Reviewerly/exHarmony huggingface dataset:
A Survey on Sentiment Lexicon Creation and Analysis

This is a survey paper that presents sentiment lexicons and discusses the state of the art in the field of methods of producing sentiment lexicons (Creation). Like websites, social networks, blogs, online portals, reviews, thoughts, suggestions, ratings, and feedback are generated by writers with accelerated Internet development. Sentiment material can be about books, people, hotels, products, surveys, activities, etc., and these sentiments are very useful to businesses, governments, and individuals. Sentiment analysis is the method for processing natural language and text analysis to define and extract subjective knowledge from text. This paper provides a survey on the problems of sentiment analysis related to their methods and techniques. There are different types of lexicons covered, differing in aspects such as coverage, development methods, lexical unit, and granularity. It aims to provide a representative sample of the field of lexicons of sentiment.
Peer text interaction in online classes during COVID-19 pandemic

This research aims: 1) to elaborate the patterns of peer text interaction in a “WhatsApp group” class; then 2) to explore the modification/utilization of the patterns in language teaching-learning. This research is a descriptive-naturalistic study. The data was triangulated from the chats of “WhatsApp group” classes and the lecturers’ notes. The number of students was 155 from five classes of English Education majors. The data was analyzed through the analytical processes, namely transcribing, pre-coding and coding, ideas growing, interpreting and conclusion-drawing. The patterns of peer-text interaction were diverse based on the course of interaction and the context of the learning activity. The dominant pattern was the “responding” goal with the “accept” and “willing” text acts. The patterns fabricated several language instruction activities for speaking and writing which are adjustable in offline and online learning. However, the practicality to draw a line from the patterns of text interaction to language instruction should be examined through further study.
Neural Network Signal Processing for LTE Receiver Application

Radio Digital Signal Processing (DSP) elements work well in the conditions for which they are designed, but degrade in edge cases outside of these conditions. In previous work we built individual physics-driven neural network (NN) models to replace standard DSP function models in an LTE-like intelligent receiver. This prevents performance degradation in the edge conditions while giving the same or better performance than the DSP models. In this work we extend these individual NN models to a more realistic LTE receiver with multiple antenna support and stitch together the individual NN models to form a complete end-to-end pipeline. We report results for these NN models as well as the complete pipeline, comparing with standard DSP element analogues using both channel bit error rate (CBER) and bit error rate (BER). The individual NN models perform equal or better than DSP blocks in the more realistic and difficult LTE receiver conditions and the end-to-end pipeline provides a much better performance improvement over a DSP pipeline, providing a modular architecture that can be used in other applications.
A Study of MOOC Course Review Topics Mining Based on LDA Topic Model

In order to dig deeper into the implied thematic information about online course review data on MOOC learning platforms and obtain the topic concerns of course learners in the process of participating in online courses, as a demand guide to improve the quality level of online classes. This study analyzes the course review data in the form of word cloud map for word frequency, and at the same time, uses LDA topic model for semantic analysis of online course review data to extract learners' topic concerns. The results show that learners focus on course content, lecture style, course discussion, learning resources, architecture, teacher quality, exercise explanation and sound effect in the learning process of MOOC online education platform . By mining online course review data for underlying themes, it is possible to understand learners' demand tendencies, which is meaningful and valuable for improving teaching quality.
Better Decision Making with Sentiment Analysis of Amazon reviews

The way we purchase products online has been revolutionized due to disruptive digital technologies making all the information available at our fingertips. This paper inspects the effect of reviews on mobile phones sales. Noting the inconsistent results on the effect of reviews in previous literature, this study examines how the sentiments on different mobile phone brands in online reviews affect their sales. In our work, we analyze reviews and comments about smartphones from Amazon as data set and classify the reviews text by negative/positive, very negative/very positive, and neutral sentiment. The information contained in reviews is very helpful for both the shoppers and product makers. In this paper, we conclude the following unique characteristics through more than 400,000 real smartphone reviews: (1) Short average length; (2) Power-law distribution; (3) Large span of length; (4) Notable difference in sentiment polarity. Based on the characteristics mentioned above, a series of comparative experiments have been done for sentiment classification and our model achieved an accuracy score of 76.80%
Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study (Preprint)

