Reviewerly/exHarmony huggingface dataset:
Bug Severity Classification Using Semantic Feature with Convolution Neural Network

Bug report classification is an important phase of software engineering process. It falls under the testing and maintenance phase which is an important and time taking process. In today’s agile world it’s very important to deliver the software in less time without affecting the quality of software. It is the job of bug trigger to classify the bugs based on criticality. If bugs are classified incorrectly then it will induce a delay in the system as bugs with a high priority will not be dealt at the right time. This task done manually is prone to errors, thus there is a need for automatic classification of bugs to help the trigger. This paper proposed convolution neural network with L1 and L2 regularization compared with machine learning approach. Experimental analysis shows that all classes achieve significant improvement in results as compared to the previous approaches.
The state of russian library and information sciences from the perspective of academic journals

Scientific challenge: A study of the current state of library and information sciences via bibliometric analysis of scientific journals and their editorial board members, i. e. geographic distribution and bibliometric analysis of their scholarly output resulting in additional approaches to evaluate serials and scientific area.Purpose: Bibliometric analyses of journals and scholarly output and geographic distribution of their editorial board members being the most authoritative experts over the last 5 years; detection of large scientific centers of library and information science; detection of a core of academic journals.Originality: For the first time in Russian literature results of the analysis of editorial boards are described for additional evaluation of library and information science journals and relevant scientific areas in general. Due to a limited number of papers on analysis of editorial boards this paper can be used as methodological one as well.Findings: Analysis of geographic distribution enabled us to detect a share of foreign members of almost 25 percent. The largest groups of Russian experts are located in Moscow and Saint-Petersburg, but also in two regional centers of the European part of Russia – Voronezh and Samara, while only Novosibirsk is the center of the Siberian expert group in library and information science. Experts mainly work at universities, followed by research organizations of The Ministry of Science and Higher Education. Wide range of bibliometric indices of editorial board members correlates with that of relevant journals. Some approaches of editors to enhance journal indices are detected including publication of their own high-level papers in journals which they are working for since these papers are attracting a larger amount of citations as compared with papers of other authors. Another strategy assumes that editorial board members cite their own journal in other serials. Co-citation data of analyzed journals predominantly demonstrate the absence of strong connections between journals.Conclusions: Editorial boards play a significant role in enhancing bibliometric indices of journals, and stronger integration of Russian library and information science society is necessary which may result in increasing currently low ranks of the studied journals among international serials.
Formal Analysis of Responsibility Attribution in a Multimodal Framework

The present article is devoted to a logical treatment of some fundamental concepts involved in responsibility attribution. We specify a theoretical framework based on a language of temporal deontic logic with agent-relative operators for deliberate causal contribution. The framework is endowed with a procedure to solve normative conflicts which arise from the assessment of different normative sources. We provide a characterization result for a basic system within this framework and illustrate how the concepts formalized can be put at work in the analysis of examples of legal reasoning.
Point-Free Spectra of Linear Spreads

Scott’s multiple-conclusion entailment relations, originally conceived as a means to clarify several aspects of many-valued logic, and later put to great use in a revised Hilbert programme for abstract algebra, are brought to application in the context of König’s lemma. An inductively generated entailment relation which describes the linear spreads of a detachable unbounded tree over a finite discrete set is given a direct non-inductive description. This immediately prompts a consistency result which serves as an elementary and constructive counterpart of the Weak König Lemma.
Modulation scheme recognition using convolutional neural network

Convolutional neural network (CNN) is an extremely powerful machine-learning tool, especially when dealing with computer vision problems. Here, the authors present a CNN-based modulation recognition model. In order to fully elaborate the powerful image feature extraction ability of CNN, the authors have created an image dataset of different complex signal spectrograms using short-time Fourier transform (STFT). In this case, the complex modulation recognition problem is converted to an image recognition problem. To study the accuracy of automatic recognition of signal spectrograms, the authors have applied two approaches recently developed for image classification. The first approach is to optimise activation functions. Experiments show that best performance can be achieved when using sigmoid as activation function. The second approach is using optimisation functions. At last, the authors compared the recognition accuracy under different signal-to-noise ratios (SNRs). The result shows that authors’ model achieves higher recognition accuracy under low SNR and stronger generalisation ability than other recognition methods.
WebProfiler: User Interaction Prediction Framework for Web Applications

