Reviewerly/exHarmony huggingface dataset:
An Experimental Evaluation of Imbalanced Learning and Time-Series Validation in the Context of CI/CD Prediction

Background: Machine Learning (ML) has been widely used as a powerful tool to support Software Engineering (SE). The fundamental assumptions of data characteristics required for specific ML methods have to be carefully considered prior to their applications in SE. Within the context of Continuous Integration (CI) and Continuous Deployment (CD) practices, there are two vital characteristics of data prone to be violated in SE research. First, the logs generated during CI/CD for training are imbalanced data, which is contrary to the principles of common balanced classifiers; second, these logs are also time-series data, which violates the assumption of cross-validation. Objective: We aim to systematically study the two data characteristics and further provide a comprehensive evaluation for predictive CI/CD with the data from real projects. Method: We conduct an experimental study that evaluates 67 CI/CD predictive models using both cross-validation and time-series-validation. Results: Our evaluation shows that cross-validation makes the evaluation of the models optimistic in most cases, there are a few counter-examples as well. The performance of the top 10 imbalanced models are better than the balanced models in the predictions of failed builds, even for balanced data. The degree of data imbalance has a negative impact on prediction performance. Conclusion: In research and practice, the assumptions of the various ML methods should be seriously considered for the validity of research. Even if it is used to compare the relative performance of models, cross-validation may not be applicable to the problems with time-series features. The research community need to revisit the evaluation results reported in some existing research.
Towards Robust Combined Deep Architecture for Speech Recognition : Experiments on TIMIT

Over the last years, many researchers have engaged in improving accuracies on Automatic Speech Recognition (ASR) task by using deep learning.In state-of-theart speech recognizers, both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) based Reccurent Neural Network (RNN) have achieved improved performances compared to Convolutional Neural Network (CNN) and Deep Neural Network (DNN).Due to the strong complementarity of CNN, LSTM-RNN and DNN, they may be combined in one architecture called Convolutional Long Short-Term Memory, Deep Neural Network (CLDNN).Similarly we propose to combine CNN, GRU-RNN and DNN in a single deep architecture called Convolutional Gated Recurrent Unit, Deep Neural Network (CGDNN).In this paper, we present our experiments for phoneme recognition task tested on TIMIT data set.A phone error rate of 15.72% has been reached using the proposed CGDNN model.The achieved result confirms the superiority of CGDNN over all their baselines networks used alone and also over the CLDNN architecture.
Research on Agent-based Economic Decision Model Systems

Based on an analysis of the development of economic decision support systems, agents are applied to construct intelligent economic decision support systems. This paper proposes a task-oriented agent design concept and designs multiple types of agents to complete the decision-making tasks with the task as the core. The structure of multi-agent based systems is provided, and the concrete realization structure of different types of agents in the system is also provided. Additionally, this study discusses the operational mechanism of the whole system and the cooperation between multiple agents in the system. Finally, these functions are implemented through a combination of VC++ 6.0, multi-threading technology and the expert system tool CLIPS. The economic decision support system combines complex system theory, decision theory, information collection, knowledge discovery technology, economic decision making and simulation technology. It can aid users in making decisions by using communication and cooperation between multiple agents.
Attribution Analysis of Grammatical Dependencies in LSTMs

LSTM language models have been shown to capture syntax-sensitive grammatical dependencies such as subject-verb agreement with a high degree of accuracy (Linzen et al., 2016, inter alia). However, questions remain regarding whether they do so using spurious correlations, or whether they are truly able to match verbs with their subjects. This paper argues for the latter hypothesis. Using layer-wise relevance propagation (Bach et al., 2015), a technique that quantifies the contributions of input features to model behavior, we show that LSTM performance on number agreement is directly correlated with the model's ability to distinguish subjects from other nouns. Our results suggest that LSTM language models are able to infer robust representations of syntactic dependencies.
Elman Neural Network for Solar Radiation Components Forecasting based on the Desired Tilt Angle

The tilt angle of solar systems affect directly the conversion of solar radiation, regardless of weather conditions. To extract the maximum power from the solar panels, the sunlight should fall with a sharp angle. Therefore, the optimum angle of inclination of fixed solar panels to the horizontal plane should be changed regularly every month and every season. The prime aim of this study is to offer for solar systems engineers and designers an efficient model based on multi-input multi output (MIMO) Elman artificial neural network (EL-ANN). To predict the three components of hourly solar radiation time series (global, direct, diffuse radiation), relative to the site of Marrakesh city, Morocco, received by different tilted solar panel surface. The prediction is based on the tilt angle desired by the user and the meteorological data, including of time variable. The obtained results prove the robustness and the efficiency of the developed model to estimate accurately the three components of hourly solar radiation time series for different tilt angles.
Book public pages in Instagram

