Reviewerly/exHarmony huggingface dataset:
Angular Features-Based Human Action Recognition System for a Real Application With Subtle Unit Actions

Human action recognition (HAR) technology is receiving considerable attention in the field of human-computer interaction.We present a HAR system that works stably in real-world applications.In realworld applications, the HAR system needs to identify detailed actions for specific purposes, and the action data includes many variations.Accordingly, we conducted three experiments.First, we tested our recognition system's performance on the UTD-MHAD dataset.We compared our system's accuracy with results from previous research and confirmed that our system achieves a 91% average performance among recognition systems.Furthermore, we hypothesized the use of a HAR system to detect burglary.In the second experiment, we compared the existing benchmark data with our crime detection dataset.We recognized the test scenarios' data by using the recognition system trained by each dataset.The recognition system trained by our dataset achieved higher accuracy than the past benchmark dataset.The results show that the training data should contain detailed actions for a real application.In the third experiment, we tried to find the motion data type that stably recognizes action regardless of data variation.In a real application, the action data are changed by people.Thus, we introduced variations in the action data using the cross-subject protocol and moving area setting.We trained the recognition system using each position and angle data.In addition, we compared the accuracy of each system.We found that the angle format results in better accuracy because the angle data are beneficial for converting the action variation into a consistent pattern.
Adapting Automatic Speech Recognition to the Radiology Domain for a Less-Resourced Language: The Case of Latvian

Gruzitis, Normunds Dargis, Roberts Lasmanis, Viesturs Julijs Garkaje, Ginta Gosko, DidzisAutomatic speech recognition (ASR) is becoming available to more and more languages. Training a quality ASR system typically requires significant amount of language resources—representative speech and text corpora. Recent advances in transfer learning and fine-tuning of transformer models show that state-of-the-art ASR models can be derived for low-resourced languages from neural ASR models pre-trained on high-resource language data. While it is relatively straightforward to implement such a scenario for a general-domain language, the language of the medical domain is very complex, which requires extra preprocessing steps to be taken before training or fine-tuning a language model. We particularly focus on radiology dictations. We make an indicative evaluation of our approach by using the standard metric of word error rate (WER); however, the actual evaluation in practice is most relevant in terms of time efficiency, which makes a crucial role for a tailored and convenient user interface for post-editing the automatic transcriptions.
Splitting Fields

Summary . In this article we further develop field theory in Mizar [1], [2]: we prove existence and uniqueness of splitting fields. We define the splitting field of a polynomial p ∈ F [ X ] as the smallest field extension of F , in which p splits into linear factors. From this follows, that for a splitting field E of p we have E = F ( A ) where A is the set of p ’s roots. Splitting fields are unique, however, only up to isomorphisms; to be more precise up to F -isomorphims i.e. isomorphisms i with i| F = Id F . We prove that two splitting fields of p ∈ F [ X ] are F -isomorphic using the well-known technique [4], [3] of extending isomorphisms from F 1 → F 2 to F 1 ( a ) → F 2 ( b ) for a and b being algebraic over F 1 and F 2 , respectively.
Activity Diagram Generation Based on Use-Case Textual Specification

The requirements specification phase is one of the most important during software development. In many cases, its outcome takes a form of a use-case model, which consists of use-case diagrams and supplementary use-case specifications. The requirements specification document is used by various stakeholders, starting from customers or their representatives, through architects, developers to testers. Each role may have specific preferences for the form of requirements specification. To solve this problem, we propose a template for writing use-cases based on the existing guidelines and a transformation method that creates an activity diagram from the use-case textual specification consistent with the proposed template. There are several tools that can generate activity diagrams based on textual specification, but none of them fully meets the requirements for the form of template or resulting diagram, which should be correct (textual specification semantics preserved), UML 2.5 syntax compliant and contain necessary data. The proposed transformation method is supported by a tool that transforms models at the same level of abstraction. The transformation itself is defined at the meta-model level. The general idea of model-to-model transformation is not new, but the meta-models are original and fit for purpose. The application of the method is demonstrated by several examples. Due to the frequent potential changes in created specifications, the automation of the process will save time. Moreover, a graphical representation of a use-case is easier to analyze and find errors or inconsistencies compared to a textual specification.
Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor

News is information disseminated by newspapers, radio, television, the internet, and other media. According to the survey results, there are many news titles from various topics spread on the internet. This of course makes newsreaders have difficulty when they want to find the desired news topic to read. These problems can be solved by grouping or so-called classification. The classification process is carried out of course by using a computerized process. This study aims to classify several news topics in Indonesian language using the KNN classification model and word2vec to convert words into vectors which aim to facilitate the classification process. The use of KNN in this study also determines the optimal K value to be used. In addition to using the classification model, this study also uses a word embedding-based model, namely word2vec. The results obtained using the word2vec and KNN models have an accuracy of 89.2% with a value of K=7. The word2vec and KNN models are also superior to the support vector machine, logistic regression, and random forest classification models.
Perceptions and Practices of Machine Translation Among 6th-12th Grade World Language Teachers

Many researchers and educators have studied the use of Machine Translation (MT) in the L2 classroom, yet little data exists on World Language 6-12th grade educators' perceptions of MT.This study inquires into the ways that middle school and high school L2 educators perceive MT and how educators are adapting their assignments in light of its use.The results of this study show that a punitive approach is prevalent, in that MT is largely banned, and that infractions result in a wide array of consequences for students.The findings also suggest that a more deliberate inclusion of MT practices in the L2 classroom would be beneficial to teachers and students.For this reason, the study concludes with pedagogical suggestions regarding the incorporation of MT in the L2 classroom.
User-Centric Health Data Using Self-sovereign Identities

This article presents the potential use of the Self-Sovereign Identities (SSI), combining with Distributed Ledger Technologies (DLT), to improve the privacy and control of health data. The paper presents the SSI technology, lists the prominent use cases of decentralized identities in the health area, and discusses an effective blockchain-based architecture. The main contributions of the article are: (i) mapping SSI general and abstract concepts, e.g., issuers and holders, to the health domain concepts, e.g., physicians and patients; (ii) presenting and instantiating an architecture to deal with the use cases mentioned, effectively organizing the data in a user-centric way, that uses well-known SSI and Blockchain technologies.
An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

Electronic Document Management is an essential workflow within every successful ERP implementation. The integration of these documents in their respective pipelines (e.g. OCR, data extraction) inside the ERP system for processing usually requires a previous classification step to improve the success rate. Unfortunately, due to the variation in type, size, and layout of business documents (i.e. invoices, checks, delivery forms), their classification is a challenging computer task and may need an additional data for model training. This paper investigates the Transfer Learning paradigm using different pre-trained deep models to extract useful features from scanned document images. In fact, the machine learning classifiers, such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB) process the extracted features for classification. The authors compared the constructed models performances based on various metrics. To overcome the over-fitting issue and dataset imbalance, we run a crossvalidation procedure at different folds sizes (4, 6, and 8) to assess the models’ generalization ability. We also inspected the effect of dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on the overall performances and execution time. We found that the best classification rate is 97.83% achieved by combining LR, LDA, and the DenseNet121 deep model. Despite the small used dataset (546 images), this excellent performance encourages the integration of this approach in an ERP system as a separate module for document preprocessing for ERP users
A Survey of Polynomial Multiplication With RSA-ECC Coprocessors and Implementations of NIST PQC Round3 KEM Algorithms in Exynos2100

Polynomial multiplication is one of the heaviest operations for a lattice-based public key algorithm in Post-Quantum Cryptography (PQC). Many studies have been done to accelerate polynomial multiplication with newly developed hardware accelerators or special CPU instructions. However, another method utilizes previously implemented and commercial hardware accelerators for RSA/elliptic curve cryptography (ECC). Reusing an existing hardware accelerator is advantageous, not only for the cost benefit but also for the improvement in performance. In this case, the developer should adopt the most efficient implementation method for the functions provided by a given legacy hardware accelerator. It is difficult to find an optimized implementation for a given hardware accelerator because there are a variety of methods, and each method depends on the functions provided by the given accelerator. In order to solve the problem, we survey methods for polynomial multiplication using RSA/ECC coprocessors and their application for Learning With Error (LWE)-based KEM algorithms of National Institute of Standards and Technology (NIST) PQC round 3 candidates. We implement all known methods for polynomial multiplication with RSA/ECC coprocessors in a platform, commercial mobile system-on-chip (SoC), the Exynos2100 Smart Secure Platform (SSP). We present and analyze the simulation results for various legacy hardware accelerators and give guidance for optimized implementation.
Moderating Effect of Self-Efficacy in the Relationship Between Knowledge, Attitude and Environment Behavior of Cybersecurity Awareness

