Reviewerly/exHarmony huggingface dataset:
Can Artificial Intelligence Help Used-Car Dealers Survive in a Data-Driven Used-Car Market?

The used-car market is notoriously untrustworthy and shady. Certified data has been shown to help mitigate the information asymmetry, one of the major factors to an untrustworthy market. In recent times, more and more used-car dealers have had problems surviving in this competitive data-driven market. In this study, we conduct 12 interviews with used-car dealers and several meetings and workshops with employees and executives from the AMAG Group, one of the largest automotive companies in Switzerland. This creates insight into current problems for used-car dealers and how artificial intelligence can help. The problems can be abstracted to the problem of high transaction cost and its subcategories. In reducing transaction costs by utilizing artificial intelligence, new secondary problems arise. People need to trust the certificate, the analytics, and the predictions. Additionally, the data and analytics need to be transparent and understandable, and privacy concerns must be addressed. The implications of this study are manifold. First, we define the problems for used-car dealers on the used-car market and introduce artificial intelligence approaches to the current data-driven used-car market. Afterward, we stress that artificial intelligence needs to follow a human-centered perspective and be designed for trust.
A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks

(1) Background: Container orchestration frameworks provide support for management of complex distributed applications. Different frameworks have emerged only recently, and they have been in constant evolution as new features are being introduced. This reality makes it difficult for practitioners and researchers to maintain a clear view of the technology space. (2) Methods: we present a descriptive feature comparison study of the three most prominent orchestration frameworks: Docker Swarm, Kubernetes, and Mesos, which can be combined with Marathon, Aurora or DC/OS. This study aims at (i) identifying the common and unique features of all frameworks, (ii) comparing these frameworks qualitatively and quantitatively with respect to genericity in terms of supported features, and (iii) investigating the maturity and stability of the frameworks as well as the pioneering nature of each framework by studying the historical evolution of the frameworks on GitHub. (3) Results: (i) we have identified 124 common features and 54 unique features that we divided into a taxonomy of 9 functional aspects and 27 functional sub-aspects. (ii) Kubernetes supports the highest number of accumulated common and unique features for all 9 functional aspects; however, no evidence has been found for significant differences in genericity with Docker Swarm and DC/OS. (iii) Very little feature deprecations have been found and 15 out of 27 sub-aspects have been identified as mature and stable. These are pioneered in descending order by Kubernetes, Mesos, and Marathon. (4) Conclusion: there is a broad and mature foundation that underpins all container orchestration frameworks. Likely areas for further evolution and innovation include system support for improved cluster security and container security, performance isolation of GPU, disk and network resources, and network plugin architectures.
A Historical Overview of Using Corpora in English Language Teaching

This chapter offers a historical account of the compilation of English frequency lists, the writing of pedagogical grammars, reference books and course materials, data-driven learning, and interlanguage analysis on the basis of corpora. Corpus approaches to English language teaching and learning are characterized by the authenticity of language data, the prioritization of (co-)frequency, the phraseological description of meaningful units, and inductive methods of learning. Most methodological innovations have been implemented in pedagogical praxis from the pre-electronic time to the cloud-computing age, and the approach has been tremendously facilitated by new technological advances and the development of applied linguistics research in general.
Statistical analysis of crowd behaviour in catastrophic situation

Machine learning (ML) is one of the emerging domains in classification and prediction. It is important to understand the responses of individuals in crowd during an earthquake emergency for making appropriate earthquake emergency management plan. Our research is focused on predicting the behaviour of individuals in a crowd during Catastrophic Situation. For this purpose, intended and actual behavioural response of crowd is collected by conducting a series of surveys. The attributes that are selected for result prediction are gender, age, affiliation, health status, training level, nearby exit, earthquake intensity, earthquake location, environmental status, and individual’s response. The dataset thus collected is divided into two crowds, Crowd 1 shows the intended behaviour whereas Crowd 2 shows actual. The decision tree, k-nearest neighbour, Naïve Bayes and neural network machine learning algorithms are used for predicting results. The results are analysed by using Rapid Miner as data mining tool. The dataset is split into two partitions. By applying randomization techniques like simple random sampling, shuffle random sampling, etc. we have trained and tested the machine learning algorithms. The results of this research will be a source of help in understanding critical details about crowd behaviour in earthquake emergency.
Prediction of Fraudulent or Genuine Transactions on Credit Card Fraud Detection Dataset Using Machine Learning Techniques

