Reviewerly/exHarmony huggingface dataset:
Code Reviews, Software Inspections, and Code Walkthroughs: Systematic Mapping Study of Research Topics

Code reviews have been used to improve code quality since the 1970s. Most practitioners in the field of software have some experience with respect to the technique. In this mapping study we illustrate what kinds of research questions are addressed in code review literature. The following themes emerged from analysis of 75 original articles: (1) description or comparison of different code review practices, (2) behavior of reviewers (e.g., eye tracking studies), (3) communication and teamwork, (4) outcomes of code reviews (e.g., what kinds of problems are identified), (5) how properties of code to be reviewed affect reviewing, and (6) reasons for conducting code reviews. About half of the studies have been conducted with students and novices. The numbers of industry papers has significantly increased when compared to the previous reviews in the field.
Unsupervised Machine Learning for Augmented Data Analytics of Building Codes

Existing automated code checking methods/tools are unable to automatically analyze and represent all types of requirements (e.g., requirements that are too complex or that require human judgement). Recent efforts in the area of augmented data analytics have proposed the use of templates to facilitate the analysis of text. However, most of these efforts have constructed such templates manually, which is labor-intensive. More importantly, it is difficult for manually-developed templates to capture the linguistic variations in building codes. More research is, thus, needed to automate the generation of templates to support the tagging and extraction of information from building codes. To address this need, this paper proposes an unsupervised machine-learning based method to extract sentence templates that describe syntactic and semantic features and patterns from building codes. The proposed method is composed of four main steps: (1) data preprocessing; (2) identifying the different groups of sentence fragments using clustering; (3) identifying the fixed parts and the slots in the templates based on the syntactic and semantic patterns of the sentence fragment groups; and (4) evaluating the extracted templates. The proposed method was implemented and tested on a corpus of text from the International Building Code. An accuracy of 0.76 was achieved.
Using Convolutional Neural Network for Image Enhancement on Mobile Devices

This paper presents an application used to automatically enhance an image captured by a camera of a mobile device. The proposed solution consists of applying 4 image enhancement algorithms: gamma correction, saturation correction, white and black level correction. In order to parameterize the algorithms, we proposed a solution based on a convolutional neural network. The network was ported on a mobile device, keeping in mind to minimize computation resources and battery consumption. The mobile platform used is a smart phone, which has an accelerator, useful for massive processing of data caused by the neural network and a graphical processor, used to apply the 4 algorithms.
System Modeling using Predictor classes

System modeling has become one of the most actively researched fields in the last decade. This new-found interest in modeling of systems can be attributed to the impact it has as an inter-disciplinary concept as well as the increased need for optimized and intelligent modeling techniques for disruptive technologies such as machine learning, artificial intelligence and computer vision.This study proposes a generalized mathematical formula to frame a designated function (Target Function) with the help of an available function (Regressor Function) and predictor variables. Categorically, this research attempts to aid designers by providing a mathematical framework for them to substitute their datasets and obtain results thereby reducing the complexity of mathematical modeling required. Furthermore, the discussed design schematics have proved capable of modeling systems with algebraic, transcendental, stochastic behaviours. The necessary calculations and mathematical formulations have been tabulated and deliberated for the same.
METHODS OF AUTOMATED CREATION OF THEMATIC ONTOLOGY

The article describes the methods, the procedures and technology of automated creation of thematic ontology based on automatic processing and semantic analysis of unstructured normative-technical texts and texts of draft documents and determining the semantic relation between detected by concept names. For an application this method necessary to possess procedures complex of automatic semantic the text processing. The article describes the hybrid methods of automated creation of technologies, based for the use of program-linguistic platform of MetaFraz. The research was supported by the Russian Foundation for Basic Research (project 18-37-00110 mol_a).
Gradient Guided Image Deblocking Using Convolutional Neural Networks

Block-based transform coding in its nature causes blocking artifacts, which severely degrades picture quality especially in a high compression rate. Although convolutional neural networks (CNNs) achieve good performance in image restoration tasks, existing methods mainly focus on deep or efficient network architecture. The gradient of compressed images has different characteristics from the original gradient that has dramatic changes in pixel values along block boundaries. Motivated by them, we propose gradient guided image deblocking based on CNNs in this paper. Guided by the gradient information of the input blocky image, the proposed network successfully preserves textural edges while reducing blocky edges, and thus restores the original clean image from compression degradation. Experimental results demonstrate that the gradient information in the input compressed image contributes to blocking artifact reduction as well as the proposed method achieves a significant performance improvement in terms of visual quality and objective measurements.
Application of Medical Feature Extraction Based on Image Technology