Today, cybersecurity is a growing issue in our education society to ensure a safe teaching and learning process for teachers and students. Reports and studies demonstrated that teachers and students are moderately aware of the cybersecurity threat surrounding them that could affect the learning curve and other fatal impacts. This study aims to identify the role of self-efficacy in the relationship between knowledge, attitude, and environment behavior of cybersecurity awareness. The researchers used a correlational design with a quantitative approach by using a questionnaire instrument to collect data from teacher respondents. A total of 350 teachers in took part in this survey voluntarily, distributed using social media applications in the midst of the Covid-19 pandemic. The researchers used the IBM SPSS Statistics to analyze the data descriptively and inferentially. The levels of knowledge, attitude, self-efficacy, and environment behavior of Malaysian school teachers towards cybersecurity awareness are low. Self-efficacy acts as a moderator in influencing the relationship between knowledge, attitude, and environment behavior of cybersecurity awareness. Strategies and programs need to be initiated by stakeholders to increase the self-efficacy of cybersecurity that could assist the positive change of environment behavior among teachers for teaching and learning benefit.
Diagnostics of psychological readiness of high school students for research activities in mathematics in the design context of a hybrid intellectual learning environment

Introduction. The problem of improving the process of organizing and supporting the project and research activities of schoolchildren through intelligent management for the purpose of self-organization of the individual, understanding and comprehending complex mathematical knowledge as a principle of personal development is relevant and far from solved. Intelligent systems provide the process of individualization of learning, the establishment of personalized and computerized feedback of cognitive and creative processes. The purpose of the article is to assess the student's readiness for research activities in the context of designing a hybrid intelligent learning environment. Materials and methods. The assessment of the student's psychological readiness for research activities in the conditions of using a hybrid intellectual environment was carried out on an experimental representative sample of students of 1-2 courses of secondary vocational education (n1=42) and students of the senior classes of secondary schools (n2=30). The diagnosis was carried out using the intelligence structure test of R. Amthauer, the creativity questionnaire of D. Johnson, the test "Individual styles of thinking" by A. Alekseev, L. Gromova, the methods of value orientations by M. Rokich, etc. The significance of the differences was established by means of Student's t-test, Fisher's angular transformation, χ2-test. The results of the study. The assessment of psychological readiness for research activities in mathematics was carried out on the basis of the developed nine parameters of scientific potential. The presented results allow us to pre-set the framework of boundary conditions in order to minimize the imprinting time of a hybrid intelligent system (including the selection of the neural network topology). For all three groups of criteria, differences by gender were established, for example, by the parameter "value orientations" (temp = 2.26 &gt; tcr = 2.02); by the parameter "creativity" (χemp2 = 6,02 ≥ χcr2 (0,05;2) = 5,99). And also by the type of educational institution, for example, by the parameter “motivation to achieve the result” (φemp = 0,186 &gt; φcr = 1,64). Conclusion. The results of the research are of practical value, as they serve as a technological basis for establishing the boundaries and boundary conditions of the most significant parameters for the effective realization of scientific potential, expressed in the work of a specialized web interface created with the student's personal account.
Reusable Security Requirements Repository Implementation Based on Application/System Components

Forming high quality requirements has a direct impact on project success. Gathering security requirements could be challenging, since it demands a multidisciplinary approach and security expertise. Security requirements repository enables an effective alternative for addressing this challenge. The main objective of this paper is to present the design of a practical repository model for reusable security requirements, which is easy to use and understand for even non-security experts. The paper also portrays an approach and a software tool for using this model to determine subtle security requirements for improved coverage. Proposed repository consists of attributes determined by examining common security problems covered in state-of-the-art publications. A test repository was prepared using specification files and Common Criteria documents. The outcomes of applying the proposed model were compared with the sample requirement sets included in the state-of-the-art publications. The results reveal that in the absence of a security requirements repository, key security points can be missed. Repository improves the completeness of the security terms with reasonable effort.
Modeling Frameworks for Knowledge Engineering Approaches

Human knowledge was regarded as a transfer process into an applied knowledge base in the early 1980s as the creation of a Knowledge-Based Systems (KBS). The premise behind this transfer was that the KBS-required information already existed and only needed to be gathered and applied. Most of the time, the necessary information was gleaned through talking to professionals about how they handle particular problems. This knowledge was usually put to use in production rules, which were then carried out by a rule interpreter linked to them. Here, we demonstrate a number of new ideas and approaches that have emerged during the last few years. This paper presents MIKE, PROTÉGÉ-II, and Common KADS as three different modeling frameworks that may be used together or separately.
A Study of Chinese University EFL Learners’ Online English Classroom Anxiety and Listening Anxiety

