Reviewerly/exHarmony huggingface dataset:
Effect of Social-Platform Educational Instructions on Self-Efficacy and Self Esteem of Patients with Coronary Artery Diseases

Background: The main cause of mortality in the developed world is coronary artery disease. Low self-efficacy and self-esteem are associated with it. Self-efficacy is a critical, adjustable personal resource that influences rehabilitation adherence and disease outcomes including health-related quality of life in persons with cardiovascular disease. Aim: This study was aimed to determine the effect of social-platform educational instructions on self-efficacy and self-esteem of patients with coronary artery diseases. Subjects and method: Design: A quasi-experimental research design was utilized. Setting: The study was applied in the cardiology unit and outpatients’ clinics at Port-Said University Hospital. Subjects: A Purposive sample of (80) adult patients diagnosed with coronary artery disease. Participants were equally and randomly allocated to an intervention and a control group, (40) patients for each one. Tools for data collection: Tool (1) a structured interviewing questionnaire, Tool (2) cardiac self-efficacy scale, Tool (3) cardiac exercise self-efficacy scale, Tool (4) Coopersmith Self-Esteem Inventory, and Tool (5) adult patients' satisfaction with social platform educational instructions. Results: The study result portrays that (82.5%) of the study group and (72.5%) of the control group were of the same age from 30- ≤ 60 groups with a mean age (45.21 ± 7.02) (46.61 ± 10.43) respectively. The majority of the patients in the study group had satisfactory knowledge levels, and had high cardiac self-efficacy, self-esteem, and practice exercise self-efficacy post-social-platform educational instructions implementation. The mean score of self efficacy in the intervention group significantly increased across the two measurement time points as well as self-esteem, while it remained significantly unchanged in the control group. Conclusion: Regarding knowledge, cardiac self-efficacy, cardiac exercise self-efficacy post, and after two months of social-platform educational instructions, there was a statistically significant difference between the study and control groups. Social-platform educational instructions were effective and significantly improved adult patients' knowledge, self-efficacy, and self-esteem. Recommendations: We recommend interventions to improve patient's compliance with treatment should be encouraged. Therefore, nurses can use this strategy to improve these patients’ self efficacy and self-esteem.
Effective components on the process of concept and meaning creation and their application in ontologies: focusing on Peirce’s triadic sign model

Purpose This study aims to focus on the interpretation component of Peirce’s triadic model of sign to identify: the components affecting the interpretation of an object and the components affecting the meaning creation by people for use in ontologies. Ashura was selected because it is a historical event and a meaningful sign in Iranian religious culture. Design/methodology/approach This qualitative study was conducted in two steps. In the first step, the Instagram pages of five groups of people were chosen as the sample and examined using qualitative content analysis. In the second step, 18 creators of Ashura works were selected, their lived experience in creating these works was collected using in-depth semi-structured interviews and analysed through phenomenology method. Findings Based on the results, the concept is context-oriented; one cannot interpret the signs according to one’s personal preferences because the context, social rules and habits limit this interpretation. Accordingly, the use of reference systems and rules of logic, as well as artificial intelligence and computer science tools alone cannot help represent meaning in ontologies. Rather, individual characteristics, contexts, presuppositions and components existing in personal, social and technological worlds are effective on concept formation and meaning creation. Thus, these factors must be considered in ontology, especially for multi-faceted and context-oriented concepts, such as Ashura. Originality/value This research adopted a new approach and method to study ontology. This study focused on interpretation and adopted Peirce’s sign model for ontology design.
Offline Writer Identification using Deep Convolution Neural Network

Deep convolutional neural networks (DCNN) are efficient in solving different pattern recognition problems and have been applied to extract image features (IFs). This paper investigates using deep learning (DL) techniques to improve the performance of the writer identification (WI) process. This work presents a novel approach for WI tasks by combining a DL technique with machine learning (ML). A convolutional neural network (CNN) is employed as a feature extractor along with a ML algorithm to classify those features. The standard Alex-Net model is utilized to extract IFs that located in the fully connected layers (FCLs). The support vector machine (SVM) model is selected as the classifier due to its efficient capabilities to improve identification performance (IP). The proposed model is tested using various types of the datasets, namely the Islamic Heritage Project (IHP) and Clusius. Furthermore, IAM and ICFHR-2012 datasets have been employed for benchmarking the proposed model. The results demonstrate the model achieves superior performance.
Decision Tree Algorithms for Predictive Modeling in Breast Cancer Treatment

