Reviewerly/exHarmony huggingface dataset:
Introducing Traceability in GitHub for Medical Software Development

Assuring traceability from requirements to implementation is a key element when developing safety critical software systems. Traditionally, this traceability is ensured by a waterfall-like process, where phases follow each other, and tracing between different phases can be managed. However, new software development paradigms, such as continuous software engineering and DevOps, which encourage a steady stream of new features, committed by developers in a seemingly uncontrolled fashion in terms of former phasing, challenge this view. In this paper, we introduce our approach that adds traceability capabilities to GitHub, so that the developers can act like they normally do in GitHub context but produce the documentation needed by the regulatory purposes in the process.
Explainable Fact-Checking Through Question Answering

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps — this separation aids models’ explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
An OpenStreetMap-Based Dataset of Building Footprints for Analysing Different Types of Label Noise

We present a dataset consisting of OpenStreetMap imagery and corresponding building footprint labels. Multiple label sets are provided, each containing a different type of label noise. The purpose of the dataset is to enable a systematic analysis of different label noise types in the earth observation domain and to provide a benchmark dataset for noise removal techniques. We also present some preliminary results from experiments on the effect of different label noise types on model performance.
Unsupervised Real-time Anomaly Detection for Multivariate Mobile Phone Traffic Series

Real-time anomaly detection in urban areas from massive data is a recent research field with challenging requirements.This paper presents a lightweight framework for real-time anomaly detection in multivariate time-series extracted from large-scale Mobile-phone Network Data (MND).Our solution relies on unsupervised machine learning applied to MND collected at individual antennas of a nation-wide French mobile phone network operator.The proposed framework is based on a two-step approach: (i) the offline stage aims at assessing the typical behaviour of the antennas; (ii) the online stage performs real-time comparison of incoming data with respect to the detected typical behaviour.Results related to a real case-study of terrorist attack in the city of Lyon show that our framework can successfully detect an emergency event almost instantaneously and locate the anomalous area with high precision.
bASES21: A Model for the Self-assessment of 21st-Century Skills in the Context of Computing Education in K-12

In today’s knowledge society, knowing how to interpret, search, communicate, and share new information are key skills for a successful citizen. Such skills, known as 21st-century skills, include critical thinking, teamwork, communication, among others. Currently, there are several proposals on how to integrate the learning of these skills in K-12 through the teaching of computing. Yet, assuming that teaching computing can contribute to the learning of 21st-century skills, there is little systematic evidence available to support this claim. One reason is the lack of 21st-century skills assessment models. Thus, the objective of this article is to present a model for the assessment of 21st-century skills in the context of computing education in K-12. Based on the results of a mapping study, the model is systematically developed as well as its measurement instrument, a self-assessment questionnaire. An evaluation of the reliability and validity of the measurement instrument, based on 394 responses from K-12 students, indicates high internal reliability (Cronbach’s alpha = 0.94). The results regarding the validity of the instrument were inconclusive and based on an explorative factor analysis, we propose a reorganization of the model in stronger alignment with prominent 21st-century skill models. In this way, the bASES21 model represents a first step supporting the assessment of 21st-century skills, aiming at the systematic improvement of teaching computing in schools.
Deep Crash Detection From Vehicular Sensor Data With Multimodal Self-Supervision

The ability to detect vehicle accidents from on-board sensor data is of the utmost importance to provide prompt assistance to prevent injuries and fatalities. In this article, we present a novel deep learning method capable of analyzing time series recorded from Inertial Measurement Units (IMU) and GPS devices to recognize the presence of an accident along with its severity. We propose a neural architecture capable of exploiting the different sensor streams (i.e., acceleration, gyroscope, and GPS speed), a multimodal contrastive self-supervised training procedure, and an ad-hoc stack of data augmentation techniques, specifically designed to counteract the extreme class imbalance and to improve the generalization capabilities of the whole pipeline. The proposed method has been validated against several state-of-the-art methods on a large and highly imbalanced dataset, composed of more than 200 thousand time series collected from US vehicles, with different vehicle sizes and traveling on different types of road. Our method achieves an average-precision score (AP) of 0.9 in the detection of crashes and 0.76 in the detection of severe crashes, significantly outperforming all the other approaches, and has small footprint and latency, so that it can easily be deployed on embedded devices.
3DFaceFill: An Analysis-By-Synthesis Approach to Face Completion

