Reviewerly/exHarmony huggingface dataset:
Generating a Generic Fluent API in Java

Context: The algorithms for generating a safe fluent API are actively studied these years. A safe fluent API is the fluent API that reports incorrect chaining of the API methods as a type error to the API users. Although such a safe property improves the productivity of its users, the construction of a safe fluent API is too complicated for the developers. The generation algorithms are studied to reduce the development cost of a safe fluent API. The study on the generation would benefit a number of programmers since a fluent API is a popular design in the real world. Inquiry: The generation of a generic fluent API has been left untackled. A generic fluent API refers to the fluent API that provides generic methods (methods that contain type parameters in their definitions). The Stream API in Java is an example of such a generic API. The recent research on the safe fluent API generation rather focuses on the grammar class that the algorithm can deal with for syntax checking. The key idea of the previous study is to use nested generics to represent a stack structure for the parser built on top of the type system. In that idea, the role of a type parameter was limited to internally representing a stack element of that parser on the type system. The library developers could not use type parameters to include a generic method in their API so that the semantic constraints for their API would be statically checked, for example, the type constraint on the items passed through a stream. Approach: We propose an algorithm to generate a generic fluent API. Our translation algorithm is modeled as the construction of deterministic finite automaton (DFA) with type parameter information. Each state of the DFA holds information about which type parameters are already bound in that state. This information is used to identify whether a method invocation in a chain newly binds a type to a type parameter, or refers to a previously bound type. The identification is required since a type parameter in a chain is bound at a particular method invocation, and that bound type is referred to in the following method invocations. Our algorithm constructs the DFA by analyzing the binding time of type parameters and their propagation among the states in a DFA that is naively constructed from the given grammar. Knowledge and Importance: Our algorithm helps library developers to develop a generic fluent API. The ability to generate a generic fluent API is essential to bring the safe fluent API generation to the real world since the use of type parameters is a common technique in the library API design. By our algorithm, the generation of a safe fluent API will be ready for practical use. Grounding: We implemented a generator named Protocool to demonstrate our algorithm. We also generated several libraries using Protocool to show the ability and the limitations of our algorithm.
Laplacian: Reversible Data Hiding Technique

Reversible Data Hiding (RDH) techniques are becoming more important in the present era of digital content security. In RDH the reciprocal nature of embedding capacity and visual quality parameters, hinder further visual quality enhancement. The trade-off between embedding capacity and visual quality is an essential characteristic of any data hiding technique. Moreover, the problem of Random noise further deteriorates the visual quality of embedded content. Hence, addressing above problems in RDH techniques widely influence applications like remote sensing, medical diagnosis and satellite imagery. In this paper we are proposing a Laplacian Reversible Data Hiding technique, that perform 4-connected neighbor pixel difference expansion. This technique simultaneously improves image quality and embedding capacity by breaking reciprocal nature of embedding capacity and visual quality. Furthermore, it removes isolated edges and eliminate smoothing effect by sharpening the edges. Moreover, this can withstand from mathematical translation and rotation attack, so it can be called as semi-fragile data hiding algorithm.
Iterative Visual Relationship Detection via Commonsense Knowledge Graph

Visual relationship detection, i.e., discovering the interaction between pairs of objects in an image, plays a significant role in image understanding. However, most of recent works only consider visual features, ignoring the implicit effect of common sense. Motivated by the iterative visual reasoning in image recognition, we propose a novel model to take the advantage of common sense in the form of the knowledge graph in visual relationship detection, named Iterative Visual Relationship Detection with Commonsense Knowledge Graph (IVRDC). Our model consists of two modules: a feature module that predicts predicates by visual features and semantic features with a bi-directional RNN; and a commonsense knowledge module that constructs a specific commonsense knowledge graph for predicate prediction. After iteratively combining prediction from both modules, IVRDC updates the memory and commonsense knowledge graph. The final predictions are made by taking the result of each iteration into account with an attention mechanism. Our experiments on the Visual Relationship Detection (VRD) dataset and the Visual Genome (VG) dataset demonstrate that our proposed model is competitive.
FEDERAL AND REGIONAL CONTEXT OF THE HIGHER EDUCATION IN RUSSIA

