Reviewerly/exHarmony huggingface dataset:
LANGUAGE LEARNING STRATEGIES USED BY THE SUCCESSFUL TOEFL TEST-TAKERS OF ENGLISH EDUCATION DEPARTMENT UIN ALAUDDIN MAKASSAR

This research aimed to know the language learning strategies that used by the successful TOEFL test-takers of English Education Department UIN Alauddin Makassar. The research design of this study is descriptive qualitative. The subject of this research consisted of 5 students of English Education Department academic year 2015. In determining the subject of this research, the researcher used criteria that the students must achieve TOEFL score 500 that undertaken TOEFL once or twice. This research used interview instrument with supporting data from document. The result showed that the successful test-takers applied several learning strategies in TOEFL. The learning strategies that the successful test-takers used are compensation strategies by guessing the meaning of unfamiliar words, metacognitive strategies by setting goals, Affective strategies by giving self-encouragement, memory strategies by grouping some new words in a certain group to make easy at remembering the words and using keyword and imagery to help remember new words, cognitive strategies by make summary about TOEFL materials and watch and read books about TOEFL, and social strategies by practice TOEFL with friends and do peer correction.
CoolTeD: A Web-based Collaborative Labeling Tool for the Textual Dataset

High-quality labeled textual data are vital for automatic mining and analysis of massive textual data produced by software systems. Several tools have been designed to facilitate manual labeling of textual data on different levels of granularity. However, these tools neither aim to provide statistics and analysis of labeled textual data, nor support collaboration among the coders to reduce the time cost in manual labeling and enhance the quality of labeling results. In this paper, we developed a Web-based labeling tool named CoolTeD (available at http://williamsriver.cn) for collaborative labeling of the textual datasets. Specifically, CoolTeD can be used: (1) to label textual data from the perspective of requirements types based on ISO 25010, (2) to review the labeling results with different confidence levels and contradictory labels, (3) to automatically calculate Cohen&#x0027;s Kappa coefficient of multiple coders, and (4) to visualize the labeling results. The tool demo is available at https://youtu.be/xVkrB_Cs1J8
Lessons from Research to Practice on Writing Better Quality Puppet Scripts

Infrastructure as Code (IaC) scripts, such as Puppet scripts, provide practitioners the opportunity to provision computing infrastructure automatically at scale. Poorly written IaC scripts impact various facets of quality (such as security and maintainability) and, in turn, may lead to serious consequences. Many of the ill-effects can be avoided or rectified easily by following recommendations derived from research and best practices gleaned from experience. While researchers have investigated methods to improve quality aspects of Puppet scripts, such research needs to be summarized and synthesized for industry practitioners. In this article, we summarize recent research in the IaC domain by discussing key quality issues, specifically security and maintainability smells, that may arise in an IaC script. We also mine open-source repositories from three organizations (Mozilla, Openstack, and Wikimedia) and report our observations on the identified smells. Furthermore, we also synthesize recommendations from the literature for software practitioners that could improve the quality of IaC scripts. Software development teams dealing with large computing infrastructure can get benefited from the actionable recommended practices. In addition, researchers in the domain may use this study to find opportunities to improve the state-of-the-art.
ProPy: Prolog-based Fault Localization Tool for Python

Fault localization involves determining the root location of a program fault. The quicker and more accurately a fault is located, the easier it is to address. Hence, the software development cycle reaps the benefits of improvements in the efficiency, effectiveness, and speed of the fault localization process. Fault localization techniques developed through research often involve running a set of test cases on the program in question, then comparing their expected and actual results. By examining which statements of the program were executed in successful versus unsuccessful test cases, then extract insight into the &#x201C;suspiciousness&#x201D; of areas of the program. Many of the tools that execute these techniques are written in imperative programming languages that are not powerful enough to support recursively-called functions such as transitive closure at scale. Our research explores the utility of declarative programming languages in the fault localization problem as they are known to support efficient, built-in functions for such recursive actions. Specifically, we build upon existing work by combining the use of the declarative language Prolog with analyzing communities detected from Python control flow graphs. The source code is available at https://github.com/jannekemorin/new-leaf.
Stone: A Privacy Policy Enforcement System for Smart Contracts

Smart contracts running on blockchain potentially disclose all data to the participants of the chain. Therefore, because privacy is important in many areas, smart contracts may not be considered a good option. To overcome this limitation, this paper introduces Stone, a privacy preservation system for smart contracts. With Stone, an arbitrary Solidity smart contract can be combined with a separate privacy policy in JSON, which prevents the storage data in the contract from being publicised. Because this approach is convenient for policy developers as well as smart contract programmers, we envision that this approach will be practically acceptable for real-world applications.
Towards a Fine-grained Analysis of Cognitive Load During Program Comprehension

