Reviewerly/exHarmony huggingface dataset:
The study of the influence of perception of different information sources on the mnemic processes of students

In the modern world, the role of information and digital technologies on our lives is so great that it is difficult to deny their influence on the psychological characteristics of people. The problem of the influence of various information sources used by modern people (from classical printed sources to smartphones) on their cognitive functions, including memory processes, seems very relevant. Mnemic processes help us navigate information flows, quickly find the material we need to make a rational decision. This article attempts to study the influence of the type of information source on the quantitative characteristics of mnemic processes. A brief analysis of scientific research on the problem under consideration (Sparrow B., Leu D.J., Dutko N.P., Davydovsky A.G., Pishchova A.V., etc.) showed that there are certain correlations between mnemic processes and modern digital information technologies. In an experimental study conducted by us and described in this article, we studied the quality of memorization and reproduction of scientific and artistic text, works of fine art, provided they are perceived from printed and digital sources (smartphone). It is shown that the perception of information from a smartphone affects to a greater extent the quality of memorization of scientific and educational text.
Formation of readiness for professional activity of technical university students as a psychological and pedagogical problem

Based on the results of empirical research, the article shows that higher education should consider the process of formation of students' readiness for professional activity as a psychological and pedagogical problem. This is due to the current system of vocational guidance in secondary schools. As a result, most applicants have no idea about their chosen profession. This makes the problem of forming students' professional readiness for future activities an urgent task of the higher education system. It is shown on the results of the study that, in the opinion of the students themselves, such a task can and should be solved. The results of the study are described, the purpose of which was to identify the level of readiness of students to perform professional activities. The forms of formation of students' readiness for professional activity are considered. Considering the problem of a person's readiness for professional activity in the article, a personality-oriented component containing the properties and qualities of a future specialist is defined as a component, according to psychology. Based on empirical data, it is concluded that the professional readiness in demand today as a set of competencies of a graduate of a higher school, acting as socially significant integrative qualities that are in demand in the field of further professional activity and personal improvement, is the basis of high-quality professional education.
Identifying Key Activities, Artifacts and Roles in Agile Engineering of Secure Software with Hierarchical Clustering

Different activities, artifacts, and roles can be found in the literature on the agile engineering of secure software (AESS). The purpose of this paper is to consolidate them and thus identify key activities, artifacts, and roles that can be employed in AESS. To gain initial sets of activities, artifacts, and roles, the literature was first extensively reviewed. Activities, artifacts, and roles were then cross-evaluated with similarity matrices. Finally, similarity matrices were converted into distance matrices, enabling the use of Ward’s hierarchical clustering method for consolidating activities, artifacts, and roles into clusters. Clusters of activities, artifacts, and roles were then named as key activities, artifacts, and roles. We identified seven key activities (i.e., security auditing, security analysis and testing, security training, security prioritization and monitoring, risk management, security planning and threat modeling; and security requirements engineering), five key artifacts (i.e., security requirement artifacts, security repositories, security reports, security tags, and security policies), and four key roles (i.e., security guru, security developer, penetration tester, and security team) in AESS. The identified key activities, artifacts, and roles can be used by software development teams to improve their software engineering processes in terms of software security.
Design, Development, and Experimental Verification of a Trajectory Algorithm of a Telepresence Robot

Background: Over the last few decades, telepresence robots (TRs) have drawn significant attention in academic and healthcare systems due to their enormous benefits, including safety improvement, remote access and economics, reduced traffic congestion, and greater mobility. COVID-19 and advancements in the military play a vital role in developing TRs. Since then, research on the advancement of robots has been attracting much attention. Methods: In critical areas, the placement and movement of humans are not safe, and researchers have started looking at the development of robots. Robot development includes many parameters to be analyzed, and trajectory planning and optimization are among them. The main objective of this study is to present a trajectory control and optimization algorithm for a cognitive architecture named auto-MERLIN. Optimization algorithms are developed for trajectory control. Results: The derived work empirically tests the solutions and provides execution details for creating the trajectory design. We develop the trajectory algorithm for the clockwise direction and another one for the clockwise and counterclockwise directions. Conclusions: Experimental results are drawn to support the proposed algorithm. Self-localization, self-driving, and right and left turn trajectories are drawn. All of the experimental results show that the designed TR works properly, with better accuracy and only a slight jitter in the orientation. The jitter is found due to the environmental factor caught by the sensors, which can be filtered easily. The results show that the proposed approach is less complex and provides better trajectory planning accuracy.
Artificial Intelligence Empowers Postgraduate Education Ecologically Sustainable Development Model Construction