With the rapid development of computer technology, it has been fully integrated into our lives, especially in the medical field, computer technology has received more attention and application.The use of computers to process medical images has made a significant contribution to the medical profession.In this paper, CT medical images are taken as an example, digital image processing theory is taken as the technical means, and Matlab is used as the simulation platform to extract the features of medical images.The image is denoised before the feature is extracted.Then, the edge features of the image are extracted by different methods such as Canny and other differential operators, wavelet transform and morphological basic operations.The simulation results are analyzed and compared.
Region Adaptive Mode Selection Algorithms for Image Super-Resolution

Image super-resolution is to generate high-resolution images from low-resolution ones. There are a variety of super-resolution methods. Some are suitable in smooth regions. Some perform well in edge regions. Some achieve good performance in regular texture regions (e.g. the methods that apply the patch with similar structure). In this paper, first, we improve the patch based methods and use the similarities of smoothness and direction to choose proper reference patches. We call it the smoothness similarity and edge-direction based interpolation (SSEDI) method. Then, since different methods are suitable for different regions, we propose an adaptive method to fuse different methods according to multi-scaled first and second order differences. Three methods are chosen for fusion: KMDCC, the cubic B-spline, and the proposed SSEDI, which have good performance in smooth, edge, and regular structure regions, respectively. The derived super-resolution method will perform well in all of the three parts of images.
User Preference Based Community Categorization Towards Video Recommendation Using Kolam Structure In Online Social Networks

A community in a network can be defined as a dense sub graph that is loosely linked to other communities. A social network graph contains nodes representing actors (generally people or organizations) and edges representing relationships or communications between the actors. Community Detection aims to divide the graph into different subsets, but an entity may belong to more than one community in reality. The existing video recommendation systems use artificial neural networks to implement the recommendation system. Kolam is a geometrical drawing consisting of lines and curves that runs around each point to form a pattern. We propose a novel method for community detection and video recommendation using the Kolam patterns considering it as a data structure.
DIAGNOSTIC ACCURACY OF FINE NEEDLE ASPIRATION AND IMPRINT CYTOLOGY OF HEAD AND NECK LESIONS (COMPARATIVE STUDY)

Background &#x0D; Fine needle aspiration cytology has been considered as the first-step procedure for microscopic evaluation of any mass or swelling in the head and neck area and the entire body following taking a full history and physical examination. It is considered as an alternative procedure to open surgical biopsy. On the other hand, imprint cytology is a well-recognized simple technique. Imprint cytology can be used as an adjunct to or as a substitute for frozen section method; since it has been proven to be more efficient than frozen section, and has higher concordance with histological results&#x0D; Objectives &#x0D; To improve clinical and diagnostic cytological experiences among oral pathologists, compare the results of cytological techniques with that of routine histopathological findings, and specify the merits and pitfalls of the above techniques.&#x0D; Materials and Methods&#x0D; A prospective study is conducted on 66 patients indicated for FNA. In 27 cases, histological examinations were available for comparison with cytological diagnosis, among which 20 cases had imprint cytology. All samples were stained with H&amp;E and Pap stain, while salivary gland lesions were additionally stained with Diff-quick. Cytological specimens were compared with the histopathological examination to find diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV).&#x0D; Results&#x0D; Fine needle aspiration cytology was performed on 62 patients, 25 males, and 37 females. The total benign lesions were the commonest (46.7%), followed by inflammatory lesions (38.7%), then malignant lesions (14.5%). The overall diagnostic accuracy of FNAC was 88.8%. The sensitivity, specificity, PPV, and NPV were 75%, 94.7%, 85.7%, and 90% respectively. Whereas in imprint cytology 4 cases were diagnosed as malignant, 11 cases as benign and 5 cases as inflammatory. The diagnostic accuracy of imprint cytology was 90%. The sensitivity, specificity, PPV and NPV were 66.6%, 100%, 100%, 87.5%, and 90% respectively. &#x0D; Conclusion&#x0D; Fine needle aspiration cytology FNAC showed 88.8% diagnostic accuracy, while imprint cytology has a slightly higher diagnostic accuracy of 90% for head and neck lesion.
Satisfiability Checking of Clinical Practice Guidelines Using an Analyzer

