Reviewerly/exHarmony huggingface dataset:
AI Based Image Restoration

The Artificial Intelligence-Based Image Restoration is a desktop application that was developed for the main purpose of restoring the quality of degraded images. Image degradation is the process where by an image loses its quality and information from its original form to its degraded and low-quality form. It can occur when the images are being compressed and decompressed, when the images are scaled, device malfunction and during transmission which cause visual distortion, loss of quality, color, information and other important details. This project comes in to deal with the effect of image degradation with the help of an artificial intelligence algorithm K-Nearest Neighbor (KNN) which helps in the realistic manner or restoring the image quality. It is able to restore the quality of an image, add color to old colorless images and extract text from old documents. The system will use methodologies where it allows the programmer to focus on certain stages that the programmer thinks it was very important or the owner of the system has suggested and to provide a working product which are called the agile and RAD methodologies
Suppression of Crosstalk in Quantum Computers Based on Instruction Exchange Rules and Duration

Abstract Crosstalk is the main noise source in quantum computing devices. In quantum computation, the parallel execution of multiple instructions will cause crosstalk, destroy the quantum state, and cause the program to fail to execute correctly. Overcoming the influence of crosstalk is a key requirement for quantum error correction and large-scale fault-tolerant quantum computing. This paper presents a method to suppress crosstalk in quantum computers based on multiple instruction exchange rules and duration. Several Benchmarks experiments verify the effectiveness of the proposed method. Compared with the prior techniques, the fidelity of the proposed method is improved by 15.97% on average.
Semantic Decision Internal-Attention Graph Convolutional Network for End-to-End Emotion-Cause Pair Extraction

Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.
Preparing Public Administration Graduates: Enhancing Public Speaking Competence

This study aimed to explore the needs of Public Administration students in relation to public speaking competence and to prepare them for future job performance.Data were collected through Focus Group Discussions (FGD) and semi-structured interviews with sixth-semester students of the PA Study Program.The FGD revealed that students viewed public speaking as essential for their future careers and highlighted the need for specific topics and teaching methods to enhance communication skills.The interviews provided clarification and support for the FGD results.The study found that students require effective teaching strategies that incorporate materials that support the learning of communication skills.The implications of the study suggest that PA programs should prioritize communication skills in their curricula to better prepare graduates for their future careers.
A three-stage fuzzy classifier method for Parkinson’s disease diagnosis using dynamic handwriting analysis

Finding low-cost and insightful methods to reinforce the diagnosis of Parkinson’s disease is a major challenge today, and using dynamic handwritten data may be one of the solutions. Artificial intelligence and machine learning methods can be used to create predictive models for disease diagnosis. In the case of machine learning models, in addition to maximising their accuracy, it is important to ensure that prediction models for disease diagnosis explain prediction results. The fuzzy classifiers using a set of IF-THEN statements with fuzzy terms often used in natural discourse suit this purpose. This study proposes a fuzzy classifier method consisting of three-stage: generating the structure, selecting the informative features, and optimising the parameters. Thirty-two variants of the method with different metaheuristic algorithms are recommended. Experiments have been conducted on publicly available handwritten signal databases for diagnosing Parkinson’s disease such as ParkinsonHW, PaHaW, NewHandPD. ParkinsonHW contains handwriting of 40 people including 25 patients with Parkinson’s disease. Handwriting tasks were drawing spirals and meanders. PaHaW contains handwriting of 75 people including 37 patients with Parkinson’s disease. Handwritten tasks consisted of drawing spirals and writing letters, bigrams, trigrams, words and sentences in Czech. NewHandPD contains handwriting of 66 people including 31 patients with Parkinson’s disease. Handwriting tasks were drawing spirals, meanders and circles. Results of statistical comparison of efficiency with alternative interpretable classifiers — decision trees and fuzzy genetic systems have shown the advantage of some variants of realisation in the accuracy of the prediction and interpretability. Achieved results may indicate the applicability of the proposed method for constructing fuzzy classifiers as a diagnostic tool.
Media Psychology Approaches in Digitalization and Human Communication

Digital communication between people is fundamentally changing the nature of communication. Recent technological advances have dramatically impacted the way people communicate. Technology has become an integral part of how people communicate with each other, increasingly replacing face-to-face communication. With the rapid development of technology, many people fear that people are getting too immersed in this digital world and not fully integrated into the real world. This chapter aims to provide an overview of media psychology theories and approaches in digitalization and human communication.
Exploring Latent Themes-Analysis of various Topic Modelling Algorithms

