Reviewerly/exHarmony huggingface dataset:
Determining the Opinions of Special Education Teachers Regarding the Use of Assistive Technologies for Overcoming Reading Difficulties

Reading is one of the basic skills that the students’ need to achieve, however, it is also one of the important competencies that is essential for reaching knowledge. Reading difficulty is a main problem that many teachers are coming across in their classrooms. Students’ with reading disabilities not only hinder the teachers’ flow of the lesson it also hinders the development of the students life skills. With the revolution of the technology in the 21ST century, assistive technologies are seen to be applied into the education settings at all levels and in all fields. Therefore, teachers are in need to know about the available technology and special technology programs in order to help their students facing difficulties in acquiring the necessary knowledge and skills. Technological enrichment of teachers' educational environment both facilitates and helps them to motivate students more easily. Technology support into the educational environments of the students with reading difficulties can improve their reading skills and enable them to succeed in reading and comprehending. The present study sought to determine the opinions of special education teachers’ regarding using assistive technology for students with reading difficulties. The research design is based on the qualitative case study design with purposeful sampling and easily accessible sampling technique. Researchers developed semi-structured interview form for data collection applied content analysis to analyze the collected data. The analysis of the study found that teacher opinions towards assistive technology usage for overcoming students’ reading difficulties are rather moderate. However, teachers’ general opinions about assistive technology utilization is promising and developing gradually.
Learning Portuguese Clinical Word Embeddings: A Multi-Specialty and Multi-Institutional Corpus of Clinical Narratives Supporting a Downstream Biomedical Task.

In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Urinary Tract Infection disease identification. Additionally, we intrinsically evaluated our main model using an adapted version of Bio-SimLex for the Portuguese language. Our empirical results showed that the larger, coarse-grained model achieved a slightly better outcome when compared with the small, fine-grained model in the proposed task. Moreover, we obtained satisfactory results with Bio-SimLex intrinsic evaluation.
When Blockchain-Enabled Internet of Things Meets Cloud Computing

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
Development of an AlphaBot2 Simulator for RPi Camera and Infrared Sensors

In recent years robots have been used as a tool for teaching purposes, motivating the development of fully virtual environments for combined real/simulated robotics teaching. The AlphaBot2 Raspberry Pi (RPi), a robot used for education, has no currently available simulator. A Gazebo simulator was produced and a ROS framework was implemented for hardware abstraction and control of low-level modules facilitating students control of the robot’s physical behaviours in the real and simulated robot, simultaneously. For the demonstration of the basic model operation, an algorithm for detection of obstacles and lines was implemented for the IR sensors, however, some discrepancies in a line track timed test were detected justifying the need for further work in modelling and performance assessment. Despite that, the implemented ROS structure was verified to be functional in the simulation and the real AlphaBot2 for its motion control, through the input sensors and camera.
A new RSA public key encryption scheme with chaotic maps

Public key cryptography has received great attention in the field of information exchange through insecure channels. In this paper, we combine the Dependent-RSA (DRSA) and chaotic maps (CM) to get a new secure cryptosystem, which depends on both integer factorization and chaotic maps discrete logarithm (CMDL). Using this new system, the scammer has to go through two levels of reverse engineering, concurrently, so as to perform the recovery of original text from the cipher-text has been received. Thus, this new system is supposed to be more sophisticated and more secure than other systems. We prove that our new cryptosystem does not increase the overhead in performing the encryption process or the decryption process considering that it requires minimum operations in both. We show that this new cryptosystem is more efficient in terms of performance compared with other encryption systems, which makes it more suitable for nodes with limited computational ability.
AutoScribe: Extracting Clinically Pertinent Information from Patient-Clinician Dialogues.

We present AutoScribe, a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. AutoScribe parses the dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant, and automatically generates a patient note and primary diagnosis.
Latest trends, challenges and solutions in security in the era of cloud computing and software defined networks

The emergence of cloud computing has changed perception of all regarding software delivery, development models and infrastructure. Cloud computing has a potential of providing elastic, easily manageable, powerful and cost effective solutions. The rapid transition to cloud computing has fueled concerns on the security issues. The migration of the user’s data and applications in a shared environment of a cloud, where there is a collocation of several users increases security related concerns. Several research efforts have been made in evaluating challenges related to security faced by the cloud computing environments, a number of solutions of such problems have also been proposed. Integrated security solutions should be devised to deal with the increasing security risks. In this paper, a detailed cloud computing survey, key services and concepts are being presented. This paper attempts to evaluate various security threats to cloud computing and a number of security solutions have also been discussed. Furthermore, a brief view of the cloud security regulatory bodies and compliance have also been presented. Despite the research efforts in cloud security field, there are still some open research problems and challenges which are discussed in this paper.
Enterprise Architectural Design Based on Cloud Computing using TOGAF (Case Study: PT. TELIN)

