Reviewerly/exHarmony huggingface dataset:
Research on the Simulation of Informationized Psychological Sand Table Based on 3D Scene

Sand table game as one of the most common technical methods in international psychological therapy, also known as box court therapy, in the current scientific research has been paid attention to, and gradually become the most common techniques in the field of applied psychology content. Because the sand table itself is too large, it is not convenient to move during the use, will be limited by the space scope, and cannot effectively save the information of the sand table. Therefore, scientific researchers put forward the use of simulation and other equipment functions in the practice of exploration, to carry out a comprehensive study of the sand table. Based on the understanding of the intelligent electronic sand table constructed based on the concept of Internet technology, this paper designs and analyzes the informatization psychological sand table simulation system with 3D scenes as the core in view of the development needs of the actual psychology field, and carries out empirical research from the perspective of the application of mobile phone App software. The final results show that the optimization of traditional psychological sand table simulation by 3D scenization and information technology can not only build a high-quality simulation environment, but also perform data simulation analysis and effective storage in the visualization of simulation results.
Innovative intelligent technology and information systems for visually impaired

The paper presents a develop innovative intelligent technology and information systems for visually impaired students. To solve this problem a comprehensive approach has been proposed, which consists in the aggregate of the application of artificial intelligence methods and statistical analysis. Creating an accessible learning environment, identifying the intellectual, physiological, psychophysiological characteristics of perception and information awareness by this category of students is based on a cognitive approach. Based on IT network program developed specifically for visually impaired students. The individually oriented learning path of PIV is constructed with the aim of obtaining high-quality engineering education with modern equipment in joint use laboratories. Additional teaching aids, like iNetSim, a universally accessible network simulator, created to allow vision-impaired and sighted students to complete Cisco Certified Network, and Microsoft HoloLens 2 (a new vision for computing, is used to transform abstract concepts into 3D experiences in the virtual classroom), which enables students to research and explore more deeply, see more clearly and learn by doing. This article aims to allow visually impaired students, as well as sighted students, to perform with similar results.
Public and Private Politics: Green Ngos Fundraising Strategies Under Political Lobbying Competition

This paper attempts to establish a link between public and private politics in the sphere of environmental regulation. We develop a three stages game where two special interests groups: a green NGO and a polluting firm compete on two levels. In the first stage, they compete over the salience of the pollution which generates donations to the NGO. In the second stage, they compete over the political influence regarding the environmental policy. In the third stage, the government set the environmental policy. Our first result shows that the intensity of the lobbying competition depends on the nature of the polluting emissions. Next, We give the firm the possibility of using both, real abatement and greenwashing as blocking fundraising strategies. We then show that when the firm uses only greenwashing, an increase in its effectiveness exacerbates the competition between the two SIGs. However, when abatement is also used, the NGO does not necessarily become more aggressive in campaigning. Finally, we show that a more effective NGO in campaigning always collects more funds resulting in a more stringent environmental policy, whilst a more effective firm in blocking fundraising manage to reduce the stringency of the environmental policy only if it uses abatement.
Anomaly Detection Using Deep Learning and Big Data Analytics for the Insider Threat Platform

Insider Threat is not a new principle, with examples of trusted insiders being malicious throughout human history, from Julius Caesar to Edward Snowden. Recently, Insiders are becoming an ever-increasing threat to organisations, being among the most damaging of security breaches; as these do not originate from external factors, but from trusted employees with access to sensitive company information and systems. Establishing whether observed behavioural data is anomalous or benign is an important task; becoming an even more complex problem when combined with the big data available to an Insider Threat platform. The work presented within this research employs a data-driven approach to the analysis of large-scale time-series data generated by a large volume of users interacting with an organisation over an extended period. First, this research identified and provided a comprehensive overview of techniques currently employed by Insider Threat teams to determine possible security threats, examining the utilised approaches in comparison to current deep anomaly detection techniques. Then, these methods were utilised to implement a process of using anomaly detection and deep learning techniques for improved identification of potential Insiders.
On the Reusability of ISIC Data for Training DL Classifiers Applied on Clinical Skin Images

The ISIC archive is an open dermoscopy dataset containing thousands of images so that new Deep Learning skin classifiers can be trained. ISIC Challenges attract many participants to build a model that will bring the best performance to the ISIC test dataset. The question is whether such a model has consistent behavior in different datasets and other clinical images. In this work, we build and study the performance of a classifier trained in the ISIC 2019 dataset in three different cases: the performance during the cross-validation training process, the performance in the separate ISIC 2019 test dataset, and dermoscopy images taken from the SYGGROS skin disease hospital. The results show a stable performance compared to the metric F1 score for the categories in which there are more than 3000 images in the training dataset. In addition, we identify the factors that make it difficult to transfer and use classifiers from a competitive to a clinical setting.
Pruning Neural Nets by Optimal Neuron Merging

