Reviewerly/exHarmony huggingface dataset:
The Role of Information Technologies in a Criminal Investigation for the Portuguese Security Forces: A Case Study of the Guarda Nacional Republicana

The aim of this study is to analyze the main contributions of the use of information technologies to enhance the operational efficiency of the investigation structure of the Portuguese Security Force (Gendarmerie Status), named Guarda Nacional Republicana (GNR). In this work, a conceptual framework of the criminal investigation and its organic structure at the level of the GNR was carried out, and, in the same way, the legal regime inherent to the criminal investigation was analyzed and its functional aspects characterized. The role of information, as a central instrument for the institution was also evaluated, their respective functionalities are considered and the main challenges that remain are highlighted. Finally, the importance of information systems and technologies in criminal investigation and in the cooperation and interoperability of the criminal investigation structure was considered. For that, a methodology based on the hypothetical-deductive model was followed, using exploratory interviews and consequent elaboration of hypothesis, verified through confirmatory interviews. Overall, information technologies are crucial for criminal investigation activity in GNR, can help predict, monitor, and adapt to the evolution of the current criminal reality—increasing situational awareness. However, it is essential to mention that these tools are only one more resource available to support the decision-making process, in which the human element continues to be central.
Modificación de un material didáctico interactivo en la búsqueda de un aprendizaje más adaptativo - [Modification of an interactive didactic material in search of a more adaptive learning]

This work presents the evolution of an interactive teaching material oriented towards the system of geometric product specifications, ISO GPS.The Educational Innovation Group Ingeniería de Fabricación of the University of Las Palmas de Gran Canaria has sought to provide a higher level of adaptivity to this material by implementing it directly on the Moodle platform.The contents have been reorganized into several blocks to improve their progressivity, making it necessary to pass basic concepts questionnaires to progress towards new contents.New resources have also been incorporated, such as interactive problem-solving videos and a graphic simulator for displaying the tolerances.Methodologies have been used with the active and collaborative participation of students, both for the development and evaluation of this new teaching material.The results obtained show that the improvements introduced have had a positive impact on student learning and it has also been shown to be a very suitable tool for use in a hybrid education modality in which face-to-face teaching and telematics are integrated.
Kandinsky Mobile

Kandinsky Mobile is a mobile device-based interactive artwork that generates and displays the social discussion landscape associated with a social mediaanchor post using a collection of colorfulcircles andconcentric circles. It draws inspiration from the famous abstract geometric art forms of Russian painter Wassily Kandinsky (1866-1944). Intuitively, a circle and a concentric circle represent a social comment and a collection of comments in a discussion thread, respectively. The artwork aims to facilitate user-friendly and effective understanding and visualization of large volumes of comments associated with ananchor post.
Universal Recurrent Neural Network Grammar

Modern approaches to Constituency Parsing are mono-lingual supervised approaches which require large amount of labelled data to be trained on, thus limiting their utility to only a handful of high-resource languages. To address this issue of data-sparsity for low-resource languages we propose Universal Recurrent Neural Network Grammars (UniRNNG) which is a multi-lingual variant of the popular Recurrent Neural Network Grammars (RNNG) model for constituency parsing. UniRNNG involves Cross-lingual Transfer Learning for Constituency Parsing task. The architecture of UniRNNG is inspired by Principle and Parameter theory proposed by Noam Chomsky. UniRNNG utilises the linguistic typology knowledge available as feature-values within WALS database, to generalize over multiple languages. Once trained on sufficiently diverse polyglot corpus UniRNNG can be applied to any natural language thus making it Language-agnostic constituency parser. Experiments reveal that our proposed UniRNNG outperform state-of-the-art baseline approaches for most of the target languages, for which these are tested.
Modularized Architecture of Attribute Generative Adversarial Network for Image Synthesis

In recent years, neural network models have achieved great success in the application of image-to-image translation based on conditional or cyclic generative adversarial networks. A variety of extended models have been developed to perform the multi-task image translation to synthesize output images according to the target attributes. In this paper, we propose a novel network architecture with multiple attribute discriminators that are pre-trained independently with attribute-relevant data, instead of being trained as a part of the generative adversarial network. Discriminators of extra attributes are added into the network incrementally, and the network is trained much faster since it is unnecessary to train the whole network for all attributes from scratch. This new design methodology makes the network modularized and extensible for new attributes in image translation tasks after the network has been trained with given attributes. We verified the proposed approach on an image-translation task that changes the attributes of the emoji images. Experimental result shows that the quality of image with multiple attributes generated by this approach is compatible with that by the conventional network.
Dynamical analysis of stress fields for a new thermo-photoelastic sample operate by magnetic induction

