Reviewerly/exHarmony huggingface dataset:
An Empirical Study on Common Bugs in Deep Learning Compilers

The highly diversified deep learning (DL) frame-works and target hardware architectures bring big challenges for DL model deployment for industrial production. Up to the present, continuous efforts have been made to develop DL compilers with multiple state-of-the-arts available, e.g., TVM, Glow, nGraph, PlaidML, and Tensor Comprehensions (TC). Unlike traditional compilers, DL compilers take a DL model built by DL frameworks as input and generate optimized code as the output for a particular target device. Similar to other software, DL compilers are also error-prone. Buggy DL compilers can generate incorrect code and result in unexpected model behaviors. To better understand the current status and common bug characteristics of DL compilers, we performed a large-scale empirical study of five popular DL compilers covering TVM, Glow, nGraph, PlaidML, and TC, collecting a total of 2,717 actual bug reports submitted by users and developers. We made large manual efforts to investigate these bug reports and classified them based on their root causes, during which five root causes were identified, including environment, compatibility, memory, document, and semantic. After labeling the types of bugs, we further examined the important consequences of each type of bug and analyzed the correlation between bug types and impacts. Besides, we studied the time required to fix different types of bugs in DL compilers. Seven important findings are eventually obtained, with practical implications provided for both DL compiler developers and users.
Automation of the Software Development Process from ReLEL Using the Praxeme Approach

Praxeme is an enterprise methodology designed to overcome the difficulties associated with the construction of enterprise information systems. The aspect that specifies the company’s intentions represents the initial phase of the software development process in Praxeme. The use of the eLEL lexicon to express the intentional aspect of Praxeme enables the knowledge in the following aspects to be clarified. However, the logical modeling (logical aspect) from the intentional aspect represented by eLEL is largely lacking in conceptual information to be exploited in the purely IT aspect of Praxeme. In this paper, we propose the use of the ReLEL lexicon to specify the intentional aspect of Praxeme in order to automate the process covering the business and IT aspects of the enterprise. ReLEL is an enterprise terminology database and has precise information concerning the conceptual representation of an information system. For this purpose, we have proposed rules to automate the articulation that covers the intentional, semantic, logical and software aspects of Praxeme. We made use of the ATL and Acceleo language for the implementation of the transformation rules. The result obtained in this article is a SOAP web service of the software aspect of Praxeme.
Justification of meanness, or The method of the cynic (digitalization of Cortez)

The violentia that Caesar confessed and applied to the Gallic (and others) tribes, and that he conducted as a principle of human existence, and that was not introduced in usage by him into use and was successfully applied in the historical specifics of the “origin of the species” before him, but he only invented a way to justify such violent paradigm — “eyewitness notes”, in which violence was rooted in the ordinariness of the language, accustomed to act rather than explain, expanding the subject-shaped series, Roman Latin retained, dissolving the body of their verbs of directional action in languages Indo-European group (English, Spanish, French, German, Portuguese, Italian, Czech, etc.), changing the principle of thinking, survived like a virus, preparing the ground for the accession of Christian ideology: depriving freedom of comprehension and introducing a system of subordination, the aggressive code becomes the summumbonum that will swaddle the whole world, promising universal happiness, which is always relative, but legitimizing the pirate lifestyle as a guarantee of instability and incredible prosperity, and as a systemic Chaos that is controlled, shows its vitality, like virulence, to the new millennium. The unfolded concept of "Trojan terrorism" testifies that the Idea, having acquired the Image of the Сoncept: capture as a formality, owns society as a sovereign, establishing its Principle, demonstrating not only quasi and ultra anarchic freedom of will (as dogma and credo) of everyone, but also quite Martian chronicles of existence in every state on the planet, wills and completely the Martian chronicles of existence, relying on the 'multiple unity' of Consciousness in the context of total human (humanistic) impoverishment.
Age-related ultrasonographic mammary gland density patterns: Implication for breast cancer risk

