Reviewerly/exHarmony huggingface dataset:
How Do Contributors Impact Code Naturalness? An Exploratory Study of 50 Python Projects

Recent studies have shown how software is comparable to natural languages, meaning that source code is highly repetitive and predictable. Other studies have shown the naturalness as indicators for code quality (i.e., buggy code). With the rise of social coding and the popularity of open source projects, the software is now being built with contributions that come from contributors from diverse backgrounds. From this social contribution perspective, we explore how contributors impact code naturalness. In detail, our exploratory study investigators whether the developers' history of programming language experience affects the code naturalness. Calculating the code naturalness of 678 contributors from 50 open-source python projects, we analyze how two aspects of contributor activities impact the code naturalness: (a) the number of contributors in a software project, (b) diversity of programming language contributions. The results show that the code naturalness is affected by the diversity of contributors and that more collaborative software tends to be less predictable. This exploratory study serves as evidence into the relationship between code naturalness and the programming diversity of contributors.
Detection Of Dysplasia From Endoscopic Images Using Daubechies 2 Wavelet Lifting Wavelet Transform

We propose herein a new feature extraction method based on the lifting wavelet transform for dysplasia detection from an endoscopic image. In the proposed method, the input endoscopic image is converted into the hue-saturation-value color space, and the S space image is used. The pattern of the abnormal area is learned from this image using Daubechies 2 (db2) wavelet lifting wavelet transform. The lifting wavelet transform is performed on the detected image using the learned filter. Each frequency component is obtained using this method. The detected image generated from the sum of the high-frequency components is divided into small blocks. A static threshold is determined herein to obtain a binary image. Discrete wavelet transform is used to exclude smooth areas. V space images are used to exclude dark areas, such as shadows. This emphasizes the contour of the abnormal part. Finally, from the idea that the area surrounded by the outline is also abnormal, the life game is limitedly applied to emphasize the abnormal area. We describe the feature extraction in detail and present the experimental results demonstrating that our method is useful for the development of dysplasia detection from an endoscopic image.
Emerging Entity Discovery Using Web Sources

The rapidly increasing amount of entities in knowledge bases (KBs) can be beneficial for many applications, where the key issue is to link entity mentions in text with entities in the KB, also called entity linking (EL). Many methods have been proposed to tackle this problem. However, the KB can never be complete, such that emerging entity discovery (EED) is essential for detecting emerging entities (EEs) that are mentioned in text but not yet contained in the KB. In this paper, we propose a new topic-driven approach to EED by representing EEs using the context harvested from online Web sources. Experimental results show that our solution outperforms the state-of-the-art methods in terms of F1 measure for the EED task as well as Micro Accuracy and Macro Accuracy in the full EL setting.
To the Problem of Modeling in Modern Education: Prospects for Model Transformations and Implementation Risks

The research featured the current state of education taking into account the specifics of national features of the implementation of a particular model. The study focused on specific characteristics of education at the beginning of the XXI century and integration of various educational models. The research objective was to identify and theoretically justify the prospects of development and the implementation risks for modern models of education in the world, as well as to prove the effectiveness of the national educational system. The paper describes the features of modern education, including the interaction of numerous local educational environments. The authors studied the mutual use of specific features of innovative environments of one country in the educational space of other countries. The paper contains a list of generalized characteristics peculiar to the educational systems of different countries, e.g. transformability, modelability, openness, adaptability, etc. The study made it possible to define directions of development of the education system in the world educational environment. The authors considered the advantages and disadvantages of each system. They highlighted some models of training that owe their efficiency to certain features. The paper also introduces the problem of development and implementation of the national model of education. A retrospective analysis made it possible to define the features of the formation of the national model at different historical stages, as well as the risks of formation. The article introduces guidelines for the construction of the Russian national model and gives practical recommendations aimed at building the national model of education.
Multi-Source Deep Residual Fusion Network for Depth Image Super-resolution