Temporal photoelastic analysis variation of the stress field in epoxy-metal embedded samples affected by a magnetic induction heating was done. Some differences of the dynamics are associated to area analyzed and steel sheet size used.
The model of the network organization of the educational process at the rural schools in conditions of digitalization

The article considers problems related to the network organization of the educational process at the rural schools of the Russian Federation in conditions of digitalization. The aim of the study is to identify and justify the peculiarities of the network organization of the educational process at the rural schools in conditions of digitalization of education. The scientific novelty of research consists ofthe following points:1. The peculiarities of the network organization of the educational process at the rural schools in conditions of their limited resource provision and partially closed educational space have been revealed.2. The model of organization of educational process at the modern rural school is presented from the point of view of the optimal combination of network and system-activity approaches, the using of which provided the quality education and the personal development to the rural students in conditions of informatization of the education. The theoretical significance of the study consists of the following points:1. The trends of formation and development of modern rural school are defined and scientifically justified.2. The theory of pedagogy is enriched by the introduction into the scientific circulation of the concept of the network organization of the educational process, the content of which emphasizes the presence of targeted impacts on its structural components by the normative body of the network school and the links between them in order to design and implement educational programs of various levels and directions.
Learning to Explore by Reinforcement over High-Level Options

Autonomous 3D environment exploration is a fundamental task for various applications such as navigation. The goal of exploration is to investigate a new environment and build its occupancy map efficiently. In this paper, we propose a new method which grants an agent two intertwined options of behaviors: "look-around" and "frontier navigation". This is implemented by an option-critic architecture and trained by reinforcement learning algorithms. In each timestep, an agent produces an option and a corresponding action according to the policy. We also take advantage of macro-actions by incorporating classic path-planning techniques to increase training efficiency. We demonstrate the effectiveness of the proposed method on two publicly available 3D environment datasets and the results show our method achieves higher coverage than competing techniques with better efficiency.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN PREDICTION OF SOFTWARE DEVELOPMENT EFFORT

Over the past two decades, there has been a great enhance in researches dealing with the software development effort estimation utilizing machine learning (ML) approaches with the objective of improving the accuracy of the estimates. Among these ML methods, artificial neural network approaches have gained significant scholarly attention thanks to their capability to learn and model non-linear and complex functions. In this paper, artificial neural network technique was considered for modelling software development effort estimation. Datasets considered for estimation were COCOMO. Evaluation measures used were MMRE and correlation R. After building and testing the ANN model, and based on the comparison between the test results of the ANN model and the SLIM, Function Points, and COCOMO-basic models it could be concluded that the ANN was a suitable model in the estimation of the effort. ANN is recommended to be used as a predictive model for software development effort estimation.
Coupling of cryptocurrency trading with the environmental goals: Is it on the cards?

Following the systematic review and bibliometric analysis of current literature, this paper attempts to investigate whether the wealth generated through cryptocurrency trading can assist in attaining the United Nation's (UN) Sustainable Development Goal (SDG) 7, affordable and clean energy and UN SDG 13 related to climate action. The critical analysis of literature indicates a growing interest in cryptocurrency, the UN's SDGs and the negative effect that crypto mining has on the use of enormous energy. However, there is a clear gap in the literature that focuses on the possibility of using the wealth generated through cryptocurrency trading in financing environmentally friendly projects and attaining the UN's SDG 7 and SDG 13. The findings and the future research direction of this study aim to firstly expand the academic literature related to SDG 7 and SDG 13 and secondly, to examine the relationship between cryptocurrency and sustainability even during an uncertain period. This study provides evidence pertaining to the theoretical models that can be applied within discussion of the complex relationship between cryptocurrency, clean energy and climate action. Our findings will provide policymakers with information regarding actions that need to be taken in order to convert cryptocurrency generated wealth, and consequently attaining sustainable socio-economic goals in the future.
Analytically Tractable Inference in Deep Neural Networks

Since its inception, deep learning has been overwhelmingly reliant on backpropagation and gradient-based optimization algorithms in order to learn weight and bias parameter values. Tractable Approximate Gaussian Inference (TAGI) algorithm was shown to be a viable and scalable alternative to backpropagation for shallow fully-connected neural networks. In this paper, we are demonstrating how TAGI matches or exceeds the performance of backpropagation, for training classic deep neural network architectures. Although TAGI's computational efficiency is still below that of deterministic approaches relying on backpropagation, it outperforms them on classification tasks and matches their performance for information maximizing generative adversarial networks while using smaller architectures trained with fewer epochs.
Emoji Discourse Strategy: Breaking The Communication Barrier Between Teachers and “Post-00” College Students

