Reviewerly/exHarmony huggingface dataset:
Computer professionals without borders

In November 2005, a group of Italian computer professionals decided to give their knowledge for bridging the digital divide and founded ISF, "Informatici Senza Frontiere" ("IT without borders") [1]. It is a non-profit organization with the goal of providing help to people living in situations of poverty and marginalization. With the main office in Treviso (in North-East Italy) now is present in most of the Italian regions with hundreds of members, men and women. Their activities span from Italy to developing countries, with courses, digitalization of hospitals and social centers, collaborating with schools, universities and prisons, retirement homes for the elderly and applications for people with disabilities. In May 2013 the UN invited ISF to Geneva for the World Summit on Information Society, recognizing its role and what IT can do for society.
Autonomously Improving Systems in Industry: A Systematic Literature Review

A significant amount of research effort is put into studying machine learning (ML) and deep learning (DL) technologies. Real-world ML applications help companies to improve products and automate tasks such as classification, image recognition and automation. However, a traditional “fixed” approach where the system is frozen before deployment leads to a sub-optimal system performance. Systems autonomously experimenting with and improving their own behavior and performance could improve business outcomes but we need to know how this could actually work in practice. While there is some research on autonomously improving systems, the focus on the concepts and theoretical algorithms. However, less research is focused on empirical industry validation of the proposed theory. Empirical validations are usually done through simulations or by using synthetic or manually alteration of datasets. The contribution of this paper is twofold. First, we conduct a systematic literature review in which we focus on papers describing industrial deployments of autonomously improving systems and their real-world applications. Secondly, we identify open research questions and derive a model that classifies the level of autonomy based on our findings in the literature review.
Digital technologies

This chapter focuses on the use of digital technologies to facilitate and enhance teaching and the learning experience in teachers classroom. It aims to understand the relevance of digital technologies for teachers and their learners. Teachers are expected to integrate digital technologies into their teaching and promote online safety. Internationally, European Schoolnet, on behalf of the European Union, advocates digital citizenship as a right for all, and that digital skills enable people to exercise this right. Good subject teachers already make good use of digital technologies and are always looking out for new ways of using them to stimulate learners and extend their learning. The chapter explores several pedagogies that support the use of digital technologies in the classroom as well as several international and national digital competence frameworks. The Substitution Augmentation Modification Redefinition model, developed by Puentedura provides a method of seeing how digital technology might impact teaching and learning.
A global stablecoin: Revolutionary reserve asset or reinventing the wheel?

This article examines the geopolitics, legal nuances and monetary policy which inform Carney's proposal. Part I sets out the application of block-chain technology in the context of payment systems, and discusses the geopolitics of Libra and the shift in focus to Central Bank Digital Currencies ('CBDCs'). It suggests that state-backed cryptocurrencies are more likely than their private sector counterparts to fit the role of true digital currencies, at least on a domestic level.Part II explains the legal distinctions between the cryptocurrency models proposed by Facebook and others, and CBDCs. The former fulfils the main functions of money by granting a claim to the holder, whereas the latter does so as an 'object' of monetary policy. A Global Stablecoin ('GSC') such as the SHC does not necessarily fall into either camp, but its success as a global reserve currency may hinge upon the fine details of its legal construction. In Part III, one conception of the SHC, based on the 'claims model' is set out. Whilst the use of block-chain technology is undoubtedly novel, there are significant similarities between this version of the SHC and previously rejected reforms to the 'Special Drawing Rights' (SDR) allocated by the International Monetary Fund ('IMF'). It is argued that the extent of the similarities indicates that the claims model is likely to face the same hurdles which stymied the campaign to develop the SDRs into a global reserve currency.This begs the question, however, of why Carney would propose the SHC if the odds in monetary policy were already so firmly stacked against it. Part IV considers the 'objects model' as applied to a SHC. It tentatively concludes that the features of the objects model which distinguish it from the claims model greatly improve its prospects of success. Specifically, the generation of network effect value, as a substitute for the convertibility value of SDRs, may be the decisive contribution of block-chain technology to a truly international currency. The article concludes that a claims-based SHC would, to a great extent, be an attempt to reinvent SDRs in a digital age, and that the advantages of block-chain technology will be insufficient to surmount well-established US monetary policy. The objects model, whilst more ambitious in some respects, constitutes a radically novel proposal which may, for that reason amongst others, gain global traction.
Experimental Study on the Effect of Medium in Optical Path on Digital Image Correlation Measurement

