Reviewerly/exHarmony huggingface dataset:
Simultaneous Refactoring and Regression Testing

Currently, refactoring and regression testing are treated independently by existing studies. However, software developers frequently switch between these two activities, using regression testing to identify unwanted behavior changes introduced while refactoring and applying refactoring on identified buggy code fragments. Our hypothesis is that the tools to support developers in these two tasks could transfer part of the knowledge extracted from the process of finding refactoring opportunities to identify relevant test cases, and vice-versa. We propose a simultasking, search-based algorithm that unifies the tasks of refactoring and regression testing, hence solving them simultaneously and enabling knowledge transfer between them. The salient feature of the proposed algorithm is a unified and generic solution representation scheme for both problems, which serves as a common platform for knowledge transfer between them. We implemented and evaluated the proposed simultasking approach on six opensource systems and one industrial project. Our study features quantitative and qualitative analysis performed with developers, and the results achieved show that the proposed approach provides advantages over mono-task techniques treating refactoring and regression testing separately.
Comparison of Accuracy in Extreme Learning Machine Based on Hidden Node Structure Variation for Lung Cancer Classification

Abstract This paper present Extreme Learning Machine to classify lung cancer nodules. Lung cancer is a type of lung disease that requires fast and specified treatment. Skills, facilities and multidisciplinary approach are required for diagnosing lung cancer. The use of Computed Tomography (CT) to detect lung cancer can reduce the number of deaths from lung cancer, but it increases the workload of the radiologist because CT screening process produces many medical images. Computer systems become one of the potential solutions to help radiologists solve the problem. Extreme Learning Machine is an algorithm that able to provide good generalization at fast learning time which is essential to help radiologists in analyzing lung cancer nodules images. In this paper, there were 877 nodules extracted from LIDC-IDRI dataset. All nodules used in this experiment consist of lung cancer nodules that diagnosed to four different level of malignancy and annotated by up-to four different radiologists. The result shows Extreme Learning Machine achieve 85.17%, 85.58% and 84.87% in accuracy and Matthew Correlation Coefficient 0.755, 0.762 and 0.749 using Hardlimit, Radial basis Function and Triangular Basis function, respectively.
Comparative Analysis of Various Classifiers for Gesture Recognition

Communication plays a very important role in human life. The ease of text-based communication in the form of emails and text-chats has increased nowadays due to the interaction of system and hardware devices. The research discussed in the present paper proposes to work upon a human–computer interaction system so that human and machine can communicate with each other without the actual use of any hardware input device like keyboards. Gesture keyboard is one such method by which we can achieve this goal of interacting with the computer using our hand gestures. The present research paper is a comparative study of seven machine earning classifiers aiming to increase the accuracy of prediction. The two main aims of this innovative research are to develop a gesture keyboard device which can be used to aid those people who have some kind of disability in vision so that they can use this device to interact with the computer and to increase the performance of the model by using methods like Bagging and Boosting.
Visual Assistant for Crowdsourced Anomaly Event Recognition in Smart City

City anomalies such as residential fires or urban floods can cause a lot of damage each year from loss of human lives to negative effects on the productivity of the city. One possible way to deal with these anomalies in a quick and effective manner is to use the information provided by the network of citizens that is likely present at the scene of any serious anomaly. However, this source of information may be of varying quality and there may be too much data for human operators to manually inspect in a speedy manner. This motivates us to propose an architecture to effectively make use of the network of citizens to deal with city anomalies. At the center of our architecture is a neural network that automatically classify incoming image data and use this information to assist anomaly handling efforts. In order for our classifier to work well, we have also collected a dataset of city anomalies specific to Vietnam, consisting of 9963 images. We experiment with several neural network models on our collected dataset. In overall, they all have decent accuracies with the best being MobileNet with 92.3% accuracy.
Method of Evaluating the User Interface of Software Systems for Compliance with the Gestalt Principles

