Reviewerly/exHarmony huggingface dataset:
Distance education during the pandemic generated by COVID- 19 in Latin America

A documentary review of the scientific production registered in the Scopus database on thestudy of Digital Distance Education in the COVID-19 pandemic in Latin America wascarried out with the purpose of knowing the main characteristics of the generation of newknowledge on this topic. The information analyzed is provided by Scopus through itsSearch tool, by means of which the data is filtered in such a way that a total of 178 researchpapers published in high impact journals are obtained, referring to distance educationduring the COVID-19 pandemic. Due to its nature, the temporal delimitation is defined bythe two semesters of 2020 and the first semester of 2021, at the date of the present research.The bibliometric review is performed on the basis of these data, which are classifiedaccording to their area of knowledge, country of origin and affiliation. Once theinformation is classified, certain papers are analyzed from a bibliographic perspective toknow and define the position of different authors with respect to the proposed topic.
Design and Implementation of Foreign Object Detection Method in Urban Rail Train Driving

The economic development has been accompanied by the continuous increase of the urban population, which has led to an increase in the city’s total transportation demand and the urban traffic pressure. As a public service, urban rail transport plays a huge and indispensable role in solving people&#39;s travel problems. There are many reasons that affect the operation of urban rail trains, and among which tracking foreign bodies is one of the more common types. So, this paper focuses on the following three tasks: foreign object detection, foreign object classification, and early warning mechanism. Firstly, it designs and implements the detection method of foreign objects in the driving of urban rail vehicles. Secondly, it provides a foreign object detection algorithm to analyze and track the status of foreign objects efficiently by analyzing the track live video generated in the train operation system. Finally, an appropriate alarm processing is proposed according to the situation.
Protests and Public Space Surveillance: From Metadata Tracking to Facial Recognition Technologies

Protest movements are gaining momentum across the world, with Extinction Rebellion, ‘Black Lives Matter, and strong pro-democracy protests in Chile and Hong Kong taking centre stage. Yet many governments have implemented various measures that severely impair the right to protest, especially during crises, like the global COVID-19 pandemic. In this submission to the Thematic Report to the 50th Session of the Human Rights Council, I draw attention to several issues:• The role of metadata tracking and data retention laws in suppressing political protests around the world;• The chilling effect of facial recognition technologies used in public spaces on the right to peaceful assembly and protest, and the absence of oversight mechanisms or adequate safeguards in many countries around the world;• The role of private actors in assisting governments to curtail protests; and the insufficiency of the international human rights legal framework to ensure the protection of human rights by private actors.
Towards Generating Summaries for Lexically Confusing Code through Code Erosion

Code summarization aims to summarize code functionality as high-level nature language descriptions to assist in code comprehension. Recent approaches in this field mainly focus on generating summaries for code with precise identifier names, in which meaningful words can be found indicating code functionality. When faced with lexically confusing code, current approaches are likely to fail since the correlation between code lexical tokens and summaries is scarce. To tackle this problem, we propose a novel summarization framework named VECOS. VECOS introduces an erosion mechanism to conquer the model's reliance on precisely defined lexical information. To facilitate learning the eroded code's functionality, we force the representation of the eroded code to align with the representation of its original counterpart via variational inference. Experimental results show that our approach outperforms the state-of-the-art approaches to generate coherent and reliable summaries for various lexically confusing code.
Intervensi Berbasis Digital Efektif Meningkatkan Kemampuan Matematika pada Siswa: Studi Metaanalisis

Numeracy is one of the most necessary skills needed. However, not every student has sufficient numeracy, therefore it is needed an effective intervention to improve students’ mathematics skill. The widely use of technology in education has provided a breakthrough for conducting digital-based interventions in learning mathematics. This study goal is to review the literature which measures the effectiveness of digital-based intervention to improve mathematics skill. Meta-analysis done to eighteen journals published in the past decade with 1,721 participants of experiment group and 1,219 participants of control groups which are relevant to this study. Based on mean differences (M, SD, and n) analysis, it is found that the effect size was g = .492 (95% CI = [.287, .706]; p &lt; .001; I2 (inconsistency) = 84.43%). This shows that the effect size obtained is similar to Benavides-Varela et al. (2020) study which found digital-based interventions are effective to improve dyscalculia students’ mathematics skill. There is also no evidence which shows level of education affects the use of digital intervention to improve students’ mathematics skill (p = .359). The effect size obtained while using level of education as moderator is g =.398 (95% CI = [-.483, .175]; p &lt; .001; I2 (inconsistency) = 84.02%). Therefore, educators can use digital media as a tool to support students’ mathematics learning, both at the preschool to college level.
Machine Learning Informed Decision-Making with Interpreted Model’s Outputs: A Field Intervention