Abstract: Credit card fraud is one of the important financial frauds that has caused a huge amount of financial losses more than the past. Several protection mechanisms such as Fraud Prevention Systems (FPSs) are used to combat the current credit card frauds, but these methods are not efficient enough to reduce the impact of these frauds. It is therefore important that credit card companies are able to recognize and prevent fraudulent credit card transactions so that customers are not charged for items that they did not purchase. In this research, we present our approach to predict legitimate or fraudulent transactions on Credit Card Fraud Detection Dataset from Kaggle. This work is based on analysis at two levels using performance evaluation metrics such as Precision, Recall, F1_Score and ROC_AUC. In the first stage of the research, the original imbalanced and skewed dataset was used to train, predict and evaluate the six supervised machine learning classifiers considered in this research including: Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT), Logistic Regression (LR), and Naïve Bayes (NB) while the same set of classifiers were also trained, predicted and evaluated with the same dataset but now resampled using SMOTETomek, i.e., a combination of both under-sampling and over-sampling technique to eliminate the imbalanced nature of the dataset during the second stage. The results of the two stages are compared to select the best overall performance. However, the result of the second stage of the experiment where models are trained, tested and evaluated with resampled dataset gave the overall best results where XGBoost, RF and DT have 100% in Precision, Recall, F1_Score and ROC_AUC respectively. While comparing the overall results of our research with all the papers reviewed in Section 2 of this work, it is worth noting that our research achieved the best performance so far where 100% were recorded from three different classifiers in all the four metrics used to evaluate our work.
Fuzzy Reasoning Procedure for Ontologies based on Rough Membership Approximation

One of the major challenges in modeling a real-world domain is how to effectively represent uncertain and incomplete knowledge of that domain. Several techniques for representing uncertainty in ontologies have been proposed with some of the techniques lacking provision for vague inference. The classical tableaux-based algorithm does not provide the flexibility for reasoning over such vague ontologies. However, several extensions of the tableaux-based algorithm have been proposed to cope with fuzzy reasoning. Similarly, several alternative reasoning methods for incomplete, inconsistent, and uncertain ontologies have been proposed. One of the major limitations of most of those techniques is that they require reengineering existing ontologies to cope with uncertainty. This paper proposes a satisfiability algorithm for vague ontologies that uses a rough set to approximate the concepts and roles. The proposed technique takes advantage of the existing ontology knowledge base to achieve vague reasoning without the need of reengineering the ontology. The results show that the proposed technique conforms to the tableaux-based algorithm while providing a way of reasoning over the uncertain aspects of ontologies.
Open Access Overtaking Academic Library Leadership: Staying Ahead of the Organisational Dynamics an Increasingly Open Future May Bring

With the fairly recent introduction of Transformative Agreements as an acquisitions and OA publishing mechanism in the context of US academic libraries, smaller institutions from Carnegie Classification R2 on down are grappling with questions of adaptation, mission response, and value proposition in a shifting environment. As Open Access continues to expand with increasing proliferation of OA content, smaller libraries are searching for answers as flat and declining budgets will eventually coincide with rising OA availability. This editorial reviews these challenges as expressed by a unique Community of Practice the authors assembled and guided through a series of conversations. Leadership-focussed solutions are offered as a way of aligning academic library management with broader trends in Higher Education.
Understanding Cybercrime and the Phenomenon of "Broutage" (Grazing) For a Media Education of Pupils and Students in Côte d'Ivoire

This article analyzes some understanding outlines of cybercrime considering its complexity and its practice by the youth population in Côte d'Ivoire for media education. On the basis of conventions, guides and scientific publications on the phenomenon, a thematic content analysis was conducted in order to identify the axes that structure the study in response to the research objective. This problematic is based on the socio-ecological model of Sallis and Owen (1998). According to the Preamble of the Council of Europe's Convention on Cybercrime, known as the "Budapest Convention" of November 23, 2001, which entered into force on July 1, 2004, cybercrime is crime in cyberspace. In the absence of a legal definition of the phenomenon, there is confusion between crimes germane to cybercrime and cybercrime itself. The Internet, a channel of cyberspace, is presented as a web, a mesh on a planetary scale that limits the control mechanisms and reveals the difficulties of quantifying the phenomenon. Through a vicarious learning among young people in Côte d'Ivoire, cybercrime colloquially called “broutage” (grazing) is a social malaise, despite the response of the Ivorian state. The use of ICTs requires education to make each user responsible for understanding, using, defending and enjoying them, because the media world is experiencing an unprecedented shock.
THE EFFECT DESCRIBE AND DRAW TECHNIQUE TOWARD STUDENTS’ SPEAKING SKILL

Speaking skill is one of the skills that students need to have. However, there are several factors that make students not master speaking, that was students feel ashamed to express opinions, afraid to make mistakes. One of them in students of MTsN 3 Indragiri Hilir. This problem is the background of the problem in this research. To find out the effect of describing and drawing techniques, researchers used experimental research with pre-experimental designs. The data collection technique in this research was a test using the oral test as an instrument. The sample of this research was 25 students of VII MTsN 3 Indragiri Hilir. After getting the data using the oral test, the researcher found that describe and drawing technique given an effect on students’ speaking skills, the score of the post-test (76.44) was better than the pre-test (66,32), and indicated by some factors, students more active, enjoy, brave. And with the result, t-observed was 22.0 while the t-table was 2.06 to 5. % and 2.81 for 1%. The t-observed score was higher than the t-table. Based on the result, H0 (null hypothesis) is rejected and H1 (alternative hypothesis) is accepted. It also means that describe and draw technique was given effect on students' speaking skills at grade VII of MTsN 3 Indragiri Hilir in academic years 2020/2021.
A STUDY ON TEACHERS' EFFORTS IN MOTIVATING STUDENTS TO LEARN ENGLISH AT GRADE VIII OF SMPN 1 TEMBILAHAN HULU