This research explores the effectiveness of four common topic modelling methods for identifying latent themes and topics in unstructured text data: Latent Dirich- let Allocation(LDA), Non-Negative Matrix Factorization(NMF), Top2Vec, and BERTopic. Topic modelling is an essential method for gaining insights from massive amounts of textual data. Top2Vec and BERTopic are recent approaches that use unsupervised neural networks to develop distributed representations of texts and words, whereas NMF and LDA are traditional techniques frequently utilised for topic modelling. This document gives a timeline of important advances in topic modelling, including the development of NMF and LDA, as well as many refinements and additions to LDA. According to the study’s findings, BERTopic surpasses the other approaches, particularly in recognising overlapping and fine- grained subjects. This work emphasises the significance of text processing quality, the variety of subjects in the text, and the right selection of topic modelling methods in efficiently breaking down topics.
Anti-pruning multi-watermarking for ownership proof of steganographic autoencoders

Model watermarking Model watermarking is a method for embedding watermark information into a neural network model. It proves the ownership of the model without affecting its performance. Since there are plenty of attacks against model pruning, it becomes more significant to design anti-pruning model watermarking algorithms. In this paper, multiple watermark embedding is performed to protect the model copyright for the image steganography auto-encoder model “Hiding Data with Deep Networks” (HiDDeN). Firstly, the appropriate model weights are selected by employing three classical model pruning algorithms of model weights. Secondly, the model watermark is spread by using Discrete Cosine Transform (DCT)-based image watermarking algorithm, which improves the noise and pruning resistance of the model watermark. Finally, the model watermark is embedded to the 4th and 5th decimal places of the selected model weights. The experimental results demonstrate that the proposed algorithm has a good robustness against model pruning without affecting the watermark extraction performance of the auto-encoder network model. Even with the embedded model watermark, the decoder's watermark extraction accuracy is still higher than 0.9993. and the autoencoder is still valuable when the model weights are pruned by 40%. Furthermore, the proposed algorithm has a certain degree of improvements in watermarking capacity.
Effects of line length, number of lines, line spacing, and font size on reading performance of Chinese text in virtual reality environment

The increasing importance of virtual reality (VR) necessitates reading in VR environments. Nonetheless, there is a lack of research on the factors influencing reading performance (speed and accuracy) in VR. The purpose of this study is to investigate the influence of line length, number of lines, line spacing, and font size on the response time and accuracy of a Chinese comprehension task in VR. Thirty-two participants were required to read Chinese passages and answer 32 multiple-choice questions in VR. The results indicated that line spacing significantly affected reading speed. Meanwhile, the interaction between the number of lines and line spacing had a significant impact on reading accuracy. To improve reading speed and accuracy, it is advisable to use single-line spacing, 20 characters per line, and ensure that the vertical distance created by the number of lines and line spacing does not exceed the optimal field of view.
A Theoretical-Practical Case Study on the Graded Multidisciplinary Model

A model of educational processes called multidisciplinary gradual is presented, whose objective is to guide the teacher in the teaching-learning of STEAM educational activities using as a use case the topic of basic logic gates, which associates mathematical, algebraic, electronic, and logical knowledge, presenting the theory and strengthening it in practice. The topic of logic gates is not directly related to the middle-higher level educational plan; however, it is possible to apply and deepen it by relating the basic mathematical operations. The work describes the three stages that make up the model, achieving that students approach a problem or topic from different contexts, each discipline contributes its own knowledge and qualities, but not necessarily integrated in the common understanding of the problem, so that they develop the foundations to solve interdisciplinary and/or transdisciplinary problems. At the end, the results obtained by using this model with data from a control group and an intervention group are demonstrated with statistical analysis.
Impact-Pose Estimation Using ArUco Markers in Structural Dynamics

