Reviewerly/exHarmony huggingface dataset:
Exploring the Effect of Tasks Difficulty on Usability Scores of Academic Websites Computed Using SUS

The prime objective of this study is to empirically determine the effect of tasks difficulty on usability scores computed using the System Usability Scale (SUS). Usability dataset is created by involving twelve end-users that evaluate the usability of 15 academic websites in a laboratory. Each end-user performs three subsets of six tasks whose difficulty is varying from easy to impossible under six different categories. Results are obtained after applying two statistical techniques, one is ANOVA and other is the correlation with regression. Results show that the SUS scores vary from higher to lower values when end-users conduct usability assessment with a list of easy, moderate, and impossible tasks on academic websites. The results also indicate the effect of tasks difficulty on the correlation between the SUS scores and task success rate. Though, the strength of the correlation is strong with each subset of tasks but it varies that depends on the nature of the tasks.
The Impact of Using Storyboards on Improving Reading skills of Third-Grade Students with Reading Disabilities in Jordanian Context

This study aimed to identify the effect of using the storyboard method on improving the reading skill of third-grade students with reading disabilities in Jordan. Many Arabic language learners with reading disabilities cannot distinguish between Arabic letters that look similar in the graph but have different pronunciations. The present study investigated the impact of the storyboard method on learning the skill of distinguishing between Arabic letters that look similar in form but those pronounced differently. The sample of the study consisted of (40) students with reading difficulties assigned equally into an experimental and control group. The experimental group consisted of 20 male and female students taught using the storyboard method, and the control group consisted of 20 male and female students taught using the traditional method. The findings of the study showed that the storyboard method improves the ability of students to distinguish between Arabic letters that look similar in written form but pronounced differently better than those involved in the traditional way of teaching. The study also found out that gender did not affect learning reading skills. The researchers recommend the use of the storyboard method in teaching different language skills. https://doi.org/10.26803/ijlter.19.1.10
Effective Approach for Sentiment Analysis on Movie Reviews

The increase in growth of social websites, many blogging platforms, and electronic media forums helps in analysis of public opinion related to various events. Sentiment analysis-based techniques are now very popular to determine the corresponding opinions related to products, movies, political election outcomes, and many more. Sentiment analysis-based techniques used NLP, computational linguistics, and text analysis techniques for effective extraction of text polarity. It is also used in decision-making process by extracting personal information and analyzing the opinion. It is also used for analyzing positive and negative reviews that allow us to measure the amount of positivity and negativity an entity is entangled. It is the field which helps us to get better understanding in analyzing people’s emotion, reviews, and attitudes from written languages. This paper has proposed a framework for effective extraction of sentiments present in movie reviews. We also need to pay attention on field of classification techniques for different opinions, performed on any kind of set of data. Nowadays, this approach is used by everyone like researchers who deal with sentiment analysis.
Search Engine Get Application Programming Interface

Application Programming Interface (API) is a function concept that can be called by other programs. The API works as a link that unites various applications of various types of platforms, commonly known as API public names. The public API has been widely spread, while its users, programmers who want to search for public APIs, must browse through various methods such as general search engines, repository documentation or directly in web articles. The user does not yet have a system specifically for collecting public-public APIs, so that users have difficulty in performing API public link searches. The solution to these problems can be solved by building a web framework with a search engine interface that provides specific public-public searches for the API, so that users can search the API public more easily. Web Service is an API that is made to support the interaction between two or more different applications through a network. Representational State Transfer (ReST) is one of the rules.
An Improved Grey Wolf Optimizer (iGWO) for Global Engineering Optimization

Metaheuristic algorithms are more popular due to their ability to solve generic problems. Selection and tuning of their algorithm specific control parameters are the key factors for their success. Grey Wolf Optimizer, an algorithm having less dependency on their control parameters and influenced by the hunting approach and social dominant hierarchy of grey wolves is considered here to solve global optimization problems. An improved GWO is proposed to achieve a perfect ratio of exploitation and exploration phases and gives more weight to the most suitable wolves to find the new position of grey wolves during the stated iterations. Simulation results of standard benchmark test functions convey the stability, efficiency, and effectiveness of iGWO compared with GWO, and other standard algorithms.
Histories and futures of research through design : From prototypes to connected things

This article discusses how the artifact of Research through Design (RtD) is changing due to data technology. The article firstly reviews the character and role of the prototype in RtD traditions informed by practices of skillful crafting and industrial design manufacturing. It then describes the move of RtD to data-enabled practices to offer a conceptualization of artifacts as connected things, that is, decentralized objects that actively collapse the division between design participation, user interaction and the creation and distribution of products and services. By considering connected things as capable of ‘making’ things too, the article positions the changing character and role of the RtD artifact in relation to three key shifts in design practice: (1) the agential shift towards the inclusion of things as partners in design, (2) the temporal shift towards always available opportunities for co-creation, and (3) the infrastructural shift towards unstable forms of value. The article concludes with a discussion on the implications of these changes for how knowledge might be generated, critiqued and shared in future data-enabled RtD practice.
[Countermeasures and treatment for aortic acute syndrome with novel coronavirus pneumonia].

