Reviewerly/exHarmony huggingface dataset:
Fix that Fix Commit: A real-world remediation analysis of JavaScript projects

While there is a large body of work on understanding vulnerabilities in the wild, little has been done to understand the dynamics of the remediation phase of the development cycle. To this end, we have done a timeline analysis on 118K commits from 53 of the most used JavaScript projects from GitHub to understand the provenance and prevalence of vulnerabilities in those projects. We used a vulnerability detector (CodeQL) to filter commits that introduced vulnerabilities and the commits that fixed a prior vulnerability. We found that in 82% of the projects, a commit fixing a prior vulnerability, in turn, introduced one or more new vulnerabilities. Among those projects, on average, 18% of the commits intended to fix vulnerabilities, in turn, introduced one or more new vulnerabilities. We also found that 50% of the total vulnerabilities found in those projects originated from a commit meant to fix a prior vulnerability, and 78% of those vulnerabilities could have been avoided if they were to use proper internal testing. We provide critical insights into how proper internal testing can avoid a significant portion of vulnerabilities, increasing organizations' security posture.
Planting trees for scalable and efficient canonical hub labeling

Point-to-Point Shortest Distance (PPSD) query is a crucial primitive in graph database applications. Hub labeling algorithms compute a labeling that converts a PPSD query into a list intersection problem (over a pre-computed indexing) enabling swift query response. However, constructing hub labeling is computationally challenging. Even state-of-the-art parallel algorithms based on Pruned Landmark Labeling (PLL) [3], are plagued by large label size, violation of given network hierarchy, poor scalability and inability to process large graphs. In this paper, we develop novel parallel shared-memory and distributed-memory algorithms for constructing the Canonical Hub Labeling (CHL) that is minimal in size for a given network hierarchy. To the best of our knowledge, none of the existing parallel algorithms guarantee canonical labeling. Our key contribution, the PLaNT algorithm, scales well beyond the limits of current practice by completely avoiding inter-node communication. PLaNT also enables the design of a collaborative label partitioning scheme across multiple nodes for completely in-memory processing of massive graphs whose labels cannot fit on a single machine. Compared to the sequential PLL, we empirically demonstrate upto 47.4x speedup on a 72 thread shared-memory platform. On a 64-node cluster, PLaNT achieves an average 42x speedup over single node execution. Finally, we show how our approach demonstrates superior scalability - we can process 14x larger graphs (in terms of label size) and construct hub labeling orders of magnitude faster compared to state-of-the-art distributed paraPLL algorithm.
A Model for Optimizing the Structure of Teaching Techniques for Distance Learning in the Russian Higher Education System

This study aims to develop a methodical approach to optimize the structure of teaching techniques and approaches to distance education, as exemplified by Russian universities, to enhance students&rsquo; professional competencies. A pedagogical experiment was conducted with the participation of 746 second to fourth year students majoring in engineering at the Ural Federal University in Russia. The coefficients for the relative importance of professional competencies were obtained through expert assessment. The number of experts was 40 faculty members with teaching experience of more than five years. The pedagogical experiment revealed the regularities of the influence of the forms of distance learning on the formation of students&rsquo; professional competency levels. The values of students&rsquo; developmental competency levels have been determined. Non-linear regression models have been developed based on the values of the integral level of professional competencies and the percentage of distance-learning forms in the number of instructional hours. The proposed methodological approach will be useful for university professors and teachers in identifying the most effective forms of distance learning and in differentiating teaching methods not only in the context of the general pandemic but also in the natural conditions of the educational process.
Einstein-Podolsky-Rosen steering in spontaneous parametric down-conversion cascaded with a sum-frequency generation

