Reviewerly/exHarmony huggingface dataset:
Paragraph Writing Efficacy among Yemeni EFL University Learners

The present study aims to explore the competency of Yemeni English as foreign language (EFL, henceforth) university learners in the construction of well-organised and well-formed paragraphs in which the basic components: the topic sentence, the bridge sentence, the supporting ideas for the topic sentence and the restatement sentence (conclusion) are included in their paragraphs. The study recruited 37 EFL learners who were enrolled in l­­evel two in the B. Ed. programme in the Department of English Language, Faculty of Education, Thamar University Yemen. Paragraph writing test was utilised as an instrument for data collection. The results revealed that the inclusion of paragraph component parts, except for the bridge sentence, by Yemeni EFL learners while developing paragraphs was high. However, those learners encountered a great difficulty in constructing well-formed sentences in all the paragraph component parts. The study offers some remedial procedures that are inferred from the instructors' experience in teaching the course of writing and supported by the results of the study data. These remedies are offered as suggestions that would be applicable and would improve paragraph writing among EFL learners when taught explicitly and comprehensively.&#x0D; Keywords: paragraph writing; topic sentence; bridge sentence; supporting ideas; conclusion; Yemeni EFL learners&#x0D; &#x0D;
Adopting a cognitive information processing approach to increase students’ career maturity

The results of a survey on students in a university in Kediri found the fact that students are still confused and do not have any idea about their future careers even though the study period is almost complete. This might be because they lack knowledge about career options that are appropriate to their field of study, feel less confident about their abilities, lack self-awareness related to their potential, and do not yet know how to prepare themselves for the work world. These phenomena illustrate the lack of student career maturity. This condition might cause an increase in the unemployment rate or job mismatch. Against this backdrop, we designed and implemented strategies to overcome these problems using a Cognitive Information Processing approach. In particular, we conducted Focus Group Discussions (FGD) with students, implemented career counselling, and carried out career socialization. This research used a pre-experimental design, particularly a one-group pretest and posttest design. The result shows that students experienced a significant increase in their career maturity. Students now have a better picture of career options that match their educational background, have short-term and long-term planning related to career readiness after completing education and have confidence in their abilities or potential.
THE PROBLEM OF BOUNDARIES OF COGNITIVE PROCESSES IN THE PHILOSOPHY OF MIND

The article deals with a controversial problem of the philosophy of mind, known as the problem of cognitive boundaries. This problem is illustrated by the polemic between two actively debating philosophical positions – internalism, according to which mental states are localized in the brain, and externalism, which assumes that cognitive acts are not limited to our body. The author analyzes Andy Clark and David Chalmers’s theory of extended cognitive processes and the extended mind, which is fundamentally new for the modern philosophy of mind and has taken on relevance today. It is shown that this concept is based on the idea of subject’s activity, which has its roots in James Gibson’s “ecological psychology”, Francisco Varela’s idea of “embodied cognition” and Alva Noë’s theory of sensorimotor enactivism. The author comes to the conclusion that the theory of extended cognitive processes has a number of deficiencies, one of which is the dependence on external resources and tools. David Chalmers’s thesis that we should not try to neutralize the negative effects of such dependency, but rather try to adapt our practices to the changing ways of thinking is seen as the way out of this problem.
Using Natural Language Processing to Extract Information from Unstructured code-change version control data: lessons learned

Context: Natural Language Processing (NLP) is a branch of artificial intelligence that extracts information from language.In the field of software engineering, NLP has been employed to extract key information from free-form text, to generate models from the analysis of text or to categorize code changes according to their commit messages.In literature, most of the approaches NLP-based focused on the impact of code changes on program execution or software architecture.Objective: In this study, we have applied NLP to code-change data to identify patterns of software code modifications and used Machine Learning techniques to build a model that determines how software has evolved over time and identifies area of code that presents problems.Method: Considering that software projects use version control systems, such as github, to manage their code, we have collected software information by using git commands.These data contain different unstructured information about the various files in a project.Each modification entry includes a message that explains the reasons for the change.According to the content of the message, it is possible to identify key terms that can be used during the classification of the entries.Results: In this study, we have considered the change history of software available on github to the High Energy Physics community.With the use of NLP techniques we have cleaned the messages and extracted some key terms to categorize both software problems and some other changes performed by developers, like the addition of a third party dependency or a script that starts a given service.We have built a code change dictionary combining the terms already in existing literature with the ones gathered directly from the software and its github repository.Finally, we have applied some Machine Learning (ML) techniques to determine any connection between code changes and software problems: we have removed redundant entries to avoid any bias in the outcomes of the ML techniques.Conclusion: We show in detail our approach adopted to construct historical code change datasets of categorized commit messages by following a multi-label classification methodology.Our model performance seems promising in terms of accuracy, precision, recall and F1-score.
Usage Efficiency of Financial Poverty Alleviation Funds: An Empirical Analysis of China’s Eastern Rural Areas

