Reviewerly/exHarmony huggingface dataset:
Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews

The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). This estimation was based on review ratings and on sentiment polarity in the comments, with respect to specific aspects, such as “lecturer”, “presentation”, etc. Between course sessions, users stop reviewing, and also, the course contents may evolve. Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera.org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses.The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes. Among the supervised methods, the J48 decision trees algorithm yielded the best performance, compared to the naive Bayes model and the SVM model.
Research Gaps and Opportunities for Secure Access Service Edge

This paper provides a contemporary discussion of security as a service from a network perspective and discusses state-of-the-art research conducted within a framework known as Secure Access Service Edge (SASE) (pronounced ‘sassy’). SASE is a network security framework proposed by Gartner (2019) (MacDonald, Orans and Skorupa, 2019). This paper gives brief description of cloud concepts and technologies, focuses on network security in the cloud and aims to provide researchers with subjects of future research in SASE. To achieve the aim, the authors evaluate existing papers on SASE and its core components to identify gaps in the literature currently available on SASE.
PENERAPAN DATA MINING DALAM PROSES PREDIKSI PERCERAIAN MENGGUNAKAN ALGORITMA NAIVE BAYES DI KABUPATEN ACEH TENGAH

Marriage in the view of Islam is something sacred, meaning worship to Allah SWT, following the sunnah of the Prophet Muhammad and carried out on the basis of sincerity, responsibility, and following legal provisions that must be heeded. In Aceh province, the divorce rate from Aceh Syari'ah Court data throughout 2017, the number of divorces occurred reaching 5,399. The data and facts about the divorce case are a reality that must be accepted even though it is bitter, and while looking for the best way and strategy to prevent divorce from an early age. Based on the divorce rate which continues to increase every year, it is necessary to have data to determine the factors that influence divorce. Thus, these data can be used as a decision-making tool that can later be a good solution for the government, society and for married couples in the future. Naïve Bayes classification is a simple probability classification based on the application of the Bayes Theorem with the assumption that the explanatory variables are independent or (independent). In this case, it is assumed that the presence or absence of a particular event from a group is not related to the presence or absence and other events. With a system to predict divorce, it can help users who want to know divorce predictions.
Addressing Evolutionary-Based Dynamic Problems: A New Methodology for Evaluating Immigrants Strategies in MOGAs

Multi-Objective Genetic Algorithms (MOGAs) have been successfully used to address dynamic problems in a wide variety of domains. In these domains, data changes over time, so a non-static analysis is required to obtain feasible solutions. In this type of environments, MOGAs are often time-consuming and require special adaptation to work properly. A number of different techniques have been proposed to adapt MOGAs to dynamic environments for tackling the previous problems such as hypermutation, memory and immigrant schemes or multi-population methods, among others. In particular, immigrant strategies are one of the most commonly used methods, for that reason, this work proposes a new methodology that allows to make a detailed evaluation of their performance when these strategies are used. The proposed methodology works on two levels, a coarse-grain one and a fine-grain one. In the former, an overall evaluation of the different immigrant strategies is made based on three different dimensions: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quality</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stability</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Speed</i> . In the latter, a detailed study of the status of the immigrant individuals during the evolution of the algorithm is carried out. This is a very relevant aspect to take into account in order to evaluate whether an immigrant strategy is working properly or not. To deploy this methodology, a new visualization technique for population mixing analysis is proposed in this work. In order to validate the proposed methodology, a test case in the context of the Dynamic Community Detection problem (DCD) has been selected using a MOGA that applies several different immigrant schemes, showing both how the methodology works and how it could be employed in a particular dynamic problem.
Thesis Title Eligibility Decision Support System using the Simple Additive Weighting Method at STMIK Palcomtech