We present a new method for structured pruning of neural networks, based on the recently proposed neuron merging trick in which following a pruning operation, the weights of the next layer are suitably modified. By a rigorous mathematical analysis of the neuron merging technique we prove an upper bound on the reconstruction error. This bound defines a new objective function for pruning-and-merging. Our new optimal algorithm provably achieves the lowest objective cost among all possible prune-and-merge strategies. We also show empirically that nuclear norm regularization can be used to obtain even better pruning-and-merging accuracy; this finding is supported by our theoretical analysis.
Perceived Value of the Augmented Reality Experience in an Industrial Context: A Stakeholder Approach

This study explores the perceived value of augmented reality (AR) experiences in B2B marketing. Drawing on the consumption values theory, the present research uses a stakeholder approach to offer a holistic vision of the value of AR. Semi-structured interviews were conducted with 15 stakeholders (buyers, sellers, 3D and AR developers) in the B2B sector. The results suggest eight different dimensions of the perceived value of the AR experience in B2B marketing, grouped into three main categories: cognitive (informational, quality, innovation and business benefits), affective (WOW effect and playfulness) and social (sharing and relationship) values. This study adds to the literature on the use of AR in marketing and B2B experiences by focusing on the specific values of AR experiences in B2B.
Proposing a Novel Scheme for Improving LSB Image Steganography with Color Image

During the most recent two years of Covid-19, the vast majority of the work will be performed web based utilizing normal correspondence channel by associations including significant reports trade. Trading significant reports are unsafe to send straightforwardly, even we are seeing different extortion cases, data burglary cases, identity robbery, marital cheats, Internet shopping fakes, and so on. In these cases assailant takes your own data or reports and abuses them for some off-base purposes. To ensure your data or report during trading them online steganography is a valuable methodology. In this paper, we will propose a significant and inventive plan utilizing LSB method with shading pictures. To work on the security of the message, we will apply AES encryption plan and afterward utilize the picture steganography with LSB method.
Eye Tracking Application to Understand the Visual Control of Locomotion

Visual information is crucial for safe locomotion because it allows individuals to adjust their stepping patterns to deal with environmental demands. Deficits in the ability to visually sample the environment expose walkers to an increased risk of falling, which have motivated researchers to investigate the visual control of locomotion. Portable eye tracking technologies allow researchers to quantify eye movements and determine how, when and what individuals look at during locomotion. However, appropriate methodological approaches are required to understand the complex link between eye movements and locomotion. This book chapter explores methodological aspects required to assess the visual control of locomotion. To offer readers examples of how we applied eye tracking technologies to investigate the role of visual information during locomotor tasks, we revisit two previous studies published by our group involving older adults and patients with Parkinson’s disease.
COVID- 19 PANDEMIC STATE: TEACHING CHILDREN COLORS WITH MOBILE APPLICATION BASED ON AUGMENTED REALITY

The spread of, COVID- 19 from Wuhan, China in December 2019 enforced Draconian measures in form of social barriers to control the COVID- 19 pandemic. Many governments replaced face-to-face education in schools with remote education through the internet. Efficient and effective teaching techniques are still being sought. Especially, lessons practical training exercises need environmental platforms to enhance teaching yet physical platforms could have Pandemic related risks for humans. The risks are even higher, for children in primary education and for children with neurodevelopmental disorders who need to learn with repeated exercises. This study proposes a color learning mobile application for children that is based on augmented reality. The application is supposed to teach colors to young pupils in an enjoyable way. In this paper, three primary colors: red, yellow, blue, and three secondary colors: orange, green, and purple are taught with 3-D ball animation based on the augmented reality technique. This animation introduces how the secondary colors originate from the combination of primary colors. This is an enjoyable, teaching-learning, and self- repeatable activity for children in a COVID-19 pandemic state.
Movie Box-Office Revenue Prediction Model by Mining Deep Features from Trailers Using Recurrent Neural Networks

