Reviewerly/exHarmony huggingface dataset:
Non-Fungible Token (NFT) in the Academic and Open Access Publishing Environment: Considerations Towards Science-Friendly Scenarios

The article describes the use and possible value creation of Non-Fungible Tokens (NFT) in the academic and open access publishing environment. It defines NFTs, describes disadvantages and possible solutions, especially in the intended scientific environment. An overview of existing NFT service providers from the publishing environment illustrates that there is not yet a suitable one for researchers. Accordingly, three possible scenarios are shown where NFT services could be located in a science-friendly way. One would be with library- or scholarly-led university presses, repositories, and other publication infrastructures (such as OJS or OMP). Another would be to use centralizing and channelling article submission platforms with which universities have contracts, such asChronosHub. The third and broadest approach would be through Digital ObjectIdentifier (DOI) registration agencies such as ChronosHub and DataCite, although complexities come into play here due to the triangular relationship with publishers registering DOIs (some of them having exclusive usage rights transferred to themselves). This complexity could be reduced by registeringNFTs only for open access publications with a Creative Commons Attribution license. A summary and outlook provide an overview of open questions and initial starting points to get started.
An Innovative Approach for Personnel Positioning

The contribution of the paper is the building of a model to predict the proper employees’ allocation in the Greek public sector. To acknowledge the set and weights of criteria upon which our model feeds data, a validated questionnaire was developed and used to conduct a primary quantitative survey amongst HR departments and employees of state organizations in Greece. On the acquired findings, several experiments were administered using linear and machine learning tools, aiming to replace time consuming and subjective procedures, followed by many organizations. Concluding, data classification algorithms are proposed to predict the best matching of employees, giving as inputs personnel qualifications as well as job specifications, leading to a model based on J48, a decision tree algorithm.
Methods and Technologies of Application of Fuzzy Models for Processing Industrial Data and Quality Management of Polymer Materials

The paper presents methods and technologies for the use of fuzzy models for the analysis and quality control of polymer materials. In accordance with the approaches under consideration, a single reconfigurable computer system was developed that allows for a comprehensive analysis of the quality of products of multi-assortment polymer industries. The methods and technologies that allow using fuzzy models to evaluate the color characteristics of polymer materials are described, which makes it possible to eliminate the incompleteness of the input data about the object of study and improve the quality of the results of the system as a whole
Performance comparison of Putnam model using new technology trends for software maintenance cost estimation

Now the concept of software estimation is added by concrete concepts of fuzzy logic and neural networks that help in monitoring as well as understanding the software development dynamic process. The goal of all these is to estimate the estimating software effort. Approximate maintenance and judgment costs for a project is defined by software maintenance cost estimation. Due to environmental, human, political and technical like number of involved variables for cost estimate calculation, the maintenance cost estimation will never be same. In this paper we have proposed a new approach using Putnam model which is an efficient software maintenance cost estimation model. For dataset acquisition we have considered Bug Id reports instances of Matlab to perform classification of Sentimental based on severity. After pre-processing the dataset Principal Component Analysis (PCA) has been used to compute and study the Eigenvector for feature extraction. Then instance selection is performed using Particle Swarm Opitmization.In order to reduce matrix size there is need to deal with instances selection that will make processing easy to deal with further proceeding input. Once an instance is selected the next step is to classify it. In this work we have used Linear Discrement Analysis (LDA) as classifier which is an efficient statistical method used in machine learning. For testing the proposed approach we have compared the results obtained using COCOMO and PUTNAM model that shows the use of Putnam model will improve the system in terms of various parameters.
A systematic review of methodologies used in calculation of pen pressure in FDE

Pen pressure is an important individual characteristic of the handwriting examination whose potential has not been fully utilized. Most of the times it is overlooked by experts because they do not think it has substantial value to be of forensic significance. Generally, it is thought to be of relevance in cases involving simple and traced forgery. One of the reasons behind pen pressure being ignored by the document examiners may be a lack of technique to calculate the absolute value of pen pressure. The existing methods require specialized setup, training, and are expensive in nature and can only be used to measure relative value of pen pressure.The aim of this paper is to discuss various methodologies used by document experts and researchers in past with an attempt to quantify pen pressure and critically analyze them to bring the reader’s and researcher’s focus on their advantages and disadvantages. The paper also discusses prospects of future works which may include techniques that can be used more routinely in handwriting examination and can also give an absolute measure of pen pressure. In this aspect, mathematical approaches can be further explored.
Integrated Classification Algorithm for Unbalanced Data Streams Based on Joint Nonnegative Matrix Factorization