In the face of increasing cross-country competition, the strategic role of higher education and science is steadily increasing. Russia largely copies Western standards in the field of science and education, having nothing to do with the development of these standards. As a result, the Russian Federation is on the periphery of the world intellectual development. The situation is exacerbated by changes in Russian higher education. In the 21st century, the higher education of Russia is characterized by the division of universities into federal and regional, which affects funding, the level of autonomy in the conduct of educational and scientific activities and other characteristics. The “nomenclature” approach to highereducation institutions of the Russian Federation turns many regional universities into noncompetitive. Probably, the lack of conditions for equal inter-university competition dramatically reduces the interest for private business investment in both federal and regional universities. The article analyzes, in application to the Russian Federation, three models of the development of modern higher education, common in the world. It is concluded that the numerous deformations of modern Russian society are transferred to higher education. Therefore, education reforms are impossible without major changes in society and without taking into account the fact that modern Russian universities reproduce the diversity of socio-economic relations.
COLNA: A Python Package to Analyze Complex, Linear Networks

The complex linear network analyzer (COLNA) python package analytically computes the propagation of complex valued signals in networks with linear nodes and directed, complex valued and delayed edges (Fig. 1).COLNA offers an easy and well-documented interface, which allows users to quickly build network models and understand their behaviour.Its main purpose is to compute coherent wave propagation through linear photonic circuits, but COLNA might be useful in any research area where signal propagation through linear complex networks is of practical relevance.
Fast Inference of Binarized Convolutional Neural Networks Exploiting Max Pooling with Modified Block Structure

This letter presents a novel technique to achieve a fast inference of the binarized convolutional neural networks (BCNN). The proposed technique modifies the structure of the constituent blocks of the BCNN model so that the input elements for the max-pooling operation are binary. In this structure, if any of the input elements is +1, the result of the pooling can be produced immediately; the proposed technique eliminates such computations that are involved to obtain the remaining input elements, so as to reduce the inference time effectively. The proposed technique reduces the inference time by up to 34.11%, while maintaining the classification accuracy.
Testing Monotonicity of Machine Learning Models

Today, machine learning (ML) models are increasingly applied in decision making. This induces an urgent need for quality assurance of ML models with respect to (often domain-dependent) requirements. Monotonicity is one such requirement. It specifies a software as 'learned' by an ML algorithm to give an increasing prediction with the increase of some attribute values. While there exist multiple ML algorithms for ensuring monotonicity of the generated model, approaches for checking monotonicity, in particular of black-box models, are largely lacking. In this work, we propose verification-based testing of monotonicity, i.e., the formal computation of test inputs on a white-box model via verification technology, and the automatic inference of this approximating white-box model from the black-box model under test. On the white-box model, the space of test inputs can be systematically explored by a directed computation of test cases. The empirical evaluation on 90 black-box models shows verification-based testing can outperform adaptive random testing as well as property-based techniques with respect to effectiveness and efficiency.
Rationally Inattentive Path-Planning via RRT*

We consider a path-planning scenario for a mobile robot traveling in a configuration space with obstacles under the presence of stochastic disturbances. A novel path length metric is proposed on the uncertain configuration space and then integrated with the existing RRT* algorithm. The metric is a weighted sum of two terms which capture both the Euclidean distance traveled by the robot and the perception cost, i.e., the amount of information the robot must perceive about the environment to follow the path safely. The continuity of the path length function with respect to the topology of the total variation metric is shown and the optimality of the Rationally Inattentive RRT* algorithm is discussed. Three numerical studies are presented which display the utility of the new algorithm.
Survey on Challenges and Achievements in Context-Aware Requirement Modeling

