Reviewerly/exHarmony huggingface dataset:
Self-play learning samples generator for checker

Computer chess games are one of the most significant research subjects in the field of artificial intelligence. Self-play learning depends only on the chess gaming process and the outcome. Neither knowledge nor experts are involved in the learning process. Although self-play learning based on the minimax algorithm, alpha-beta pruning algorithm and Monte Carlo search algorithm has achieved remarkable achievements, there is a lack of research on the quality of the learning samples. We consider that the shortage of convenient software tools is a primary reason why researchers have not sufficiently studied learning samples. Therefore, this paper designs and implements a Self-play Learning Sample Generator for Checkers system (SLSG system).
Research on single object tracking algorithm based on Siamese network and Kalman filter

The single object tracking algorithm based on Siamese Network transforms the object tracking problem into the similarity calculation problem of features. By inputting the template and the search frame into the Siamese Network with the same structure and sharing weights, the corresponding features are extracted respectively, and the feature map is output. Then, convolution operation is performed on the feature map to find the area where the search frame is most similar to the template frame, so as to achieve the tracking of a single object. However, this method only uses the appearance feature of the object to track the object, and does not use the motion feature of the object to predict the position of the object. In a scene with multiple objects with similar appearance, it is easy to overlap the spatial positions of similar objects. Or the tracking frame drift phenomenon occurs due to occlusion or other reasons, resulting in the loss of tracking of the object. Kalman Filter is a linear system that can predict the next state of the object based on the current state and known information, and continuously predict the object state through continuous iteration. This feature can be used to predict the motion trajectory of the object, and combining it with the Siamese Network can greatly improve the anti-interference ability of the Siamese Network. This paper uses SiamRPN as the basic network and introduces the Kalman Filter algorithm to predict the object trajectory. The purpose of this paper is to fuse the appearance and motion features of the object, and conduct a comparative experiment with the tracking of a single feature to study the impact of different features on the object tracking performance.
Design and implementation of a python-based earthquake emergency assessment model

The earthquake emergency assessment model proposed in this paper uses the Python Programming Language to achieve automatic output. The overall framework includes data stacking and analysis and automatic output of the epicenter, casualties, and economic loss. When an earthquake occurs or an earthquake drill is simulated, the program can, by inputting the three elements of the earthquake, automatically output the economic loss and casualties caused by the earthquake, calculate disaster-related information in a timely and rapid manner, and quickly generate an earthquake emergency assessment report. This is essential for formulating a proper emergency rescue strategy in a timely manner and improving the efficiency and quality of rescue efforts.
Tinker Hunt

With tech taking over most of the life process in recent times, primary education still lacks interactive processes using technology in the process of “Learning by Doing”. Most of the Ed. techs offer visual learning with no or near-to-zero input by children. Tinker Hunt can be a possible and one of its kind of interactive learning application used to tackle the same. A rough sketching input by children can yield results over the internet in the form of pictures, detailed information, etc. Tinker Hunt is created using Google’s QuickDraw Dataset, using hundreds of sketch patterns for multiple categories of icon-level doodles, collected from hundreds of individuals that are recognized by CNN trained model. Also, the app is tested for high accuracy in sketch recognition and categorization. Key Word: Machine Learning; QuickDraw Dataset; Interactive Learning; Convolutional Neural Network; Artificial Neural Network
Visualization of Sounds: Improving the Listening Experience Through Digital Visualization

&lt;p&gt; &lt;/p&gt; &lt;p&gt;In this project we will observe the persuasiveness of visualization when pertaining to audio data. Specifically, we look at instrumental music composition and observe how visualization can facilitate the communication of a music-maker's state of mind. We first examine, through a research process, how visualization can facilitate the processing of information. Then, steps are taken using data bending and digital data visualization techniques – with minimal creative input – to generate animated visuals from the audio data. Finally, the digital visualizations are introduced to participants via a survey to measure how effective these visualizations were at translating intentionality. &lt;/p&gt;
Considerations regarding the resource of human creativity in the context of various educational systems of countries