Comparing with color images, depth images are often in lack of texture information in high quality. Depth image super-resolution provides an efficient solution to enhance the high frequency information of LR depth image. In this paper, we propose a novel multi-source residual fusion neural network named "MSRFN", which fully uses the fruitful texture information of color images to guide the depth images reconstruction. Initially, color and depth images are used to extract residual features in two-branch network. Then, color residual and depth residual are fused by the fusion network. Finally, the high-resolution (HR) depth map is reconstructed by fusing multi-source high-frequency information. Experimental results on MPI Sintel and Middlebury public databases show that MSRFN outperforms some state-of-the-art approaches in subjective and objective measures.
Evolving Pictures in Image Transition Space

Evolutionary art creates novel images through a processes inspired by natural selection. Images are high dimensional objects, which can present challenges for evolutionary processes. Work to date has handled this problem by evolving compressed or encoded forms of images or by starting with prior images and evolving constrained variations of these. In this work we extend the prior-image concept by evolving interesting images in the transition-space between two bounding images. We define new feature metrics based on proximity to the two bounding images and show how these metrics, combined with other aesthetic features, can be used to drive the creation of new images incorporating features of both starting images. We extend this work further to evolve sets images that are diverse in one and two feature dimensions. Finally, we accelerate this evolutionary process using an autoencoder to capture the transition space and reduce the dimensionality of the search space.
Improving Just Noticeable Difference Model by Leveraging Temporal HVS Perception Characteristics

Temporal HVS characteristics are not fully exploited in conventional JND models. In this paper, we improve the spatio-temporal JND model by fully leveraging the temporal HVS characteristics. From the viewpoint of visual attention, we investigate two related factors, positive stimulus saliency and negative uncertainty. This paper measures the stimulus saliency according to two stimulus-driven parameters, relative motion and duration along the motion trajectory, and measures the uncertainty according to two uncertainty-driven parameters, global motion and residue intensity fluctuation. These four different parameters are measured with self-information and information entropy, and unified for fusion with homogeneity. As a result, a novel temporal JND adjustment weight model is proposed. Finally, we fuse the spatial JND model and temporal JND weight to form the spatio-temporal JND model. The experiment results verify that the proposed JND model yields significant performance improvement with much higher capability of distortion concealment compared to state-of-the-art JND profiles.
Decidability of affine solution problems

Abstract We present formula equations—first-order formulas with unknowns standing for predicates—as a general formalism for treating certain questions in logic and computer science, like the Auflösungsproblem and loop invariant generation. In the case of the language of affine terms over $\mathbb{Q}$, we translate a quantifier-free formula equation into an equivalent statement about affine spaces over $\mathbb{Q}$, which can then be decided by an iteration procedure.
Possibilities of Position Determination

The article presents an implementation of methods for obtaining the form and appearance of real objects. The analysis of existing approaches, as well as recognition algorithms. The best methods were selected for determining an object in three-dimensional space. Reviewed by method of object recognition to determine the distance from the camera to the marker and methods for determining the position of the object in space. Algorithm for positioning an object in space is developed with the using of the OpenCV library functions. As a result, calculations of relative coordinates of the found object are carried out. Information selection rules based on information are suggested.
Classification Performance Improvement of Keystroke Data

In this work, the classification of keystroke data, a behavioral biometrics modality, is investigated. The users degrading the overall classification performance are identified by classifying two different databases composed of different number of individuals and passphrases. The improvement in performance is demonstrated by eliminating those users in the training phase of the classifier.
A Novel Sequence Graph-Based Approach to Find Academic Research Trends

Research trends are dynamic, changing from time to time. It is an indicator of the latest innovations in each field of research, current areas of research, the latest technologies, and developments in each field of research. It also helps with future innovations and developments by providing current challenges and opportunities. This article proposes an efficient method to find research trends in each field of research of any subject area by using the graph-based subject classification of published papers. This methodology can be efficiently used to find research trends at any point of time, based on the published year of academic publications. A study of change in research trends in three subject areas - physics, mathematics, and computer science have been successfully conducted based on a total of 4500 publications since 2004.
On the Complementary Role of DNN Multi-Level Enhancement for Noisy Robust Speaker Recognition in an I-Vector Framework

