Reviewerly/exHarmony huggingface dataset:
On (Mis)perceptions of testing effectiveness: an empirical study

A recurring problem in software development is incorrect decision making on the techniques, methods and tools to be used. Mostly, these decisions are based on developers’ perceptions about them. A factor influencing people’s perceptions is past experience, but it is not the only one. In this research, we aim to discover how well the perceptions of the defect detection effectiveness of different techniques match their real effectiveness in the absence of prior experience. To do this, we conduct an empirical study plus a replication. During the original study, we conduct a controlled experiment with students applying two testing techniques and a code review technique. At the end of the experiment, they take a survey to find out which technique they perceive to be most effective. The results show that participants’ perceptions are wrong and that this mismatch is costly in terms of quality. In order to gain further insight into the results, we replicate the controlled experiment and extend the survey to include questions about participants’ opinions on the techniques and programs. The results of the replicated study confirm the findings of the original study and suggest that participants’ perceptions might be based not on their opinions about complexity or preferences for techniques but on how well they think that they have applied the techniques.
Web application to convert English into helpful characters for pronunciation learners

The International Phonetic Alphabet (IPA) is the most commonly used set of phonetic symbols but it can be difficult to understand and too abstract for non-phoneticians, such as English learners and foreign language educators. One of the factors that makes it difficult for English learners is the number of vowels used in English. In previous research, this problem was solved by proposing a condensed list of 13 English vowels and 24 consonants that are logically organized for North American English, and by assigning a new phonetic symbol font called Sound Spelling to these phonemes. However, there are currently no English texts that have Sound Spelling to show learners this easy way to pronounce and it is difficult for non-phoneticians to write transcriptions right away, because materials using those symbols are lacking for English learners. In this research, we developed a web application, now publicly available, that converts input English into Sound Spelling quickly and accurately to solve this problem.
Type Inference for Rank 2 Gradual Intersection Types

In this paper, we extend a rank 2 intersection type system with gradual types. We then show that the problem of finding a principal typing for a lambda term, in a rank 2 gradual intersection type system is decidable. We present a type inference algorithm which builds the principal typing of a term through the generation of type constraints which are solved by a new extended unification algorithm constructing the most general unifier for rank 2 gradual intersection types.
Challenges and Limitation of Resource Allocation in Cloud Computing

Cloud computing is internet-based computing era. The resources that are provided by cloud computing is easily accessible by the cloud clients when they are demanding. The infrastructure of cloud computing is dynamic in nature and resources are optimally allocated. These resources shared in cloud computing, like any other paradigm resource management is main issue in cloud computing. It is very challenging to provide all demanding resources, as the number of available shared-resources are increasing. This paper reviews sharing of resources (like servers, applications and data) over cloud and consider techniques to make adaptive algorithms for management of resources in cloud computing.
GAMIFICATION TO IMPROVE STUDENTS’ ENGAGEMENT IN LEARNING ENGLISH

ABSTRACT&#x0D; This paper aim to review some theories and previous studies on the application of game-based learning platform to improve students’ engagement in learning English. Learning English is crucial and is not negotiable in an Industry 4.0 era to communicate and to collaborate globally. In fact, not a few students find it difficult and lack of enthusiasm in learning English. One of the major problems is the teaching method which is less interesting and tends to be boring. Therefore, one of the solutions to resolve this problem is through the implementation of gamification. One of the most well-known employed gamification tools is Kahoot. Kahoot is one of the most used application to impove students’ engagement in learning English. Kahoot is a free game-based learning platform that can be used by teachers and students. Hence, this paper will describe at first the challenges in teaching English language in digital era. Further, it discusses the theory of gamification in education, advantages of gamification, the description of Kahoot, advantages of using Kahoot and how to apply Kahoot in English classroom. Some previous research highlighting the effect of using Kahoot on the students’ engagement in learning English is the main point covered next. The last part of this paper will present conclusion and pedagogical implication as a closing note.&#x0D; Keywords : gamification, Kahoot, students’ engagement
Fast and Bounded Probabilistic Collision Detection for High-DOF Robots in Dynamic Environments