Postgraduate education provides valuable intellectual resources for the development and progress of human society. At present, the development of postgraduate education in China is at a moderate level. The level of internationalization is not high, and there remain objective problems, such as a shortage of educational resources. To solve these problems, this paper proposes the use of artificial intelligence technology to build a sustainable development model for graduate students. It is aims to study the means of building a favorable environment for the development of postgraduate education and to optimize the educational structure of postgraduate studies, so as to improve the training model and enhance China’s international influence. Under the influence of the sustainable development model of educational ecology, developed under the background of artificial intelligence, this study included a questionnaire survey of current tutors, as well as doctoral and master’s students; a total of 30% of master’s students and 37% of doctoral students were “very satisfied” and “relatively satisfied” with the evaluation of “teaching content”.
Aligning Federated Learning with Existing Trust Structures in Health Care Systems

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders' needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners' nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients' FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient-practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training.
TrainAR: An Open-Source Visual Scripting-Based Authoring Tool for Procedural Mobile Augmented Reality Trainings

Mobile Augmented Reality (AR) is a promising technology for educational purposes. It allows for interactive, engaging, and spatially independent learning. While the didactic benefits of AR have been well studied in recent years and commodity smartphones already come with AR capabilities, concepts and tools for a scalable deployment of AR are still missing. The proposed solution TrainAR combines an interaction concept, a didactic framework and an authoring tool for procedural AR training applications for smartphones. The contribution of this paper is the open-source visual scripting-based authoring tool of TrainAR in the form of a Unity Editor extension. With this approach, TrainAR allows non-programmer domain experts to create (“author”) their own procedural AR trainings by offering a customized editor, while at any time programmers may decide to utilize Unity’s full capabilities. Furthermore, utility and usability evaluations of several already developed TrainAR trainings (combined n = 317) show that TrainAR trainings provide utility in several contexts and are usable by the target groups. A systematic usability evaluation of the TrainAR Authoring Tool (n = 30) shows that it would be usable by non-programmer domain experts, though the learning curve depends on the media competency of the authors.
StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized - (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.
Advanced Investigation of Steering Performance with Error-Accepting Delays

The Steering Law is a robust model to predict the movement time (MT) for steering through a constrained path, and the most representative example in human-computer interaction (HCI) is navigating cascaded menus. In typical implementations of cascaded menus, however, users can deviate from the path for a short time; we call this error-accepting delay, or Tdelay. Yamanaka modified the Steering Law to predict MT under several Tdelay conditions, and our goal is to investigate the reproducibility of his model with more various Tdelay values. In addition, HCI researchers have recently formed a consensus that the goodness of models should be judged by the prediction accuracy for future (untested) task conditions. Thus, for the sake of completeness, we conducted two analyses: a shuffle-split cross-validation and leave-one-Tdelay-out cross-validation. The results showed that, regardless of the all-data and cross-validation analyses, Yamanaka’s modified model outperformed the baseline Steering Law, which strengthened his original experimental report.
Medical imaging and nano-engineering advances with artificial intelligence

Medical imaging is a broad field of research and artificial intelligence used to explore such images is termed as AI-Imaging. AI-imaging is further divided into sub-branches including the computational, theoretical and practical experiments in wet and dry labs. The current research focuses on the background of medical imaging, recent advances in the field of medical imaging for oncology, challenges and possible solutions. During this research, some computational and programing tools are outlined. The process of image segmentation is important as it can help to explore the medical images in more detail. During this research, the steps involved in image segmentation are outlined and the numerical experiments are performed on a set of breast cancer medical images. It is concluded during this research that the achievements in this domain are always credited by the smart programing &amp; computational tools and computer vision. The current research also outlines the step-wise protocols of deep learning, designed for different types of medical imaging such as X-rays, CT-scan and MRI are documented to provide a comprehensive understanding, that can help in bridging the two domains of medicine and computer vision, in a reliable and fruitful manner.
Information Security on Learning Management System Platform from the Perspective of the User during the COVID-19 Pandemic

Mujiono Sadikin, Rakhmat Purnomo, Rafika Sari, Dyah Ayu Nabilla Ariswanto, Juanda Wijaya, and Lydia Vintari. Journal of information and communication convergence engineering 2023;21:32-44. https://doi.org/10.56977/jicce.2023.21.1.32
Disease Diagnosis and Data Protection System for IoT-based Wearable Sensor Network

Big Data analytics is used in many areas of smart healthcare. Leakage or correction of this data while transferring is not only violating patients' privacy but endangering their exists. Many scientists have worked on encoded Personal Health Records. Conversely, around are quiet contests such as data leak while learning, training models outflow and exactly operators don't like to breach their statistics in the analytics organization. This paper presents a method for forecasting disease and alerts, and using Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR) to analyze and display patient health information. In order to defend health data privacy, we approve a secure data deletion method, whereby the information owner will be able to choose to deny some users admission to their health data. The presented analysis and the experimental results show the safety and efficacy of our proposed approach.
Photovoltaic Power Forecasting With an Ensemble Multi-Input Deep Learning Approach