: A clinical practice guideline consists of the best practices required for managing a particular disease. Designing a consistent guideline is difficult and error-prone; hence, checking the consistency of guidelines is crucial. Due to the complexity of guidelines, a formal language is an appropriate choice for modeling and analyzing a guideline. IMPNL has been introduced as a metric interval-based temporal logic to model such guidelines. Moreover, a sound and complete tableau-based algorithm has been designed for checking the satisfiability of an IMPNL formula. In this paper, we introduced a clinical practice guideline analyzer suitable for modeling and checking the consistency of a guideline. The analyzer can also determine points, in which inconsistencies occur, and help designers to quickly and easily fix a guideline. Moreover, physicians can use the output of the analyzer (the calendar model) to check whether a patient is coherently treated with a specific guideline.
Impacts of adversarial inputs in associative memory models and its iterative learning variants

Adversarial attacks have always been a bane to neural networks. Most of the research focus is on adversarial networks and its defence for deep neural networks. Associative memory models are different class of neural models used in image recognition tasks. There are fundamental differences between Deep neural networks and Associative memory models in terms of the learning procedures. These fundamental differences in turn have different effects on adversarial attacks. In this paper we have attempted an empirical study on various flavors of an associative memory models viz.Hopfield model and two different forms of iterative learning rules and its resilience towards an adversarial attack.
Human Density Estimation by Exploiting Deep Spatial Contextual Information

We propose in this paper a deep learning method for human density estimation, targeted to sparse density scenarios. We divide the image into a grid of cells and realize that a person does not span in one cell but several cells. To estimate the human density appeared in one cell, the information of surrounding cells plays a crucial role, so-called spatial contextual information. The key insight of our approach is the employment of Convolutional Neural Networks (CNN) to extract image features and Long Short-Term Memory (LSTM) to exploit the spatial contextual information. The shared-weight mechanism of CNN models generalizes the learned features of humanity in all corners of the images. The feedback mechanism of LSTM captures spatial contextual information of neighboring cells in a sub-grid of 3x3 cells to estimate the human density of the central cell. We demonstrate the validity of the proposed method on Mall dataset with a competitive performance in comparison with state-of-the-art methods, validated on the same dataset.
Mining Cross-platform User Behaviors for Demographic Attribute Inference

In this paper we seek to utilize the behavior information and the attribute labels of users from an auxiliary platform to help make attribute prediction. We propose a cross-platform demographic attribute inference model (CDAIM for short), in which we first learn the user representations with the behavior data, and then regulate the vectors of the same users from both platform to be close via a transfer function, and finally train a classifier with the feature vectors and attribute labels of all the users. We conduct extensive experiments on real datasets and the results show that our CDAIM outperforms the baselines.
User Preference Aware Multimedia Pricing Model using Game Theory and Prospect Theory for Wireless Communications

Providing user satisfaction is a major concern for on-demand multimedia service providers and Internet carriers in Wireless Communications. Traditionally, user satisfaction was measured objectively in terms of throughput and latency. Nowadays the user satisfaction is measured using subjective metrices such as Quality of Experience (QoE). Recently, Smart Media Pricing (SMP) was conceptualized to price the QoE rather than the binary data traffic in multimedia services. In this research, we have leveraged the SMP concept to chalk up a QoE-sensitive multimedia pricing framework to allot price, based on the user preference and multimedia quality achieved by the customer. We begin by defining the utility equations for the provider-carrier and the customer. Then we translate the profit maximizing interplay between the parties into a two-stage Stackelberg game. We model the user personal preference using Prelec weighting function which follows the postulates Prospect Theory (PT). An algorithm has been developed to implement the proposed pricing scheme and determine the Nash Equilibrium. Finally, the proposed smart pricing scheme was tested against the traditional pricing method and simulation results indicate a significant boost in the utility achieved by the mobile customers.
Detecting Missing Translations in Neural Machine Translation Using Information Quantity in Sentences

This study attempts a method based on the information quantity in the source sentence, for detecting missing translations in neural machine translation. It assumes the necessary information quantity for the sentence as a whole, based on the word information quantity in the source sentence and, in the same way, detects missing translations by making a comparison with the presumed target sentence information quantity.
Optical Methods in Stress Measurement