The purpose of this paper is to study the unbalanced data flow integration classification algorithm based on joint nonnegative matrix factorization, in order to solve the problem that the basic clustering results obtained from the original data set have some information loss, thereby reducing the effective information in the integration stage. In this paper, the accuracy of the unbalanced data and the detection time consumption are selected as the research object. Six data sets with imbalanced proportions of minority and majority samples are selected for experiments. Mathematical statistical analysis is first used to observe text classification, disease diagnosis, and network intrusion detection and the classification accuracy of majority class and minority class; the commonly used algorithm for unbalanced data is statistical analysis method. Comparing the univariate method for comprehensive classification of unbalanced data flow based on nonnegative matrix factorization with the unbalanced data algorithm, the observation has accurate rate and detects time-consuming changes. Among them, the comprehensive classification algorithm of unbalanced data flow is based on the classification of data, classifying the data, judging whether two data points belong to the same category, and determining their degree of balance. The research data shows that the unbalanced data flow integrated classification algorithm based on joint nonnegative matrix decomposition can reasonably evaluate the classification performance of the classifier for a few classes, and the detection speed is faster and saves more time. The experimental research shows that the algorithm combines the relationship matrix and information matrix from the original data set into a consensus function, uses NMF technology to obtain the membership matrix, effectively uses potential information, improves the accuracy rate of 69.73%, and shortens 71.65% of the time consumed.
Artificial Intelligence Technologies and Their Application for Reform and Development of Table Tennis Training in Complex Environments

The development of information technology has been deployed in almost all sectors and is making life easier. In this development, the sports sector has seen tremendous expansion. In traditional table tennis training, the coach and the players have to meet daily to take appropriate training for the game. This process is time-consuming in a complex environment, and it will have a significant impact on the reformation and development of table tennis training. The utilization of improved technologies can overcome this challenge by performing training of the game online with an intelligent wireless system with advanced training mechanisms. Carrying the traditional and heavier intelligent wireless devices will be difficult for the players. In this research, the fine-grained evaluation (FGE) system is incorporated into the deep learning model to analyze the player's body postures during the training and event sessions and make them develop after each session through online training in any circumstance. The proposed FGE was compared with the traditional statistical model, and it was observed that the proposed FGE had obtained higher precision and recall values of 70% and 98.9% than the statistical model.
The Quantum Circuits Configurations for the Module of Conjugate Coefficients Permutations when Performing QFT

The module of conjugate coefficients permutation for unification of the quantum Fourier transform is considered. The scheme of quantum circuit adaptation from the logical level of the layout of multi-qubit quantum gates to the physical level of representation based on a set of basic gates is presented. An example of an automatically generated specification of a quantum circuit in the form of a program code in the OpenQASM language is given.
Development of Quantum Corrective Repeated Code

This work is a study of the influence of the medium on the quantum system of qubits. The article assumes a description of the fundamentals of the quantum information theory, as well as a place in it the concept of quantum entanglement. Quantum error correction is a set of methods for protecting quantum information and quantum state from unwanted interactions of the environment (decoherence) and other forms and types of noise. Today, there are modern quantum technologies that are capable of supporting fundamentally new computational algorithms based on the principles of quantum mechanics. The dependences of data distortion on noise and measures of decoherence on noise in one qubit are demonstrated. This article is devoted to solving the problem of research and development of corrective codes for correcting several types of quantum errors. These errors appear during computational processes in quantum algorithms and models of quantum computing devices. The aim of the work is to study existing methods for correcting various types of quantum errors and to create a corrective code for quantum error correction. The scientific novelty of this research is expressed in the one shortcoming exclusion of the quantum computing process. Currently, in many advanced countries of the world, intensive research is being carried out to develop and create quantum computers and their software, and there has been a rapid increase in interest in quantum computers. A large number of articles and monographs are published.
CLUSTER-BASED TRANSFORMATION OF REGIONAL EDUCATION

