Reviewerly/exHarmony huggingface dataset:
Age in Place, Move in VR: Investigating the Feasibility and Usability of a Custom‐made Virtual Reality Exergame to Promote the Well‐being of Community‐Dwelling Older Adults

Abstract Background Interactive virtual reality (VR) games combined with exercise (exergames) are a plausible strategy to encourage physical activity (PA) among older adults. However, there is little systematic evaluation of the feasibility, usability, and potential benefits of deploying at‐home VR exergames among this population. Method Fifteen community‐dwelling older adults (M = 67.33 ± 5.3, age range 60‐77) (Table1) were recruited to play 18 sessions (each 15‐20 min) of a custom‐made VR exergame at home over six weeks. Recruitment, retention, and adherence rates were calculated to determine feasibility. The usability of the exergame was assessed through i) a game user experience questionnaire, ii) a self‐reported physical/emotional discomfort questionnaire, and iii) a perceived enjoyment scale. The exploratory outcome measures included changes in i) PA, ii) exercise self‐efficacy, iii) affect, iv) various cognitive and perceptual tasks and were assessed i) before and after acute data, and ii) before and after the intervention. Result Thirteen participants completed the study (13% attrition rate) without missing any exergaming session (100% adherence rate). The completion rate of cognitive assessments, perceptual tasks, and self‐reported questionnaires was ≥ 97%. Low levels of cybersickness were reported (M = 0.9 ±1.0 out of 10). The average perceived enjoyment and perceived rate of exertion were 3.2. ±1.1 (out of 5) and 2.4±1.9 (out of 10), respectively. Most of the participants (92.3%) found the exergame’s instructions easy to follow and had an overall positive experience (84.6%). Whereas the exergame was perceived as useful for increasing PA levels (61.6%), mood enhancement was reported among less than half of participants (46.2%) and only 38% indicated that they would choose VR exergaming for at‐home PA. The likelihood of playing VR exergames in the future was also low (38.5%). The exploratory outcome measures are presented in Table 2. Conclusion Our study provides insights into the feasibility and usability of a custom‐made VR exergaming intervention to promote PA in community‐dwelling older adults which may potentially benefit their well‐being. Our findings inform the future adoption of VR at home. Our results can also be used to inform large‐scale trials and support the design and deployment of other remote exergames and assessments.
Fund Transfer Tracking System using DLT

Blockchain is technology that is being adopted globally due to its decentralized, secure, immutable, and tamper-proof features. In India, funds are a contentious topic, and large sums of money are allocated to various schemes in the public interest. However, the lack of transparency in this process can be overcome by using blockchain to provide a fully secure and immutable environment for tracking funds. This paper proposes a blockchain-based government fund transfer and tracking system to enhance transparency, traceability, and accountability in the public sector. The system provides a secure, immutable, and decentralized platform for tracking funds, subsidies, and benefits provided by the government to citizens. The proposed system utilizes smart contracts to automate the transfer and tracking process and enable efficient verification of transactions. The paper highlights the benefits of using blockchain technology, such as increased trust, reduced corruption, and enhanced efficiency in government operations. The proposed system is designed to address the challenges faced by the Indian government in providing transparent and efficient services to its citizens.
Age in Place, Move in VR: Investigating the Feasibility and Usability of a Custom‐made Virtual Reality Exergame to Promote the Well‐being of Community‐Dwelling Older Adults

Abstract Background Interactive virtual reality (VR) games combined with exercise (exergames) are a plausible strategy to encourage physical activity (PA) among older adults. However, there is little systematic evaluation of the feasibility, usability, and potential benefits of deploying at‐home VR exergames among this population. Method Fifteen community‐dwelling older adults (M = 67.33 ± 5.3, age range 60‐77) (Table1) were recruited to play 18 sessions (each 15‐20 min) of a custom‐made VR exergame at home over six weeks. Recruitment, retention, and adherence rates were calculated to determine feasibility. The usability of the exergame was assessed through i) a game user experience questionnaire, ii) a self‐reported physical/emotional discomfort questionnaire, and iii) a perceived enjoyment scale. The exploratory outcome measures included changes in i) PA, ii) exercise self‐efficacy, iii) affect, iv) various cognitive and perceptual tasks and were assessed i) before and after acute data, and ii) before and after the intervention. Result Thirteen participants completed the study (13% attrition rate) without missing any exergaming session (100% adherence rate). The completion rate of cognitive assessments, perceptual tasks, and self‐reported questionnaires was = 97%. Low levels of cybersickness were reported (M = 0.9 ±1.0 out of 10). The average perceived enjoyment and perceived rate of exertion were 3.2. ±1.1 (out of 5) and 2.4±1.9 (out of 10), respectively. Most of the participants (92.3%) found the exergame’s instructions easy to follow and had an overall positive experience (84.6%). Whereas the exergame was perceived as useful for increasing PA levels (61.6%), mood enhancement was reported among less than half of participants (46.2%) and only 38% indicated that they would choose VR exergaming for at‐home PA. The likelihood of playing VR exergames in the future was also low (38.5%). The exploratory outcome measures are presented in Table 2. Conclusion Our study provides insights into the feasibility and usability of a custom‐made VR exergaming intervention to promote PA in community‐dwelling older adults which may potentially benefit their well‐being. Our findings inform the future adoption of VR at home. Our results can also be used to inform large‐scale trials and support the design and deployment of other remote exergames and assessments.
A two-stage fine-tuning method for low-resource cross-lingual summarization