In order to improve the noise robustness of automatic speaker recognition, many techniques on speech/feature enhancement have been explored by using deep neural networks (DNN). In this work, a DNN multi-level enhancement (DNN-ME), which consists of the stages of signal enhancement, cepstrum enhancement and i-vector enhancement, is proposed for text-independent speaker recognition. Given the fact that these enhancement methods are applied in different stages of the speaker recognition pipeline, it is worth exploring the complementary role of these methods, which benefits the understanding of the pros and cons of the enhancements of different stages. In order to use the capabilities of DNN-ME as much as possible, two kinds of methods called Cascaded DNN-ME and joint input of DNNs are studied. Weighted Gaussian mixture models (WGMMs) proposed in our previous work is also applied to further improve the model's performance. Experiments conducted on the Speakers in the Wild (SITW) database have shown that DNN-ME demonstrated significant superiority over the systems with only a single enhancement for noise robust speaker recognition. Compared with the i-vector baseline, the equal error rate (EER) was reduced from 5.75 to 4.01.
A Multi-granularity Genetic Algorithm

The genetic algorithm is a classical evolutionary algorithm that mainly consists of mutation and crossover operations. Existing genetic algorithms implement these two operations on the current population and rarely use the spatial information that has been traversed. To address this problem, this paper proposes an improved genetic algorithm that divides the feasible region into multiple granularities. It is called the multi-granularity genetic algorithm (MGGA). This algorithm adopts a multi-granularity space strategy based on a random tree, which accelerates the searching speed of the algorithm in the multi-granular space. Firstly, a hierarchical strategy is applied to the current population to accelerate the generation of good individuals. Then, the multi-granularity space strategy is used to increase the search intensity of the sparse space and the subspace, where the current optimal solution is located. The experimental results on six classical functions demonstrate that the proposed MGGA can improve the convergence speed and solution accuracy and reduce the number of calculations required for the fitness value.
ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources

The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.
Dynamic Analysis of a UAS Glider Using Advanced Aircraft Analysis and Athena Vortex Lattice

A dynamic analysis of the DG808S unmanned aerial system glider is preformed using two separate aircraft modeling programs. Athena Vortex Lattice and Advance Aircraft Analysis are both used to create dynamic models for the DG808S and compared in an attempt to find uncertainties in different stability and control derivatives. All geometric parameters for the models are gathered through hand measurements while the mass moment of inertia for the aircraft is found through the bifilar pendulum method. Dynamic models are created from the stability and control derivatives calculated from these programs along with MATLAB/Simulink. The dynamic models that are created verify that the modes of the aircraft are all stable besides the spiral mode. The handling qualities are then assessed and found to lie within acceptable levels.
Modified Edge-Weighted Centroidal Voronoi Tessellation for Image Segmentation

The edge-weighted centroidal Voronoi tessellation (EWCVT) algorithm has been proved to be very efficient for image segmentation, especially when segmenting images with strong noise. However, when the objects to be segmented consist of special or complex small-scale structures, e.g. the pores in a core CT image, the EWCVT algorithm might fail to identify them from noisy background. In this work, we propose two modifications for the EWCVT algorithm to extend its ability for segmenting objects full of small or complex structures. The proposed model and algorithm encode two kinds of prior information: local spatial coherence of the image pixels and local spatial coherence of small structures. The effectiveness and efficiency of the proposed model and algorithm will be demonstrated by various numerical experiments.
Content based Image Retrieval with Rocchio Algorithm for Relevance Feedback Using 2D Image Feature Representation

This paper presents Content based Image Retrieval with Relevance Feedback to retrieve relevant images based on an image query. Three main steps are proposed, first, obtain 2D feature representation of an image query and image database using the Integrated Color Co-Occurrence Matrix. This feature extraction method captures two features simultaneously, they are color and texture features. Second, compute cosine similarity measurement to retrieve similar images between features of an image query and features of all images in the database. Third, update the query features using Rocchio algorithm based on the user's relevance feedback, and recalculation of the cosine similarity between the updated feature of query and features of all images in the database. Experiments are conducted using Corel Image database that consists of 1000 images from ten classes. The proposed model for retrieving similar images achieved higher performance accuracy compare to the Content based Image Retrieval without Relevance feedback.
ICS Cyber Attack Analysis and a New Diagnosis Approach

