Reviewerly/exHarmony huggingface dataset:
An Attribute Matcher for Test-to-Code Traceability

Execution of test cases without considering the related source files makes test-to-code traceability difficult. Most of the current approaches depend on the idea of directly accessing from test relevant dataset or programs to source files. But the data needed to access source files is already generated and tracked as work items and change sets. Since in most of the software companies, development and testing teams and their processes are separated, the relevant data is also generated and tracked redundantly, without considering the test case-to-work item traceability. In this paper, we propose a matching method, AMT (Attribute Matcher for Traceability) for matching test cases and work items. The proposed method is based on mining the attributes and data of test and work item artifacts. AMT enables two-way matching; such as test to work item and work item to test matching. In this way, an estimated work item set can be generated for a test case. We apply the proposed method on the two sub-repositories of a proprietary collaborative lifecycle management software (PS). The results show that AMT can match test and work item objects with a level of high accuracy.
A Comparative Study and Simulation of Object Tracking Algorithms

With the development of artificial intelligence technology, the problems of object tracking are getting more and more attention. This article introduces the popular object tracking algorithms, from common problems in object tracking to the classification of algorithms: Early classic tracking algorithms, tracking algorithms based on kernel correlation filtering, and tracking algorithms based on deep learning. Then the original version and various improved versions of each type of tracking algorithm are introduced, analyzed, and compared. Finally, we use the OTB-2013 data set to test the above 50 object tracking algorithms. The simulation experiment obtained the accuracy figure and success figure of the overall performance comparison of these 50 object tracking algorithms and then selected 11 attributes to compare the performance of various algorithms. The 11 attributes are illumination variation, out-of-plane rotation, scale variation, occlusion, deformation, motion blur, fast movement, in-plane rotation, out of view, background clutter, and low resolution. The accuracy figures and success figures of these 50 algorithms are obtained respectively under the different conditions. The experiment mainly analyzes the results through three indicators of accuracy, success rate, and tracking speed of various algorithms, and draws the following conclusions: Compared with traditional algorithms, the tracking speed of correlation filtering algorithms can reach more than 100 frames per second, while the precision of deep learning methods can reach more than 0.7. The algorithms using convolution features and multi-features fusion algorithms have more advantages in tracking accuracy than the algorithm using a single feature, but the tracking speed will also drop rapidly. Using a powerful classifier is the basis of achieving good tracking. Tracker with scale estimation module can get better performance of success figures. The updating mechanisms of the model also affect the tracking accuracy.
Realistic Text Replacement With Non-Uniform Style Conditioning

In this work, we study the possibility of realistic text replacement. The goal of realistic text replacement is to replace text present in the image with user-supplied text. The replacement should be performed in a way that will not allow distinguishing the resulting image from the original one. We achieve this goal by developing a novel non-uniform style conditioning layer and apply it to an encoder-decoder ResNet based architecture. The resulting model is a single-stage model, with no post-processing. We train the model with a combination of adversarial, style, content and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> losses. Qualitative and quantitative evaluations show that the model achieves realistic text replacement and outperforms existing approaches in multilingual and challenging scenarios. Quantitative evaluation is performed with direct metrics, like SSIM and PSNR, and proxy metrics based on the performance of a text recognition model. The proposed model has several potential applications in augmented reality.
mlf-core: a framework for deterministic machine learning

Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas. To allow appropriate verification of predictive models before deployment, models must be deterministic. Solely fixing all random seeds is not sufficient for deterministic machine learning, as major machine learning libraries default to the usage of nondeterministic algorithms based on atomic operations.Various machine learning libraries released deterministic counterparts to the nondeterministic algorithms. We evaluated the effect of these algorithms on determinism and runtime. Based on these results, we formulated a set of requirements for deterministic machine learning and developed a new software solution, the mlf-core ecosystem, which aids machine learning projects to meet and keep these requirements. We applied mlf-core to develop deterministic models in various biomedical fields including a single-cell autoencoder with TensorFlow, a PyTorch-based U-Net model for liver-tumor segmentation in computed tomography scans, and a liver cancer classifier based on gene expression profiles with XGBoost.The complete data together with the implementations of the mlf-core ecosystem and use case models are available at https://github.com/mlf-core.
Development of math education comics through digital android

