Reviewerly/exHarmony huggingface dataset:
A systematic review and mapping of the literature of virtual reality studies in earth science engineering education

Today, earth sciences engineering education programs face with serious problems. It is often not possible to provide experience on different geographical environments or materials. Considering these problems of earth science related engineering education programs, virtual reality environment potentially can provide technology enhanced training alternatives. However, in the literature there are not many studies showing how virtual reality can enhance the earth science related engineering education programs. Accordingly, the aim of this mapping of the literature and systematic review study is to explore studies dealing with virtual reality in the earth sciences engineering education programs. Through a rigorous screening process, the first search in the Scopus database provided 150 studies, where 19 of them were selected in the course of this study and the mapping of the literature report was drawn with all these articles. Additionally, with seven of these articles a systematic review is conducted. The mapping of the literature report shows that studies on this topic have increased in recent years and have been published in a variety of journals. The number of China and USA origins studies are remarkable among the examined studies, which are predominantly in the fields of mining engineering and geological engineering. As a result of the in-depth analyses, the systematic review report indicates that virtual reality applications in the earth sciences engineering education positively contribute to the learning outcomes and experiences of students/trainees from different perspectives.
Pairing-Free Identity-Based Proxy Signature Scheme With Message Recovery

In many real-world situations, signature schemes with message recovery plays a very important role to minimize the bandwidth for efficient communications. A proxy signature scheme is a kind of digital signature scheme that allows an original signer to designate his/her signing capacity to a proxy signer. The proxy signer generates a signature on a message on behalf of the original signer. Such signatures are very useful in various applications where the delegation rights is quite common, especially in distributed systems and grid computing. This paper presents a pairing-free proxy signature with message recovery scheme that integrates the advantages of proxy signatures and message recovery in identity based setting. This scheme improves the computational and communicational efficiency due to pairing-free and message recovery features. The proposed scheme is proven secure in the random oracle model under the hardness assumption of the ECDLP. The comparison results shows that the proposed scheme is superior to the related works from the aspect of security and performance.
From Pandemic Control to Data-Driven Governance: The Case of China’s Health Code

Current debates over digital contact tracing mainly focus on the tools and experiences in the West. China’s health code, while often seen as one of the earliest and most widely adopted apps since the outbreak of COVID-19, has not been studied specifically. This paper provides a detailed analysis about the health code, draws comparison with the contact tracing apps developed by Google and Apple, and seeks to understand the specifications and contradictions internal to the health code’s development and deployment in China. Looking at both technical features and the mode and process of its adoption, the paper argues that the health code is strictly speaking not a contact tracing tool, but a technology of population control which is integrated in traditional forms of control and facilitates the enhancement of such control. As a technology of ruling the population, rather than the virus as such, the health code also reveals crucial problems in the modernization and informatization of the state governance and public administration. A critique on the health code solely informed by privacy and personal data protection runs the risk of being co-opted by the government and technology companies deploying such tools to expand their surveillance and regulatory power.
A new method to build an associative memory model

An associative memory is an artificial neural network model designed to store and recall input-output patterns pairs by association. A new method for obtaining two associative memories +M and -M is presented in this article, which uses a framework focused on two new binary operations boxplus and boxminus, and a unary operation called projection. The conditions to obtaining perfect recovery and the noise boundaries that the memories can tolerate are studied. Memories +M and -M are robust to additive and subtractive noise, respectively, both types converge in one-step and operate in heteroassociative and autoassociative modes. The performance of the proposed memories is tested against other memory models under identical conditions using the gamma binary distance for measuring similarity between binary patterns. The computer simulation results based on the average of 500 trials with the binary set of 26 lowercase letters of 7x7 pixel size, showed that the proposed memories in autoassociative mode exhibited a gamma binary distance of above .8, .75 and .7 by distorting up to 10 pixels by additive, subtractive, and mixed noise, respectively, which implied that the recovered images had a high similarity. In absence of noise the performance was excellent, i.e., the 100% of the recovered images were identical.
A Lattice-Based Fully Dynamic Group Signature Scheme Without NIZK