The peculiarity of the Russian-language Instagram segment is that for many users it has become the platform for text self-expression and discussions (including on reading issues). This explains the fact that many publishers and bookstores create their own Instagram accounts to promote book products. The purpose of the article is to identify the marketing potential of book public pages in Instagram. The author sets the following tasks: to identify successful practices of publishers and bookselling organizations in Instagram; analyze them from the point of view of text content, visual design; identify the main forms of users engagement; calculate the engagement rate (ER); identify possible "recipes" for the success of book public pages. Viewing the official websites of publishers and bookstores allowed the identification of book public posts with a significant number of subscribers and that of forms to attract users, and the determination of content features and visual design. A comparison of the activity level of public pages: @ eksmolive and @mifbooks of two major publishing houses. For this purpose, the number of posts, likes and comments for the period from 23.10.2019 to 22.11.2019 was calculated, as well as the engagement rate using the ER method. The author comes to the conclusion that the Instagram is widely used by publishing organizations and booksellers as a promising platform for book promotion and reading support, active exchange of information with the audience, increasing people’s loyalty, brand promotion, organization of direct sales. Effectiveness of the presence of booksellers in Instagram largely depends on the competent segmentation of the audience, the uniqueness of the content and visual design, and the use of various techniques and methods to enhance user engagement.
Revisiting Gilbert’s known-key distinguisher

Abstract Known-key distinguishers have been introduced by Knudsen and Rijmen in 2007 to better understand the security of block ciphers in situations where the key can not be considered to be secret, i.e. the “thing between secret-key model and hash function use-cases”. Trying to find a rigorous model to fit this intuition is still ongoing. The most recent advance by Gilbert (Asiacrypt 2014) describes a new model that—even if it is well justified—seemingly does not match this intuition. AES is often considered as a target of such analyses, simply because AES or its building blocks are used in many settings that go beyond classical encryption. Consider AES-128. Results in the secret-key model cover up to 6 rounds, while results in the chosen-key model reach up to 9 rounds. Gilbert however showed a result in the known-key model that goes even further, covering 10 rounds. Does it mean that the use cases corresponding to the cryptanalysis of hash-function use-cases are inherently less efficient, or is it rather an artifact of the new model? In this paper we give strong evidence for the latter . In Gilbert’s work, two types of arguments or rather conjectures are put forward suggesting that the new model is meaningful. Firstly that the number of “extension rounds” due to the new model is limited to two. And secondly that only a distinguisher that exploits the uniform distribution property can be extended in such way. We disprove both conjectures and arrive at the following results: First, we are also able to show that more than two extension rounds are possible. As a result of this, we describe the first known-key distinguishers on 12 rounds of AES that fit into Gilbert’s model. The second conjecture is disproven by showing that the technique proposed by Gilbert can also be used to extend a known-key distinguisher based on another property: truncated differentials. A potential conclusion of this work would be that the counter-intuitive gap between Gilbert’s known-key model and the chosen-key model is wider than initially thought. We however conclude that results in Gilbert’s model are due to an artifact in the model. To remedy this situation, we propose a refinement of the known-key model which restores its original intent to fit the original intuition.
STUDY ON THE DIFFUSION OF THE WESTERN NAIL AND BACKGROUND OF THE COMBINED ON THE USE OF THE JAPANESE NAIL IN THE FIRST HALF OF THE MEIJI PERIOD

The author has so far considered the spread of the Western nails in the Meiji era from the viewpoint of the price. As a result, it has already shown that it was the price of a 1.5 inch long nail is 1/21 in 1885, 1/50 at the end of the Meiji period, compared to the same length of the Japanese nails in 1875. By the way, the question remains as to how the nails spread before 1885. Although the authors have already reported that the Japanese nails -square nails- and the Western nails - round nails- were used together from the late Meiji 10s to the Meiji 20s, in spite of such a price difference, the reason why square nails and round nails are used together cannot be clearly shown. Therefore, this article aims to clarify the background of the combined use of square nails and round nails by examining the spread of the Western nails in the first half of the Meiji era according to the actual situation of imports.
Case-Sensitive Neural Machine Translation

