Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving k-means Clustering: an Application to Driving Style Recognition

With the advent of connected vehicles, drivers will communicate personal information describing their driving style to their vehicles manufacturers, stakeholders or insurers. These information will serve to evaluate remotely vehicle state via an e-diagnostics service, to provide over-the-air update of vehicles controllers and to offer new third parties services targeting profiled drivers. An inherent problem to all the previous services is privacy. Indeed, the providers of these services will need access to sensitive data in order to propose in return an adequate service. In this paper, we propose a privacy-preserving k-means clustering for drivers subscribed to the pay how you drive service, where vehicles insurance fees are adjusted according to driving behavior. Our proposal relies on secure multi-party computation and additive homomorphic encryption schemes to ensure the confidentiality of drivers data during clustering and classification.
O intangível na produção do cuidado: o exercício da inteligência prática em uma enfermaria oncológica

This article brings the results of a research of which main goal was to investigate the use of practical intelligence by an oncology nurse team in an oncological ward, in a hospital in the city of Rio de Janeiro, Brazil. We assume that, in spite of the suffering inherent to health care work, it can have a creative and potent dimension in the exercise of care, even in the presence of the challenges faced by the precarious working conditions, the growing demand of patients and the demands of productivity. Participant observation, in-depth interviews and group interviews were carried out with a selected nursing staff. We observed two main forms of practical intelligence manifestations. The first is the exercise of speaking and listening. The second, as an exercise in the production of comfort. Such forms of practical intelligence interpenetrate and cannot be captured by numbers, indicators, and methods of performance evaluation. They are invisible to instruments that do not go through the word of the workers. We conclude that it is necessary to create collective spaces in health organizations where managers and workers can express and dialogue on such issues, socially validating this know-how and those workers'experiences.Este artigo resulta de uma pesquisa cujo objeto é o saber-fazer dos trabalhadores de enfermagem. O objetivo principal foi investigar os usos da inteligência prática, em uma equipe de enfermagem de uma enfermaria oncológica no Rio de Janeiro. Partimos do pressuposto de que, a despeito do sofrimento inerente ao trabalho em saúde, este pode conter uma dimensão criativa e potente no exercício do cuidar, mesmo diante dos desafios colocados pelas precárias condições de trabalho, pela demanda crescente de pacientes e pelas exigências de produtividade. Foram realizadas observação participante, entrevistas em profundidade e entrevistas em grupo com trabalhadores da enfermaria selecionada. Observamos duas principais formas de manifestação da inteligência prática. A primeira, como exercício da palavra e da escuta. A segunda, como exercício da produção de conforto. Tais formas de inteligência prática se interpenetram e não são passíveis de captura por números, indicadores e métodos de avaliação do desempenho. São invisíveis para instrumentos que não passem pela palavra dos trabalhadores. Conclui-se pela necessidade de criação de espaços coletivos em que gestores e trabalhadores possam expressar e validar socialmente o saber-fazer e as experiências dos trabalhadores.
A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained ones), particularly when they are trained on high-dimensional state spaces such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations. It enables trained agents to adapt to new domains by learning robust features which are invariant across varied and randomized input observations. Furthermore, we propose an inference method based on Monte Carlo approximation to reduce the variance induced by this randomization. The proposed method significantly outperforms conventional techniques including various regularization and data augmentation techniques across 2D CoinRun, 3D DeepMind Lab exploration, and 3D robotics control tasks.
A Method for Vulnerability Database Quantitative Evaluation

During system development, implementation and operation, vulnerability database technique is necessary to system security; there are many vulnerability databases but a lack of quality standardization and general evaluation method are needed. this paper summarized current international popular vulnerability databases, systematically introduced the present situation of current vulnerability databases, and found the problems of vulnerability database technology, extracted common metrics by analyzing vulnerability data of current popular vulnerability databases, introduced 4 measure indexes: the number scale of vulnerabilities, the independence level, the standardization degree and the integrity of vulnerability description, proposed a method for vulnerability database quantitative evaluation using SCAP protocol and corresponding standard, analyzed a large number of vulnerabilities in current popular vulnerability database, quantitative evaluated vulnerability database by the law of normal distribution, the experimental results show this method has strong versatility and science, and it is beneficial to improve the quality and standardization construction for vulnerability database development.
Automated Language Identification of Bibliographic Resources