Emoji discourse is the "expression body language" of social networking."Post-00" college students use emoji instead of words to express themselves on the Internet.One of the characteristics of their interpersonal communication is the popularity of online fighting pictures and Internet memes.The use of memes not only merely meets their needs that can help them to express most instinctively and emotionally, but also is an effective way of communicating and reducing pressure.Education is a two-way communication between teachers and students to transfer knowledge and cultivate talents.Teachers in colleges who are good at using emoji discourse strategies will effectively break the communication barriers with the "post-00" college students, and will be conducive to promote the growth of college students.It is the embodiment of the modern education concepts that keep pace with the times, which is of great significance to improve the quality of education.
Perbandingan Algoritma RED, SFQ, Dan AQM Pada Jaringan Enterprise Dengan Vmware ESXI Dan Router OS

The use of the internet network since the covid-19 pandemic is increasingly needed as the backbone of the implementation of mobile technology, websites, and desktops where all of them utilize network infrastructure and servers. The use of the right enterprise network combined with adequate server management results in optimal access and stable network conditions. The more complex the network that is built, the more server needs are needed and the more complicated the queuing algorithm that is built to produce the appropriate access speed and optimal storage. Behind these needs, there are several algorithms that can be implemented to optimize the application of enterprise networks. In this study, the implementation of a comparison between the RED, SFQ, and AQM algorithms on RouterOS and VMware ESXi in an enterprise network, in this case a university-scale internet network, is carried out. RouterOS was chosen because most of the networks in the agency, especially using Mikrotik RouterOS for network management and monitoring, while VMware ESXi is used as a virtualization server that can replace many physical servers into one server by implementing virtualization in it to be more practical and efficient. The implementation of the RED, SFQ, and AQM algorithms was tested during March to July and the results showed that the AQM throughput test value was greater than RED and SFQ by 91.4%, the delay and packet loss test values of AQM were better than RED and SFQ by 7% and 8856 ms. Keywords: VMware ESXi, RED, SFQ, AQM,, routerOS.
Multifaceted Risk Assessment and Risk Countermeasure Portfolio for Internet of Things

As global businesses now extensively depend on data, Internet of Things (IoT) sensors have become the primary source of real-time data that enable the digital transformation. According to Bain’s Insight, the IoT market will grow to more than $520 billion by 2021. The technology has already been adopted for a wide array of use cases, but due to the ever-expanding threat landscape, many customers have indicated that security remains the primary barrier when it comes to their acceptance of IoT. The current security risk management methodologies focus mostly on the cyber view. In this work, we identify 28 risk factors extracted using the risk breakdown structure method and expand this traditional view to include others (physical, psychological) that are critical to business operations. Next, we proposed risk countermeasures for all the extracted risk factors using a risk matrix method. Further, from a practical point of view, a portfolio of the proposed risk countermeasures was clearly indicated to enable the gradual introduction of risk countermeasures. Finally, the effectiveness of the risk countermeasures was quantitatively evaluated on the basis of the risk values. Our findings help clarify IoT security and its relation to non-cyber risks for proper implementation of IoT systems.
Using digital pathology in competency based case scenarios as an efficient pathology learning platform

Abstract Introduction/Objective Case-based learning has been utilized for implementation in learning pathological and clinical features of diseases in an integrated, facile manner for some time now. We applied certain novel technological advances to improve case-based learning - short clinical vignettes describing all pertinent pathologic features with the help of digital pathology images - including both negative and positive findings. The clinical cases combined with digital pathological and radiological images and labs were uploaded on Moodle platform for student access, to be followed later by an online interactive discussion via Zoom. Methods/Case Report Clinical cases meticulously woven with digital pathological and radiological images and labs were centralized on 3 fundamental competencies – disease processes, systemic pathology and diagnostic &amp; therapeutic pathology. This was followed by corresponding Q-A session using the “breakout room” feature of Zoom to form a differential diagnosis, reach a final diagnosis and discuss appropriate management options. Students exited the breakout rooms after individual groupwork to return to the main Zoom session for final discussion. Novel web- based tools, social media assistance, and other online resources were utilized to enhance the online learning experience. Results (if a Case Study enter NA) Student feedback confirmed that our online instructional methodology was incredibly stimulatory, particularly in topics that were difficult to grasp using traditional pedagogical methods. Summative and formative assessments indicated performance gain, enhanced motivation, improved critical thinking and evolved ability to solve clinical cases. Drawbacks included irregular student participation, lack of face-to-face interaction, network issues and time differences. Conclusion Our pathology pedagogy provides flexibility and self-directed learning skills, while simultaneously covering the geographical limitations. This not only helps students in developing critical clinical reasoning, but also enables them to acquire life-long learning and problem-solving skills. The technique proved to be a powerful educational tool that facilitated an interactive learning experience beyond the classroom setting, with integration of multidisciplinary aspects in learning Pathology. Additionally, the communication competency was also covered during case discussion among students, promoting literary, verbal and team-working capabilities.
ANALISIS KETERBACAAN WACANA PADA BUKU TEKS BAHASA INDONESIA SEKOLAH MENENGAH PERTAMA MENGGUNAKAN GRAFIK FRY