Abstract This research aims to develop mathematics education comics through digital android. This type of research is a combined RND research from Brog and Gall and MPI. The product was developed through several stages by manually designing, scanning, providing colour with photoshop, giving text balloons after that the process of digitizing android is done using PowerPoint, Ispring and Web2APK which can be operated through android, gadgets offline and online. The evaluation of this product was assessed by several experts namely experts in language, material, graphic design and colleagues. Data collection techniques using a questionnaire and then analyzed descriptively quantitative. The results of the assessment of experts get good criteria from several expert validators, language, material, and graphic design conclude that this product is feasible to use.
A Gesture Cognition Strategy for High-speed Train Drivers on Reconstructed Multiple Views

The driver of high-speed trains usually is required to perform certain gestures to confirm the signals before implementing some operations, which is an essential validation for driving safety. However, the accuracy of gesture recognition is difficult to guarantee due to the jamming background and limited perspective. In this paper, the features of the side view and vertical view are integrated to assist classification decisions. Firstly, point clouds of the gesture are generated with RGB-D data and then projected onto two orthogonal planes to reconstruct the side and vertical view of the gesture. Secondly, multiple-view 3D Convolution Neural Network architecture is proposed with three branches of Convolution Neural Network. Combined with the front view obtained by frame difference, the model learns convolution features from three aspects of the gesture. Further, multiple-view classification results are adaptively fused to acquire the final decision. Experiments show that our approach is superior to the state-of-the-art gesture recognition methods on challenging dataset.
Multi-dimensional Impact Detection and Diagnosis in Cellular Networks

Performance impacts are commonly observed in cellular networks and are induced by several factors, such as software upgrade and configuration changes. The variability in traffic patterns across different granularities can lead to impact cancellation or dilution. As a result, performance impacts are hard to capture if not aggregated over problematic features. Analyzing performance impact across all possible feature combinations is too expensive. On the other hand, the set of features that causes issues is unpredictable due to the highly dynamic and heterogeneous cellular networks. In this paper, we propose a novel algorithm that dynamically explores those network feature combinations that are likely to have problems by using a summary structure Sketch. We further design a neural network based algorithm to localize root cause. We achieve high scalability in neural network by leveraging the Lattice and Sketch structure. We demonstrate the effectiveness of our impact detection and diagnosis through extensive evaluation using data collected from a major tier-1 cellular carrier in US and synthetic traces.
Research on the Balanced Development of High Quality Compulsory Education

The research follows the basic principles of "more emphasis on hardware and software, more emphasis on qualified indicators, more emphasis on mass satisfaction, and more emphasis on quantity and quality" In order to solve the problem of imbalance between urban and rural teachers, it is necessary to explore effective ways of balanced development of compulsory education from the five aspects of technical support and mechanism coordination, and to infiltrate modern information technology into the optimal allocation of teachers.
The History, Features, and Prospects of the Chinese Style Teaching Research System

The Chinese teaching research system was created, improved, and developed in response to the ongoing process of addressing issues and needs in teaching in China's primary and secondary education system. The teaching research system is an important and integral part of the Chinese approach to managing teaching. Its 70-year course of development can be divided into six periods: initial creation period (1949–1956)–starting from nothing; development period (1957–1965)–gradual improvement; setback period (1966-1976)–at a low ebb; restoration period (1977–1984)–regrouping; standardization period (1985–1999)–great prosperity; improvement period (2000–)–transformation and innovation. The teaching research system developed alongside the changes in China's basic education and the ongoing development of primary and secondary education curricula, teaching materials, and teaching, becoming the core supporting mechanism ensuring reforms to curricula, teaching materials, and teaching, and improving the quality of teaching education, and the professionalization of teachers. The teaching research system has formed a set of relatively mature, improved, and effective multi-level province, city (district, county), and school, teaching research systems, and has formed teaching research groups that have accumulated rich teaching experience, substantial academic achievement, and strong management and leadership capabilities. The teaching research work emerges out of the practical needs of teachers, and its objective is to resolve teachers’ day to day problems encountered in the classroom and in the implementation of the curriculum. The Chinese teaching research system faces new challenges as curriculum reform deepens, the education level and level of professional development of primary and secondary teachers improves, and multimedia technology advances. These changes require a deepening of the work of the teaching research system, demanding greater professionalization, standardization, and scientific backing.
Research on Quantum Programming Framework Based on NISQ Function

