Reviewerly/exHarmony huggingface dataset:
Micro Evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic

This work introduces the algorithm Micro Evolutionary Particle Swarm Optimization (MEPSO), based on the Particle Swarm Optimization algorithm (PSO). MEPSO's main idea is to replace PSO's update of velocity with Evolutionary mutations and crossovers that occur probabilistically. MEPSO uses two control hyperparameters of the evolutionary operators, α and β, to improve convergence ability. These hyperparameters allow the algorithm to adapt to different problems. MEPSO algorithm has a crucial feature, the use of Micropopulations. This results in reduced computational complexity, faster convergence rate, prevention of premature convergence, and easy implementation. Micropopulations also potentiate the study of MEPSO's suitability for dynamic and multiobjective optimization in the future. We evaluated MEPSO and PSO against 14 chosen benchmark functions. PSO's results revealed that it had an average success rate of 84% and a median computation time of 49.9 s. MEPSO, on the other hand, had a success rate of 81% and a computation time of 8 s. We found MEPSO to be faster when we performed a right-tail Wilcoxon Signed-Rank statistical test against the Mean Computation Time of 30 runs for each function for each algorithm.
Toward domain adaptation with open-set target data: Review of theory and computer vision applications

Open-set domain adaptation is a developing and practical solution to training deep networks using unlabeled data which have been collected among unknown data and are under domain shift with other labeled data. This scenario transfers knowledge from a source domain enriched with labeled data to the unlabeled target domain, meanwhile, unknown target samples which are not present in the source domain are separated. Existing methods aim to bridge the domain gap of shared classes between source and target domains in a trustworthy manner and avoid negative transfer learning using keeping away unknown data from the domain alignment step. In this review article, we present a unified framework of theory advances for network risk and a new categorization of open-set domain adaptation along with listing evaluation metrics and popular datasets. Then we accentuate challenges and gaps in existing studies to organize a road map for future research using detailed analysis of investigations. To bring things full circle, we also point out different assumptions and outlooks in the settings of this research area.
Care4U: Integrated healthcare systems based on blockchain

During the crisis of COVID-19, the need to stay at home has raised dramatically. In addition, the number of sick people, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patients can ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT). The IoMT can monitor and transmit sensitive health data, however, it may be vulnerable to various attacks. In this paper, an efficient healthcare security system is proposed for IoMT's applications. In the proposed system, the medical sensors can transmit sensed encrypted health data via a mobile application to the doctor for privacy. Then, three consortium blockchains are constructed for load balancing of transactions and reducing transaction latency. They store the credentials of system entities, doctor's prescriptions, and recommendations according to the data transmitted via mobile applications and the medical treatment process. Besides, cancelable biometrics are used for providing authentication and increasing the security of the proposed medical system. The investigational results show that the proposed system outperforms existing work where the proposed model consumed less processing time by values of 18%, 22%, and 40%, and less energy for processing a 200 KB file by values of 9%, 13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. From these results, it is clear the proposed system gives a very reliable and secure performance for efficiently securing medical applications.
Commitment-based negotiation semantics for accountability in multi-agent systems

Abstract Negotiation is a key form of interaction in multi-agent systems. Negotiation enables agents to come to a mutual agreement about a goal or plan of action. Current negotiation approaches use traditional interaction protocols which do not capture the normative meaning of interactions and often restrict agent autonomy. These traditional negotiation approaches also have difficulty specifying accountability. This paper seeks to address this gap in maintaining autonomy and capturing accountability during negotiation through the use of normative commitments. We propose Nala , a commitment-based negotiation semantics. Nala uses commitments to provide normative meaning to agent interactions. The nature of commitments support in capturing accountability through the violation of created commitments. We illustrate Nala ’s usage via a case study using a game scenario where agents participate in negotiation to bring about their goals in a research constrained environment.
Discrimination of quarry blasts from earthquakes in Northern and Central Egypt using linear and quadratic discriminant functions

