Reviewerly/exHarmony huggingface dataset:
UMLsec4Edge: Extending UMLsec to model data-protection-compliant edge computing systems

Edge computing enables the processing of data - frequently personal data - at the edge of the network. For personal data, legislation such as the European General Data Protection Regulation requires data protection by design. Hence, data protection has to be accounted for in the design of edge computing systems whenever personal data is involved. This leads to specific requirements for modeling the architecture of edge computing systems, e.g., representation of data and network properties. To the best of our knowledge, no existing modeling language fulfils all these requirements. In our previous work we showed that the commonly used UML profile UMLsec fulfils some of these requirements, and can thus serve as a starting point. The aim of this paper is to create a modeling language which meets all requirements concerning the design of the architecture of edge computing systems accounting for data protection. Thus, we extend UMLsec to satisfy all requirements. We call the resulting UML profile UMLsec4Edge. We follow a systematic approach to develop UMLsec4Edge. We app UMLsec4Edge to real-world use cases from different domains, and create appropriate deployment diagrams and class diagrams. These diagrams show UMLsec4Edge is capable of meeting the requirements.
Negative Transfer in Cross Project Defect Prediction: Effect of Domain Divergence

Cross-project defect prediction (CPDP) models are used in new software project prediction tasks to improve defect prediction rates. The development of these CPDP models could be challenging in cases where there is little or no historical data. For this reason, researchers may need to rely on multiple sources and use transfer learning-based CPDP for building defect prediction models. These data are typically taken from similar and related projects, but their distributions can be different from the new software project (target data). Although, transfer learning-based CPDP models are designed to handle these distribution differences, but if not correctly handled by the model, may lead to negative transfer. To this end, recent works have focused on building transfer CPDP models, but little is known about how similar or dissimilar sources should be to avoid negative transfer. This paper provides the first empirical investigation to understand the effect of combining different sources with different levels of similarities in transfer CPDP. We introduce the use of the Population Stability Index (PSI) to interpret whether the distribution of the combined or single-source data is similar to the target data. This was validated using an adversarial approach. Experimental results on three public datasets reveal that when the source and target distribution are very similar, the probability of false alarm is improved by 3% to 7% and the recall indicator is reduced from 1% to 8%. Interestingly, we also found that when dissimilar source data are combined with different source datasets, the overall domain divergence is lowered, and the performance is improved. The results highlight the importance of using the right source to aid the learning process.
The Role Of Post-Release Software Traceability in Release Engineering: A Software-Intensive Embedded Systems Case Study From The Telecommunications Domain

Modern release engineering practices such as continuous integration and delivery have allowed software development companies to transition from a long release cycle to a shorter one. The shorter release cycle has led to more software releases available to customers. At the same time, companies developing high-volume software-intensive embedded systems often deliver patch releases and maintenance releases on top of major and minor releases to customers who pick and choose what releases apply to them and decide when to upgrade the system, if to upgrade at all. While release engineering has been studied before in web-based, desktop-based, and embedded software, the focus has been on pre-release activities. Few studies have investigated what happens after the release, particularly the role of tracing software from release to deployment in high-volume software-intensive embedded systems. To address this gap, we conducted a qualitative case study at a multi-national telecommunications systems provider focusing on Radio Access Network (RAN) software. RAN software is a complex and large-scale embedded software used in mobile networks Base Stations (BS), providing software functionality for RAN mobile technologies ranging from 2G to 5G. Our study shed light on post-release software traceability and how it is used in the release engineering process.
The Student’s Barries in Speaking English at STIKes Darmais Padangsidimpuan

Based on the concept presented in the introduction, it can be concluded that there are many difficulties students encounter when learning the English language, especially as a foreign language because the language is used in any condition and certain people which is not in daily activities. Therefore, I interest to dig deeper into that obstacle experienced by students on language skills, especially on speaking skill, which is about the student‟s barriers in speaking English. This research, an exploratory sequential mixed method research (MMR) design was selected in order to broadly explore students' barriers in speaking English. The observations showed that students‟ main barrier was individual barriers; nervous, lack of vocabulary, feel unable to speak precisely, grammar error, underestimated in presenting information in order, lack of confidence. The students confirmed that individual barrier was the main barriers they face, and then language barrier, environment also interpersonal barriers followed. Since they did not feel able to speak English in a natural way, and thinking about how to pronounce correctly a word or how to use the correct grammar structures, they said that it was difficult to grow the confident.
PENINGKATAN KEMAMPUAN MENULIS CERPEN DENGAN PEMANFAATAN MEDIA KOMIK DIKELAS XI IPA NEGERI 2 LAWE BULAN TAHUN AJARAN 2021-2022