<sec> <title>BACKGROUND</title> Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app—TraceTogether—in March 2020 to augment Singapore’s contact tracing capabilities. </sec> <sec> <title>OBJECTIVE</title> This study aims to compare the performance of the contact tracing app—TraceTogether—with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening. </sec> <sec> <title>METHODS</title> All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians’ 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period. </sec> <sec> <title>RESULTS</title> RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them. </sec> <sec> <title>CONCLUSIONS</title> TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic. </sec>
NooJ Grammars for Italian Negation System and Sentiment Analysis

The main purpose of this study is the formalization of the Italian morphosyntactic negation system, in order to assess the relevance of this system as regards Sentiment Analysis (SA) practices and routines. The study will be conducted through the construction, application, evaluation, and debugging of specific NooJ grammars.Italian Negation may be of three different types, that is: lexical/morphological, syntactic, and semantic. Here, we will only deal with those features of Italian negation that are subject to formalization.As for sentence negation (SN), Italian employs above all the form non (not). It precedes the predicate inflecting in person and number, and produces sentences such as Non piove (It is false that it is raining), which are in opposition to their affirmative counterparts, i.e. Piove (It is true that it is raining).
Geometry of Deep Neural Networks

In this chapter, which is mathematically intensive, we will try to answer perhaps the most important questions of machine learning: what does the deep neural network learn? How does a deep neural network, especially a CNN, accomplish these goals? The full answer to these basic questions is still a long way off. Here are some of the insights we’ve obtained while traveling towards that destination. In particular, we explain why the classic approaches to machine learning such as single-layer perceptron or kernel machines are not enough to achieve the goal and why a modern CNN turns out to be a promising tool.
Wrapping up

During the first half of this book, we have introduced the concept of dynamic variables and showed how they can be used to implement new kinds of functionality: subsystems of hooks and choices whose behavior depends on the dynamic context in which they are executed. We have drawn direct parallels between these two subsystems and the existing standard systems of Common Lisp condition handlers and restarts. We have shown how these subsystems tie in with the existing standard mechanisms of transferring control in CL programs. Finally, we have described the interactive means by which the condition system and the Lisp debugger are allowed to interact with the programmer.
Social-Aware Cross-Chain Authentication Strategy in Mobile Edge Computing

With the rapid development of mobile edge computing (MEC), the amount of mobile devices and users is growing rapidly, and the distributed management architecture based on the blockchain technology is gradually used for the demand of trust and privacy protection in MEC. While most of the works focus on how to use blockchain to provide a secure and trusted environment in MEC, the problem of excessive authentication cost caused by repeated authentication has not been solved. This paper proposes a social-aware cross-chain authentication strategy in MEC. The concept of social network is introduced into MEC, based on data collected from the users, we can derive the social connection among users and select notaries from users with high credibility to cross the chain. Simulation results show that the proposed strategy can effectively reduce the system cost of distributed management architecture in MEC.
Security Attacks on the Improved SMS4-BSK Encryption Transmission System

The improved S MS 4-BS K algorithm is a symmetric key block cipher with a message block size of 128-bit. The algorithm is developed to protect the data packets, over the unsecured wireless medium, against cryptanalysis attacks. From the analysis like key sensitivity analysis and the plaintext sensitivity analysis and the security attacks on the design like brute-force attack, Chosen Plaintext attack, Known Plaintext attack, Chosen Ciphertext attack and Ciphertext Only attack,it is evident that the improved SMS4-BSK cryptosystem has the immunity against all the types of cryptanalysis attacks.
Server-Side Distinction of Incoming Traffic Transmission Medium Using Machine Learning

In the two decades, there have been rapid advancements in the field of communication and information technology. Today, infrastructures include high-speed broadband networks that serve users at a fixed location which is referred to as fixed broadband networks. Another type serves users on the move which is referred to as mobile broadband networks. Fixed broadband networks offer fixed speed and reliable connections. Meanwhile, mobile broadband networks offer mobility, but they are less reliable. Therefore, some of the critical operations such as system update require users to be connected to a fixed broadband network. Different type of networks results in different type of traffic pattern. This paper utilizes a machine learning model at the server-side to help servers differentiate between data transmitted over fixed networks and data transmitted over mobile networks. Supervised training was used to build the model. The proposed system was tested and it showed an accuracy of 92.24 percent. This work is novel and the first of its kind since it is the first to attempt the detection of the nature of the network used for transmission based on the pattern of the arrival of packets.
Authenticity Verification Scheme Based On Tee and Blockchain