User interaction prediction for Web applications is crucial to improve browsing experience. With the prediction, for instance, a browser can prefetch the content to be accessed next reducing long wait times in advance. However, predicting the user interaction is challenging in practice. Collecting detailed interaction data is difficult due to the constraints on target application and platform. Moreover, Web navigation prediction mechanisms for general applications have a low accuracy with conventional machine learning models. To this end, in this paper, we propose a Web interaction profiling framework, WebProfiler, which collects user interaction data in a generic way and accurately predicts the next navigation. Both navigation and click events are collected by using JavaScript event handlers and clicked objects are identified reliably through a document object model based approach. Furthermore, we adopt gated recurrent unit (GRU), a representative deep learning technique suitable for coping with time series Web interaction data, and present two advanced techniques for training the GRU-based model: uniform resource locator (URL) grouping to handle the variant URLs of a Web page and Web embedding to represent both events in a unified vector space. The experimental results based on the real user interaction data showed that click events within an application improved the overall prediction performance by 13.7% on average, which were overlooked by most of the previous research. In addition, WebProfiler achieved an average F-measure of 0.798 for top three candidates where URL grouping and Web embedding contributed to 52.4% of the performance improvement.
Process of Requirement Analysis Link to Software Development

This paper is giving an overview of the process of requirement analysis for software development.Here I have discussed about key parts in requirement analysing, gathering relevant materials, functional analysis and allocations, how to improve and make a quality process and also document development as well and many more which relates to requirement analysis process.The scope of this study is not a generalized approach but rather discuss through specific cases such as like Dutch flower case.It describes the main areas of requirement process in practice, and highlights.I hope that readers will find this paper useful in guiding them toward the knowledge and resources they needed.
An Efficient Data Compression Approach based on Entropic Codingfor Network Devices with Limited Resources

The expansion of sensitive dataderiving from a variety of applications has requiredthe need to transmit and/or archivethem with increased performance in terms of quality, transmission delay or storage volume.&#x0D; However, lossless compression techniques are almost unacceptable in the application fields where data does not allow alterations because of the fact that loss of crucial information can distort the analysis.&#x0D; This paper introduces MediCompress, a lightweight lossless data compression approach for irretrievable data like those from the medical or astronomy fields. The proposed approachis based on entropic Arithmetic coding, Run-length encoding, Burrows-wheeler transform and Move-to-front encoding. The results obtained on medical images have an interesting Compression Ratio (CR) in comparison with the lossless compressor SPIHT and a better Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) than SPIHT and JPEG2000.
Exploring the Use of 360-degree Video for Teacher-Training Reflection in Higher Education

360-degree videos offer new features to extend video-based learning scenarios. In combination with virtual reality (VR) displays such as full-feature or cardboard displays the use of 360-degree video can further support the feeling of immersion and provide more degrees of freedom to look around. This paper presents preliminary results from an interpretive case study (n=16) evaluating the use of 360-degree videos for video reflection of teacher training sessions in a business education context. In two courses students were offered to record their teaching sessions with a 360-degree video camera in order to reflect on their teaching performance. The process of video reflection consisted of watching the video multiple times and answering specific questions on didactics, content and performance. For this case study participants could explore three viewing experiences, a head-mounted display, a cardboard viewer and a web-based player. Based on their experiences made, they were interviewed focusing on the usefulness for video-based self- reflection. Preliminary results show that 360-degree videos are embraced positively and perceived more useful than regular video for reflection. However, challenges were identified for the scalability and usability of 360-degree video content.
IPO: Interior-point Policy Optimization under Constraints

In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the objective with logarithmic barrier functions, inspired by the interior-point method. Our proposed method is easy to implement with performance guarantees and can handle general types of cumulative multiconstraint settings. We conduct extensive evaluations to compare our approach with state-of-the-art baselines. Our algorithm outperforms the baseline algorithms, in terms of reward maximization and constraint satisfaction.
Representation Disentanglement for Multi-task Learning with Application to Fetal Ultrasound

One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms. In this paper we propose a novel representation disentanglement method to extract semantically meaningful and generalizable features for different tasks within a multi-task learning framework. Deep neural networks are utilized to ensure that the encoded features are maximally informative with respect to relevant tasks, while an adversarial regularization encourages these features to be disentangled and minimally informative about irrelevant tasks. We aim to use the disentangled representations to generalize the applicability of deep neural networks. We demonstrate the advantages of the proposed method on synthetic data as well as fetal ultrasound images. Our experiments illustrate that our method is capable of learning disentangled internal representations. It outperforms baseline methods in multiple tasks, especially on images with new properties, e.g. previously unseen artifacts in fetal ultrasound.
Experimental determination of critical strain energy density of ductile materials

The method of experimental determination of strain energy density of plastic materials is developed.The technique for complete true stress-strain curves plotting is formulated.The standard hydraulic testing machine is equipped with specially designed experimental setup for Bridgman specimens testing at strain controlled tension loading with digital camera and light source for using digital image correlation method -a non-contact technique for strain and displacement measurement.The digital image correlation method was used to determine the local strain at the onset of fracture in the neck of Bridgman specimen.The technique takes into account the change of cross-section area in the neck of specimen due to internal crack propagation when calculating the true stresses.The complete true stress stress-curve of 40Kh alloyed steel is plotted end critical strain energy density of steel is determined from it.It is shown that the critical strain energy density of material, determined from the curve obtained by developed technique is 1.8 times higher than determined from the classical true stress-strain curve and is close to the value of the specific heat of fusion of steel.The curves built using the proposed technique can be used for setting material properties in stress-strain state calculations by finite element method at large scale yielding conditions, for instance at pressure vessels critical pressure calculation.The critical strain energy density value can be considered as a material property at fatigue life-time calculation using energy approach.
A Sociolinguistic Analysis of the Use of Arabizi in Social Media Among Saudi Arabians

The aim of this sociolinguistically-oriented study is to explore the Arabizi phenomenon which is characterized by spelling Arabic words using the Latin script. It is prevalent in the text-based computer-mediated communications among Saudi Arabians. The study focuses on why Arabizi is used, how, particularly in respect to with whom and in which topics, it is used, the attitudes of its users toward its use and the perceived advantages and disadvantages of its use. Using an online survey, data were collected from 241 participants, 72 of which were users of Arabizi. The findings revealed that the primary reasons for using Arabizi were its being a communication code among youths and a compensation for the lack of Arabic keyboard from technological devices as well as being more expressive than Arabic language. It was also found that Arabizi was primarily used to communicate with friends and individuals of the same age, but not with parents and older people or in formal relationships. In addition, the study revealed that Arabizi is used in occasional conversations and social matters, but not in academic, scientific, business, economic, religious or poetry and literature- related topics. Arabizi users were found to hold both positive and negative attitudes based on different advantages and disadvantages of the phenomenon. These findings will be discussed and recommendations for future research will be given.
BYOD, Personal Area Networks (PANs) and IOT: Threats to Patients Privacy

The passage of FISMA and HIPPA Acts have mandated various security controls that ensure the privacy of patients’ data. Hospitals and health-care organizations are required by law to ensure that patients’ data is stored and disseminated in a secure fashion. The advent of Bring Your Own Devices (BYOD), mobile devices, instant messaging (such as WhatsApp) and cloud technology however, have brought forth new challenges. The advent of Internet of Things (IOT) have complicated the matters further as organizations are not fully cognizant to the all facets of threats to data privacy. Physicians and health care practitioners need to be made aware of various new avenues of data storage and transmission that need to be secured and controlled. In this paper we look at various threats and challenges that IOT, Bring Your Own Device (BYOD) and Personal Area Networks (PANs) technologies pose to the patients’ privacy data. We conclude the paper by providing the results of a survey that gauge the depth of understanding of healthcare professionals regarding the emerging threats to patients’ privacy.
Comedy "The Forest" by Alexander Ostrovsky: holding out for a hero. Experience of a comment

The publication of comedy "The Forest" and its premier performances in St. Petersburg and Moscow in 1871 germinated considerable number of critical responses; at that, in most of them there was lack of understanding of artistic-moral and psychological problems the playwright was uneasy about and showed in his play. Alexander Ostrovsky was considered to be, first of all, a portrayer of ordinary life who was keeping exposing the cloyed social sores of the Russian life. Only few reviewers paid attention that in the comedy, luminaries of art were opposed to the spiritless world of powers that be. They were not ideal, but capable to rise beyond mercantile interests, and their restless existence was, in the opinion of the author, more attractive, than life of men in the street filled with falseness, intrigues and lie. Alexander Ostrovsky understands that art, unfortunately, is not omnipotent, but it also is not powerless; it well influences the person. The comedy "The Forest" carries on tradition of search of the positive fundamentals in the Russian life by the playwright and opens the important topic of theatre and figures of theatre – carriers of humanistic values – in his creative work; it plans a new vector of holding out for the hero.
A CNN-Based Subpixel Level DSM Generation Approach via Single Image Super-Resolution