This research is to find out whether there is an increase in the ability to write short stories by using comics in the XI IPA class of SMA Negeri 2 Lawe Bulan in the 2021/2022 academic year. This type of research is quantitative research. Data were collected using tests for work in the pretest and posttest classes. The population in this study was 33 people. Data collection techniques were carried out in two meetings. First, before using comic media. Second, after using comic media. Data analysis technique. First, read short stories written by students. Second, converting comments into values. Third, describe the level of students' short story writing skills by finding the average. Fourth, clarifying the ability to write short stories before and after using comic media for class XI students of SMA Negeri 2 Lawe Bulan. The results of this study are First, the short story writing skills of class XI students of SMA Negeri 2 Lawe Bulan before using comic media obtained an average grade of 63.03 which was categorized as less. The ability to write short stories for class XI students of SMA Negeri 2 Lawe Bulan after using comic media obtained an average grade of 82.12 which was categorized as Good.
Picture Archiving and Communication Systems (PACS) as a Solution to Inequality in the Number of Radiological Resources in West Java

Inequality of health care facilities, especially radiology resources, occurs in West Java Province. There are many class A hospitals in provincial capitals, while in areas far from the provincial capital and from DKI Jakarta Province, the quantity and quality of hospitals are still lacking. Likewise with the quantity of radio diagnostic instruments and human resources. 12 radiology specialists and an additional 192 radiographers are needed in West Java Province. Archiving and image communication systems (PACS) can be used as a solution so that health workers in hospitals located far from the city or district centers can consult, and expert conclusions can be obtained from radiology specialists at referral centers.&#x0D; Keywords: PACS, radio diagnostic, radiology specialist, radiographer, West Java
EXPLORING STUDENTS’ RESPONSES TO THE “MISSYOU” LYRIC GAME ON LEARNING LISTENING

The purpose of this study is to see how students react to using the "Missyou" Lyric Game in listening activity. This study uses a qualitative research design. The participants in this study were 20 students from SMPN 1 Srengat who were chosen at random. A questionnaire was used to gather information. The purpose of the questionnaire is to collect student responses and determine whether the "Missyou" Lyric Game model explores and develops students' listening ability as an alternative media for learning to listen. The results revealed that 81,65 percent of respondents agreed that song lyrics may enhance their listening skills.
Stratifying large software files to improve prediction performance in software defect prediction

Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explored the effect of size by stratifying our sample based on each unit’s size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48, and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is better than predicting those in small files. In addition, the results showed higher median values of all performance measures and less variation in each measure.
THURSDAY: A Web Platform to Support AutoML

THURSDAY is a web platform that aids users in building machine learning models by providing easily accessible tools to either create models manually, or through the use of automated machine learning (AutoML) libraries like AutoKeras. As part of THURSDAY’s key innovations, users are given the opportunity to configure and run multiple machine learning models. The results of these model executions can then be compared with built-in performance metrics. Finally, THURSDAY allows users to analyze hyperparameter changes, as well as the changes created by AutoML libraries, in order to provide a vital tool that aids in the revision of existing models. To meet the high volume demands of machine learning, THURDAY adopted a microservice-based design pattern that supports containerization, orchestration, and scalabability. In this paper, the design, implementation, and impact of the THURSDAY system is explored in detail. In order to evaluate the capability of THURSDAY, its core functionality is compared against similar platforms that provide machine learning support.
Identification of intra-domain ambiguity using transformer-based machine learning

Recently, the application of neural word embeddings for detecting cross-domain ambiguities in software requirements has gained a significant attention from the requirements engineering (RE) community. Several approaches have been proposed in the literature for estimating the variation of meaning of commonly used terms in different domains. A major limitation of these techniques is that they are unable to identify and detect the terms that have been used in different contexts within the same application domain, i.e. intra-domain ambiguities or in a requirements document of an interdisciplinary project. We propose an approach based on the idea of bidirectional encoder representations from Transformers (BERT) and clustering for identifying such ambiguities. For every context in which a term has been used in the document, our approach returns a list of its most similar words and also provides some example sentences from the corpus highlighting its context-specific interpretation. We apply our approach to a computer science (CS) specific corpora and a multi-domain corpora which consists of textual data from eight different application domains. Our experimental results show that this approach is very effective in identifying and detecting intra-domain ambiguities.
Masked Face Detection with Anchor-level Attention and Local Feature

