Reviewerly/exHarmony huggingface dataset:
Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems

Expert systems are a form of highly understandable artificial intelligence that allow humans to trace the decision-making processes that are used. While they are typically software implemented and use an iterative algorithm for rule-fact network processing, this is not the only possible implementation approach. This paper implements and evaluates the use of hardware-based expert systems. It shows that they work accurately and can be developed to parallel software implementations. It also compares the processing speed of software and hardware-based expert systems, showing that hardware-based systems typically operate two orders of magnitude faster than the software ones. The potential applications that hardware-based expert systems can be used for and the capabilities that they can provide are discussed.
Analyzing Public Concern Responsesfor Formulating Ordinances and Lawsusing Sentiment Analysis through VADER Application

Purpose–Thispaperaimed to develop a system that applies VADER Sentiment Analysis to tweets collected using adevelopedtwitter scraper toolto identify the insights of public responsesbased on their tweetson certain government servicesrendered to them thus providing legislators of the province of Laguna an additional tool in writing future legislations.Method–This study may serve as an additional tool tothe Sangguniang Panlalawigan of Laguna in identifying sentiments of the public in terms of government services that are rendered and lack thereof based on the collected tweets written in Tagalog, English or Taglish(Tagalog and English).Data collected through the Twitter scraper tool are preprocessed taking into consideration the special characters that also have impact on scoring sentiments, emojis,and emoticons. The compound score is computed by normalizing the sum of the polarityscores foreach tweet.Results–Aside from a tabular visualization of VADER’s results, the system also provides graphical representation of the evaluation result with the percentage of positive neutral and negative tweets. Based on the result of the testing and evaluation, the VADER model is 80.71% accurate and had an F-score of 84.33%.Conclusion–The reports generated from the system be utilized to serve as potentially additional basis for legislators of the province of Laguna in writing legislations such as resolutions and ordinances based on the sentiment or voice of the community. Recommendations–It is recommended to collaborate with linguists to develop a native language of VADER’s lexicon to improve the accuracy of the sentiment scores.
Novel Machine Learning for Human Actions Classification Using Histogram of Oriented Gradients and Sparse Representation

Recognition of human actions is a trending research topic as it can be used for crucial medical applications like life care and healthcare. In this research, we propose a novel machine learning algorithm for the classification of human actions based on sparse representation theory. In the proposed framework, the input videos are initially partitioned into several temporal segments of a predefined length. From these temporal segments, the key-cuboids are then obtained. These cuboids are obtained based on the locations having maximum variation in orientation. From these regions, key-cuboids are extracted. From the key-cuboids, Histogram of Oriented Gradient (HOG) features are extracted. This new descriptor has the capability to express the dynamic features in the action videos. Using these features, a single shared dictionary is created from the videos belonging to different classes using K-Singular Value Decomposition (K-SVD) algorithm. This dictionary has the combined features of all the action classes. This shared dictionary is generated during the training phase. During the testing phase, the features belonging to a test class is classified using a novel Sparse Representation Modeling based Action Recognition (SRMAR) Algorithm using Orthogonal Matching Pursuit (OMP) and the shared dictionary. The proposed framework was evaluated using popular benchmark action recognition datasets like KTH dataset, Olympic dataset and the Hollywood dataset. The results obtained using these datasets were represented in the form of a confusion matrix. Evaluation was performed using metrics like overall classification accuracy, specificity, precision, recall and F-score that were obtained from the confusion matrix. This system achieved a high specificity of about 99.52%, 99.16% and 96.15% for the KTH dataset, Olympic dataset and the Hollywood datasets, respectively. Similarly, the proposed framework attained very good precision of 97.64%, 90.46% and 73.39% for the KTH dataset, Olympic dataset and the Hollywood datasets, respectively. Also, the average value of recall achieved was 97.58%, 90.86% and 74.09% for the KTH dataset, Olympic dataset and the Hollywood datasets, respectively. It was also observed that the proposed machine learning algorithm achieved outstanding results compared to the existing state-of-the-art human action recognition frameworks in the literature.
Research on Face Image Digital Processing and Recognition Based on Data Dimensionality Reduction Algorithm

Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.
Entanglement entropy and monotones in scattering process

In this paper, we study the entanglement property of a four-particle system. In this system, two initially entangled electrons A and C are scattered by two uncorrelated positrons B and D, respectively. We calculate the entanglements among the particles both before and after the double QED scattering ($AB\ensuremath{\rightarrow}AB,CD\ensuremath{\rightarrow}CD$). We find that the change of entanglement entropy between subsystems A and B during the scattering process is proportional to the total cross section, ${\ensuremath{\sigma}}_{\mathrm{tot}}={\ensuremath{\sigma}}_{AB}\ifmmode\times\else\texttimes\fi{}{\ensuremath{\sigma}}_{CD}$. Even though there is no direct interaction between subsystems A and C (or B and D), the scattering process induces entanglement change among them which is also proportional to ${\ensuremath{\sigma}}_{\mathrm{tot}}$. This result shows some kind of entanglement sharing property in multipartite system. In order to further investigate the entanglement sharing, we calculate the entanglement monotones that quantify the genuine multipartite entanglement in a multipartite system. For our chosen scattering process, ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$, however, we find that the outgoing state is a W-type four-partite entangled state that has no genuine four-partite entanglement.
Importance of Corporate Governance: Evidence from Kosovo’s Banking Sector

This study identifies and assesses the impact and effect of corporate governance (CG), as a good practice mechanism, as well as some specific financial indicators on the performance of the banking sector in Kosovo. The data used in the research are defined as secondary data that include nine (9) commercial banks and cover the period 2013–2020. The analysis applied to data processing is the dynamic approach through 2SLS estimation for the dependent variables ROA, ROE, and NIM. The results obtained at the end of the study show that all variables applied in this research, depending on the variable defined for evaluation, have a significant impact on the performance of the banking sector. The results also show that the most adequate measure for assessing a bank’s performance is the net interest margin (NIM). This research paves the way for debate and discussion on the governing structures of financial institutions and policymakers.
Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm

The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.
Review in IoT for Healthcare in Our Life Engineering

Over the previous decade, significant research has been conducted in the field of healthcare services and their technological advancement. To be more precise, the Internet of Things (IoT) has demonstrated potential for connecting numerous medical devices, sensors, and healthcare professionals in order to deliver high-quality medical services in remote locations. This has resulted in an increase in patient safety, a decrease in healthcare expenses, an increase in the healthcare services’ accessibility, and an increase in the industry’s healthcare operational efficiency. This paper provides an overview of the possible healthcare uses of Internet of Things (IoT)-based technologies. The evolution of the HIoT application has been discussed in this article in terms of enabling technology, services of healthcare, and applications for resolving different healthcare challenges. Additionally, effort difficulties and drawbacks with the HIoT system are explored. In summary, this study provides a complete source of information on the many applications of HIoT together the purpose is to help future academics who are interested in working in the field and making advances gain knowledge into the issue.
The Literacy Habituation To Grow Early Childhood Language Skills Through Picture Story Books in Early Childhood Education

Literacy is a language skill possessed by someone in communicating. Literacy habituation needs to be done from an early age because it will make children accustomed to literacy which will also be able to cultivate their imagination. At this time, many parties apply literacy to early childhood with an inappropriate process, some children are required to be able to read and write in a method that requires children to think hard. By using picture storybook media, it will foster children's literacy skills. In this quantitative method, the authors take samples from several children with observational data collection techniques, namely interviews and questionnaires. The results of data collection stated that eight out of ten children succeeded in following the literacy habits that the authors applied. Therefore, literacy is very important to be applied to early childhood so that children can have the ability to communicate effectively that can develop their potential and participate in community life in the future.
Applying Assessment Principles during Emergency Remote Teaching: Challenges and Considerations

