Reviewerly/exHarmony huggingface dataset:
GOSSEC: Goal Oriented Software Sustainability Evaluation Criteria

The concepts of sustainability is now aware among the software engineering researchers. It has direct and indirect impacts on three dimensions which are environment, economic and social that results from the development and implementation of the software. Although there are studies on software sustainability evaluation that defines the software sustainability criteria unfortunately, most of the studies are focusing on single criterion rather than come out with holistic criteria of software sustainability. Additionally, the studies also focused on what need to be measured instead of how to perform the evaluation systematically. This limitation was occurred due to lack of defining the measurement goal of each criteria of software sustainability dimensions. Therefore, this study aimed to develop a Goal Oriented Software Sustainability Evaluation Criteria and organize the sustainability criteria using Quality Function Deployment. On top of that, the Goal Oriented Software Sustainability Evaluation Criteria has been constructed using Goal Oriented Measurement approach by adapting the Goal Question Metric method to assist in defining the goal that clearly defined the purposes, perspectives, and point of views of measurement of software sustainability. Hence, the Goal Oriented Software Sustainability Evaluation Criteria provides nine (9) goals and thirty four (34) sub goals for measuring the software sustainability criteria and sub criteria. The findings from the study present a set of criteria and measurement goals which can be used for evaluating software sustainability. The criteria were organized into three dimensions which are environment, economic and social.
Improving bearings-only target state estimation tracking problem by using adaptive and nonlinear kalman algorithms

&lt;p&gt;&lt;span style="font-family: 'Times New Roman'; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"&gt;Finding the best estimate of the process state from noisy data is the main problem in tracking systems, many efforts and researches have been done to remove this noise. More useful information about the target’s state can be extracted from observations by using a more appropriate model for the target’s motion or using additional sensors. In this paper, we will introduce two methods to improve the estimation of bearing-only target tracking problem in two dimensions (2D). The first method is by adding a third sensor and making a good alignment of those sensors, and at the same time an extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) are implemented. The second method is by applying an adaptive nonlinear Kalman filter (ANKF) for two sensors to solve the problem of measurement variance uncertainty.&lt;/span&gt;&lt;/p&gt;
ARGESIM Benchmarks on Modelling Approaches and Simulation Implementations – Development, Classification and Basis for Simulation Education

ARGESIM, a non-profit association for information and publication on simulation, started in 1990 in co-operation with EUROSIM, the Federation of European Simulation Societies, the series 'ARGESIM Comparison of Simulation Software' in the journal Simulation Notes Europe (SNE).The comparisons have been developed successfully towards the 'ARGESIM Benchmarks for Modelling Approaches and Simulation Implementations', with up to now 24 benchmarks defined, and up to now 350 benchmark solutions, benchmark reports, or benchmark studies published in SNE Simulation Notes Europe.Interestingly, it turned out, that along with the various benchmark publications in SNE, the benchmarks are used as examples, case studies, and lab work in simulation education in academia.This contribution sketches the development of the benchmarks and introduces a new classification of the benchmarks with respect to system type, model approach, and required experiments with the model(s).In the following, main emphasis is the investigation of the benchmarks as source and basis for simulation education, together with a classification of the up to now defined benchmarks with respect to their suitability in education -from simulation methodology view, and from simulation application view.The contribution concludes with an overview table on benchmark data: definition date, number of solutions, type classification, and classification for educational use.
Computer aided system for lymphoblast classification to detect acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a disease that is detected by the presence of lymphoblast cell. Basically, lymphoblast cell is the abnormal cell of lymphocyte which is one of the White Blood Cell (WBC) types. Early prevention is suggested as this disease can be fatal and caused death. Traditionally, ALL is detected by using manual analysis which is challenging and time consuming. It can also yield inaccurate result as it is highly dependent on the pathologist’s skills. Industry has come out with hematology counter which is fast, accurate and automated. However, these machines are costly and cannot be afforded by some countries. For that reason, Computer Aided System (CAS) will be a great help to the pathologist for assisting purposes and it also can act as second opinion for the pathologist. This system contains six main steps which are color space correction, WBC segmentation, post processing, clumped area extraction, feature extraction and lymphoblast classification. Firstly, color space correction is apply by using l*a*b* color space to standardize the image’s intensity. Next, WBC segmentation is made to prune out WBC region using color space analysis with Otsu thresholding. However, segmented image contains noises that need to be eliminated and it is accomplished by applying morphological filter with Connected Component Labelling (CCL). There is an overlapping WBC which need to be separated by using Watershed method to extract the individual cells. Next, feature extraction is made to collect the cell’s data to be fed into the classifier. Classifier used in this system to classify lymphoblast is Support Vector Machine (SVM) and this system is able to achieve 96.69% of accuracy.
Enhancing User Experience for Mobile Learning Using Augmented Reality and Learning Style