Objective: The focus in this study is on how to improve the disharmony between the financial poverty alleviation investment model and the economic development efficiency. Methods: In this paper, based on the efficiency measurement principle of DEA method, an input-output model for evaluating the performance of poverty alleviation funds in counties of Shandong Province is constructed. Then, the data published by the government of 30 poverty-stricken counties in Shandong Province are selected and usedto measure the performance of poverty alleviation funds by Frontier Analyst 4.0 software according to indicators, including comprehensive efficiency analysis (CCR model), pure technical efficiency analysis (BCC model) and scale efficiency analysis (SE model). Results: (1) The average comprehensive efficiency of the use of financial poverty alleviation funds in 30 poverty-stricken counties in Shandong Province is 0.7898, of which 16 values are lower than the average;(2) Among the 30 poverty-stricken counties, the pure technical efficiency of poverty alleviation funds in 15 counties is 1, and the average value of pure technical efficiency is 0.9670, while the pure technical efficiency of poverty alleviation funds in 10 counties is lower than the average level; (3) From the perspective of average scale efficiency, the average scale efficiency of the use of financial poverty alleviation funds in 30 poverty-stricken counties involved in the study is 0.8168, and the use of poverty alleviation funds in more than half of counties shows increasing returns on scale. Conclusion: In poverty-stricken counties, the overall efficiency of the use of financial poverty alleviation funds is not high, among which the pure technical performance of input and output of financial poverty alleviation funds is relatively low. Therefore, under the same investment, more detailed and differentiated capital investment projects should be built to improve the efficiency of resource investment. For some counties with declining returns to scale, poverty alleviation projects with different characteristics can be added to improve the overall efficiency of poverty alleviation funds in counties.
Personality-Oriented Approach in Teaching a Foreign Language

Annotation: The relevance of this work is due to the conditions associated with the change in the system of higher education. As well as the growing needs of society for highly qualified specialists engineering specialties. The application of a personalityoriented approach in foreign language lessons is one of the main problems in modern education. Much attention should be paid to creating an educational environment at the university in which further socialization of the individual takes place and professionally oriented skills of each student are developed. Each student is a person with their own needs and desires. The teacher needs to take into account all aspects of a personality-oriented approach when developing and implementing a foreign language course. Keywords: personality-oriented approach, personality, student, assessment, method.
Reversible and Secured Image Watermarking Technique for IoMT Healthcare

In recent days, IoMT based medical treatment and diagnosis is so popular in health-care domain. The IoMT collects the medical data from patients on real time and sends over insecure channels. This leads to various authentic and security threats. To address these concerns, in this paper a region based reversible Medical Image Watermarking (MIW) approach is proposed in spatial domain to ensusing low real time computational complexity. First the cover image is divided into Border Region (BR) and Non Rorder Region (NBR). Binary hospital logo image is embedded in BR using LSB to achieve authentication. Encryted Electronic Patient Record (EPR) information is embedded in NBR to achieve confidentiality. A reversible quadratic difference expansion approach with low computational cost and high embedding capacity is proposed for EPR embedding. To achive high EPR security, a Pseudo Random key is used in encryption. A Pseudo random key is generated using Playfair cipher approach. A <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16 \times 16$</tex> Playfair is used for generation of Pseudo Random key. The obtained experimental results show the proposed scheme is superior as compared to previous works in terms of security, embedding capacity and have low computational time complexity. Thus the scheme can be used in real time healthcare industries for IoMT.
Eye-tracking research

Eye movements provide information on subconscious reactions in response to stimuli and are a reflection of attention and focus. With regard to visual activity, four types of eye movements—fixations, saccades, smooth pursuits and blinks—can be distinguished. Fixations—the number and distribution, total fixation time or average fixation duration are among the most common measures. The capabilities of this research method also allow the determination of scanpaths that track gaze on the image as well as heat- and focus maps, which visually represent points of gaze focus. A key concept in eye-tracking that allows for more in-depth analysis is areas of interest (AOI)—measures can then be taken for selected parts of the visual stimulus. On the other hand, the area of gaze outside the scope of analysis is called white space. The software allows for comparisons of static and non-static stimuli and provides a choice of template, dataset, metrics or data format. In conducting eye-tracking research, proper calibration is crucial, which means that the participant’s gaze should be adjusted to the internal model of the eye-tracking software. In addition, attention should be paid to such aspects as time and spatial control. The exposure time for each participant should be identical. The testing space should be well-lit and at a comfortable temperature.
Semantic Repeatability Screening Mechanism of Intelligent Learning Platform Based on Bi-LSTM