The present study examined Chinese university EFL (English as a foreign language) undergraduates' classroom anxiety and listening anxiety in online English language classrooms.261 freshmen from a Chinese state-owned university answered a 56-item questionnaire on their English language classroom and listening anxiety.Findings from the survey data were as follows: (1) the participants were slightly higher than intermediate level in both overall English classroom anxiety and overall English listening anxiety; (2) gender does not play a significant role in differentiating undergraduates' overall English classroom anxiety, overall English listening anxiety, and their subscales; (3) the ELCAS and its subscales were significantly positively intercorrelated and correlated with the ELLAS and its subscales; (4) students' classroom anxiety and listening anxiety predicts each other.The results of the study add to the limited number of studies on foreign language anxiety in online learning environment and can offer teachers pedagogical suggestions inclusive of providing more opportunities for students to practice spoken English, relieving students' fear of the new instruction technology by changing task assignment, and endeavoring to help students with technical problems.
Unimodular matrix and bernoulli map on text encryption algorithm using python

One of the encryption algorithms is the Hill Cipher. The square key matrix in the Hill Cipher method must have an inverse modulo. The unimodular matrix is one of the few matrices that must have an inverse. A unimodular matrix can be utilized as a key in the encryption process. This research aims to demonstrate that there is another approach to protect text message data. Symmetric cryptography is the sort of encryption utilized. A Bernoulli Map is used to create a unimodular matrix. To begin, the researchers use an identity matrix to generate a unimodular matrix. The Bernoulli Map series of real values in (0,1) is translated to integers between 0 and 255. The numbers are then inserted into the unimodular matrix's top triangular entries. To acquire the full matrix as the key, the researchers utilize Elementary Row Operations. The data is then encrypted using modulo matrix multiplication.
Fusion Deep Learning and Machine Learning for Heterogeneous Military Entity Recognition

With respect to the fuzzy boundaries of military heterogeneous entities, this paper improves the entity annotation mechanism for entity with fuzzy boundaries based on related research works. This paper applies a BERT-BiLSTM-CRF model fusing deep learning and machine learning to recognize military entities, and thus, we can construct a smart military knowledge base with these entities. Furthermore, we can explore many military AI applications with the knowledge base and military Internet of Things (MIoT). To verify the performance of the model, we design multiple types of experiments. Experimental results show that the recognition performance of the model keeps improving with the increasing size of the corpus in the multidata source scenario, with the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -score increasing from 73.56% to 84.53%. Experimental results of cross-corpus cross-validation show that the more types of entities covered in the training corpus and the richer the representation type, the stronger the generalization ability of the trained model, in which the recall rate of the model trained with the novel random type corpus reaches 74.33% and the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>F</mi> </math> -score reaches 76.98%. The results of the multimodel comparison experiments show that the BERT-BiLSTM-CRF model applied in this paper performs well for the recognition of military entities. The longitudinal comparison experimental results show that the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>F</mi> </math> -score of the BERT-BiLSTM-CRF model is 18.72%, 11.24%, 9.24%, and 5.07% higher than the four models CRF, LSTM-CRF, BiLSTM-CR, and BERT-CRF, respectively. The cross-sectional comparison experimental results show that the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>F</mi> </math> -score of the BERT-BiLSTM-CRF model improved by 6.63%, 7.95%, 3.72%, and 1.81% compared to the Lattice-LSTM-CRF, CNN-BiLSTM-CRF, BERT-BiGRU-CRF, and BERT-IDCNN-CRF models, respectively.
An IoT smart clothing system for the visually impaired using NFC technology

The selection of appropriate clothing to match one's daily life is an important ritual for individuals across the globe.But this can be a Herculean task, especially for the visually impaired.Together with the rise of the Internet of Things (IoT) paradigm and enabling technologies, solutions to improve the quality of life for visually impaired people have arisen.This paper proposes an IoT-based smart clothing system (EZwear) to enable visually impaired individuals to select and find appropriate clothing within their closets.The main underlying technical components in the solution are the use of an NFC (Near Field Communication) and a smartphone and its applications.By combining these technologies, we can transform a smartphone into an NFC reader that can provide sound information to the visually impaired user.The system enhances the ability of the visually impaired person to independently and comfortably manage their closets.The results show that the system performed reasonably and achieved the usability requirements.