Reviewerly/exHarmony huggingface dataset:
Smart Contract: Security and Privacy

Smart contracts are simply self-activated contracts between two parties. The idea behind their implementation relies on the concept of blockchain, wherein the details and execution of the contract are turned into code and distributed among users of a network. This process controls counterfeiting and money laundering by its ability to trace who owes whom. It also boosts the general economy. This research paper shows how smart contracts in modern-day systems have changed the approach to money tracing. We present case studies about the uses of smart contracts with high levels of security and privacy. As a building block of smart contracts, a brief description of blockchain is provided in an introduction. Among other cryptography methods and techniques, the usage of hashing and hash functions in blockchain security are also explained. We also explore the real-time applications of blockchain and smart contract techniques in real estate. The main advantage of this research paper is that it discusses a state-of-the-art subject, as most of the articles referenced in this paper are from 2018 and onward.
Conversational Agent for Industrial Processes Through Virtual Environments

This paper presents the implementation of a conversational agent in virtual environments oriented to industrial control processes in order to contribute to the improvement of teaching-learning processes. The application simulates the level control process in two virtual environments, the first one as a virtual laboratory and the second environment as an industrial process. The environments have been created with three-dimensional models based on Piping and Instrumentation Diagram (P&ID) diagrams. The models have been simulated and animated using Unity 3D software. The environments exchange information in real time with MATLAB mathematical software to simulate the industrial process controller, achieving a high degree of realism. Each environment is assisted by a conversational agent who communicates with Dialogflow, a natural language understanding platform, to guide the user during their training. Test users were asked to provide feedback through a usability questionnaire, and these results are discussed at the end of this article.
Basic digital skills to guarantee the academic continuity caused by Covid-19

The migration from face-to-face to non-face-toface education due to the pandemic caused by Covid-19 has led to the transformation of learning scenarios. In this research, the perceptions of the students regarding the implementation of teaching strategies based on communicative interaction, the selection of learning materials, and for the evaluation based on the use of technologies were analyzed. The study subjects were high school students from a private educational institution located in Mexico. The study was descriptive, based on the application of a Likert-type scale questionnaire. The recovery of the data was carried out using a digital form designed expressly for the investigation. 248 students who were about to complete the fourth semestre in the period January-June 2020 participated. The results revealed that there was a favorable acceptance of the activities carried out by teachers with digital tools, some areas of opportunity were evident, such as face-to-face interaction, the scarce diversification of the content selected for learning and evaluation, and the absence of evaluation actions based on the use of discussion forums.
Subjective assessment of display stream compression for stereoscopic imagery

Abstract High‐resolution display bandwidth requirements often now exceed the capacity of display link channels necessitating compression. The goal of visually lossless compression codecs such as VESA DSC 1.2 is that viewers perceive no difference between the compressed and uncompressed images, maintaining long‐standing expectations of a lossless display link. Such low impairment performance is difficult to validate as artifacts are at or below sensory threshold. We have developed a 3D version of the ISO/IEC 29170‐2 flicker paradigm and used it to compare the effects of image compression in flat images presented in the plane of the screen (2D) to compression in flat images with a disparity offset from the screen (3D). We hypothesized that differences in the location and size of the compression errors between the disparate images in the 3D case would affect their visibility. The results showed that artifacts were often less visible in 3D compared to 2D viewing. These findings have practical applications with respect to codec performance targets and algorithm development for 3D movie, animation, and virtual reality content. In particular, higher compression should be attainable in stereoscopic compared to equivalent 2D images because of increased tolerance to artifacts that are binocularly unmatched or have disparity relative to the screen.
Is Requirements Similarity a Good Proxy for Software Similarity? An Empirical Investigation in Industry

[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman's rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.
Robustness of Meta Matrix Factorization Against Strict Privacy Constraints

In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction to preserve users' privacy. We reproduce the experiments of Lin et al. on five datasets, i.e., Douban, Hetrec-MovieLens, MovieLens 1M, Ciao, and Jester. Also, we study the impact of meta learning on the accuracy of MetaMF's recommendations. Furthermore, in our work, we acknowledge that users may have different tolerances for revealing information about themselves. Hence, in a second strand of experiments, we investigate the robustness of MetaMF against strict privacy constraints. Our study illustrates that we can reproduce most of Lin et al.'s results. Plus, we provide strong evidence that meta learning is essential for MetaMF's robustness against strict privacy constraints.
What are agile, flexible, or adaptable employees and students? A typology of dynamic individual differences in applied settings