This article describes experiments in the use of machine learning techniques at the British Library to assign language codes to catalog records, in order to provide information about the language of content of the resources described. In the first phase of the project, language codes were assigned to 1.15 million records with 99.7% confidence. The automated language identification tools developed will be used to contribute to future enhancement of over 4 million legacy records.
Evaluation of Child-Friendly Augmented Reality Tool for Patient-Centered Education in Radiology and Bone Reconstruction

The use of augmented reality (AR) has a rich history and is used in a number of fields. Its application in healthcare and anatomy education is developing considerable interest. However, although its popularity is on the rise, its use as an educational and practical tool has not been sufficiently evaluated, especially with children. Therefore, this study presents the design, development and evaluation of an educational tablet-based application with AR functionality for children. A distal radius fracture was chosen, as it is one of the more common fractures in the younger age group. Following a standardized software engineering methodology, we identified functional and non-functional requirements, creating a child-friendly tablet based AR application. This used industry standard software and incorporated three-dimensional models of a buckle fracture, object and image target marker recognition, interactivity and educational elements. In addition, we surveyed children at the Glasgow Science Centre on its usability, design and educational effectiveness. Seventy-one children completed a questionnaire (25 also underwent a short structured interview). Overall, the feedback was positive relating to entertainment value, graphic design, usability and educational scope of the application. Notably, it was shown to increase user understanding of radiology across all age groups following a trial of the application. This study shows the great potential of using digital technologies, and more particularly augmented information, in engaging future generations in science from a young age. Creation of educational materials using digital technologies, and evaluating its effectiveness, highlights the great scope novel technology could have in anatomical education and training.
Knowledge Graph-Enhanced Blockchains by Integrating a Graph-Data Service-Layer

No doubt that blockchain technology has revolutionized a decentralized consensus technique allowing for increased security and trust among people and machines. This has paved the way for many and various types of applications to make use of the notions of this promising technology. These applications are concerned with persisting historically attested and tamper resistant datasets. Having these datasets persisted calls for techniques that fulfill the emerging requirements for managing these stored datasets efficiently. This includes providing efficient and direct ways to access, query, interlink, and retrieve those datasets. The absence of such solutions imposes many hindrances to make use of the full extent of the blockchain technology. In this paper, we introduce our approach which aims at adding a data-graph service-layer to the component stack of a blockchain system. This added service-layer employs data graphs such as RDF-encoded data, reasoning, and the underlying inference rules allowing to implement applications with advanced requirements for sophisticated data models that need to be built on top of blockchain infrastructure.
Empirical Evaluation of Activation Functions and Kernel Initializers on Deep Reinforcement Learning

Hyperparameter optimization has a considerable impact on system performance. However many researchers use the default hyperparameter configurations provided by the machine learning frameworks they use because it require a lot of effort, time and computing resources. In this paper, we analyze the effect of the activation function and the kernel initializer on the performance of deep reinforcement learning. In detail, we compared the performance of 42 combinations of 7 activation functions and 6 kernel initializers on two different network types, namely, multi-layer perceptron (MLP) and convolutional neural network (CNN). From the results, it has been revealed that not only did the optimal combinations differ but also, even with the same hyperparameter, the performance difference was dramatic depending on the network type. This phenomenon was prominent in the kernel initializer.
Identification of Wireless Communication Signals Based on Cumulants and Wigner-Vile Distribution

In order to improve the recognition effect of wireless communication signal under non-cooperative conditions, the electromagnetic fingerprint sets representing the differences of wireless signals are constructed by combining high-order cumulants and Wigner-Power Distribution (WVD). Meanwhile the Fisher discriminant criterion method is introduced to evaluate and refine the electromagnetic fingerprint set. The simulation results show that the proposed method can significantly improve the identification effect of wireless communication signals in specific electromagnetic space compared with a single fingerprint feature.
Improvement in Deep Networks for Optimization Using eXplainable Artificial Intelligence