There is a renewed and growing demand for interpretable and explainable machine learning (ML) systems, propelled by the increased use of these systems for making high-stakes decisions affecting individuals. Despite having set theoretical ground for explainable intelligent systems a few decades go, the information system scholars have given little attention to new developments, and especially to the use of ML trained models in decision-making with humans-in-the-loop in real-world problems. In this paper, we take the sociotechnical system lenses and employ quantitative and qualitative analysis of a field intervention in a public employment service setting to study the machine learning informed decision-making with interpreted models' outputs. Contrary to theory, our results suggest a small positive effect of explanations on confidence in the final decision, and a negligible effect on the decisions' quality. We uncover complex dynamic interactions between humans and algorithms, and the interplay of algorithmic aversion, trust, experts’ heuristic, and changing uncertainty-resolving condititions. We discuss theoretical and practical implications of our findings.
ANALISIS STUDI KASUS KRISIS KETERSEDIAN AIR MUSIM KEMARAU DALAM UPAYA MENANGGULANGI PADA MASYARAKAT DESA BUTUH

Water is a source of life that must exist in human life. Water supply crises have hit a lot due to drought disasters from the dry season. Drought is a disaster that occurs slowly due to changes in weather patterns that last a long time. A village that is located in Kediri Regency, namely Butuh Village has experienced a drought. Efforts that must be made can help reduce the impact of extreme climate change disasters. So, this article is used to add insight into the efforts and solutions that can be implemented. Respondents in the study were sixteen respondents with ten respondents who experienced the dry season, five people came to each house for observation. The results of the analysis that the author conducted through interviews resulted in a process carried out by the community by drilling wells and five people using bucket wells. However, if the drilling of wells by means of continuous deepening of the ground hole can have a dangerous impact on the environment. So, the authors provide another solution by making biopores in each house.
Visual analysis of geospatial multivariate data for investigating radioactive deposition processes

Abstract The Fukushima nuclear accident of 2011 raised awareness of the importance of radioactive deposition processes, especially for proposing aerosol measures against possible air pollution. However, identifying these types of processes is often difficult due to complicated terrains. This paper presents an application study for identifying radioactive deposition processes by taking advantage of visual interaction with topographic data. The idea is to visually investigate the correspondence of the spatial positions to the air dose rate along with relevant attributes. This is accomplished by composing scatterplots of pairwise attributes, onto which we project terrain areas to interactively find specific patterns of such attributes. We applied our approach to the analysis of air dose rate distribution data around the Fukushima nuclear plant after the accident. Our visualization technique clearly distinguished contamination areas derived from different deposition processes and thus is useful for elucidation of the deposition process.
Quantitative X-ray mapping of Au-Ag ratios in native electrum from the Fire Creek epithermal vein deposit, Lander County, Nevada (USA)

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DYNAMICS OF STUDENT YOUTH VALUES IN RUSSIAN SOCIETY IN THE CONTEXT OF DIGITALIZATION OF HIGHER EDUCATION

Objective of the research is to study the dynamics of student youth values in the Russian society in the context of digitalization of higher education. The methodological basis of the research was the subject-activity approach, which contains neoinstitutional, socio-dispositional, structural-activity procedures. Research results. It is proved that digitalization, being a process of influence on the professional socialization of students, and actualizing the assessment of social and cultural differences in the youth environment in the mass moods, becomes a mediating factor in the transition to the synthesis of instrumental and fundamental values of modern students. Prospects of the study are connected with further research on the study of youth values in the modern world.
A Pilot Study on Video-based Eye Movement Assessment of the NeuroEye Examination

The ability to perform automated neurological assessment could enhance diagnosis and treatment in the pre-hospital setting such as during telemedicine or emergency medical services (EMS) encounters. In this pilot study we present the preliminary analysis of a video-based eye movement assessment workflow (NeuroGaze) for neurological eye examinations. Our workflow uses a pupil detector to detect the center of the pupil for both eyes for a given video and determine the conjugacy of eye movement. The study evaluated the proposed workflow on 18 healthy volunteers while performing a digitally adapted neurological eye examination (NeuroEye) to assess conjugacy of gaze in all cardinal positions. The NeuroGaze performance was compared to the COTS eye tracker. The video-based eye movement assessment workflow reported an average Pearson correlation coefficient of 0.84, compared to the COTS eye tracker with an average of 0.95. The preliminary analysis indicates that NeuroGaze can be used to determine the individuals ability to perform conjugate eye movements. Future study will assess the performance of NeuroGaze in patients with pathological eye movements.
Two-dimensional jointly sparse robust discriminant regression

