Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Big Data Applications in Cloud Providers

Due to the amount of computational data generated each year and most often without standardization, the need arose for the development of specific Big Data tools. Currently cloud computing has been chosen as it is widely used in conjunction with Big Data due to its reduced cost and elasticity. Utilization of cloud services has increased over the years and many companies have migrated their computing services to the cloud to reduce the operational costs of maintaining the technology infrastructure. Due to the number of cloud providers available, benchmarking is now considered important for your choice, especially the performance of the specific application in the cloud environment. This paper aims to evaluate MongoDB database performance using the Yahoo Cloud Serving Benchmark tool, with 3 cloud application profiles from Google and Microsoft.
Design and Implementation of a Multi-scale Object Detection Algorithm on TensorFlow

Abstract The technology of object detection is a very important subject in computer vision task. It has been widely used in intelligent transportation, face detection, aerospace and medical image equipment. In this paper, a kind of object detection algorithm based on regression and region proposals is studied. We use a popular deep learning framework—TensorFlow, as the experimental platform. We propose a multi-scale object detection algorithm based on RPN region proposal network, which can extract the features of large and small objects by using the level of feature map. In addition, this paper also improves the classification regression model and proposes a two-dimensional loss function, which makes the region proposals closer to the groundtruth boxes in the final training, which makes the training process of the improved network easier. The experimental data set used in this paper is PASCAL VOC data set, the accuracy and speed of each category in 20 detection objects are calculated and analyzed. A number of experiments have proved that the proposed multi-scale object detection algorithm based on RPN have improved in accuracy and speed. The average detection precision on the PASCAL VOC dataset increased to 74.4%.
Development of a mobile application: optimization problems for differential calculus

This article presents the development of a technological tool that results in student learning about solving differential calculus optimization problems. This work is framed in a research project endorsed and developed at the Universidad de La Salle in Colombia, called Learning to solve differential calculus optimization problems through mobile technology. Initially, evaluation of a mobile application analysis is required, to evaluate characteristics such as: acceptability, navigability, costs, operating system and impact on learning to solve problems. The development followed the analysis methodology, where the teacher's requirements can be classified and the service is personalized; design, where the technological scenario is defined, and the interfaces and navigability of the application are structured; development, where the design is made; Functional tests, where the application is emulated and simulated, and the teacher presents elements for improvement, to finally assess its effectiveness.
Open Access is not a Panacea, even if it’s Radical – an Empirical Study on the Role of Shadow Libraries in Closing the Inequality of Knowledge Access.

Library Genesis is one of the oldest and largest illegal scholarly book collections online. Without the authorization of copyright holders, this shadow library hosts and makes more than 2 million scholarly publications, monographs, and textbooks available. This paper analyzes a set of weblogs of one of the Library Genesis mirrors, provided to us by one of the service’s administrators. We reconstruct the social and economic factors that drive the global and European demand for illicit scholarly literature. In particular, we test if lower income regions can compensate for the shortcomings in legal access infrastructures by more intensive use of illicit open resources. We found that while richer regions are the most intensive users of shadow libraries, poorer regions face structural limitations that prevent them from fully capitalizing on freely accessible knowledge.We discuss these findings in the wider context of open access publishing, and point out that open access knowledge, if not met with proper knowledge absorption infrastructures, has limited usefulness in addressing knowledge access and production inequalities.
Information security risk management model for mitigating the impact on SMEs in Peru

This paper proposes an information security risk management model that allows mitigating the threats to which SMEs in Peru are exposed. According to studies by Ernst & Young, 90% of companies in Peru are not prepared to detect security breaches, and 51% have already been attacked. In addition, according to Deloitte, only 10% of companies maintain risk management indicators. The model consists of 3 phases: 1. Inventory the information assets of the company, to conduct the risk analysis of each one; 2. Evaluate treatment that should be given to each risk, 3. Once the controls are implemented, design indicators to help monitor the implemented safeguards. The article focuses on the creation of a model that integrates a standard of risk management across the company with a standard of IS indicators to validate compliance, adding as a contribution the results of implementation in a specific environment. The proposed model was validated in a pharmaceutical SME in Lima, Peru. The results showed a 71% decrease in risk, after applying 15 monitoring and training controls, lowering the status from a critical level to an acceptable level between 1.5 and 2.3, according to the given assessment.
Collaborative transmedia learning environments for the promotion of students’ autonomy and motivation : A study developed with students at risk of dropout and school failure

