Reviewerly/exHarmony huggingface dataset:
Towards understanding and fixing upstream merge induced conflicts in divergent forks

Divergent forks are a common practice in open-source software development to perform long-term, independent and diverging development on top of a popular source repository. However, keeping such divergent downstream forks in sync with the upstream source evolution poses engineering challenges in terms of frequent merge conflicts. In this work, we conduct the first industrial case study of frequent merges from upstream and the resulting merge conflicts, in the context of Microsoft Edge development. The study consists of two parts. First, we describe the nature of merge conflicts that arise due to merges from upstream. Second, we investigate the feasibility of automatically fixing a class of merge conflicts related to build breaks that consume a significant amount of developer time to root-cause and fix. Towards this end, we have implemented a tool MrgBldBrkFixer and evaluate it on three months of real Microsoft Edge Beta development data, and report encouraging results.
Timely: A Chain of Custody Data Visualizer

Digital forensics is a growing field with a high need for qualified professionals but a lack of people to fill this need. As a result, there is a need for the creation of forensic tools to help streamline this process and to allow those in the field and those that are breaking into the field to be able to learn and succeed in their respective careers. In the field, chain of custody reports are used to track and document changes in possession or ownership of evidence. While the written report is standard, timelines have been shown to be effective visualization tools in both organizing and displaying information, as well as educating those who use them. In this paper, we are proposing a web based interactive data visualization timeline that organizes the chain of custody and evidence information in an easy to understand and easily accessible interface.
Phase information extraction for moiré fringes based on multiresolution analysis

In this paper, the multiresolution analysis (MRA) method is used to preprocess moiré fringes, which can reduce the number of data points and increase computation speeds. To discuss the applicability of the method, a candle combustion flow field is chosen as an example for experiment by moiré deflectometry. First, moiré fringes are preprocessed by the MRA method. Then, phase information extraction and refractive index reconstruction are performed on the three-level low-frequency approximation components. Finally, the involved results prove that the calculation time required for phase information extraction and refractive index reconstruction is greatly reduced based on the moiré fringes preprocessed by MRA method. The relative error could be accepted if the suitable approximation level is applied.
Algorithm for Motion Video Based on Basketball Image

To enhance quantitative evaluation of basketball motion, basketball video and image analysis are carried out in basketball training, and a feature extraction algorithm for basketball motion video based on edge contour gray detection is proposed. The image noise of the original basketball video frame is reduced by wavelet denoising method. The gray histogram analysis and edge contour extraction are carried out. Combined with the distribution of the adjacent frames of the basketball image, the electronic image stabilization compensation is carried out. According to the results of the electronic image stabilization of the basketball video, the fast capture and feature extraction of the basketball motion action are carried out, and the accurate analysis of the motion video is realized. The proposed algorithm can achieve high frame extraction accuracy of basketball video analysis and enhance recognition of basketball motion.
Question to Question Similarity Analysis using Morphological, Syntactic, Semantic, and Lexical Features

In the digitally connected world that we are living in, people expect to get answers to their questions spontaneously. This fact increased the burden on the Question/Answer platforms such as Stack Overflow and many others. A promising solution to this problem is to detect if a question being asked similar to a question in the database and present the answer of the detected question to the user. To address this challenge, we propose a novel Natural Language Processing (NLP) approach that detects if two Arabic questions are similar or not using their extracted morphological, syntactic, semantic, lexical features. Our approach involves several phases including Arabic text processing, novel feature extraction, and text classifications. To conduct our experiments, we used a real-world questions dataset consisting of 4,000 pairs of Arabic questions in which our approach achieved 78.2% accuracy using XGBoost model on the best features selected by the Random Forest feature selection technique. This high accuracy shows the ability of our approach to correctly detect the similarity between two Arabic questions.
Smooth Viewport Bitrate Adaptation for 360 Video Streaming