Decision Support System (DSS) is a system that can assist a person in making accurate and targeted decisions. Many problems can be solved using SPK, one of which is the feasibility of the thesis title at STMIK PalComTech Palembang. There are several methods that can be used in building a DSS including Simple Additive Weighting (SAW). As in the SPK the eligibility of the thesis title. This study uses the SAW method in determining the feasibility of the thesis title at STMIK PalComTech Palembang. In determining the feasibility of the thesis title, there are several criteria that become the basis for making decisions, including Journal, unique title, 5W 1H (What, Where, When, Why, Who and How)(Fitriani &amp; Alasi, 2020). Journal means whether the student fulfills all the journals to meet the eligibility requirements for the thesis title. A unique title means that students must be able to submit a title that has never existed or an existing title is then redeveloped so that it is not the same as the previous title. 5W 1H is the title eligibility criteria that must be met. And in the development of the model using a prototype. The final result in this research is to get an assessment that is ordered from the lowest to the highest, so that the supervisor and the Head of Study Program can easily make decisions by looking at the results of the SPK.
Aggregated Traffic Anomaly Detection Using Time Series Forecasting on Call Detail Records

Mobile network operators store an enormous amount of information like log files that describe various events and users’ activities. Analysis of these logs might be used in many critical applications such as detecting cyber attacks, finding behavioral patterns of users, security incident response, and network forensics. In a cellular network, call detail records (CDRs) is one type of such logs containing metadata of calls and usually includes valuable information about contacts such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call), and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper’s primary goal is to study subscribers’ behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and neural network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies.
Connecting Bitcoin Blockchain with Digital Learning Chain Structure in Education

In the present paper, an extension of the Bitcoin (BTC) structure is introduced, with a proprietary and unique DLCC (Digital Learning Chain Structure) blockchain, through which an automated bursary payment system could be constituted, which has, until this day, never been implemented at universities.Thus, an alternative is offered to achieve a bursary payment system, operating in a fully automatic, periodically repeating manner, never requiring supervision.In the DLCC blockchain, specifically, in the Smart Contract, the terms of contract are kept, concerning the bursaries, terms agreed by both parties (students and universities alike).A great advantage of the DLCC system is that it is able to extract the required grades from the electronic grade book, thus supervising the students' progress throughout the semesters.Upon reaching the end of each semester, if the student qualifies, meeting the requirements, the system will transfer the required quantity of cryptocurrency from the university's account automatically to the student's account.
Reduction of Online Fraudulent Activities in Freelancing Sites Using Blockchain and Biometric

Digital freelancing refers to hiring potential employees to work on specific projects on a contract basis by organizations, companies, and individuals. Existing freelancing systems are based on client-server models and managed in a centralized way. Centralized freelancing systems are originating several issues: lack of trust, payment delays, central control over all operations, less reliability, online frauds, and lack of fairness. Hence, we propose a decentralized freelancing system based on blockchain and biometrics. Blockchain in the freelancing system makes contracts decentralized, and no one can control the interaction and deals between buyers and sellers. Biometric signatures on smart contracts in the freelancing system enhance the authenticity and validity of agreements between buyers and sellers. Our decentralized freelancing system is an online web-based platform based on blockchain and biometrics and built on Python. It makes the interaction and deals between buyers and sellers via the innovative approach of smart contracts. The proposed work illustrates the development, implementation, and functionalities of the freelancing system, such as registration as buyer or seller, logging in to the system, posting a job, bidding on jobs, hiring process, finalizing deals with smart contracts having a biometric signature of both parties and distributing smart contracts among all stakeholders via blockchain. Our system proves the fairness, clarity, security, and reliability in all processes of posting and hiring without the involvement of any central management authority.
The Berry–Esseen-type bound for the G-M estimator in a nonparametric regression model with <i>α</i>-mixing errors

Consider the fixed design nonparametric regression model: Yi=g(xi)+εi,i=1,2,…,n,n≥1, where xi are known fixed design points from [0,1] satisfying 0=x0≤x1≤⋯≤xn−1≤xn=1 and g(x) is an unknown function defined on the closed interval [0,1], Yi are the response variables, and εi are zero mean α-mixing random variables with α(n)=O(n−λ)forsomeλ>(2+δ)(1+δ)/δ, where δ>0. We derive the Berry–Esseen-type bound for the G-M estimator of g(⋅) under some mild conditions, which approximates to O(n−14) provided that weights and moments are appropriate. Our results generalize some known ones in the literature. In addition, a simulation study is given to illustrate the validity of the theoretical result.
Dimensional Reduction Techniques for Huge Volume of Data

