Reviewerly/exHarmony huggingface dataset:
Health Data Privacy During Pandemic: Benefiting from Heath Data Without Compromising Health Data Privacy

Health emergency during COVID-19 caused health authorities and authorized organisations to widely collect, store, transmit and use health data for research and public health purposes. Such use may limit the right to data privacy for the purpose of prevention of spread of decease and protection of public health. The limitation of data privacy, however, should have an appropriate balance. The European Union (EU) member countries are pioneers in implementing better data privacy protection. The EU member countries’ data privacy in general is enshrined in the European Convention on Human Rights (ECHR) (Council of Europe 1950), the Charter of Fundamental Rights of European Union (CFREU) and Data Protection Regulation 2018 (GDPR). The pandemic created an opportunity to look at the existing EU laws, regulations, and guidelines to understand the proportionate use of health data for public health in case of emergency. In Canada, privacy laws in general are non-harmonized for different categories of entities: private sector, public sector, and health sector entities. In the light of COVID-19, the regulators leaned towards creating exceptions in using, storing, and transmitting the health data for clinical and research purposes internally and internationally. Nonetheless, there is no clear guidance on how the usage is going to be balanced against competing interests. Saudi Arabia has provided protection for personal data in addition to limitation of such protection in case of national and international crisis as well. However, there is no provisions on how the limitation would be exercised and time limit of such limitation. Hence this research will analyse various laws and regulation in EU, Canada, and Saudi Arabia with a view to encapsulating the proposition of standard that could be used in enhancing public health by benefiting from heath data while ensuring acceptable protection of personal health data privacy.
Role and Function of Ethnology in Developing the Aesthetic Competence

The article is devoted to the experimental testing of aesthetic competence of future philologists by means of ethnology. The principles (national character, humanization, ethnicization, cultural conformity, integration) are considered and stages of model implementation (orientative, training, creative) are elaborated and characterized. Structural and functional components, criteria and indicators of the levels of forming the future philologists' aesthetic competence are defined. The characteristic of levels of aesthetic competence development in philology students (reproductive, constructive, creative) is given. The characteristics of the levels with the help of the coefficient of tasks completeness related to knowledge, skills and abilities for aesthetic competence development of the philology students by means of ethnology were determined. The paper concludes by calling attention to the enhancement of the educational process forms and methods in order to develop the aesthetic competence of future philologists by means of ethnology, urging to instill the aesthetic competences more purposefully and integrally throughout the Bachelor program.
On the Logical Strength of Confluence and Normalisation for Cyclic Proofs.

In this work we address the logical strength of confluence and normalisation for non-wellfounded typing derivations, in the tradition of cyclic proof theory. We present a circular version CT of Godel's system T, with the aim of comparing the relative expressivity of the theories CT and T. We approach this problem by formalising rewriting-theoretic results such as confluence and normalisation for the underlying coterm rewriting system of CT within fragments of second-order arithmetic.We establish confluence of CT within the theory RCA₀, a conservative extension of primitive recursive arithmetic and IΣ1. This allows us to recast type structures of hereditarily recursive operations as coterm models of T. We show that these also form models of CT, by formalising a totality argument for circular typing derivations within suitable fragments of second-order arithmetic. Relying on well-known proof mining results, we thus obtain an interpretation of CT into T; in fact, more precisely, we interpret level-n-CT into level-(n+1)-T, an optimum result in terms of abstraction complexity.A direct consequence of these model-theoretic results is weak normalisation for CT. As further results, we also show strong normalisation for CT and continuity of functionals computed by its type 2 coterms.
Evaluating Innovation Strategies in Online Education in Higher Education

The impact of rapid changes to online course content delivery via platforms such as Zoom and the new demands placed on learning management systems such as Canvas and Blackboard to maintain a semblance of order, were staggering for many educators. However; out of the rapidly changing higher education landscape, there were successes that emerged and we witnessed a burgeoning of online education communities for sharing what can be best described as DIY teaching survival strategies. This research describes a process for sharing success strategies between educators using a mixed method data collection survey and an online participatory group brainstorming protocol using Zoom, an online whiteboard, and an observation method to identify and evaluate teaching strategies in face to face, hybrid and online formats. This online education evaluation strategy, “Innovation in Teaching (I-Teach) Event,” will be discussed with regard to the tools and the techniques of documenting, comparing, and evaluating teaching strategies and technologies.
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena

In this paper we present our observations and evaluations by observing the linguistic performance of the system on several steps on the training process of various English-to-German Neural Machine Translation models.The linguistic performance is measured through a semi-automatic process using a test suite.Among several linguistic observations, we find that the translation quality of some linguistic categories decreased within the recorded iterations.Additionally, we notice some drops of the translation quality of certain categories when using a larger corpus.
Zero Knowledge Authentication Modification for Drone and Server Communication Security

Drones are now being used in various fields. One of the uses of drones is for delivery service. When delivering goods, the drone needs to report its condition while traveling to the server so that the server can monitor the drone and the server can give some commands to the drone. However, when the drone receives data from the server, there are many vulnerabilities it has against the attacker that can take control of the drone device. Taking control of the drone can occur if the attacker manages to send a script that can take over control of the drone. If the attacker can take control over the drone device, the attacker can steal the drone both physically or data of the drone. For this reason, in this study, we propose an authentication algorithm that can filter out who is allowed to send data to the system. The algorithm used is Zero-Knowledge Authentication. To improve the performance of the Zero-Knowledge Authentication algorithm in terms of authentication speed, we try to combine it with the secret key generated from the Secret Key Generation process. From the results of authentication testing, the effectiveness of the proposed algorithm after a Brute Force attack experiment is 100% for single attacker scenarios and for multiple attackers the effectiveness is 100% in LOS and NLOS conditions. Because drones have limited resources, the authentication time experiment is also performed and the result is that in all scenarios the time required to authenticate is in microseconds so that the proposed algorithm can be implemented on drones with limited resources.
Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos

Person re-identification in surveillance camera videos is attracting widespread interest due to its increasing number of applications.It is being applied in the field of security, healthcare, product manufacturing, product sales and more.Though there are a variety of methods to do person reidentification, face verification-based methods are very much effective.In this study, a deep learning framework to perform face verification in videos is proposed.Face verification deep learning model development includes different stages like face recognition, cropping, alignment, augmentation, image enhancement and face image selection for model training.The authors have put forward innovative methods to be adopted in various stages of this sequence to improve the performance of the models.The focus of this study is on these image preprocessing stages of the process, rather than the deep learning part, which makes the approach unique.The overall model is improvised by increasing the efficiency of each of these stages by adopting methods like face recognition and cropping based on face landmarks, effective training image selection using face landmark symmetry, various image augmentation techniques including perspective transformation and image enhancement methods like contrast stretching and histogram equalization.An average two percent increase is obtained in the accuracy of the face verification models by applying these methods.
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)

In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context.Our top system, based on a combination of lexical, syntactic, word embeddings and Transformers-derived features and on a Gradient Boosting Regressor, achieves a top correlation score of 0.754 on the subtask 1 for single words and 0.659 on the subtask 2 for multiword expressions.
INTRODUCING KEPLAIR - A PLATFORM FOR INDEPENDENT LEARNERS

1 University of Bari (ITALY)2 LO*OP Center, Inc. (UNITED STATES)3 Unfold Learning, LLC (UNITED STATES)4 University of Oxford (UNITED KINGDOM)
Deep spatio-temporal emotion analysis of geo-tagged tweets for predicting location based communal emotion during COVID-19 Lock-down

Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people’s socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%.
Secure And Fragile Watermarking Of Medical Images With Fingerprint Based Biometric Data Using Matlab

In the modern era, the Internet has turn out to be the primary hub for exchanging information among users. At the same time, data attacks and threats may override the safety of these digital information. Many techniques like watermarking and digital signature are employed to protect these types of information in the field of data processing. Watermarking strategies are carried out in various fields that allows you to guard the content material from unauthorized user. It also assures the user on fact integrity and authenticity, patent evidence and content ownership.Robust watermarking strategies intent to show the patent and the proprietor issues. These strategies have the cap potential to withstand against malware issues like image compression, noise, and change in dimension, etc. However, semi-fragile watermarking, which is also called as soft authenticate, tries to exhibit only the integrity and accuracy of thematerial. This method cannot withstand against the malware attacks.
Modelling and Analysis of Various Machine Learning Algorithms for Human Activity Recognition