Abstract In this study, seismic events in Northern and Central Egypt are inspected to discriminate quarry blasts from earthquakes. We examine a collection of 639 events in both time and frequency domains with local magnitudes of 1.5 ≤ ML ≤ 3.3 from the Egyptian Seismological Network’s seismic event catalogue between 2009 and 2015. The maximum S-wave to the maximum P-wave amplitude ratio, complexity (C), spectral ratio (S r ), and power of events (P e ) classifiers as well as two statistical approaches, linear discriminant function (LDF) and quadratic discriminant function (QDF), are used to distinguish between earthquakes and quarry blasts. The usage of the LDF and QDF forms did not result in any major differences in the discrimination. The results obtained by the LDF and QDF from (P e -C) are the best of all approaches. The findings of all approaches were compared to get a final categorization for each event, and a decision was achieved when at least three of the four methods provided the same event category. In Northern Egypt, 243 earthquakes and 308 quarry blasts could be identified as final decisions, with two misclassified events, resulting in an overall success rate of 99.6%. In Central Egypt, 48 earthquakes and 36 quarry blasts were classified as a final decision, with two misclassified events, for an overall success percentage of 97.6%.
Thangka mural style transfer based on progressive style-attentional network and multi-level loss function

Style transfer is a challenging computer vision task. As an important cultural heritage in the Tibetan region, Thangka murals cover history, culture, and religion. The stylistic recreation of Thangka images not only promotes better appreciation of Thangka art but also provides a new art form for Thangka. Two challenges prevent the existing methods from solving Thangka stylization problems: (1) feature point based attention networks ignore manifold alignment of features between content images and style images and are unable to achieve regional consistency of stylized Thangka images well. (2) Due to the lack of comprehensive understanding of content and style losses in previous studies, the style-content trade-off of the stylized Thangka is unable to be guaranteed. To solve these problems, we propose a progressive style-attentional network (PSANet) and a multi-level loss function strategy for Thangka style transfer. The proposed method consists of two parts: (1) a PSANet to align content manifolds and style manifolds. (2) A multi-level loss function strategy to achieve a balance between content and style of the stylized Thangka images and to ensure better content preservation. Qualitative and quantitative experiments show that our proposed method is able to achieve satisfactory stylized effects on Thangka images.
Graphic image classification method based on an attention mechanism and fusion of multilevel and multiscale deep features

The paper explores the issue of privacy breaches caused by sharing and publishing images on social media platforms, which is becoming increasingly serious. While existing deep learning-based methods have achieved remarkable results in private image classification, they only consider single-level and single-scale features and neglect the problem of multilevel and multiscale features. To address this limitation, the paper proposes a novel graphic image classification method that fuses multilevel and multiscale deep features. The proposed method employs a multibranch convolutional neural network to extract multilevel features, which are then processed by a multilevel pooling layer to obtain multiscale features. Additionally, two feature fusion methods based on attention mechanism, Privacy-MSML (Privacy Multi-Scale Multi-Level) and Privacy-MLMS (Privacy Multi-Level Multi-Scale), are designed to fuse the multilevel and multiscale features for image classification. Both methods utilize Bi-LSTM and a self-attention mechanism to capture feature dependencies. The experimental results on public datasets demonstrate that the proposed methods effectively fuse multilevel and multiscale features, leading to a significant improvement in classification performance, which highlights the innovative contribution of the paper in addressing the issue of multilevel and multiscale features in private image classification.
Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images

Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model's accuracy to get a faster and more accurate prediction.
An Intelligent System for Video-Based Proximity Analysis

Since the emergence of COVID-19 pandemic in early 2020, digital technologies played a significant role in the public-health response to COVID-19 worldwide. Rapid deployment of various technological solutions early in the course of the pandemic, especially in the context of digital epidemiological surveillance and contact tracing, is associated with a number of success stories leading to the rapid suppression of community transmission and reduction of incidence rates. Now about two years since the onset of the pandemic, public attention increasingly shifts towards finding optimal exit strategies, including adaptation of these technologies and finding their place in the post-pandemic society. Here we propose an AI-based framework for proximity tracing based on video surveillance in public places, and show how it can be used in different scenarios ranging from individual contact tracing or epidemiological surveillance of crowds to the improved public spaces planning.
Application of Virtual Reality in Education