The article aims to analyse the considerations on the human creativity in the context of educational systems of the countries of the world. The complexity of the aspects and impact of creativity resource is very wide, and involves synthesizing knowledge, experience and wisdom, on the one hand, and on the other, the efficient strategic planning and implementation of educational policies’ processes are directly dependent on how creative the stakeholders as main actors are to contribute and facilitate the interest and love for learning, research and development of pupils and students involvement. It is inconceivable that anyone would overlook the imperative connection between the quality of the education system and the quality of life in any society, be it in the west or in the east of the world. The authors of the research show that the more creative the persons are due to individual perspectives and development or to educational system contribution, the more creative economic activity can be generated, and the educational system can strategically facilitate and promote this concept and phenomena.
Role of ICT in Managerial Effectiveness in the Changing Era of Technology: A Quantitative Investigation

Information and Communication Technology is one of the vital forces important for the growth and survival of an organization. It is crucial for every organization to perceive the idea of ICT. ICT in today’s technological era is more than just a study; it is a kind of innovation and new business style which is important for the managerial effectiveness of an organization. It is vital for the top managers to successfully implement the notion of Information and Communication Technology. It doesn’t matter whether the organization is small, medium or large ICT is essential for the smooth running of the business. In a capital intensive business ICT helps in the efficient functioning of the business. As the world is drastically transforming towards digitization ICT has emerged as a tool to bring effectiveness in the managerial activities of an organization. This paper will highlight the role of ICT in managerial effectiveness and some of the issues and challenges related with the same.
A Study of Use of the Knowledge Management Process for Organizational Growth in Public Sector Banks

An organization's knowledge may be put to good use in the form of financial and social returns for its customers and network via the implementation of a system known as "Knowledge Management." Knowledge management is a multi-step process that enables the free flow of information across all VIP clients of a business. Common knowledge is that banks are crucial in a nation's ability to expand its economy, create new jobs, innovate, and build its credit infrastructure. It is important to recognize the role that commercial banks play in a country's economic progress because of the effective mobilization of resources for productive purposes. The purpose of this research is twofold: first, to identify the ways in which banks have benefited from implementing knowledge-management practices, and second, to identify the barriers that have prevented these advantages from being fully realized.
Internet of Things as a Transformation Agent for New Age Businesses: An Empirical Study Current Status and Future Challenges

Internet of Things is transforming the world in every aspect. Businesses are one of the biggest beneficiaries of IoT as they are adapting with the transformation swiftly. Not only the businesses but everything which is connect to these businesses are getting advantages be it a Consumer, Government or other organizations. Digitization and IoT has changed the working approach of the companies. They are shifting more towards a digital path rather staying on a traditional way. This shift has benefitted the companies in many ways like the reach to the potential customer has increased and only interested customer can be targeted. Though the IoT proved to be beneficial but it does posses many threats to the business and to the consumer in the form of security breach and data exploitation etc. This paper with will discuss the current status of IoT in business and also highlight the future challenges might face by them.
A network security situational awareness model based on multi-source heterogeneous sensors

In the research of network security situational awareness, an important problem that needs to be solved urgently is that in the face of a large number of noisy multi-source data, the accuracy of obtaining network security situational awareness indicators will be affected. This paper proposes a data fusion processing method based on deep neural network to solve the above problems, which first uses natural language processing technology to process high-noise data in the data, and does a good job of word reduction of the data through the WordNetLemmatizer module. The TF-IDF feature extraction algorithm is applied to extract features through the frequency of data occurrence and the weight of the data. The experimental results show that the accuracy of obtaining network security situational awareness indicators by using the data fusion method based on deep neural network reaches more than 85% compared with the data fusion methods of other machine learning algorithms, and can be applied to the network security situational awareness model.
Overcoming barriers to digital transformation of higher education

The modern world can hardly be imagined without digital technologies. They are used in any sphere of human activity, education being one of them. Using digital technologies in higher education is not just an option anymore; it is an imperious necessity conditioned by a number of factors. Digital technologies are widely used by students for developing their personalized learning environment. However, technology adoption and use by instructors is uneven and often quite modest. Of course, there are faculty members actively and effectively using and promoting available digital teaching tools, as well as those just willing (or unwilling) to do it due to various types of barriers. Sometimes it looks contradictory enough, as most instructors are active users of digital technologies in their personal lives, but in classrooms, some of them encounter serious technical, logistical, and pedagogical problems. Understanding the reasons for some reluctancy to incorporate technology, as well as teachers’ concerns in this respect, can essentially contribute to the enhancement of learning environment and professionalism. The present study aims to extend what is known about the factors and issues negatively affecting the adoption and use of modern technologies by faculty members, as well as to offer some recommendations for solving problems and reducing the barriers to technology adoption.
Bitcoin and Blockchain Technology and Some Perspectives on its Future- A Mini Review