&lt;abstract&gt;&lt;p&gt;Cross-lingual summarization (CLS) is the task of condensing lengthy source language text into a concise summary in a target language. This presents a dual challenge, demanding both cross-language semantic understanding (i.e., semantic alignment) and effective information compression capabilities. Traditionally, researchers have tackled these challenges using two types of methods: pipeline methods (e.g., translate-then-summarize) and end-to-end methods. The former is intuitive but prone to error propagation, particularly for low-resource languages. The later has shown an impressive performance, due to multilingual pre-trained models (mPTMs). However, mPTMs (e.g., mBART) are primarily trained on resource-rich languages, thereby limiting their semantic alignment capabilities for low-resource languages. To address these issues, this paper integrates the intuitiveness of pipeline methods and the effectiveness of mPTMs, and then proposes a two-stage fine-tuning method for low-resource cross-lingual summarization (TFLCLS). In the first stage, by recognizing the deficiency in the semantic alignment for low-resource languages in mPTMs, a semantic alignment fine-tuning method is employed to enhance the mPTMs' understanding of such languages. In the second stage, while considering that mPTMs are not originally tailored for information compression and CLS demands the model to simultaneously align and compress, an adaptive joint fine-tuning method is introduced. This method further enhances the semantic alignment and information compression abilities of mPTMs that were trained in the first stage. To evaluate the performance of TFLCLS, a low-resource CLS dataset, named Vi2ZhLow, is constructed from scratch; moreover, two additional low-resource CLS datasets, En2ZhLow and Zh2EnLow, are synthesized from widely used large-scale CLS datasets. Experimental results show that TFCLS outperforms state-of-the-art methods by 18.88%, 12.71% and 16.91% in ROUGE-2 on the three datasets, respectively, even when limited with only 5,000 training samples.&lt;/p&gt;&lt;/abstract&gt;
A Novel A-star Algorithm for Vehicle Path Planning in Complex Scenes

In this paper, a novel A-star algorithm with an improved heuristic function is proposed for autonomous vehicle path planning. Firstly, the direction guidance operator and distance weight coefficient are added to the heuristic function, which greatly reduces the expanded nodes and the search area of the algorithm in the search process. Secondly, the generated path is re-optimized to eliminate redundant nodes, this can improve the smoothness of the path, and further shorten the length of the path. Finally, the algorithm is tested in several different complex scenes. The experimental results demonstrate that the proposed algorithm performs exceptionally well in regards to path length, search time, and smoothness.
Immersive Assistance System for Intuitive Robot Programming using Mixed-Reality and Digital Twin

Enabling technologies influence our everyday life. Robot manipulators are prominent examples in the industrial realm. They carry out repetitive production tasks with repeatable accuracy at high speed for a wide range of payloads. However, enterprises still refrain from adopting industrial robots. Reasons include the complexity and efforts necessary to program and adapt the robot behavior. In fact, employees are seldom suitably skilled to complete this goal and hiring experts increases production costs. As customer demands fluctuate in terms of volume and individualization, this situation gets worse since time windows to reprogram the robot might be substantially shortened and standard vendor-specific motion patterns fail to accommodate irregular Cartesian profiles. To address these challenges, this work combines mixed-reality and digital twin technologies to develop a natural, dynamic, and fast approach to (re-)program robots. It decouples motion planning driven by augmented reality, application-agnostic connectivity, and physical motion execution to augment even novice users with the capability to move a robot between freely and intuitively specified Cartesian frames without writing any line of code. The usefulness and effectiveness of the approach is demonstrated in many illustrations in practice.
BFNet: Brain-like Feedback Network for Object Detection under Severe Weather