The applied psychology literature has discussed and used a variety of different definitions of dynamic individual differences. Descriptions like dynamic, agile, adaptive, or flexible can refer to a variety of different types of constructs. The present article contributes to the literature by presenting an organizing typology of dynamic constructs. We also conducted a literature review of four major applied journals over the last 15 years to validate the taxonomy and to use it to map what type of dynamic individual differences constructs are typically studied in the applied psychology literature. The typology includes six basic conceptualizations of dynamic individual differences: Variability constructs (inconsistency across situations), skill acquisition constructs (learning new skills), transition constructs (avoiding “loss” in behavior/skill after unforeseen change), reacquisition constructs (relearning after change), acceleration/deceleration constructs (losing or gaining energy by displaying the behavior), and integration/dissolution constructs (behavior becomes more or less uniform). We provide both verbal and statistical definitions for each of these constructs, and demonstrate how these conceptualizations can be operationalized in assessment and criterion measurement using R code and simulated data. We also show how researchers can test different dynamic explanations using likelihood-based R 2 statistics.
Regulating Central Bank Digital Currencies: Towards a Conceptual Framework

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A general framework for reversible data hiding in encrypted images by reserving room before encryption

In this paper a general framework to adopt different predictors for reversible data hiding in the encrypted image is presented. Employing linear regression, we propose innovative predictors that contribute more significantly to accomplish more payload than conventional ones. Reserving room before encryption (RRBE) is designated in the proposed scheme making possible to attain high embedding capacity. In RRBE procedure, pre-processing is allowed before image encryption. In our scheme, pre-processing comprises of three main steps: computing prediction-errors, blocking and labeling of the errors. By blocking, we obviate the need for lossless compression to when a content owner is not enthusiastic. Lossless compression is employed in recent state of the art schemes to improve payload. We surpass the prior arts exploiting proper predictors, more efficient labeling procedure and blocking of the prediction-errors.
Remote Reporting During a Pandemic Using Digital Pathology Solution: Experience from a Tertiary Care Cancer Center

Remote reporting in anatomic pathology is an important advantage of digital pathology that has not been much explored. The COVID-19 pandemic has provided an opportunity to explore this important application of digital pathology system in a tertiary care cancer center to ensure patient care and staff safety. Regulatory guidelines have been described for remote reporting following the pandemic. Herein, we describe our experience of validation of digital pathology workflow for remote reporting to encourage pathologists to utilize this facility which opens door for multiple, multidisciplinary collaborations.To demonstrate the validation and the operational feasibility of remote reporting using a digital pathology system.Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months. Three pathologists with different models of consumer-grade laptops reviewed the cases remotely to assess the diagnostic concordance and operational feasibility of the modified workflow. The slides were digitized on a USFDA-approved Philips UFS 300 scanner at ×40 resolution (0.25 μm/pixel) and viewed on the Image Management System through a web browser. All the essential parameters were reported for each case. After successful validation, 886 cases were reported remotely from March 29, 2020, to June 30, 2020, prospectively. Light microscopy formed the gold standard reference in remote reporting.100% major diagnostic concordance was observed in the validation of remote reporting in the retrospective and prospective studies using consumer-grade laptops. The deferral rate was 0.34%. 97.6% of histopathology and 100% of frozen sections were signed out within the turnaround time. Network speed and a lack of virtual private network did not significantly affect the study.This study of validation and reporting of complete pathology cases remotely, including their operational feasibility during a public health emergency, proves that remote sign-out using a digital pathology system is not inferior to WSIs on medical-grade monitors and light microscopy. Such studies on remote reporting open the door for the use of digital pathology for interinstitutional consultation and collaboration: Its main intended use.
Sentiment Analysis of Social Network Content to Characterize the Perception of Security

The study of the perception of security helps to understand the feeling of citizens in the face of risk and the magnitude of its consequences, to understand the fear of crime. Traditionally the measure of this kind of subjective perception was made through surveys with low costly-effective performance, to a small sample of the population in specific time periods. To solve those inconveniences, this work analyzes take advantage of the amount of data and the real-time monitoring allowed by the social networks to quantifies the Perception of the security in Bogota. The quantification is made through different methods that involve ruled-based and supervised learning approaches for the sentiment analysis of the data coming from Spanish text from Twitter.
Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning

With the increasing of the data size and the development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and also makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates at all iterations. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both strongly convex and non-strongly convex problems, while existing asynchronous algorithms (e.g., KroMagnon and ASAGA) only have convergence guarantees for strongly convex problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical experimental results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++.
Augmented reality technology as a tool to support chemistry learning: a scoping review

Abstract The purpose of this scoping review was to provide a comprehensive overview of relevant research regarding the use of Augmented Reality, the links to pedagogy and educational outcomes, specifically in the context of chemistry learning. The scoping review is underpinned by the five-stage Arksey and O’Malley framework. First, research questions are identified. Second, the last ten years studies are explored by using the key word ‘augmented reality + chemistry learning.’ Third, studies are investigated through inclusion and exclusion criteria, and PRISMA model is utilized for article selection. PRISMA model used in the form of four-phase flow diagram, including: identification, screening, eligibility, and included. Fourth, selected articles are charted with respect to numerous dimensions and summaries. Finally, findings are reported in the light of research questions. The findings of the scoping review illustrated a set of studies that provide evidence of improved conceptual understanding, increase in students’ interest, engagement, motivation, and satisfaction through the educational environments that are enriched with AR applications. The findings of the scoping review are discussed with respect to multiple dimensions that are explored under research questions.
Warped Language Models for Noise Robust Language Understanding