A context-aware system always needs to adapt its behaviors according to context changes; therefore, modeling context-aware requirements is a complex task. With the increasing use of mobile computing, research on methods of modeling context-aware requirements have become increasingly important, and a large number of relevant studies have been conducted. However, no comprehensive analysis of the challenges and achievements has been performed. The methodology of systematic literature review was used in this survey, in which 68 reports were selected as primary studies. The challenges and methods to confront these challenges in context-aware requirement modeling are summarized. The main contributions of this work are: (1) four challenges and nine sub-challenges are identified; (2) eight kinds of methods in three categories are identified to address these challenges; (3) the extent to which these challenges have been solved is evaluated; and (4) directions for future research are elaborated.
CAPTURING ACTUAL STRAYING BEHAVIORS USING THREE-DIMENSIONAL LASER SENSOR

In this research, a method for extracting straying behaviors in a facility using a three-dimensional laser sensor will be described by taking an example of EBINA SA(up line).
Vehicle Brand Detection Using Deep Learning Algorithms

Today, information technologies are used in almost every stage of life. It seeks to find solutions too many issues and problems. Image processing applications have been widely used in many areas in recent years and are trying to solve problems. Many applications which perform tasks such as classification, counting, measurement, target tracking have been developed. The aim of this study is to provide a solution for different applications using an effective and cost-effective method to detect the brand and model of vehicles. A classification method is implemented using deep neural network in the determination of the vehicle brand. The proposed solution is tested on various images taken from different angles and obtained from different sources. Faster-RCNN method which is one of deep neural networks is used to brand detection of vehicles in this study. It is observed that Faster-RCNN method performs 67.66% classification accuracy.
Artistic Image as a Result of Reality Reflection and Construction

The article is devoted to the analysis of artistic image as a gnoseological image. The concept of variety of cognition types, produced by contemporary epistemology, allows to consider art as a special type of cognitive relation to reality, and artistic image as a form of artistic cognition, a result of reality comprehension. In this case, the artistic image is studied from the viewpoint of two concepts — the knowledge understanding as a reflection of reality and as its construction. The analysis of the artistic image within the frame of the first concept (as a result of reality reflection) is traditional for Russian aesthetics. From this point of view, the artistic image is considered in three aspects — objective (it appears through the reality reflection by art), estimative (the artistic image includes an obvious or concealed estimation of reality) and operational (it is connected with creative transformation of reality in the process of its artistic comprehension). The concept of constructivism allows to consider the artistic cognition in other way, as an intense activity of the subject to construct reality. This article bases its argumentation on the idea, by V.A. Lektorsky, of removing the opposition between realism and constructivism through the recognition of the concept of constructive realism. Thereby, the artistic image is presented as the result of the creative activity of the artwork’s author, who transforms the reality, constructs a new, artistic reality, and the addressee, who is included in the artistic reality through actualization, identification, construction of meanings.
Word Representations for Neural Network Based Myanmar Text-to-Speech System

The main objective of this paper is to improve the naturalness of Myanmar Text-to-Speech (TTS) system without using time-consuming and expensive annotation of training corpus.Recently, word embedding, which has the advantage of training directly from a large amount of raw text data, has been used as the additional input features with the conventional input features or as the replacement of that conventional input features on the acoustic modelling of TTS systems.In this paper, the effectiveness of applying word vectors as the additional input features are investigated for Myanmar speech synthesis on the three acoustic modelling techniques, Deep Neural Network (DNN), Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), and a hybrid of DNN and LSTM-RNN (Hybrid-LSTM-RNN).For the purpose of achieving better TTS performance, we built our own word vectors for Myanmar language.We further explore the best modelling method and vector dimension of word embedding for Myanmar TTS systems.Both objective and subjective evaluations are done on DNN, LSTM-RNN and Hybrid-LSTM-RNN based Myanmar TTS systems with and without additional input features such as Part-of-Speech (POS) and word vectors.According to the subjective results, applying additional input features in DNN, LSTM-RNN, and Hybrid-LSTM-RNN based Myanmar TTS systems can improve the naturalness of the synthesized speeches though objective results cannot lead to the significant improvement in LSTM-RNN and Hybrid-LSTM-RNN based systems.To the best of our knowledge, this is the first attempt to apply word vector features in neural network based Myanmar TTS systems.
New XML Signature Scheme That is Resistant to Some Attacks

