Reviewerly/exHarmony huggingface dataset:
A LambdaMart-Based High-Accuracy Approach for Software Automatic Fault Localization

Software debugging or fault localization is a very significant task in software development and maintenance, which directly determines the quality of software. Traditional methods of fault localization rely on manual investigation, which takes too much time in large-scale software development. To mitigate this problem, many automatic fault localization techniques have been proposed which can effectively lighten the burden of programmers. However, the quality of these techniques is not enough to meet the practical requirements. In order to improve the accuracy of fault localization, we propose LBFL, a LambdaMart-based high-accuracy approach for software automatic fault localization, which can integrate software’s diversified features and achieve very high accuracy. To realize that, LBFL first extracts the static and dynamic features and normalizes them. Then these features are gathered on LambdaMart algorithm for training. Finally, LBFL sorts the code statements according to the model and generates a list which can help developers to locate faults. Exhaustive experiments indicate that LBFL can locate 76 faults in Top-1, which has at least 217% improvements over nine single techniques and has 55% improvements over ABFL approach on the Defects4J dataset.
A Multi-person collaborative Simulation System For Circuit Experiment System Base on Virtual Reality

The learning of physical circuit experiments has the characteristics of both theory and practice. In order to enhance students' hands-on ability and reduce the difficulty and cost of practical teaching, a virtual reality-based multi-person collaborative circuit 3D virtual experiment simulation system is proposed. We use 3ds max to construct a circuit simulation lab, and use socket technology to realize multi-person online interaction. And use Photon network engine to realize multiplayer linkage. The system includes two main parts, circuit experiment practice and custom experiment, and circuit simulation simulation through 3D interface. The system can use wires to connect the circuit components and display the experimental results in real time through the interface, visually reflecting the impact of different circuit connections and parameter changes on the simulation results, and has the function of students using the equipment library to connect and create circuits independently, so that they can build circuits independently to realize the simulation of innovative experiments.
ANALYSIS OF PROPOSAL NEW CONCEPT FOR PRIMARY EDUCATION - A NEW APPROACH TO THIS EDUCATION CYCLE

Education today is a very important problem all over the world, as well as in our country, so its reform is the main intention of the leaders of this digester. Education reforms have started to be implemented in previous years and continue to be relevant today because quality and success depend on these reforms. In relation to this issue, the Concept for primary education has been drafted, according to which a contemporary approach to learning is provided. This paper aims to analyze the Concept and verify how much its content actually offers something new in this cycle of education and is suitable for teachers. Like any research that is associated with the methodological part, in this paper we will use the method of theoretical analysis, to have clearer results we will use tools and techniques such as the questionnaire for teachers, from which we will get the opinions and attitudes of them in relation to this Concept. We will present the achieved results in a tabular way in order to have a clear picture about them and in this way we will notice how the new Concept will be a new opportunity for quality assurance in primary education in our country.
Analyzing and Recommending Development Order Based on Design Class Diagram

In the system design process, it is an important issue to consider the order of class development. Different orders of class development may have great impact on the cost, efficiency and fault tolerance of the project. Because of that, it is an essential issue to consider which class should be developed before the others. In this paper, we present an approach to recommend a reasonable development order of classes with minimum development cost based on design class diagram and genetic algorithm. It helps the designer to improve their development strategy and to prevent mistakes resulted from improper development order of classes. We also provide a phase tree to help developers visualize and analyze the details of each development phase. At last, we implement a tool and illustrate that the proposed approach is sound and effective with two case studies.
Progressive Mesh-based HTTP Adaptive Augmented Reality Streaming System

This work presents a progressive mesh-based HTTP adaptive augmented reality (AR) streaming system that provides high quality AR streaming services with low latency over a time-varying wireless network. In the proposed system, a progressive mesh is employed to support the scalability of AR streaming services, a novel meta-file is proposed to efficiently provide the rate and view-dependent quality information. By using this meta-file, the proposed system determines the next request chunk to enhance human visual perceptual quality considering the view-dependent projected images of AR objects on the screen. The proposed system is realized using open sources (e.g., Android, OpenCV, OpenGL, and Nexus). Finally, experimental results are given to verify the performance improvement of the proposed system.
Learning Distributional Programs for Relational Autocompletion

Abstract Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DCs), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML – an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation–maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.
EFL UNDERGRADUATE STUDENTS’ SUMMARIES: THE PRODUCT AND THE RESPONSE