The inherent intermittency of solar power due to meteorological factors presents a significant challenge in integrating them into grids on large scales. An accurate Photovoltaic (PV) power generation forecasting effectively supports achieving optimal planning and operational stability of power systems. Motivated by satisfactory performance of deep learning methods in the energy sector, an ensemble multi-input deep learning method combining three-dimensional convolution (Conv3D) networks and bidirectional long short-term memory (BiLSTM) networks is proposed in this study. First, one-dimensional (vector) PV power production and atmosphere parameters series are rearranged into a bi-dimensional matrix (2D image) and stacked in the third dimension (3D image) to preserve dependency between the input data. Then, the Conv3D network is utilized to extract non-linear spatial features of the 3D image. Finally, the BiLSTM network is implemented to learn the long-term dependencies of the extracted spatial features. The proposed method is utilized for one-day, three-day, five-day, and seven-day ahead PV power forecasting with one-hour intervals for Tennent operators, Germany dataset. Experimental results show reduced prediction error compared to two-dimensional Convolution (Conv2D), Conv3D, long short-term memory (LSTM), BiLSTM, Conv-LSTM, and ConvLSTM networks.
Long- and Short-term Prediction of Bitcoin Energy Consumption

Bitcoin”s (BTC) mining process utilizes the proof of work (PoW) concept as the consensus algorithm., which consumes electricity. As a result., there are concerns regarding its sustainability and carbon emission. If one wants to estimate these, they first should have an estimation of the network's energy consumption which is hard to calculate due to its decentralized nature. To do so., two methods are conceivable. First, considering electricity price and miner's revenue (electricity costs are considered as a part of total revenue., then divided by electricity price to obtain electricity consumption), second, using hash rate along with the efficiency of mining devices. The latter is utilized in this study for short- and long-term predictions. In the short-term, recurrent neural network (RNN) is used, whereas in the long-term logistic functions are applied. It has been concluded that the ultimate energy consumption of the Bitcoin network, which will happen around 2025, is in the range of 40.4 to 73.41 TWh annually and its estimated value is 58.56 TWh.
Multisensor Multitarget Tracking and Collision Avoidance Based on the Labeled RFS Void Probability and KL-PaC Cost Function

Existing sensor control methods for multisensor and multitarget tracking seldom consider comprehensive control strategy and collision avoidance between sensors and targets or between sensors together. In this article, we first propose a cost function for the multisensor control strategy, which includes Kullback–Leibler (K–L) divergence, detection probability, and sensor usage cost (KL-PaC cost function). Second, to solve collision avoidance between sensors and targets, we propose a collision avoidance strategy based on the labeled random finite set (RFS) void probability. The strategy is essentially to set up a safe region around the sensor or target and then combine the void probability and cost function in the safe region as the evaluation function. The final simulation and experimental results verify the effectiveness of the proposed approach.
Robust Nonlinear Filtering: A Non-Bayesian Approach

Today, the field of practical estimation is dominated by linear-Bayesian estimators as these familiar techniques are computationally efficient and can be quickly tailored to many systems of interest. However, beyond the common weaknesses of linear estimators to non-Gaussian noises and nonlinear transformations, Bayes' rule itself lacks robustness, as it inherently assumes that all statistical models are exactly known. Therefore, in lieu of filters outfitted with linear-Bayesian updates, this work proposes a method to derive nonlinear, non-Bayesian updates from first principles using the theory of generalized variational inference (GVI)—an optimization approach to information fusion. By selecting different divergence measures, loss functions, and feasible distributions, a wide variety of robust/conservative filter updates are quickly built and tested using numerical optimization. From these GVI candidates, a suitable update that uses the robust γ-loss function is selected based upon its desirable estimation behavior. Via analytical optimization and Gaussian mixture modeling, the γ-loss GVI update is realized as a nonlinear, closed-form filter that is well-suited for practical estimation.
Optimal Linear Multilateration Combined With the Kalman Filter for Range-Only Tracking

This article presents the optimal estimation for the extensively concerned linear multilateral positioning issues and further improves the accuracy of range-only tracking significantly. The traditional linear multilateration method has been used to achieve target positioning for range-only measurements, but the completeness of the theory and implementation effect is limited due to the imperfect model and the lack of proper statistical error analysis. Moreover, the performance of the representative nonlinear filters is not satisfactory because of the strong nonlinearity between the range observations and kinematic states of the moving target. This article reveals the essence of the linear fusion of multiple distance sensors from a geometric perspective. The multisensor fusion model is reconstructed in the linear feature space by transforming the likelihood function into the probability function and separating the expectation of the compound random variable and residual. After that, a new linear multilateration method is developed based on the precise derivation of statistical characteristics corresponding to the linear fusion model, and also, minimizing the squared Mahalanobis distance is introduced to replace minimizing the mean square error. The first-order moment, as a pseudomeasurement used to improve the positioning accuracy, and the second-order moment, as the covariance of the pseudomeasurement noise, are provided by this method, which has been proved to be the best linear unbiased according to the Gauss–Markov theorem. Combined with the pseudomeasurement, a standard linear Kalman filter is capable of tracking the maneuvering target. Several simulations and experiments verify the superior performance of the proposed method.