We present a novel approach to performing probabilistic collision detection between a high-DOF robot and imperfect obstacle representations in dynamic, uncertain environments. These uncertainties are modeled using Gaussian distributions. We present an efficient algorithm for bounded collision probability approximation.We use our probabilistic collision algorithm for trajectory optimization in dynamic scenes for 7- DOF robots. We highlight its performance in challenging simulated and real-world environments with robot arms operating next to dynamically moving human obstacles.
TRAJECTORY SIMULATION OF BADMINTON ROBOT BASED ON FRACTAL BROWN MOTION

This paper focuses on the design of badminton robots, and designs high-precision binocular stereo vision synchronous acquisition system hardware and multithreaded acquisition programs to ensure the left and right camera exposure synchronization and timely reading of data. Aiming at specific weak moving targets, a shape-based Brown motion model based on dynamic threshold adjustment based on singular value decomposition is proposed, and a discriminative threshold is set according to the similarity between the background and the foreground to improve detection accuracy. The three-dimensional trajectory points are extended by Kalman filter and the kinematics equation of badminton is established. The parameters of the kinematics equation of badminton are solved by the method of least squares. Based on the fractal Brownian motion algorithm, a real-time robot pose estimation algorithm is proposed to realize the real-time accurate pose estimation of the robot. A PID control model for the badminton robot executive mechanism is established between the omnidirectional wheel speed and the robot’s translation and rotation movements to achieve the precise movement of the badminton robot. All the algorithms can meet the system’s requirements for real-time performance, realize the badminton robot’s simple hit to the ball, and prospect the future research direction.
Service Quality Assurance in Cloud Data Centers Using Migration Scaling

The continuous and swift progress in the number of the cloud data centers have led to establishment of multitudes of the computational nodes and the huge paradigm. But the assuring the quality of services through these paradigms is still questionable. So tit has become a prominent areas of research. As the quality of service of the data centers plays a vital role in the user satisfaction. The present work carried out in the paper survey the service quality rendered in the previous similar work, identifies the drawbacks and proposes a strategy of migration taking into consideration the multiple metrics. The proposed structure is validated through the cloud simulator to evince its capability in efficiently handling the resources and guaranteeing the quality of service.
A Brief Survey of Sentiment Analysis

Sentiment analysis has been a very active area of research in recent times because of the wide number of applications and the issues that it has. Over a period of time, many approaches and algorithms have been proposed by many scholars to solve these issues. This paper attempts to give a brief introduction to the different approaches to sentiment analysis and various algorithms. Also, the paper performs analysis of different algorithms as it is felt that it will help in the proper selection of an algorithm for a given problem.
A high security and noise immunity of speech based on double chaotic masking

It is known that increasing the security of the information and reducing the noise effect through public channels are two of the main priorities in developing any communication system. In this article, an efficient, secure communication system with two levels of encryption has been applied to the speech signal. The suggested security approach was implemented by using two different stages of chaotic masking on the signal; one masking was conducted by using Lorenz system and the other masking was built by using Rӧssler chaotic flow system. The main goal of developing this two-chaotic masking approach is to increase the key space and the security of the information. Also, an immunity technique has been implemented in the suggested approach to reduce the noise effect. For practical application purposes, this system was tested with additive white gaussian noise (AWGN) channel. The simulation results show that the quality of reconstructed speech signal is changeable according to the used signal to noise ratio (SNR); therefore, a proposed technique based on digital processing method (DPM) was applied to the first masked signal by converting the sampled data from the analog to the binary format. The simulation results show that an 22 dB (SNR) is sufficient to recover the speech signal with minimum noise by using the suggested approach.
Design Flaws Prediction for Impact on Software Maintainability using Extreme Learning Machine