Successful treatment of breast cancer increases a high chance of survival among women. Machine Learning could support the discovery of knowledge and important patterns from medical data by a good predictive model. The study aims to identify the effective and predictive modeling for the primary treatment of breast cancer after diagnosis. The data-set was collected from patients records who have malignant from the Benghazi Medical Center. Decision Tree algorithms J48, CART, and Random Forest were used to build three models by WEKA software. The data-set was divided into two subsets training and testing. Accuracy of Prediction, Sensitivity test, Specificity test, Area under curve, Kappa statistics, and Mean Absolute Error was used to compare models’ performance. The study results showed that the Random Forest was the better model for "training data" and "test data" compared to J48 and CART models. Also, the Random Forest model could provide good information and is able to recognize the important patterns. This study concluded that the Random Forest might a good model for detecting breast cancer treatment.
Precision Motion Measurement for Linear Motor Based on FNCC and Phase Difference M-Estimator Robust Regression in a VLSM System

This paper investigates a low-cost vision-based line-scan measurement (VLSM) system to achieve the high-precision positioning of linear motors. And an improved two-step phase difference method is performed for detecting subpixel translation of 1-D multi-frame images. In the first step of the algorithm, a fast normalized cross-correlation (FNCC) is adopted for coarse estimation to extract the overlapping portions of the two frames. In the second step, M-Estimator robust regression method is used to estimate the displacement parameters of the phase difference matrix of the overlap further accurately. The simulation cases indicate that the proposed approach outperforms the other existing methods in accuracy and robustness. In addition, the system and proposed algorithm are effectively validated in experiments and achieve a displacement detection of 0.01 mm grade of the mover.
AI as Agents

The AI agent does not have legal personality. It cannot be sued in court and it has no patrimony. Therefore, applying the law of agency seems to depend on granting intelligent agents a legal personality. Without legal personality, no declaration of intent would exist; without a declaration of intent, the law of agency is not applicable. Applying agency law principles to the problems that arise as a result of transactions generated by AI agents creates more problems than solutions. Therefore, applying the law of agency seems to depend on granting intelligent agents a legal personality.
AI and Legal Personhood

This chapter focuses on the question of whether, and under what circumstances, we might wish to attribute legal personality to AI-systems. It examines the issue of victims seeking compensation for physical harm or harm to property caused by AI-systems in a private law context, with a particular emphasis on recent developments and discussion at an EU level. The chapter proceeds from a distinction between a compensation model, where the AI is accorded independent legal personhood and victims are granted compensation directly from the AI itself, and a model where the AI is not accorded independent personhood and victims receive compensation from some other party held responsible for the harm caused by the AI. The rejection of personhood, which characterizes much of the current EU discussion, is often based on unconvincing arguments or, at least, an overly simplified account of the personhood model. There are therefore genuine difficulties with the various versions of the liability model and, given these difficulties, there may be some value in revisiting personhood, at least for harm caused by AI in certain situations.
Optimising enterprise financial sharing process using cloud computing and big data approaches

With the rise and application of new information technology led by big data and cloud computing, traditional enterprise financial work faces the pressure of transformation and upgrading. To improve the efficiency of enterprise management and reduce the cost of financial treatment, in this exploration, the differences, advantages and disadvantages between the traditional financial management mode and the sharing model are compared; the basic situation of Company H is analysed under the operation status of financial shared service centre. Company H's total asset exceeds 10 billion Chinese yuan, and its annual construction capacity exceeds 40 billion Chinese yuan. As its development scale continues to increase, the drawbacks of its decentralised financial management model gradually become prominent. For the problems of Company H's current financial management mode, such as high management cost, complex process, low efficiency and personnel redundancy, the solution to optimise the financial shared service centre is proposed.
IoT Application Model in Secondary Education

The implementation of certain smart classroom services in the educational process makes it possible to increase the efficiency and quality of the teaching process, as well as the level of competence of students, with low financial investments and a short process of teacher training. The application of the Internet of Things and mobile technologies improves the quality of teaching, making it more interesting for students, thus technologies have significant potential for improving teaching and learning in all educational sectors. The advantage of this way of working is in the application of mobile technologies that students already use. Organizing the teaching process in smart learning environments enables quality and faster communication of students with teachers, active participation of students, and a better type of teaching as well as the efficient exchange of information in the teaching process. The research presented in the paper aims to point out the possibilities of improving the learning process in secondary education by applying pervasive technologies. The proposed model was evaluated with secondary school students.
Axial Force Measurement Using Digital Image Correlation