Supporting the execution of transactions through the use of electronic documents requires security. The scope of this security primarily involves ensuring the integrity, authenticity and non-repudiation of the origin of information. The signed XML document is a powerful tool that ensures the above features and the ease of processing and integration with various systems. An XML document may have many signatures, and each of them may sign different parts of the document. This feature is highly attractive, but in order to use it, the signature and structure of the document must be carefully designed. This article presents the existing risks associated with the use of XML signatures, focusing on XML signature wrapping vulnerability. This vulnerability is a consequence of the relationship between the XML signature and the signed document. The authors suggest that without neglecting the need for protection against the possibility of moving and replacing a fragment of the document, the use of secure XML signature references should also be considered and applied. The article proposes the use of secure signature templates as a countermeasure against the threat of an improper indication of the signed content defined in the signature reference. This threat is serious in automatic signature processing, where it is important to correctly indicate the signed content.
Quality Control for Automated Scoring in Large-Scale Assessment

In this chapter we discuss best practices for quality control of automated scoring (AS) systems in large-scale assessment contexts, oriented around four key guiding principles for oversight of an AS modeling pipeline: (1) that it produces valid scores based soundly in the target construct, (2) that defensible procedures and standards are employed in its execution, (3) that it produces accurate scores that reliably measure mastery or ability, and (4) that the processes and methods used to produce those scores are interpretable by key stakeholders. We highlight the interdependencies between the various technical systems used in computing AS scores and the human/organizational systems that provide supporting context in the overall ecology of the AS modeling pipeline while illustrating the positive and negative impacts these systems can have on each other. We review specific quality-control practices and statistical measures that can provide greater insight into the efficacy of the AS pipeline and instill confidence in the quality of scores produced by both human and machine scores. We discuss key aspects of feature engineering, calibration pool scoring, model evaluation analyses, and documentation of design decisions.
The use of regression analysis in the evaluation of the impact of digitalisation and technological inovations in three measures of the development of economy and society

The objective of this paper is to examine the correlation between the achieved level of technology and innovations and the development of the financial market, index of human development and gross domestic product per capita.The research question reads: "Is the level of technology and innovations related to the market development index, the human development index and GDP per capita, and in what way?"The research objective is to determine whether there is a reason to believe that, by imitating a certain country in relation to the level of digitalisation and innovations, it is possible to further develop the financial market, affect the level of human development or the increase in GDP per capita.Methods used in this paper are regression analysis, i.e. simple linear regression, as well as the analysis and consolidation of preceding research and theoretical findings in order for conclusions to be drawn through an induction method.The paper represents the authors' contribution to the theory and application of economics, as well as to the wider public.The research results also indicate that it would be rational for financial intermediaries in developing countries to consider changing their business models and new possibilities for their adjustment to accelerated technological changes.
Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose descriptions of physics which directly allow us to leverage optimization methods for physical reasoning and sequential manipulation planning. The proposed multi-physics formulation enables the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. As an essential ingredient, we propose a specific parameterization of wrench exchange between object surfaces in a path optimization framework, introducing the point-of-attack as decision variable. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target.
The Implications of Social Media Use on Development in Africa: A Development Theory Perspective

Social media, most obviously, have an effect on development. Global trends show that, countries that have liberalized the usage of social media have experienced extensive development across diverse fields. Despite the extant literature on social media, little effort has been made to assess the role of social media in development in Africa based on the development theory perspective. This article therefore used the development theory to assess the role of social media on Africa’s development. Development theory serves as an ideological function and therefore, social media users are able to articulate and share ideas on this platform. Overtime, they are able to form a coalition directed towards an ideology for the purpose of development. The study also uses a desktop approach to describe the various phenomena associated with development in Africa. The study realized that despite the negative connotations associated with social media in Africa, they have led to massive development on the continent especially in the field of health, culture, politics and religious convictions and education.