With the recent advancements in the field of explainable artificial intelligence (XAI), which covers the domain of turning deep network architectures from black boxes to comprehensible structures, it became easy to understand what goes on inside a network when it predicts an output. Many researchers have successfully shown the `thought process' behind a network's decision making. This rich and interesting information has not been utilized beyond the scope of visualizations once the training finish. In this work, a novel idea to utilize this insight to the network as a training parameter is proposed. Layer-wise Relevance Propagation (LRP), which obtains the effect of each neuron towards the output of the whole network, is used as a parameter, along with learning rate and network weights, to optimize the training. Various intuitive formulations have been proposed, and the results of the experiments on MNIST and CIFAR-10 datasets have been reported in this paper. Our proposed methodologies show better or comparable performances against conventional optimization algorithms. This would open a new dimension of research to explore the possibility of using XAI in optimizing the training of neural networks.
Information Retrieval of Physical Force Using the TF-IDF

Current information retrieval is done using a search engine or information retrieval system, users write queries and search engines will display search results. Existing and widely used search engines now provide many search results (many documents are taken), so it takes time to determine the relevant search results. Therefore we need a way to classify the amount of information available, which is needed by the user, making it easier for users to get the desired documents. Information retrieval system is a system that is used to find information that is relevant to the needs of its users, by applying the system problem information retrieval Physics force can provide relevant results according to the needs of users. There are two main processes in information retrieval systems, namely indexing and retrieval. One of the weighting algorithms is the TF-IDF which is influenced by the frequency of occurrence of words in each document and the frequency of documents that have the word. The result in a recall value close to 1 in each test which shows that almost all relevant documents can be found by the system, the precision values are between 0.6666667 to 1 which shows that there are other documents besides the relevant documents found by the system.
Implementation of Naïve Bayes Classification Algorithm on Infant and Toddler Nutritional Status

Body weight and length are often used as a reference index in classifying the nutritional status of infants and toddlers. In Posyandu Anggrek in Limo, Depok, Indonesia, the anthropometric index calculation used is still manual, using the z-score list or WHO NCHS standard deviation found in KMS (Healthy Cards for Toddlers). Nutritional status is only seen based on the colors in the KMS without calculation or looking at the index in the anthropometric table. Naïve Bayes, one of the methods used in classifying by calculating probability data, will be used in this study to measure the nutritional status of infants and children. The variables used in evaluating the nutritional status of infants and toddlers are data about body weight and body length. Based on the results of research and discussions conducted, 46 data about infants and toddlers are divided into 90% testing data and 10% training data by type of sampling using stratified sampling at RapidMiner. Based on the data obtained, the accuracy of Naïve Bayes in classifying the nutritional status of infants and toddlers up to 75%.
Environment Based Optimal Path Planning Realization for Autonomous Agents

The shortest path searching algorithms are broadly utilized as an essential part of autonomous agents and navigation systems. As applications get extensively complex, the shortest path calculations implemented via programming produce a lot of time overhead. In this paper, different path planning techniques are analyzed on the bases of their time effectiveness under dynamic constraints; varying spaces and environments having a different level of complexities. Bi-RRT algorithm is proposed as the most time efficient algorithm in complex environments with high spaces. In addition, the algorithms are tested in different environments and comparison among all simulations results are presented in the form of time and path length.
An Approach to Cluster Tamil Literatures Using Discourse Connectives

Tamil literary works have many valuable information hidden in it which may be a guide to even to the current modern society. Tamil literary works prove to be a treasure that retains the historical values and also a solution provider to almost each and every aspect of life such as, traditions, love, war, governance, trade etc. Now a days, many Tamil literary works have been translated into various world languages. When such information is made available to the global society through World Wide Web (WWW), it requires computational analysis which needs computational analysis of Tamil literary works. Clusters of Tamil literary works can be one such kind of computational analysis in order to be accessed by the Search Engine. This paper proposes an approach to cluster the Tamil Literatures using K-Means Clustering using discourse connectives as the predominant features. Discourse connectives are the words which connect two coherent clauses, phrases or sentences. The identification of discourse connectives signal the semantic relation between the texts fragments. This is explored in clustering the semantically similar Thirukkurals which is a famous Tamil literary work. This clusters of Thirukkural can form basis to identify efficient indices for many Natural Language Processing (NLP) applications, such as, Information Retrieval Systems, Summary Generation Systems, and Question Answering Systems. The proposed work is tested using all 1330 Thirukkural couplets and it has been found that 1050 Thirukkural couplets are correctly clustered into five clusters. This research has been evaluated using cluster purity metric and has achieved 0.79 purity, overall rand index of 0.92, precision of 0.79, recall of 0.8, and F-score of 0.79.
Online anomaly detection in ECG signal using Hierarchical Temporal Memory