Program comprehension is inherent to all software development activities. This task may require a high mental effort (or so-called &#x201C;cognitive load&#x201D;), which in turn can hinder the performance of developers. In the literature, several authors have investigated the ability of biosignals to estimate developers&#x0027; cognitive load during program comprehension. While the majority of these studies provide estimates at the task level, we aim for a more fine-grained level of analysis allowing to pinpoint the critical parts of code that could be associated with cognitive load. We infer these critical parts solely from eye fixation features and investigate qualitatively their relationship with those perceived as challenging by users. Being able to pinpoint critical parts in the source-code, is a first stride towards a very handy approach providing targeted support to developers to prevent them from committing errors. Furthermore, such a lightweight approach can be adapted in online settings.
Collaborative Writing Tools for Predicting Verb Tense Using Syntax Parsing on Learning Networks

Collaborative writing tools and Natural language processing are plays a vital role in learning networks. These tools are involved with the dealings among computer systems and human languages. It processes the data through syntax analysis, parsing and lexical analysis, and etc. Syntax analysis is used for syntactic parsing deals with the syntactic structure of a sentence. The collaborative writing tools and natural language processing applications are used for verb tense prediction and it it encodes the temporal order of activities in a sentence. Recognizing the syntactic structure is beneficial in identifying the means of a sentence. The model in this paper is introduced to predict verb tense based on lexical and syntactic features. This model works on English articles, every article will be split to sentences using the tokenization process. Every token in sentences will be analyzed and model will parse the sentences the use of tenses algorithms that represent grammar rules of the English language. This model is given precise accuracy when it is examined on articles/ stories.
On the Influence of Biases in Bug Localization: Evaluation and Benchmark

Bug localization is the task of identifying parts of the source code that needs to be changed to resolve a bug report. As this task is difficult, automatic bug localization tools have been proposed. The development and evaluation of these tools rely on the availability of high-quality bug report datasets. In 2014, Kochhar et al. identified three biases in datasets used to evaluate bug localization techniques: (1) misclassified bug report, (2) already localized bug report, and (3) incorrect ground truth file in a bug report. They reported that already localized bug reports statistically significantly and substantially impact bug localization results, and thus should be removed. However, their evaluation is still limited, as they only investigated 3 projects written in Java. In this study, we replicate the study of Kochhar et al. on the effect of biases in bug report dataset for bug localization. Further investigation on this topic is necessary as new and larger bug report datasets have been proposed without being checked for these biases. We conduct our analysis on a collection of 2,913 bug reports taken from the recently released Bugzbook dataset that fix Python files. To investigate the prevalence of the biases, we check the bias distributions. For each bias, we select and label a set of bug reports that may contain the bias and compute the proportion of bug reports in the set that exhibit the bias. We find that 5&#x0025;, 23&#x0025;, and 30&#x0025; of the bug reports that we investigated are affected by biases 1, 2, and 3 respectively. Then, we investigate the effect of the three biases on bug localization by measuring the performance of IncBL, a recent bug localization tool, and the classical Vector Space Model (VSM) based bug localization tool, which was used in the Kochhar et al. study. Our experiment results highlight that bias 2 significantly impact the bug localization results, while bias 1 and 3 do not have a significant impact. We also find that the effect sizes of bias 2 to IncBL and VSM are different, where IncBL has a higher effect size than VSM. Our findings corroborate the result reported by Kochhar et al. and demonstrate that bias 2 not only affects the 3 Java projects investigated in their study, but also others in another programming language (i.e., Python). This highlights the need to eliminate bias 2 from the evaluation of future bug localization tools. As a by-product of our replication study, we have released a benchmark dataset, which we refer to as CAPTURED, that has been cleaned from the three biases. CAPTURED contains Python programs and therefore augments the cleaned dataset released by Kochhar et al., which only contains Java programs.
Rebot: An Automatic Multi-modal Requirements Review Bot