One of the main challenges higher educational institutions encounter amid the recent COVID-19 crisis is transferring assessment approaches from the traditional face-to-face form to the online Emergency Remote Teaching approach. A set of language assessment principles, practicality, reliability, validity, authenticity, and washback, which can be applied to any academic subject, are critical within the design of any task that aims to assess learning. This review paper discusses how assessment approaches need to be modified in a time of crisis. It determines the position assessment should adopt within emergency remote teaching methods and analyzes the fundamental characteristics of five principles of assessment and how they can be accomplished with emergency remote teaching approaches. Furthermore, this paper evaluates the vulnerability and viability of the five assessment principles, examines the application of online assessment on a holistic level, and puts forward a set of recommendations to ensure the assessment principles are achieved within emergency remote learning contexts. The paper concludes with the notion that the construct of time, which is inherent within the principle of practicality, is the most significant when developing online assessments as it is this characteristic that is the most at risk. In addition, we suggest that the assessments that are implemented during emergency remote learning involve open-ended, as opposed to close-ended, questions and highlight the importance of educators demonstrating flexibility and understanding toward their students.
A Convergent Approach to the Viability of the Dynamical Systems: The Cognitive Value of Complexity

The integration of information and industrial technologies, digitalization and differentiation of sciences are accompanied by an increase in various types of complexity. This limits the capabilities of computer modelling, data mining, and predictive analytics. The increasing cognitive complexity of information flows and their diversity creates problems of safety, reliability and stability of the functioning of a complex dynamic system in extreme conditions. Here we show the possibility of cognitive visualization of signals of different nature through their geometrization in the form of a topological 3D model of functioning. Its projections are spatio-temporal signatures, the configurations of which reflect the dynamic, energetic and structural features of the model. An increase in the number of components of the signature configuration and its area under external influence indicates an increase in structural and functional complexity. Therefore, the signal structure can be analyzed in real time using complementary probabilistic and deterministic methods. A set of tools for the synthesis and analysis of 3D models has innovative potential for monitoring the functioning of elements of complex dynamic systems, risk management and predictive analytics.
Automatic Short Answer Grading System in Indonesian Language Using BERT Machine Learning

A system capable of automatically grading short answers is a very useful tool. The system can be created using machine learning algorithms. In this study, a machine system using BERT is proposed. BERT is an open-source system that is set to English by default. The use of languages other than English Language is a challenge to be implemented in BERT. This study proposes a novel system to implement Indonesian Language in the BERT system for automatic grading of short answers. The experimental results were measured using two measuring instruments: Cohen's Kappa coefficient and the Confusion Matrix. The result of measuring the BERT output of the implemented system has a Cohen Kappa coefficient of 0.75, a precision of 0.94, a recall of 0.96, a Specificity of 0.76 and an F1 Score of 0.95. Based on the measurement results, it can be seen that the implementation of the automatic short answer grading system in Indonesian Language using BERT machine learning has been successful.
Image Steganography Technique Based on Integer Wavelet Transform Using Most Significant Bit Categories

In this paper, an image steganographic technique based on Integer Wavelet Transform (IWT) is proposed.The cover image is transformed using IWT to suppress the secret message into the high frequency bands HH, LH, and HL of the cover image.The coefficients of these bands are labelled into six categories according to their most significant bits (MSBs).All coefficients from different bands which belong to the same category are collected.The embedding process starts from the highest category and continues to the next category by controlling the number of coefficients to match the size of the secret message.The test results show that an average PSNR of 54 dB is achieved with a message size of 67.7 kb which considered reasonable result compared to other steganographic techniques.Up to 70% embedding rate can be provided with average PSNR of 47 dB with full reversibility and perfect reconstruction of data.
Immersive Technologies in Media: Towards the Concept of Generative Mediatization?