For several years now, education has been inseparably connected with information technology. The latter significantly expand the possibilities of traditional education, enriching it with new teaching methods and tools used in the study of a variety of disciplines. Lately, virtual reality technology has been actively developing. This technology will significantly expand the horizons of existing classical and electronic education. This article deals with modern information resources, which are used for creating virtual educational environment. The possibilities of using virtual reality technology in education through the creation of VR-application, increasing the level of practice-oriented training of students are considered. #CSOC1120.
Automation of Data Processing for Patient Monitoring in the Smart Ward Environment

This paper considers automation of retrieval and processing of medical data using mobile applications as a component of the smart ward. The paper shortly describes the current state of research and development in the fields of smart wards, medical cyber-physical systems and mobile applications, and formulates an automation task for data retrieval and processing for a specific kind of diagnostic techniques based on scoring. Architectural solutions for implementation of such techniques are proposed. Its implementation in the form of a mobile application is described. Possible effects consist in automating the determination of disease severity, simplifying the choice of patient treatment algorithms depending on the presented data, minimizing the human factor in scoring. Further research includes deeper integration with devices and systems for data storage and retrieval, providing information security, specifying and modeling the scenarios of smart ward operation.
Asymptotic Exact Formulas for Characteristics of the Joint Maximum Likelihood Estimates Under a Partial and Complete Violation of the Regularity Conditions of the Decision Determining Statistics

The aim of this study is to obtain general asymptotic exact formulas for the characteristics of the maximum likelihood estimates of the signal parameters under various conditions of the decision determining statistics regularity, which makes it possible to analytically determine the efficiency of the synthesized estimation algorithm. Three possible cases are considered: when the decision determining statistics is differentiable with respect to all unknown parameters (the regular case); when the decision determining statistics is not differentiable with respect to any of the unknown parameters (the singular case); and when the decision determining statistics is differentiable with respect to some of the parameters but non-differentiable with respect to the others (the case of partial violation of the regularity of the decision determining statistics). In the first case, the formulas for the characteristics of the estimates are obtained based on a multidimensional generalization of the small parameter method; in the second case, a generalization of the local Markov approximation method by means of an additive-multiplicative representation of the moments of the decision determining statistics is applied; in the third case, a generalization of the two indicated approaches is carried out. The characteristics of the estimates to be passed in all considered cases are established. #CSOC1120
Marginal Models: An Overview

Marginal models involve restrictions on the conditional and marginal association structure of a set of categorical variables. They generalize log-linear models for contingency tables, which are the fundamental tools for modelling the conditional association structure. This chapter gives an overview of the development of marginal models during the past 20 years. After providing some motivating examples, the first few sections focus on the definition and characteristics of marginal models. Specifically, we show how their fundamental properties can be understood from the properties of marginal log-linear parameterizations. Algorithms for estimating marginal models are discussed, focussing on the maximum likelihood and the generalized estimating equations approaches. It is shown how marginal models can help to understand directed graphical and path models, and a description is given of marginal models with latent variables.
Head-Mounted Augmented Reality Support for Assemblers of Wooden Trusses

Wooden-house assembly is an area where still a big part of the work is done manually. In this case study, pairs of operators compose large wooden pieces together based on paper-print instructions complemented by visual guidance in the shape of laser marks projected from lasers mounted in the ceiling, based on Computer-aided design (CAD) data. Augmented Reality (AR) head-mounted displays (HMD) offer a unique platform for providing instructions and additional information superimposed in the work environment and thus can provide guidance in a cognitively ergonomic way. A particular advantage compared to other computing platforms is that the operators have free hands and can perform the manual work and follow guidance simultaneously. We present an evaluation of a prototype that dynamically transforms a CAD data file with design and measurements of wooden trusses to be manufactured, into an AR-based guidance system developed in Unity for Microsoft HoloLens 2 devices. We used an iterative participatory design process for prototyping and think-aloud protocol combined with observations for evaluation, involving professional assemblers in different stages of the process. Participants found the solution to potentially save time in their everyday work and simplify the task by offering increased visibility of the marks compared to the existing laser projection. Large-scale deployment of the system is still facing design challenges of which some are also discussed in the paper.
A Mixed Reality-enhanced Rapid Prototyping Approach for Industrial Articulated Products