Even as an important lexical information for Latin languages, word case is often ignored in machine translation. According to observations, the translation performance drops significantly when we introduce case-sensitive evaluation metrics. In this paper, we introduce two types of case-sensitive neural machine translation (NMT) approaches to alleviate the above problems: i) adding case tokens into the decoding sequence, and ii) adopting case prediction to the conventional NMT. Our proposed approaches incorporate case information to the NMT decoder by jointly learning target word generation and word case prediction. We compare our approaches with multiple kinds of baselines including NMT with naive case-restoration methods and analyze the impacts of various setups on our approaches. Experimental results on three typical translation tasks (Zh-En, En-Fr, En-De) show that our proposed methods lead to the improvements up to 2.5, 1.0 and 0.5 in case-sensitive BLEU scores respectively. Further analyses also illustrate the inherent reasons why our approaches lead to different improvements on different translation tasks.
Modern library and reading in multimedia environment

The article identifies the library's ability to stimulate reader activity in the context of the multimedia culture development. It also considers main changes in the library activities as an organizer of reading in multimedia culture including the increase of emphasis on readers, their subjectivity, interaction dialogue forms development, multimedia projects implementation, online services and digital resources introduction, education of readers on literate media behavior and text activity in the Internet. The author states that in the context of multimedia, library can become an active participant in overcoming information chaos, streamlining information flows, enhancing reading and also contribute to the development of inter-reader communication. The article concludes that it is necessary to prepare library specialists, tutors, ready to organize communication about books and reading in a real and virtual environment, network navigation, analysis of Internet resources, recognition of misinformation, and teaching readers various techniques of textual activity.
Non-idempotent Intersection Types in Logical Form

Intersection types are an essential tool in the analysis of operational and denotational properties of lambda-terms and functional programs. Among them, non-idempotent intersection types provide precise quantitative information about the evaluation of terms and programs. However, unlike simple or second-order types, intersection types cannot be considered as a logical system because the application rule (or the intersection rule, depending on the presentation of the system) involves a condition expressing that the proofs of premises satisfy a very strong uniformity condition: the underlying lambda-terms must be the same. Using earlier work introducing an indexed version of Linear Logic, we show that non-idempotent typing can be given a logical form in a system where formulas represent hereditarily indexed families of intersection types.
Using Leader Election and Blockchain in E-Health

The development of electronic healthcare systems requires to adopt modern technologies and architectures.The use of Electronic Personal Health Record (E-PHR) should be supported by efficient storage such as cloud storage which enables more security, availability and accessibility of patients' records.Actually, the increase of availability of E-PHR enhances parallel access, which improves the performance and the throughput of the system.Using distributed systems, users are able to communicate and to share resources to achieve specific goals.Such kind of access needs to have more coordination to maintain parallelism, which can be provided through leader election algorithms.In leader election algorithms, users elect one of them as a leader to coordinate the work and to prevent conflicts.This paper introduces an adoptive leader election algorithm (ALEA) that considers medical and healthcare specifications, since it uses leader election algorithm for E-PHR in the cloud environment.The use of ALEA improves performance by allowing more parallelism and reducing the number of coordinating messages within the system, as well as facilitating the medical specifications such as having a primary doctor or handling emergency situations.Moreover, the paper highlights the strengths and weaknesses of using Blockchain technology in the field of healthcare.In fact, the paper investigates the implementation challenges of ALEA concepts using Blockchain technology.
Specialists’ training by integration of the high school and business structures

According to global trends, universities should now be responsible for training a new generation of researchers and highly qualified managers who are ready to implement innovative activities in the modern market economy. This article focuses on the problems of professionalization and training of a modern specialist through the integration of educational and business structures. The purpose of this study is to substantiate the importance of the interaction of universities with business structures as a factor contributing to the solution of these problems and improving the quality of higher education in general. The article provides examples of the interaction of the Higher School of Media Communications and Public Relations (SPbPU) with enterprises based on the implementation of project activities. We have analysed students 'essays regarding participation in this kind of project activity, reflecting positive and negative experiences and allowing us to assess the impact of project activity on students' professional skills. More than 71% of students noted the significant contribution of project activity to their creativity and professional skills, and thus confirmed the importance of the interaction of higher education with the business environment, primarily for the educational process.
A Collaborative Mechanism for Private Data Publication in Smart Cities