The implementation of systems and information technology has become the company's main strategy to remain able to compete in an increasingly competitive business climate. Information systems have a role to help manage business processes effectively and efficiently. Case studies on PT XYZ companies show that the use of IS / IT has not been optimal in supporting the sustainability of its business processes. This is due to the absence of information technology architecture planning that is aligned with the company's business strategy, especially related to the Enterprise Architecture (EA) which consists of Business Architecture, Data Architecture, Application Architecture and Technology Architecture. Business processes and organizational activities. This study aims to design cloud computing-based Enterprise Architecture (EA) for PT. TELIN using the TOGAF ADM method as a framework. The integration between Enterprise Architecture (EA) and cloud computing brings great benefits to the company. Cloud Computing provides elasticity in software and hard solutions in organizations, while Enterprise Architecture (EA) guarantees compatibility between business strategies and IT strategies. This study produced the architectural design of the company PT. TELIN is done by looking at business strategies in the process of production and product distribution with the aim of supporting the implementation of the company's information system as a whole, resolving the problem of information system integration, and increasing effectiveness and efficiency in the implementation of information technology infrastructure.
The Barriers to High-Growth Enterprises: What Do Businesses in Africa Experience?

This study examines the challenges that micro, small and medium enterprises (MSMEs) face and provide insights on African business environment and entrepreneurial ecosystem. In the context of growth-oriented entrepreneurship, good policies and favourable institutional environments support firms' growth, productivity and development, while adverse formal and informal institutions constrain business development. Secondary data from World Bank Enterprise Survey (WBES) was used to capture barriers to entrepreneurship and high-growth opportunities which include – lack of finance, lack of innovation and technology, low skilled workforce, poor infrastructure, unfavorable regulations and more pervasively corruption. These barriers are evident in the micro, meso and macro environments. These have implications on the unemployment rate, poverty rates and economic growth of the African region.&#x0D; Keywords: Micro, Small and Medium Enterprises; Entrepreneurial Ecosystem; African Entrepreneurship; African Business Environments.&#x0D;
Foreground algorithms for detection and extraction of an object in multimedia

Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.
A Comparative Study on the Washback Effects of Teacher Feedback plus Intelligent Feedback versus Teacher Feedback on English Writing Teaching in Higher Vocational College

For college students majoring in English, English writing is a relatively difficult project. How to effectively improve the English writing ability of students of English majors in vocational colleges is an important issue that every college teacher needs to pay attention to. As a teaching tool, the intelligent computer automated essay evaluation system can help students improve their English writing ability more objectively, efficiently and accurately. This paper employs the intelligent computer automated essay evaluation system as a teaching tool, taking college students as the research object, and carried out an 18-week online self-writing teaching experiment and traditional writing experiment. This paper conducts a comparative study on the washback effects of teacher feedback plus intelligent feedback versus teacher feedback on English writing teaching in higher vocational college, which provides a prominent research value and research significance for the reform and innovation of English writing teaching in higher vocational colleges.
Non-Autoregressive Video Captioning with Iterative Refinement

Existing state-of-the-art autoregressive video captioning methods (ARVC)generate captions sequentially, which leads to low inference efficiency.Moreover, the word-by-word generation process does not fit human intuition ofcomprehending video contents (i.e., first capturing the salient visualinformation and then generating well-organized descriptions), resulting inunsatisfied caption diversity. In order to press close to the human manner ofcomprehending video contents and writing captions, this paper proposes anon-autoregressive video captioning (NAVC) model with iterative refinement. Wethen further propose to exploit external auxiliary scoring information toassist the iterative refinement process, which can help the model focus on theinappropriate words more accurately. Experimental results on two mainstreambenchmarks, i.e., MSVD and MSR-VTT, show that our proposed method generatesmore felicitous and diverse captions with a generally faster decoding speed, atthe cost of up to 5% caption quality compared with the autoregressivecounterpart. In particular, the proposal of using auxiliary scoring informationnot only improves non-autoregressive performance by a large margin, but is alsobeneficial for the caption diversity.
Early Prediction of 30-Day ICU Re-admissions Using Natural Language Processing and Machine Learning

ICU readmission is associated with longer hospitalization, mortality and adverse outcomes.An early recognition of ICU readmission can help prevent patients from worse situation and lead to lower treatment cost.As the abundance of Electronics Health Records (EHR), it is popular to design clinical decision tools with machine learning techniques manipulating on healthcare large scale data.To this end, we designed data-driven predictive models to estimate the risk of Intensive Care Unit (ICU) readmission.The discharge summary of each hospital admission was carefully represented by natural language processing algorithms.Unified Medical Language System (UMLS) was further used to standardize inconsistency of discharge summaries.5 machine learning classifiers including naïve Bayes, support vector machine, logistic regression, gradient boosting decision tree and 2 feature representations including Bag-of-Words and Bag-of-CUIs were adopted to construct predictive configurations.The best configuration yielded a competitive AUC of 0.748.High contribution words and medical terms were further investigated to ensure that they were clinical meaningful.A comparative study between two feature representations were also discussed.Our work suggests that natural language processing of discharge summaries is capable to extract meaningful information from discharge summary automatically and to send clinicians the warning of unplanned 30-day readmission upon discharge.
An approach to designing and developing an LMS framework appropriate for young pupils