Students can do some ways to help them in the learning process in English class. One of them is summarizing from both lectures or books. This research focuses on the book summarization written by EFL students. Summarizing may help the students to comprehend the materials from the sourcebook.  Therefore, this research aimed to identify the type of summaries used by EFL undergraduate students and to describe their responses toward summary writing. This study was conducted in a university in Surabaya. The researcher used two instruments to collect the needed data which were the Type of Summaries rubric proposed by Horney (2009), and the second is focus group discussion. After getting the data, the results were analyzed qualitatively by occupying the three stages; familiarizing & organizing, coding & reducing, and interpreting & representing. The results showed that type of summaries used by the students were copy one sentence, copy multiple sentences, own words, and combination type. Moreover, it is also found that the students had difficulties in the process of summarizing. In conclusion, it is proven that students still plagiarized when summarizing from the source book and, they did not know how to summarize effectively. The implications of these findings are considered for academic individual to explore their students with summarizing and paraphrasing strategies.
Why you walk like that: Inferring Body Conditions from Single Gait Cycle

Gait is a key barometer to analyze human body conditions. We propose a personalized gait analysis framework which can diagnose a possible muscularskeletal disorders with a single gait cycle. Our framework built over a gait manifold which reveals the principle kinematic characteristics in the temporal pose sequence. Body parameters such as muscle, skeleton, and joint limits for an arbitrary gait cycle can be approximated by measuring similarity in the small latent space. We present a physical gait simulator to enrich the gait space paired with the body conditions.
Sarcasm Detection for Japanese Text Using BERT and Emoji

In this paper, we propose methods to detect sarcasm from Japanese text on Twitter by using the BERT language model and analyzing emoji as well as text. After constructing a Japanese Twitter dataset, we extract feature vector for both text and emoji. Our experimental results show that the usage of BERT and emoji can help improve the performance of sarcasm detection.
Web Crawling on News Web Page using Different Frameworks

Now a days Internet is widely used by users to find required information. Searching on web for useful information has become more difficult. Web crawler helps to extract the relevant and irrelevant links from the web. Web crawler downloads web pages through the program. This paper implements web crawler with Scrapy and Beautiful Soup python web crawler framework to crawls news on news web sites.Scrapy is a web crawling framework that allow programmer to create spider that define how a certain site or a group of sites will be scraped. It has built-in support for extracting data from HTML sources using XPath expression and CSS expression. BeautifulSoup is a framework that extract data from web pages. Beautiful Soup provides a few simple methods for navigating, searching and modifying a parse tree. BeautifulSoup automatically convert incoming document to Unicode and outgoing document to UTF-8.Proposed system use BeautifulSoup and scrapy framework to crawls news web sites. This paper also compares scrapy and beautiful Soup4 web crawler frameworks.
Enhancement of Big Data Privacy Preserved of Map Reduce by Using Data Anonymization Technique

The objective of the present research paper is to provide the services to clients about the imparting private information like Electronic wellbeing records for information or mining, going with security concerns a wide number of cloud administrations. It is set through speculation to give anonymous information and fulfill specific security prerequisites, for example, k. k anonymous mostly provides classification of protection protecting procedures. In the present time, according to big data pattern the size of information increases in multiple cloud application. It is a test for generally utilized programming device to catch, oversee and processing such huge scale of information inside an average glided by time. It is a test for persisting anonymization ways to reach for protection and security of fragile but it is delicate extensive scale information sets because of its lack of flexibility. The present research work is worker node process that contacts to the cache manager every time before it starts processing an input data file. The file name and operations make plan for the application of the file to cache manager is sent by the worker node process. The cache manager mechanically spots the best-matched cache items for feeding every reducer, which is in the original input file in the maximum overlapping in the map phase.
On some punctured codes of ℤq-Simplex codes

In this paper, we have constructed some new codes from [Formula: see text]-Simplex code called unit [Formula: see text]-Simplex code. In particular, we find the parameters of these codes and have proved that it is a [Formula: see text] [Formula: see text]-linear code, where [Formula: see text] and [Formula: see text] is a smallest prime divisor of [Formula: see text]. When rank [Formula: see text] and [Formula: see text] is a prime power, we have given the weight distribution of unit [Formula: see text]-Simplex code. For the rank [Formula: see text] we obtain the partial weight distribution of unit [Formula: see text]-Simplex code when [Formula: see text] is a prime power. Further, we derive the weight distribution of unit [Formula: see text]-Simplex code for the rank [Formula: see text] [Formula: see text].
Fuzzy Ontology-Based Possibilistic Approach for Document Indexing Using Semantic Concept Relations