Previous work for subpixel level Digital Surface Model (DSM) generation mainly focused on data fusion techniques, which are extremely limited by the difficulty of multisource data acquisition. Although several DSM super resolution (SR) methods have been developed to ease the problem, a new issue that plenty of DSM samples are needed to train the model is raised. Therefore, considering the original images have vital influence on its DSM's accuracy, we address the problem by directly improving images resolution. Several SR models are refined and brought into the traditional DSM generation process as an image quality improvement stage to construct an easy but effective workflow for subpixel level DSM generation. Experiments verified the validity and significance of bringing SR technology into this kind of application. Statistical analysis also confirmed that a subpixel level DSM with higher fidelity can be obtained more easily compared to directly DSM interpolation.
An Improved Adaptive Correction Algorithm For Non-uniform Illumination Panoramic Image

An adaptive non-uniform illumination panoramic image enhancement algorithm was proposed, which can improve the visual effect of panoramic images captured under non-uniform illumination. Firstly, according to the illuminance-reflection imaging model, we adopt a bilateral filtering method to decompose the brightness into illuminance component and reflection component. Secondly, an adaptive gamma correction function is constructed, which can dynamically adjust the illuminance component. In the reflection component processing, an improved morphological operation is proposed to denoise and enhance the image details. Finally, we combine the illumination component with the reflection component to get the enhanced panoramic image. The experimental results show that the proposed algorithm can improve the effect of non-uniform illumination on the panoramic image, and can obtain better image contrast, detail information and visual effect.
Preliminary dating of martite goethite iron ore in the Hamersley Province (Western Australia)

SummaryIn Western Australia, iron ore deposits are divided into three types, Channel Iron Deposits (CID), Detrital Iron Deposits (DID) and Bedded Iron Deposits (BID). The latter are hosted in the banded iron-formation (BIF) of the Marra Mamba and Brockman Iron Formations. Two types of BID are mined, the Martite-Microplaty and the Martite-Goethite (M-G), the latter representing the current bulk of the direct shipping ore. The Upper to Lower Miocene CID have been successfully dated using the (U-Th)/He method. Here we report the first dating of three samples from M-G mineralisation occurring in the Dales Gorge member from the Brockman Iron Formation. In unweathered MG ores, the original texture of the BIF, from the micro- to the macroscale, is fully preserved. The primary hematite is unchanged, but primary magnetite is oxidised to martite and the original gangue minerals such as chert, silicates, and iron-rich carbonates are pseudomorphed by goethite. Three samples were collected from a diamond drill hole at depths of 163m, 116m and 90m from the surface. The samples were characterised using an optical microscope and after microscopic identification of goethite and martite, samples were microdrilled. The average ages of iron oxides were 45 ± 6 Ma or Mid-Eocene for the sample at 163 m; 42 ± 5 Ma or Mid-Eocene for the sample at 116 m and 28 ± 3 Ma or Early Oligocene for the sample at 90 m.
Evaluating Research Novelty Detection: Counterfactual Approaches

In this paper, we explore strategies to evaluate models for the task research paper novelty detection: Given all papers released at a given date, which of the papers discuss new ideas and influence future research?We find the novelty is not a singular concept, and thus inherently lacks of ground truth annotations with cross-annotator agreement, which is a major obstacle in evaluating these models.Test-oftime award is closest to such annotation, which can only be made retrospectively and is extremely scarce.We thus propose to compare and evaluate models using counterfactual simulations.First, we ask models if they can differentiate papers at time t and counterfactual paper from future time t + d.Second, we ask models if they can predict test-of-time award at t + d.These are proxies that can be agreed by human annotators and easily augmented by correlated signals, using which evaluation can be done through four tasks: classification, ranking, correlation and feature selection.We show these proxy evaluation methods complement each other regarding error handling, coverage, interpretability, and scope, and thus altogether contribute to the observation of the relative strength of existing models.
Bloat Factors and Binary Specialization

Code bloating in software has been proven to be pervasive in recent research. However, each study provides a different approach to measure bloat. In this paper, we propose a system of metrics to effectively quantify bloat in binaries called bloat factors. Subsequently, we conducted an extensive study to calculate bloat factors for over 3000 Linux applications and 896 shared libraries. Using these metrics as pointers, we introduce a static approach to perform debloating for closed-source binaries by creating corresponding specialized versions to cater for a specific program requirements. We evaluated our debloating technique on large programs and achieved a maximum code reduction of 19.7%.