The practices of so-called immersive media have been developing in the past few years. The immersive media situation characteristics, infrastructure, content and social aspects have been identified through the use of a multilevel structural and functional methodology, and make it possible to fix its specificity at all levels. The new format of the immersive media situation leads to changes in approaches to the mediatization studies. In the article, to study the media immersive communicative situation a generative approach is proposed for the first time. It is relevant to topological thinking, and to the modern immanent picture of the world, when a person and technology co-create a new form. Along with the generative approach and generative design, the necessity of applying relevant paradigms and methods of psychology to form new theoretical and methodological foundations of immersive user-centric media communication is substantiated. Several new concepts and terms are introduced, in particular, the term immersive hypermediation, which is opposite to immediacy as a classical criterion of media effectiveness. As a result, the analysis of the essence and features of immersive media projects allows fixing the premises of immersive paradigm shift in mediatization studies.
Agent-Oriented Software Engineering Methodologies: Analysis and Future Directions

The Internet of Things (IoT) facilitates in building cyber-physical systems, which are significant for Industry 4.0. Agent-based computing represents effective modeling, programming, and simulation paradigm to develop IoT systems. Agent concepts, techniques, methods, and tools are being used in evolving IoT systems. Over the last years, in particular, there has been an increasing number of agent approaches proposed along with an ever-growing interest in their various implementations. Yet a comprehensive and full-fledged agent approach for developing related projects is still lacking despite the presence of agent-oriented software engineering (AOSE) methodologies. One of the moves towards compensating for this issue is to compile various available methodologies, ones that are comparable to the evolution of the unified modeling language (UML) in the domain of object-oriented analysis and design. These have become de facto standards in software development. In line with this objective, the present research attempts to comprehend the relationship among seven main AOSE methodologies. More specifically, we intend to assess and compare these seven approaches by conducting a feature analysis through examining the advantages and limitations of each competing process, structural analysis, and a case study evaluation method. This effort is made to address the significant characteristics of AOSE approaches. The main objective of this study is to conduct a comprehensive analysis of selected AOSE methodologies and provide a proposal of a draft unified approach that drives strengths (best) of these methodologies towards advancement in this area.
Deriving Distributive Laws for Graded Linear Types

The recent notion of graded modal types provides a framework for extending type theories with finegrained data-flow reasoning.The Granule language explores this idea in the context of linear types.In this practical setting, we observe that the presence of graded modal types can introduce an additional impediment when programming: when composing programs, it is often necessary to 'distribute' data types over graded modalities, and vice versa.In this paper, we show how to automatically derive these distributive laws as combinators for programming.We discuss the implementation and use of this automated deriving procedure in Granule, providing easy access to these distributive combinators.This work is also applicable to Linear Haskell (which retrofits Haskell with linear types via grading) and we apply our technique there to provide the same automatically derived combinators.Along the way, we discuss interesting considerations for pattern matching analysis via graded linear types.Lastly, we show how other useful structural combinators can also be automatically derived.
MAPPING POLITICAL THEORY USING BIBLIOMETRIC ANALYSIS

Politics is a discipline of science that is part of social science. What distinguishes political science from other social sciences is the object studied. The development of research on the theory of political science and politics becomes a question in this paper. Topic mapping and topic classification based on keywords, countries, and themes discussed were the main focus of this research, including visual density based on keywords, showing the level of saturation political science theory. In this study, the data used were data that had been downloaded from the Scopus database with several limitations to limit and be more specific to the results of the discussion. After the documents to be reviewed and analyzed are bibliographical using the VOSViewer and NVivo 12 Plus software, the data is exported .CSV and .RIS formats. This study aims to provide insights into subsequent research on the theory of political science
Argumentation of Prospective Mathematics Teachers in Fraction Tasks Mediated by an Online Assessment System With Automatic Feedback

This article reports the argumentation work of a group of trainee mathematics teachers in an experiment carried out in a virtual class (due to the emergence of COVID-19) during 2020. They worked with a task on fractions in an online assessment system with questions with random parameters and infinite possible correct answers. This was followed by a discussion of the strategies and the justifications, arguments and validations of these strategies and other conjectures that emerged. This article analyzes this work from a qualitative approach using the Mathematical Working Space as a theoretical framework. The results show that the discussion work led the trainee teachers to find interpretations for the algorithms processed by the computer, enhancing epistemic discourse and argumentation in the context of the use of technological artifacts. In turn, the same discourses allowed the future teachers to instrumentalize the processes to be used in new tasks.