Reviewerly/exHarmony huggingface dataset:
An Empirical Investigation of Visual Reinforcement Learning for 3D Continuous Control

Sample-efficient reinforcement learning (RL) methods that can learn directly from raw sensory data will open up real-world applications in robotics and control. Recent breakthroughs in visual RL have shown that incorporating a latent representation alongside traditional RL techniques bridges the gap between state-based and image-based training paradigms. In this paper, we conduct an empirical investigation of visual RL, which can be trained end-to-end directly from image pixels, to address 3D continuous control problems. To this end, we evaluate three recent visual RL algorithms (CURL, SAC+AE, and DrQ-v2) with respect to sample efficiency and task performance on two 3D locomotion tasks (‘quadruped-walk' and ‘quadrupedrun') from the DeepMind control suite. We find that using data augmentation, rather than using contrastive learning or an auto-encoder, plays an important role in improving sample efficiency and task performance in image-based training.
Tweet Analysis: What changed over time?

This work focuses on analyzing linguistic variations in India over time using TwitterAPI as a data source. The study aims to discover the correlation between political matters and the nature of tweets between 2012 and 2021. The research involves extracting prominent tweet text parameters and conducting sentimental analysis to identify patterns in the language shift. We use two different clustering algorithms to track the nature of clusters based on each parameter. The results indicate that the number of parameters used in tweets increases over time, with a significant increase in 2019, possibly due to a societal event. The usage of parameters such as hashtags, emojis, and mentions generally increases with time. The study suggests negative judgments of politics, indicating that the government fails to meet people’s expectations. Overall, the work sheds light on the linguistic variations and political sentiments prevalent in India through Twitter data analysis.
Blockchain qualified: Verification system

In this competitive world, it is very difficult to achieve your dream job or even a job for that matter. People are ready to go to any limits to be a stand out candidate for an organization. With everything working in online mode and a large number of editing applications available in the market for free or at a very cheap rate, privacy and integrity of documents have become a big issue. It has become very easy to forge or duplicate a document with zero knowledge of editing. For instance, one can easily change his or her scores on a certificate or even change the name and claim the certificate to be theirs. As of today, organizations check manually with the respected certificate issuers to verify a document. This is a very tiring and time-consuming process. There is a need to resolve this issue to ease and secure the document verification procedure. Thus, this paper discusses the development of a Blockchain Based Verification System to maintain data purity. Blockchain is evolving in every sector these days due to its strong security and transparency. It should now be implemented in the Education sector as well to use it to its full advantage. The certificates will have a key limited to the certificate holder. The verification of the certificate can be done using that key. Moreover, only the certificate issuer can upload the certificate on the Blockchain network. The data immutability and security offered by Blockchain will solve the above addressed issue.
Development of practical thinking in the professional activities of future law officers

The article is devoted to the peculiarities of the development of practical thinking in the professional activity of future law enforcement officers, as one of the types of thinking that regulates activity, ensuring its adequacy. Such thinking achieves its goals due to the fact that, along with the properties and nature of the object, as well as the peculiarities of the action situation, its conditions, as well as the means of implementation available in this case, are taken as the conditions of the task. The following psychological features are characteristic of practical thinking: observation, the ability to quickly move from thinking to action and back, the ability to see a problem, since the very formulation of a problem is an act of thinking that often requires a lot of hard work. Practical thinking is determined by such psychodynamic determinants as motivation - a set of psychological processes that direct human behavior (at high levels of development, future law enforcement officers are motivated to succeed); an attitude is an individual's tendency to a certain form of response, with the help of which this or that need can be satisfied; representative systems (auditory, visual, kinesthetic), the auditory sensory modality prevails in the subjects, which is expressed in the perception of information by ear and better memorization. All these psychodynamic determinants affect the development of practical thinking of future law enforcement officers. Key words: practical thinking, future law enforcement officers, professional activity, motivation for success, representative systems (auditory, visual, kinesthetic).
ORDINARY AND SCIENTIFIC KNOWLEDGE: METAMORPHOSES OF RELATIONSHIPS

