Reviewerly/exHarmony huggingface dataset:
CNN - Based Priority Prediction of Bug Reports

When considering software maintenance, priority prediction is an essential part of it. Thousands of bugs are reported daily in the Bug Tracking System (BTS); Bugzilla, JIRA, and GitHub are commonly used. Priority assignment for the reported bugs is conducted manually. Therefore, this task takes considerable time to do, and there is also a high possibility of making a mistake. Therefore, it is imperative to have a way to predict the bug report's priority automatically. Our study proposed a model based on the Convolutional Neural Network (CNN) to predict the bug report's priority. First, preprocess the textual content in bug reports using natural language processing (NLP) approaches. Then extract the features from the textual context (short description) using the Bag-of-word feature extraction method. Finally, train a CNN-based classifier to make priority predictions based on its input. Then our result is compared with the Support Vector Machine (SVM) and Temporal Convolutional Network (TCN) to find a better model for priority prediction. The final results show that the proposed approach based on the CNN classifier performs better than the other approaches, and it shows a 71% accuracy while others have low accuracy, like 63% and 48% for SVM and TCN, respectively. The proposed model's performance was evaluated using the Bugzilla dataset, which included over 25,000 bug reports.
Study on a Flipped Learning-based Online Discussion Class Case and its Effectiveness

This study aims not only to search for how to apply ‘Flipped Learning’ to online learning situations, but also to prove its relevant effectiveness. In order to achieve the research goals, the study targeted foreign students studying in a domestic university and proposed a case in which Flipped Learning is applied to an online discussion class. The class was composed of three stages, namely Pre-learning, Learning in Progress and Advanced Learning. At the stage of Pre-learning, after acquiring the basic knowledge of culture which they are supposed to know as a university student, the learners autonomously understand a topic in the process of asking themselves questions. At the stage of Learning in Progress, after hearing a short lecture by a professor which addresses some of their questions, the learners get involved in problem-solving and discussion topic-analyzing Afterwards, the learners engage in a group discussion and share their results. At the stage of Advanced Learning, with feedback from the professor, the learners carry out a reflective evaluation and engage in self-evaluation journal writing, and finally internalize their learning results.&lt;/br&gt;The study applied the learning method above mentioned for one semester, and verified its effectiveness by conducting a Korean language proficiency-based satisfaction survey on the learners. The findings show that for a majority of students this method helped them attain new knowledge and improved their debating abilities. However, when it came to the beginner learners, their lack of linguistic resources resulted in a decrease of their satisfaction with this program compared with the level of satisfaction found among the intermediate-post intermediate learners. We suggest that this problem be investigated further in following studies.
Continuous Learning with the Sandwich of Happiness and Result Planning

With an increase in fast time-to-market and keeping up with fast mandatory legal changes, we observe a demand for continuous software development which is reflected by the emergence of Lean, Agile, and DevOps approaches. At the same time, we observe the phenomenon of lifelong learning that is both manifest and propagated by government, industry, and education. We introduce two patterns that match these two phenomena: the Sandwich of Happiness and Result Planning. Together, these patterns support learning for students in an educational setting and continuous learning for professionals in industry, especially in the context of Continuous Software Development.
Research on trajectory correction model of dual magnetic navigation differential mobile robot

Abstract In order to improve the pose accuracy of magnetic navigation mobile robot, differential driving mobile robot is taken as the research object. The kinematics and deviation model are analyzed by means of double magnetic strip sensor navigation. The kinematic model is built in SIMULINK, and a fuzzy controller with double input and single output is designed through MATLAB, and the motion process is simulated and analyzed. The feasibility of the kinematic model is verified and the fuzzy rectifying controller designed in this paper has good rectifying control performance and response characteristics.
Analysis of Emphasis and Prosodic Features on Face-to-Face Discussions

we focus on recognizing speakers' emphasized sections during face-to-face discussions. There is a problem that a speaker’ s emphasized sections are not necessarily conveyed to listeners. Thus, we compared sections that speakers intend as em-phasized with sections that listeners perceive as emphasized. We also analyzed the influence of prosodic features on emphasized section recognition. Two types of discussion were designed for our experiment. One is a free discussion and the other is a scripted discussion that contains the direction of emphasized sections. The utterances were recorded during the discussions. The emphasized sections were answered by listener individually after listening to the recorded experiment. After the experiment, the percentage of correspondence of the emphasized sections between the speakers and listeners was calculated. The results showed that there was an insignificant difference in the percentage of correspondences of emphasized sections between the free and scripted discussions. It was difficult for speakers to convey the emphasized sections to the listeners correctly, even if the emphasized sections were directed in advance. The emphasized sections could not be estimated from the pitch and power standard deviations clearly. Finally, we analyzed the reasons listeners recognized emphasized sections, Key words of affirmation.
Attention Based Data Augmentation for Knowledge Distillation with Few Data