Einstein-Podolsky-Rosen (EPR) steering has potential applications in quantum information processing. Here the genuine tripartite EPR steering is investigated in cascaded nonlinear process of spontaneous parametric down-conversion cascaded with a sum-frequency generation in an optical cavity for a wide range of the nonlinear parameters. The threshold properties of the cascaded nonlinear precess are also analyzed both below and without threshold regime, respectively. The genuine tripartite EPR steering is demonstrated based on the criteria for genuine multipartite EPR steering. Our scheme of the generation of genuine tripartite EPR steering can be used as a suggestion for the potential experiments and the applications in quantum communication and computation.
Method research on ship detection in remote sensing image based on Yolo algorithm

Small target recognition is a classic challenge in the field of target recognition. In this paper, an improved algorithm based on Yolo V3 is proposed for ship detection in remote sensing images. In order to solve the problem that the input satellite image size is too large, the window sliding segmentation technology is used to cut the large image into several small images; because the detection target only has a single kind of ship, K-means algorithm is used to re cluster anchor Box, to improve the detection accuracy of the algorithm; in view of the small ship target in the ocean background, the multi-level feature extraction method is used to highlight the feature vector of small target to improve the recognition ability of small target. Through the above-mentioned methods, using the data set of Airbus ship detection provided by kaggle, the ship target detection has achieved a better comprehensive accuracy rate, which is higher than the accuracy and recall rate of similar target detection algorithms.
Application of the gradient descent for data balancing in diagnostic image analysis problems

The article proposes an algorithm for data balancing based on gradient descent. The proposed algorithm is able to partially mitigate the influence of the data imbalance problem which is commonly seen in the tasks of diagnostic image analysis. The authors have investigated the influence of the proposed algorithm on the accuracy of a fully convolutional neural network. The neural network was trained on unbalanced data as well as on the balanced by the algorithm. Recommendations on how to use the proposed algorithm are also formulated.
External Patch-Based Image Restoration Using Importance Sampling

This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The Minimum Mean Squared Error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means (NLM), uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.
Comparing the randomized benchmarking figure with the average infidelity of a quantum gate-set

Randomized benchmarking (RB) is a popular procedure used to gauge the performance of a set of gates useful for quantum information processing (QIP). Recently, Proctor et al. [Phys. Rev. Lett. 119, 130502 (2017)] demonstrated a practically relevant example where the RB measurements give a number $r$ very different from the actual average gate-set infidelity $\epsilon$, despite past theoretical assurances that the two should be equal. Here, we derive formulas for $\epsilon$, and for $r$ from the RB protocol, in a manner permitting easy comparison of the two. We show in general that, indeed, $r\neq \epsilon$, i.e., RB does not measure average infidelity, and, in fact, neither one bounds the other. We give several examples, all plausible in real experiments, to illustrate the differences in $\epsilon$ and $r$. Many recent papers on experimental implementations of QIP have claimed the ability to perform high-fidelity gates because they demonstrated small $r$ values using RB. Our analysis shows that such a statement from RB alone has to be interpreted with caution.
Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.
Deep multitask learning for pervasive BMI estimation and identity recognition in smart beds

Smart devices in the Internet of Things (IoT) paradigm provide a variety of unobtrusive and pervasive means for continuous monitoring of bio-metrics and health information. Furthermore, automated personalization and authentication through such smart systems can enable better user experience and security. In this paper, simultaneous estimation and monitoring of body mass index (BMI) and user identity recognition through a unified machine learning framework using smart beds is explored. To this end, we utilize pressure data collected from textile-based sensor arrays integrated onto a mattress to estimate the BMI values of subjects and classify their identities in different positions by using a deep multitask neural network. First, we filter and extract 14 features from the data and subsequently employ deep neural networks for BMI estimation and subject identification on two different public datasets. Finally, we demonstrate that our proposed solution outperforms prior works and several machine learning benchmarks by a considerable margin, while also estimating users’ BMI in a 10-fold cross-validation scheme.
Real-Time Collision Warning System Based on Computer Vision Using Mono Camera