On the characteristic features of everyday and scientific types of knowledge, their interaction (interrelation and difference) in the historical aspect is studied. The problem is analyzed &#x0D; on the basis of historical and philosophical material. The difference and relationship between everyday practical and scientific types of knowledge is illustrated by examples of acquired knowledge from various fields of science. Particular attention is paid to the comparative characteristics of the language of science and the language of everyday knowledge. The analysis carried out allows us to avoid extremes – both &#x0D; the absolute opposition of these types of cognition and their fusion. The attributes of scientific knowledge – rationalistic elements (argumentation and proof, methodological approach, universality, etc.) – &#x0D; are complemented by irrationalistic elements (faith, intuition, etc.), without which no scientific research is possible. The use of the dialectical method of analysis can be a sufficient basis for criticizing anti-scientism and reduce the currently increasing intensity of criticism of science.
Experience design: A tool for the valorization of the archaeological site of Sbeitla - Tunisia

This article presents a research path interesting the valorization of the archaeological site of Sbeïtla (Tunisai) by the tool of experience design. The objective of this research is to contribute to the creation of a specific experience to visitors. To carry out this work, we have adopted an analytical approach. Using experience design, we have conducted a site observation followed by a survey whose purpose is to analyze the visit of a group of experiencers in order to assess the level of their satisfaction.&#x0D; Based on the results obtained, this research proposes the use of the new technology, specifically the augmented reality, as a catalyst to amplify the level of satisfaction of experiencers. It is through experience design and through the integration of new non-destructive technology that the archaeological site of Sbeïtla can be both valorised and preserved.
Towards Kyrgyz stop words

The concept of stop words introduced by H. P. Lun in the mid-20th century plays a huge role in today’s NLP practice. Stop words are used to reduce noisy text data, remove uninformative words, speed up text processing, and minimize the amount of memory required to store data.The Kyrgyz language is an agglutinative Turkic language for which no scientific study of stop words has been previously published in English. In our study, we combined frequency analysis with rule-based linguistic analysis. First, we found the most frequently used words, set a threshold, and removed words below the threshold. This way we got a list of the most frequently used words. Then we reduced the list by excluding from the list all words that do not belong to the category of function words of the Kyrgyz language. Finally, we got a list of 50 words that can be considered stop words in the Kyrgyz language. In our analysis, we used a single corpus of sentences collected and posted as an open source project by one of the local broadcasters.
The crisis of the professional identity of the teacher in the context of the formation of a new information and communication educational paradigm: Aspect of research and publication activity

The problem of the study - the genesis of the problem of the crisis of professional identity of a teacher in the context of the formation of a new information and communication educational paradigm in the second half of the twentieth century - early twenty-first century. The analysis of the scientific and pedagogical literature of the second half of the twentieth century - the beginning of the twenty-first century demonstrates that the transition to a new paradigm of education was marked by the emergence of a crisis of professional identity, which is also emphasised by the ambiguity of definitions of the concept of professional identity. In the modern scientific and pedagogical discourse, a teacher is seen as a person with numerous roles. The scientific, theoretical and philosophical foundations for considering research activities and publication activity as key indicators of the level of professional identity of a teacher are identified and substantiated. An empirical study of the opinions of practicing teachers in Poland, Slovakia, Ukraine and Sweden on research and publication activity is carried out. The results of the international empirical research state the presence and reveal specific manifestations of the professional identity crisis.
Filtering problem for periodically correlated stochastic sequences with missing observations

The problem of the mean-square optimal estimation of the linear functionals which depend on the unknown values of a periodically correlated stochastic sequence from observations of the sequence with missings is considered. Formulas for calculation the mean-square error and the spectral characteristic of the optimal estimate of the functionals are proposed in the case where spectral densities of the sequences are exactly known. Formulas that determine the least favorable spectral densities and the minimax spectral characteristics are proposed in the case of spectral uncertainty, when spectral densities of sequences are not exactly known but the class of admissible spectral densities is given.
Appointment Booking and Drug Inventory System in Healthcare Services Using Blockchain Technology

Blockchain technology has the potential to revolutionize the healthcare industry by improving data security, reducing administrative inefficiencies, and enabling the seamless sharing of medical information. In the healthcare sector, blockchain can be applied to a wide range of use cases, such as medical record-keeping, clinical trials, drug traceability, and telemedicine. By using a secure, decentralized system, healthcare organizations can ensure that sensitive patient data is kept confidential and can be easily accessed by authorized parties. Additionally, the use of smart contracts can contribute to reducing the risk of errors and saving time and resources. In the proposed work, a decentralized application integrates healthcare services with blockchain technology to ensure transparency and security and prevent tampering with electronic medical records. The three main functionalities implemented in this work are to provide a transparent appointment booking system where patients can view the real-time availability status of the doctors and book an appointment with the doctor of their choice, and to store and retrieve data in an efficient and secure manner. To maintain a transparent and tamper-resistant medical inventory to prevent the unauthorized sale of medicines and drugs and also verify the availability of the drug. This helps the patients get the drugs in the quickest manner possible. Compared to existing centralized storage techniques, the proposed decentralized storage technique provides higher data availability, the fastest response time, and immutable storage of existing data. Experimental results show that, compared to existing work, the proposed work provided better results in throughput and latency. The communication cost of the proposed technique is 7% lesser than the existing Telecare Medicine Information system.
Blockchain Enabled Hadoop Distributed File System Framework for Secure and Reliable Traceability