Abstract: Huge volume of data and information is needed with the expanding advancement in the current collection of tools, cloud storage, strategic techniques and increasing development of science technology. With the appearance of complete genome successions, the biomedical area has encountered an exceptional progression. This genomics has prompted the advancement of new high-produced strategies techniques that are huge amounts in measures of information and data, which inferred the exponential development of numerous organic and biological data sets. This paper represents different linear and non-linear dimensionality reduction techniques and their validity for different kinds of data information datasets and application regions. Keywords: High dimensional data, Dimensionality reduction, Linear techniques, Non-linear techniques, feature extraction, feature selection, Machine Learning
THE RELATIONSHIP BETWEEN PRESERVICE ENGLISH TEACHERS’ PERCEPTION OF WATCHING ENGLISH VIDEO ON YOUTUBE AS A HABIT AND LISTENING SKILL ACHIEVEMENT

Watching English videos on YouTube is one of the alternatives in order to improve students’ listening comprehension. The aim of this study is to discover whether or not there is a significant correlation between the perception of habit of watching English videos on YouTube and listening skill achievement. The participants of the study were 51 students of English Education Study Program in Palembang. There were two instruments used: (1) The Habit of Watching English Videos on YouTube Questionnaire, and (2) listening mastery score from listening course they took. The data were then analysed using Pearson Product Moment in SPSS 25. The results show that there is no significant correlation between the habit of watching English videos on YouTube and listening mastery of the students, r(49) = - .26, p &lt; .01. This study also provided discussions and recommendations for future research.
Strategy Politeness of Werkudara in Wayang purwa

Wayang purwa is a noble culture of Javanese people which is the pride of the Indonesian people and is recognized internationally as a masterpiece. The nobleness of the puppet culture is inseparable from the strong characterization of each character in the puppet. One of the strongest characters in wayang purwa is Werkudara. This research focused on the analysis of Werkudara politeness strategies in wayang purwa in order to explore the characterizations played by Werkudara characters. This research used the politeness theory of Brown &amp; Levinson to deepen the results of the politeness strategy analysis. This study also used descriptive qualitative methods to explain the phenomenon of speech acts of Werkudara in explaining the politeness strategies used. This research data was Werkudara's speech taken from the data source, namely the recording of Ki Nartosabdo's purwa puppet show with Dewa Ruci's play. The results of this study concluded that Werkudara was more dominant in using negative politeness in speaking. The negative politeness strategies used include giving deference; being pessimistic, and using indirect expressions. Furthermore, positive politeness strategies are used by Werkudara, including the use of group identity markers; avoiding disagreement; offering promises, and exaggeration.
Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator

Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.
A Modular Framework for Generic Quantum Algorithms

We describe a general-purpose framework to design quantum algorithms. This framework relies on two pillars: a basic data structure called quantum matrix and a modular structure based on three quasi-independent modules. These latter include a loading module, a tool-kit of basic quantum arithmetic operations and a read-out module. We briefly discuss the loading and read-out modules, while the arithmetic module is analyzed in more depth. Eventually, we give explicit examples regarding the manipulation of generic oracles and hint at possible applications.
Search geometric ranges efficiently as keywords over encrypted spatial data

With the increasing popularity of location-based services (LBS), data outsourcing toward clouds is an emerging paradigm for ease of data management by LBS providers. Geometric range queries are one of the fundamental search functions in LBS, which are to find points inside geometric areas (e.g., circles or polygons). To ensure data confidentiality, the service users tend to encrypt the data before outsourcing it. However, regarding encrypted data, only a few consider geometric range queries, where the rationale is the high-dimension calculations make these queries particularly harder. In this paper, we propose a novel scheme for geometric range queries, that can provide the privacy of data stored at a cloud server and queries. Our scheme supports querying encrypted spatial data with irregular-shaped areas, achieves fast searches and enables dynamic updates. Experimental results over real-world spatial datasets demonstrate that our scheme results in fewer communication rounds and can speed up the search time 4× compared to state-of-the-art schemes, without carrying any potentially visible leakage in the structure.
Human Errors: A Cybersecurity Concern and the Weakest Link to Small Businesses