As a basic task for computer vision, face detection plays an important role in the application of face recognition. However, in real-world applications, masked face detection is still a challenging problem. In this paper, we present a novel face detection framework for masked faces. Firstly, we use the anchor-level attention mechanism to reduce the impact of complex environments and occlusion on face detection. We select the ground truth with the minims attention loss to supervise the attention layer. Besides, we depart the face features and each part corresponds to the different channel of the feature vector. By the means, the occlusions on the face can be restricted in the local part of the features. The experimental results illustrate that our model improves the accuracy of the face detection task, especially in the masked face detection. Compared to SSH, the average precision of our model has an average of 2.1%, 2.1% and 5.4% improvements on WIDER FACE easy, normal and hard validation datasets, respectively, and an average of 1.6% improvement compared to FAN on MAFA dataset.
The Effectiveness of Graphic Organizer Method on Teaching Descriptive Writing at Mambaus Sholihin Junior High School

This study investigated the effect of the Graphic Organizer Method on Teaching Descriptive Writing at First Grade in Mts. Mambaus Sholihin Gresik. The population of this research was the students of first grade of Mts. Mambaus Sholihin used simple random sampling. The research method was pre-Experimental design, which was conducted in one group pre-test post-test. To analyze the data, the researcher used paired sample t-test to determine whether there was significant effect before and after treatment in Experimental class. The result showed that the experimental group scores had a significant difference before being taught the Graphic organizer method or after. That can be known based on the results of the pre-test and post-test. with the comparison of the magnitude of t that we get in the manual counting (t0) = 12,59 and the magnitude of t listed in the table of values (t.ts.5% = 2.03), we can know that t0 is greater than tt .2.03 &gt; 12,59. So it can be concluded that the hypothesis is accepted or Ha is accepted, while Ho is rejected. descriptive writing.
Interactive Journaling with AI: Probing into Words and Language as Interaction Design Materials

Conversational Agents (CAs) are making human-computer interaction more collaborative and conversational through using natural language. The HCI and interaction design communities, have been experimenting with and exploring the area of designing conversational interactions. Furthermore, interaction designers may need to acquire new skills for designing, prototyping, and evaluating artifacts that embody AI technologies in general, and CAs in particular. This paper builds upon a previous study on principles of designing interactive journaling experiences with CA and explores the practice of designing such experiences, using words, language, and conversations as design materials. We present a prototype for interactive and reflective journaling interaction with CA and the result of a Wizard of Oz experiment. Our findings suggest that designing interactions with CA challenges designers to use materials with inherently different natures and qualities. Despite this challenge, words appear to have unique characteristics to support designers to externalize and iterate on ideas, e.g., tone and intent. Hence, we suggest considering words, language, and conversations as the primary design materials, and the AI’s predictability, adaptivity, and agency as secondary materials, while designing human interactions with Conversational Agents.
A Semantic Text Processing System for Free-Write English Papers

We present the development and evaluation of a semantic text processing system that evaluates student essays. The system processes n-many documents and suggests a letter grade, identifies papers that may need additional teacher action based on component/composite scores, and allows optional teacher input on features to generate the grade. The system was developed in Python using open-source libraries and is also available as open-source. Using a human-in-the-loop approach, expert teachers were interviewed as part of the design process. Assessing the documents on token, sentence, readability, dependency distance, and part of speech with user guided feature selection the system generated automated results where the true letter grade and machine letter grade corresponded exactly in ~46% of papers and in a ±1 letter grade interval in ~86% of papers. The program can be further extended to flag grades for potential human review based on user defined criteria with example code provided for papers marked as written above the high school level.
Automated Robot (Car) using Artificial Intelligence

This paper discusses about researching an automated robot car using artificial intelligence; training its neural network using AlexNet model, using YOLO (you only look once algorithm) for object detection phase and for practical deduction and judging component we have used Open Neural Network Exchange (ONNX) format. Our Robot Car model is agile and cost efficient. It detects objects efficiently in front of it and movement of it is smooth. It moves through sensors in motors which makes it different than other models in the world.
Information Delayering Safety Management (IDSM): A New Method of System Safety in Urgent Situations Needs to Be Established

Organizational safety decisions rely heavily on safety information in today's data-driven era, but there is a significant danger of information distortion that can compromise system safety. To address the issue of information distortion and enhance system safety, a new approach called information delayering safety management (IDSM) has been developed and implemented. The IDSM method combines delayering management mode and graph theory to study the relationship between information distortion management and delayering management. By using the delayering mode as a theoretical foundation for safety information management, information distortion can be reduced. The implementation of this approach from a graph theory perspective has been tested using a case study and has been proven to effectively enhance the reliability of safety information and ensure system safety. The minimum control set of the directed graph algorithm can be used to realize the whole network management of safety information distortion. The amount of safety information and signal noise can be controlled by adjusting connectivity, and safety information distortion can be regulated through the adjustment of structural holes and flow direction. Overall, IDSM offers a new, effective method for accident analysis and safety management, allowing safety professionals to make informed decisions based on robust advanced evidence.
Indonesian Children English Development through Home And School Learning