Despite the demonstrated promising results achieved by deep learning-based object detection methods on conventional datasets, the object detection presents a significantly greater challenge when confronted with poor perception captured under adverse weather. Existing methods are studying how to improve the ability of the model to extract features by reducing the impact of severe weather on images, but these methods contradict the human visual cognition process. Based on the mechanism of visual cognition, we propose a novel object detection model based on brain-like feedback, which improves the detection performance of the model and has strong interpret ability. Specifically, an initial environment cognition assessment method is proposed, which is used to evaluate high-level prior knowledge and improve the ability to process and integrate low-level information; In addition, the amodal predictive completion method is proposed, which solves the problem of object blur and uncertainty caused by limited visual perception, and realizes the shape prediction of unknown objects. The experimental results are surprising, demonstrating the effectiveness of our method in severe weather.
LEGAL EDUCATION AS A PEDAGOGICAL CONDITION OREVENTION OF DEFORMATION OF LEGAL CONSCIOUSNESS OF PERSONALITY

The relevance of the study lies in the sphere of formation of legal consciousness and prevention of its deformation using pedagogical methods of legal training, education and development in the educational process. At present, when society is faced with rapid sociocultural and technological changes, it is important to understand how a person's legal consciousness can be formed more effectively through pedagogical methods. The purpose of the study - conduct an analysis of effective methods for forming legal consciousness within the framework of the educational process, highlight key aspects that contribute to the education of law-abiding citizens and the formation of a sustainable legal outlook, worldview and worldview in the educational environment. Research methodology. In this article, to systematize theoretical and empirical data on the problem of forming legal consciousness through the educational process, the method of analyzing scientific and methodological literature is used. To identify the main advantages and problems associated with the development of legal training, education and development in the educational process, a descriptive method is used. When considering examples of successful practices in legal education and countering the deformation of legal consciousness, the method of analysis and generalization of pedagogical experience is used. Results. The article discusses current methods and strategies used in educational institutions for the effective formation of legal consciousness among students. Particular attention is paid to the influence of legal education on the prevention of deviations of legal consciousness and the correction of its deformation. The role of educational programs in creating a conscious attitude towards the law is explored, and factors contributing to the deformation of the legal consciousness of students are identified. The article discusses problems associated with the information environment and media literacy that affect the perception of laws. The presented analysis allows us to identify key strategies and techniques that contribute to the sustainable and balanced development of legal consciousness in the educational environment. Conclusion. The article provides practical recommendations for educational organizations in order to optimize the processes of forming legal consciousness, ensuring effective prevention and countering the deformation of legal values in the modern educational context.
Relational neurostrategy

Purpose Neurostrategy is an abductive decision-making process developed from the knowledge generated within the network of decision-makers. It links cognitive style with the team's decisional adaptability in relation to the organization's purpose. Neurostrategy differs from traditional methods, in which it addresses negative utility or decisional trauma, highlighting the variety of interests that are the main cause of team misalignment and allowing for the development of strategies to address them. Neurostrategy enables the classification of strategy deficiencies based on cohesion, coordination, communication and conduction (Co4). Design/methodology/approach This paper introduces a novel approach called relational neurostrategy (RNS) to elucidate the knowledge processes influencing decision-making. RNS aims to “capture” the intricate processes guiding decisions, enabling the network's decisional plasticity in both forms and contents. This adaptability is crucial for effectively addressing posed challenges, while simultaneously mitigating the impact of diverse interests. The methodology also ensures transparency in the decision-making process and generates an effective solution strategy. Findings The RNS addresses two critical aspects of the decision-making process. Firstly, it reduces unnecessary variety stemming from multiple interpretations and secondly, it minimizes the adverse impact of diverse interests within the decision-making network. This approach results in strong and credible decisions that reflect the collective intelligence, cooperation and collaborative efforts of the network, rather than being imposed as absolute truths. Originality/value The RNS stands out as a distinctive decision-making method, setting itself apart from existing approaches. Its uniqueness becomes evident in its ability to address the question “what prevents the authors from … ?” from this inquiry, RNS successfully integrates unrequired variety and negative utility. By doing so, it strategically narrows down the search field to the universe of distinctions that truly constitute the problem. This innovative process not only enhances efficiency but also fosters a high level of participation in the strategic design of potential solutions. In essence, RNS brings unprecedented value by effectively navigating the intricacies of decision-making and maximizing the relevance of the identified problem space.
Harmonizing Image Forgery Detection &amp; Localization: Fusion of Complementary Approaches