Abstract To explore the effect of optical media on digital image correlation measurement, the experiments of digital image correlation measurement under different optical media conditions were carried out. By analyzing the speckle images non-special optical path medium, common ordinary glass medium and quartz glass medium in the optical path, the effect of medium change in the optical path on digital image correlation measurement is discussed. The results show that the effect of different optical media on the accuracy of digital image correlation measurement is different, and the effect of optical media on absolute displacement measurement is much greater than that of rigid body displacement measurement. The effect of optical medium on different measuring areas is also different. The effect of quartz glass state on strain measurement is greater than that of ordinary glass. The effect of optical medium on the measurement of vertical optical axis displacement is much less than that of optical axis displacement measurement. Whether ordinary glass or quartz glass is added, the measurement error of camera optical axis direction is much larger than that of vertical optical axis direction; the stability of DIC displacement measurement in different optical path media is in turn ordinary glass medium, quartz glass medium and non-special optical path medium.
Application Monitoring Using Libc Call Interception

Generally available application monitoring solutions comprise basic aggregated metrics. This paper collects such metrics in the form of raw data. We have worked out a very primitive piece written in Golang, which manifests results from HTTP/HTTPS calls from/to the system being monitored. This process can be easily extended and applied to various architectures that use the libc library to make low-level system calls to interact with sockets or, in a more basic sense, files. The experimentation and the proof of concept were carried out on various languages, viz. C, Java, Python, and Golang itself using Docker and some classic open-source compilers.
AWK and GNU Octave Programming Languages Integrated with Generic Mapping Tools for Geomorphological Analysis

The paper presents a sequential use of the AWK and GNU Octave programming languages integrated with Generic Mapping Tools (GMT) for geospatial data analysis. The geographic scope of the research is focused on the Kuril-Kamchatka Trench, north Pacific Ocean. Practical research aim is to analyse and compare bathymetry in the southern and northern part of the trench using digitized cross-section profiles. The initial mapping and geospatial analysis was performed in GMT scripting toolset. The GMT was used for cartographic mapping based on the raster ETOPO1 grid and automatic digitizing of the profiles crossing the trench perpendicularly. Besides visualized map, the processed geodata were received in a numerical form as a complex multi-field table for each segment. These tables were generated by the GMT in its native format and could not be directly processed by the MATLAB/Octave. Therefore, the tables were exported to AWK, a data-driven programming language and a powerful tool for data extraction. The table was then restructured, sorted and reshaped by the AWK script. Because the total amount of profiles overstepped 100 (62 and 52 for the northern and southern trench segments), only selected profiles were visualized. For this purpose, at the next step the modified tables were converted to GNU Octave language for visualizing and plotting selected profiles. Finally, the geomorphology was analysed and two segments compared. The results show that the southern part has deeper bathymetric values, vary in geomorphic structure and has steeper gradient slopes comparing to the north, which is caused by the seismicity, volcanism, geologic and tectonic settings. Three full scripts of GMT, AWK and GNU Octave programming languages are presented for replicability in the Appendix.
Discovering Localized Information for Heterogeneous Graph Node Representation Learning

Representation learning for heterogeneous graphs aims at learning meaningful node (or edge) representations to facilitate downstream tasks such as node classification, node clustering, and link prediction. While graph neural networks (GNNs) have recently proven to be effective in representation learning, one of the limitations is that most investigations focus on homogeneous graphs. Existing investigations on heterogeneous graphs often make direct use of meta-path type structures. Meta-path-based approaches often require a priori designation of meta-paths based on heuristic foreknowledge regarding the characteristics of heterogeneous graphs under investigation. In this paper, we propose a model without any a priori selection of meta-paths. We utilize locally-sampled (heterogeneous) context graphs “centered” at a target node in order to extract relevant representational information for that target node. To deal with the heterogeneity in the graph, given the different types of nodes, we use different linear transformations to map the features in different domains into a unified feature space. We use the classical Graph Convolution Network (GCN) model as a tool to aggregate node features and then aggregate the context graph feature vectors to produce the target node's feature representation. We evaluate our model on three real-world datasets. The results show that the proposed model has better performance when compared with four baseline models.
A Group-Based Optimized Practical Byzantine Fault Tolerance Consensus Algorithm