The lifestyle of the new people generation, called in the scientific literature Generation Z, is closely related to the Internet, computer and information technology. Therefore, people and children belonging to this group can be viewed in terms of software technology as specific users who have high requirements regarding the functions and interface of the software applications, connectivity to social networks and instant communication via the Internet. This influenced not only on the teaching and learning methods but also on the software applications used in the learning process. In recent years, new theoretical teaching methods have emerged, and the number of electronic learning systems increased. However, students lack motivation for the learning process. This requires developing new conceptual models of training and learning software, tailored to the skills and preferences of the end-users. The young students up to 12 years of age: from kindergartens to preschools and primary schools are special users who have not been studied exhaustively. In order to present the problem related to the development of learning and training software thoroughly, the most commonly used standards and current trends, as well as the advantages and disadvantages of LMS platforms have been reviewed. Attention is drawn to the commonly used software design and development technologies. This is the reason to propose a strategy for developing a web-based e-learning management system according to the possibilities of young pupils as a specific user. Having in mind this strategy we described a software architecture, based on SCORM's specification, and we developed an LMS prototype. Its design was tailored to the skills of young children. The basic methodology used in the design and creation of the system we propose is user-centered design. The document is intended for developers, educators and scientists, studying child-computer interaction.
Detection of Generic Human-Object Interactions in Video Streams

The detection of human-object interactions is a key component in many applications, examples include activity recognition, human intention understanding or the prediction of human movements. In this paper, we propose a novel framework to detect such interactions in RGB-D video streams based on spatio-temporal and pose information. Our system first detects possible human-object interactions using position and pose data of humans and objects. To counter false positive and false negative detections, we calculate the likelihood that such an interaction really occurs by tracking it over subsequent frames. Previous work mainly focused on the detection of specific activities with interacted objects in short prerecorded video clips. In contrast to that, our framework is able to find arbitrary interactions with 510 different objects exploiting the detection capabilities of R-CNNs as well as the Open Image dataset and can be used on online video streams. Our experimental evaluation demonstrates the robustness of the approach on various published videos recorded in indoor environments. The system achieves precision and recall rates of 0.82 on this dataset. Furthermore, we also show that our system can be used for online human motion prediction in robotic applications.
Improving Named Entity Linking Corpora Quality

Gold standard corpora and competitive evaluations play a key role in benchmarking named entity linking (NEL) performance and driving the development of more sophisticated NEL systems.The quality of the used corpora and the used evaluation metrics are crucial in this process.We, therefore, assess the quality of three popular evaluation corpora, identifying four major issues which affect these gold standards: (i) the use of different annotation styles, (ii) incorrect and missing annotations, (iii) Knowledge Base evolution, (iv) and differences in annotating co-occurrences.This paper addresses these issues by formalizing NEL annotations and corpus versioning which allows standardizing corpus creation, supports corpus evolution, and paves the way for the use of lenses to automatically transform between different corpus configurations.In addition, the use of clearly defined scoring rules and evaluation metrics ensures a better comparability of evaluation results.
Vulnerability Analysis of E-voting Application using Open Web Application Security Project (OWASP) Framework

This paper reports on security concerns in the Evoting used for the election of village heads.Analysis of the system and server uses two different tools to determine the accuracy of scanning vulnerabilities based on the OWASP Framework.We reported that the results of the scanning using the ZAP tool got vulnerability information with the following risk level, one high level, three medium levels, and eleven low levels.The Arachni tool got vulnerability information with the following risk level, one high level, three medium levels, and two low levels.ZAP has a more complex vulnerability view than Arachni.Fatal findings on E-voting in this E-voting system is XSS, which impacts clients, which can be exploited by attackers to bypass security.Directory Traversal allows attackers to access directories and can execute commands outside of the web server's base directory.Cyber Hiscox Readiness report in 2018 in several European countries such as The United States, Britain, Germany, Spain, and the Netherlands, that the Attackers target through the most vulnerable security holes such as injection, Broken Authentication, Sensitive Data Exposure, XXE, Merged, Security Misconfiguration, XSS, Insecure Deserialization, Using Components with Known Vulnerabilities, Insufficient Logging, and Monitoring.The purpose of cyberattacks alone can threaten the stability of the country and disturb other factors.E-voting, as part of an electronic government system, needs to be audited in terms of security, which can cause the system to disrupt.
Control and Interaction Algorithms for Industrial and Service Robots

The paper is considering the design of a transport workshop according to the concept Industry 4.0.At the stage of development and design of the transport system, the main attention was focused on the mPC and sensors frequencies coordination and the problem of accurate positioning.To organize timely stopping and accurate positioning of the trolley, it was used a set of proximity switches with a special processing programs for data transfer, filtering and simulation.Creating of efficient electronics to control transport robots is considered.