Abstract. The article deals with the development trends of the modern Russian system of secondary vocational education (SVE). The current state of SVE in the Tomsk region is described. Regional peculiarities and strategic goals of transformation of SVE in the context of socioeconomic development of the region are presented. A theoretical rationale for the cluster approach to the development of the SVE system is provided. The need for productivity changes in the regional SVE system using the cluster approach is demonstrated. A project for updating the SVE infrastructure in the Tomsk region, reflecting the principles and mechanisms of cluster policy, is proposed. The basic characteristics of two clusters – educational clusters and educational-industrial clusters – are presented. Scientifically justified hypotheses about the regularities and the effective functioning of these clusters are formulated. Cluster-driven organizational and pedagogical methods of integrating SVE practices and continuous professional development of the current personnel of the real economic sector are pointed out. Methods, management tools, and conditions for successful integration are mentioned. Conditions for the realization of pedagogical interaction between subjects participating in industrial cluster communities are presented. The existing practical experience of the Tomsk region with clusters in SVE transformation is summarized. Socioeconomic and pedagogical prospects of interaction between clusters and networks in SVE for solving urgent and future tasks at the regional level are predicted. The materials of the article are based on the results of educational design and research methods of theoretical analysis, survey, and expert method.
Applications of Distributional Soft Actor-Critic in Real-world Autonomous Driving

Reinforcement learning (RL) plays an important role in the decision-making of high-level autonomous vehicles due to the self-evolving ability without reliance on labeled data. Although existing RL-based decision-making studies have yielded fruitful results, most of them are carried out based on simulation platforms. Due to the inherent difference between simulation and the real world, it is of great significance to verify the efficacy of RL-based decision-making in practical applications. In this paper, a multi-lane driving task and the corresponding reward function are designed to provide a basis for RL-based policy learning. The distributional soft actor-critic algorithm is used to learn an offline policy based on a simulated environment. Then, we implement the learned policy to a real car on a two-lane park road. Both objective and subjective experiments are carried out to verify the effectiveness and robustness of the learned policy in practical applications. Experimental results show the trained policy can not only complete driving tasks smoothly and robustly, but also acquire fair satisfaction from subjects. Our work provides certain evidence for the feasibility of RL in real-world driving tasks.
Centralized and Accelerated Multiagent Reinforcement Learning Method with Automatic Reward Setting

For multiagent environments, a centralized reinforcement learner can find optimal policies, but it is time-consuming. A method is proposed for finding the optimal policies acceleratingly, and it uses the centralized learner in combination with supplemental independent learners. In order to prevent the failure of learning, the independent learners must stop in a timely manner, which is done through finely tuning a reward. The reward tuning, however, requires additional time and effort. This paper proposes a reinforcement learning method in which the reward is automatically set.
Trainable Power-of-2 Scale Factors for Hardware-friendly Network Quantization

Quantization can efficiently reduce the bitwidth of parameters in neural networks and accelerate both inference and data transmission, which is essential for deploying networks with a large parameter scale on resource-limited edge devices. However, most of the existing quantization methods use non-linear function (e.g., tanh, non-linear polynomial function), it is not only difficult to implement on hardware but also occupy plenty of computing resources. In addition, previous quantization methods seldom consider the hardware implementation of operators, which only simulate the quantization accuracy at the algorithm level. To address these problems, we propose novel \emph{Trainable Power-of-2 Scale Factors Quantization} (TPSQ) to combine power-of-2 quantization scale factor and trainable clamp interval, to benefit from the advantages of both. Several experiments over current mainstream vision tasks show that the performance of TPSQ surpasses most of the previous quantization methods, these prove the effectiveness of TPSQ. Finally, we implemented an FPGA accelerator for object detection to demonstrate the hardware friendliness of TPSQ. The experimental results show that the peak performance of our system is 300 GOP/s under 300 MHz working frequency, which is 72 times higher than the implementation on an ARM-A9 processor.
Face Recognition System for Medical Information Modeling Using Machine Learning