Abstract In structural dynamics a structure’s dynamic properties are often determined from its frequency-response functions (FRFs). Commonly, FRFs are determined by measuring a structure’s response while it is subjected to controlled excitation. Impact excitation performed by hand is a popular way to perform this step, as it enables rapid FRF acquisition for each individual excitation location. On the other hand, the precise location of impacts performed by hand is difficult to estimate and relies mainly on the experimentalist’s skills. Furthermore, deviations in the impact’s location and direction affect the FRFs across the entire frequency range. This paper proposes the use of ArUco markers for an impact-pose estimation for the use in FRF acquisition campaign. The approach relies on two dodecahedrons with markers on each face, one mounted on the impact hammer and another at a known location on the structure. An experimental setup with an analog trigger is suggested, recording an image at the exact time of the impact. A camera with a fixed aperture is used to capture the images, from which the impact pose is estimated in the structure’s coordinate system. Finally, a procedure to compensate for the location error is presented. This relies on the linear dependency of the FRFs in relation to the impact offset.
ROLE OF ICT IN VALUE BASED EDUCATION: A CONCEPTUAL STUDY

The present conceptual research focused on the study is to discuss the role of ICT in value based education system. Value Education and conversation technologies (ICT) are an important part of daily life, including the teaching-learning process. Now a days Value Based Education and Mindfulness peace activities change in human life because with connection in the Information and Communication Technology. ICT has bought about many dramatic changes in how teachers teach and how students learn. Education use ICT to design and assess learning activities, to communicate with students and the value based education for sustainable development. The present paper focused on the study of Role of Information and Communication Technology (ICT) in Value Based Education System with prime objectives are (i) To understand the innovative best practices through ICT in Value Based Education System. (ii) To analyses the role of ICT in Value Based Education System. (iii) To discuss the Role of Teacher in Value Based Education System. The methodology of the research is a different type involving an interpretative, conversation, observation and study secondary sources, like books, articles, journals, thesis, university news, expert opinion, and websites, etc.
Reducing the Loss Cost by Setting the Optimal Replacement Cycle for Cutting Tools using FOM-Tool Monitoring

In precision machining, the condition of cutting tools deteriorates the product quality, defect rate, and productivity. Most small- and medium-size enterprises set the cutting tool replacement cycle through experience; however, this significantly differs from the actual number of tool replacements. To solve this problem, in this study, based on tool replacement data, FOM-Tool Monitoring, a smart factory operation management system, was used to perform 4M multi-dimensional analysis to analyze Min-Max and deviation in the number of times of tool use. The tool load waveform trend was also analyzed to verify the suitability of replacement, optimal replacement cycle, and conduct verification.
Novel randomization and iterative based algorithms for the transactions assignment in blockchain problem

This study focuses on the load balancing of the transactions in the blockchain. The problem is how to assign these transactions to the blocks. The objective is to guarantee a load balancing of the workload in the time of blocks. The proposed problem is an NP-hard one. To face the hardness of the studied problem, the challenge is to develop algorithms that solve the problem approximately. Finding an approximate solution is a real challenge. In this paper, nine algorithms are proposed. These algorithms are based on the dispatching-rules method, randomization approach, clustering algorithms, and iterative method. The proposed algorithms return approximate solutions in a remarkable time. In addition, in this paper, a novel architecture composed of blocks is proposed. This architecture adds the component "Balancer". This component is responsible to call the best-proposed algorithm and solve the scheduling problem in a polynomial time. In addition, the proposed work helps users to solve the problem of big data concurrency. These algorithms are coded and compared. The performance of these algorithms is tested over three classes of instances. These classes are generated based on uniform distribution. The total number of instances tested is 1350. The average gap, execution time, and the percentage of the best-reached value are used as metrics to measure the performance of the proposed algorithms. Experimental results show the performance of these algorithms and a comparison between them is discussed. The experimental results show that the best algorithm is best-mi-transactions iterative multi-choice with 93.9% in an average running time of 0.003 s.
A postulate-driven study of logical argumentation

Logical argumentation is a well-known approach to modeling non-monotonic reasoning with conflicting information. In this paper we provide a comprehensive postulate-based study of properties of logical argumentation frameworks and a full characterization of their semantics and inference relations. In this way we identify well-behaved formal argumentative models of drawing logically justified inferences from a given set of possibly conflicting defeasible, as well as strict assumptions. Given some desiderata in terms of rationality postulates, we consider the conditions that an argumentation framework should fulfill for the desiderata to hold. One purpose of this approach is to assist designers to "plug-in" pre-defined formalisms according to actual needs. To this end, we present a classification of argumentation frameworks relative to the types of attacks they implement. In turn, for each class we determine which desiderata are satisfied. Our study is highly abstract, supposing only a minimal set of requirements on the considered underlying deductive systems, and in this way covering a broad range of formalisms, including classical, intuitionistic and modal logics.
Sentiment Classification of Social Network Text Based on AT-BiLSTM Model in a Big Data Environment