The purpose of this study is increasing the usability of the user interfaces (UI) by ensuring their compliance with Gestalt principles. The developed method of evaluating the compliance of the UI with Gestalt principles provides: quantitative evaluation of the compliance of the UI with each of the Gestalt principles; quantitative evaluation of the compliance of the UI to all Gestalt principles in the complex; selection of elements of the UI, which meet to the largest and smallest quantity of Gestalt principles; the formation of the set of UI elements, which require further re-work in order to provide them the compliance with Gestalt principles; increase of compliance of the UI with the Gestalt principles (by 15% for the real UI of webpage for selling the jewelry).
Control of quantum correlation of high dimensional system in squeezed vacuum reservoir

We study the quantum correlation and entanglement for a high-dimensional spin-S system in a squeezed vacuum reservoir. The exact analytical solution is presented, which shows that the decoherence function governs the quantum correlation and quantum entanglement of corresponding dynamical process. The squeezed amplitude parameter for quantum correlation and quantum entanglement has a negative impact in the whole process, the better the squeezing effect, the greater the negative impact. Furthermore, in the super-Ohmic reservoir, the influence of Ohmic parameter on quantum correlation and quantum entanglement can be divided into two regions in terms of the critical value s=2.5⁡. If s<2.5, the decay rate of quantum correlation slows down obviously, and the environmental decoherence effect is much smaller than that of Ohmic and sub-Ohmic reservoirs. On the contrary, if s>2.5, the decay rate of quantum correlation accelerates obviously, and the decoherence effect surpasses the cases of Ohmic and sub-Ohmic reservoirs greatly.
Opinion Words Extraction and Sentiment Classification with Character Based Embedding

In recent years, sentiment analysis from customer comments has received widespread attention in deep learning and recognition computing area. In the field of fine-grained sentiment analysis, aspect level sentiment classification aims to detect the sentiment polarity towards a particular aspect in a sentence. Most of previous research in this task focus on sentiment polarity, and ignore the importance of opinion words. As the specific embodiment of sentiment, opinion words provide diversified representation of the aspect and contribute to sentiment polarity analysis. In this work, character level word embedding is applied to our model for enhanced semantic expression, and additional position attention based on opinion words is used in sentiment classification. Our work shows considerable improvement in opinion words extraction and acquires comparable results in sentiment polarity classification on SemEval 2014 datasets.
Multiple Thresholding Methods for Extracting &amp; Measuring Human Brain and 3D Reconstruction

Abstract Thresholding changes the gray image to binary imagery and improves image quality. This study applies the multiple thresholding method to extract and calculate the area of bleeding in the human brain. 10 samples of human brain image using multiple threshold method (Otsu and hybrid thresholding). The results of the Otsu method that still have noise can be overcome by the hybrid thresholding method. The results of the original image research with images that have been processed using the multiple thresholding method yield the optimum threshold value, the method applied produces excellent image quality. Results of calculation of MSE, RMSE and PSNR. the average PSNR Otsu thresholding: an average of 60.93 dB, the average PSNR Hybrid thresholding: 59.06 dB, and the average PSNR Multiple thresholding: 56.96 dB, the average MSE Otsu thresholding: 38.27, the average MSE Hybrid thresholding 36.15, mean MSE multiple thresholding: 34.30, mean RMSE Otsu: 9.49, mean RMSE Hybrid thresholding: 8.88, average RMSE Multiple thresholding: 7.58. The results of the calculation of the area of cerebral haemorrhage and the level of accuracy indicate a better multiple thresholding method. The results of the calculation of the bleeding area were carried out by 3D reconstruction using linear interpolation method.
A UML Profile for Software Requirements Prioritization

The increasing complexity of software systems makes Requirements Engineering an important phase in a system's development life cycle. When RE is carried out poorly, multiple problems may arise, such as failures, cost overrun and delays. In this context, prioritization is of significant importance to organize requirements in their order of comparative significance and consequent development releases. It is the most important step taken in making fundamental resolutions so as to increase the commercial significance of a system. Evaluating requirement prioritization in model-driven context is a super difficult task. Hence, there exists a solid need for a model-based approach that allows for the ranking procedure in SDLC. This article introduces UML profile for Analytic Hierarchy Process (AHP); to rank the software requirements in the context of model-driven engineering. Mapping its basic concepts into stereotypes to extend SysML's Requirement's Diagram. Thus, providing a way of ranking requirements in the model-driven context for the first time. AHP is a well-proven process for collaborative decision making to achieve better outcomes. Later, the applicability of this profile is authenticated through BuyMobilePhone case study.
Research in Breast Cancer Imaging Diagnosis Based on Regularized LightGBM