Abstract Knowledge distillation has attracted great attentions from computer vision researchers in recent years. However, the performance of student model will suffer from the absence of the complete dataset, which is used to train the teacher model. Especially for conducting knowledge distillation between heterogeneous models, it is difficult for student model to learn and receive guidance with few data. In this paper, a data augmentation method is proposed based on the attentional response of teacher model. The proposed method utilizes the knowledge in teacher model without requiring homogeneous architecture between teacher model and student model. Experimental results demonstrate that combining the proposed data augmentation method with different knowledge distillation methods, the performance of student model can be improved in knowledge distillation with few data.
Test Problem in Which Bits Used for Fitness Calculation Depend on Bit Pattern

This paper proposes a new test problem for model-based GAs (MBGAs) called the overlapping chains problem (OCP). The investigation of overlapping linkages has been an important issue in MBGAs that adopt linkage detection. Like some other existing test problems, OCP consists of sub-problems with overlapping linkages. Nevertheless, unlike others, the bits used for fitness calculation in OCP are uniquely designed such that they depend on the pattern of the solution candidate. The experimental analysis shows that if the Hamming distance between two randomly generated solution candidates increases, the difference in the bits used for their fitness calculations increases. The analysis also shows that the Hamming distance between two solution candidates with the same or similar high fitness values is quite large and the bits used for the fitness calculation vary slightly even between two such good ones. These results suggest that at each search stage, the bits used for fitness calculation are not completely fixed and that the linkages are difficult to identify. Several state-of-the-art MBGAs are then tested on OCP. Empirical results suggest that OCP provides a new aspect of difficulty for MBGAs to correctly identify linkages and generate high-quality solutions, applying trial-and-error methods to existing ones is essential.
Identifying User Behavior Profiles in Ethereum Using Machine Learning Techniques

Ethereum is one of the largest blockchain platforms currently that has become a digital business environment for users. This platform is designed to allow decentralized transactions between anonymous users. Thus, the development of methods to identify user behavior profiles, keeping their identities anonymous, has the potential to leverage business on this platform. In this work, we investigate the use of machine learning to classify a user profile as professional or common based on the attributes of their transactions. This classification is challenging due to the small fraction of publicly labeled users in Ethereum and still the considerably smaller fraction of professional users. To conduct this investigation, we train models considering carefully balanced sets of transactions with labeled users. Our results show high performance models for the classification of profiles, achieving a performance greater than 90% for accuracy, precision, and other related measures. In addition, we have identified the most relevant features in transactions for this classification.
Autonomous Learning with Automatically Created Models and a Novel Model Selection

An autonomous learning approach is presented here for expansive problem domains that may undergo frequent changes. It is hard to train and adapt learning-models to changes when the problem domain is very large. With the autonomous learning approach, a system performs knowledge refinement automatically by determining what can be improved and trains itself without explicit guidance to do so. It uses novel techniques to automatically create the learning-models and select an optimal model for each prediction. It automatically builds dynamic ensembles of models that incorporate specific improvisations, such as improvements based on past knowledge or specific trends reflected in the results, and overshadows sub-optimal portions of existing model. A reinforcement learning based model selector identifies which model is optimal for handling a request using a novel approach with automatically created hierarchical states. As a result, smart adaptive solutions that go beyond initial training become possible. This also alleviates the need for big complex monolithic models that require extensive training, automatically adapts to changes in the domain and offers better control over its performance. Experimental results show this approach learns quickly, adapts to changes very rapidly and performs quite well against prevalent learning methods.
EL-service-YU: An Android Mobile Application for Errand Services