Learning English as a Foreign Language (EFL) starting at an early age is considered a better alternative than learning EFL at an older age. This study aims to examine the development of children's English capability especially in speaking after they have studied at home with Indonesian parents and at school with Indonesian teachers. This study is a longitudinal case study examining 2 children aged 80 and 52 months. The data were in the form of children's English utterances and were taken within 1-2 year intervals. The data were collected by recording their speech, interviewing both children and parents, and carrying out participant observations during data collection. Data analysis was performed using MLU (mean utterance length); UB (Upper Bound) or the number of morphemes of the longest utterance and Morpheme Acquisition (the types of morphemes acquired). The result is that in general they were able to converse in English, but they had different levels of MLU, UB, and Morpheme acquisition. This difference was ascribed to differences in the EFL intervention and learning experience. The first research subjects learned English with Direct Learning in courses and schools, she got her English in no time and her MLU was moderate. The second research subject experienced learning English from a very early age at home with his parents, his MLU was high.&#x0D; &#x0D;
SPEAKING IN PROCEDURE TEXT BY RETELLING USING PICTURE SERIES

AbstractThe main objective of the research was to know the effect of speaking of procedure textby retelling using picture series on students’ speaking achievement of MTs Miftahul JannahPalangka Raya. Quasi-experimental research was done in randomized control group pre-testpost-test design to the answer of the research problem. The population was the students of grade7 of MTs Miftahul Jannah Palangka Raya, and the samples of grades 7A and 7B were takenrandomly from the population to be the control and exerimental groups. The data were obtainedfrom the scores of speaking taken from the samples of the research. To test the hypothesis of theresearch, statistical calculation was done using t-test. Based on the result of hypothesis testcalculation, it was found that the value of tobserved was greater than the value of ttable. i.e. 20.019 &gt;1.671 that Ha was accepted and H0 was rejected. It means picture series gave effect on thespeaking achievement of retelling of students of MTs Miftahul Jannah Palangka Raya.Keywords : Picture series, retelling, speaking achievement
English Students’ Motivation and Their Learning Styles in Speaking Class at Sawerigading University of Makassar

ABSTRACT&#x0D; This research aimed to find out the correlation beetwen students’ motivation and their learning style in speaking subject. The questions of the research is “How does the correlation beetwen students motivation and their learning style in speaking class at the English Students of Sawerigading University of Makassar?”.&#x0D; Questionnaires were distributed to 42 students, while the questionnaire of students’ motivation and learning style consisted of 20 questions of each variable in the form of questions, from each of the questions was followed by 5 (five) alternative answers that consecutively very Strongly Agree (SA), Agree (A), Undecided (U), Disagree (D) and Strongly Disagree (SD) with consecutive scores 5,4,3,2 and 1. While the value obtained from the students’ motivation instrument. To get the data, researcher used a questionnaire with 20 items about distributed to 20 sample respondents.&#x0D; The result of findings about correlation between students’ motivation and their learning style in speaking class at the third semester students of Sawerigading University of Makassar, compared prices count with r-r- table. With the 5% significance level of 0.288 was obtained rtable rhitung at 0.882. When compared both rhitung = 0.882&gt; rtable = 0.288, so that Ho accepted and Hi rejected or the correlation between students’ motivation and their learning style in speaking class was 0.882. This meant that correlation between them was strong correlation. &#x0D; &#x0D; Keywords : Motivation, Learning style. Speaking&#x0D;
A fast PSO algorithm based on Alpha-stable mutation and its application in aerodynamic optimization

提出了一种基于Alpha stable分布的新型变异方法。针对粒子群算法容易陷入局部最优的缺点, 通过对比分析确定了一种调整Alpha stable分布的稳态系数动态变异策略, 使粒子群算法能够在搜索初始阶段具有更强的种群多样性以及算法探索能力, 减少陷入局部最优的可能; 在算法末期增强粒子群优化算法的局部搜索能力, 提高解的精度。将基于Alpha stable变异的粒子群优化算法(Alpha stable particle swarm optimization, ASPSO)与多种改进型粒子群优化算法以及差分进化算法(differential evolution algorithm, DE)进行了比较, 基准测试函数结果表明新建立的ASPSO算法极大地提高了算法的收敛速度和精度。将其应用到RAE2822翼型的单点跨声速减阻优化中, 在保持种群规模等参数相同的情形下, ASPSO算法的优化效果和效率都远高于传统PSO算法, 最终得到的翼型也比PSO优化的翼型具有更高的升阻比, 翼面波阻有明显降低。