Reviewerly/exHarmony huggingface dataset:
Features of professional self-actualization of university students – future secondary school teachers taking into account the level of development of the non-verbal component of their social intelligence

The work aims to identify the features of professional self-actualization of university students – future secondary school teachers in connection with the development of the non-verbal component of their social intelligence. The paper describes the process of conducting a study consisting of the theoretical and the empirical stages and oriented towards achieving the specified aim. The scientific novelty of the research lies in identifying the nonlinear nature of the relationships between the features of professional self-actualization of future teachers and the level of development of the non-verbal component of their social intelligence. As a result, the work has shown that the majority of future teachers have an average level of development of socio-perceptual abilities (the non-verbal component of social intelligence) and an average level of self-actualization. Concerning university students – future secondary school teachers, self-actualization and the level of development of the non-verbal component of their social intelligence are interrelated, but these relationships are nonlinear. Teachers with the low level of socio-perceptual abilities have less pronounced components of self-actualization, such as time orientation, values and creativity (compared with teachers with both high and medium levels of socio-perceptual abilities).
Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrencies

Abstract Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and $$f_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.
Simultaneously Here and There: Situating online organizing in our embodied material practices

Online organizing is situated in offline practices. Recent theorizing has examined how online and offline practices co-evolve over time, but given limited attention to how they co-exist in time. Yet, without being ‘here’ in an embodied material practice, we cannot experience being ‘there’ online. Understanding the co-existence of online and offline practices is important as organizing becomes actively negotiated in the moments of being simultaneously here offline and there online. In this article, we pull the field of view back from the computer screen to theorize the simultaneity of online organizing with multiple offline practices in which participants are embodied and engage materially. We articulate how, in the case of the video conference, the shared experience of presence, proximity and participation in online organizing is constituted in relation to multiple – but mostly unshared – offline practices. We argue that this interconnection with multiple offline practices can blur boundaries, divert purposes and lead to glitches and breakdowns in online organizing. We make two contributions. First, we recast online organizing as embedded in multiple, mostly unshared, offline practices. Second, we develop understanding of the co-existence of online and offline in time, and its implications for interacting and organizing online. We conclude by suggesting new directions for research on organizing across simultaneously co-existing online and offline practices.
Image Encryption Using Quadrant Level Permutation and Chaotic Double Diffusion

Information security is becoming more and more important as information technology advances. In the fields of telecommunication networks, diagnostic imaging, and multimedia applications, information security is crucial. The use of images as a medium for the conveyance of information is integral to the information age. However, image data differs from traditional text-based information in that it is dense and has strong pixel correlation. As a consequence, image encryption no longer works with traditional encryption techniques. Consequently, a safe and difficult-to-crack image encryption technique is required. In this research, a new symmetric image encryption technique based on improved Henon chaotic system with byte sequences and a unique method of shuffling a picture’s pixels is suggested. This algorithm produces effective and efficient encryption of images. The suggested image encryption technique created a new dimension for safe image transfer in the realm of digital transmission by causing more confusion and diffusion, according to statistical analysis and experimental key sensitivity analysis.
Nuclei Instance Segmentation in Colon Histology Images with YOLOv7

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7.
EntangleVR++: evaluating the potential of using entanglement in an interactive VR scene creation system

Interactive digital stories provide a sense of flexibility and freedom to players by allowing them to make choices at key junctions. These choices advance the narrative and determine, to some degree, how the story evolves for that player. As shown in prior work, the ability to control or participate in the construction of the narrative can give the player a high level of agency that results in a stronger sense of immersion in the narrative experience. To support the design of this type of interactive storytelling, our system, EntangleVR++, borrows the idea of entanglement from quantum computing. Our use of entanglement allows creators and storytellers control over which sequences of story events take place in correlation with each other, initiated by the choices a player makes. In this work, we evaluated how well our idea of entanglement enables creators to easily and quickly design interactive Virtual reality narratives. We asked 16 participants to use our system and based on user interviews, analyses of screen recordings, and questionnaire feedback, we extracted four themes. From these themes and the study overall, we derived four authoring strategies for tool designers interested in the design of future visual interface for interactively creating virtual scenes that include relational objects and multiple outcomes driven by player interactions.
Advancements in super-resolution methods for smart meter data