Requirements review is the process that reviewers read documents, make suggestions, and help improve the quality of requirements, which is a major factor that contributes to the success or failure of software. However, manually reviewing is a time-consuming and challenging task that requires high domain knowledge and expertise. To address the problem, we developed a requirements review tool, called Rebot, which automates the requirements parsing, quality classification, and suggestions generation. The core of Rebot is a neural network-based quality model which fuses multi-modal information (visual and textual information) of requirements documents to classify their quality levels (high, medium, low). The model is trained and evaluated on a real industrial requirements documents dataset which is collected from ZTE corporation. The experiments show the model achieves 81.3&#x0025; accuracy in classifying the quality into three levels. To further validate Rebot, we deployed it in a live software development project. We evaluated the correctness, usefulness, and feasibility of Rebot by conducting a questionnaire with the users. Around 76.5&#x0025; of Rebot&#x0027;s users believe Rebot can support requirements review by providing reliable quality classification results with revision suggestions. Furthermore, Around 88&#x0025; of the users believe Rebot helps reduce the workload of reviewers and increase the development efficiency.
Knowledge Management Challenges for AI Quality

Developing an AI-based system is uniquely challenging as it requires knowledge across multiple domains. Though the project team is required to be versatile, it is possible that their repertoire cannot cover all of the requirements of the system, which results in damage to the software quality. Therefore, it is critical to have an effective team knowledge management (KM) strategy to detect the valuable &#x201C;unknown&#x201D;, optimize the &#x201C;known&#x201D; task assignment, and enlarge the team knowledge base. Moreover, it is more effective to support the process with data-driven approaches.
Program Transformations for Precise Analysis of Enterprise Information Systems

Programs written in a high-level language such as C and Java, interleaved with declarative database processing and inter-service communication statements are common in Enterprise Information Systems (EIS). IT companies that offer re-engineering services for such EIS rely on manual analysis or code analysis tools to understand the flow of control and data within and across the programs during the reverse-engineering phase. Generally, the code analyzers are developed for commonly used programming languages, and lack support for analyzing database processing and inter-service communication statements. Therefore, such analysis tools cannot be readily re-used for re-engineering projects. In this paper, we share the key challenges of handling program analysis requirements for large embedded Structured Query Language (SQL) applications written in C language. We describe our approach to transform a program written in C and embedded SQL into a plain C program which can be automatically analyzed using any available C code analyzer that has no explicit support for domain specific extensions like SQL. We deployed the data dependency analysis based on our transformation approach as a feature in an industrial code analysis tool. We present the evaluation and results that show a precision improvement for slices as compared to the na&#x00EF;ve translation provided by the standard pre-compiler.
Exploration of Virtual Simulation Teaching Mode of Landscape Design with OBE Concept as Core in Post-pandemic Era

Based on the background of the "online + offline" hybrid teaching of college environmental design courses in the post-epidemic era, the advantages and disadvantages of online teaching of college landscape design courses during the epidemic are analyzed to propose the OBE concept as the core of design education. The virtual simulation of teaching is introduced into the landscape design teaching curriculum. The design process of the virtual simulation teaching experiment is analyzed, including design tasks, on-site inspection, landscape analysis, epidemic prevention analysis, scheme discussion, plane planning. For this study, we took the online teaching practice of the "Virtual Simulation Experiment of Landscape Reconstruction of Sunlight Rock under the Background of Epidemic Normalization" as the research subject. There are ten links of function analysis, style positioning, effect display, and effect verification. In the aspects of virtual simulation experiment teaching method, the following is constructed: teaching content setting, digital information construction, school-enterprise cooperation mode, and teaching effect evaluation, and a virtual simulation teaching mode of college landscape design course oriented by learning output. The purpose is to improve students' autonomous learning by analyzing the ability to think and solve problems. To obtain better results and teaching effects, the paths and methods of teaching reform and innovation of landscape design courses in colleges and universities need to be explored.
Analysis of Piano Teaching in Colleges and Universities from Perspective of Big Data

The two characteristics of big data technology are excellent data perception ability and strong data processing ability. At the same time, it also has innovative technologies for distributing, storing, parsing, capturing, and processing data with different structures. Big data technology can be used as a means of college teaching mode analysis. From this point of view, it is necessary to study piano teaching in colleges and universities with the technology. Corresponding to the problems of low data results and accuracy in the traditional algorithm of piano teaching in colleges and universities, we propose an analysis and evaluation model of piano teaching in universities based on big data. The big data analysis of the constraint parameters of the piano teaching mode in universities is promoted, too, and the quantitative recursive analysis of piano teaching in universities is carried out. Finally, the model is optimized, and the simulation test is carried out with MATLAB. The average evaluation accuracy of this method reaches 95.99%, and the average utilization rate reaches 94.12%. Compared with the conventional two methods, this method is better than the other two methods, which shows that the big data method is better applied to the evaluation of piano teaching mode in universities.
Pilot Study of Using ARCS Model to Investigate Motivational Affordances of EduVenture VR from Perspective of Construction Management Students