Anomaly detection in time series is a well-studied subject, and it is well-documented in the literature such as ECG signal. Many successful algorithms for analyzing ECG signals are proposed and applied successfully to detect anomalies. However, ECG signal is a complex signal since it is contextual time series. The heartbeats changes over time and each user has his own heartbeats morphology. Consequently, there is no a specific model for this kind of signals. In this paper, we propesed to apply Hierarchical Temporal Memory (HTM). HTM is compatible with the changeable pattern because it is able to integrate a contextual properties to make prediction. A real dataset called MIT-BIH Arrhythmia is applied to evaluate HTM anomaly detection in ECG signals. Compared with Deep Learning, HTM is more efficiently to anomalies detection in ECG heartbeats.
Investigation of the Most Effective Meta-Heuristic Optimization Technique for Constrained Engineering Problems

One of the most common areas of meta-heuristic search (MHS) algorithms is optimization problems. In addition, the performance of only a few of the hundreds of MHS algorithms in the literature is known for constrained engineering design problems. The reason for this is that in most of the studies in which MHS algorithms have been developed, only classical benchmark problems are used to test the performance of the algorithms. Besides, applying MHS techniques to engineering problems is a costly and difficult process. This clearly demonstrates the importance of investigating the performance of new and powerful MHS techniques in engineering problems. In this paper, we investigate the search performance of the most recent and powerful MHS techniques in the literature on constrained engineering problems. In experimental studies, 20 different MHS techniques and five constrained engineering problems most commonly used in the literature have been used. Wilcoxon Runk Sum Test was used to compare the performance of the algorithms. The results show that the performance of MHS algorithms in classical benchmark problems and their performance in constrained engineering problems do not exactly match.
El boom tecnológico en las escuelas de los años 80: una aproximación al programa ATENEA español

Aims: This paper aims to analyse the debate concerning the incorporation of Information and Communication Technologies (ICT) by the Spanish Educational System, specifically the background and development of the first state-sponsored experimental programme, the Atenea project (1985-1992). Methodology and sources: To this end, we performed a review of documents pertaining to the programme and its evaluation, also taking into account the debates it generated. These documents were supplemented by reports and evaluations on related programmes, the conclusions of which we analyse in comparison with some of the contemporary reports, such as the one published by the OECD in 2015. Conclusions: Some of the fundamental debates that arose as a result of these programmes promoting the use of computers, in particular the Atenea, are strikingly valid but still lack a clear response. In the case of the promotion of ICT as an educational tool, we must recognize the relevance of corporate and business interests that saw the Education System as a large market, and therefore encouraged this type of programme, favouring an application focused more on the instrumental level than on the pedagogical one.
Extensive Survey on Feature Extraction and Feature Selection Techniques for Sentiment Classification in Social Media

Data Mining is a process of generating new information from the existing datasets involving the machine learning, statistics and database systems. Major role of data mining is to turn raw data into useful information by using mathematical algorithms for predicting future events. Data mining is known as knowledge discovery in data (KDD). The key properties of data mining are automatic discovery of patterns, prediction of likely outcomes, creation of actionable information and focuses on large datasets and database. Mining the information related to a content-oriented extraction on the basics of feature of data. For accessing the features of data that is data retrieval can be done by feature extraction mechanism. Various feature extraction methods have been used along with a feature selection algorithm for mining the large datasets.
Automation of Usability Inspections for Websites

To allow website portals to be designed according to the functional needs of users, the concept of usability has gained great importance in recent years. In this sense, according to ISO 9241-11, usability focuses on fulfilling a user’s objectives with effectiveness, efficiency, and satisfaction. At usability level in websites, a set of usability inspection heuristic tests exists, such as Torres-Burriel, which, based on a set of usability criteria and heuristics, allows identifying typical usability problems. In spite of the above, execution of these types of usability tests does not only involve the inspection and evaluation of the criteria, but also the processing and analysis of the data, given that the templates for these tests are usually found in text documents. In this paper, we propose as a contribution, a software system to inspect the usability of websites, which allows evaluating the criteria associated with the heuristics and the graphic analysis of the heuristics evaluated. This software system seeks to support developers and experts to carry out usability inspections on websites.