The collection of high-confidence data has been one prominent step for many services in smart city systems. However, the privacy issues have been thwarting the seamless publication of data, especially as the data from different aspects of daily life may provide unprecedented coverage of contributors. Current solutions have been carefully designed to perturb or suppress the data before publication, so as to balance the privacy and utilities. However, they cannot fit the practice in smart cities, where multiple service providers request information on heterogeneous domains and regions of the city. Therefore, this article proposes a novel framework for data publication of workers in smart city systems. The framework allows workers and requestors to own and request various types of contents in different regions. The objective is to maximize the number of service providers receiving qualified utilities under privacy constraints. Furthermore, differential privacy is applied to guarantee that workers will not disclose personal information to requestors. In the technical part, the problem is proved to be NP-complete. Then two algorithms and strategies are proposed toward different cases: 1) workers apply identical privacy budgets for all published data and 2) workers are flexible on privacy settings. Both algorithms are theoretically analyzed on their performance of the released results. Finally, the evaluation of data sets of local businesses reveals that proposed algorithms can outperform baseline methods.
Buena práctica docente para el diseño de aula virtual en Google Classroom

&nbsp; Google Classroom is a tool that allows to manage educational processes in an agile and easy way, allowing to create classes, assign homework, grade, send comments and give access to the entire educational process in one place. The objective was the analysis of the incidence of Google Classroom in the pedagogical management in the participants of the Master in Education of the Universidad Tecnológica Indoamérica. As a conclusion, its use shown advantages for the students, who were very participative and demonstrated a high level of immersion in the virtual environment, from the use of different collaborative work tools.
Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning.

In this paper, we focus on automatic disease diagnosis with reinforcement learning (RL) methods in task-oriented dialogues setting. Different from conventional RL tasks, the action space for disease diagnosis (i.e., symptoms) is inevitably large, especially when the number of diseases increases. However, existing approaches to this problem employ a flat RL policy, which typically works well in simple tasks but has significant challenges in complex scenarios like disease diagnosis. Towards this end, we propose to integrate a hierarchical policy of two levels into the dialogue policy learning. The high level policy consists of a model named master that is responsible for triggering a model in low level, the low level policy consists of several symptom checkers and a disease classifier. Experimental results on both self-constructed real-world and synthetic datasets demonstrate that our hierarchical framework achieves higher accuracy in disease diagnosis compared with existing systems. Besides, the datasets (this http URL) and codes (this https URL) are all available now.
Implementing Augmented Reality for the Holographic Assembly of a Modular Shading Device

The development of innovative digital design and fabrication tools for material processing and manufacturing of complex and non-standard forms, apart from their advantages, have brought a number of challenges.These might be related to the effectiveness and sustainable potential of implementation associated with environmental, cost and time-related parameters, particularly in cases of large number of elements construction and complex assembly.Augmented Reality (AR) is an emerging technology with great potential for implementation in the construction industry, since it can enhance the real world with additional digital information, and thus, can assist towards manufacture and assemble of these particular systems.This study presents an AR methodology for assembling a modular shading device and discusses the advantages and disadvantages that this application can bring to the Architecture, Engineering and Construction (AEC) industry by taking into account precision and construction time issues based on the handling of the process by skilled and unskilled users/workers.Our aim is to investigate the potential implementation of AR in the assembly, and consequently, in the construction process as a whole.Also, this study aims at exploring existing constraints of the technology and suggests ways of improvement.
Modular Inference of Linear Types for Multiplicity-Annotated Arrows

Abstract Bernardy et al. [2018] proposed a linear type system $$\lambda ^q_\rightarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>λ</mml:mi> <mml:mo>→</mml:mo> <mml:mi>q</mml:mi> </mml:msubsup> </mml:math> as a core type system of Linear Haskell. In the system, linearity is represented by annotated arrow types $$A \rightarrow _m B$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:msub> <mml:mo>→</mml:mo> <mml:mi>m</mml:mi> </mml:msub> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> , where m denotes the multiplicity of the argument. Thanks to this representation, existing non-linear code typechecks as it is, and newly written linear code can be used with existing non-linear code in many cases. However, little is known about the type inference of $$\lambda ^q_\rightarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>λ</mml:mi> <mml:mo>→</mml:mo> <mml:mi>q</mml:mi> </mml:msubsup> </mml:math> . Although the Linear Haskell implementation is equipped with type inference, its algorithm has not been formalized, and the implementation often fails to infer principal types, especially for higher-order functions. In this paper, based on OutsideIn(X) [Vytiniotis et al., 2011], we propose an inference system for a rank 1 qualified-typed variant of $$\lambda ^q_\rightarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>λ</mml:mi> <mml:mo>→</mml:mo> <mml:mi>q</mml:mi> </mml:msubsup> </mml:math> , which infers principal types. A technical challenge in this new setting is to deal with ambiguous types inferred by naive qualified typing. We address this ambiguity issue through quantifier elimination and demonstrate the effectiveness of the approach with examples.