Reviewerly/exHarmony huggingface dataset:
Characterizing Wealth Inequality in Cryptocurrencies

Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy. In this paper, we explore the free market-based economy of eight major cryptocurrencies: Bitcoin, Ethereum, Bitcoin Cash, Dash, Litecoin, ZCash, Dogecoin, and Ethereum Classic. We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration. We also draw a parallel between the crypto economies and real-world economies. To adequately address these two points, we devise a generic econometric analysis schema for cryptocurrencies. Through this schema, we report on two primary econometric measures: Gini value and Nakamoto Index which report on wealth inequality and 51% wealth concentration respectively. Our analysis reports that, despite the heavy emphasis on decentralization in cryptocurrencies, the wealth distribution remains in-line with the real-world economies, with the exception of Dash. We also report that 3 of the observed cryptocurrencies (Dogecoin, ZCash, and Ethereum Classic) violate the honest majority assumption with less than 100 participants controlling over 51% wealth in the ecosystem, potentially indicating a security threat. This suggests that the free-market fundamentalism doctrine may be inadequate in countering wealth inequality within a crypto-economic context: Algorithmically driven free-market implementation of these cryptocurrencies may eventually lead to wealth inequality similar to those observed in real-world economies.
Cognitive Electronic Jamming Decision-Making Method Based on Improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Q</mi> </math>-Learning Algorithm

In this paper, a cognitive electronic jamming decision-making method based on improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>Q</mi> </math> -learning is proposed to improve the efficiency of radar jamming decision-making. First, the method adopts the simulated annealing (SA) algorithm’s Metropolis criterion to enhance the exploration strategy, balancing the contradictory relationship between exploration and utilization in the algorithm to avoid falling into local optima. At the same time, the idea of stochastic gradient descent with warm restarts (SGDR) is introduced to improve the learning rate of the algorithm, which reduces the oscillation and improves convergence speed at the later stage of the algorithm iteration. Then, a cognitive electronic jamming decision-making model is constructed, and the improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>Q</mi> </math> -learning algorithm’s specific steps are given. The simulation experiment takes a multifunctional radar as an example to analyze the influence of exploration strategy and learning rate on decision-making performance. The results reveal that compared with the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>Q</mi> </math> -learning algorithm, the improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>Q</mi> </math> -learning algorithm proposed in this paper can fully explore and efficiently utilize and converge the results to a better solution at a faster speed. The number of iterations can be reduced to more than 50%, which proves the feasibility and effectiveness of the method applied to cognitive electronic jamming decision-making.
Distance Compression in the HTC Vive Pro: A Quick Revisitation of Resolution

Spatial perception in immersive virtual environments, particularly regarding distance perception, is a well-studied topic in virtual reality literature. Distance compression, or the underestimation of distances, is and has been historically prevalent in all virtual reality systems. The problem of distance compression still remains open, but recent advancements have shown that as systems have developed, the level of distance compression has decreased. Here, we add evidence to this trend by beginning the assessment of distance compression in the HTC Vive Pro. To our knowledge, there are no archival results that report any findings about distance compression in this system. Using a familiar paradigm for studying distance compression in virtual reality hardware, we asked users to blind walk to a target object placed in a virtual environment and assessed their judgments based on those distances. We find that distance compression in the HTC Vive Pro mirrors that of the HTC Vive. Our results are not particularly surprising, considering the nature of the differences between the two systems, but they lend credence to the finding that resolution does not affect distance compression. More extensive study should be performed to reinforce these results.
Strength and Stability

In this paper, I present two presumed alternative definitions of metavalidity for metainferences: Local and Global. I defend the latter, first, by arguing that it is not too weak with respect to metainference-cases, and that local metavalidity is in fact too strong with respect to types. Second, I show that although regarding metainference-schemas Local metavalidity is always stable, Global metavalidity is also stable when the language satisfies reasonable expressibility criteria (and that in fact, both concepts collapse in those cases).
A Bag of Tricks for Dialogue Summarization

Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization.In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding.Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data.Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.
Multi-Modal Open-Domain Dialogue

Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020;Roller et al., 2020).However, if we want to build agents with human-like abilities, we must expand beyond handling just text.A particularly important topic is the ability to see images and communicate about what is perceived.With the goal of getting humans to engage in multi-modal dialogue, we investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models.We study incorporating different image fusion schemes and domain-adaptive pre-training and fine-tuning strategies, and show that our best resulting model outperforms strong existing models in multi-modal dialogue while simultaneously performing as well as its predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based conversation.We additionally investigate and incorporate safety components in our final model, and show that such efforts do not diminish model performance with respect to human preference.
The determinism of the information society and the educational system: Enhancing academic attainments within distance learning