Introduction: Mammary gland/breast density is important because it is a known biomarker for breast cancer risk. However, the sensitivity of mammography decreases with high breast density found in younger age group. Ultrasound is considered as the first-line examination in the classification of breast density and in the detection and characterization of breast lesions. This study aims to evaluate the relationship between age and ultrasonographic breast density pattern and its implication for breast cancer risk. Materials and Methods: This was a community-based cross-sectional, exploratory, descriptive study involving 658 females. Breast ultrasonographic scans were performed using a Sonoace X1 Machine with a 7.5 MHz transducer. The lesions detected and classified by ultrasonography as benign or malignant were further subjected to cytopathology. Results: Modal age group of the participants ranged from 33 to 43 years representing 29.8%. There was significant correlation (P<0.01) between ultrasonographic mammary gland density pattern and age, the age group of <33 years demonstrated predominant fibroglandular density pattern with mostly benign lesions, while the age group of 33–53 years demonstrated predominant heterogeneous breast density pattern with most of the malignant lesions in this age group, making it the high-risk group for breast cancer. Conclusion: The relationship between age and ultrasonographic breast density is inversely proportional and not absolute. It also concludes that ultrasonography is a reliable screening tool in the diagnostic process for mammary gland lesions, and as an imaging tool, it is the preferred modality in dense breast. The heterogeneous fibroglandular pattern emerged as the high-risk group for breast cancer, especially in middle age.
An Improved Range Proof with Base-3 Construction

Zero-knowledge protocols (ZKPs) allow a party to prove the validation of secret information to some other party without revealing any information about the secret itself. Appropriate, effective, and efficient use of cryptographic ZKPs contributes to many novel advances in real-world privacy-preserving frameworks. One of the most important type of cryptographic ZKPs is the zero-knowledge range proofs (ZKRPs). Such proofs have wide range of applications such as anonymous credentials, cryptocurrencies, e-cash schemes etc. In many ZKRPs the secret is represented in binary then committed via a suitable commitment scheme. Though there exist different base approaches on bilinear paring-based and RSA-like based constructions, to our knowledge there is no study on investigating the discrete logarithm-based constructions. In this study, we focus on a range proof construction produced by Mao in 1998. This protocol contains a bit commitment scheme with an OR-construction. We investigate the effect of different base approach on Mao's range proof and compare the efficiency of these basis approaches. To this end, we have extended Mao's range proof to base-3 with a modified OR-proof. We derive the number of computations in modulo exponentiations and the cost of the number of integers exchanged between parties. Then, we have generalized these costs for the base-u construction. Here, we mainly show that comparing with other base approaches, the base-3 approach consistently provides approximately 12% efficiency in computation cost and 10% efficiency in communication cost. We implemented the base-3 protocol and demonstrated that the results are consistent with our theoretical computations.
Sentiment Analysis in Social Networks Using Convolutional Neural Networks

Nowadays, with the proliferation of user reviews, emotions, feedback, and opinions in social networks towards a specific topic, product, event, or such a service. Sentiment analysis has become one of the essential research fields, that lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics, and Data Mining. It concerns at classifying a given piece of a post into sentiment polarity, i.e. determining whether the expressed opinion is positive, negative, or neutral. Furthermore, Deep Learning has shown good data modelling capabilities when dealing with complex and large datasets and is considered as the state-of-art model in various languages. In this paper, we aim to investigate the feasibility of the deep learning model, we develop a based Convolutional Neural Networks (CNNs) model for sentiment analysis tailored to the Arabic tweets. We focus on the Arabic language due to its ambiguity, morphological richness, and lack of its resources. The resulting performance is evaluated in a holistic setting across three benchmark Arabic Sentiment Tweets datasets, where we find that our model achieves an accuracy of 74.53%, which outperforms the state-of-art method’s accuracy of 64.30%.
A Comparative Study on the Art Textbooks of the Elementary School Third Grade between Korea and China

Purpose - The purpose of this paper is exploring the similarities and differences of the art textbooks of the elementary School third grade between Korea and China. Design/Methodology/Approach - Take two art textbooks used in third grade of elementary school as the object of comparative research, the third volume of the textbook named “The Art” which was published by genius education publishing house approved by South Korean Ministry of Education in 2017 and the “compulsory education textbook art” which was published by the Chinese People’s Fine Arts Publishing House approved by Chinese Ministry of Education in 2013. The comparative research is taken from two dimensions of the overall analysis of the curriculum and the content of the textbooks. The overall analysis of the curriculum is divided into basic curriculum concepts and teaching material objectives. The contents of the textbooks are divided into framing design, overall structure, unit content and legend. Findings - The presentation of art textbooks in the two countries has its own characteristics, and the differences in the nature and positioning of art textbooks in the two countries can be found through multi-dimensional comparison. Research Implications - In order to obtain the educational cutting-edge and innovative idea for textbooks compilation and promote the development of the research in art textbooks, the characteristics of art textbooks in both countries are summarized, furthermore, current art educators’ awareness to the art textbooks can also be improved.
Efficient Privacy-Preserving Federated Learning For Electricity Data