This article reports on a study carried out with vocational education students with a significant risk of failure or dropping out in their school career. Bearing in mind that these students have reduced levels of autonomy and motivation, an action-research plan was designed in which, through a transmedia collaborative approach and using digital technologies, it was intended to act, gradually, in the development of these 2 aspects over 2 research cycles, each with 4 and 2 weeks, respectively. The results obtained in the 1st implementation cycle indicate an improvement in mathematical knowledge. However, there were no statistically significant differences in motivation and autonomy.
Optimized parallel implementation of sequential clustering-based event detection

The ACM 2020 DEBS Grand Challenge focused on Non-Intrusive Load Monitoring (NILM). NILM is a method that analyzes changes in the voltage and current going into a building to deduce appliance use and energy consumption. The 2020 Grand Challenge requires high performance and high accuracy NILM implementations. In this paper, we describe the technical details of our solution for the 2020 Grand Challenge, a NILM program based on Apache Flink. We employ a Divide-and-conquer strategy to implement our parallel algorithm and designed a verify stage to improve the accuracy. For performance, our method achieves a great overall run time and the highest accuracy.
Design and Implementation of InBlock—A Distributed IP Address Registration System

The current mechanism to secure Border Gateway Protocol relies on the resource public key infrastructure (RPKI) for route origin authorization. The RPKI implements a hierarchical model that intrinsically makes lower layers in the hierarchy susceptible to errors and abuses from entities placed in higher layers. In this article, we present InBlock, a distributed autonomous organization that provides decentralized management of IP addresses based on blockchain, embedding an alternative trust model to the hierarchical one currently implemented by the RPKI. By leveraging on blockchain technology, InBlock requires consensus among the involved parties to change existent prefix allocation information. InBlock also fulfills the same objectives as the current IP address allocation system, i.e., uniqueness, fairness, conservation, aggregation, registration, and minimized overhead. InBlock is implemented as a set of blockchain smart contracts in Ethereum, performing all the functions needed for the management of a global pool of addresses without human intervention. Any entity may request an allocation of addresses to the InBlock registry by solely performing a (crypto) currency transfer to the InBlock. We describe our InBlock implementation and we perform several experiments to show that it enables fast address registering and incurs in very low management costs.
Ritchie’s Smart Watch Data Analytics and Visualization

The popularity of smartwatches has significantly risen in the past decade owing to advancements in technology, cost reduction, materials availability, production optimization and industrial automation. These factors have compounded the growth of manufacture of smartwatches and coupled with the internet-of-things boom in recent years, this field has seen rapid development and large investments of time and resources. Consequently, with a large amount of data being generated by these smartwatches, the question arises – is it feasible to obtain this data from a watch, and then visualize it in an interpretable manner? Our work answers this with a positive “yes” and demonstrates the proof-of-concept. We propose to build a platform that can extract data from a smartwatch (Mi Band 3 used in proof-of-concept) and visualize this information on a web application dashboard in order to enable the user to obtain insights regarding their physical health and data with ease and convenience.
Karl Marx and the Blockchain

Blockchain is often presented as a technological development; however, clearly it is not only that: the `Blockchain buzz' exists in the context of current social and political developments. In this essay, we analyse blockchain technology and its social and political context from a perspective of Marxist economic theory. Since arguably the last great inflection point in society and technology was analysed by Marx in terms of labour and capital and since we seem to be experiencing a shift in the balance between these forces today, it makes sense to revisit the Marxist ideas and apply them to the current situation, to see how well they still apply and if necessary to update them for current events.
BCube and DCell Topology Data Center Infrastructures Performance

Abstract Data Center (DC) quality is if it can provide optimal service. Optimal service means that the access provided should be free from interference. Interference can come from outside the system or from within the system itself. One of the variables used to measure the performance of data center architecture is a routing protocol. Routing Information Protocol is a variable to measure performance on BCube and DCell topologies. Testing is done using a simulator that can produce information architecture that has the best performance when communicating from the server to the client. BCube is a server-centric network structure, while DCell is a recursively defined architecture. This study was conducted to compare the two data center topologies with experimental methods. The results show that DCell has a better speed in data transmission. However, when viewed from the side of security and integrity of BCube delivery much better, this is because BCube is able to send data completely without any failure or inhibition in the delivery.
Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture

Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation.In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it.Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57% in terms of accuracy compared to a simple majority baseline.
Covid-19 Pandemic, Outbreak Educational Sector and Students Online Learning in Saudi Arabia

Measures to restrict the dissemination of the COVID-19 virus amongst the older and younger communities in many nations have led to the widespread closing of hospitals, schools, colleges and other educational establishments. According to the Saudi Ministry of Education, health authorities have been recommending preventive and precautionary steps to ensure full protection for students and staff. The ministry asked for the approval of virtual schools so that the learners will not mentally fall behind. The outbreak took a lot of hurdles like poverty, unemployment, social pressure and Education deficiencies.After the announcement of University closures, Saudi Arabia is one of the nation where students are worried and going through a phase of learning, where they have no tutor, a lot of course work and only themselves to help them study. No team work, juts digital libraries are making it even worse. Many learning management systems are introducing, but none of them can come even closer to the impact and quality physical classrooms can provide. As graduate students, all students and professors are situated inside the liminal room of just being. This ensures they will contribute to both their students' and their own scholarly duties at the duration of the COVID-19 pandemic and campus suspensions. They've seen learning consortia and coalitions taking shape over the last few weeks, with numerous actors-including states, retailers, educational experts, infrastructure companies, and mobile network operators-working forward to leverage new channels as a potential response to the problem.
Achieving Fast Convergence and High Efficiency using Differential Explicit Feedback in Data Center

Since most flows are short-lived in data center networks, fast convergence becomes very important to help the short flows effectively utilize high bandwidth. Though current feedback-based transport control protocols (TCPs) provide fast convergence via fine-grained explicit congestion information from customized switches, they unavoidably incur large traffic overhead for widely existing small packets in data center applications, resulting in suboptimal network efficiency. To solve this issue, we propose a datacenter TCP based on differential feedbacks, called DECN, to achieve fast convergence without any traffic overhead. Specifically, DECN feeds rate difference between the target and current rate back to the source by using multiple consecutive packets. The experimental results of NS2 simulation and testbed implementation show that DECN achieves comparable fast convergence as XCP without incurring any extra feedback overhead. Compared with the state-of-the-art feedback-based TCPs, DECN reduces the flow completion time by up to 34.1% in typical data center applications.
Contextual Adaptative Interfaces for Industry 4.0

Information technologies are intrinsically connected to the manufacturing processes, with more data generated each second. To efficiently operate machines, users must sort out information that is relevant to them in specific moments and contexts. In this paper, we propose an architecture that combines context – e.g. location, type of order, available assets, previous actions – with information established through user stereotypes.
Inferential Semantics as Argumentative Dialogues

This paper is at the same time a first step towards an “implementation” of the inferentialist view of meaning and a first proposal for a logical structure which describes an argumentation. According to inferentialism the meaning of a statement lies in its argumentative use, its justifications, its refutations and more generally its deductive relation to other statements. In this first step we design a simple notion of argumentative dialogue. Such dialogues can be either carried in purely logical terms or in natural language. Indeed, a sentence can be mapped to logical formulas representing the possible meanings of the sentence, as implemented with some categorial parsers. We then present our version of dialogical logic, which we recently proved complete for first order classical logic. Next we explain, through examples, how argumentative dialogues can be modeled within our version of dialogical logic. Finally, we discuss how this framework can be extended to real argumentative dialogues, in particular with a proper treatment of axioms.
Human Emotion Recognition from Spontaneous Thermal Image Sequence Using GPU Accelerated Emotion Landmark Localization and Parallel Deep Emotion Net

In recent years, the interest of researchers in facial emotion recognition from thermal infrared images has seen as increasing graph. Despite active research activities in this domain, existing algorithms for thermal image processing lacks robustness and accuracy. The proposed work presents an approach for facial emotion recognition based on long wave infrared imaging. Unlike all the present research works, our proposed methodology operates on a continuous sequence of emotional images instead of a collection of single images. Here, we have manually annotated the NVIE dataset images in 73 emotional landmark points. At first, detection of a human face from the input image, using the algorithm defined in our previous work, is done. Detected face is then auto-annotated using a trained DenseNet model. ROI are extracted, and GLCM algorithm is used to create a matrix containing features extracted from a given sequence. A parallel deep emotion net has been proposed which provides the probability values that the given sequence belongs to a particular emotion. This method of training the model to identify the pattern within a sequence is more reliable and also follows the idea that human emotions are complex in nature. MPI model along with GPU processing has been used to achieve parallelism at different levels in this proposed work.
Crash reproduction using helper objectives