Spherical Video-based Immersive Virtual Reality (SV-IVR) is regarded as an affordable and creator-friendly VR-based technology to be adopted by educational practitioners. Nevertheless, from the instructional point of view, a major limitation of SV-IVR is its incapability to offer interactivities between students and SV-IVR content, leading to the demotion of students&#x2019; motivation in the course of learning. EduVenture VR (EVVR), which is an integrated interactive learning system riding on SV-IVR to support students in conducting interactive and immersive virtual expeditions, has been developed to address this instructional gap. In this pilot research, on a qualitative basis, we adopted the instructional motivation model of ARCS (composed of 4 dimensions: Attention, Relevance, Confidence, and Satisfaction) to investigate the motivational affordances of EVVR from the perspective of higher-education students from a program of Bachelor Applied Science in Construction Management in Australia. It involved retrospective semi-structured interviews with 4 construction management students who had experienced EVVR-supported learning activities in their coursework. Results show that, in general, the participants positively perceived EVVR&#x2019;s motivational affordances in all 4 ARCS dimensions. This pilot work lays an empirical foundation for further research into the motivational affordances of EVVR in supporting instructional activities in construction management through a larger-scale study.
Block-chain Encryption Search Algorithm for Educational System Using Improved Neural Network

The interconnection of the educational system to the blockchain means the challenge of security, reliability, credibility, and confidentiality of information and data. Information search and data access of blockchain are realized by accessing PHR from a database and vulnerable to a variety of security vulnerabilities, even though blockchains provide a secure search mechanism However, network physical attack, deception injection attack, electromagnetic crosstalk attack, malicious code injection, and other vulnerability attacks always bring high risk to the information and data security of education system. Based on the model of security threat and intrusion detection, we present a security search and access model to information and data of blockchain with an improved BP neural network. By introducing a dynamic self-adjusting coefficient, the search and access efficiency, real-time performance, and throughput are improved. In this model, homomorphic encryption technology is used to secure search and database access based on keywords. A key revocation and regeneration strategy is proposed to integrate blockchain and the security chain. Compared with the existing models of search and access based on BP and Fuzzy networks, the method proposed in this paper provides the premise of data sharing, effective data access, and better information search security for education system block chain information flow.
Study on Labor Consciousness Training Model of English Majors Based on Deep Learning

BP neural network is applied to forward multi-layer backpropagation learning. Its main idea is forward propagation and backpropagation of error in the studying process. The BP neural network has a three-tier structure. Aiming at the problem of cultivating English majors' labor consciousness, we propose a research method based on BP neural network. Firstly, 2433 questionnaires were collected. According to the questionnaire survey, two approximate primary indexes and five detailed secondary indexes are set to train and test BP neural network. The accuracy, recall, and AUC values of the trained BP neural network were evaluated. The BP neural network had high accuracy, recall, and AUV values, which were 0.962, 0.702, and 0.6456, respectively. This indicates that the BPNN model was reliable and accurate. We further study and compare BPNN with the Decision tree model and SVM. With calculation, analysis, and comparison, the results show that BPNN has high accuracy in various indicators, and the recall rate is 30.9% higher than that of the Decision tree model, which greatly improves the accuracy of the model and provides great advantages.
Exploring Extended Reality Extension and Impact of Talent Cultivation in Post-Epidemic Era

We explored the impact of Extended Reality (XR) and talent cultivation in the post-epidemic era. XR is cross-disciplinary and requires different resources to support. With the KJ method, the results of the SmartPLS validated structural model revealed that the combination of remote XR with Qualia improves the design level, exquisite texture, and the design field. The findings and conclusions show a positive impact on customer satisfaction to validate the architecture of Qualia's art of technology-inspired design and have valuable implications for XR designers and talent cultivation.
Research on Cultural Ant Colony Algorithm in Robots Path Planning

With the low speed of convergence and the local optimal problem-solving in the ant colony algorithm (ACO), d, we propose a cultural ant colony algorithm for robotic path planning. By introducing a cultural algorithm into ACO, the spatial evolution of the population can be guided by belief space evolution, and the convergence speed of the algorithm can be further improved. Ant fallback strategy and adaptive adjustment strategy of pheromone evaporation coefficient are added to its population to expand the search of the algorithm and improve the robustness of the path planning method. The circuitous route strategy is used to optimize the path set and improve the safety of the robot and energy efficiency. The simulation results show that the cultural ant colony algorithm has better search efficiency and convergence speed than the basic ant colony algorithm in robot path planning in the same environment.