection of VR and cognitive disorders. The risk of bias in the included studies was assessed using the MMAT tool. Publication bias was assessed using funnel plots and Egger's test. The collected data were utilized to calculate the standardized mean differences (Hedges's g) between the treatment and control groups. The heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was conducted using Stata version 17.0.Ten studies were included in the analysis out of a total of 3,157 retrieved articles. VR had a statistically significant improvement in cognitive impairments among patients (Hedges's g = 0.42, 95% CI: 0.15, 0.68; p_value = 0.05). games (Hedges's g = 0.61, 95% CI: 0.30, 0.39; p_value = 0.20) had a more significant impact on cognitive impairment improvement compared to cognitive training programs (Hedges's g = 0.29, 95% CI: -0.11, 0.69; p_value = 0.24). The type of VR intervention was a significant moderator of the heterogeneity between studies.VR-based interventions have demonstrated promise in enhancing cognitive function and addressing cognitive impairment, highlighting their potential as valuable tools in improving care for individuals with cognitive disorders. The findings underscore the relevance of incorporating virtual reality into therapeutic approaches for cognitive disorders.
Face photo-line drawings synthesis based on local extraction preserving generative adversarial networks

Authors:
raw_author_name: Po Wang
raw_affiliation_string: College of Electrical Engineering, Xinjiang University, Urumqi, China

Abstract: Facial photo-to-sketch synthesis is crucial for entertainment and criminal investigations, yet challenges persist, including local detail blurring and identity feature loss. To mitigate these problems in face sketch synthesis, We propose a technique for synthesizing photographic sketches of faces using generative adversarial networks that focus on image localization to extract and preserve image identity features – namely LEPGAN. Our method introduces an image local extraction module, an attention mechanism in the generator, and an extended U-Net structure to enhance critical image features and preserve identity information. Multi-scale perceptual and local loss functions further enhance synthetic image quality. LEPGAN outperforms existing methods, producing images with crisper facial details that closely align with human visual perception, effectively preserving unique identity characteristics. Our approach represents a significant advancement in face sketch synthesis for various applications.
Elk herd optimizer: a novel nature-inspired metaheuristic algorithm

Authors:
raw_author_name: Mohammed Azmi Al-Betar
raw_affiliation_string: Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE
raw_author_name: Mohammed A. Awadallah
raw_affiliation_string: Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
raw_author_name: Malik Shehadeh Braik
raw_affiliation_string: Department of Computer Science, Al-Balqa Applied University, Al-Salt, 19117, Jordan
raw_author_name: Sharif Makhadmeh
raw_affiliation_string: Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE
raw_author_name: Iyad Abu Doush
raw_affiliation_string: Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait

Abstract: Abstract This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman’s test post-hocked by Holm’s test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems.
In defense of local descriptor-based few-shot object detection

Authors:
raw_author_name: Shichao Zhou
raw_affiliation_string: Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China
raw_author_name: Zhuowei Wang
raw_affiliation_string: Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China
raw_author_name: Zekai Zhang
raw_affiliation_string: Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China

Abstract: State-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples. In contrast, human intelligence can robustly learn a new concept from just a few instances (i.e., few-shot detection). The distinctive perception mechanisms between these two families of systems enlighten us to revisit classical handcraft local descriptors (e.g., SIFT, HOG, etc.) as well as non-parametric visual models, which innately require no learning/training phase. Herein, we claim that the inferior performance of these local descriptors mainly results from a lack of global structure sense. To address this issue, we refine local descriptors with spatial contextual attention of neighbor affinities and then embed the local descriptors into discriminative subspace guided by Kernel-InfoNCE loss. Differing from conventional quantization of local descriptors in high-dimensional feature space or isometric dimension reduction, we actually seek a brain-inspired few-shot feature representation for the object manifold, which combines data-independent primitive representation and semantic context learning and thus helps with generalization. The obtained embeddings as pattern vectors/tensors permit us an accelerated but non-parametric visual similarity computation as the decision rule for final detection. Our approach to few-shot object detection is nearly learning-free, and experiments on remote sensing imageries (approximate 2-D affine space) confirm the efficacy of our model.
Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

Authors:
raw_author_name: Lukas Morand
raw_affiliation_string: Fraunhofer Institute for Mechanics of Materials IWM, Freiburg, Germany
raw_author_name: Dirk Helm
raw_affiliation_string: Fraunhofer Institute for Mechanics of Materials IWM, Freiburg, Germany

Abstract: Abstract In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for Support Vector Classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.
Correlation enhanced distribution adaptation for prediction of fall risk

Authors:
raw_author_name: Ziqi Guo
raw_affiliation_string: Department of Systems Science and Industrial Engineering, The State University of New York at Binghamton, Binghamton, USA
raw_author_name: Teresa Wu
raw_affiliation_string: School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
raw_author_name: Thurmon E. Lockhart
raw_affiliation_string: School of Biological and Health Systems Engineering, Arizona State University, Tempe, USA
raw_author_name: Rahul Soangra
raw_affiliation_string: Department of Physical Therapy, Chapman University, Orange, USA
raw_author_name: Hyunsoo Yoon
raw_affiliation_string: Department of Industrial Engineering, Yonsei University, Seoul, Korea