Research in Medical & Engineering Sciences Bitcoin and Blockchain Technology and Some Perspectives on its Future- A Mini Review Chetna* and Shikha Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, India *Corresponding author: Chetna, Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Submission: July 12, 2022Published: August 22, 2022 DOI: 10.31031/RMES.2022.09.000725 ISSN: 2576-8816Volume9 Issue5
A meta-analysis: Investigating the impact of virtual and augmented reality technologies on brand equity and brand loyalty in sports sponsorship

Virtual and augmented reality (VR/AR) technologies have gained increasing attention in the sports sponsorship industry as a means to enhance brand equity and loyalty.This meta-analysis investigates the impact of virtual and augmented reality technologies on brand equity and brand loyalty in sports sponsorship.A total of 16 studies were included in the analysis, with a combined sample size of 5,426 participants.The results indicated a significant positive effect of virtual and augmented reality technologies on both brand equity (SMD = 0.77, 95% CI = 0.40 to 1.14) and brand loyalty (SMD = 0.51, 95% CI = 0.23 to 0.79).However, substantial heterogeneity was observed among the studies, with an Isquared value of 87.6%.Subgroup analyses did not reveal significant differences in effect sizes based on the type of sport or the target audience.The risk of bias was assessed using the Cochrane Risk of Bias tool and the quality of evidence was evaluated using the GRADE approach.Sensitivity analyses were conducted to test the robustness of the results.The findings of this meta-analysis suggest that virtual and augmented reality technologies can have a positive impact on brand equity and brand loyalty in the context of sports sponsorship.However, further research is needed to explore the mechanisms underlying these effects and to identify the specific conditions under which VR and AR are most effective.These results have implications for marketers and sports organizations seeking to enhance their brand image and cultivate customer loyalty using immersive technologies.Overall, this meta-analysis provides valuable insights for both academics and practitioners in the sports sponsorship industry, highlighting the potential of VR/AR technologies in enhancing brand outcomes.
Procedural Terrain Generation by Sampling a 2D Monochrom Perlin Noise Map in Unity

This article presents a study on procedural terrain generation using Perlin noise in the Unity game engine, building upon prior research and established techniques [1,2]. The objective of the this study is to create realistic and visually appealing landscapes by leveraging algorithmic generation [3]. By utilizing a two dimensional monochrome Perlin noise map, the algorithm generates terrains with organic features such as hills, valleys, and mountains [4]. The research employs the Unity game engine, which offers a wide range of tools and technologies for implementing procedural generation algorithms [5].&#x0D; The performance analysis conducted during this study yielded important insights. The execution time test revealed that the algorithm's execution time increased significantly with larger grid sizes, emphasizing the need to consider the scale and resolution of the terrains [6]. This finding highlights the trade-off between generating detailed terrains and the time required for the algorithm to complete its computations. Additionally, the memory usage test demonstrated a quadratic relationship between memory usage and grid size, indicating the necessity of efficient memory management [7] for generating higher-resolution terrains. Optimizing memory usage is crucial to avoid excessive consumption and ensure efficient resource utilization.&#x0D; These findings from the performance analysis provide valuable guidance for optimizing and refining the algorithm discussed in this article. Techniques such as algorithmic refinements, parallelization [8,6], and memory optimization strategies can be employed to reduce execution times and minimize memory overhead.&#x0D; By understanding the algorithm's performance characteristics and leveraging the insights gained from the analysis, developers can make informed decisions to generate visually compelling and diverse landscapes within the Unity game engine.
Fractal analysis of the sulfide composition and structure of interspersed ores in picrite gabbro-dolerites of the Oktyabrsky deposit, Norilsk ore field