The problem of this research was that students' motivation in learning English is good, so it is needed to know what teacher's effort in motivating them. Therefore, the purpose of this research is to know what teachers’ efforts in motivating students to learn English at grade VIII of SMPN 1 Tembilahan Hulu are. Therefore, this research was descriptive quantitative research. The researcher distributed the questionnaire to twenty students of the eighth grade of excellent class and interviewed the English teacher as a sample to know what teachers' efforts in motivating Students to learn English. The sample was taken using total sampling because there are only twenty students and one teacher for an excellent class. After collecting the data, the researcher found that teacher has reasonable effort in motivating students to learn English. The efforts are teacher uses various activities in teaching, prepares some engaging media, gives students assignment based on what they have learned, relates topics in teaching to students' daily activities, asks the students to think about their experience, gives reword to students, says positive words, gives students a chance to present their course work, and gives memorable score when students finish group work soon. Therefore, students can be motivated to learn when their teacher keeps supporting them by making some efforts in teaching.
Effect of Apperception on Learning Readiness of Class IV Elementary School Students

The purpose of this study was to determine the effect of apperception on the learning readiness of fifth grade elementary school students. The research was conducted in Soppeng Regency, precisely at UPTD SPF SDN 254 Mappatongai. The type of research used is an experimental quasi-experimental design that uses one group pretest-posttest. The independent variable in this study is the provision of apperception, while the dependent variable is readiness to learn. The population in this study amounted to 47 people. The sampling technique used was random sampling by selecting class IV, which consisted of 12 people as samples. The research instruments used were observation sheets and questionnaires. The results of the data that have been collected, processed qualitatively and quantitatively using the SPSS application. The results of the pretest showed that the average student learning readiness was 62.17 which was in the sufficient category. The results of the observations showed that the students' learning readiness had progressed from the first meeting to the fourth meeting. After the posttest was conducted, it was found that the average student learning readiness was 82.25 which was in the high category. From the results of the research that has been done, it can be concluded that there is an average difference in learning readiness between pretest and posttest of 20.08 and there is an effect of apperception on learning readiness of fifth grade students at UPTD SPF SDN 254 Mappatongai, Soppeng Regency. This is indicated by the value of sig. (2-tailed) is 0.0005 &lt; 0.05, which indicates that H0 is rejected and H1 is accepted.
The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems.
Antecedents of Perceived Business Performance among Private Business Organizations: A Partial Least Square Approach

This study is very important to ensure that business organizations can formulate and plan a strategic plan to continue to ensure that their business performance remains viable and sustainable.The aim of this study is to assess the direct relationships between corporate image, employee engagement, organizational culture, employee loyalty, and business performance among private business organizations.This study is vital to be carried out because many private organizations face difficulty to maintain their business performance in the coming years.The research model of this study consists of three independent variables: corporate image, employee engagement, organizational culture, employee loyalty as a mediator, and business performance as a dependent variable.This study adopts a quantitative approach by using primary data for analysis.Primary data were utilized in this study and a survey questionnaire which was adopted and adapted from previous studies was used for data collection.329 clean data were used in the data analysis by utilizing the structural equation modeling (SEM) technique.Smartpls3 was used in this study to run the multivariate data analysis and test the proposed hypotheses.In addition, the model measurement and structural model assessment procedures also were performed by using Smartpls3.The PLS-SEM technique was employed for this study due to its assessment ability.Initially, the convergent validity was evaluated on the measurement model by assessing the construct reliability and validity.Then, the discriminant validity was assessed and confirmed through cross-loading and Hetrotrait-Monotrait (HTMT) ratios.Subsequently, the structural model was assessed and the hypotheses testing reveals that corporate image, employee engagement, and organizational culture, have a positive and significant influence on employee loyalty and business performance was strongly affected by employee loyalty.This shows that corporate image, employee engagement, and organizational culture are very important factors and business organizations need to pay serious attention if they want to ensure that the planned business performance can be achieved.
Comparison Performance Prediction between some Artificial Neural Networks (Box Jenkins) methodology with application

This paper compares a prediction accuracy between the statistical time series method that use (Box – Jenkins) methodology, and some artificial neural networks, which differ from them through the feedback in their structure. These networks are Feed Forward Neural Network (FFNN), Elman Neural Network (ENN), and Nonlinear Autoregressive with Exogenous Input (NARX). By using a set of data, the average monthly maximum temperatures in Mosul for the years (1983– 2009), which numbered 324 observations, twelve observations were kept as Truncated samples in order to compare with the results of prediction models for the above two methods. The results of prediction with performance of neural networks with feedback is better than others, and the performance methodology of (Box - Jenkins).
A Novel Hybrid Methodology for Anomaly Detection in Time Series

Abstract Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments