Reviewerly/exHarmony huggingface dataset:
Towards automatic evaluation of the Quality-in-Use in context-aware software systems

Abstract Context-aware systems adapt their services to the user’s intentions and environment to improve the user experience. However, how to evaluate the quality of these systems in terms of user perception and context recognition is still an open problem. Our goal in this work is to evaluate the Quality-in-Use (QinU) for context-aware software systems according to the ISO/IEC 25010 standard and in an automated manner. This evaluation is oriented to be model-based, with domain specification and log data as input, while quality metrics and representations of users’ behavior as output. In this process, we use probabilistic models to discover user patterns, heuristic metrics as QinU estimation, clustering techniques to obtain user profiles according to their QinU, and feature selection to identify relevant factors of context. We propose a framework for assessing the QinU in context-aware software systems called Framework for Assessing Quality-in-use of Software (FAQuiS). FAQuiS includes a set of models to represent all dimensions of context, a methodology to apply the quality analysis to any system, and a set of tools and metrics to support and automate the process. We seek to test the impact and ease of integration in the industry for this framework. A case study in a company allows us to validate the applicability in a real environment. We analyze the mechanisms that support the QinU evaluation in context-aware systems, the feasibility of the QinU quantification, and the suitability of the integration in companies. Compared to previous works, our proposal offers a novel data-driven approach with general-purpose and industrial viability. FAQuiS can be used as a solution to assess the QinU based on the ISO 25010 standard and the models of user behaviors in different contexts. This solution analyzes the context changes in the user interaction, can quantify the quality loss in these contexts, and does not require big efforts to be integrated into a software development process.
Active Learning for Capturing Human Decision Policies in a Data Frugal Context

Modeling human expert decision patterns can potentially help create training and decision support systems when no ground truth data is available. A cognitive modeling approach presented herein uses a combination of supervised learning methods to mimic expert strategies. Yet without historical data logs on human expert judgments in a given domain, training machine learning algorithms with new examples to be labelled one by one by human experts can be time-consuming and costly. This paper investigates the use of active learning methods for example selection in policy capturing sessions with an oracle in order to optimize frugal learning efficiency. It also introduces a new hybrid method aimed at improving predictive accuracy based on a better management of the exploration/exploitation tradeoff. Analyses on three datasets evaluated data exploration, data exploitation and finally hybrid methods. Results highlight different tradeoffs of those methods and show the benefits of using a hybrid approach.
Gender Recognition Using a Gaze-Guided Self-Attention Mechanism Robust Against Background Bias in Training Samples

We propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples.
BAYESIAN NETWORK AND DEMPSTER-SHAFER THEORY FOR EARLY DIAGNOSIS OF EYE DISEASES

An accurate self-diagnosis expert system would prevent the progression of chronic eye disease. However, developing an expert system for medical diagnose requires a robust reasoning capability.Â  In the knowledge acquisition phase, a knowledge engineer faces several issues. For example, an eye disease may contain several similar symptoms to another eye disease. Even worse, a patient may input a set of symptoms that can be attributable to several diseases, and these symptoms may not be readily quantifiable. Dempster-Shafer Theory (DST) and Bayesian Network (BN) are two commonly used techniques for combining uncertain evidence. The literature review showed that there have been no studies, either using BNs or DST, to diagnose eye diseases with a comparative study about both methods, BNs and DST. This paper study the effectiveness and reliability of DST and BN as the reasoning engine of an expert system for early diagnose of eye disease. The primary sources of knowledge on eye diseases are the patient files and human experts. Data were collected from hospitals and ophthalmologists in Riau, Indonesia. BN and DST framework was used to model and estimate the probability of eye diseases in supporting decision making, i.e. diagnosis. Rule-Based Reasoning and the Forward Chaining methods are applied in developing the reasoning structure. The Expert System Development Life Cycle (ESDLC) methodology is used to structure, plan and control the process of developing the expert system. In this study, 20 physical symptoms of illness obtained from the patients' files are used for diagnosing six types of eye diseases. The result of this study is accomplished by comparing the expert system diagnostic results with a human expert diagnostic result. Based on the testing of 10 eye diseases cases, the accuracy of the BN is higher compared to DST.
Conditions of Use an Extreme Regulator in the Control Loop of Large-Scale Economic Activities

The problem of calculating the autonomous control of a non-stationary system using only the values of the target function on a discrete set of times is considered. The algorithm of the extreme regulator is proposed for calculating the control. The conditions of its working capacity are formulated. An estimate of the exact lower margin of error of the decision, due to the size of the step in time and the rate of change of the target function, is obtained. The application of the algorithm is demonstrated in the output control model.
Aplikasi Analisis Data Kualitatif Menggunakan Algoritma Rough Set

Data analysis is a process carried out to produce a data modeling with a view to finding useful information so that it can provide clues to a study in making decisions on existing data. One algorithm that is used in the process of data analysis is the rough set algorithm used for qualitative data types. The rough set algorithm is widely used because this algorithm is easy to understand and can find a minimum set of data and can evaluate the significance of the data. To process large amounts of data in data analysis using this rough set algorithm, we need a tool that can simplify and speed up the calculation process. In this study an application was developed as a tool that can help the process. The application is built based on a GUI that can run on Windows, Linux and MacOS platforms. With this application the analysis process can be accelerated with the ability to process a small time and direct implementation of the rules that are owned. The number of objects in the decision system that can be processed is also greater than the application of its predecessor Rosetta. In the algorithm, the rules generated by this application meet the rough set principle, from the results of the comparison test of accuracy with the rules produced by Rosetta. With an average questionnaire score of 85.33%, proving that the application built provides ease and high use value.
Construction of multi-scale grid for massive land survey data

In the face of ever-growing and complex massive multi-source spatiotemporal data, the traditional vector data model is increasingly difficult to meet the needs of efficient data organization, management, calculation and analysis. Based on the simple and widely used geographic grid data organization model, this paper designs a technical method to convert vector data into multi-scale grid data, establishes a unified, standardized and seamless land spatial grid data model, and analyses the area accuracy of multi-scale grid data. Practice shows that the model can better meet the needs of multi-scale geospatial information integration and analysis, and it is easy to carry out distributed data processing, which provides technical support for the efficient organization, fusion and analysis of spatiotemporal data.
Using Cyber Applications Towards Positive Psychology Interventions in Africa

This paper describes “Positive Psychology” and its origins as well as its current status within the field of Psychology. Though the field is gaining rapid support in the rest of the world, it has not made the same progress in Africa, although its approach seems to form part of the continent’s indigenous knowledge systems and practices. In order to comment on possible future developments in this field, Africa’s fast increasing exposure to the cyber domain is illuminated, followed by examples of cyber domain interventions that have improved the well-being of Africans. Access to information and interactive communication seems to facilitate an increase in positive experiences, development of positive individual traits and positive communities, especially in the field of agriculture. This suggests that mobile applications and the cyber domain can be used as a platform for positive psychology interventions.
The Use Of Mind Mapping In Improving Writing Skill At Fourth Semester Of English Students Of University Qamarul Huda Badaruddin Bagu: The Use Of Mind Mapping In Improving Writing Skill At Fourth Semester Of English Students Of University Qamarul Huda Badaruddin Bagu

Regarding to the preliminary study, the researcher concerned with improving writing skill of English students of University of Qamarul Huda Badaruddin . Certainly, the students of this class are selected students having high intelligence than others so that they must get the perfect mean score. Unfortunately, it was not achieved. The mean score of writing was 6.5 as the same as other classes. It was caused by some reasons such as having difficulty in organizing and developing ideas and monotonous technique used daily by the teacher. To solve this problem, the researcher used mind mapping technique. It is an effective way for organizing ideas before writing. In implementing this technique, students could organize some ideas as many as possible and focus of the ideas that were going to construct. Then they had to develop the ideas into good sentences. In the other word, they could write in sequences so that students felt more easily to compose their ideas well. The research design of this study was Classroom Action Research which had four regular procedures such as planning, implementation, observation, and reflection. The subject was 37 English students of University of Qamarul Huda Badaruddin. To analyze the data in the observation, the researcher used quantitative data obtained from a test and qualitative data gotten from observation checklists and field notes.  All were used during teaching and learning process. The finding of this research indicated that implementing mind mapping technique, the mean score of writing skill of English students of University of Qamarul Huda Badaruddin reached the improvement .This was proven by knowing that they could organized and develop their ideas more easily, they were more active, enthusiastic, and motivated, and the mean score of their writing skill was 78 from 65.5 in the preliminary study. It meant that mind mapping was an effective technique to help students in improving writing skill. Obviously, the researcher had met the criterion of success. As the rule, the research was carried out on a cycle. To sum up, mind mapping was an appropriate way to enhance writing skill based on the finding. Moreover, students could create and decorate their own mind mapping in which they felt more enjoyable to write well.
Neutrosophic Sociogram for Group Analysis