The analysis of ore samples taken from wells drilled on the Eastern flank of the Oktyabrskoye deposit was carried out. In the vein-disseminated sulfide copper-nickel ores of the deposit, three types of mineralization are distinguished, which clearly differ from each other: interstitial-teardrop-like, interstitial, tear-shaped-interstitial. A sample of each type of ore was examined by 3D tomography and analyzed using fractal theory. Based on the results of the study, a conclusion was made about the relationship between the fractal dimensions of ore minerals and the dynamics of the formation of various types of ores. The pattern of changes in the intensity of mineralization from the thickness of the layers is established. The results obtained may be of great importance in relation to the technology of enrichment of new types of ores from the flanks of the deposit, and also be an additional criterion for identifying in disseminated ores zones with the highest intensity of ore mineralization during prospecting and exploration in the Norilsk ore region.
Self-compositional data augmentation for scene text detection

Scene text detection aims to detect text regions in the complex background, and deep learning-based methods have been the mainstream. To obtain robust performance, deep learning-based methods are data-hungry, and one efficient technique to obtain sufficient data is data augmentation. However, current data augmentation for scene text detection is 1) changing the whole image, which ignores the instance-level diversity, or 2) generating synthetic data using generative models, which requires extra training data. In this paper, we propose a self-compositional data augmentation (SDA) for scene text detection. Our SDA generates new data by changing the original text regions of one image with four types of variations: translation scaling, rotation, and curving, and putting the changed text regions back into random places of the same image. In specific, our SDA is an instance-level augmentation, which could be combined with image-level augmentation; SDA requires no extra training data, which could be easily adopted in different methods. We conducted extensive experiments with three state-of-the-art scene text detection methods on two public datasets. Using our SDA improves all methods on all datasets, and the improvements demonstrate the effectiveness and generality of our SDA.
An improved LSTM model for correcting grammatical errors in English text

In English teaching, grammar teaching is still an indispensable part. Teachers pay more attention to the correction of written form than oral form in grammar teaching so that students can see their mistakes intuitively and clearly. Whether from the perspective of teachers or students, the correction of grammatical errors and good learning results are inseparable. At present, the public grammar error correction corpus is too small and the quality is uneven, which makes the parameters of the grammar error correction model can not be fully trained. The performance of the model is also a bottleneck. Graph neural network-based model for grammar error detection is studied. The errors in the text data are detected. Neural network modeling is adopted as the basic structure of the model. In addition to predicting the correct label of each word in the sentence, an auxiliary task of predicting the context word of the word is introduced to further improve the detection performance of the model. Furthermore, the graph neural network with a gating mechanism is adopted to model the dependency syntax tree of the statement, which provides important information features for error detection and effectively improves the performance of model checking. Finally, good results in English grammar error detection and test data sets are achieved. As one of the core technologies in online education and other fields, the research of grammar error correction has great research and application value.
Predict, Identify and Alert on Suspicious Activity by Multiple Zone

Abstract: Suspicious human activity detection in security capture is a study topic in image processing and vision. The mysterious identification of human activity from video surveillance is an area of study in both fields. Human activity can be monitored visually in conspicuous public spaces like bus depots, airports, railway stations, financial institutions, malls, schools, and universities to avoid terrorist activity, vandalism, accidents, prohibited parking spaces, vandalism, fighting chain theft, criminality, and other unusual behavior. Extremely difficult to continually monitor public spaces, thereby an innovative video surveillance installation system that can track people's movements in real-time, classify them as routine or odd, and send out an alert is needed. The field of visual surveillance to identify aberrant actions has seen a significant amount of publications in the last ten years. Furthermore There are a few surveys in the literature for recognising various abnormal activities, but none have reviewed various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the stateof-the-art in the field of recognizing suspicious behavior from surveillance recordings during the past tenyears. We provide a brief outline of the risks and challenges associated with detecting suspicious human activity. This article examines six aberrant behaviors, including the identification of abandoned objects, theft, falls, traffic accidents, and unlawful parking, as wellas the detection of violence and fire. Generally speaking, we have covered all the processes that have been [1] Foreground object extraction, object identification based on tracking or non-tracking approaches, feature extraction, classification, activity analysis, and recognition are some ofthe techniques that have been used to identify human activity from surveillance movies in the literature. This paper's goal is to give field researchers a literature assessment of six different suspicious activity identificationsystems together with its broad framework.[1]