To tackle the challenge of ineffective sentiment prediction using current sentiment classification methods, this paper introduces a method social network text sentiment classification. The method leverages a bidirectional short and long-term memory model (AT-BiLSTM), specifically designed for a big data environment. First, a vectorized representation of text is realized by introducing a pre-trained BERT model, and the classification results are dynamically adjusted according to the semantic information of the words. Then, the BiLSTM combined with the attention mechanism performs aspect-level sentiment analysis, and the corresponding model AT-BiLSTM is formulated. Finally, the BERT model randomly selects input tags for information masking and pre-trains the proposed model. The proposed method was evaluated against three alternative methods using an identical dataset. The results show that the novel method achieved the highest accuracy, recall, and F1-score, reaching 93.72%, 93.91%, and 92.38%, respectively. Consequently, the proposed method demonstrates superior performance compared to the other three methods evaluated.
When Literacies Collide

The integration of computer programming and music-making has a rich history dating back to the 1950s. While there has been substantial prior work on the creative and cognitive affordances of programming languages for engaging in musical tasks, there is less work that attempts to understand the theoretical implications of music and code as literacies in collision. In this paper, we report on a study in which five undergraduate students with experience in both music and coding completed two creative musical tasks: one using conventional instruments and tools and one using Python code in an online music-coding environment. In combining representational infrastructures from music and code, both undergo transformations. We introduce semiotic theories of translation and transcription to make sense of the music-coding process and describe strategies that participants devised in their creative process.
Globally Guided Confidence Enhancement Network for Image-Text Matching

Image-text matching is a crucial aspect of multi-modal intelligence. The main challenge in this area is accurately measuring the relevance between the image and text, using evidence obtained through matching. Previous studies either concentrated on obtaining a well-represented global feature to measure similarity directly or on investigating complex matching patterns at a local level before aggregating them, with little attention paid to combining them. We propose a Globally Guided Confidence Enhancement Network that combines both approaches by obtaining a good global representation to guide fine-grained local interactions. In this process, content that better matches the text from a global perspective is enhanced and represented with confidence scores. Extensive experiments demonstrate that the approach we have employed achieves superior performance on Flickr30K and MSCOCO datasets.
Social inquiry for higher education in Russia: Sociological context

The purpose of the work is to determine the characteristics of the social demand for higher education in Russia. The methodological basis of the research is built around the neo-institutional theory, namely D. North's theory of institutional evolution. A theoretical model of the social inquiry for higher education in Russia was developed; four factors with the greatest influence on the social demand were formulated: 1) social inertia; 2) market laws; 3) students' intentions; 4) fashion and trends. The research was conducted and the results of comprehension of features of the social demand for higher education in Russia were presented. The qualitative empirical research aimed at verification of the proposed model of the social inquiry along with the analysis and comparison of the importance of the formulated factors. The theoretical model of the social demand for higher education in Russia was developed; a comparison of concepts of the social inquiry was made.
Identifying Behavior Indicators for Flight Instructor Competencies and Training Needs Analysis for General Aviation Flight Instructor

This study aims to provide information necessary for developing a “Competency-Based Training and Evaluation Plan” for general aviation flight instructors in Korea. General aviation training experts designed a survey by selecting “Behavior Indicators” from the ICAO Flight Instructor Competency Model. This study analyses the importance of Behavioral Indicators, the level of instructor performance, and the training needs for flight instructors. According to the factor analysis, ICAO “Flight Instructor Competency Model” is found as a valid tool to measure general aviation flight instructor competencies. According to the training needs analysis, flight instructors training program needs to be improved on “Training Environment Management”, “Teaching Method”, and “Interaction” competencies. When compared with trainee group, flight instructors &amp; examiners have a higher training demand for “Teaching &amp; Learning Environment Management”. This study suggests the flight instructor training program requires to be reorganized based on the competencies related to the flight instructor duties in the training environments.