As a simple and highly accurate axial force management method during manufacturing and maintenance of bolt-tightened structures, we examined a method to calculate the axial force from images of the bolt head. In this method, the axial force is calculated using the strain on the bolt head measured by digital image correlation (DIC) technique from images of the bolt head and the relationship between strain on the bolt head and axial force calculated by finite element analysis (FEA). We examined this method through two comparisons. The first was to compare the strain on the bolt head measured by the DIC method with the strain measured by strain gauges. The second was to compare the axial force calculated by this method with the axial force obtained from strain gauges on the bolt neck. The strain measured using the DIC method on M10 bolts had a mean absolute error of 15 μstrain compared to the strain measured by strain gauges. We confirmed that the DIC method can be used to measure the strain on the bolt head generated by bolt tightening with a high degree of accuracy. In all tightening tests in which M10 bolts were applied to axial forces of 16 kN, close to the proof load of 18 kN in strength category 4.8, the tightening coefficient was 1.11 based on the axial force calculated using this method. The coefficient of tightening was also 1.33 when considering geometric tolerances and variations in the coefficient of friction. We confirmed that the axial force management method based on the axial force calculation method using images of bolt heads can be used to manage axial forces with the same or higher accuracy than the conventional general torque management method with a tightening coefficient of 1.4 to 3.0.
Aspect Based Sentiment Analysis Using Rule Based Approach

There is an immense measure of media information like overpowering news and different pictures that can be effortlessly acquired from the Web, which thus has brought about an extraordinary challenge of consequently grouping, breaking down, and summing up the information. The analysis not just performs seriously over conventional methodologies as far as point demonstrating and archive order, yet additionally can recognize the discriminative force of each word regarding its relegated theme. The fundamental thought hidden is to encode the interchange among points and discriminative force for the words in the reports in a managed way to such an extent that which will be helpful and can likewise be applied in different fields with huge results. The estimation of the info is controlled by thinking about different components. In this work, we proposed an algorithm using rule-based approach for sentiment analysis and also additionally applied POS tagging in Pre-preparing interaction of the information. We used python coding language to implement the algorithm as it has required bundles and modules for analysis. The frontend part is planned utilizing HTML including featured sentences, emojis, gif to depict the emotion of the input. According to the analysis, the proposed algorithm outperforms well in predicting the sentiments of the sentences and takes very less time period for analysis.
Construction of self-learning classroom history teaching mode based on human-computer interaction emotion recognition

Due to the continuous epidemic in recent years, the traditional teaching mode of history classroom has been gradually replaced by the teaching mode of self-learning classroom. The teaching mode of autonomous learning classroom has become a popular teaching mode in recent years. However, in the autonomous learning classroom under the current history teaching mode, the lecturer cannot always pay attention to the various states of the students. It is also difficult to understand and receive the information the teacher wants to convey in real time. For this reason, human-computer interaction emotion recognition technology has been proposed and developed. In order to construct and realize the teaching mode of self-learning classroom history, this paper studies the emotion recognition technology of human-computer interaction. The research results show that the introduction of human-computer interaction emotion recognition technology into the construction of autonomous learning classroom teaching mode can fully understand students' emotional behavior. It improves the accuracy of students' emotion recognition by 2.67%, enables students to maintain a good learning motivation, and make reasonable plans and arrangements for the historical time and progress of learning. At the same time, it enhances the history teaching intensity and autonomous learning ability, and improves the original single learning mode. By establishing a new teaching-teacher-student relationship, it creates a good and active autonomous classroom atmosphere.
Subjective Examination Evaluation Based on Spelling Correction and Detection Using Hamming Distance Algorithm

The usage of Online examination systems in education is not a new concept for the past several years, Objective assessments have been conducted using examination systems. This research examines E-examinations that include an E-assessment system that can be used for subjective questions. The present work aims to investigate the spelling errors, for experiment 12th standard Business studies paper is collected from a CBSC school. The exam was conducted on Microsoft teams. Hamming distance for word matching or spelling mistakes is deployed on one word and one sentence. Types of Error considered while evaluating spell mistakes are Inserting, Missing, Replacement or Substituting and Transposition error or Swap which resulted in a 46.62% correction on the overall result of subjective inspection for spell mistake in answer assessment.
Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector

Static hand gesture recognition is critical in the development of a system for human-computer interaction. Many human-computer interactions, such as human-robot interaction, game control, control of smart home devices, and others, use hand gestures as a fundamental and natural language of the body. The direction of rotation of static hand gestures is the subject of this research, and the focus is on six degrees of rotation (0°, 45°, 90°, 180°, 270°, and 315°). This work presents an ideal approach that can recognize the angle of hand gestures based on the Aggregate Channel Features (ACF) detector. This approach consists of three main stages: preprocessing (image labelling), computer training, and hand angle detection based on the ACF detector. The training process consists of 25 stages. The static hand gesture dataset contained 569 images (361 for training and 208 for testing). The average time cost to detect all hand gesture angles was 0.9445 seconds, and all hand angles were recognized with 100% accuracy. This is a strong indication that supports our approach.
A Deep Learning-Based Cluster Analysis Method for Large-Scale Multi-Label Images

Large-scale multi-label image classification requires determining the presence or absence of a target object in a large number of sample images. For highly specialized and complex multi-label image sets, it is especially important to ensure the accuracy of image classification. Traditional deep learning models usually don’t take into account image-label correlation constraints when classifying multi-label images, and the strategy of classifying images based only on their own features greatly limits the model performance. In this context, this paper focuses a deep learning-based cluster analysis method for large-scale multi-label images. We constructed a model for large-scale multi-label image category recognition, which consists of a global image feature extraction module, a feature activation vector generation module and an image category inter-label connection module. Using a graph convolutional network (GCN), we aggregated the information of image category label nodes in the constructed multi-label graph structure, while exploring the correlation between image category labels. A detailed description is presented on how to introduce the attention mechanism into the constructed model mentioned above for image category recognition. Experimental results have validated the effectiveness of the constructed model.
Inferring Cyber Threat Intelligence -- A Knowledge Graph-based Approach

Security analysts prepare threat analysis upon investigating an attack, an emerging cyber threat, or a recently discovered vulnerability. Threat intelligence on malware attacks and campaigns is shared on blog posts, reports, analyses, and tweets with varying technical details. Other security analysts use this intelligence to inform them of emerging threats, indicators of compromise, attack methods, and preventative measures. Collectively known as threat intelligence, it is typically in an unstructured format and, therefore, challenging to integrate seamlessly into existing IDPS systems. In this paper, we propose a framework that aggregates and combines CTI - the openly available cyber threat intelligence information. The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts. We propose the first semi-supervised open-source knowledge graph (KG) framework, TINKER, to capture cyber threat information and its context. Following TINKER, we generate a Cyberthreat Intelligence Knowledge Graph (CTI-KG). We demonstrate the efficacy of CTI-KG using different use cases and its application for security analysts.
The Digitisation of Agriculture in India: How AgriStack Infringes Informational Privacy

Recently, the Government of India signed a Memorandum of Understanding with Microsoft to launch 'AgriStack' for creating a 'Unified Farmer Service Interface' through its cloud computing services that aim to create a nationalized database of farmers' details, land holdings, crops produced, and credit details. Its pilot will be in 100 selected villages in 10 districts of 6 states (Uttar Pradesh, Madhya Pradesh, Gujarat, Haryana, Rajasthan, and Andhra Pradesh). This piece discusses the trajectory of AgriStack, its intended benefits, and the violations of Indian farmers' human rights if implemented in its current form.
Affective Knowledge-enhanced Emotion Detection in Arabic Language: A Comparative Study

Online opinions/reviews contain a lot of sentiments and emotions that can be very useful, especially, for Internet suppliers which can know whether their services/products are meeting their customers&amp;rsquo; expectations or not. To detect these sentiments and emotions, most applications resort to lexicon-based approaches. The major issue here is that most well-known emotion lexicons have been developed for English language; nevertheless, in other languages such as Arabic, there are fewer available tools, and many times, the quality of them is poor. The goal of this study is to compare the performance of two different types of algorithms, shallow machine learning-based and deep learning-based, when dealing with emotion detection in Arabic language. To improve the performance of the algorithms, two lexicons, which were originally developed in other languages and translated into Arabic language, have been used to add emotional features to different information models used to represent opinions. All approaches have been tested using the dataset SemEval 2018 Task 1: Affect in Tweets and the dataset LAMA+DINA. The semantic approaches outperform the classical algorithms, that is, the information provided by the lexicons clearly improves the results of the algorithms. Particularly, the BiLSTM algorithm outperforms the rest of the algorithms using word2vec. On the contrary to other languages, the best results were obtained using the NRC lexicon.