Ridge regression is an important method in feature extraction and has been extended in many different versions. Robust discriminant regression aims to solve the Small Sample Size Problem (SSSP) in ridge regression form by designing a novel regression model and imposing on L2,1-norm as the main metric instead of the regularized term. However, when the dimensions of data are very high, RDR will be faced with the problem of the curse of dimensionality and high memory space cost. The computation cost of RDR will be extremely high in the iterative procedures. To address this problem, we propose an improved method called Two-dimensional jointly sparse RDR (2DJSRDR) for image-based feature extraction. Unlike previous vector-based methods which stretch the data into a high-dimensional vector as input, the proposed 2DJSRDR uses the two-dimensional image matrix directly as the computational unit so that the drawbacks in RDR can be naturally avoided. Besides, we also introduce L2,1-norm as regularization term to obtain jointly sparse projections for feature selection, which is helpful to improve the performance of the model. Experiments on some benchmark datasets demonstrate the superior performance of the proposed method.
HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme

It is well known that conventional digital signature algorithms such as RSA and ECDSA are vulnerable to quantum computing attacks. Hash-based signature schemes are attractive as post-quantum signature schemes in that it is possible to calculate the quantitative security level and the security is proven. SPHINCS is a stateless hash-based signature scheme and introduces HORST few-time signature scheme which is an improvement of HORS. However, HORST as well as HORS suffers from pretty large signature sizes. HORSIC is proposed to reduce the signature size, yet does not provide in-depth security analysis. In this paper, we propose HORSIC+, which is an improvement of HORSIC. HORSIC+ differs from HORSIC in that HORSIC+ does not apply f as a plain function to the signature key, but uses a member of a function family. In addition, HORSIC+ uses the chaining function similar to W-OTS+. These enable the strict security proof without the need for the used function family to be a permutation or collision resistant. HORSIC+ is existentially unforgeable under chosen message attacks, assuming a second-preimage resistant family of undetectable one-way functions and cryptographic hash functions in the random oracle model. HORSIC+ reduces the signature size by as much as 37.5% or 18.75% compared to HORS and by as much as 61.5% or 45.8% compared to HORST for the same security level.
Extracting Tweets Using R in The Context of Ethical Issues

Twitter is a well-known social networking platform where users exchange information and express opinions. Since many people's interactions now take place on social media, this medium has rapidly become a source of capturing knowledge from users. The aim of this study is to find tweets using R that are then related to Ibn Khaldun's thoughts. The analysis was carried out with a simple algorithm written in R Programming. 45 keywords based on Ibn Khaldun's thoughts were constructed as the hashtag to retrieve data from Twitter. As a result of the data extraction, 1075 public tweets were collected through the search API. The simple algorithm is capable to facilitating the process of extracting data from Twitter, such that it easily known to the public tweets that exist in a given time period and this can be used as reference material for further development process. For further work, it is possible to use text mining activities and sentiment analysis approaches, as well as explore various social media platforms using R packages for decision makings.
Development of a face-mask detection software using artificial intelligence (AI) in python for covid-19 protection

Infectious diseases like Covid-19 transmits and contaminates via dispersal through aerial medium has become a serious issue nowadays that stimulated increasing the facemask usage. However, in densely populated countries it is very hard for law enforcing organizations to monitor the number of people wearing mask and those who are not wearing any in public. For this reason, a facemask detection model can be developed for security cameras in public places and surveil on people. In this research, researchers developed a prototype of facemask detector using python codes and artificial intelligence. Since Machine cannot understand human language, developers use different types of programming languages for training machine. Here, Python was used with the help of its packages to train models and two separate python files were developed to complete this research;the first one is to develop a mask detector and the other one to combine mask and face detector, combined together to develop facemask detector in real time. Data was collected, analyzed and visualized with python programming language, model was trained using ConvNet (Convolutional Neural Network) and finally an output was received from a raw input, which can detect mask in human face. © 2021
Variational Model-based Policy Optimization

Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is often challenging because the bias in simulated data may overshadow the ease of data generation. A potential solution to this challenge is to jointly learn and improve model and policy using a universal objective function. In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a variational lower-bound of a log-likelihood. This allows us to use expectation maximization (EM) and iteratively fix a baseline policy and learn a variational distribution, consisting of a model and a policy (E-step), followed by improving the baseline policy given the learned variational distribution (M-step). We propose model-based and model-free policy iteration (actor-critic) style algorithms for the E-step and show how the variational distribution learned by them can be used to optimize the M-step in a fully model-based fashion. Our experiments on a number of continuous control tasks show that our model-based (E-step) algorithm, called variational model-based policy optimization (VMBPO), is more sample-efficient and robust to hyper-parameter tuning than its model-free (E-step) counterpart. Using the same control tasks, we also compare VMBPO with several state-of-the-art model-based and model-free RL algorithms and show its sample efficiency and performance.
An OWL-Based Ontology Structure for representing Multimodel Process Improvement Framework

Software and systems improvement requests to merge various interpretations from several improvement models and techniques. A particular challenge is the multitude of models for requirements and quality, which can get time consuming and error prone to trace, change, and verify. Lately, Ontologies have been used across several domains and for numerous purposes to be applied for many applications. Besides, recent work in Artificial Intelligence is discovering the use of formal ontologies as a way of identifying content-specific agreements for the sharing and reuse of knowledge among software entities. Therefore, this paper describes how ontology engineering is used to construct an Ontological structure of the proposed SPI-CMMI framework –which based on using Six sigma approach integrated with CMMI-Dev model and Quality Function Deployment (QFD) technique- with its progressive phases, related activities, recommended tools and the CMMI-Dev 1.3 representation. The SPI-CMMI Ontology provides a shared improvement terminology, defines precise and unambiguous semantics for the software enterprises and enables reuse of improvement phase’s knowledge; in addition it makes domain assumptions explicit and separate domain knowledge from the operational knowledge.
GoTrip: An Automatic Ontological Travel Itinerary Planner Using SPARQL Inferencing

The paper proposes Semantic web-based travel agent which will provide best tour plan for a user, based on his current requirements and past profile. The proposed travel agent will perform all the tasks of a planned trip including availability and booking of trains, flights, hotels and a complete travel schedule depending on the destination, number of people travelling, number of days of travel, user spending capacity, his interest etc. In case, user is not ready for a destination, it will also guide him for the possible destinations which will match the weather and his rest of the current requirements. The complete Travel Ontology is developed in OWL using Protege 5.2 version and the validity of the above functionalities is checked by executing SPARQL queries against the semantic annotated data and Rule ML. The paper not only automates the process of vacation planning for the user but also consider his personal requirements while showing him the results.
A Survey of Kalman Filter Algorithms and Variants in State Estimation

The Kalman filter is one of the most extensively used approaches for estimating system states with unknown statistics in the areas of modem control, communication applications, and signal processing. By selecting the appropriate estimate technique, a correct and accurate state estimation of a linear or non-linear system can be enhanced. By using several mathematical techniques to linearize the nonlinear system, state estimation can be improved.Kalman filter methods are a common methodology for nonlinear systems that produce linear, unbiased, and minimum variance estimates of unknown state vectors.We attempted to bridge the gap between the Kalman filter and its variants in terms of algorithm and performance when applied to a non-linear system in this study.When only noisy observation data is provided, the techniques mentioned here have been shown to be more effective. This work can be used as theoretical basis for further studies in a number of different directions such as to achieve high computational speed for high dimensional state estimation.
Possessives in indefinite nominal phrases: A comparison between Italo-Romance and Daco-Romance

&#x0D; Southern Italian dialects exhibit a peculiar morphosyntactic device in licensing possessives in non‑definite noun phrases, i.e. the insertion of the functional element de followed by the definite article. This strategy shows striking similarities with the functional element A(-) employed in Romanian to introduce a possessive or a noun phrase marked as a genitive when it is not linearly preceded by a noun phrase marked as definite. This paper proves that an identical syntactic structure is detectable in both groups of varieties which stems from the definiteness requirements of the genitive.&#x0D;
A Multi-Tier Architecture for the Management of Supply Chain of Cloud Resources in a Virtualized Cloud Environment

As cloud resource demand grows, supply chain management (SCM), which is the core function of cloud computing, faces serious challenges. Quite a number of techniques have been proposed by many researchers for such a challenge. As such, numerous proposed strategies are still under reckoning and modification so as to enhance its potential. An optimized dynamic scheme that combined several algorithms' characteristics was proposed to map out such a challenge. The hybridized proposed scheme involved the meta-heuristic swarm mechanism of ant colony optimization (ACO) and deterministic spanning tree (SPT) algorithm as it obtained faster convergence chain, ensured resource utilization in least time and cost. Extensive experiments conducted in cloudsim simulator provided an efficient result in terms of minimized makespan time and throughput as compared to SPT, round robin (RR), and pre-emptive fair scheduling algorithm (PFSA) as it significantly improves performance.