Face-recognition systems must be capable of identifying patient faces in an uncontrollable situation. Facial detection is a distinct problem from facial recognition in that it needs to report the location and size of all of the faces in a given image, which is not possible with face recognition. There are numerous changes in the images of the same face, which makes it a difficult problem to solve because of their general likeness in appearance. Face recognition is a very difficult procedure to do in an uncontrolled environment, since the lighting and angle of the face, as well as the quality of the image to be recognized, all have a considerable influence on the outcome of the process. This chapter provides information regarding the various face recognition machine learning models. The performance of the models is compared on the basis of values derived for FAR, FRR, TSR, and ERR.
Materialising New Forms of Journalism: A Process Model

Design is a discipline that gives form to alternative futures. Through a process of material exploration, it explores novel configurations of technology, people and values, and in doing so, seeks to understand the implications of new designs. It is this practice of making and creating that is valuable to journalism as it grapples with how to respond to communication technologies that are increasingly subtle, physical and sensuous. In this article we examine the theory of materiality as it has been discussed in communication and design literature, proposing a conceptual approach for a design-led view of materiality in journalism studies. This view sees materiality from a form-giving perspective in which technologies and journalistic principles are materials to shape in order to explore potentials and possibilities. This article extends the concept of journalism design by examining how journalistic values can be incorporated into the design of public interest technologies. In doing so, it frames journalism as a process of designing things that respond to public concerns. The article also contributes to scholarship that sees tinkering, iteration and participation as a way to understand how news could be structurally different.
Identification of Secondary Breast Cancer in Vital Organs through the Integration of Machine Learning and Microarrays

Breast cancer includes genetic and environmental factors and is the most prevalent malignancy in women contributing to the pathogenesis and progression of cancer. Breast cancer prognosis metastasizes towards bones, the liver, brain, and lungs, and is the main cause of death in patients. Furthermore, the selection of features and classification is significant in microarray data analysis, which suffers from huge time consumption. To address these issues, this research uniquely integrates machine learning and microarrays to identify secondary breast cancer in vital organs. This work firstly imputes the missing values using K-nearest neighbors and improves the recursive feature elimination with cross-validation (RFECV) using the random forest method. Secondly, the class imbalance is handled by employing K-means synthetic object oversampling technique (SMOTE) to balance minority class and prevent noise. We successfully identified the 16 most essential Entrez gene ids responsible for predicting metastatic locations in the bones, brain, liver, and lungs. Extensive experiments are conducted on NCBI Gene Expression Omnibus GSE14020 and GSE54323 datasets. The proposed methods have handled class imbalance, prevented noise, and appropriately reduced time consumption. Reliable results were obtained on four classification models: decision tree; K-nearest neighbors; random forest; and support vector machine. Results are presented having considered confusion matrices, accuracy, ROC-AUC and PR-AUC, and F1-score.
Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model

This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global features utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction. MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification. Model testing used 10,000 faces derived from the Olivetti Research Laboratory (ORL) library. Model performance was evaluated in comparison with three state-of-the-art models depending on Frequency Partition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms of image features (low-resolution issues and occlusion) and facial characteristics (pose, and expression per se and in relation to illumination). The MDCT-based model yielded promising recognition results, with a 97.70% accuracy demonstrating effectiveness and robustness for challenges. Furthermore, this work proved that the MDCT method used by the proposed FFLFRM is simpler, faster, and more accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). As well as that it is an effective method for facial real-life applications.
A RL Based Model for Improving Human Task Management Performance

This paper discusses an Reinforcement Learning (RL) based system to improve human performance in task selection and management by incorporating various factors. A simulated task management environment of a coaching center is considered. Different factors includes task urgency, task status, and task importance as well as which task to attend to next, and that an even more important factor of task management is the capability to avoid low importance tasks. Five algorithms such as: Boltzman Sampling, Epsilon-decreasing, Random, Softmax, and Thompson Sampling have been used in experiments.
AI based activity tracking and analysis system

With the increase in the automation in this world, the accuracy of the systems has increase and the cost is decreased. Due to automation the need of activity detection systems has increased which can eliminate the need of human intervention and costly infrastructure. To solve this problem deep learning is used widely. It refers to the use of neural network which classifies the various human activities. In this paper, the model used to classify the human activity is ResNet-34 which is methodology of neural network. The model has an average accuracy of 71%. The error detection and reduction algorithm is used which can increase the accuracy of prediction data considerably.