This paper aims to help self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems in real life. Crash avoidance is a complex system that depends on many parameters. The forward-collision warning system is simplified into four main objectives: detecting cars, depth estimation, assigning cars into lanes (lane assign) and tracking technique. The presented work targets the software approach by using YOLO (You Only Look Once), which is a deep learning object detector network to detect cars with an accuracy of up to 93%. Therefore, apply a depth estimation algorithm that uses the output boundary box's dimensions (width and height) from YOLO. These dimensions used to estimate the distance with an accuracy of 80.4%. In addition, a real-time computer vision algorithm is applied to assign cars into lanes. However, a tracking proposed algorithm is applied to evaluate the speed limit to keep the vehicle safe. Finally, the real-time system achieved for all algorithms with streaming speed 23 FPS (frame per second).
The role of financial development on economic growth in the emerging market countries of the sub-Saharan African (SSA) region

Purpose This paper aims to investigate the effect of financial development (FD) on economic growth and growth-enhancing transmission channels for the sub-Saharan African (SSA) region in three different periods: the pre-Millennium Development Goals (MDGs) era (1990–1999), during the MDGs (2000–2017) era and the main period (1990–2017). Design/methodology/approach The study used the system generalized method of moments (SGMM) approach on 45 SSA countries from 1990 to 2017, using the data collected from the World Bank and the International Monetary Fund (IMF). Findings The long-run effect of the study showed mixed results in pre-MDGs and during MDGs periods but was positive in the main period. For growth-enhancing transmission channels, the results were mixed, although in many cases, institutional (INST) quality, human capital (HC) and foreign direct investment (FDI) were the main transmission channels. Research limitations/implications Some of the countries were dropped from the analysis due to data inadequacy. Practical implications The empirical results of this study provide evidence that the financial sector has robust positive effect throughout 1990–2017. Furthermore, the financial sector depends on several factors to improve economic growth. The SSA region has to focus on improving HC, INST quality in terms of good governance and create environment that is attractive to FDI since they were the main growth-enhancing channels. Originality/value Most of the studies in SSA countries assessed the direct effect of FD on economic growth without considering its transmission channels in different time frames. Moreover, they often used specific variables but not the financial index. This study extended the scope by considering various financial sector transmission channels, in different time periods and the financial index.
Quantum Approach to Fast Protein-Folding Time<sup>*</sup>

In the traditional random-conformational-search model, various hypotheses with a series of meta-stable intermediate states were proposed to resolve the Levinthal paradox in protein-folding time. Here we introduce a quantum strategy to formulate protein folding as a quantum walk on a definite graph, which provides us a general framework without making hypotheses. Evaluating it by the mean of first passage time, we find that the folding time via our quantum approach is much shorter than the one obtained via classical random walks. This idea is expected to evoke more insights for future studies .
Object Tracking Using Spatio-Temporal Networks for Future Prediction Location

We introduce an object tracking algorithm that predicts the future locations of the target object and assists the tracker to handle object occlusion. Given a few frames of an object that are extracted from a complete input sequence, we aim to predict the object’s location in the future frames. To facilitate the future prediction ability, we follow three key observations: 1) object motion trajectory is affected significantly by camera motion; 2) the past trajectory of an object can act as a salient cue to estimate the object motion in the spatial domain; 3) previous frames contain the surroundings and appearance of the target object, which is useful for predicting the target object’s future locations. We incorporate these three observations into our method that employs a multi-stream convolutional-LSTM network. By combining the heatmap scores from our tracker (that utilises appearance inference) and the locations of the target object from our trajectory inference, we predict the final target’s location in each frame. Comprehensive evaluations show that our method sets new state-of-the-art performance on a few commonly used tracking benchmarks.
Novel metaheuristic based on multiverse theory for optimization problems in emerging systems

Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization problems such as non-linear and multi-level programming problems as well as applied optimization problems for CPS systems. MVA algorithm inspires the creation of the next population to be very close to the solution of initial population, which mimics the nature of parallel worlds in multiverse theory. Additionally, MVA distributes the solutions in the feasible region similarly to the nature of big bangs. To illustrate the effectiveness of the proposed algorithm, a set of test problems is implemented and measured in terms of feasibility, efficiency of their solutions and the number of iterations taken in finding the optimum solution. Numerical results obtained from extensive simulations have shown that the proposed algorithm outperforms the state-of-the-art approaches while solving the optimization problems with large feasible regions.
Learning with differentiable perturbed optimizers

Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g. sorting, picking closest neighbors, finding shortest paths or optimal matchings). Although these discrete decisions are easily computed in a forward manner, they cannot be used to modify model parameters using first-order optimization techniques because they break the back-propagation of computational graphs. In order to expand the scope of learning problems that can be solved in an end-to-end fashion, we propose a systematic method to transform a block that outputs an optimal discrete decision into a differentiable operation. Our approach relies on stochastic perturbations of these parameters, and can be used readily within existing solvers without the need for ad hoc regularization or smoothing. These perturbed optimizers yield solutions that are differentiable and never locally constant. The amount of smoothness can be tuned via the chosen noise amplitude, whose impact we analyze. The derivatives of these perturbed solvers can be evaluated eciently. We also show how this framework can be connected to a family of losses developed in structured prediction, and describe how these can be used in unsupervised and supervised learning, with theoretical guarantees. We demonstrate the performance of our approach on several machine learning tasks in experiments on synthetic and real data.
Hazard-Recognition Training Using Omnidirectional Cinemagraphs: Comparison between Virtual Reality and Lecture-Based Techniques

This research conducted a case study to understand the use of omnidirectional cinemagraphs for training purposes, enhancing the hazard-recognition of construction struck-by hazards. Traditional lecture-based training using classroom teaching was compared to virtual reality omnidirectional cinemagraph-based training employing a head-mounted display. A set of six struck-by hazards were identified to train students utilizing both experimental conditions, and posteriorly tested using a paper-based assessment tool. Measures for hazard identification, attitudes, transportability, cognitive absorption, and sense of presence were collected experimentally from junior level construction management students. The results of this investigation found that students presented a higher average of hazard identification score using traditional lectures (Mean: 81%; STD = 14%) with respect to virtual reality training (Mean: 79%; STD = 23%). However, the survey responses suggest that participants felt more positively engaged during the virtual reality training with respect to the lecture-based training as this condition presented lower overall scores.
BERT Knows Punta Cana is not just beautiful, it’s gorgeous: Ranking Scalar Adjectives with Contextualised Representations

Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.
Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine

This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by machine learning approaches. The most commonly used and well-known machine learning algorithms (i.e., k-nearest neighbor, support vector machine, artificial neural network, and extreme learning machine) were compared to determine the one with the best detection performance. After identifying the best algorithm, several improved versions of the algorithm are compared and analyzed according to their characteristics. Their quantitative performances and abilities to correctly classify the state of the urban water system under cyber-attack were measured using various performance indices. Among the algorithms tested, the extreme learning machine (ELM) was found to exhibit the best performance. Moreover, this study not only has identified excellent algorithm among the compared algorithms but also has considered an improved version of the outstanding algorithm. Furthermore, the comparison was performed using various representative performance indices to quantitatively measure the prediction accuracy and select the most appropriate model. Therefore, this study provides a new perspective on the characteristics of various versions of machine learning algorithms and their application to different problems, and this study may be referenced as a case study for future cyber-attack detection fields.
Research on Multi-sensor Data Fusion Technology

Abstract Multi-source sensor information fusion mainly collects various types of information from multiple independent decentralized sensors. Due to the different types of information, specific combinations of time and events are required to make the collected information more advanced and useful information. From the level of fusion, it will be merged from the three levels of data set, feature level and decision-making level. For different practical problems, it is necessary to use a certain level of fusion or a certain two levels of fusion according to the situation, so as to obtain the most optimal integration scheme.