Data has become the core driving force for the development of digital economy. Electricity data is also known as weather vane of national economic operating status. There is a huge challenge in terms of mechanism, technology and security on sharing and application of energy data on a larger scale. Based on the characteristics of power data, this paper proposes an efficient model federated-training method with data privacy protected, realizing the security co-construction of analysis models. This approach is combined with federated learning and secret sharing technology, help breaking the barriers between government and power enterprises, as well as among differnet departments of power enterprises. In addition, this paper makes a detailed analysis on the security of the approach, which ensures that the data privacy can be guaranteed in the semi-honest and malicious model of no more than one server being corrupted. Finally, the proposed scheme is verified by experimental simulation, and the experimental results are compared with plaintext training. The results show that the proposed scheme still be highly efficient and practical even if the security computing technology is introduced on.
Agile Readiness Measurement in Organizations using Agile Adoption Framework : A Case study on Indonesian Automotive Company

Many organizations have adopted agile methodology in their project process and management to take advantage of benefits to organizations. The benefits are quicker good software quality, return of Investment, and customer satisfaction. For some organizations that have mature software development methodology using traditional approaches, they face some difficulties in adopting agile methodology. Instead of being successful in IT project implementation, the organization has failed in IT project, bad quality software, and running out of budget. The Organization conducted an agile readiness assessment using an agile adoption framework called SAMI (Sidky Agile Measurement Index) to know their agility level. Based on this assessment framework, there is a questionnaire which assesses the process for agile practices adoption in project and organization. Organization involved customer manager, IT manager, IT Section head and IT developers as respondents to fill in the questionnaire related to their experience in IT project. The assessment result mentioned that this organization has passed the criterias to continue the adoption, and agile adoption of project level is at level 3 (Effective), and organizational readiness level is at level 1 (Collaborative). To increase agility level up to maximum level at level 5 (Encompassing), Organization can choose to improve failed assessment items through follow up the recommendation or lower the expectation through reducing the project level into level 1, which is similar level to organization readiness. Knowing agility level in organization through assessment and proceed the recommendation, can increase agile level and ease agile adoption especially on software development in organization.
Introduction

This introduction refers to the title of the book. In the first section, the goal of the book is depicted. The second section outlines the most crucial aspect, i.e., explainability. The third tries to answer what is a recommender system, presenting different types of recommenders. Next, the interpretability of recommenders is discussed within the Explainable Artificial Intelligence, including the neuro-fuzzy approach. Finally, the content and main results of the book are described. This chapter can also be treated as a literature review of a few narrow research areas—Explainable Artificial Intelligence, Interpretable Machine Learning and Recommender Systems.
Teachers and Their Use of Digital Technologies in School

In this chapter, we present one of the important contexts of the present-day life of young people: school. Students spend a large part of their time in school and they encounter digital technologiesdigital technologies in various (learning) situations. We look in more detail into the life of schools, focusing on how Czech schools are equipped with digital technologies, how teachers use the digital technologies in their lessons, what the teachers’ skills and abilities to use ICT are, and how the teachers approach the digital technologies and their use in teaching and learning. We do not limit ourselves to national or international qualitative studies; we also let the students themselves speak. In the areas outlined above, we present the results of the relevant quantitative studies and then describe how the students see the given situation on the basis of our qualitative research. We are interested in how the students evaluate the school equipment, what they think of their teachers’ ability to use ICT in education, and how they perceive their teachers’ use of digital technologies directly in lessons. The combination of quantitative and qualitative approaches enables us to look into the life of teachers and students in the school environment in a more complex way. In the first part of the chapter, we delimit the topic of this chapter as a whole. We then present our research questions and describe the methodology of this part of the research, including the sources of the empirical data. In the subchapters, we gradually present answers to the defined research questions. In the conclusion, we summarize all our findings and interpret them in wider contexts.
An Graph Neural Network and Feature Interaction Based Fraud Detection

Modern social networks contain various types of objects and rich interactive information, the effect of traditional detection methods is limited. Many researchers proposed graph neural network methods to solve this problem, but most of them have ignored the heterogeneous nature of social networks. So, we proposed a new model named Multi-view Similarity-based Graph Convolutional Network(MSGCN) to address this problem. Our model adopted a multi-view method to deal with rich interaction and heterogeneous graph data. Firstly, we converted the heterogeneous graph into many single-views by using meta- path. Then, we employed a similarity-based graph convolutional network to learn node representation for every single- view. Finally, the multiple views features were aggregated together through the attention mechanism to generate the final representation. Experimental results show that our MSGCN is better than other network representation learning baselines.
A Brief Study on Cybercrimes and Practices in Mizoram