Image manipulation is easier than ever, often facilitated using accessible AI-based tools. This poses significant risks when used to disseminate disinformation, false evidence, or fraud, which highlights the need for image forgery detection and localization methods to combat this issue. While some recent detection methods demonstrate good performance, there is still a significant gap to be closed to consistently and accurately detect image manipulations in the wild. This paper aims to enhance forgery detection and localization by combining existing detection methods that complement each other. First, we analyze these methods' complementarity, with an objective measurement of complementariness, and calculation of a target performance value using a theoretical oracle fusion. Then, we propose a novel fusion method that combines the existing methods' outputs. The proposed fusion method is trained using a Generative Adversarial Network architecture. Our experiments demonstrate improved detection and localization performance on a variety of datasets. Although our fusion method is hindered by a lack of generalization, this is a common problem in supervised learning, and hence a motivation for future work. In conclusion, this work deepens our understanding of forgery detection methods' complementariness and how to harmonize them. As such, we contribute to better protection against image manipulations and the battle against disinformation.
A QUANTUM-LIKE APPROACH IN UNDERSTANDING MODELING COGNITION AND DECISION-MAKING PROCESSES (REVIEW OF FOREIGN LITERATURE)

The article presents a review of foreign scientific literature on the quantum-like approach to understanding the modeling of cognition and decision-making processes. It is shown that the current application of quantum physics approaches to consciousness, psychological approaches such as perception, cognition, emotions, consciousness and learning, involves the search for a single objective mathematical model of what underlies the experience of the brain. The use of quantum physics algorithms can help in understanding the mechanisms of brain malfunctions and memory impairments that are found in pathologies such as dementia caused by Alzheimer's disease, schizophrenia, etc. Quantum physics approaches are applicable to issues of modeling cognition and decision-making processes, as well as in cognitive research, information processing by the human brain, artificial intelligence development, psychology, social and political sciences. Application of the abstract mathematical principles of quantum theory can help in understanding human consciousness and behavior. Quantum probability theory is applied to modeling human behavior due to the prevalence of uncertainty in human interaction with the world. Quantum cognitive models are especially useful in research in such areas as errors in decision making, order effects of questions, conceptual combinations, accumulation of evidence, perception, etc. Of particular interest to researchers are issues of quantum-like modeling of cognition and decision-making processes, the apparatus of quantum information and probability theory, useful for such modeling, as well as the use of such apparatus in cognitive research, the development of artificial intelligence, psychology, social and political sciences.
ANALYSIS OF THE INFLUENCING FACTORS AFFECTING STUDENTS' COLLEGE ENTRANCE EXAMINATION SCORES

The college entrance examination scores are significant for students. In China, the result of the college entrance examination is the only criterion for judging whether a student can enter a university. This study takes some students from China West Normal University to analyze the factors that may influence the scores. Through the statistical analysis of SPSS, it is found that gender is related to the score of college entrance examination, and the place of origin is significantly related to the score of college entrance examination. The causes of the various phenomena are analyzed, and targeted suggestions are put forward to provide valuable references for improving students' performance.
The Influence of the Federal Reserve System Interest Rate Hike on Tesla: Empirical Research Based on ARIMA Model

On 23 March 2022, the Federal Reserve System announced interest rate hikes and increased 25 basis points. After that, a series of negative monetary policies were carried out. Tesla CEO stated that the price of cars must be reduced when interest rates rose sharply. This means that Tesla's stock price will continue to decrease. The 25 -basis points raised the rate of interest rates and the highest interest rate reached 5 % to respond to inflation. The rate hikes of The Federal Reserve System have a great impact on the world. This article selected all stock data from Tesla from the listing to August this year (including daily data, weekly data, and monthly data) and uses ARIMA model to model and analyses the data. It will compare with the actual value of the Federal Reserve System. According to the study, the trend of the decline in stock prices after the RECERAL Reserve System raised interest rates is the same as the trend of research prediction in this article. Its interest rate hikes will cause Tesla's stock price to fall.
Bitcoin Price Prediction: ARIMA &amp; SARIMA vs Linear Regression

This paper illustrates the working process of predicting the Bitcoin price applying ARIMA, SARIMA and linear regression. Since more and more machine learning models were developed and tested in the financial field, these three models are selected to examine their reliabilities. In this study, three methodologies have been used for the Bitcoin predictions under the data set of Bitcoin historical prices. With the help of python notebook, order (1, 1, 1) and seasonal order (0, 1, 1, 12) were applied to the predictions in ARIMA and SARIMA respectively. In terms of linear regression, this paper used two independent variables including historical data and trading volume to predict the Bitcoin prices. It was discovered that the predictive graph for these three methodologies can match the actual value well, and linear regression performs the best. Considering the rapid development of machine learning methods, adopting alternative methods deserve in-depth investigations.