IoT devices constitute the key infrastructure to support various important IoT applications. To ensure the high reliability of these devices and their generated data, a dual verification framework based on a trusted execution environment and Blockchain was proposed to verify the device identity and data authenticity in this paper. In addition, the security of the framework is also analyzed. The scheme provides a data verification reference for the expansion of the ecological application of the IoT.
An analysis of YouTube videos on oral hygiene practices during coronavirus disease 2019 pandemic

Objectives: The objectives of the study were to examine the usefulness and content of YouTube videos regarding additional oral hygiene practices that need to be taken during the coronavirus disease 2019 (COVID-19) pandemic. Materials and Methods: A search of YouTube videos was performed using the terms “oral health,” “dental hygiene,” “oral health child” and “dental hygiene child” in combination with “COVID-19”. When analyzing each video, the descriptive characteristics such as title, country of origin, date of upload, duration of the video, the number of views, comments, likes, and dislikes were recorded. The quality of the videos were assessed through indices: Video ınformation and quality ındex (VIQI), modified DISCERN, and global quality score (GQS). The content quality score of the videos was examined. Results: The majority of the analyzed videos were uploaded by health-care professionals (67%). About 40% of the videos were slightly useful, and 6% were useful. There was no statistically significant correlation between the source of upload and GQS, VIQI, total content, or modified DISCERN scores ( P &gt; 0.05). Highly-watched videos appeared to have a higher total content, VIQI, Modified DISCERN, and GQI scores than poorly-watched videos, but this difference was not statistically significant ( P &gt; 0.05). Conclusion: YouTube videos related to oral hygiene practices during COVID-19 pandemic were a limited source for children. Oral health-care professionals and organizations should play an active important role for providing educative oral hygiene-related information on YouTube videos.
Formation of Professional Expert Community Leaders’ Competency Profiles

In the difficult conditions of transformation of socioeconomic and labor relations, professional expert communities (PEC) acquire a special role, ensuring both the development of individual professions, experts, and, in general, affecting the quality of the education system and the national qualifications system in the country. At the same time, community leaders play a key role, often acting as initiators of the creation and locomotives of PEC development. The aim of this study is to systematize the approaches of modern researchers to modeling competencies and develop, on its basis, a competency profile for PEC leaders. The theoretical and methodological basis of the study is a systematic approach based on a multidimensional model of competencies, which involves considering both professional and personal competencies. The empirical basis of the study is a series of in-depth expert interviews with twenty leaders of professional expert communities, selected based on the results of PEC monitoring, implemented in 2019–2020 within the framework of the RFBR grant. The study made it possible to formulate the main goal of the communities’ activities, which is the exchange of experience, the development of the profession and the community of professionals, the dissemination and implementation of best practices, and the transfer of knowledge. Based on this, the community leader must demonstrate a sufficiently high level of intellectual qualities, as well as managerial and expert knowledge. The study shows that one of the key competencies characteristic of PEC leaders is developed skills of expert activity in the framework of the professional activity of the community. In addition, PEC leaders have a high and equal level of the following competencies: continuous development, confidence, self-awareness and control, honesty and ethical behavior; they all have a high level of intellectual abilities, the skill of demonstrating leadership expertise and expert examination. Cognitive competences and meta-competencies are the most pronounced among PEC leaders. Among the socio-ethical competencies, honesty and ethical behavior are the most manifested. Formation of the competence profile of PEC leaders will allow determining the most manifested competencies of community leaders and preparing recommendations regarding the formation of individual development plans for current and future community leaders. The stronger communities become embedded in socioeconomic relations, the more important it is to understand the capabilities of their leaders. In addition, the understanding of PEC managers’ competency profiles will allow integrating them more effectively into the activities of professional qualifications councils, whose activities are aimed, among other things, at standardizing areas of professional activity.
Application of GIS-AI for Detecting Geological Structure and Prospective Zoning of Mineral Resources in Vanyen Area