The novel coronavirus pneumonia (NCP) has cost a great loss to the health and economic property of Chines people. Under such a special circumstance, how to deal with such patients with acute aortic syndrome has become a serious challenge. Rapid diagnosis of concomitant NCP, safe and effective transportation, implementation of the interventional procedure, protection of vascular surgical team and postoperative management and follow-up of such patients have become urgent problems for us. Combined with the latest novel government documents, the literature and the experiences from Wuhan, we answered the above questions briefly and plainly. It also hopes to inspire the national vascular surgeons to manage critical emergencies in vascular surgery and even routine vascular diseases with NCP, as a final point to limit the severe epidemic situation, and minimize the damage of NCP.新型冠状病毒肺炎对我国人民的生命健康和经济财产造成了巨大的损失。在这样的特殊形势下，如何应对主动脉急症患者成为了一个严峻的挑战。快速诊断患者是否合并新型冠状病毒肺炎、安全有效地转运患者、实施合并肺炎患者的介入手术、保护血管外科医疗团队，以及对患者的术后管理和随访等，都成为血管外科医师亟待解决的问题。结合最新的国家政策、文献报道和个人所在单位支援武汉的防控经验，本文对上述问题进行了简要阐述，希望引发全国血管外科医师对新冠肺炎疫情下如何实施血管外科危重急症乃至常规血管疾病诊疗的思考，共同应对疫情，并在最大程度上将疫情的损害降至最低。.
Approach to user interfaces development based on semantic model of user activity

Abstract In this paper there is proposed a method of user interface design, which is based on semantic model of user activity in the domain, which is formed by the end user himself in natural language, as a source data for design. This description is further transformed into a mechanism of action, the combination of which can be called a model. Also the paper contains the analysis of possible models used in model-oriented approaches to interface development, advantages and features of the proposed model are highlighted. Based on the model in the form of action mechanisms, the user interface of the information system is formed. The paper contains the general example of the process of user interface construction.
Fusion-partitioning genetic task scheduling algorithm based on deterministic annealing technology in DAG blockchains

The traditional blockchain structure cannot adapt to large-scale and real-time-application scenarios because of its inherently slow response. To solve this problem, a theoretical framework of DAG (directed acyclic graph) blockchain is proposed, transforming the chain processing of a traditional blockchain into parallel processing. On this basis, the non-independent task-scheduling problem in the DAG blockchain environment is studied, and a fusion-partitioning genetic task-scheduling algorithm based on deterministic annealing technology in DAG blockchains is proposed. The experimental results show that the algorithm can adapt to the heterogeneity, dynamism, and wide area of DAG blockchain nodes, and its scheduling performance is better than that of the traditional scheduling algorithm. While optimizing the task-completion time, the algorithm takes account of the load-balancing problem and effectively improves the response speed. It is a feasible method for solving the non-independent task-scheduling problem in the DAG blockchain environment.
Semantic Filtering of Twitter Data Using Labeled Property Graph (LPG)

Hidden interests of an individual can be inferred by keenly observing their social profile data and blending this data with a semantic network. Getting user interests without user’s manual intervention is very beneficial for companies feeding on user’s regular behavior. This paper provides the entire idea of how to retrieve the user’s hidden interests and what is a semantic network. Twitter is the preferred social platform for entities extraction. We started basically by gathering entities like hashtags and keywords from the tweets posted by an individual. And simultaneously created a Semantic Network using Wikipedia’s taxonomy of categories and subcategories and pages implementing a concept called Labeled Property Graph (LPG). Matching the pre-obtained tweet entities with the Wikipedia graph of Categories and Pages a graph is generated called Hierarchical Interest Graph (HIG) which contains so called hidden interests of user. HIG of an individual is an isolated entity and may never match with others’ HIG.
Hexa-Directional Feature Extraction for Target-Specific Handwritten Digit Recognition