Focusing on the problems of high energy consumption, low efficiency and poor scalability of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm existed in consortium blockchain, this paper presents a group-based optimized Practical Byzantine Fault Tolerance (GPBFT) consensus algorithm, which is a multi-stage algorithm. First, we propose a comprehensive reputation evaluation model to judge the credibility of a node from two aspects of transaction behavior and consensus performance. Then, the nodes with higher reputation value will be selected to enter the consensus group, and the other nodes with block packaging ability are selected as candidate nodes. Finally, we optimize the PBFT based on the application scenario of blockchain, change the three stages to two, which reduces the number of messages delivered by 50% at least. The experimental results demonstrate that GPBFT gains better performance. While ensuring the security and reliability of the system, it shows good scalability, thus it can be used in large-scale consortium blockchain system.
Convergence of a Finite-Time Zhang Neural Network for Moore–Penrose Matrix Inversion

Moore-Penrose or pseudo inverse is examined for a time varying matrix. The Finite time model for Moore-Penrose left and right inverse is proposed and investigated. Infinite time for convergence in the original Zhang network is said to converge in finite time when the proposed Finite time Moore-Penrose inverse is employed. Nonlinear activation functions are tested to quicken the speed of the Finite time Zhang Neural Network. Theoretical and experimental results show that Finite time Zhang Neural network converges much faster than Zhang neural network. In addition the hyperbolic sine activation function applied to the proposed system converges faster than the linear activation function.
Determinants of corruption: can we put all countries in the same basket?

This paper aims to study the determinants of corruption by examining specificities relating to the region and the level of economic development. Starting from a cross-sectional study on 130 countries, we rely on the Bayesian Model Averaging (BMA) approach to address the issue of model uncertainty and identify the key determinants of corruption according to the level of development and the region. Our results highlight the need for specific remedies in the fight against corruption given the regional, sociocultural, economic and institutional specificities. Indeed, the key determinants of corruption in sub-Saharan Africa are not the most relevant in the East Asia and Pacific region. Similarly, the most important determinants in developed countries are not the most worrying in developing countries.
A Coverage-Based Effective Data Augmentation Framework

Data augmentation is one of the common methods to improve the performance of neural networks. People usually use text mutation and synonym replacement techniques for text generation in order to augment the original data set in the field of natural language processing. However, these generated texts often contain ineffective data which could significantly corrupt the performance of learning systems. To address this challenging problem, in this paper, we propose a coverage-based framework to identify effective data of the augmented data set using three novel XLNet structural test coverage criteria on story ending prediction task. Experimental results demonstrate that the proposed framework can improve the data quality of the augmented data set and make the model more reliable after retraining. Specifically, the effective data obtained by our method for data augmentation cause 0.03% to 33% prediction accuracy higher than the directly generated data used.
Anlık Sorgu Geri-Getirimi Test Veri Kümesi Hazırlamak İçin Bir Web Aracı

The main purpose of this study is to develop a web tool, which helps out on making relevant/nonrelevant judgement of documents used in the test datasets prepared for text retrieval.The test dataset for the adhoc retrieval contains a collection of documents, a certain number of queries, and relevant documents corresponding to these queries.Especially, the process of judging whether documents fit query targets or not is quite complex and time consuming.There is a need for an application that will perform the process of marking the documents relevant to the queries in a digital environment.Because a large number of documents need to be examined by users who are concerned with relevance.In this study, this process has become manageable by the tool developed on the basis of the method applied in the TREC (Text Retrieval Conference).
Artificial Dendrites: an Algorithm

This paper outlines preliminary work on an algorithm to create dendritic, tree-like structures by arranging spines in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> using directional forces. Each spine is attracted to its neighbor based on the temporal coincidence of afferent action potentials. The resulting spine location spatially encodes activation patterns. Proximity to the soma and to each other determines connectivity within an acyclic graph. Dendritic branches carry back-propagating action potentials used for credit assignment by Hebbian-style learning. Preliminary results suggest this method could be used to create spiking neural networks consisting of dendritic neurons capable of pattern recognition.
PENGGUNAAN USER PERSONA UNTUK ELISITASI KEBUTUHAN PERANGKAT LUNAK

Requirement elicitation is an important phase in the software development process. The elicitation stage needs to be the most crucial stage because it determines the final results of the software. One of the HCI techniques is commonly used to elistate software requirements is the persona technique. The focus of the previous research was the use of several methods in exploring the user persona, but in this study the focus is the application of the persona technique that developed by John W. Castro with an iterative process modification as a process of validation of the user persona as itself. From this research, the result is persona can be one of the software requirement elicitation options. The use of user persona in persona techniques clarifies the source of the problem and the expectations of the user. Modification of the iterative model on Alan Cooper and John W. Castro’s persona techniques in this study also allows validation.
A Framework to Data Integration for an Internet of Things Supporting Manufacturing Supply Chain Operation