This study aims to analyze the level of awareness of cybersecurity and crimes in Mizoram by focusing on various threats from the internet, Social Networking Sites (SNSs), and other communication or computer-oriented crime, in cyberspace along with their significant awareness. In modern technological years, cybercrime is a hot challenge in all areas and aspects including personal security, national security, and public safety. To prevent from being victimized, everyone needs to be informed about some sort of measures and about cybercrimes. A well-structured questionnaire was prepared for the survey as per the cases history recorded provided by the Mizoram police department. The results of this study are important recommendations and need to be carried out by various circles of the general public and specifically for Mizoram. While crime victims are mostly unaware of stranger behaviour to capture their information details, consequently the public awareness level on cybercrime is below average. This study presents a brief overview of all evolving crime, case studies, and the working approach to combat cybercrime in the Mizoram context.
Unscented Filtering of Angles-Only Measurements of Orbital Debris

The purpose of this project is to implement unscented filtering of angles-only measurements to locate space debris for capture and removal. Previous research has explored the orbit determination procedure for capture scenario and utilized an Extended Kalman Filter (EKF) to predict the orbital position of space debris using an orbital model and line-of-sight (LOS) measurement. For this research, an Unscented Kalman Filter (UKF) was implemented with the same orbital model and LOS measurement. The results show that the UKF is more stable and more accurate at larger magnitudes of uncertainty in the prediction model compared to the EKF.
Challenges and Solution Recommendation in Large-Scale Agile Implementation: A Systematic Literature Review

In the recent years, Agile methods have been widely-used in large organization to improve their performance. Since these methods initially designed for small projects and co-located teams, organization faces many challenges when implementing them in large-scale. During the past few years, challenges in implementing Large-Scale Agile have become a popular research topic. However, studies exploring the challenges in large scale Agile development along with best practices solutions through secondary studies is still scarce. This study aims to fill this gap by conducting Systematic Literature Review to identify various challenges as well as the proposed practices and solution to deal with them. A total of 12 papers, the results of SLR process, were analysed. This study identified 28 challenges classified in six categories. The biggest challenge arises from communication and collaboration category, which includes challenges related to team coordination, managing autonomous team, uneven team maturity, and distributed team. The solution of each challenge obtained from related studies and best practices.
Engineering Optimization Using an Advanced Hybrid Algorithm

Among numerous metaheuristic algorithms, differential evolution (DE) and particle swarm optimization (PSO) are found to be efficient and powerful optimization techniques. Similarly, it has been observed that their hybrid algorithms provide a reliable estimate to global optimum. Therefore, based on multi-swarm approach, an advanced hybrid algorithm (haDEPSO) is introduced to solve engineering optimization problems, instead of naïve way. Proposed aDE and aPSO (an advanced DE and PSO) are incorporated in haDEPSO. Also, the population of one is combined with the other in a predefined way in haDEPSO, to create balance between global and local search capability. Introduced hybrid haDEPSO and its integrating component aDE and aPSO have been applied over two engineering optimization problems. Results show supremacy of introduced algorithms comparing to so some modern algorithms. Finally, on basis of performance, the introduced haDEPSO is endorsed in solving engineering optimization problems.
A Novel Framework for Fast Feature Selection Based on Multi-Stage Correlation Measures

Datasets with thousands of features represent a challenge for many of the existing learning methods because of the well known curse of dimensionality. Not only that, but the presence of irrelevant and redundant features on any dataset can degrade the performance of any model where training and inference is attempted. In addition, in large datasets, the manual management of features tends to be impractical. Therefore, the increasing interest of developing frameworks for the automatic discovery and removal of useless features through the literature of Machine Learning. This is the reason why, in this paper, we propose a novel framework for selecting relevant features in supervised datasets based on a cascade of methods where speed and precision are in mind. This framework consists of a novel combination of Approximated and Simulate Annealing versions of the Maximal Information Coefficient (MIC) to generalize the simple linear relation between features. This process is performed in a series of steps by applying the MIC algorithms and cutoff strategies to remove irrelevant and redundant features. The framework is also designed to achieve a balance between accuracy and speed. To test the performance of the proposed framework, a series of experiments are conducted on a large battery of datasets from SPECTF Heart to Sonar data. The results show the balance of accuracy and speed that the proposed framework can achieve.