Evolutionary-based crash reproduction techniques aid developers in their debugging practices by generating a test case that reproduces a crash given its stack trace. In these techniques, the search process is typically guided by a single search objective called Crash Distance. Previous studies have shown that current approaches could only reproduce a limited number of crashes due to a lack of diversity in the population during the search. In this study, we address this issue by applying Multi-Objectivization using Helper-Objectives (MO-HO) on crash reproduction. In particular, we add two helper-objectives to the Crash Distance to improve the diversity of the generated test cases and consequently enhance the guidance of the search process. We assessed MO-HO against the single-objective crash reproduction. Our results show that MO-HO can reproduce two additional crashes that were not previously reproducible by the single-objective approach.
Learning a Planning Domain Model From Natural Language Process Manuals

Artificial intelligence planning techniques have been widely used in many applications. A big challenge is to automate a planning model, especially for planning applications based on natural language (NL) input. This requires the analysis and understanding of NL text and a general learning technique does not exist in real-world applications. In this article, we investigate an intelligent planning technique for natural disaster management, e.g. typhoon contingency plan generation, through natural language process manuals. A planning model is to optimise management operations when a disaster occurs in a short time. Instead of manually building the planning model, we aim to automate the planning model generation by extracting disaster management-related content through NL processing (NLP) techniques. The learning input comes from the published documents that describe the operational process of preventing potential loss in the typhoon management. We adopt a classical planning model, namely planning domain definition language (PDDL), in the typhoon contingency plan generation. We propose a novel framework of FPTCP, which stands for a Framework of Planning Typhoon Contingency Plan, for learning a domain model of PDDL from NL text. We adapt NLP techniques to construct a ternary template of sentences of NL inputs from which actions and their objects are extracted to build a domain model. We also develop a comprehensive suite of user interaction components and facilitate the involvement of users in order to improve the learned domain models. The user interaction is to remove semantic duplicates of NL objects such that the users can select model-generated actions and predicates to better fit the PDDL domain model. We detail the implementation steps of the proposed FPTCP and evaluate its performance on real-world typhoon datasets. In addition, we compare FPTCP with two state-of-the-art approaches in applications of narrative generation, and discuss its capability and limitations.
ODE-Inspired Analysis for the Biological Version of Oja’s Rule in Solving Streaming PCA

Oja's rule [Oja, Journal of mathematical biology 1982] is a well-known biologically-plausible algorithm using a Hebbian-type synaptic update rule to solve streaming principal component analysis (PCA). Computational neuroscientists have known that this biological version of Oja's rule converges to the top eigenvector of the covariance matrix of the input in the limit. However, prior to this work, it was open to prove any convergence rate guarantee.\n\nIn this work, we give the first convergence rate analysis for the biological version of Oja's rule in solving streaming PCA. Moreover, our convergence rate matches the information theoretical lower bound up to logarithmic factors and outperforms the state-of-the-art upper bound for streaming PCA. Furthermore, we develop a novel framework inspired by ordinary differential equations (ODE) to analyze general stochastic dynamics. The framework abandons the traditional step-by-step analysis and instead analyzes a stochastic dynamic in one-shot by giving a closed-form solution to the entire dynamic. The one-shot framework allows us to apply stopping time and martingale techniques to have a flexible and precise control on the dynamic. We believe that this general framework is powerful and should lead to effective yet simple analysis for a large class of problems with stochastic dynamics.
Work-in-Progress—A Web-based Virtual Reality Training Simulator for Field Service Telecommunications Engineers

The increasing complexity of field service operations in different industries, presents companies with the challenge of maintaining and improving their workforce expertise. Employees need to be efficiently trained in different key specialized equipment and procedures. To do so, companies need to consider challenges such as access to equipment (or physical mock-ups) and the challenging logistics of getting people to them, among other issues. In this paper, we present a work-in-progress web-based VR simulator that aims to enhance and facilitate the hands-on training process for field service telecommunication engineers. We present an implementation of the simulator and report preliminary results of user evaluation.