Handwritten numeral recognition (HNR) is the most challenging task in the area of optical character recognition (OCR). OCR process involves both feature extraction and selection that is being generated by central or distributed sources. Presence of unimportant features in a feature set may lead to the “curse of dimensionality” and causes malfunctioning of the recognition system. A feature set is claimed as a good feature set if it contains only useful and discriminative features. In this research, the proposed model considers the projection distance of available black pixels from sharp boundary edges from all four directions (left, right, top and bottom) and directional longest run length of black pixels for rows, columns, principal diagonal and off-diagonal of any handwritten digit/character. This feature extraction algorithm is advantageous because it yields less number of features compared to zone and projection distance-based approach thus reduces computation cost without compromising the classification accuracy. Two levels of experiments have been performed to validate the authenticity of the proposed approach. In the first level, we consider the group of confusing classes, e.g. (0, 6, 8 and 9) and perform one-class classification for target-specific mining using support vector data description (SVDD); whereas, in the second level we consider all classes from 0 to 9 and perform one-class classification. Experiments are performed on own generated and MNIST data sets. For both data sets, the proposed model demonstrates better results as compared to zoning and directional-based approach of feature extraction. This paper considers classification accuracy, training time and feature set size as comparison parameters.
Evolution of Blockchain Technology in Business Applications

Blockchain technology is an invention where digital transactions refer to distributed database without the need of third party intervention and authentication. In present time several organizations in various disciplines are adopting this technology which has transparency, integrity, efficiency in cost and time. Its unique technology is being adopted in various industries like banking, legal, healthcare, rentals, education and many more. The intention of this paper is to outline the Blockchain technology and its function in various domain.
Let the story unfold

The best reading experiences at school can stay with children, and be treasured, for a very long time. Sally Player describes how she delivers rich literacy opportunities for her Reception class every day.
Parallel Bisection-based Distribution Matching for Probabilistic Shaping

We propose a parallel bisection-based distribution matching for constant composition probabilistic shaping. The number of serial operations can be significantly reduced without performance loss, making it a suitable architecture for large block lengths.
Accessibility and acceptability of voice assistants for people with acquired brain injury

Acquired Brain Injury can cause cognitive impairments, such as problems with memory and scheduling, and executive dysfunction. Assistive Technologies (AT), usually in the form of prompting and reminding systems, can improve remembering and task completion in people with ABI. Studies, however, show that the use of AT in this context is rather low, and that there are several factors that impact their uptake and effectiveness, indicating there is room for better design. Voice Assistants (VA), i.e. smart speakers with integrated virtual assistants, offer a versatile set of tools to provide support to cognitively impaired users, through a more accessible and pervasive interaction. Research however shows that there are several obstacles that can prevent the uptake and effective use of VAs by people with ABI. This research aims to explore how the conversational interfaces of VAs can be designed to become more accessible and acceptable by people with ABI. An online survey (n=114) and several focus groups (n=29) were conducted to gather requirements for the design of such a system in a user-centred way. Additionally, the key design considerations of a conversational UI for calendar management were investigated in a user study with students (n=16). This paper outlines the background and motivation behind the research, gives a brief overview of the planned course of action, and presents the results up to this point. The envisioned contributions of this research to the field of HCI are also addressed.
A High-Level Programming Library for Mining Social Media on HPC Systems

The convergence between HPC and Big Data processing can be pursued also providing high-level parallel programming tools for developing Big data analysis. Software systems for social data mining provide algorithms and tools for extracting useful knowledge from user-generated social media data. ParSoDA (Parallel Social Data Analytics) is a high-level library for developing data mining applications on HPC systems based on the extraction of useful knowledge from large dataset gathered from social media. The library aims at reducing the programming skills needed for implementing scalable social data analysis applications. To reach this goal, ParSoDA defines a general structure for a social data analysis application that includes a number of configurable steps and provides a predefined (but extensible) set of functions that can be used for each step. User applications based on the ParSoDA library can be run on both Apache Hadoop and Spark clusters. The goal of this paper is to assess the flexibility and usability of the ParSoDA library. Through some code snippets, we demonstrate how programmers can easily extend ParSoDA functions on their own if they need any custom behavior. Concerning the usability, we compare the programming effort required for coding a social media application using or not using the ParSoDA library. The comparison shows that ParSoDA leads to a drastic reduction (i.e., about 65 %) of lines of code, since the programmer only has to implement the application logic without worrying about configuring the environment and related classes.
Application of Adaptive Neuro Fuzzy Logic Method of Noised Electrical Signals for Correcti