In Virtual Reality, 360-degree video is a major content type that provides immersive viewing experience for users. In this paper, we propose S-VAS, a new adaptation method for 360-degree video streaming, which can provide users with smooth viewport bitrate under network bandwidth variations and time-varying user head movements. Our proposed method adapts the number of extension tiles according to viewport prediction errors. Besides, variations in bitrate between adjacent tiles in a segment are mitigated by a novel tile version selection algorithm. Experimental results show that the proposed method can reduce the inter-segment bitrate variation by up to 46% and the intra-segment bitrate variation by up to 32%.
Clustering Big Datasets Using Orthogonal Gray Wolf Optimizer

Grey wolf optimization (GWO) is one of the many nature-inspired algorithms that is popular for solving labyrinthine optimization problems. The algorithm uses properties of hunting mechanism and leadership hierarchy of Grey wolves. This manuscript proposes a new variant of GWO called as Orthogonal Grey Wolf Optimization (OGWO). It is different from the original GWO in a sense that the position of wolves are not merely updated by averaging the movement towards three global leaders. Instead a combination termed as orthogonal methodology is used to determine the effective update position of the leader wolves. Here the methodology objective is to obtain the best possible combination of positions from the three global leaders. The simulation analysis on standard benchmark function reveals that the results obtained from the proposed algorithm are more optimal and have lesser standard deviation than the previous approach. In addition to this, the proposed algorithm is also successfully used on cluster analysis and very competent results are obtained when compared to other nature-inspired algorithms like original GWO, Particle Swarm Optimization (PSO), Orthogonal PSO (OPSO), Orthogonal Genetic Algorithm with Quantization (OGA).
Applying k-Nearest Neighbors to Increase the Utility of k-Anonymity

Nowadays, there are many organizations publishing and sharing their databases with other parties for different purposes, such as to conduct statistical surveys, business investigations, or health studies. However, this shared information is mostly public, and adversaries can use it to reveal and expose real identities; therefore, it is important for database owners to preserve the privacy of the individual's data. Previous researches in data anonymity have provided different privacy-preserving methods for protecting published data. However, the utility of the anonymized databases remains an important challenge and requires further studies. In this paper, we proposed a new way to increase the utility of the anonymized databases. We integrated kNN, a classification method, with k-anonymity to measure the similarities among multiple records. Finally, we show an example of how kNN can significantly maximizing the utility of the released databases while preserving data privacy.
Mold - A Framework for Entity Extraction and Summarization

With the dawn of Wikis, the collaboration of endless users and the rise of automated agents scraping thorough the internet in search of actionable information, it has become a necessity to create a framework that could serve as a translator between these two parties. Currently, moderators or developers serve this purpose by manually adding the required structure to natural language input while being assisted by one or more assisting applications. In short, the process still requires human intervention and that is what we are aiming to eliminate using the framework suggested in this paper. The framework is divided into two parts, one natural language processing module that uses state-of-the-art Part-of-Speech (PoS) taggers and our own algorithms to convert a natural language text into triples made up of predefined predicates, which are provided to the machine users (autonomous agents). The generated triples are also complemented with their own schema thus enabling machine-based reasoning on the text. The other part i.e. the summarization takes the triples generated by the previous step and creates a natural language text out of it so that the human users of the system could access the information without any knowledge of underlying triples and schema. The framework is trained on a large corpus of English text to optimally find the subject and the object of a given sentence along with the most probable predicate. The predicates will be stored in a separate place in an XML based syntax giving the users the functionality to add/update the schema and predicates as the need arises enabling easy adaptation of the framework to a specific domain.
Mobile Application and Its Universal Impact

The current market of mobile application market is expanding with variety and diversity. The rise of Android and iOS based mobile operating system providing similar playing field for all the applications. In this article, some credible evidence from the modern market and the new figures clarified the current situation of mobile applications and their position on the global market. In fact, we expect this paper to assist with issues relating to mobile applications in general and mobile phone in general in the fields of study.
Data Security Risk and Preventive Measures of Virtual Cloud Server Based on Cloud Computing