Introduction. The article discusses ambiguous attitudes of information society citizens to the use of digital technologies in education. The aim of the research is to reveal and describe the deterministic nature of society and education, to explain the reasons for the ambiguous attitudes towards the use of digital technologies in education, and to analyze and summarize their advantages and capabilities to improve academic outcomes in the context of distance learning. Materials and Methods. By adopting personality-centered, activity-based and environmental approaches, the study considers the individual as a subject of learning and teaching, who can freely choose modes and means of learning, as well as communicative strategies within learning environments. Research methods include analysis of scholarly literature, synthesis of findings, and generalization. Empirical data were collected via a questionnaire and analyzed using statistical data processing methods. The sample consisted of students and teachers. Results. The literature review has been used to characterize and describe the information society in order to reveal its contradictory nature and multi-vector impact on the individual in the educational environment. Changes in the psycho-biological mechanisms of learners belonging to Generation Z are outlined and summarized. The authors emphasize the need for adjusting teaching methods to the peculiarities of modern learners. The study indicates that learners’ attitudes towards digital technologies are determined by their ICT competence. The recommendations on the improvement of instruction in a virtual learning environment are presented. Conclusions. The information society has predetermined the inevitability of the introduction and use of information technologies in the education, which , on the one hand, increases learners’ educational and research capabilities, and, on the other hand, requires a high level of ICT competence as well as major expenditures on such changes. The identified contradictions have led to restrained attitudes to distance (online) learning.
Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network

The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology.The term "forms of communication" refers to the actions used by hearingimpaired people to communicate.These actions are difficult for ordinary people to comprehend.The recognition of Arabic Sign Language (ArSL) has become a difficult study subject due to variations in Arabic Sign Language (ArSL) from one territory to another and then within states.The Convolution Neural Network has been encapsulated in the proposed system which is based on the machine learning technique.For the recognition of the Arabic Sign Language, the wearable sensor is utilized.This approach has been used a different system that could suit all Arabic gestures.This could be used by the impaired people of the local Arabic community.The research method has been used with reasonable and moderate accuracy.A deep Convolutional network is initially developed for feature extraction from the data gathered by the sensing devices.These sensors can reliably recognize the Arabic sign language's 30 hand sign letters.The hand movements in the dataset were captured using DG5-V hand gloves with wearable sensors.For categorization purposes, the CNN technique is used.The suggested system takes Arabic sign language hand gestures as input and outputs vocalized speech as output.The results were recognized by 90% of the people.
Protocol for generation of high-dimensional entanglement from an array of non-interacting photon emitters

Abstract Encoding high-dimensional quantum information into single photons can provide a variety of benefits for quantum technologies, such as improved noise resilience. However, the efficient generation of on-demand, high-dimensional entanglement was thought to be out of reach for current and near-future photonic quantum technologies. We present a protocol for the near-deterministic generation of N -photon, d -dimensional photonic Greenberger–Horne–Zeilinger (GHZ) states using an array of d non-interacting single-photon emitters. We analyse the impact on performance of common sources of error for quantum emitters, such as photon spectral distinguishability and temporal mismatch, and find they are readily correctable with time-resolved detection to yield high fidelity GHZ states of multiple qudits. When applied to a quantum key distribution scenario, our protocol exhibits improved loss tolerance and key rates when increasing the dimensionality beyond binary encodings.
Lightweight and Anonymous Mutual Authentication Protocol for Edge IoT Nodes with Physical Unclonable Function

Internet of Things (IoT) has been widely used in many fields, bringing great convenience to people’s traditional work and life. IoT generates tremendous amounts of data at the edge of network. However, the security of data transmission is facing severe challenges. In particular, edge IoT nodes cannot run complex encryption operations due to their limited computing and storage resources. Therefore, edge IoT nodes are more susceptible to various security attacks. To this end, a lightweight mutual authentication and key agreement protocol is proposed to achieve the security of IoT nodes’ communication. The protocol uses the reverse fuzzy extractor to acclimatize to the noisy environment and introduces the supplementary subprotocol to enhance resistance to the desynchronization attack. It uses only lightweight cryptographic operations, such as hash function, XORs, and PUF. It only stores one pseudo-identity. The protocol is proven to be secure by rigid security analysis based on improved BAN logic. Performance analysis shows the proposed protocol has more comprehensive functions and incurs lower computation and communication cost when compared with similar protocols.
Contributions to the design of mobile applications for visitors of Botanical Gardens

Botanical Gardens are among the most visited touristic attractions, offering scientific, educational, cultural and leisure activities to preserve and enhance heritage and disseminating specialised knowledge on science and history. There are several mobile applications (apps) to support visitors of Botanical Gardens, which explore augmented reality technologies to enrich their experience. Our work aims to systematize a set of requirements that must be considered in the development of these apps. We have applied them in the development of an app for a Botanical Garden available for Android and iOS. Preliminary data analysis of the use of our app revealed some characteristics of the visitors and the preferred tours. Meanwhile we are evaluating our app by conducting a user study.
Can Character-based Language Models Improve Downstream Task Performances In Low-Resource And Noisy Language Scenarios?

Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen highresource languages.Building language models and, more generally, NLP systems for nonstandardized and low-resource languages remains a challenging task.In this work, we focus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi, found mostly on social media and messaging communication.In this low-resource scenario with data displaying a high level of variability, we compare the downstream performance of a character-based language model on part-of-speech tagging and dependency parsing to that of monolingual and multilingual models.We show that a characterbased model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank of this language leads to performance close to those obtained with the same architecture pretrained on large multilingual and monolingual models.Confirming these results a on much larger data set of noisy French user-generated content, we argue that such character-based language models can be an asset for NLP in low-resource and high language variability settings.
Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on ‘Raspberry Pi 3’ and ‘STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%.
Statistical Analysis of Cybersecurity Awareness Issues in Higher Education Institutes

Now-a-days, computers and the Internet are becoming increasingly indispensable tools in several aspects of our lives, including academic study, professional work, entertainment, and communication.Despite the significant advantages of information technology, particularly in information accessibility and internet applications, cyber security has risen to become a national concern in Saudi Arabia, and cyber security threats now need to be taken more seriously.Therefore, computer and network security are a concern not only for traditional security awareness organisations, for example, military, bank, or financial institutions, but also for every individual and government official who use computers.Besides, nowadays, more and more organisations' valuable assets are stored in the computerised information system; security has become an essential and urgent issue.However, it is remarkable that most systems today are designed with little attention to security concerns.This study aims to examine and analyse cyber security issues, including cyber risk, cyber security, cyber security awareness, and cyber trust, among higher education students in Saudi Arabia.Based on an analysis of the collected data using SPSS, the findings of this study highlight a lack of awareness of basic information related to cyber security among Saudi students.In addition, the number of students attending training programs was very low.Considering other security issues, this study reveals that while Saudi students are aware of cyber risk, they are not aware of cyber security.In addition, Saudi students are not aware of and do not have cyber trust.
Smart Face Detection and Recognition in Low Resolution Images Using Alexnet CNN Compare Accuracy with SVM

Aim: Machine learning algorithm plays a vital role in various biometric applications due to its admirable result in detection, recognition and classification. The main objective of this work is to perform comparative analysis on two different machine learning algorithms to recognize the person from low resolution images with high accuracy. Materials &amp; Methods: AlexNet Convolutional Neural Network (ACNN) and Support Vector Machine (SVM) classifiers are implemented to recognize the face in a low resolution image dataset with 20 samples each. Results: Simulation result shows that ACNN achieves a significant recognition rate with 98% accuracy over SVM (89%). Attained significant accuracy ratio (p=0.002) in SPSS statistical analysis as well. Conclusion: For the considered low resolution images ACNN classifier provides better accuracy than SVM Classifier.
Generic Constructions of (Boolean and Vectorial) Bent Functions and Their Consequences

This article is devoted to Boolean and vectorial bent functions and their duals. Our ultimate objective is to increase such functions’ corpus by designing new ones covering many previous bent functions’ constructions. To this end, we provide several new infinite families of bent functions, including idempotent bent functions of any algebraic degree, bent functions in univariate trace form, and self-dual bent functions. Those bent functions are of great theoretical and practical interest because of their special structures and relationship with self-dual codes. In particular, many well-known bent functions are special cases of our bent functions. Moreover, we extend our results to vectorial bent functions and obtain three new infinite classes of vectorial bent functions of any possible degree by determining the explicit duals of three classes of well-known bent functions.
Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications

Warning: this paper contains content that may be offensive or upsetting.Workplace communication (e.g.email, chat, etc.) is a central part of enterprise productivity.Healthy conversations are crucial for creating an inclusive environment and maintaining harmony in an organization.Toxic communications at workplace can negatively impact overall job satisfaction and are often subtle, hidden or demonstrate human biases.The linguistic subtlety of mild yet hurtful conversations has made it difficult for researchers to quantify and extract toxic conversations automatically.While offensive language or hate speech has been extensively studied in social communities, there has been little work studying toxic workplace communications.Specifically, the lack of corpus, sparsity of toxicity in enterprise emails and a well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale.We take the first step towards studying toxicity in workplace communications by providing (1) a general and computationally viable taxonomy to study toxic language at workplace (2) a dataset to study toxic language at workplace based on the taxonomy and (3) analysis on why offensive language and hate-speech datasets are not suitable to detect workplace toxicity.Our implementation, analysis and data will be available at https://aka.ms/ToxiScope.
Development of Comic Mathematics Teaching Materials on Flat-Building Material to Increase Reading Interest in Class IV Elementary School Students

The problem of the student’s low reading interest is the variety of learning media in delivering material. The lack of reading interest that students have about books provided by schools is less attractive. Research aims to develop the raw materials of comics in flat waking materials to increase interest in reading elementary school students. Research and development carried out are four steps, namely: definition, design, development, and propagation. The analysis process collects qualitative and quantitative data from materials experts, design experts, the specific educators in math subjects, and class iv student participants using the likert scale. Based on the validity of expert media and material experts obtained from a first validity test with an 83.3% rating categorized perfectly worthy of use, a second validity test with an average of 90% is categorized perfectly worthy of use and an 80% is categorized asa learning medium. The development of comics for mathematical materials in flat waking materials to increase interest in reading the fourth graders is worth using and can serve asa guide in learning.