This study focused on the development of EL-service-YU: AN ANDROID MOBILE APPLICATION FOR ERRAND SERVICES which aimed to provide more job opportunities and at the same time help people to lighten up their tasks. El-service-Yu sought answers to the following objectives: develop the Value Proposition Canvas and Business Canvas Model and validate its acceptability using convenience and quota sampling; develop an android mobile errand service application using agile extreme programming; and determine the level of acceptability using ISO/IEC 25010-2011-SQUARE. Design Science Research Methodology was used as research design to develop the required artifact of the study along with extreme programming framework as the software development methodology.
Peculiarities of Using Game Activities for Raising Students’ Socio-Cultural Competence

The purpose of this research is to highlight the importance of enhancing socio-cultural competence in foreign language teaching and to highlight the benefits of using play activities in the classroom to develop social and cultural competence. As an object of scientific work, game activities aimed at developing the socio-cultural competence used in the lesson are indicated. In order to uncover the essence of the topic, a group was selected for the practice and results were obtained through inspection and observation processes.
Real-Time Scheduling Models in Diverse Multi-core OpenMP Applications

This research presents some of the critical information required to understand the concept of parallel programming and the implementation of OpenMP in parallel programming. Parallelism is the preferred tool for expediting an algorithm, as demonstrated by the evolution of computing architectures (multi-core and many-core) towards a greater number of processing cores. The report will focus on OpenMP parallel programming models and further examine its implementation and features. Parallel programming OpenMP model is increasingly preferred for its ability to deliver real-time processing, thereby, meeting system requirements performance wise. Furthermore, the study of implementing OpenMP in enhancing the efficiency of 3D discontinuous deformation analysis (3D-DDA) for expansive simulation using parallel block Jacobi (BJ) and Pre-conditioned conjugate gradient (PCG) algorithms. The absence of synchronization of data in parallel programming makes the system more prone to errors in programming since the parallel environment is much more complicated than perceived. The studies performed will highlight how synchronization is managed using OpenMP model. In the field of biometrics, the most important issue faced in DNA sequencing and pattern discovery is locating the longest common subsequence (LCS) among sequences. To identify the LCS of DNA sequences, we will look into the solutions achieved using OpenMP tools based on CPU, that extend major improvements in processing speed, capital, and ubiquity, and the results based on the analysis are discussed.
On Nonuniqueness of Quantum Channel for Fixed Input-Output States: Case of Decoherence Channel

For a fixed pair of input and output states in the space HA of a system A, a quantum channel, i.e., a linear, completely positive and trace-preserving map, between them is not unique, in general. Here, this point is discussed specifically for a decoherence channel, which maps from a pure input state to a completely decoherent state like the thermal state. In particular, decoherence channels of two different types are analyzed: one is unital and the other is not, and both of them can be constructed through reduction of B in the total extended space HA⊗HB, where HB is the space of an ancillary system B that is a replica of A. The nonuniqueness is seen to have its origin in the unitary symmetry in the extended space. It is shown in an example of a two-qubit system how such symmetry is broken in the objective subspace HA due to entanglement between A and B. A comment is made on possible relevance of the present work to nanothermodynamics in view of quantum Darwinism.
Design and implementation of special Data Encyclopedia system

Abstract:The traditional data construction in the military field mainly focuses on the vertical business field, realizing the digitization of the military business and failing to meet the application requirements of horizontal data fusion.This paper realizes the design of a special data encyclopedia system in the military field, and uses advanced big data processing technologies such as web crawler, knowledge spectrum, and full-text retrieval to realize the horizontal integration of data in the military field, mine the value of data in the military field, and improve the practicability and timeliness of data.
Enabling Type Checking on Columns in Data Frame Libraries by Abstract Interpretation

Data frames are a tabular data structure widely used in transforming data to an appropriate form in data analysis, especially in data wrangling. However, when data frames are implemented with libraries rather than supported at the language level, it is hard to find out errors due to the limitation of type checking on columns. Data scientists may encounter errors due to missing column labels or inconsistent types, especially when they reuse code snippets for new data. These errors are usually left to runtime, and it is sometimes difficult to find out where the problems are. To address this issue, we propose using abstract interpretation to perform type checking on data frame columns. We defined the type for data frames based on column labels and developed semantics to verify the typing in general operations. A static checker can be implemented based on the semantics to help programmers quickly fix errors without executing the code. To show the feasibility, we implemented a proof-of-concept for the pandas library as an example, PDChecker, to discuss the limitation and usage. It is then used to compare the functionalities with existing solutions. The results show our approach can fulfill the function of type checking for data frames. Supporting more data frame operations is included in our future work.
Research on Employee Assistance Program: Progress and Trend