The sociogram is a technique of sociometry widely used in the field of sociology due to its simplicity and effectiveness. The purpose of this method is the graphical visualization of the relationships among the members of a social group. The sociogram has been extended to the fuzzy framework to include uncertainty in the so-called fuzzy sociograms. However, there could be indeterminate relationships among some members of the group, because they have not experience in performing some activities together, although potentially either future links or disagreements could be established among them. In this paper, we propose a neutrosophic sociogram, which allows representing the indeterminacies in the relationships among some members of a group. The advantage of neutrosophic sociograms over fuzzy sociograms is that the representation and calculation considering indeterminacy, allow us to achieve greater accuracy in the results, and a greater approach to the potentialities of the group in terms of the future bond among the members. A hypothetical example is proposed to illustrate the applicability of the method.
Reactive Tabu Search for Optimal Vehicle Routing Method of Trucks Considering Allocation of Secondary Depots in Case of Natural Disaster

In recent years, large earthquakes such as the Great East Japan Earthquake have occurred in Japan. Unfortunately, large-scale disasters are expected to occur in the near future. In such a case, logistics of emergency relief supplies is so important for us to survive in the situation. This paper proposes a reactive tabu search based optimal vehicle routing method in the distribution area when natural disaster occurs. The effectiveness of the proposed method is verified using the actual evacuation facility data of Chiba city, Japan, which are actually planned to utilize when a natural disaster occurs, by comparing with the conventional tabu search based method.
BUILDING UP STUDENTS’ MOTIVATION IN LEARNING ENGLISH THROUGH FUN ENGLISH LEARNING STRATEGY (FELS)

Motivation is one of the factors that helped determine effectiveness in learning, even in language learning like English. Therefore, to gain an optimal effectiveness in the learning process, a developed learning strategy is needed. Recently, it is revealed that fun learning situation has potential characteristics to be used as a strategy to increase students’ motivation in learning English. This article discussed the effect and incensement of students’ motivation through Fun English Learning Strategy (FELS). The strategy was well-designed in two class of sample, experiment and control class. It was conducted to obtain data quantitatively and qualitatively for the need of analysis. FELS showed a great effect and also an improvement to the learning process, especially to the students’ motivation in learning English, compared to those without the application of it. Furthermore, FELS showed an improvement of students’ motivation in learning English, and gave positive effect to the learning process indicated by the value of t-test was higher than t-table, 25.93 >2.00, with degree of freedom were 64. Finally, these results suggest that Fun English Learning Strategy has a potential to be used in improving students’ motivation in learning English. Keywords: Learning Strategy, Learning Motivation, , Fun English Learning Strategy
Deep learning network security

The explosion of data usage has contributed to the requirement of processing extensive amount of data for most of the applications on smart devices and edge- and fog computing nodes. Due to the scale and complexity of the tasks, decision support systems can greatly benefit from the use of machine learning (ML) techniques to correlate multimodal sensing to make accurate predictions and powerful inferences. Traditional ML algorithms have to be fed with previously extracted features. These features are usually identified in advance to reduce the complexity of the data and increase the visibility of the patterns to the learning algorithms. Furthermore, in some circumstances, like multiple object detection, the task needs to be divided into parts and solved individually and the partial results are combined at the final stage. The required human intervention and discontinuity in the process of accomplishing the tasks contribute to the reduced efficiency of the conventional ML algorithms in the face of massive raw data and intricate tasks. Deep learning (DL), also referred to as deep neural network (DNN), has overcome the weakness of the need for human&apos;s participation on effective feature identification and hard-core feature extraction. It learns the high-level features from raw data in an incremental manner and solves the problems end-to-end. As a result, DL has now become a preferred option for handling majority of the challenging tasks in image classification, speech recognition and language processing.
Well-Rounded Lattices and Applications to Physical Layer Security