Hadoop Distributed File System (HDFS) is a distributed file system that allows large amounts of data to be stored and processed across multiple servers in a Hadoop cluster. HDFS also provides high throughput for data access. HDFS enables the management of vast amounts of data using commodity hardware. However, security vulnerabilities in HDFS can be manipulated for malicious purposes. This emphasizes the significance of establishing strong security measures to facilitate file sharing within Hadoop and implementing a reliable mechanism for verifying the legitimacy of shared files. The objective of this paper is to enhance the security of HDFS by utilizing a blockchain-based technique. The proposed model uses the Hyperledger Fabric platform at the enterprise level to leverage metadata of files, thereby establishing dependable security and traceability of data within HDFS. The analysis of results indicates that the proposed model incurs a slightly higher overhead compared to HDFS and requires more storage space. However, this is considered an acceptable trade-off for the improved security.
THE ROLE OF THE YOUTH SCIENTIFIC SOCIETY IN THE SYSTEM OF ORGANIZED SCIENTIFIC ACTIVITY OF THE HIGHER EDUCATIONAL INSTITUTION OF EMERCOM OF RUSSIA

The article reveals the question of the need for the existence of a youth scientific society as an organizational structure for organizing extracurricular and scientific activities of young scientists and students of a higher educational institution. The paper discusses some aspects of the functioning of the youth scientific society of the university, which have a positive effect not only on students engaged in scientific activities, but also on the whole for the system of training scientific personnel of a higher educational institution.
Unlocking the Potential of Non-Fungible Tokens: A Novel Approach for Privacy-Preserving in EHR

This paper proposes a novel approach to securing electronic health records(EHRs) using non-fungible tokens (NFTs). NFTs are unique digital tokens that can be used to represent ownership or proof of authenticity of digital assets. The proposed framework explores the use of NFTs for secure patient data storage, secure data transfer, data provenance, and patient ownership of data. By using NFTs to establish secure and auditable records of patient data, electronic health records management systems can better protect patient privacy and security. The paper also discusses the challenges and opportunities of using NFTs in EHRs, as well as the legal and ethical implications of giving patients ownership and control over their medical data. This paper presents a new avenue for research into securing EHRs using NFTs, which has the potential to improve patient outcomes and advance the field of health informatics.
Content, Consumption, and Productivity: An Empirical Analysis of Compact Streaming and Reel Content's Effects on the Productivity of Today's Emerging Generation

In today's generation, short streaming video content on various platforms has become pervasive. More importantly, it has a significant impact on the productivity of today's generation. This paper delves into the intertwined connection between productivity and short video contents and reels with the help of ML models like Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes Classifier. For our research work, we have assembled 751 data of certain people under the age group of 15 to 35. The individuals within this data set are characterized by their active engagement with short streaming video content. While analyzing, among different ML models, the Naive Bayes Classifier has the highest accuracy of 76.06%. Additionally, to demonstrate our research, we have also used Ensemble Learning which combines the result of many different models to reduce the bias, to increase model stability, and accuracy.
Challenges in Distributed Software Development: A Systematic Review

In an increasingly connected and technologically advanced world, Distributed Software Development has gained considerable prominence due to its ability to utilize the talent, experience, and resources of geographically distributed teams. However, this development approach can also result in a number of problems caused by distance and a lack of communication. In this article, we have put together a systematic review that aims to identify the main problems when working with Distributed Software Development, as well as their possible solutions. The search was conducted in three different databases, and included only studies published after and including the year of 2020. After applying the selection criteria, 25 studies were selected and analyzed, we found that the main problems were project management, bad communication and geographical distance. We could also found that the main solutions listed were tools for improving communication and use of agile methodologies. These results suggest that the biggest difficulty in working with distributed projects is project management, and this is what solutions should be focus on.
Kun Khmer Posture Recognition Using Body Landmarks: A Comparative Experiment