Based on citation analysis and knowledge map visual analysis, a study of 1031 journal literature data in the field of employee assistance program (EAP) research in the web of science database from 1900 to 2021 found that: since the first research article related to Employee Assistance Program published in 1974, EAP research has entered an outbreak period since the 1990s, and the number of literature and citations have basically increased year by year; It mainly focuses on the subthemes of workplace, stress, depression, care, health, alcohol, burnout, etc; The United States, Canada and Australia ranked the top three in number and quality of the research articles, forming eight types of cooperation among countries (regions) and academic institutions, and the aggregate of the total volume and quality of Chinese mainland have a big gap. Roman, PM and other scholars and their literature have great influence in the field of employee assistance program, which has formed five academic community factions.
An Approach to Ensure Secure Inter-Cloud Data and Application Migration Using End-to-End Encryption and Content Verification

Cloud Computing is one of the most popular platforms in recent times and has many services to offer. The resources are deployed on the Cloud and are made available to cloud users over high-speed internet connectivity. Many enterprises think of migrating the data or application hosted from one Cloud to another based on the requirements. Migration from one Cloud to another Cloud requires security as it is vital for any data. This article presents a novel secure framework called ‘InterCloudFramework,’ considering well-established criteria to migrate various services across clouds with minimal supervision and interruption. Security is the primary concern to migrate the data among inter-clouds. The study incorporates the Elliptical-Curve Diffie-Hellman algorithm to encrypt the data and Merkle Hash Trees to check the integrity of the data. In addition to security during migration, the framework reduces the migration time of data or applications.
A Regularized Wasserstein Framework for Graph Kernels

We propose a learning framework for graph kernels, which is theoretically grounded on regularizing optimal transport. This framework provides a novel optimal transport distance metric, namely Regularized Wasserstein (RW) discrepancy, which can preserve both features and structure of graphs via Wasserstein distances on features and their local variations, local barycenters and global connectivity. Two strongly convex regularization terms are introduced to improve the learning ability. One is to relax an optimal alignment between graphs to be a cluster-to-cluster mapping between their locally connected vertices, thereby preserving the local clustering structure of graphs. The other is to take into account node degree distributions in order to better preserve the global structure of graphs. We also design an efficient algorithm to enable a fast approximation for solving the optimization problem. Theoretically, our framework is robust and can guarantee the convergence and numerical stability in optimization. We have empirically validated our method using 12 datasets against 16 state-of-the-art baselines. The experimental results show that our method consistently outperforms all state-of-the-art methods on all benchmark databases for both graphs with discrete attributes and graphs with continuous attributes.
Optimal Geometry of Elliptical Target Localization

This letter unifies the optimal geometry analysis for elliptical target localization by a new notion called virtual agents (VAs), which allows the conversion of a bi-static time-of-arrival (TOA) measurement to a direct TOA measurement with equivalent Fisher information. Using the notion of VAs, we determine the optimal geometries with different types of measurements based on D-optimality. In particular, the optimal geometry is attained when the angles between transmitter-to-target and target-to-agent directions are ±π/3 for the TOA case, or the agents have an equal angular spacing around the target with equal ranging information intensity (RII) for the angle-of-arrival (AOA) case. Moreover, we also show that for the TOA/AOA fusion case, the optimal geometry occurs if the transmitter, agents, and target are collinear under both single-agent with arbitrary RII and multi-agent with identical RII between two measurements conditions. Finally, numerical results are given to validate our theoretical analysis.
Text-Enhanced Question Answering over Knowledge Graph

Question answering over knowledge graph is an important area of research within question answering. Existing methods mainly focus on the utilization of information in knowledge graphs and ignore the abundant external information of entities. However, knowledge graphs are usually incomplete and entities in knowledge graphs are not completely described. In this paper, we propose a novel text-enhanced question answering model over knowledge graph by taking advantage of the rich context information in a text corpus. We believe the rich textual context information can effectively alleviate the information loss in knowledge graphs and enhance the knowledge representation capability in the answer end. To this end, we apply an attention model to realize dynamic fusion of internal and external information. Besides, Transformer Encoder network is used to obtain the representation of input question and descriptive text. The experiments on the WebQuestions dataset prove that compared with other state-of-the-art QA methods, our method can effectively improve the accuracy.