The software that contains flaws in its design is an indication that the design and coding standards have been violated by the developer during the software system’s development. It has been observed in recent empirical studies that classes with flaws in the design flaws have a higher probability of change proneness or fault proneness when compared to classes without flaws in the design. There is an exponential increase in terms of the effort required to remove bugs due to design flaws in cases where the flaws are not detected in the early stages of the development of the software. The use of source code metrics for the prediction of design flaws can be implemented in the initial stages of the life cycle of the software development for the reduction of the testing effort and the maintenance of the software as well as the improvement of its quality. This empirical research study examines and assesses a variety of techniques for classification, feature selection, and data sampling in order to deal with the imbalance data for prediction of several categories of design flaws. The assessment of more than 20 application packages is the basis of the conclusions of this study. The results of the experiments indicate that the estimating capability of various classifiers, feature selection, and data imbalance techniques for the development of prediction models for design flaws can be confirmed. In addition, it was also revealed that the models that were developed through the use of one particular technique were found to be superior to the models that were developed with the use of other techniques, according to our analysis.
Lingering Effects Associated with Virtual Reality: An Analysis Based on Consumer Discussions Over Time

Since the release of the Oculus Rift CV1 in 2016, millions of VR headsets became available to consumers across the globe. Since then, many users have logged their experiences through online discussion forums. We found that many reported experiencing various “lingering effects” generally after spending at least a full hour in VR. Two major categories were identified and divided into sub-categories: perceptual effects and behavioral effects. Users agreed that these effects completely disappeared after several weeks and felt no long-term side effects. The topics of interest we identified here could be foundations for future research in laboratory settings.
3D modeling in the study of the basics of computer graphics

Abstract Teaching the basics of computer graphics is included in the process of training bachelors of different directions. Knowledge of the theoretical foundations of computer graphics is important for future successful professional activity. There are methods of increasing the efficiency of their study through the implementation of practical tasks in this article. Students are interested in performing tasks related to 3D modeling. They are proposed to develop three-dimensional interactive models for use in the study of other disciplines. Students develop independence, creative thinking, ability to plan their activities, when they create such models.
Recognition of Isothetic Arc Using Number Theoretic Properties

In this paper, we propose an arc recognition method based on the number theoretic properties of isothetic covers. A definition of digital circles is given based on the dilation of Euclidean circles with unit squares. We show that a variant of the digital circle is equivalent to the grid centers between the isothetic covers of disks. Number theoretic properties of the isothetic covers of disks are explored and show that the distribution of square numbers can be used to find the run lengths of the isothetic covers. Arc recognition algorithms are developed based on the number theoretic properties.
A Hyperledger Technology Approach to Mitigate the Risks of the Database in Foreign Trade Management

It was analyzed blockchain proposals such as architectures and models applied in different areas which increase the level of information security. The problem is the low diversity of proposals applied in hyperledger to the foreign trade area. The objective is to generate a Hyperledger-based architecture to mitigate the risks of information in foreign trade management. It was used the quantitative, narrative and deductive logic to study the referenced articles. It turned out a conceptual model for information management, architecture of data management, functions of Smart Contract and algorithm to record the information. It was concluded that this proposal on hyperledger platform mitigates the risks of information due to its characteristics of privacy, authentication and consensus among the participants; on simulations the architecture obtained 91.08% of average load.
TG-GAN: Deep Generative Models for Continuously-time Temporal Graph Generation.

The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design. However, many real-world problems involve temporal graphs whose topology and attribute values evolve dynamically over time, including important applications such as protein folding, human mobility networks, and social network growth. As yet, deep generative models for temporal graphs are not yet well understood and existing techniques for static graphs are not adequate for temporal graphs since they cannot 1) encode and decode continuously-varying graph topology chronologically, 2) enforce validity via temporal constraints, or 3) ensure efficiency for information-lossless temporal resolution. To address these challenges, we propose a new model, called ``Temporal Graph Generative Adversarial Network'' (TG-GAN) for continuous-time temporal graph generation, by modeling the deep generative process for truncated temporal random walks and their compositions. Specifically, we first propose a novel temporal graph generator that jointly model truncated edge sequences, time budgets, and node attributes, with novel activation functions that enforce temporal validity constraints under recurrent architecture. In addition, a new temporal graph discriminator is proposed, which combines time and node encoding operations over a recurrent architecture to distinguish the generated sequences from the real ones sampled by a newly-developed truncated temporal random walk sampler. Extensive experiments on both synthetic and real-world datasets demonstrate TG-GAN significantly outperforms the comparison methods in efficiency and effectiveness.
Research on Radar Echo Signal Noise Processing and Adaptive RLS Noise Reduction Algorithm