With the usage of the technologies like the internet of things (IoT) and Semantic Web service spanning across the spectrum of manufacturing, production, and distribution, typical supply chain management (SCM) systems depend on a multitude of services for operation purpose. The unprecedented growth of valuable data produced by decentralised information systems along the global manufacturing supply chain has led to a persuasive appeal for a semantic approach to integrating distributed data facilities in the field of collaborating logistic services. This technology combines a set of new mechanisms with grounded knowledge representation techniques to address the needs of formal information modelling and reasoning for web-based services. This chapter describes a framework, apparel business decentralised data integration (ABDDI), which exploits knowledge representation techniques and languages (e.g., description logics – DLs) to annotate relevant business activities. Finally, a simple business case is presented to demonstrate the framework's semantic similarity assessment functionality.
Model for Data Access of Command Information System Based on Blockchain

In order to solve the problems in the data access process of command information system, such as the low matching degree between using authority and command task, the imperfect mechanism of data exception handling, and the unsynchronized data updating of the whole network, this paper proposes a model for data access of command information system based on blockchain. The model uses blockchain to manage and supervise the use process of command information, so as to realize the response of information access application on demand, real-time discovery and processing of data exceptions, and synchronous update of data throughout the network. Through analysis and evaluation, it is proved that the model can provide reliable, safe and consistent data access for commanders in complex and harsh command environment.
Comprehensive Analysis of State-of-the-Art Techniques for VQA

The visual question answering is an upcoming field in the domain of computer vision and NLP, i.e., natural language processing community. In this domain, a person will be giving any image and can answer certain questions using common sense about the given image, this combines various visual and linguistic processing using the understanding of the user by analyzing the image. This discussion concerned the various datasets that are used in these studies and using the various question and the answer format and which increases the robustness and efficiency of different machine learning models. Also, here, various methods are discussed, which are classified using the machine learning mechanism taking the help of popular textual and visual modalities. The latest convolutional and neural networks of recurring nature are used to map several questions and images in the feature space, which is very common in deep learning. Also, numerous question answers are examined from the diverse depth and the project of visual modalities to understand the relevance of the annotations of structured images and photographs using a visual question answering method in this study.
Design and Development of a Search and Rescue Robot with TriSTAR Locomotion Units

In this paper the authors present a search and rescue robot with hybrid locomotion system. The robot uses a locomotion system with two TriSTAR units and two conventional wheels. Details are also presented regarding the design of the robot and the TriSTAR locomotion unit, the robot locomotion and the robot actuation and control. In order to provide the possibility to manipulate small objects the robot will be equipped with a manipulator presented in detail.
Modification of Select Operation Model for Multilevel Security: Medical Database Systems as an Application

Information security (IS) is essential and important in different application fields. Medical databases (MD) and healthcare applications (HA) are not an exception; information security plays a prominent role in the protection of patient data by encryption. Securing the privacy of patients is an imperative challenge. MD and HA adopted encryption as a technique to secure the data, especially for patients with disabilities. Previous research works in the field of data security reported the application of a new select operation model in the encryption-based on multilevel security approach to multilevel database; these models enhanced the security level (SL) of databases due to the encryption based on multilevel security (MLS) but their performance is not as efficient in medical database management systems (DBMS) due to the huge size of records and several decryption operations needed. The original select model becomes very inefficient when the number of records (size of data) becomes large. In this paper, we modify the select operation model in multilevel database to enhance its performance, when the encrypted data is retrieved. Nevertheless, the obtained results in this paper show that the proposed modifications give better performance as compared with results obtained from the original model.
Few-Shot Learning with Unlabeled Outlier Exposure

Few-shot learning aims to train a classifier which can recognize a new class from a few examples like a human. Recently, some works have leveraged auxiliary information in few-shot learning, such as textual data, unlabeled visual data. But these data are positive data, they are close related to the training data. Different from such experimental settings, people can also get knowledge from negative data to better recognize a new class. Inspired by this, we exposure a few unlabeled outliers in each few-shot learning tasks to assist the learning of the classifier. To the best of our knowledge, we are the first ones who propose to utilize outliers to improve few-shot learning. We propose a novel method based on meta-learning paradigms to utilize unlabeled outliers. We not only utilize unlabeled outliers to optimize the meta-embedding network but also adaptively leverage them to enhance the class prototypes. Experiments show that our outlier exposure network can improve few-shot learning performance with a few unlabeled outliers exposure.