To overcome the weaknesses of current information retrieval system and to utilize the strengths knowledge extraction a novel approach based on fuzzy ontology and possibility theory is proposed for indexing documents. Possibility theory allows to model and quantify the relevance of a document given a controlled vocabulary through two measures: necessity and possibility. Fuzzy ontology is used to improve the indexing process on information retrieval by means of external resource. Besides, the fuzzy approach has been proposed in order to make the availability of terms representations in a document more flexible. It allows a formal representation of a knowledge domain in the form of a hierarchical terminology provided with semantic relationships. As a result, the proposed approach has been made on different corpora, had better performance than other indexing approaches and it prove important results.
Information-analytical semantic model of an expert system for studying the co-evolution of the sociotechnical landscape in digital reality

Abstract Currently, the development of society is largely determined by the expansion of digital technology into various social practices. Prediction and management of the co-evolution of society in digital reality is determined by the need to ensure sustainable development of society and adequate satisfaction of practices with information technology. The aim of the study was the development and pilot study of the capabilities of the information-analytical semantic model of an expert system for the analysis and management of socio-technical landscapes (STL) in digital reality. The network semantic structure of the knowledge base with unified decision modules in network nodes is substantiated. An enlarged diagram of the model is given and its functioning is described. Examples of the pilot application of the crucial modules of the expert system are examined: social practice “medicine” and digital technologies: “Artificial Intelligence”, “Information Technology”, “Mobile Technologies”, “Internet of Things”, “Big Data”. There are sharp changes in the ratios of feasibility and demand for technologies that occurred in the first half of 2020 compared to 2019 (which is associated with the consequences of the COVIN-19 pandemic). It is shown that the STL reaction is modeled by a transient process characteristic of the vibrational link.
Identification of Pocket and Wrist Watches of the Second Half of the XIX - the First Half of the XX Century: Basic Methods, Recommended Algorithm

The purpose of the research is to determine an algorithm of proceeding with the study and identification of pocket and wrist watches and watch mechanisms of the second half of the XIX - the first half of the XX century and propose it to the experts. Scientific novelty lies in the proposed algorithm for collecting and analysing information that makes it possible to identify the manufacturer, place of production and the history of an object manufacture itself, as well as technical novelty of the studied process. The result of the conducted research of pocket and wrist watches amounts to the algorithm and methodology for studying analogous items for the purpose of their identification.
علاقة الريادة التكنولوجية بنجاح المشروعات الصغيرة (The Relation Between Technological Entrepreneurship and the Success of Small Businesses)

Arabic Abstract: يهدف البحث إلى دراسة أثر الريادة التكنولوجية على نجاح المشروعات الصغيرة ، وقد استخدم الباحث المنهج الوصفي التحليلي في إجراء الدراسة ، وتم تطبيق الاستبانة كأداة لجمع المعلومات على عينة البحث المكونة من (39) مفردة ، وقد توصل الباحث إلى التوصيات الآتية : حث وزارة التربية والتعليم على تهيئة المناهج وطرائق التدريس بحيث تحتوي مقومات خلق التفكير الإبداعي لدى الطلبة وخريجي الجامعات، والعمل على خلق بيئة تتلاءم مع الريادين حتى يتمكنوا من تطوير تفكيرهم في خلق أفكار يمكن تحويلها إلى فرص حقيقية، ووجود حضانات تكنولوجية لكل مؤسسة أو جامعة من أجل دورها الفاعل في تعزيز نجاح الأعمال الصغيرة، وتوجيه الاهتمام من قبل الحكومة والمجتمع نحو أهمية المشروعات الصغيرة لغرض المساهمة في التنمية الاقتصادية.English Abstract: The research aims to study the effect of technological entrepreneurship on the success of small projects. The researcher used the descriptive analytical approach in conducting the study, and the questionnaire was applied as a tool to collect information on the research sample consisting of (39) individuals. The researcher reached the following recommendations: Urging the Ministry of Education to Create curricula and teaching methods that contain the foundations of creating creative thinking for students and university graduates, and work to create an environment appropriate for entrepreneurs so that they can develop their thinking in creating ideas that can be converted into real opportunities, and the existence of technological incubators for each institution or university for its effective role in promoting success small project, directing attention by the government and society towards the importance of small projects for the purpose of contributing to economic development.
Design and Research of Virtual Reality Teaching Assistant System for Colleges and Universities Experimental Course Under the Background of Wisdom Education