To solve the excessive utilization of back-end data caused by the sharp increase in the visit and consultation on the intelligent learning platform in the era of novel coronavirus epidemic, this study proposed to introduce Co-attention mechanism (Co-attention) into the Bidirectional Long Short Term Memory model (Bi-LSTM). The study employed Multi-layer Perception Network (MLP) for classification and screening to accurately judge the semantic repeatability. Lastly the study carried out contrast experiments for different models, using 1150 consultation posts about transposed determinant, using Newton’s Leibniz formula to calculate definite integral, using Laplace’s theorem to calculate determinant, how to do model analysis of STATA panel data, under what circumstances is the weighted least square method applicable, how to realize the Pareto optimality and finding the area of trapezoid with curve side from MOOC platform of Chinese universities. Results show that this model performs better than other existing models on the judgment accuracy and its accuracy is up to 89.42%.
ANALYSIS OF THE USE OF BANKSY IMAGE IN IMPROVING STUDENTS’ WRITING SKILL

This study aims to improve students’ Writing abilities using Bansky Image. The method used in research is a quantitative method with Pre Experimental as a research design. Both pre-test and post-test used as the instruments in collecting the data. The writer used one classes of the tenth grade of MA Al Fathiyah, Jakarta Timur, for the population. The sampling technique used was cluster sampling, in which one class was determined as experimental and the control class. The data was analyzed statistically by using a t-test. The result of this analysis indicated that the average value of the students was 75.7, the statistical calculation showed that the results of to (t-observation)&gt; tt (t-table) in which to obtained was 25.357 while the value of tt was 2.037 with a significant level of 5% (0.05), since, to was higher than tt. Therefore alternative hypothesis (Ha) was accepted while null hypothesis was rejected. From these analysis results, this research summarized that Banksy Image was an effective medium to improve students’ Writing skills.
An Architecture to Facilitate Security Assurance and Legal Compliance for Call Auditing in the Wholesale Electricity Market

The auditability of telephone call records plays an essential governance role in the electricity industry in Australia as non-compliance with the Australian National Electricity Rules can lead to financial charges and result in developing a poor reputation.The existing telephone call recording processes using manual logbook entries or a recording system without verification and auditing capabilities are labourintensive and prone to human error.This study is motivated to address this real-world problem by designing a system that streamlines telephone call audit processes.This can be verified with digital technologies to meet security requirements as well as legal requirements stipulated by the Australian National Electricity Rules.In meeting security and legal compliance requirements of the Australian National Electricity Rules, this study develops a novel approach using the Clark-Wilson Integrity Model and blockchain technology for an automatic telephone call audit system with security provisions to prevent unauthorized access to and manipulation of telephone call records nationally.Although the application of blockchain has generated great interest in other areas, few studies have been conducted on its application to auditing.This study uses the Clark-Wilson Integrity Model to verify metadata records' integrity at the systems where metadata are generated.The proposed architectural design not only enhances data integrity and confidentiality but also enables the automatic execution of telephone call audit processes for auditors.The auditing system we propose presents a higher level of security compared to the existing system.
Image quality improvement using local adaptive neighborhood-based dark channel prior

In-vehicle cameras and surveillance cameras are used in many situations in our daily lives. Visibility degradation in foggy environments is caused by the scattering of reflected light from real objects by minute water droplets or fog in the medium through which light passes. The degree of degradation depends on the density of suspended microparticles existing between the observed object and the observation point in the medium. In general, the farther the object is from the camera, the more it is affected by the fog. The purpose of image de-fogging is to improve the clarity of an object by removing the effects of fog in the image.
Exploration Across Small Silos: Federated Few-Shot Learning on Network Edge

Federated Learning (FL) has been drawing significant attention from both academia and industry working on distributed machine learning. In practice, learning over mutually isolated datasets residing at the network edge, also known as silos, FL clients can suffer from a lack of samples, due to many reasons (e.g., expensive annotation), and this has potentially significant negative impact on FL performance. Few-Shot Learning (FSL) has been considered as a promising solution, but unfortunately cannot be directly applied to practical Cross-Silo Federated Learning (CSFL) systems. In this article, as far as we know, we conduct the first systematic discussion of the specific challenges of FSL in CSFL systems. We extract essential design issues found in Federated Few-Shot Learning (FFSL), and develop a new FFSL method based on Model-Agnostic Meta Learning (MAML). Through experiments using real-world federated datasets, we comprehensively demonstrate our method's advantages over existing FL and FSL methods in different practical CSFL scenarios where hitherto FL and FSL methods failed. We also highlight some promising future research directions.
Robust Single-Image Dehazing