Predicting a film's box-office revenue before its release has become an emerging demand, as it affects financial decisions and the creation of promotional efforts by advertising businesses that create trailers. Decision-makers have a complex and challenging task due to a lack of historical data and several complex considerations. This paper proposed a novel Deep Multimodal Predictive Cross-Input Neural Network model (DMPCNN) for predicting the film's opening weekend box office revenue using deep multimodal visual features extracted from the trailer content and a cross-input neighborhood feature fusion. DMPCNN is an end-to-end recurrent neural network with two architectures: one for extracting and learning mid-level temporal visual content and another for uncovering and fusing high-level spatial features in trailers to predict movie revenue. The first section focuses on learning various trailer segments, while the second section simultaneously learns and combines features from movie trailers and metadata and corresponding similarity metrics. DMPCNN aided in developing a decision support system that incorporates useful revenue-related trailer features. We evaluated DMPCNN's performance on the Internet Movie Database by collecting metadata for 50,186 movies from the 1990s to 2021 and comparing it to various state-of-the-art models. The learned information in movie trailers and the predicted outcomes outperformed baseline models, achieving a 61% feature precision and 84.40% prediction accuracy.
Connected Things: People, Machines, and Plants in the Internet of Things (IoT)

If we let the word “network of everything,” as the Internet of Things (IoT) is also called, roll off our tongue for a moment, it sounds a lot like science fiction and not like normality—to me, at least, but maybe it’s different for you. But the fact is that this “network of everything” is no longer fiction. It is reality.
PRISM: Principal Image Sections Mapping

Rapid progress in machine learning (ML) and artificial intelligence (AI) has brought increased attention to the potential vulnerability and reliability of AI technologies. To counter this issue a multitude of methods has been proposed. Most of them rely on Class Activation Maps (CAMs), which highlight the most important areas in the analyzed image according to the given model. In this paper we propose another look into the problem. Instead of detecting salient areas we aim to identify features that were recognized by the model and compare this insight with other images. Thus giving us information: which parts of the picture were common, which were unique for a given class. Proposed method has been implemented using PyTorch and is publicly available on GitHub: https://github.com/szandala/TorchPRISM .
Leveraging the Kinect Sensor to Correct Improper Bowling Form

When we go to bowling alleys, more often than not, we are often mesmerized by the one person who seems to get astrike with every swing. Is it the way they swing the ball? Or is it due to their feet placement? However, the answeris more straightforward than one might think; the form we use to bowl can make a massive difference betweengetting a strike and getting a gutter ball. Having poor performance at this sport can make playing it frustrating andembarrassing due to the constant gutter balls and inconsistent swings. To increase the chance of getting a strike,proper form is needed to do so. In this paper, we propose an application, Bowler Correcter, that will correct the formof a bowler in real-time. To improve the form of a poor bowler, the Xbox Kinect sensor V2 was used to provide thenecessary adjustments to correct a bowler's form. To train the sensor with proper form, proficient bowlers providedtraining data to the sensor. When selecting bowlers with poor form, the following was observed in non-proficientbowlers during a screening: tensed shoulders, an improper swing of the ball, not crouching in the follow-throughstage, and skipping the stance stage. We capture the skeleton of the bowler instead of the movement, which allowsus to assess the form of the bowler more clearly and accurately. Since this is a complex gesture, it had to be split upinto three gestures that would be later stored in the bowling database - Follow Through, Stance, and Swing ArmBack; breaking this gesture into parts makes it easier for the sensor to detect and to provide better feedback. TheKinect SDK, along with the Kinect library, was used to provide the necessary drivers for the Kinect sensor andprovide the needed libraries for creating the application. When gathering training data for the sensor, the applicationKinect Studio was used; this application and the Visual Gesture Builder can be found in the Kinect SDK. To trainthe Kinect sensor, clips recorded in the Visual Gesture Builder are loaded into Kinect Studio, where each clip istagged where the stance is present. To test the effectiveness of the application, Sports Champion 2 for PlayStation 3using the PlayStation Eye and Move controller was used as the increase in popularity in AR and VR allowedparticipants to experience bowling without needing to travel to a bowling alley. Additionally, with Covid concerns,this provided a way of ensuring cleanliness, comfort, and the safety of our participants as they bowled. The bowlerswith poor form were asked to play two games of bowling, one game without the corrector and the other with thecorrector, with the difficulty level set to championship; this increased the sensitivity of the Move controller andremoved all aides that prevented the ball from going into the gutter. It was found that this application provided thenecessary feedback needed for poor bowlers to correct their form and improve their performance.
Shoot Share: A Cloud Data Sharing System Using a Mediation Device