Masked Language Models (MLM) are self-supervised neural networks trained to fill in the blanks in a given sentence with masked tokens. Despite the tremendous success of MLMs for various text based tasks, they are not robust for spoken language understanding, especially for spontaneous conversational speech recognition noise. In this work we introduce Warped Language Models (WLM) in which input sentences at training time go through the same modifications as in MLM, plus two additional modifications, namely inserting and dropping random tokens. These two modifications extend and contract the sentence in addition to the modifications in MLMs, hence the word "warped" in the name. The insertion and drop modification of the input text during training of WLM resemble the types of noise due to Automatic Speech Recognition (ASR) errors, and as a result WLMs are likely to be more robust to ASR noise. Through computational results we show that natural language understanding systems built on top of WLMs perform better compared to those built based on MLMs, especially in the presence of ASR errors.
Toward More Robust and Real-Time Unmanned Aerial Vehicle Detection and Tracking via Cross-Scale Feature Aggregation Based on the Center Keypoint

Unmanned aerial vehicles (UAVs) play an essential role in various applications, such as transportation and intelligent environmental sensing. However, due to camera motion and complex environments, it can be difficult to recognize the UAV from its surroundings thus, traditional methods often miss detection of UAVs and generate false alarms. To address these issues, we propose a novel method for detecting and tracking UAVs. First, a cross-scale feature aggregation CenterNet (CFACN) is constructed to recognize the UAVs. CFACN is a free anchor-based center point estimation method that can effectively decrease the false alarm rate, the misdetection of small targets, and computational complexity. Secondly, the region of interest-scale-crop-resize (RSCR) method is utilized to merge CFACN and region-of-interest (ROI) CFACN (ROI-CFACN) further, in order to improve the accuracy at a lower computational cost. Finally, the Kalman filter is adopted to track the UAV. The effectiveness of our method is validated using a collected UAV dataset. The experimental results demonstrate that our methods can achieve higher accuracy with lower computational cost, being superior to BiFPN, CenterNet, YoLo, and their variants on the same dataset.
The Use of Augmented Reality and Virtual Reality in Ergonomic Applications for Education, Aviation, and Maintenance

Practicing engineers associated with military applications performed a literature review to detail the use of augmented reality and virtual reality (AR/VR) related to ergonomic applications across education, aircraft crew stations, and maintenance. This review examines articles based on both breadth of applications for and relevance to ergonomic AR/VR applications. Results of the review were summarized across multiple industries to highlight specific use cases of AR/VR applications. The purpose of this article is to inform ergonomics practitioners of current AR/VR applications addressing ergonomic issues across a selection of three military-related usage areas and to highlight research insights, implementation ideas, and commonalities between applications.
A Contextual Model for Information Extraction in Resume Analytics Using NLP’s Spacy

The unstructured document like resume will have different file formats (pdf, txt, doc, etc.), and also, there is a lot of ambiguity and variability in the language used in the resume. Such heterogeneity makes the extraction of useful information a challenging task. It gives rise to the urgent need for understanding the context in which words occur. This article proposes a machine learning approach to phrase matching in resumes, focusing on the extraction of special skills using spaCy, an advanced natural language processing (NLP) library. It can analyze and extract detailed information from resumes like a human recruiter. It keeps a count of the phrases while parsing to categorize persons based on their expatriation. The decision-making process can be accelerated through data visualization using matplotlib. Relative comparison of candidates can be made to filter out the candidates.
Graph Representation Ensemble Learning

Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links and classifying and recommending nodes. Most embedding methods aim to preserve specific properties of the original graph in the low dimensional space. However, real-world graphs have a combination of several features that are difficult to characterize and capture by a single approach. In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently. We provide analysis of our framework and analyze - theoretically and empirically - the dependence between state-of-the-art embedding methods. We test our models on the node classification task on four realworld graphs and show that proposed ensemble approaches can outperform the state-of-the-art methods by up to 20% on macro-F1. We further show that the strategy is even more beneficial for underrepresented classes with an improvement of up to 40%.
Spacecraft Relative Trajectory Planning Based on Meta-Learning

Spacecraft relative trajectory planning is central to many space missions like on-orbit service and debris removal. While early attempts of machine-learning-based trajectory planning have been witnessed, they still face the open problem of how to acquire sufficient training samples to conduct robust training. In this article, we have introduced a meta-learning framework to improve the adaptation ability of the planner when facing new initial conditions. To achieve this goal, we divided the training trajectories as sub-training samples and fake testing samples. Then, the meta planner is trained by repeatedly conducting a trail training-and-testing process. To this end, the gradient information of the meta learner is initially obtained on the subtraining sets and is further adjusted by looking at its testing performance on those fake testing data. Therefore, the meta planer explicitly take account of the potential testing performance into consideration and, hence, alleviate the overfitting phenomena with few training trajectories. Simulation results substantiate the effectiveness of our approach, as well as the advantages of quick adaptation to new initial conditions without overfitting.