In the process of signal processing, noise will affect the accuracy of data processing and research. The accuracy obtained by different algorithms is different [1]. In this paper, a variety of radar signal denoising algorithms are discussed and simulated. Finally, the self-adaptive noise processing method [2] is described and optimized. The self-adaptive noise cancellation method is obtained. The comparison between the simulated data and the actual measurement data is obtained. It is proved that the self-adaptive RLS noise reduction effect studied in this paper is better, thus reducing the influence of noise.
A Multi-label Classification Approach for ICT Fault Text Analysis

The ICT customer service from State Grid has accumulated a large number of electric power fault reports and texts, which are difficult to classify and analyze because an occurrence of power fault may be triggered possibly by multiple different causes. In order to track different causes of electric power faults from historical fault texts, a multi-label based text classification approach is proposed for fault-assisted decision. Firstly, Chinese fault texts are sequentially preprocessed by word segmentation, stop words removal and feature representation. Then, we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm. The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification.
Color Volume and Hue-preservation in HDR Tone Mapping

The electrical-electrical transfer function (EETF), described in International Telecommunications Union—Radiocommunication (ITU-R) BT.2390–7, offers four variations, which allow high luminance imagery to be tone-mapped to a lower luminance in different ways. This is useful when converting high dynamic range (HDR) encoding spaces or rendering HDR content on screens that have limited performance. However, for some input colors, these variations introduce drastic changes in hue, in some cases, and do not restrict the output to the desired color volume, in other cases. In this article, a fifth variation is proposed, that preserves the input color’s hue while also restricting the output to the specified color volume. This proposed variation is compared and shown to outperform the four standardized variations.
Design and Implementation of Content-Based Natural Image Retrieval Approach Using Feature Distance

Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.
Building the Virtual Stage: A System for Enabling Mixed Reality Theatre

In this work, we present an infrastructure for enabling live theatrical productions that make use of virtual stage elements viewed using augmented reality devices. We present challenges that guided the design of the system and illustrate use of the framework through examples from live productions that utilize our prototype implementation.
High speed collision localization based on surface vibration processing

High speed or ultra-high speed collision localizations have been found wide applications in many fields, such as the protection of manned spacecraft, robots, vehicles, ships, machine tool collision damage detection and localizations, etc. In this paper, we develop a high speed collision detection and localization algorithm based on discrete wavelet transform (DWT) and kernel extreme learning machine (KELM). The collision localization is finally achieved using the estimated distances of multiple joint sensors. To validate the effectiveness of the proposed method, real vibrations collected from the collisions generated by three kinds of high speed bullets striking on the aluminium alloy plate are employed for experiments.
The Use of Social Media as E-Learning of Writing Skill in Learning English

To increase the student’s skills in learning English, the lecturer needs hard thinking to create a new method or model to teach the student in learning English. Based on the technology updates requirement the lecturer should be up to date about the information which can be linked to the English materials and it should be learning joyful to the students. Revolution of 4.0 is one of the lecturer target to get involved in English material, and the lecturer created by using the social media (Instagram) as e-learning of writing skill in learning English. This is one of the best method to stimulate the students to increase their knowledge in technology and English class. From the basic learning English by introducing their self to the senior class and lecturer as the method of the lecturer to find out the good skill in speaking and writing, means that speaking to other people and write down their experienced of positive and negative sentences in the comment of social media (Instagram). Based on this method the lecturer found that the students very attractive to study English more and more, and this method accepted by the students to increase their knowledge in speaking and writing skills
Reading on 3D Surfaces in Virtual Environments

While text tends to lead a rather static life on paper and screens, virtual reality (VR) allows readers to interact with it in novel ways: the reading surface is no longer confined to a 2D plane. We conducted two user studies, in which we assessed text rendered on different surface shapes in VR and their effects on legibility and the reading experience. Comparing differently curved surfaces, these studies disclose the impact of warp angles and view box widths on reading comfort, speed, and distraction. Our results suggest that text should be warped around the horizontal rather than the vertical axis, and we provide recommendations for the extent of warp and view box width. In a proof-of-concept application, we used everyday 3D objects as text canvases and studied them through an information-seeking task. The studies’ implications inform VR interfaces and, more generally, the rendering of text on 3D objects.