Data storage is gradually shifting from local disk to online cloud storage. Consequently, users are increasingly sharing data stored in their cloud storage with others. The data sharing methods in cloud services at present include sharing unique uniform resource locators (URLs) and adding accounts associated with email addresses. However, users cannot efficiently share data using these sharing operations because they must use multiple tools such as browsers, e-mail, and social networking services (SNSs) to share strings of text, such as URLs and e-mail addresses, with others. To address the abovementioned issues, we developed Shoot Share, which is a web application that enables cloud data sharing between different accounts using a smartphone. Users can easily share data by holding their smartphones over the source and shared devices. This study is a forerunner for smoother cloud data sharing assuming that data will be managed by cloud storage in future.
About University Autonomy in Institutional Execution

The principle of university autonomy and academic freedom laid down by Wilhelm von Humboldt, one of the founders of modern higher education in Europe, does not just take root in Russian higher education. The state (federal) administration, which we called “institutional execution”, determines the main forms of university activity. Nevertheless, the entry into the Bologna process (USE, GIFO, educational loans, etc.), the successful implementation of the 5-100 and 2030 megaprojects allow us to talk about the sustainable development of higher professional education. The issue of access to quality education and overcoming inequalities is discussed.
The Soul of a New Machine: Promises and Pitfalls of Artificial Intelligence in Finance

After decades of development and many false dawns Artificial Intelligence (AI), in its various guises, finally appears poised for mainstream commercial adoption. The financial sector, in particular, is looking with great interest at a broad range of applications. In April 2021, the European Commission published draft legislation that endeavours to create a comprehensive regulatory framework for the civilian use of AI. With its proposal for an "Artificial Intelligence Act" (AI Act) the Commission aims at striking a balance between the twin objectives of promoting the uptake of AI in the European Union and the need to address the risks associated with some of its uses. To this end the AI Act identifies a number of applications that are either deemed "high risk", and therefore subject to specific requirements and enhanced supervision, or prohibited outright. Only one of the “high risk” applications listed in the initial proposal relates to the financial services sector. This contribution examines other potential intersections between the proposed AI Act and the extensive body of existing financial-sector legislation and seeks to provide an initial assessment of the proposed regulatory framework.
Securing the Accounts of the Deceased: Implications of Compromised Profiles

Increasingly social media accounts are created across multiple platforms with potential long-term use over a person’s lifespan. Social media use over long periods of time can be problematic when the account owner dies as it contains a wealth of information and connections to other users. Messages, pictures, likes, connections, and other social media interaction, over time construct a form of a digital narrative that both may need protection. Problems can occur when these accounts of deceased users are compromised as these profiles can become a treasure trove of content for hackers and can potentially be used to attack other accounts. In this project, we explore possible security implications of compromised social media accounts of deceased users and propose future exploratory studies. This includes a discussion of transitional weaknesses; the time between a person’s death and public knowledge of their death which is when accounts are especially susceptible to being attacked. In addition, we discuss concerns in order to better protect these accounts.
Movement and cognition from the perspective of new technologies.

This paper discusses the relationship between movement and mental and cognitive function, as well as the potential for it to be utilized by new technologies. The first section of the paper presents the cerebral mechanisms responsible for associating physical with mental activity, and discusses examples of this influence on cognitive and emotional processes, as well as learning. The second section focuses on physical activity as an element of human interaction with a computer, including, but not limited to, so-called “exergames”. The subject of physical activity and cognitive functions is presented in the final section of the paper from the perspective of immersive virtual reality technology – a tool which appears to be highly compelling. Virtual reality seems uniquely suited to research on the relationship between movement and cognitive function.
Automated Correlation Selection Method for Unsupervised Learning

The world is flooded with all types of information, which allows for extensive AI research and development in industry and academia. One significant challenge with the enormous number of variables and features is defining, selecting, and prioritizing the most relevant and influential features for each organization's research, growth, and development. Currently, in many cases, this can be done via manual ETL processes of data correlation, which entails a lengthy, demanding, and expensive analysis. In this work, we will demonstrate and implement our AutoML method and examine the competence of the advanced AutoML procedure using three datasets. The innovative AutoML method allows the determination of a recommended efficient [sub]dataset after analyzing and eliminating all threshold dataset correlations. The procedure automatically operates (based on the dataset’s behavior) an effective threshold and produces a subset of features that may lead to improved performance, reduce complexity, and enable advanced AI models, outcomes, and services. One of the major advantages of this method is that it is not reliant on the AI model and/or analysis. Moreover, outcomes can be analyzed with conclusive results, independent of other models. This proposed method enables adjustments that are suited to different needs and, therefore, can achieve flexible outcomes.