Artificial Intelligence and Machine Learning technologies have a widespread use in many disciplines thanks to raw data processing and computational power. The capabilities of these technologies will enable the identification of legal and illegal traffic/behavior by classifying these data rapidly and without damaging the continuity of the system through the high amount of network traffic/behavior, which is one of the biggest problems in the field of cyber security. In this respect, artificial intelligence and machine learning technologies will provide valuable contributions in protecting Industrial Control Systems (ICS), which play an important role in the control of critical infrastructures such as electrical power generation-transmission-distribution systems, nuclear power plants, gas and water, against cyber-attack. In this study, it is aimed to reveal the anatomy of the attacks by executing denial of service, Start/Stop, and the man in the middle attacks to PLCs, an important component of ICS. In the test environment created in the study, the attacks on the real two types of PLCs were analyzed. The analyzes focused to obtain the rule sequences, which can be used by the artificial intelligence and machine learning technologies, by benefitting from data sets obtained in the test environment. In this way, a new security approach has been created for ICS. The results also revealed the importance of PLCs’ vulnerability to attacks and the continuous monitoring of the network in order to detect and identify the attacks as soon as possible and to protect the ICS and to maintain its continuous functioning.
Modelling Sources of Inconsistent Information in Paraconsistent Modal Logic

Epistemic logics based on normal modal logic are notoriously bad at handling inconsistent and yet non-trivial information. This fact motivates epistemic logics based on paraconsistent logic, examples of which can be traced back at least to the 1980s. These logics handle inconsistent and non-trivial information, but they usually do not articulate sources of the inconsistency. Yet, making the origin of an inconsistency present in a body of information explicit is important to assess the body—can we trace the mutually conflicting pieces of information to sources of information relevant to the body or is the inconsistency a result of an error unrelated to any outside sources? Is the inconsistency derived from various equally trustworthy sources or from a single inconsistent source? In this article we show that a paraconsistent modal logic, namely, the logic B K introduced by Odintsov and Wansing, is a first step toward a formalism capable of making these distinctions explicit. We interpret the accessibility relation between states in a model as a source relation—states accessible from a given state are seen as sources of potential justification of the information contained in the original state. This interpretation also motivates the study of a number of extensions of B K. We focus here on extensions of B K able to articulate the relation of compatibility between bodies of information and extensions working with labels explicitly differentiating between bodies of information. In the case of compatibility-based extensions a more detailed technical study including a completeness proof is provided; technical features of the simpler case of label-based extensions, on the other hand, are discussed without going into details.
The Difficulties in Using Weak Relevant Logics for Naive Set Theory

We discuss logical difficulties with the naive set theory based on the weak relevant logic DKQ. These are induced by the restrictive nature of the relevant conditional and its interaction with set theory. The paper concludes with some possible ways to mitigate these difficulties.
Role of AI and Bio-Inspired Computing in Decision Making

Decision making has been one of the challenging tasks in solving real-world problems. Decision making mimics human activity which in turn has noteworthy impacts. Several researches have proved that the quality of decisions has been improved by developing computer-based technologies that aid and extend the human capabilities. Decision support systems (DSS), which augment in making proper decisions, to an extent, do sometimes lack intelligence. The recent advances in the field of artificial intelligence (AI) have made this possible in a wide range of multifarious applications. AI has the capability of mimicking human decision making and has also shown that it is capable of assisting and improving human decision making in real-time complex environments. Embedding intelligence into the DSS using the AI capabilities has been progressing thus forming the Intelligent Support Systems for Decision Making (ISSDM). These support systems have been used in simplifying the decision-making process in several areas, namely, health care, cyber security, finance, marketing, and commerce. This chapter reviews the role of artificial intelligence tools such as artificial neural networks, fuzzy logic, and bio-inspired algorithms in the decision-making process. A brief introduction to the application of these algorithms to Industry 4.0 paradigm is also provided.
Pareto-Based Self-organizing Migrating Algorithm Solving 100-Digit Challenge

In this article, we describe the design and implementation of a variant version of SOMA named SOMA Pareto to solve ten hard problems of the 100-Digit Challenge. The algorithm consists of the following operations: Organization, Migration, and Update. In which, we focus on improving the Organization operation with the adaptive parameters of PRT and Step. When applying the SOMA Pareto to solve ten hard problems to 10 digits of accuracy, we achieved a competitive result: 85.04 points.