Abstract In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints.
Character Set Construction for Chinese Language Learning

To promote efficient learning of Chinese characters, pedagogical materials may present not only a single character, but a set of characters that are related in meaning and in written form. This paper investigates automatic construction of these character sets. The proposed model represents a character as averaged word vectors of common words containing the character. It then identifies sets of characters with high semantic similarity through clustering. Human evaluation shows that this representation outperforms direct use of character embeddings, and that the resulting character sets capture distinct semantic ranges.
MI-FGSM on Faster R-CNN Object Detector

The adversarial examples show the vulnerability of deep neural networks, which makes adversarial attacks widely concerned. However, most of the attack methods are based on image classification model. In this paper, we use Momentum Iterative Fast Gradient Sign Method (MI-FGSM), which stabilize optimization and escape from poor local maxima, to generate adversarial examples on the Faster R-CNN object detector. We have made some improvements on the previous object detection attack methods. The best current attack method, Project Gradient Descent (PGD) on object detection, starts from a random value, resulting in the uncertainty of the attack result. In contrast, our attacks are more stable and powerful in both white-box attacks and black-box attacks, and can better adapt to various neural network architectures. Experiment on Pascal VOC2007 shows that, under same setting of white-box attack, PGD has 0.23% mean average precision (mAP) on Faster R-CNN with VGG16, while our method achieves 0.17%. In addition, we analyze the difference between classification and detection attacks, and find that in addition to misclassification, the adversarial examples produced by detection models can also lead to mislocation.
Fuzzy Fault Tree Analysis Method and Its Application in Fault Diagnosis of Denitration System in Thermal Power Plant

The Fuzzy Fault Tree (FFT) analysis method is an important fault diagnosis tool in the current engineering field, and has become one of the research hotspots at home and abroad. This paper introduces the basic principle of FFT analysis method and compares the advantages and disadvantages of various FFT algorithms and their impact on system performance in detail. It also proposes an FFT analysis method in the fault diagnosis of the denitration system in thermal power plants, and finally forecasts the development prospects of FFT.
Hybrid Genetic Algorithms to Solve the Traveling Salesman Problem

This research report introduces two new hybrid genetic algorithms and compares them to solve the traveling salesman problem (TSP). The new algorithms are a combination of a genetic algorithm (GA) with a nearest neighbor algorithm (NNA). The NNA is used to influence the population of the GA and the selection of the parents in the GA. The results of the conducted experiments, performed in MATLAB environment, have shown that the new algorithms can be successfully used to solve the TSP. Depending on the size of the TSP, the new algorithms achieve better results than the initial GA, which is also used. However, in terms of selecting the best algorithm, no conclusive results were obtained and the best choice depends on the particular TSP.
Use of Cryptography for Monetary Issuance and Securing Financial Transactions

During the financial subprime crisis (2007/2008), the banking system showed its limits and at the same time lost the privilege and monopoly of trading in money. The immediate consequence of this banking commoditization was the decompartmentalization of markets, leading in its wake to the dematerialization of the borders of the economical system. This deregulation movement is characterized by the emergence of complex innovations combdigitqapointing financial theory and mathematical applications. Among them we have cryptography applied to monetary creation (crypto currency) which continues to push the limits of financial engineering. Despite a hesitant penetration into the financial ecosystem, it is revolutionizing the conception of money and third-party holding. In this article we analyze the use of cryptography tosecure monetary issuance and financial transactions in the block chain technology.
Performance Analysis of Activation Functions on Convolutional Neural Networks Using Cloud GPU

One of the tools used for images is Convolutional Neural Networks (CNN) which is a sub-group of the Artificial Neural Network. The activation function is the main characteristic element in CNN. As for any complex application, the starting point is the results of the activation function. The activation function is used to limit the amplitude of the output of a neuron. In order to compute complex functions, non-linearity is introduced by activations to the model. Activation functions of different types can be used with a Convolutional Neural Network in different applications, but better results are given by effective activation functions; they also improve the model performance. The work explores the ability of 10 most used activation functions to evaluate their efficiency in terms of accuracy along with the training time. Sigmoid, Hyperbolic, Tangent (Tanh), Exponential Linear Unit (ELU), Rectified Linear Unit (RLU), Scaled Exponential Linear Unit (SELU), linear, hard sigmoid, softsign, Parametric Rectified Linear Unit (PReLU), and Leaky Rectified Linear Unit (LReLU) activation functions are under consideration. The effects of networks, datasets and processors on training are analyzed.
Supplementing Machine Learning with Knowledge Models Towards Semantic Explainable AI