This paper presents a comprehensive review of super-resolution methods for smart meter data analysis. Smart meters provide valuable insights into household electricity consumption, but their low-frequency data limits the ability to capture detailed patterns. Super-resolution techniques address this challenge through the reconstruction of high-resolution data from low-resolution measurements. The review covers both non-machine learning-based methods (interpolation, signal processing, and statistics) and machine learning-based methods (CNNs, GANs). Four selected methods are discussed in detail, highlighting their principles, advantages, and limitations. These methods demonstrate superior accuracy in enhancing data completeness, capturing complex relationships, and improving resolution. The review contributes to the advancement of super-resolution techniques for smart meter data analysis, providing researchers and practitioners with valuable insights for efficient energy management and forecasting.
An artificial neural network for digital image correlation dynamic subset selection based on speckle pattern quality metrics

Abstract Precise and accurate digital image correlation computed displacement data requires sufficient noise suppression and spatial resolution, which improve and diminish, respectively, with increased subset size. Furthermore, spatially varying speckle pattern quality and displacement field complexity ideally necessitate a location‐specific optimal subset size to obtain a favourable compromise between noise suppression and spatial resolution. Although dynamic subset selection (DSS) methods have been proposed based on speckle pattern quality metrics (SPQMs), they do not ensure such a favourable compromise. This work investigates using an artificial neural network (ANN) for DSS. An ANN is trained to predict the displacement error standard deviation of a subset from multiple SPQMs and the standard deviation of image noise, such that the smallest subset offering sufficient noise suppression, dictated by a displacement error standard deviation threshold, is appointed. Validation, both within and outside the domain of the training images, shows that the smallest subset providing sufficient noise suppression offers a favourable compromise for up to moderate displacement gradients. Additionally, the proposed method is shown to perform with greater consistency and reliability relative to existing SPQM‐based DSS methods. The novel proposition lies in utilising an ANN as an error prediction tool, based on multiple SPQMs, and hence, is an attractive alternative for DSS.
Psychological training program to support student technological entrepreneurship: “psycho-correctional targets” and the basics of construction

The psychological training program for supporting student technological entrepreneurship in the Russian educational environment of an educational institution of higher education is discussed in the article. Ways to develop entrepreneurial activity are proposed, based on the “psychocorrectional targets” identified by the authors and the psychological problems of beginning entrepreneurs.
PV Output Power Prediction Using Regression Methods

Over the past few years, the general public has become increasingly aware of climate change and the role of greenhouse gas emissions, especially carbon dioxide, in contributing to it. Therefore, individuals, businesses, and governments around the world have taken steps to reduce their emissions. One of these steps is to increase adoption of renewable energy sources, such as solar power which provides clean energy, in addition to low building and operation costs and minimal maintenance requirements. Accurate estimation of solar energy production is crucial to ensure the stability of electrical networks as the transition to renewable energy sources such as solar power increases. In this study, machine learning regression algorithms including artificial neural networks, support vector regression, regression trees, and k-nearest neighbor are performed to estimate hourly solar energy production of one month using historical production data and various meteorological parameters. The models are optimized using grid search and validated using K-fold cross validation method. The performance of the models is evaluated using the RMSE, MAE, and R2 evaluation metrics. The results showed that the k-nearest neighbor regression model achieves the highest performance with an R2 score of 0.9715.
A Proposal for a Federated Learning Protocol for Mobile and Management Systems

In this research paper, we introduce a federated learning communication protocol tailored for emergency management applications. Our primary objective is to tackle the communication challenges that arise in such critical scenarios. In order to overcome the limitations associated with centralized server architectures, we present an innovative communication protocol. This protocol empowers the framework to effectively cooperate with multiple centralized servers, fostering efficient knowledge sharing and model training while ensuring the utmost data privacy and security. By harnessing this protocol, our framework elevates the performance and resilience of vital infrastructure systems operating on the Android platform, thereby facilitating real-time operational scenarios. This research makes a substantial contribution to the field of emergency management applications, as we offer a comprehensive solution that optimizes communication and enables seamless collaboration with numerous centralized servers.
Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy

The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population’s capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm’s performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms.
Autonomous Agent Using AI Q-Learning in Augmented Reality Ludo Board Game