Breast cancer is the main cause of cancer death in women, and it is increasingly threatening women’s health. The research of breast cancer imaging radiology aims to replace the traditional artificial diagnosis and promote the accurate diagnosis and treatment of breast cancer. Using deep learning technology to extract the features of breast images and to construct a breast cancer imaging diagnostic system is essential for promoting the development of diagnostic efficiency in the field of imaging medicine. Firstly, the existing mammography datasets are rotated 50 times at random angles (0°–360°) with the data enhancement technology. Then the images are encoded, and then the image features are extracted by ResNet50. In the process of classification, a regularized LightGBM model is added, the combination of the two models constitutes a model for breast cancer diagnosis. The addition of LightGBM improves the classification accuracy and performance of the model. The experimental results show that, the accuracy of the model on the two datasets of INbreast and DDSM is 91.7% and 93.6% respectively when doing the three-classification (normal/benign/malignant), when doing the binary classification (benign/malignant), the AUC on the two datasets are 0.942 and 0.962 respectively.
Іnternalization of information flow using techniques of visualization in the process of future philologists’ professional training

The academic paper deals with the mechanisms of іnternalization of information flow using techniques of visualization. Information competence is an integral part of a foreign language teacher’s professional competence and translator’s and interpreter’s professional competences. Any information is divided into two types: according to the presentation form and the method of obtaining information. Knowledge, that has passed the stage of personal appropriation (internalization), is especially important. The classification of visualization techniques includes: Mindmap, Timeline, Infographics, Scribing. Each type of visualization has its own peculiarities and advantages. Mind maps represent the process of thinking through schemes. Timelines help students understand and memorize quickly the temporal relationships of events. Infographics in the form of interactive posters, tag clouds, visual organizers present information logically, quickly and clearly. Scribing is the latest technique to introduce new information. It is a dynamic, simultaneous process of information presentation and its visual display in real time. Visualization acts as an intermediate link between the educational material and the learning outcomes. It develops a number of professional sub-competences. These innovative techniques help students simplify and optimize processes of perception and cognition.
Binary Binomial Tree Based Secure and Efficient Electronic Healthcare Record Storage in Cloud Environment

Electronic Health Records (EHRs) is a key form of healthcare records that attracts a great deal of attention. It is regarded that the sharing of healthcare records is a vital strategy for improving the quality of healthcare services and reducing medical expenses. Presently, the cloud computing paradigm supports many significant characteristics in Electronics Health Records (EHRs). However, in the cloud model, the patients have no longer control over their crucial healthcare records after outsourcing the records to cloud servers, which can lead to severe data integrity related concerns. Therefore, in this manuscript, we introduce a novel secure and efficient cloud based EHR data storage system by constructing Binary Binomial Tree like data structure. Furthermore, we extend the proposed model to attain significant cloud model based public auditing characteristics such as data dynamics, batch auditing, privacy preserving, blockless verification, data traceability, and recoverability. Extensive experiments and results demonstrate that the proposed cloud based EHR system is secure and efficient over other existing strategies.
Color Image Watermarking with Watermark Hashing

Color images are extensively used, published, and exchanged through internet. The need to establish ownership of digital images has resulted in the development of many image watermarking techniques. Many of these techniques are based on Singular Value Decomposition (SVD) of images. But, the major issue with SVD based watermarking is that it can be fooled easily to reproduce false data. This paper uses SVD with three different hashing techniques to counter this issue. These techniques for hash value generation are compared with respect to imperceptibility and their recovery when two copies of 128-bit hash values are embedded in addition to the watermark itself. The hash value of the claimed watermark at the receiver end will be first verified with the extracted value. If verified, the watermark is extracted. Experiments show that the PSNR of the watermarked image is nearly 41.5 dB for Lena image, irrespective of the method used for hashing. Embedding two copies of 128-bit unique hash value of the watermark exhibits an average correlation of 0.7966 during retrieval of watermark after authenticating it.
Iterative Learning of Multiple Univariate Zero-Order T-S Fuzzy Systems