Summary Nowadays Geographic Information System (GIS) has become ubiquitous in geospatial data management. Modern AI algorithms, especially artificial neural networks (ANN) are being developed and increasingly demonstrate the effectiveness of the method with the ability to detect potential relations in large amount of information. In the GIS-AI system: GIS is a tool for spatial data management. The effectiveness of ANN’s identity classification depends a lot on the accuracy and abundance of these layers of information. AI with the ability to detect potential relations in large number of layers and predict events / processes based on incomprehensible, scattered and discrete large data with many statistical rules. The GIS-AI system will be a new methodology in the integrated processing and interpretation of geological - geophysical - remote sensing data, helping to improve the accuracy of prospecting. The application of GIS-AI (ANN Kohonen and SOM) to data interpretation of Van Yen region has classified geological objects into 37 groups according to geological - geophysical - remote sensing properties, 4 of those are potential groups (area) of some minerals (Ta, Py and Pb-Zn).
Batman Reimagined

Inspired by the “Batman Equation” of 2011, this article presents a challenging and engaging process for graphing complicated designs from just a single parametric equation pair. Reinforces numerous analytic geometry skills. Works in popular graphing software such as Desmos or GeoGebra, or even graphing calculators.
Virtual Reality in Health Care: Bibliometric Analysis (Preprint)

<sec> <title>BACKGROUND</title> Research into the application of virtual reality technology in the health care sector has rapidly increased, resulting in a large body of research that is difficult to keep up with. </sec> <sec> <title>OBJECTIVE</title> We will provide an overview of the annual publication numbers in this field and the most productive and influential countries, journals, and authors, as well as the most used, most co-occurring, and most recent keywords. </sec> <sec> <title>METHODS</title> Based on a data set of 356 publications and 20,363 citations derived from Web of Science, we conducted a bibliometric analysis using BibExcel, HistCite, and VOSviewer. </sec> <sec> <title>RESULTS</title> The strongest growth in publications occurred in 2020, accounting for 29.49% of all publications so far. The most productive countries are the United States, the United Kingdom, and Spain; the most influential countries are the United States, Canada, and the United Kingdom. The most productive journals are the Journal of Medical Internet Research (JMIR), JMIR Serious Games, and the Games for Health Journal; the most influential journals are Patient Education and Counselling, Medical Education, and Quality of Life Research. The most productive authors are Riva, del Piccolo, and Schwebel; the most influential authors are Finset, del Piccolo, and Eide. The most frequently occurring keywords other than “virtual” and “reality” are “training,” “trial,” and “patients.” The most relevant research themes are communication, education, and novel treatments; the most recent research trends are fitness and exergames. </sec> <sec> <title>CONCLUSIONS</title> The analysis shows that the field has left its infant state and its specialization is advancing, with a clear focus on patient usability. </sec>
Implementation of discrete positive operator valued measures on linear optical systems using cosine-sine decomposition

Positive operator valued measurements (POVMs) play an important role in efficient quantum communication and computation. While optical systems are one of the strongest candidates for long distance quantum communication and information processing, efficient methods to implement POVMs in these systems are scarce. Here we propose an all-optical scheme to implement an arbitrary POVM using linear optical components on $m$-dimensional Hilbert space of internal degrees of freedom. Linear optical nature of the proposed scheme makes it efficient and robust. We show how the scheme can be applied for state tomography and for preparing arbitrary mixed states.
Future Application of Block chain Technology in Verification for Recruitment

Conventional verification in recruitment processes are demanding, especially when there are many candidates. Failure in this process can lead to wrong hiring for the job. The blockchain technology can solve most of these issues. However the uses of blockchain in verification for recruitment among Malaysia companies are not known. Therefore, this study is aim to determine the future blockchain technology for recruitment verification in Malaysia. By using STEEPV analysis, drivers of blockchain in verification for recruitment were identified. It is found that social driver is the highest which refer to trust and transparency issues of the system. Distribution of questionnaire was used to identify the top two drivers of future blockchain technology in verification for recruitment. The second phase would include impact-uncertainty analysis where cost and time effectiveness as well as data transparency and trust are the top two drivers. Based on these two drivers, four future scenarios have been developed which are impressive technology, overpriced technology, low adoption of technology and cost based technology.