Cybersecurity is essential for all organizations, especially during this menacing Covid-19 global pandemic. The sudden transition of leaving the offices to work from home – the 'new normal' – has introduced information security-related risks associated with human factors. For example, both criminals and employees use the same platform for information exchange but with starkly different intentions. But both their actions compromise information and computer security. Criminals intentionally exploit systems to gain unauthorized access for their benefit, while employees make careless mistakes, leaving systems exposed and vulnerable. The present study examines human errors influenced by actions, attitudes, and behaviors that affect overall information security. Purposive sampling within the qualitative approach was used to select thirty (30) small business managers. Data was collected using a qualitative online survey as a Google Form. The study used thematic analysis. The results revealed that repeated human mistakes compromise information security principles and render employees the weakest link. The study explained the risks caused by employees due to ignorance or poor decision making, technical-related errors, and skills- and policy-based errors. Even though small businesses do not require a 'one-size-fits-all' security approach, recommendations to reduce human mistakes were made.
Specialised Media Monitoring Tool to Observe Situational Awareness

The 21st century witnessed the rise of the digital age via the evolution of the Internet. A direct result of the established digital era is the growth of digital media. Although traditional media, such as print and broadcasting, remains relevant, the low accessibility and limited reach impact the effectiveness of such media. In comparison, digital media, such as social media, provides access to vast collections of open-source information. The open-source information available on digital media platforms is current and easily accessible. However, conversion of open-source information into intelligence becomes a time-consuming and quite ineffective process due to the large quantities of information available. Open-source intelligence (OSINT) tools attempt to address such limitations by offering technological solutions that provide access to up to date, as well as easily searchable digital information. Thus, OSINT tools, such as Shodan and Maltego, streamline the conversion of open-source information into intelligence. This paper introduces the design of a new but specialised media monitoring tool to assist with the monitoring of open-source information towards enabling situational awareness. The key design feature of this new media monitoring tool is to offer versatility by guiding the collection of open-source information. Controlling the amount of information gathered in turn improves the efficiency of the processes used to extract intelligence. The obtained intelligence, thus, enables the observation of situational awareness of chosen aspects within a secure environment. Also discussed in this paper is the motivation behind the design of the specialised media monitoring tool, as well as various design considerations, are taken to ensure effective usage of the tool. The paper concludes by discussing the various benefits offered by the specialised media monitoring tool concerning observing situational awareness.
LEARNING INDONESIAN LANGUAGE IN THE TIME OF THE COVID-19 PANDEMIC

During the Covid-19 pandemic, the learning process is carried out using established regulations. Through this regulation, the implementation of Indonesian language learning is carried out using Distance Learning. This study then tries to see how the implementation of Indonesian language learning occurs during the Covid-19 pandemic. This research will be carried out using a qualitative approach using previous studies and research data. The results of this study found that Indonesian language learning had not been implemented optimally during the pandemic. Then the understanding of teachers and other teachers is still lacking in the emergency curriculum. Finally, the achievement of learning outcomes during the pandemic found that students’ skills in writing texts were still lacking.
Building Pillars Where Bridges are Easy to Imitate - Mediating Technologies in Knowledge-Based Economies

We carried out a qualitative investigation of a pioneer of the Industrial Internet of Things operating in Silicon Valley since 1989. We reconstructed a sequence of strategic turns and conceptualized their current business model in a way that is relevant for several other industries enabled by the Internet, generalizing Thompson’s concept of mediating technologies in order to adapt it to contemporary, knowledge-based economies. We propose an interpretive framework for mediating technologies that we express by means of semantic networks.