Cloud computing is another technological revolution of the Internet. It is the product of the development of the information age, which has changed the way people use data and applications. The core idea of cloud computing is to virtualize server entities into logical servers, forming a large cloud resource pool, and providing services such as common computing, parallel processing, and shared resources. However, with the continuous development of cloud computing technology, the large amount of user and application data processed on the virtual cloud server is vulnerable to various security threats. This paper mainly analyzes the data security risks of the virtual cloud server from three aspects of data transmission, data storage, and data application, and proposes corresponding data security precautions to ensure the data security of the virtual cloud server and build a good cloud service network security environment.
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modelling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited–bootstrapping from the features returned by Word Embedding mechanisms–to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.
Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions

The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user’s free-text responses, and probes answers whenever needed. To investigate the effectiveness and limitations of such a chatbot in conducting surveys, we conducted a field study involving about 600 participants. In this study with mostly open-ended questions, half of the participants took a typical online survey on Qualtrics and the other half interacted with an AI-powered chatbot to complete a conversational survey. Our detailed analysis of over 5,200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses measured by Gricean Maxims in terms of their informativeness, relevance, specificity, and clarity. Based on our results, we discuss design implications for creating AI-powered chatbots to conduct effective surveys and beyond.
CHOOSING SEEDS FOR SEMI-SUPERVISED GRAPH BASED CLUSTERING

Though clustering algorithms have long history, nowadays clustering topic still attracts a lot of attention because of the need of efficient data analysis tools in many applications such as social network, electronic commerce, GIS, etc. Recently, semi-supervised clustering, for example, semi-supervised K-Means, semi-supervised DBSCAN, semi-supervised graph-based clustering (SSGC) etc., which uses side information, has received a great deal of attention. Generally, there are two forms of side information: seed form (labeled data) and constraint form (must-link, cannot-link). By integrating information provided by the user or domain expert, the semi-supervised clustering can produce expected results. In fact, clustering results usually depend on side information provided, so different side information will produce different results of clustering. In some cases, the performance of clustering may decrease if the side information is not carefully chosen. This paper addresses the problem of efficient collection of seeds for semi-supervised clustering, especially for graph based clustering by seeding (SSGC). The properly collected seeds can boost the quality of clustering and minimize the number of queries solicited from the user. For this purpose, we have developed an active learning algorithm (called SKMMM) for the seeds collection task, which identifies candidates to solicit users by using the K-Means and min-max algorithms. Experiments conducted on real data sets from UCI and a real collected document data set show the effectiveness of our approach compared with other methods.
STEM CLUB FOR EXTRACURRICULAR WORK - A WAY TO STIMULATE STUDENTS

This article shares experience related to students' assessment of STEM education at English Language School “Geo Milev“, Ruse, Bulgaria. Observing students (and their parents) motivation and commitment is one of many indicators of success for a STEM club. The tools provided in the development are easy to use and can provide rapid feedback to students and measure their effectiveness. They are intended to be ancillary tools for collecting data for the club, for its progress, for evaluating its development and for planning its next goals. STEM training was conducted with a total of 7 groups of students aged 14 to 17 years. The study covers a period of several months. The classes are in groups for optional training in information and communication technologies. In this case, standard methods for assessing progress are not applicable. There are no tests or control tasks. The teacher enters a new role - mentor and facilitator. The main approach in training is project training. Therefore, the development pays particular attention to the formulation of the task, the study of student progress and the evaluation of teamwork. STEM clubs and optional STEM training groups are a free space for extracurricular training. In them, students acquire and master competences in science, technology, engineering and mathematics. The teacher designs the design of this type of training as a mentor and facilitator and works in an environment other than traditional formal learning spaces.
RETRACTED: Design Method of English Education Informationization Platform Based on 5G