In this article, the correction of the distorted electrical signal with an adaptive neural fuzzy logic (ANFIS) due to noise forming in is discussed. An electrical signal, such as a sinus signal, is widely used both in load supply and in modulation techniques for control and information transport. While the deterioration in the sinus structure causes energy losses, it also causes damage to the control and information transmission signals. Therefore, after considering the structure of the clear reference signal equation, a noise that may occur in the sinus structure is included in the clear reference signal structure. An interference signal must be formed with the unknown nonlinear process from another noise source for measuring of the information signal because of an interference signal needs. After an interference signal is generated, the measuring signal is given in the sum of the pure reference signal and the interference. For the correction phase of the signals, at inputs of the adaptive fuzzy logic system in the Matlab toolbox, the source signal with noise and the measuring signal values are entered and the experiment is performed via a 3-membership function. When the results are observed, it is seen that the signal which is distorted after the correction operations is very close to the reference signal
Identificación de cambios en el estilo de escritura literaria con aprendizaje automático

This research aims to identify changes in the writing style over time of 7 authors of Englishspeaking novels. For each author, an organization of the novels was carried out according to&nbsp;the date of publication. The novels were classified in three stages called initial, intermediate&nbsp;and final; each stage contains 3 novels. Between two consecutive stages there are at least&nbsp;2 years of separation between the publication dates of the novels. To solve the problem of&nbsp;detecting changes in writing style over time, it is proposed to use a supervised automatic&nbsp;learning-based approach. Vector space models were created from the frequencies of use of&nbsp;n-grams of different types and lengths. In addition, the algorithm of Principal Component&nbsp;Analysis (PCA) was used as the n-gram selection method. The solution was addressed as a classification problem using the Vector Support Machine algorithms (Support Vector Machine, SVM), Naive Bayes Multinomial (Multinomial Naive Bayes, MNB), Logistic Regression (LG) and Liblinear as classifiers. The metric to measure the efficiency of the learning algorithms&nbsp;was accuracy. The research showed significant changes in five of the authors with an average&nbsp;accuracy between 70% and 80% in the different types of n-grams.
Paved and Unpaved Road Segmentation Using Deep Neural Network

Semantic segmentation is essential for autonomous driving, which classifies roads and other objects in the image and provides pixel-level information. For high quality autonomous driving, it is necessary to consider the driving environment of the vehicle, and the vehicle speed should be controlled according to types of road. For this purpose, the semantic segmentation module has to classify types of road. However, current public datasets do not provide annotation data for these road types. In this paper, we propose a method to train the semantic segmentation model for classifying road types. We analyzed the problems that can occur when using a public dataset like KITTI or Cityscapes for training, and used Mapillary Vistas data as training data to get generalized performance. In addition, we use focal loss and over-sampling techniques to alleviate the class imbalance problem caused by relatively small class data.
The effectiveness of 7E learning cycle model to improve student motivation in work and energy topic

Abstract Motivation determines students’ learning creativity through the learning behavior presented in the class. This research was done to indicate the effectiveness of 7E learning cycle model implementation in order to increase student motivation during learning about work and energy. The research was pre-experimental study with one group pretest and posttest design. It used purposive sampling to select the research sample with criteria of previous students’ cognitive score on the last learned topic. The data were collected using motivation questionnaire which has been validated and the result of reliability test was 0.826, which was categorized high. Parametric tests such as N-gain and t-test were used to analyze the data. The results showed that the average score of motivation at the first stage was 59.86 and increased to 89.54 at the final stage with N-gain of 0.72, which represented the improvement of student motivation as high category. The t-test also showed t count &gt; t table (9.574 &gt; 2.052), thus there was significant difference between initial and final motivation. According to the results, it can be concluded that the implementation of learning cycle 7E model was effective to be used to improve student motivation in learning about work and energy.
VIDEO MAKING TASK TO DECREASE ANXIETY IN STUDENTS’ SPEAKING PERFORMANCE

Anxiety is one of the factors influencing students’ speaking performance in the classroom. A video making task is an alternative strategy that can decrease anxiety besides it provides a positive influence on students’ self-assessment of speaking task. Hence, the objective of this study was to describe how video making task could decrease students’ anxiety in speaking class. This study used a descriptive qualitative method in which the data source was the students’ performance in video to require the task of procedure text presentation in speaking class. The data were collected by using observation and interview. And the result of the observation of the video showed that most of the students seemed less anxious and more relaxed in explaining the assigned materials. In the interview, the students also said that they felt more confident in presenting the task. In conlusion, the video making task could decrease students’ anxiety in students’ speaking performance.