Abstract: With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient's condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models.
Autonomous multicopter landing on a moving vehicle based on RSSI

Authors:
raw_author_name: Jongwoo An
raw_affiliation_string: Department of Electronics Engineering, Pusan National University, Kumjeong-ku, Republic of Korea
raw_author_name: Hosun Kang
raw_affiliation_string: Department of Electronics Engineering, Pusan National University, Kumjeong-ku, Republic of Korea
raw_author_name: Jangmyung Lee
raw_affiliation_string: Department of Electronics Engineering, Pusan National University, Kumjeong-ku, Republic of Korea

Abstract: Abstract Currently, most of the studies on unmanned aerial vehicle (UAV) automatic landing systems mainly depend on image information to determine the landing location. However, the system requires a camera, a gimbal system and a separate image-processing device, which increases the weight and power consumption of the UAV, resulting in a shorter flight time. In addition, a large amount of computation and slow reaction speed can cause the camera to miss a proper landing moment. To solve these problems, in this study, the moving direction and relative distance between an object and the automatic landing system were measured using a receive signal strength indicator of the radio-frequency (RF) signal. To improve the movement direction and relative distance estimation accuracy, the noise in the RF signal was minimised using a low pass filter and moving average filter. Based on the filtered RF signal, the acceleration of the multicopter to reach the object was estimated by adopting the proportional navigation algorithm. The performance of the proposed algorithm for precise landing on a moving vehicle was demonstrated through experiments.
Unmasking the oligarchs - Using open source data to detect sanctions violations

Authors:
raw_author_name: Simon Marsden
raw_affiliation_string: University of Portsmouth, Institute of Criminal Justice Studies, School of Criminology and Criminal Justice, Museum Road, Ravelin House, Portsmouth, PO1 2QQ, United Kingdom

Abstract: Russia’s invasion of Ukraine in February 2022 led Western governments to impose extensive sanctions on the Russian economy and the people who maintain the regime - the so-called oligarchs. The enforcement of these sanctions and the manoeuvres of these oligarchs to evade them have created new challenges for the authorities, industry and society at large. This study aims to unmask sanctioned oligarchs who are beneficial owners of companies in the UK. The approach is based on the assumption that the actions of oligarchs leave detectable traces in the UK company register. Inspired by simple tools developed by volunteers for citizens doing their own intelligence work, software has been developed to achieve three different research objectives: to develop indicators of sanctions evasion, to demonstrate that it is possible to do useful intelligence work using only the resources available to the average citizen, and to develop a software tool to identify officers in the UK company register who are suspected of being linked to sanctioned oligarchs. The findings underscore the importance of open source investigations, reflecting a burgeoning intelligence phenomenon introduced as ‘citizen intelligence’, which has the potential to enhance the protection of society by building a more democratic and diverse intelligence community. This study opens the door for further research into the actions and tactics of sanctioned individuals, providing insights to protect global financial integrity and security.
A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

Authors:
raw_author_name: S. Sajini
raw_affiliation_string: Department of Computer and Information Science, Faculty of Science, Annamalai University, India
raw_author_name: B. Pushpa
raw_affiliation_string: Department of Computer and Information Science, Faculty of Science, Annamalai University, India

Abstract: This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.
Mammography with deep learning for breast cancer detection

Authors:
raw_author_name: Lulu Wang
raw_affiliation_string: Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen, China

Abstract: X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.
AI-based real-time hand gesture-controlled virtual mouse

Authors:
raw_author_name: Pranoti Kavimandan
raw_affiliation_string: Department of Computer Science &amp; Business System, Bharati Vidyapeeth (Deemed to be University), Pune, India

Abstract: Hand gesture control technology is an emerging alternative to conventional input devices like keyboards and mice, enabling users to interact with computers through natural hand movements. Although, many researchers worked on gesture recognition and its challenges, yet wide range of gestures has not been addressed. In this direction, the author proposed a novel gesture-controlled virtual mouse system that uses hand gestures and hand tip detection with a webcam or internal camera for computer navigation that accurately recognizes various gestures, including cursor movement, left and right clicks, double clicks, scrolling, drag and drop, multiple item selection, volume control. To deal with this real-world problem, the authors proposed a solution for limited space situations and assist individuals with hand impairments who cannot use a physical mouse. Furthermore, it surpasses the limitations of traditional mice, such as mechanical wear and tear, adaptability to different environments, and restricted functionalities. By leveraging hand gestures and tip detection through a camera proposed system offers an efficient and adaptable approach to operating a PC mouse. The proposed methodology uses computer vision and AI for the accurate detection of gestures, even in challenging environments. It also offers a comprehensive set of gestures for various actions and functions.