Group signature allows members in a group to sign messages anonymously on behalf of the group. In this paper, we propose the first lattice-based fully dynamic group signature scheme without NIZK based on the work in [Katsumata and Yamada, EUROCRYPT2019]. In order to realize our idea, we present a new indexed ABS scheme by using the Bonsai tree structure [Cash et al., EUROCRYPT2010]. Our fully dynamic group signature scheme satisfies CCA-selfless anonymity, traceability, weak non-frameability, and tracing soundness under the LWE and SIS assumptions. The size of keys and signature grow linearly in the upper bound of the group size in the system.
Parallelization of the k-means Algorithm in a Spectral Clustering Chain on CPU-GPU Platforms

k-means is a standard algorithm for clustering data. It constitutes generally the final step in a more complex chain of high quality spectral clustering. However this chain suffers from lack of scalability when addressing large datasets. This can be overcome by applying also the k-means algorithm as a pre-processing task to reduce the input data instances. We describe parallel optimization techniques for the k-means algorithm on CPU and GPU. Experimental results on synthetic dataset illustrate the numerical accuracy and performance of our implementations.
Detection and Classification of Legitimate and Spam Emails using K-Nearest Neighbor Augmented with Quadratic Sieve Algorithm

Spam in emails is a major challenge that is inherent in today's internet as it endangers financial institutions and poses a threat to individual users.Various techniques have been proposed by different studies to prevent spam in emails; however, classification and filtering technique using machine intelligence methods are the most efficient among the several methods.This study employed a K-Nearest Neighbor (KNN) augmented with the Quadratic Sieve algorithm to detect and classify legitimate emails and spam.The sieve algorithm revealed all the prime numbers for all the dataset used, starting from the input dataset to reduce the errors that may cause an imbalance in the classification.The result from this study shows that implementation of KNN augmented with Quadratic Sieve algorithm detects and properly classifies legitimate e-mail as well as spam much better.
Deep associative learning for neural networks

Neural learning plays an important role in many applications. In this paper, we derive a new learning paradigm for neural networks. Most existing neural models train network parameters including connecting weights and biases via optimizing a loss or energy function. Inspired from the associative learning in brain, we propose to associate different patterns via modeling joint distribution of them based on a hierarchical architecture. We first define an energy function based on the distance between hierarchical features of different patterns. Then a Gibbs typological distribution is constructed according to the energy field. To optimize the model, it is necessary to estimate the gradient expectation via sampling. Different from the simple architecture of existing probabilistic neural models such as restricted Boltzmann machine, the difficulty of optimizing this model lies in uncomputable probability for sampling from the distribution. Then we propose an optimization based sampling method. After learning, conditional probability can be derived and the unknown pattern can be generated via sampling as well. Compared with existing neural learning models, the proposed deep associative learning can associate different patterns and can be directly applied to many learning problems. Experiments on problems of classification, image transformation, and image change detection verify the effectiveness of the proposed learning paradigm.
Mixed Hill Cipher methods with triple pass protocol methods

Hill Cipher is a reimbursement coding system that converts specific textual content codes into numbers and does no longer exchange the location of fixed symbols. The symbol modifications simplest in step with the English letter table inclusive of (26) characters handiest. An encoded Hill Cipher algorithm was used that multiplication the square matrix of the apparent text with a non-public key and then combined it with the Triple Pass Protocol method used to repeat the encryption three times without relying on a personal key. Also, you could decode the code and go back it to the express textual content. The cause of mixing algorithms is to cozy the message without any key change among the sender and the recipient.
Asset Market Equilibria in Cryptocurrency Markets: Evidence from a Study of Privacy and Non-Privacy Coins

This paper explores whether asset market equilibria in cryptocurrency markets exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users’ demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each sub-market in the 2016–2018 period, we find that privacy coins and non-privacy coins exhibit two distinct market equilibria. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the asset market equilibrium of privacy coins appears to be unrelated to the market equilibrium in the non-privacy coin markets, implying that the privacy coin market is emerging as a distinct asset market among cryptocurrency markets.
Aadhaar Identity System using Blockchain Technology