Abstract In recent years, when carrying out educational activities, modern information technology and Internet technology have been integrated with each other to form a variety of educational information platforms, which have gradually replaced the traditional education and teaching methods. Using Internet thinking to improve the application level of education informatization is helpful to improve the quality of education and is of great significance to comprehensively promote the quality education of students. English is an important subject in student learning activities. English learning can improve the comprehensive ability of students, promote the healthy development of students, and enable students to better meet the needs of society. In order to improve learning efficiency, optimize the learning platform, and meet the needs of users, it is imperative to establish a 5G English education information platform. 80% of people in the current society are expecting the arrival of 5G platforms. Considering that in a high-density scenario, the number of terminals is large and the traffic is intensive. If the event scheduling method is still used to process data packets, there will be problems of too many event generations and too long processing time. Computing packet processing architecture. This architecture divides the time axis into multiple time slots. Based on the length and generation time of each data packet, the source terminal number of each data packet is mapped to the corresponding time slot position. By calculating the effective signals and interference in each time slot The strength of the signal is used to obtain performance indicators such as system capacity and throughput.
Datalog with Negation and Monotonicity.

Positive Datalog has several nice properties that are lost when the language is extended with negation. One example is that fixpoints of positive Datalog programs are robust w.r.t. the order in which facts are inserted, which facilitates efficient evaluation of such programs in distributed environments. A natural question to ask, given a (stratified) Datalog program with negation, is whether an equivalent positive Datalog program exists.In this context, it is known that positive Datalog can express only a strict subset of the monotone queries, yet the exact relationship between the positive and monotone fragments of semi-positive and stratified Datalog was previously left open. In this paper, we complete the picture by showing that monotone queries expressible in semi-positive Datalog exist which are not expressible in positive Datalog. To provide additional insight into this gap, we also characterize a large class of semi-positive Datalog programs for which the dichotomy `monotone if and only if rewritable to positive Datalog' holds. Finally, we give best-effort techniques to reduce the amount of negation that is exhibited by a program, even if the program is not monotone.
Application of Video Recognition Technology in Sports Stunt Teaching

Abstract In recent years, with the rapid development of sports in our country, the national importance of sports is also increasing. In order to further improve the level of physical education in our country, we began to pay attention to the teaching of physical stunt. Sports stunt is difficult and dangerous. These characteristics determine that sports stunt teaching should improve teaching efficiency as much as possible. Therefore, the decomposition and identification of sports stunt is particularly important. The traditional sports stunt teaching can not identify the movements accurately, which brings some difficulties to the sports stunt teaching. Video recognition technology provides a new means for the teaching of physical stunt and greatly improves the teaching effect. This paper first gives an overview of video recognition and sports stunt, analyzes the advantages of video recognition technology in sports stunt teaching, and analyzes its feasibility based on data feature screening algorithm. It not only promotes the further integration of video recognition technology and teaching, but also deepens the research on this aspect.
Learning Multimodal Fixed-Point Weights using Gradient Descent

Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-point quantization, we propose a gradient-based optimization strategy to generate a symmetric mixture of Gaussian modes (SGM) where each mode belongs to a particular quantization stage. We achieve 2-bit state-of-the-art performance and illustrate the model's ability for self-dependent weight adaptation during training.
Phased Groupwise Face Alignment

A face does not only have rigid variations but also non-rigid distortions, which has influenced the performance of groupwise face alignment. A novel method for groupwise face alignment which considers both rigid variations of a face and non-rigid distortions was presented in the paper. The process for groupwise face alignment was divided into two stages, i.e. affine transformations and non-rigid distortions. At the stage of the affine transformations, the key points of a face were categorized into five groups and the affine transformations were used for each group of the key points. At the stage of the non-rigid distortions, a novel method was used for all of the key points in a face. Two stages were independent of each other, and moreover, iterations were made in each stage. Besides, the results from the stage of the affine transformations were used as the input of the non-rigid distortion stage. Experiments show that the method for groupwise face alignment in the paper is better than that only considering global affine variations, and is also better than that considering global affine variations and local non-rigid distortions. The method in the paper can be used as a novel method for groupwise face alignment.