Explainable Artificial Intelligence (XAI) aims at making the results of Artificial Intelligence (AI) applications more understandable. It may also help to understand the applications themselves and to get an insight into how results are obtained. Such capabilities are particularly required with regard to Machine Learning approaches like Deep Learning which must be generally considered as black boxes, today. In the last years, different XAI approaches became available. However, many of them adopt a mainly technical perspective and do not sufficiently take into consideration that giving a well-comprehensible explanation means that the output has to be provided in a human understandable form. By supplementing Machine Learning with semantic knowledge models, Semantic XAI can fill some of these gaps. In this publication, we raise awareness for its potential and, taking Deep Learning for object recognition as an example, we present initial research results on how to achieve explainability on a semantic level.
Development Experience and Application of Machine Learning

Machine learning is the foundation of computer intelligence, known as the core of artificial intelligence. Nowadays, the main development direction of information industry such as computer science and artificial intelligence has gradually become machine learning technology. By analyzing the connotation, research purpose and development process of machine learning, this paper briefly and systematically expounds how to better apply machine learning, so as to promote the application of machine learning in real life and better promote the intelligent development of the computer and the progress of the society.
Information Mandala: Statistical Distance Matrix With Clustering

In machine learning, observation features are measured in a metric space to obtain their distance function for optimization. Given similar features that are statistically sufficient as a population, a statistical distance between two probability distributions can be calculated for more precise learning. Provided the observed features are multi-valued, the statistical distance function is still efficient. However, due to its scalar output, it cannot be applied to represent detailed distances between feature elements. To resolve this problem, this paper extends the traditional statistical distance to a matrix form, called a statistical distance matrix. The proposed approach performs well in object recognition tasks and clearly and intuitively represents the dissimilarities between cat and dog images in the CIFAR dataset, even when directly calculated using the image pixels. By using the hierarchical clustering of the statistical distance matrix, the image pixels can be separated into several clusters that are geometrically arranged around a center like a Mandala pattern. The statistical distance matrix with clustering is called the Information Mandala.
Publication Output with Citation based Performance of Selected DBT Institutes in India

This scientometric study examines the publication outputs from six institutes of the Department of Biotechnology (DBT) in India as cited in the Scopus database over the past 24 years, 1996-2019. Research in biotechnology and other allied areas were analysed in terms of their chronological growth, activity index, collaborations, preferred journals for publication, country collaborators, popular keywords and scholarly impact. Scientists from the six institutes published 6, 076 journal articles representing 73.65 per cent of nationally collaborated articles and 25.03 per cent of internationally collaborated articles. Of the DBT institutes, the National Institute of Immunology (NII) published the highest number of articles and the Institute of Life Sciences (ILS) shared most patents. Publication frequency was the highest for Plos One journal and the countries with which scientists collaborated included the United States, Germany, United Kingdom and France in that order. The publishing outputs of DBT institutes suggest a need for greater international collaborative research in order to gain scientific competency and increase the quality of research outputs. Also this study may be helpful to government officials and policy makers in determining allocation of resources to boost the scholarly outputs of DBT institutes.
Analysis on the Judicial Interpretation of the Crawler Technology Infringing on the Intellectual Property Rights of Enterprise Data

In the actual process of web crawler infringement and criminal identification, there is a theory of “weakening the infringement typology and strengthening the presumption of legal interest”. This is also the basic method for the subsequent identification of infringement in the commercial application of enterprise data through crawler technology. This article summarizes the criminal risks of abusing crawler technology based on previous work experience. The author discusses the judicial interpretation of crawler technology infringing the legal benefits of corporate data intellectual property rights from the categories of data types crawled by various crawlers and the types of data crawled by crawlers determine the types of applicable laws, increase the concept of trade secrets, and determine the standards and other judicial interpretation clauses three sides.