Current m-learning media are available in both native application and web-based application forms which create different user experience. Also, as the learners generally have diverse preferences and needs, a single style of m-learning may not meet such requirements. In order to formulate better and more attractive interaction between the learner and m-learning system, this study introduces augmented reality (AR) to both native and web applications as a means to improve captivation and create new user experience. A single group of learners is selected to try both types of application. The researcher groups the learner by learning style, using Felder-Silverman Learning Style Model, and then determines effectiveness of both forms of m-learning through an experiment. Experiment showed that all 4 learner groups were more satisfied with AR-enhanced native application based on user experience design due to its attractiveness and entertainment.
JavaNeighbors: Improving ChuckyJava’s neighborhood discovery algorithm

In this paper, a study to protect Java Card source codes against fuzzing attacks is presented. This work is based on the tool ChuckyJava. This tool aims at automatically detecting anomalies in Java source codes in a Machine Learning way, without the knowledge of their specification. First, we propose a definition of neighbor methods. Based on this same definition, this study focuses on the improvement of the neighborhood discovery step for the tool ChuckyJava. To achieve this task, we have created a new Machine Learning tool: JavaNeighbors. It is based on different Natural Language Processing techniques: both local and global weighting schemes adjustement for term extraction and Latent Semantic Analysis to mitigate the curse of dimensionality. As a result, JavaNeighbors solves four limitations of ChuckyJava and it performs faster and with a better accuracy.
Research on Natural Language Processing and Aspose Technology in the Automatic Generation of Ocean Weather Public Report

Combining with the requirements of the production of the ocean weather public reports, based on the analysis of a large number of historical documents issued daily by National Meteorological Center (NMC), it proposes an automatic generation method of ocean weather public reports by using Natural Language Processing technology and Aspose technology. Furthermore, it focuses on three key issues for further discussion, such as historical text content analysis and feature extraction, text description rules and their natural language processing, report template customization and automatic generation of report document from the template by Aspose technology. Meanwhile, it proposes the implementation in detail for these issues. Compared with the content between forecasters’ manuscript and the computer report document, the results show that the automatically generated forecast text is correct in content, semantics and syntax, smooth in sentences, and the text style conforms to the habits of forecasters. The automatically generated report document effectively avoids the common errors of weather elements and time interval selection, illustration sorting and text style in the process of manually editing documents. It can effectively improve the efficiency of forecasters.
A new approach to gather similar operations extracted from web services

&lt;span lang="EN-US"&gt;A web service is an autonomous software that exposes a set of features on the Internet, it is developed and published by providers and accessed by customers who discover it, select it, invoke and use it. Several research policies have been implemented such as searching through keywords, searching according to semantics and searching by estimating the similarity. A customer is looking for a service for the operations he/she carries out, hence the interest of guiding the search for services towards a search for operations: finding the desired operations amounts to finding the services. For this, groupings of similar operations would make it possible to obtain all the services that can meet the desired functionalities. The customer can then select, in this set the service or services according to its non-functional criteria. The paper presents a study of the similarity between operations. The proposed approach is validated through an experimental study conducted on web services belonging to various domains.&lt;/span&gt;
Behavior Recognition Based on RBAC-Ensemble Model

In terms of people's lifestyles, behavioral data can be easily collected and analyzed. How to protect and utilize these data is a hot topic today. There are many behavioral recognition models in academia, but the means of identifying behavior are still based on regular matching in industry. The main reason is the impact of work circles. Data can reflect behavioral patterns, but work circles have a catastrophic effect on behaviors. The working circle here is defined as a group of people working in daily life. The work circle is related to permissions. Therefore, we introduce the RBAC model to incorporate the impact of permissions on behavior recolonization. We propose a method for dynamically generating RBAC models. Then, we divide the “min-privilege groups” and combine the results of the groups to get our RBAC-ensemble model. In the experiments, we compare the effects of several models before and after using “min-privilege groups”. The results prove that permissions do have an impact on behaviors. Based on “min-privilege groups”, we compare the effects of the other three models. The model we propose achieves the best recognition.
Protecting sensitive information utilizing an efficient association representative rule concealing algorithm for imbalance dataset