In order to overcome the problems of single teaching mode, lack of physical space, high cost of equipment and low initiative of students in traditional laboratory education in colleges and universities, a virtual reality teaching assistant system based on virtual reality technology (VR) is designed. The design of the system is mainly divided into the design of the preliminary plan of the system, the design of the system functions as well as the design of the system application scenarios. Among these, the functions of the system mainly include the display function of VR experiment effect, the simulation function of VR experiment, and the evaluation function of VR experiment knowledge. Students interact with the virtual scene through VR helmet and VR handle to understand the purpose and effect of the experiment, participate in the operation of the experiment, master the experimental process and acquire experimental knowledge. In the system test, taking the computer host assembly experiment as a case, the experimental result indicated that the application of the system in the experimental teaching process of colleges and universities is conducive to improving students’ learning initiative and knowledge absorption efficiency.
Robust Discriminant Subspace Clustering With Adaptive Local Structure Embedding

Unsupervised dimension reduction and clustering are frequently used as two separate steps to conduct clustering tasks in subspace. However, the two-step clustering methods may not necessarily reflect the cluster structure in the subspace. In addition, the existing subspace clustering methods do not consider the relationship between the low-dimensional representation and local structure in the input space. To address the above issues, we propose a robust discriminant subspace (RDS) clustering model with adaptive local structure embedding. Specifically, unlike the existing methods which incorporate dimension reduction and clustering via regularizer, thereby introducing extra parameters, RDS first integrates them into a unified matrix factorization (MF) model through theoretical proof. Furthermore, a similarity graph is constructed to learn the local structure. A constraint is imposed on the graph to guarantee that it has the same connected components with low-dimensional representation. In this spirit, the similarity graph serves as a tradeoff that adaptively balances the learning process between the low-dimensional space and the original space. Finally, RDS adopts the l2,1 -norm to measure the residual error, which enhances the robustness to noise. Using the property of the l2,1 -norm, RDS can be optimized efficiently without introducing more penalty terms. Experimental results on real-world benchmark datasets show that RDS can provide more interpretable clustering results and also outperform other state-of-the-art alternatives.
A Novel Approach for Emotion Detection from Text Data using Natural Language Processing and Machine Learning

Abstract - Emotion can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Emotion Detection in text documents is essentially a content– based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Learning. In this paper we are proposing a solution for emotion recognition based on textual data. The emotion expressed in a blog, review or any kind of textual content remains unused until the text is analyzed and the emotion is retrieved from the data. It is impossible to analyze the huge amount of data manually and gain information from it.
Deep Learning and Digital Pathology Power Precision Treatment of Triple-Negative Breast Cancer

Background: Molecular stratification and tailored target therapy bring clinical benefit for patients with triple-negative breast cancer (TNBC), but it is difficult to implement comprehensive molecular testing in clinical practice. Thus, we aim to devise an approach based on digital pathology and deep learning for the molecular and prognostic stratification of TNBC.Methods: We collected digital whole slide images (WSIs) (N=425) of our previously established TNBC cohort with multi-omics data. A deep learning-based workflow was developed and applied to these WSIs to train and validate neural network models to predict multi-omics molecular features, to identify molecular subtypes, and to improve prognostic evaluation. The models that showed high prediction accuracy were further validated on the TNBC cases from TCGA (N=143).Findings: A model was first developed for automatic tissue type classification, which enabled selection of certain tissue types on WSIs for the following prediction. Numerous molecular features can be inferred from WSIs including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 expression. All of the four molecular subtypes of TNBC can be accurately identified based on WSIs and distinctive morphological patterns were revealed for each subtype. The addition of image features to clinical prognostic factors significantly improved the accuracy of relapse risk assessment. The models for predicting PIK3CA mutation, PD-L1 expression, TNBC subtypes and relapse risk can be well generalized to the TCGA TNBC cases. The complete prediction workflow along with validated models was modularized and deployed on an online platform, which can realize real-time one-stop prediction for newly-uploaded WSIs.Interpretation: We proposed a deep learning-based workflow and developed neural network models to predict clinically relevant information of TNBC from pathological WSIs. Our findings and established platform may enable the implementation of artificial intelligence guided precision treatment in clinical trials and future routine practice.Funding: Fudan University Shanghai Cancer Center.Declaration of Interest: None to declare Ethical Approval: The tissue samples used in our study were obtained after the approval from the FUSCC Ethics Committee.