Stress will be produced in most of the engineering components related to their manufacturing process or because of their loading condition. For some special cases, both types also combined together and produce stress. Manufacturing processes like casting, welding, machining, and hot forming are creating the stress in the components. This stress will produce due to alteration of the microstructure (size, shape, phase composition, and orientation). Loading conditions are also produced stresses in the engineering components. This stress may be classified into compression, shear, tension, and fatigue. These depend on the load. Measuring the stresses in the components is very important because it can save a lot in terms of money, material, and manpower. A lot of techniques are used in industries to measure the stresses. Based on that, measurement techniques are broadly classified into two category, namely destructive and non-destructive techniques. Each method has its own advantages and limitations too. In this chapter, the optical method of measuring stress is discussed briefly.
A Study About Safety Technology of Control System and Information System in Nuclear Power Plant

Based on the security incident of Bushehr nuclear power plant in Iranian, this paper analyzes the research direction of the nuclear power plant control system and information system under the new situation. This study contains functional analysis and specification development. At the same time, security flaws are discovered and security protection system is established. During the normal operation and accident situation of the nuclear power plant, the security of control system and information system is guaranteed, and the reliability of information transmission is improved. Therefore, the safe operation of nuclear power plant has been strongly supported.
Enhanced Graphics Techniques for Improving Depth Perception Accuracy in Magnified Virtual Environments

Improving depth perception accuracy in virtual environments is an important research topic to use 3D vision technology for quantitative practice. This study examines the impacts of various depth cues on human depth perception in a specialized virtual environment where images are magnified for viewing. It expands on the results of preliminary research that suggested promising techniques to improve depth perception accuracy. The study evaluates a focused area of the medical industry; it analyzes the application of enhanced graphics techniques selectively onto 3D medical images. Expanded experimental tests were conducted on a one-wall 3D projection system to display a scene with redesigned depth cues. The previous study showed viewers tend to underestimate depth in virtual environments. This follow-up experiment revealed the enhanced graphic techniques improved accuracy in depth estimation; it suggested future research directions for utilizing 3D vision technology in medical tasks where quantitative analysis can provide significant benefits and advantages for examining medical images.
IUPTIS: Fingerprinting Profile Webpages in a Dynamic and Practical DPI Context

In this paper, we propose an extended overview of a novel webpage fingerprinting technique ‘IUPTIS’ that allows an adversary to identify webpage profiles in an encrypted HTTPS traffic trace. Our approach works by identifying sequences of image resources, uniquely attributed to each webpage. Assumptions of previous state-of-the-art methods are reduced by developing an approach that does not depend on the browser utilized. Additionally, it outperforms previous methods by allowing webpages to be dynamic in content and permitting a limited number of browser and CDN-cached resources. These easy-to-use properties make it viable to apply our method in DPI frameworks where performance is crucial. With practical experiments on social media platforms such as Pinterest and DeviantArt, we show that IUPTIS is an accurate and robust technique to fingerprint profile webpages in a realistic scenario. To conclude, we propose several defenses that are able to mitigate IUPTIS in privacy-enhanced tools such as Tor.
Interactive Search and Exploration in Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations and compare different embedding strategies. We demonstrate the capabilities of the proposed approach on a multimedia collection originating from the violent online extremism forum Stormfront, which is particularly interesting due to the high semantic level of the discussions it features.
Design of Cockpit Ergonomic Evaluation Simulation System Based on Augmented Reality

To solve problems of ergonomic evaluation in cockpit collaborative design(co-design), it needs to design shared ergonomic evaluation system to rapid response ergonomic requirements of cockpit co-design, and which it can be used for real-time dynamic evaluation by participants in every iterative interval. A design of Cockpit Ergonomic Evaluation Simulation System(C2ES) is proposed and research in ARtoolkit development platform. The C2ES, which based on specific demand of aviation cockpit ergonomic evaluation, has been designed a modified configuration in typical video see-through augmented reality system. Its design achieved two majors' parts, which one is infrared camera and infrared marker to track head movements, and another is mixture of synthetic instrument and cockpit physical model by visual registration algorithm. The C2ES, whose underlying infrastructure come from mature algorithm and typical system has preferable robustness and practicability of cockpit ergonomic evaluation. Verification experiments show that it can be as a platform to simulate more layout change in cockpit which will then achieve knowledge sharing and find commonalities. For the new ergonomic design conflict that get unveiled, build on them further to conflict resolution.