&lt;span&gt;In data mining, discrimination is the detrimental behavior of the people which is extensively studied in human society and economical science. However, there are negative perceptions about the data mining. Discrimination has two categories; one is direct, and another is indirect. The decisions depend on sensitive information attributes are named as direct discrimination, and the decisions which depend on non-sensitive information attributes are called as indirect discrimination which is strongly related with biased sensitive ones. Privacy protection has become another one of the most important problems in data mining investigation. To overcome the above issues, an Efficient Association Representative Rule Concealing (EARRC) algorithm is proposed to protect sensitive information or knowledge and offer privacy protection with the classification of the sensitive data. Representative rule concealing is one kind of the privacy-preserving mechanisms to hide sensitive association rules. The objective of this paper is to reduce the alternation of the original database and perceive that there is no sensitive association rule is obtained. The proposed method hides the sensitive information by altering the database without modifying the support of the sensitive item. The EARRC is a type of association classification approach which integrates the benefits of both associative classification and rule-based PART (Projective Adaptive Resonance Theory) classification. Based on Experimental computations, proposed EARRC+PART classifier improves 1.06 NMI and 5.66 Accuracy compared than existing methodologies.&lt;/span&gt;
Decision Tree Algorithm Using Particle Swarm Optimization To Improve The Accuracy Of Detection Malnutrition In Toddler

Malnutrition in Indonesia is still relatively high, it is recorded that there are 19.6% of children under five years old who suffer from malnutrition throughout Indonesia.Malnutrition will give impacts tochildren's health in the future.Therefore, the action to detect the malnutrition occurrence should be conducted as early as possible; thus, the patient will immediately getthe right health care.Many methods have already implemented to determine whether a toddler suffers from malnutrition or not.One of them is by using data mining techniques to create a grouping.Toddlers will be categorized into 4 groups namely Good Nutrition, Lack of Nutrition, Over Nutrition and Malnutrition.The data used are Toddler data, which is consisted of 4 predictor attributes and 1 result attribute.In the previous research the algorithm used was C 4.5 that was compared toBack-propagation.The result of the data processing by using C 4.5 algorithm is 88.24% and Kappa with the amount of 0.725.In order to improve the accuracy of the C 4.5 algorithm, the algorithm of Particle Swarm Optimization is implementedfor the optimization.Having implemented Particle Swarm Optimization, the accuracy is obtained in the amount of 98.04% and Kappa 0.954.Accordingly, the Particle Swarm Optimization increases the accuracy of C 4.5 by 9.80%.The feature selection, which is conducted, indicates that the attribute of family status must be omitted to obtain higher amount of accuracy.
A Novel Approach of Fair Scheduling to Enhance Performance of Hadoop Distributed File System

Nowadays, big data is one of the most challenging issues for managing big amounts of data more effectively and efficiently. It widely used in E-commerce, social media, online business and such types of applications. Hadoop Distributed File System (HDFS) is one of the widely used frameworks which can easily handle and store large amounts of data set frequently. For HDFS job scheduling is more challenging because it plays an important role in time optimization in big data. For resolving this issue in this paper, we introduce a job scheduling algorithm which is more time efficient and accurate than existing fair job scheduling algorithm. We optimize the time cycle of fair scheduling by minimizing iteration. We have accelerated with the different number of jobs in the existing algorithm and proposed an algorithm for experimentally proving the time complexity and time measurement. It is observed that the proposed method is computationally efficient than the existing one and our algorithm has reduced the number of iterations and improved the time efficiency on average 26.719%.
A multi-pose face frontalization method based on encoder-decoder network

Multi-pose face frontalization can alleviate the influence of pose variance on face analysis.The traditional method of synthesizing a frontal face image directly from a multi-pose face image presents a problem in missing face details.To overcome this problem, we propose a face frontalization method based on the encoderdecoder network, namely multitask convolutional encoder-decoder network (MCEDN).The MCEDN introduces a frontal raw feature network to synthesize the global raw features of the frontal face.Then, the network utilizes the decoder to synthesize a clearer frontal face image by fusing local features extracted by the encoder and global raw features.We use a multitask learning mechanism to build an end-to-end model.The method then integrates three modules, namely local feature extraction, global raw feature synthesis, and frontal image synthesis.The model performance was improved by sharing parameters.In comparison with existing methods, MCEDN can synthesize frontal face images with a stable structure and rich details on multiple datasets.Then, we use the synthesized frontal images for face recognition and face expression recognition, and the state-of-the-art results demonstrate that the MCEDN preserves a number of face details.
The Effect of Integrating Mobile Application in Language Learning: An Experimental Study