Aadhaar is a 12 digit unique identification number, provided to each citizen of India.This includes the biometric data and personal information such as full names, addresses and birthdates.As per the government rule nowadays Aadhaaris linked to the Bank account, PAN Card, Voter ID Card, LPG Connection Card, Ration Card, Mobile number of the citizen of India.In order to ensure the security of Aadhaar, Blockchain technology has the potential to overcome the security and privacy challenges in Aadhaar.Blockchain is a decentralized, cryptographically signed digital ledger where transactions are framed into blocks.The type of information stored in the transactions can be varied (e.g.currency, intellectual property, identity data, location data, etc).Every transaction (and hence block) has a timestamp associated with when it was recorded to the blockchain.Subsequent blocks require the identifier (or hash) of the preceding block, and this is often the link that chains all the blocks together.The distributed consensus nature of the blockchain would prevent malicious attacks, until 51% of the nodes would be compromised.The proposed system AIMS (Aadhaar Identity Management System) provides self sovereign identity to the people through which one can create and control their verifiable credentials without any centralized authority and gives control over how their personal data is shared and used.This article also provides the comparative analysis of consensus algorithm which determines the performance and security of the blockchain system.
Deep Cognitive Gate: Resembling Human Cognition for Saliency Detection

Saliency detection by human refers to the ability to identify pertinent information using our perceptive and cognitive capabilities. While human perception is attracted by visual stimuli, our cognitive capability is derived from the inspiration of constructing concepts of reasoning. Saliency detection has gained intensive interest with the aim of resembling human 'perceptual' system. However, saliency related to human 'cognition', particularly the analysis of complex salient regions ('cogitating' process), is yet to be fully exploited. We propose to resemble human cognition, coupled with human perception, to improve saliency detection. We recognize saliency in three phases ('Seeing' - 'Perceiving' - 'Cogitating), mimicking human's perceptive and cognitive thinking of an image. In our method, 'Seeing' phase is related to human perception, and we formulate the 'Perceiving' and 'Cogitating' phases related to the human cognition systems via deep neural networks (DNNs) to construct a new module (Cognitive Gate) that enhances the DNN features for saliency detection. To the best of our knowledge, this is the first work that established DNNs to resemble human cognition for saliency detection. In our experiments, our approach outperformed 17 benchmarking DNN methods on six well-recognized datasets, demonstrating that resembling human cognition improves saliency detection.
Two language samples for maximizing linguistic variety

This paper presents two language samples for maximizing linguistic variety and explain the criteria followed to develop them.
Politics and Incivility in the Online Comments: What is Beyond the Norm-Violation Approach?

The article offers an empirical analysis of the disrespectful online comments—in total, 17,581,659—in Hungary between 2017 and 2019. Considering the name-calling and obscene and abusive phrases as communication practice, we rely on computational linguistics to investigate incivility in comments posted on online news stories and news portals’ social media sites across time, platforms, and topics. The results of the dictionary-based quantitative content analysis show that incivility’s frequency is steady in the examined three-year period. The topic and platform make the real difference: Politics attract more uncivil comments than business news, and less incivility is detected on Facebook than on the news sites. Contrary to popular perceptions, we find that incivility has not become significantly intensive in election campaign periods. Incivility, however, correlates to the dynamics of interaction. The comment-level analysis suggests that uncivil messages are likely to be surrounded by the same communication feature.
Comparison of Algorithms for Fuzzy Decision Tree Induction

The objective of the classification is to assign a class label to a data sample based on previous, learned experience. Despite the long tradition of classification algorithms research, there is not a unique technique that yields the best classification performance in all scenarios. Many real-world problems are uncertain. In this case, the crisp classification can be difficult to perform. The usage of fuzzy logic can be useful to describe real-world problems with higher accuracy. In this paper, we describe and compare algorithms of Fuzzy Decision Trees (FDTs), which are an extension of traditional decision trees. These algorithms are popular for their easy understandability and interpretability. But today there are many algorithms for FDT induction. These algorithms differ in many aspects. The most common difference is in information measures for selecting splitting attributes. Generally, the goal of decision tree induction is to create the smallest tree which is the most accurate as possible. In this paper, we use various information measures to induct FDTs and compare the accuracy and size of obtained FDTs. In comparison, information measures that are based on a generalization of the Shannon entropy and cumulative mutual information are included. The comparison shows that FDTs based on the cumulative mutual information acquired the best results. These results will also be included in a course on data mining that is taught at Faculty of Management Science and Informatics of University of Zilina.
Label Rectification Learning through Kernel Extreme Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.
Implementasi Kampus Mengajar Perintis (KMP) sebagai Cikal Bakal Penggerak Pembelajaran Literasi dan Numerasi di Sekolah Dasar