The design of industrial articulated products is challenging due to the consideration of multiple poses, movements, and especially in-field constraints, such as the design of a personalized robot arm or the layout of articulated hoists. Traditionally, depicting 2D sketches from multiple perspectives and poses of the product is tedious. Meanwhile, on-site evaluation after fabricating the physical prototype is costly and time-consuming. In order to assist in context-aware decision-making at the early design stage, this research proposes a mixed reality-enhanced rapid prototyping approach for industrial articulated products. In a 3D mixed reality environment that incorporates the surroundings, designers can depict key joints and rough shapes through intuitive gesture-based interaction. After a quick prototype creation, the kinematics preview is provided to designers for preliminary functional verification. Finally, a user study shows that the proposed approach can help designers rapidly complete the MR prototype of articulated products at the early design stage, provide a better understanding of the product movement, assist in-field solution generation and facilitate communication. It is hoped this explorative study can offer insightful ideas with effective toolkits for assisting the new product design process more intuitively and interactively.
Application of Augmented Reality (AR) in the Laboratory for Experimental Physics

This paper presents a concept of how AR can be implemented for learning in university laboratories. The concept includes videos from the first-person perspective and virtual 3D elements to impart knowledge as well as a database connection to check understanding. It is based on improvement potential identified in an experimental physics laboratory. General recommendations for actions are derived as to how AR can be used in the various laboratories of technical courses. The influences on the learning effect are evaluated using the ”Confidence-based Assessment” method.
Product Development Process for complex hardware-based solutions: current trends

Innovation processes have evolved significantly. Starting with traditional Product Development Process in the 1990s, novel development approaches have been disseminated, especially approaches originating in the software and service sectors. However, understanding the influence of recent approaches to developing complex hardware-based solutions is still a research issue. This study aims to identify emerging approaches that have already demonstrated adequacy to support hardware design. Therefore, a systematic literature review has been conducted. The research screened 292 papers and analyzed in detail 25 articles published between 2018 and 2022. Our findings indicate increased adoption of creativity-oriented methods and extended prototyping in early phases.
Generative AI for Immersive Experiences: Integrating Text-to-Image Models in VR-Mediated Co-design Workflows

Text-to-image AI models can generate novel images for design inspiration. Yet, their applications for collaborative design (co-design) purposes and interoperability within simulation-based, immersive settings have been scarcely explored. In this paper, we propose a novel, multi-modal approach for interactive public participation in urban design projects. The main objectives of our research are (a) to describe a methodological workflow of integrating text-to-image AI models into VR-mediated co-design workshops, and (b) to investigate the applicability of the proposed workflow through a set of completed and prospective case studies. Both studies are parts of a broader research project, which aims to revitalize the city of Derby, UK through producing a series of sustainable design visions. Through these case studies, we discuss some preliminary results and introduce our future work.
The Process of Creating Interactive 360-Degree VR with Biofeedback

The paper aims to demonstrate the interconnectedness of filmmaking techniques and iterative software design methods during the creation of interactive 360-degree Virtual Reality (VR) scenarios. These scenarios, which were designed to treat social anxiety disorder (SAD), comprised of six distinct simulations. To track patients' biodata, biosensors were integrated with the developed scenarios. In the paper, we provide an overview of the combined development process method and demonstrate how these methods were employed. Our goal is that this combined method for developing interactive 360-degree applications will serve as a useful reference for future designers.
Towards Modelling Elaborateness in Argumentative Dialogue Systems

To provide an engaging and natural interaction with an argumentative dialogue system, we introduce a model to adapt the system utterances to the user's communication style in an ongoing discussion. Therefore, we propose an "Elaborateness Score" (ES) considering the length of the user utterances and requested content and adapts its utterance length as well as the amount of provided meta-information accordingly. To evaluate our approach we conducted a laboratory user study with a total of 30 participants who had a conversation with our ADS. In a pre- and post-survey, the participants had to rate statements to subjectively assess each dialogue and indicate which level of elaborateness they preferred as well as their general impression of the system. The results show that the system's elaborateness style has a significant influence on user's perception of the dialogue and imply that the preference of the system's elaborateness is individual for every user.