Innovation in Language Teaching Methodology should address the needs and demands of the 21st century learners and the birth of Mobile Aided Language Learning created abundant possibilities for the field of language education. This study investigates the effect of the Mobile Application: Moodle in teaching ESL learners. A quasiexperimental research with 100 respondents in Junior High School at Rizal Technological University-Laboratory High School of the Academic Year 2017-2018. The research data were gathered from the pre-test and post-test results during the betatesting activity and Focus Group Discussion conducted among the experimental set during the 4th grading period. Experimental Set used the Mobile Application: Moodle in their daily classroom discussion and activity tasks. Results shows that there is an increase with the performance of the Experimental Group compared to the Controlled Group test results. Also, the students in the experimental set express positive feedback about the integration of Mobile App in teaching and learning process. The study suggests that with further research and development of the program, it will enhance the possibility of MALL acquisition in teaching ESL Keywords: mobile aided language learning (MALL); e-learning; m-learning; mobile applications
A new optimization-driven path planning method with probabilistic completeness for wheeled mobile robots

Wheeled mobile robots are widely utilized for environment-exploring tasks both on earth and in space. As a basis for global path planning tasks for wheeled mobile robots, in this study we propose a method for establishing an energy-based cost map. Then, we utilize an improved dual covariant Hamiltonian optimization for motion planning method, to perform point-to-region path planning in energy-based maps. The method is capable of efficiently handling high-dimensional path planning tasks with non-convex cost functions through applying a robust active set algorithm, that is, non-monotone gradient projection algorithm. To solve the problem that the path planning process is locked in weak minima or non-convergence, we propose a randomized variant of the improved dual covariant Hamiltonian optimization for motion planning based on simulated annealing and Hamiltonian Monte Carlo methods. The results of simulations demonstrate that the final paths generated can be time efficient, energy efficient and smooth. And the probabilistic completeness of the method is guaranteed.
Study of Language Style in Hillary Clinton's speech

This study aims to gain an understanding and description of the Language Style used by Hillary Clinton are: to describe the Language style used in Hillary Clinton's speech, entitled: "Hillary Clinton makes history speech" and to determine the Language style used by Hillary Clinton in her speech entitled: "Hillary Clinton makes history speech". The goal of this study is a study of the Language Style in a speech on Youtube com with its subtitle, on March 14, 2018 entitled "Learn English Hillary Clinton makes history speech". The data collection method used in this research is by listening, reading, writing and recording Hillary Clinton's speech texts repeatedly and understanding, identifying, and clarifying the style of language used during the speech. This research is qualitative descriptive research because in this research is explored to the whole speech of Hilary Clinton with the aim of analyzing and determining the type of suitable Language Style. The subject of this study is Hillary Clinton's speech entitled: "Hillary Clinton makes history speech". The use of language style in Hillary Clinton's speech can facilitate understanding. Complex ideas may be conveyed more clearly through figurative language and slogan rethorically. Stored messages can help to 'alert' a message. The use of various syntactic patterns or figurative words can help audiences remember an important element in the message. The messages conveyed can increase the persuasion appeal
Proposal of a questionnaire to measure the level of interaction in the university classrooms: version for teachers, students and observers

&lt;p class="Abstract"&gt;Throughout the literature, there are several studies that highlight the importance of interaction in the classroom as a means to promote student learning. Based on this idea, and due to the absence of previous studies that do it, the objective of this work is to propose a questionnaire that allows to measure the level of interaction in university classrooms, as well as to identify the best interactive practices, from a triple perspective: observed teacher, external observer and student. This work is part of a teaching innovation project carried out by a multidisciplinary group of lecturers from the University of Cantabria. In this study, in addition to presenting the aforementioned questionnaire proposal, the research process for the collection of data and the analysis of results will be described. It is considered that the main contribution of this work is the proposal of an instrument for measuring interaction.&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;