In the context of Kun Khmer, a traditional combat technique in Cambodia, the challenge of correcting trainees' postures and movements is significant due to its impact on performance and outcomes. Moreover, incorrect movements during training can lead to physical injuries. This paper presents a machine learning-based system that aims to recognize Kun Khmer postures and potentially facilitate posture correction. The proposed solution utilizes anatomical landmarks and explores multiple classifiers, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine. Experimental results demonstrate a recognition rate of 96.32% using the Support Vector Machine classifier, with an average recall and precision of 96.22% and 96.32% respectively.
Research on Network Slicing Resource Prediction Method Based on Ring Federated Learning

This paper proposes a prediction method for network slicing resources based on ring federated learning. By obtaining the performance index data required for network slicing service in each VNFs sequence, and then training the network slicing resource prediction model according to the performance index data required for network slicing service in each VNF continuous time. Get a prediction of the resources required for network slicing. Among them, the network slicing resource prediction model is constructed by using the ring federated learning framework. The physical node where each VNF resides is taken as the client participating in federated learning, and the node resource prediction model is constructed for each client. Then, the performance indicator data required for the network slicing service in each VNF continuous time is input into the node resource prediction model of the physical node where the VNF is located, and the resource prediction results required at the next moment of network slicing are output, which improves the accuracy of network slicing resource prediction.
Landscape-Based Genetic Algorithm with Quantum Entanglement for Dynamic Optimization Problems

The research on dynamic optimization problems (DOPs) has attracted significant attention due to its wider presence in many areas and industries. However, the changing nature and generally limited computational resources in DOPs bring challenges to optimization algorithms that perform well in static environments, such as evolutionary algorithms. This paper introduces quantum entanglement into a landscape-based genetic algorithm, where both the quantum entanglement operator and genetic algorithm are used to search for the optimal solution, while a landscape-based strategy is employed to utilize the knowledge learned from previous problem environments. The performance of the proposed algorithm is tested on the bench-mark generator for IEEE CEC'2009 Competition on dynamic optimization with four types of landscape measures, and quantum entanglement has achieved average fitness improvements of 10.86%, 10.79%, 12.26% and 12.36%, respectively on the four landscane measures.
Facial Beauty Analysis Using Distribution Prediction and CNN Ensembles

Facial Beauty Prediction (FBP) is a computer vision task of quantifying the beauty of a face. Several solutions to this problem have benefitted immensely from the recent developments in deep learning. However, the majority of current methods train machine learning models to purely predict mean beauty scores, treating FBP solely as a regression task. In addition, deep learning based FBP approaches so far use transfer learning from models trained on general classification tasks such as ImageNet. We propose fine-tuning an ensemble of convolutional neural network (CNN) models originally trained on face verification tasks using a variety of loss functions such as Earth Mover's Distance (EMD) based loss. With this approach, our method can predict the entire beauty score distribution rather than just the mean, and the predicted mean scores have a higher Pearson Correlation (PC) compared to the ground truth scores. This method achieves state of the art results on the MEBeauty dataset in terms of mean absolute error, root mean squared error and PC between the predicted and the ground truth mean scores.
Traffic Density Identification From UAV Images Using Deep Learning: A Preliminary Study

This study presents a preliminary investigation into the application of deep learning techniques for the identification of traffic density from unmanned aerial vehicle (UAV) images. The primary objective is to categorize the traffic flows into three classes: low, moderate, and high. The study proposes a VGG16-based framework with the aim of achieving high classification rates. The experimental results, obtained from a curated dataset with a 70:30 data-splitting ratio, demonstrate a good accuracy at 95.67%. The initial findings regarding traffic density identification are notably satisfactory, especially considering the challenges posed by the UA V -based images utilized in the experiments.
Turning Pages, Facing Challenges: Unveiling Business Inactivity and SMEs' Challenges in Romania's Book Publishing Market

Abstract Book publishing companies navigate the interplay between localization and globalization seamlessly, yet the cadence at which global dynamics infiltrate local markets unfolds at different rhythms. Using publicly available data about the performance of Romanian book publishers, this paper explores the local market and its dynamics from 2018 to 2021. The results indicate "an inactivity trend" among enterprises: more than 40% of companies could not generate a net profit, and more than 50% had no employees. The prevalence of SMEs (small and medium-sized enterprises) over big companies is an intrinsic characteristic of the Romanian market. Although compared to bigger companies, SMEs manage to translate sales into profitability, over 70% of small and medium-sized companies have a turnover below 60,000 Euros. The paper concludes by highlighting growth opportunities, such as tapping into the Romanian diaspora and exploring the correlation between book sales, university locations, and the role of academic institutions in shaping reading habits.