This paper proposes a new single-image dehazing method, which is an important preprocessing step in vision applications to overcome the limitations of the conventional dark channel prior. The dark channel prior has a tendency to underestimate transmissions of bright regions or objects that can generate color distortions during the process of dehazing. In order to suppress the distortions in a large sky area or a bright white object, the sky probabilities and the white-object probabilities calculated in the non-sky area are proposed. The sky area is detected by combining the advantages of a region-based and a boundary-based sky segmentation in order to consider various sky shapes in road scenes. The performance of the proposed methods is evaluated using synthetic and real-world datasets. When compared to conventional methods in the reviewed literature, the proposed method produces significant improvements concerning visual and numerical criteria.
A Manifold Learning based Video Prediction approach for Deep Motion Transfer

We propose a novel manifold learning based end-to-end prediction and video synthesis framework for bandwidth reduction in motion transfer enabled applications such as video conferencing. In our workflow we use keypoint based representations of video frames where image and motion specific information are encoded in a completely unsupervised manner. Prediction of future keypoints is then performed using the manifold of a variational recurrent neural network (VRNN) following which output video frames are synthesized using an optical flow estimator and a conditional image generator in the motion transfer pipeline. The proposed architecture which combines keypoint based representation of video frames with manifold learning based prediction enables significant additional bandwidth savings over motion transfer based video conferencing systems which are implemented solely using keypoint detection. We demonstrate the superiority of our technique using two representative datasets for both video reconstruction and transfer and show that prediction using VRNN has superior performance as compared to a non manifold based technique such as RNN.
Rapid Digital Transformation Using Agile Methodologies for Software Development Projects

Now a day’s all organizations are moving towards digitalization. These consequences of the use of digital technologies made organizations seek for best and fast digital solutions. All software developer companies are also trying to draw consumer's attention by offering prompt services. In this regard, the critical issue in information technology and other areas of computation is how software can be created easily and rapidly for complex businesses. In this context, the main aim of the research is to show the agile methodology role in the rapid digital transformation. In this paper, we have surveyed different agile methodologies and tools for rapid software development and introduced an agile management tool having a backlog. We identified the key practices of agile methods and after a survey, it is suggested that the agile approach can help to achieve a balance between the applications generated by developers on customer demand. This paper illuminates and translates agile methodologies into agile project management tools for simple and rapid application development. Empirical research based on a case study is provided for better understanding and showing the importance of agility in software development
𝝎Mc –functions and 𝑵Mc-functions

In this paper, we presented new types of Mc-function by using 𝜔-open and 𝑁-open sets some of them are weaker than Mc-function and some are stronger, which are 𝜔Mc-function, M𝜔c-function, 𝜔M𝜔c-function, 𝑁Mc-function, M𝑁c-function and 𝑁M𝑁c-function, also we submitted new kinds of continuous functions and compact functions and we illustrated the relationships between these types. The purpose of this paper is to expand the study of Mcfunction and to get results that we need to find the relationship with the types that have been introduced.
Multistage Sentiment Classification Model using Malaysia Political Ontology

Now-a-days, people use social media platforms such as Facebook, Twitter, and Instagram to share their opinions on particular entities or services.The sentiment analysis can get the polarity of these opinions, especially in the political domain.However, in Malaysia, current sentiment analysis can be inaccurate when the netizen tempts to use the combination of Malay words in their comments.It is due to the insufficient Malay corpus and sentiment analysis tools.Therefore, this study aims to construct a multistage sentiment classification model based on Malaysia Political Ontology and Malay Political Corpus.The reviews are carried out in sentiment analysis, classification techniques, Malay sentiment analysis, and sentiment analysis on politics.It starts with the data preparation for Malay tweets to produce tokenized Malay words and then, the construction of corpus using corpus filtering, web search, and filtering using linguistic patterns before enhancing with political lexicons.The process continues with the classifier construction.It started with a generic ontology with Malaysia's political context.Lastly, twelve features are identified.Then the extracted features are tested using different classifiers.As a result, Linear Support Vector Machine yields an accuracy of 86.4% for the classification.It proved that the multistage sentiment classification model improved the Malay tweets classification in the political domain.