Existing face completion solutions are primarily driven by end-to-end models that directly generate 2D completions of 2D masked faces. By having to implicitly account for geometric and photometric variations in facial shape and appearance, such approaches result in unrealistic completions, especially under large variations in pose, shape, illumination and mask sizes. To alleviate these limitations, we introduce 3DFaceFill, an analysis-by-synthesis approach for face completion that explicitly considers the image formation process. It comprises three components, (1) an encoder that disentangles the face into its constituent 3D mesh, 3D pose, illumination and albedo factors, (2) an autoencoder that inpaints the UV representation of facial albedo, and (3) a renderer that resynthesizes the completed face. By operating on the UV representation, 3DFaceFill affords the power of correspondence and allows us to naturally enforce geometrical priors (e.g. facial symmetry) more effectively. Quantitatively, 3DFaceFill improves the state-of-the-art by up to 4dB higher PSNR and 25% better LPIPS for large masks. And, qualitatively, it leads to demonstrably more photorealistic face completions over a range of masks and occlusions while preserving consistency in global and component-wise shape, pose, illumination and eye-gaze.
Software Quality Measurement Modelling Using AHP and Promethee Methods

As competition in the industries relying on software increases, software product quality has become a prominent factor in the competition since quality issues within software-applications have a drastic effect on the organizations’ reputation, and their customers experience. It has become more important to define and follow software quality metrics in order to determine the current situation in terms of quality of the developed software applications and to ensure continuous improvement. Within the scope of this paper, software products belonging to a technology company were ranked in terms of quality based on the scores assigned to software development metrics. The metric values were evaluated by Analytic Hierarchy Process (AHP) method and that result was used by PROMETHEE method for ranking the software products. This software quality measurement model can help people to produce their institutional standards over international standards by considering the best practices generated over the time.
Design of a Parallel and Scalable Crawler for the Hidden Web

The WWW contains huge amount of information from different areas. This information may be present virtually in the form of web pages, media, articles (research journals / magazine), blogs etc. A major portion of the information is present in web databases that can be retrieved by raising queries at the interface offered by the specific database and is thus called the Hidden Web. An important issue is to efficiently retrieve and provide access to this enormous amount of information through crawling. In this paper, we present the architecture of a parallel crawler for the Hidden Web that avoids download overlaps by following a domain-specific approach. The experimental results further show that the proposed parallel Hidden web crawler (PSHWC), not only effectively but also efficiently extracts and download the contents in the Hidden web databases
Hyperspectral Image Super-Resolution Based on Multiscale Residual Block and Multilevel Feature Fusion

Hyperspectral images have high spectral resolution, but this is often at the expense of spatial resolution. Although deep learning-based super-resolution (SR) algorithms have shown comparative performance for spatial resolution enhancement, most of them cannot effectively extract features of different size objects because of single scale convolution. In deep architectures, low level features also tend to disappear during transmission. In this paper, an efficient network (MRBMFF) for enhancing the spatial resolution of hyperspectral image is proposed. Based on the multiscale residual block (MRB), features at different scales can be effectively extracted and fused. Meanwhile, the multilevel feature fusion (MFF) is introduced to concatenate the low and high level features. Effective SR images could be recovered after inputting their low-resolution counterparts to the proposed network. Experimental results show that the proposed network achieves superior reconstruction performance compared with the state-of-the-art approaches.
Personality in information systems professions: identifying archetypal professions with suitable traits and candidates' ability to fake-good these traits