Textbook research is an important so that the quality of text books is always maintained. The purpose of this study is to describe and report the results of an analysis of the readability or feasibility of Indonesian textbooks that are widely used in schools in Indonesia. This study uses a qualitative descriptive method with observation techniques. The collected data were analyzed according to the symptoms assessed in the study and then described. The results of the study concluded that 44.4% were in the appropriate category, 33.3% were in the inappropriate category, and 22.2% were in the invalid category.
Robust Human Physical Activity Recognition Using Smartphone Sensors

Human physical activity recognition using gyroscope and accelerometer sensors of smartphones has attracted many researches in recent years. In this paper, the performance of principle component analysis feature extraction method and several classifiers including support vector machine, logestic regression, Adaboost and convolutional neural network are evaluated to propose an efficient system for human activity recognition. The proposed system can improve the classification accuracy in comparison with the state of the art researches in this field. The performance of a physical activity recognition system is expected to be robust on different smartphone platforms. The quality of smartphone sensors and their corresponding noises vary considerably between different smartphone models and sometimes within the same model. Therefore, it is beneficial to study the effect of noise on the efficiency of the human activity recognition system. In this paper, the robustness of the investigated classifiers are also studied in various level of sensor noises to find the best robust solution for this purpose. The experimental results, which is provided on a well-known human activity recognition dataset, show that the support vector machine with averaged accuracy of 96.34% perform more robust than the other classifiers on different level of sensor noises.
The COVID-19 pandemic and plans for economic reopening in Brazil: a documental analysis

BACKGROUND: In March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic. In Brazil, the high rate of dissemination made it necessary to adopt restrictive measures nationwide with the discussion regarding the resumption of economic activities starting in April. In mid-December the country had 6,970,034 cases diagnosed and 182,799 deaths from COVID-19.&#x0D; OBJECTIVE: To analyze the content and characteristics of official documents, which guided the period of transition and resumption of economic activities in Brazil based on health indicators. METHODS: This is a documental research, carried out between May and July 2020, using official websites and publications from the state governments of the 27 federative units in Brazil as sources. In the study, only documents that used epidemiological and health indicators were included as determining criteria for decision making in relation to the easing, permanence or regression of social isolation measures adopted during the COVID-19 pandemic.&#x0D; FINDINGS: Plans, decrees and technical notes were identified for 18 Brazilian federal units. In most documents, the scientific team was made up exclusively of technicians (n = 10). The number of indicators found ranged from 2 to 11, being stratified into 5 categories: frequency and distribution of the disease; social and collective adherence; installed capacity or service profile; productive potential; and availability of supplies.&#x0D; MAIN CONCLUSIONS: Knowing governmental strategies, adopted in the easing of restrictive measures, in the face of the coronavirus (SARS-CoV-2) pandemic based on indicators and with the possibility of comparison between different federative units, provides subsidies for understanding the outcome of the disease by place of occurrence, allowing the construction of a panorama pathology in the country. The appropriation of the findings of this study by Brazil and other countries also serves as an instrument for reflection and planning of policies adopted during the COVID-19 pandemic.
The Impact of Online Learning on Self-Regulated Learning and Learning Outcomes in Junior High School Students

This research was based on the COVID-19 pandemic conditions which forced offline/face-to-face learning had been replaced with online learning. It was necessary for the teachers to package online learning which provided benefits for students in increasing their competence. This research was conducted to determine the impact of online learning which was packaged by teachers on self-regulated learning and learning outcomes for Junior High School Students. This research used descriptive quantitative method by using Google form taken from 250 students from several Junior High Schools in Semarang. Based on the research result, it can be concluded that online learning which was packaged by teachers has the effect of decreasing students’ self-regulated learning and learning outcomes that are not entirely complete from several existing subjects.