This thesis is devoted to the analysis and construction of well-rounded lattices. The main motivation for studying these objects comes from problems in wireless communications.  In particular, we study lattice codes for wiretap channels in the context of physical layer security. The set of well-rounded lattices has been investigated from topological, geometric and arithmetic point of views.  We contribute to this study by first answering various questions about well-rounded lattices arising from real quadratic fields. Then, motivated by number theoretic constructions, we derive a more general generic construction of lattices generated by their minimal vectors in every dimension.  The last part of the thesis is dedicated to analysing the performance of lattice codes carved from well-rounded lattices. We then motivate the use of such codes over fading channels to achieve both reliable and secure communication. We show that these codes provide nearly optimal solutions in terms of reliability for both additive white Gaussian noisy channels and Rayleigh fading channels. For the security part, we led an experimental and analytical study of well-rounded lattice codes on the wiretap channel.  We conclude that, similarly as well-rounded lattices provide maximisers of lattice sphere packings, they also provide excellent candidates for minimising the eavesdroppers' chances of intercepting a transmitted message.
Karakteristik Kelayakan Teknologi RFID sebagai Kebutuhan Bisnis pada Perusahaan Logistik di Indonesia: Sistematik Literature Review

Developments in information technology affects an organization to become more advanced and better. Logistics company is one of the companies to implement information technology. One of the technologies that can be implemented RFID technology. RFID is a chip that stores a record or data. Thus RFID technology can be used as business requirements on a company that can help all company activities. In this study will explore the feasibility of a characteristic of RFID technology can be applied or can as the business needs of the logistics company is INDONESIA. In this paper use you methods Systematic literature review (SLR). This research resulted in three RFID characteristics: Real-time data processing, data tracking continuous and discrete data, reuse. Expected by this research can contribute to how a user can select and measure the characteristics of RFID technology so that the views of the system and the implementation of RFID technology into added value for the company rather as complementary technologies.
PERBANDINGAN METODE FUZZY SUGENO DENGAN FUZZY TSUKAMOTO PADA SISTEM PREDIKSI HARGA SMARTPHONE BEKAS BERBASIS ANDROID DI WILAYAH MAKASSAR

This study aims to compare the Fuzzy Sugeno and Fuzzy Tsukamoto methods in the used android smartphone price prediction system based on Android in the Makassar region. The expected benefits of this research are as a service to get used Smartphone price information and pricing before the transaction. The theorems / methods used in this study are Fuzzy Sugeno and Fuzzy Tsukamoto. The research began by designing the Unified Modeling Language (UML). This program is tested using the Black Box Testing and testing method directly in the field. Based on the results of the study it can be concluded that the Fuzzy Sugeno method produces a better predictive value than the Fuzzy Tsukamoto method, with the MAPE value of 11.8% compared to 25.67%.
Development Of Human Figure Proportions System And Its Application In Art Pedagogy

We adhere to the concept of the phased formation of mental actions developed by the Soviet psychologist P. Ya. Halperin. The confident work of an artist without a model, using only imagination, is a prime example of fully formed mental actions. For this, an artist should know by heart three interrelated things: proportions, shapes and mechanics of movement of a human figure. In the academic system, when teaching these fundamental things, the emphasis is on the long-term development of observation and intuition. In addition, there are schemes of proportions, intended for the training of artists. But they are rarely used in practice. The reason is that these schemes do not give students a complete system of reference points. Artists and teachers more than once addressed the problem of proportions. However, the topic remains relevant. In the framework of the modern discipline Drawing there are no separate tasks aimed at studying proportions. The discipline Composition, although operates with the writing of sketches, also does not provide such training. Based on the study of Leonardo da Vinci’s “Vitruvian Man” and other materials related to proportions, we were able to develop a complete system of reference points for constructing a human figure. Using this system, students from the very beginning build figures in different movements without using any additional samples.
A Study on the Use of ‘Compliment Responses’ Strategies According to the Korean Learning Environment - Focused on Vietnamese Korean Learners in the KSL and KFL Environments.

This study divided Vietnamese learners into groups of KSL and KFL, and compared and analyzed the patterns of using strategies for praise and response according to their environment with groups of KNS. The results confirmed that all three groups use the ‘thanks’ strategy of active acceptance the most. However, there were some differences in the use of other strategies, except for ‘thanks’. KNS used ‘smile/laughing’ as the second most frequently after ‘thanks,’ a passive acceptance as a nonverbal behavior, and KSL used ‘denial’ as the second most often. KFL used many complex strategies in the form of ‘thanks+α’ by mixing other passive acceptance strategies into ‘thanks’. In addition, ‘Number of Combined Strategies Used, Number of Strategies Used Only in the Group’ was generally followed by ‘KNS < KSL < KFL’. Among them, the KFL group used the largest number of combined strategies and many kinds of strategies. This shows that the KFL group is overusing a compound strategy with more meaning than necessary, and found that the extent to which it is more severe than that of the KSL.