Purpose The importance of information systems (IS) professions is increasing. As personality–job fit theory claims, employees must have suitable personality traits for particular IS professions. However, candidates can try to fake-good on personality tests towards the desired personality type. Thus, the purpose of this study is to identify archetypal IS professions, their associated personality types and examine the reliability of the Myers–Briggs Type Indicator (MBTI) personality test in IS recruitment decisions. Design/methodology/approach The authors reviewed academic literature related to IS professions to identify job archetypes and personality traits for IS professions. Then, the authors conducted an experiment with 452 participants to investigate whether candidates can fake-good on personality tests when being tested for a particular IS profession. Findings The identified job archetypes were IS project manager, IS marketing specialist, IS consultant, IS security specialist, data scientist and business process analyst. The experimental results show that the participants were not able to fake-good considerably regarding their personality traits for a particular archetype. Research limitations/implications The taxonomy of IS professions should be validated further. The experiment was executed in an educational organisation and not in a real-life environment. Actual work performance was not measured. Practical implications This study enables a better identification of suitable candidates for a particular IS profession. Personality tests are good indicators of the candidate's true personality type but must be properly interpreted. Originality/value This study enhances the existing body of knowledge on IS professions' archetypes, proposes suitable MBTI personality types for each profession and provides experimental support for the appropriateness of using personality tests to identify potentially suitable candidates.
Precision of Product Reviews Using Naive Bayes and Linear Regression

As the digital information technology domain is growing unprecedently, the process of making the devices mimic human like actions has gained an increased research attention. One of the main features of human like system is the human emotion or sentiment detection. Online shopping has made it easy for everyone in the world to buy different varieties of products from a single place. It also gives the opportunity for them to try new products. While trying to buy new products, people check for the reviews and ratings of those products given by the other customers. Those reviews and ratings can be used to determine the sentimentality of users, who have bought those products by using text mining techniques and classify them either as positive or negative reviews. To analyze these data, the proposed research work has used both Naive Bayes and linear regression algorithms. In our analysis, the accuracy rate of both algorithms is checked.
An Efficient Computation of Dempster-Shafer Theory of Evidence Based on Native GPU Implementation

Interest in Dempster-Shafer's theory of evidence has often run up against problems associated with the inherently exponential complexity of the calculation of the Belief and Plausibility measures. This can be mitigated by looking at the technological possibilities offered by GPU computing. Some preliminary attempts have been oriented towards parallelization of the computation, but none of them natively use the support offered by lower-level GPUs. In this paper, we introduce a set of Python functions for operations related to the Dempster-Shafer theory and outline its implementation based natively on GPU computing, highlighting the speedup possibilities in relation to a CPU-based or CPU-derived implementation.
Motivation for the Development of Digital Skills and Competencies of Employees

The introduction of digital technologies in production and everyday life significantly changes the labor market, the ratio of age groups, areas of activity, forms of organization of production and employment, which requires new skills and competencies of workers. Digital skills become critical for both employees and employers. Knowledge in the field of search, filtering, systematization, evaluation and analysis of a large amount of data, digital content and information becomes mandatory for managers and professionals. The peculiarity of modern work is the change of traditional boundaries of labor activity, strengthening the role of individual professional qualities of employees, their competencies. Therefore, the work of a modern worker becomes more pronounced. This fact should be taken into account when implementing new forms of motivation. Motivation to mastering the digital competencies of staff is a process of creating conditions under which employees can realize their values, needs, potential and which have an active influence on the purposeful change of human behavior. In the conditions of economy digitalization the staff motivation methods to digital skills and competences reception, their realization in digital technologies acquire special value. The list of digital skills and competencies is outlined, the expediency of using gamification to activate staff is substantiated, measures of motivation the generation Z to master and use digital skills for work efficiency are developed. Emphasis is placed on the qualities of management and employees in enterprises – "hidden champions".
Mathematical Modeling of Transaction Latency on Ethereum

Applications on blockchain are currently limited by the relatively poor performance of the blockchain network such as low TPS, high latency and the resulting high transaction fees. Hence, performance optimization is one crucial problem of blockchain. Focusing on the most prosperous public blockchain Ethereum, we model the on-chain transaction confirmation process with the knowledge of Poisson process and queueing theory, derives the mean transaction-confirmation time, and explores the effect of different transaction fees on latency. We also conduct a numeric simulation of the model, which indicates that the model fits in well with the real world blockchain.
Mitigating thermal‐induced image drift for videogrammetric technique in support of structural monitoring applications