An autonomous agent works with Artificial Intelligence (AI) can decide its actions to adapt and respond to the changes in a dynamic environment. The autonomous agent can be developed in games as a Non-Player Character (NPC) to interact with the changes of state in the game environment. Traditional board games such as Ludo have had many players since the olden days but slowly lost attraction to the public, especially the younger generations as digital games become more popular. Although the Ludo board game can be digitized to fascinate the players through implementing Augmented Reality (AR) technology in handheld devices, common NPCs found in games have determined actions and are unable to learn from experience and adapt to the changes of the game environment. Therefore, this research aims to develop an autonomous agent for board game in handheld AR (HAR). The first step in the three main phases is to examine the autonomous agent for the HAR board game. The second phase is developing the AR board game with Q-learning and Minimax algorithms for board game agents. Finally, the third phase is integrating the AR board game with Q-learning and Minimax agents in handheld. The novel contribution of this research is the redesign of Ludo for AR with autonomous agent and generate the training data using the Q-Learning algorithm to create autonomous agent in AR.
Voice Commands with Virtual Assistant in Mixed Reality Telepresence

Mixed Reality (MR) telepresence is an expanding field in marketing research and practice especially in computer vision due to its ability to connect people in a remote location. As voice command and speech recognition technology have also been significant milestones in the twenty-first century, this technology has the advantage to be used in MR telepresence for interaction. Meanwhile, the virtual assistant avatar’s implementation in MR telepresence can provide support for the interaction by reducing the workload and improving the performance of the interaction in collaborative MR telepresence. Therefore, in this paper, we propose to explore the voice commands interaction and describe the implementation with the virtual assistant avatar in MR telepresence. We implement speech-to-text (STT) conversion and utilize the semantics of Natural Language Processing (NLP) for the voice command. Meanwhile, for the avatar to respond, the speech service is used to convert the text-to-speech (TTS) input. This paper ends with a conclusion, limitations and suggestions for future works.
Triplet MAML for Few-Shot Classification Problems

In this study, we propose a TripletMAML algorithm as an extension to Model-Agnostic Meta-Learning (MAML) which is the most widely-used optimization-based meta-learning algorithm. We approach MAML from a metric-learning perspective and train it using meta-learning tasks composed of triplets of images. The idea of meta-learning is preserved while generating the meta-learning tasks and training our novel meta-model. The experimental results obtained on four few-shot classification datasets show that TripletMAML that is trained using a combined loss yields in high quality results. We compared the performance of TripletMAML to several metric learning-based methods and a baseline method, in addition to MAML. For fair comparison, we used the reported results of those algorithms that were obtained using the same shallow backbone. The results show that TripletMAML improves MAML by a large margin, and yields better results than most of the compared algorithms in both 1-shot and 5-shot settings. Moreover, when we consider the classification performance of other meta-learning algorithms that use much deeper backbones, we conclude that TripletMAML is not only competitive in terms of the classification performance but also very efficient in terms of the complexity.
Secure Future Healthcare Applications Through Federated Learning Approaches

The healthcare field is so sensitive to data privacy and security due to including medical and personal information. Almost all healthcare applications are required to increase data security and privacy, which use traditional machine learning approaches relying on centralized systems, both computing resources and the entirety of the data. Federated learning, a sort of machine learning technique, has been used to exactly address this issue. The training data is disseminated across numerous devices in federated learning, and the learning process is collaborative. There are numerous privacy attacks on Deep Learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in healthcare applications that use sensitive medical data. This paper provides a comprehensive review of federated learning on future healthcare applications. It also discusses the types of federated learning along with its implementation in healthcare applications.
Generating Sub-emotions from Social Media Data Using NLP to Ascertain Mental Illness

It is predicted that mental illness will be one of the leading causes of death in 2030. Many people will not share their details of the illness detail with others, including family and friends. Also, many are unaware that their mental disorder is affecting their thinking and behavior. Early detection and medical intervention are necessary, otherwise it leads to severe problems. More than half of the world population, that is around 58.4% of the people use Social Media (SM) to express their thoughts and feelings. By fetching their timely thoughts and feelings expressed in social media we can analyze their emotions and sub-emotions. In this study, we developed a novel model to generate the sub-emotions of social media users from EmoLEX lexicon using the Affinity Propagation (AP) algorithm and word2vect conversion word2vec-google-news-300. The number of clusters and vocabulary obtained for ten emotions such as Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, Positive and Negative is evaluated based on sub-emotions generated. By using word2vect conversions and AP algorithm it is found that the consistency of Mean words (µW) per cluster are equally distributed in each cluster with respect to all emotions on an average of 19, 20, 21 and 22. The obtained sub-emotions is used to mask the SM user post and it could be further used for detecting the mental illness of SM users.