The focus of this research was to determine the implementation of Pioneer Teaching Campus (PTC) 2020 activities as a forerunner to driving literacy and numeracy learning at elementary schools. The type of this research was qualitative research with a narrative approach. The stages in this narrative research included (1) identification of phenomena to be studied, (2) determining participants allowing researchers to study the phenomena, (3) gathering information (experiences) from participants, (4) retelling information (experiences) from participants, (5) collaborating with participants, and (6) writing narratives based on information (experiences) from participants. The results showed that the implementation of PTC 2020 was very helpful for teachers in target schools, especially in literacy and numeracy learning since it was common for many teachers to be reluctant to carry out literacy and numeracy learning in their classes, even though literacy and numeracy programs had been promoted for a long time. Many factors caused this condition, ranging from age that affected motivation, teachers' understanding of literacy and numeracy learning, to the limited ability of teachers to use technology.
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries

In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training with noise perturbations. Most existing studies assume a fixed perturbation level for all training examples, which however hardly holds in real tasks. In fact, excessive perturbations may destroy the discriminative content of an example, while deficient perturbations may fail to provide helpful information for improving the robustness. Motivated by this observation, we propose to adaptively adjust the perturbation levels for each example in the training process. Specifically, a novel active learning framework is proposed to allow the model interactively querying the correct perturbation level from human experts. By designing a cost-effective sampling strategy along with a new query type, the robustness can be significantly improved with a few queries. Both theoretical analysis and experimental studies validate the effectiveness of the proposed approach.
Approximating Probabilistic Models as Weighted Finite Automata

Abstract Weighted finite automata (WFAs) are often used to represent probabilistic models, such as ngram language models, because among other things, they are efficient for recognition tasks in time and space. The probabilistic source to be represented as a WFA, however, may come in many forms. Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a WFA such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized. The proposed algorithm involves a counting step and a difference of convex optimization step, both of which can be performed efficiently.We demonstrate the usefulness of our approach on various tasks, including distilling n-gram models from neural models, building compact language models, and building open-vocabulary character models. The algorithms used for these experiments are available in an open-source software library.
Analyzing and Modeling Critical Risks in Software Development Projects: A Study Based on RFMEA and Systems Dynamics

This article aims to propose a method of qualitative analysis and risk management in software development project environments, using a combination of RFMEA and System Dynamics. Therefore, it was possible to analyze the main risks in software development projects from the literature review. Based on those risks, a survey was applied with experts of one of the projects of the Brazilian Civil Aviation Agency (ANAC) to estimate the probability of occurrence, impact, and detection of each risk. Through RFMEA, it was possible to identify and prioritize the project's risks, and with Systems Dynamics, to evaluate the causal relationships among them. This is a case study and qualitative research carried out with the validation of ten risks in a total of 23, establishing the strategies and recommended actions for each one of them.
Manifold Learning for Real-World Event Understanding

Information coming from social media is vital to the understanding of the dynamics involved in multiple events such as terrorist attacks and natural disasters. With the spread and popularization of cameras and the means to share content through social networks, an event can be followed through many different lenses and vantage points. However, social media data present numerous challenges, and frequently it is necessary a great deal of data cleaning and filtering techniques to separate what is related to the depicted event from contents otherwise useless. In a previous effort of ours, we decomposed events into representative components aiming at describing vital details of an event to characterize its defining moments. However, the lack of minimal supervision to guide the combination of representative components somehow limited the performance of the method. In this paper, we extend upon our prior work and present a learning-from-data method for dynamically learning the contribution of different components for a more effective event representation. The method relies upon just a few training samples (few-shot learning), which can be easily provided by an investigator. The obtained results on real-world datasets show the effectiveness of the proposed ideas.