The thermal effect that leads to spurious image drift is one major concern in the videogrammetric measurement. On the ground that the image drift originates from the thermal-induced displacement of the image sensor board, this study proposes to establish the thermal-induced displacement function of the image plane for the prediction of image drift and the elimination thereof. The displacement function of the image plane is calibrated with stationary targets easily available in laboratory. In measurements other than the thermal effect calibration, the thermal-induced displacement of the image plane is predicted from the precalibrated displacement function. It is then subtracted from the measured pixel coordinates of measurand, resulting in normalized pixel coordinates in which the thermal-induced image drift has been removed. To exemplify this approach, two thermal effect calibration tests with different camera orientations are fulfilled. The displacement function of the image plane is simulated with a first-order polynomial model. Verification tests are performed in both temperature-controlled and outdoor environments. The approach achieves satisfactory performance in both environments.
Research on classification method of new energy vehicle information security risk assessment

Abstract With the rapid development and popularization of intelligent Internet connected vehicle, the automobile and the physical world are combined together, which makes users have strong technology experience and life convenience. While enjoying the convenience and experience brought by the intelligent Internet connected vehicle, we are also faced with the risk of information security in the network world, even the security problems of the network world directly affect the security of the physical world. With the improvement of automobile ‘four modernizations’, the research on information security has become an important topic and research hotspot in the field of automobile. Risk assessment is one of the important research and work contents of automobile information security. Impact valuation is an important indicator of risk assessment. It is very important to determine the optimal cluster number for risk assessment model and risk assessment. In this paper, the fuzzy clustering impact estimates are divided into 2-12 categories during Evergrande’s risk assessment of an electric vehicle. The optimal number of clusters is determined by calculating the mixed F-statistics of each type of data. Conclusion shows that the best clustering number of impact estimates is 5.
Learnable Adaptive Cosine Estimator (LACE) for Image Classification

In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator [43] (ACE), our proposed method incorporates angular information that is inherently learned by artificial neural networks. Our learnable ACE (LACE) transforms the data into a new "whitened" space that improves the inter-class separability and intra-class compactness. We compare our LACE to alternative state-of-the art softmax-based and feature regularization approaches. Our results show that the proposed method can serve as a viable alternative to cross entropy and angular softmax approaches. Our code is publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Improving Pseudo-Label Training For End-To-End Speech Recognition Using Gradient Mask

In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results. In this paper, we propose a novel approach to combine their ideas for end-to-end speech recognition model. Without any extra loss function, we utilize the Gradient Mask to optimize the model when training on pseudo-label. This method forces the speech recognition model to predict from the masked in-put to learn strong acoustic representation and make training robust to label noise. In our semi-supervised experiments, the method can improve the model’s performance when training on pseudo-label and our method achieved competitive results comparing with other semi-supervised approaches on the Librispeech 100 hours experiments.
The Necessity of Minimal Representation of Genericity in a Newly Developed Language, Unish

The current study compares generic representations in different languages including English and Korean.It reviews important universal concepts of genericity denotation in languages and compares how genericity is realized in different languages.By comparing different genericity representations in different languages, the current study predicts possible acquisition difficulties.It also suggests difficulties of acquiring English article systems are also caused by the complex nature of article uses.Particularly, using a
A Modified Version of K-Means Algorithm

In this work is presented a modified version of the K-Means which identifies cluster stability. The stability is defined by a threshold based on a percentage of the largest displacement of centroid at first iteration. A cluster is considered stable when the largest centroid displacement in the current iteration achieves the 10% of threshold, and objects that remains in the same cluster in two consecutive iterations are removed from the classification phase in subsequent iterations. Eight different instances were used to validate the proposal, three synthetics and five reals. The modified version was compared against the standard and three related work versions. Results shows that the proposal reduced the execution time up to 92.14% regarding the standard version with only a 3.73% in the quality reduction. Despite the new version do not has the major reduction time in all cases, the algorithm reaches the best values for quality of grouping.