This paper proposes an iterative learning approach to learn a fuzzy system composed of a sum of multiple univariate zero-order Takagi-Sugeno (T-S) fuzzy systems. The learning algorithm is based on the backfitting algorithm, and new fuzzy rules are iteratively added based on a novelty detection criterion, which gives the novelty degree of a new data by a value between zero and one, allowing an easier rule creation threshold's definition. In order to validate the performance of the proposed approach, 10 benchmark data sets are used to compare the proposed approach with two well-known state-of-the-art methods, the Extreme Learning Machine (ELM), and the Support Vector Regression (SVR), and with the GAM-ZOTS approach, which model is similar to the proposed approach. From the results, it is concluded that the proposed approach outperforms ELM, SVR and GAM-ZOTS in almost all data sets.
Testing of Image Resolution Enhancement Techniques Using Bi-cubic Spatial Domain Interpolation

Abstract The technique for increasing digital image resolution from low-resolution image to high-resolution image based on digital image processing is called the super-resolution technique. In this paper, a super-resolution technique is presented using a two-dimensional bi-cubic interpolation method in the spatial domain. The order of the super resolution method applied is as follows: (1) selecting ten images as samples, (2) decrease the sample image resolution to one-fourth of the original resolution by deleting three quarters of the pixel number, (3) increasing the image resolution of a quarter of the part becomes like the initial resolution using bi-cubic interpolation for three quarters of the additional new pixels, (4) testing this bi-cubic interpolated image with the same pixel-sized initial image, (5) using parameters: average value, minimum value, maximum value and standard deviation value as a comparison parameter between bi-cubic interpolated images and the same pixel-sized initial image. The results obtained from the super-resolution technique using spatial bi-cubic interpolation are: (1) The average error value of the bi-cubic interpolation method in image objects in this study is between 4% to 10% or still quite low, (2) Bi-cubic interpolation methods can work well on square pixel-sized images (m = n) compared to non-square pixel-sized images, (3) Bi-cubic interpolation turns out to produce an array of image pixel values that mirror symmetry against the main diagonal lines of the image before being interpolated.
The use of Bayesian methods in the task of localizing the narcotic substances distribution

This paper proposed a methodology for the use of static Bayesian networks (BN) in the problems of localizing the distribution of narcotic substances. Methods for constructing the BN structure, their parametric training, validation, sensitivity analysis and “What-if' scenario analysis are considered.
Multistage identification method for real-time abnormal events of streaming data

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.
Increasing the Environment Feel in Learning through the Double S Application

Abstract In this era of globalization, English has become the second language for some non-English speaking countries. But in Indonesia, English is actually being a foreign language. This is because learning English in junior high school is monotonous and boring. So students’ interest in learning is reduced. The purpose of this study is to create a media for learning English so that it can increase interest the environment feel in learning and trigger students’ enthusiasm in learning English subjects. The learning media is spinning wheel and a jumbo puzzle with picture of the Siak Palace. This learning media was tested limited to students of Pekanbaru Az-Zuhra Islamic Middle School. Students were divided into 3 groups. The group representatives would spin the spinning wheel which contained the difficulty level of the quiz and the group would discuss to answer the quiz about the material. After that, the group with the correct answers would paste several puzzles on the board according to the score that they obtained from the previous quiz. From the results of this limited test, based on questionnaires given after learning, it appears that there is an increase in interest and enthusiasm of students in learning English through this media. This learning media in general can build interest and increase student motivation in learning English and can build skills to work together in learning groups.
Gradient Density Estimation in Arbitrary Finite Dimensions Using the Method of Stationary Phase

We prove that the density function of the gradient of a sufficiently smooth function :, obtained via a random variable transformation of a uniformly distributed random variable, is increasingly closely approximated by the normalized power spectrum of exp iSas the free parameter 0 τ → .The frequencies act as gradient histogram bins.The result is shown using the stationary phase approximation and standard integration techniques and requires proper ordering of limits.We highlight a relationship with the well-known characteristic function approach to density estimation, and detail why our result is distinct from this method.Our framework for computing the joint density of gradients is extremely fast and straightforward to implement requiring a single Fourier transform operation without explicitly computing the gradients.