The objective of this research paper is to analyse a dataset consist of, dataset consist of slog from accelerometer & gyroscope of a smartphone carried by diff men and women volunteers while doing activities using different machine learning analysed and compared in terms of precision & efficiency. We also try to see whether we can separate the individuals based on their walking styles and find any additional verdict Such insights can be utilized to implement real-time human asset monitoring in highly secure locations, track older citizens with movement disorders or illnesses for any issues based on movement patterns, and so on., decide whether an individual is fatigued or not, and so on. We discovered that our work has a recognition accuracy of over 94%.
The Impact of News Media on Cryptocurrency Prices: Modelling Data Driven Discourses in the Crypto-Economy

Natural Language Processing was adopted in this study to model data driven discourses in the crypto economy. Utilising topic modelling, specifically Latent Dirichlet Allocation (LDA), a text analysis of cryptocurrency articles (N=4218) published from over 60 countries in international news media, identified key topics associated with cryptocurrency in the international news media from 2018 to 2020. This study provides empirical evidence that 18 key topics were framed around the following key macro discourses: crypto related crime, financial speculation and investment, financial governance and regulation, political economy (with reference to specific geographical areas), cryptocurrency actors and communities and specific crypto projects and their respective markets. Analysis showed that the identified cryptocurrency macro discourses may have had a ‘social signal’ effect on movements in the crypto financial markets, including potential effects of crypto price volatility. Further in some cases, that the source of the news may have amplified the effect, particularly in terms of geographical region, relative to broader market conditions.
History of Image Digital Formats Using in Information Technology

An important type of digital media is images, and in this article, we are going to review how images are represented and how they can be manipulated with simple mathematics. This is useful general knowledge for anyone who has a digital camera and a computer, but for many scientists, it is an essential tool. Printed publications use different formats to specify the properties of the information published; Thus, the uses that each type of each publication have been defined. Also, the formats of the digital images have been developed for use with a certain type of images and this article presents the benefits and uses of the different digital image formats.
VUS at IWSLT 2021: A Finetuned Pipeline for Offline Speech Translation

In this technical report, we describe the fine-tuned 1 ASR-MT pipeline used for the IWSLT shared task.We remove less useful speech samples by checking WER with an ASR model, and further train a wav2vec and Transformers-based ASR module based on the filtered data.In addition, we cleanse the errata that can interfere with the machine translation process and use it for Transformer-based MT module training.Finally, in the actual inference phase, we use a sentence boundary detection model trained with constrained data to properly merge fragment ASR outputs into full sentences.The merged sentences are postprocessed using part of speech.The final result is yielded by the trained MT module.The performance using the dev set displays BLEU 20.37, and this model records the performance of BLEU 20.9 with the test set.
Performing/Watching Artificial Intelligence On Stage

In the last two decades, we have observed the experimenting with Artificial Intelligence algorithms as a tool for performances. From the early tests, mostly carried out in the Universities’ Labs, to the first public performances, the practice has produced several evidence, enough to consider the implications, both from the point of view of the design and of the reception. Computer scientists have used performance and drama to test the potential of AI. Artists have used AI as a new criticism to both presence and disembodiment. As robots became relevant in our society, they walked their first steps on stage; Artificial Agents have been dialoguing with actors; some algorithms have managed the interaction among participants in the interactive performances. Therefore, we can foresee a new multifaceted practice in theatre, and this may further develop the notion of live event. Yet, the analysis of this practice isn’t relevant only for the creators but may open new consideration from the point of view of the audience experience. The notion of the “here and now” that instantiated the aesthetic of performance as relation between performer and spectator, might be reconsidered in the light of the artificial agency of the algorithm.Keywords: Artificial Intelligence; algorithm; artificial agents; intermediality; drama; audience
Sorting Short Integers.

We build boolean circuits of size $O(nm^2)$ and depth $O(\log(n) + m \log(m))$ for sorting $n$ integers each of $m$-bits. We build also circuits that sort $n$ integers each of $m$-bits according to their first $k$ bits that are of size $O(nmk(1 + \log^*(n) - \log^*(m)))$ and depth $O(\log^{3}(n))$. This improves on the result of Asharov et al. arXiv:2010.09884 and resolves some of their open questions.
The Effect of Feature Selection on Credit Card Fraud Detection Success

In this study, a deep learning based credit card fraud detection system has been designed and implemented. The information provided by credit card transactions is not sufficient in fraud detection systems. In order to increase the success of the model, new features were created by grouping the previous transactions according to features such as merchant category, transaction type, and payment type. Then these features were selected with feature selection methods and the most impact ones were determined. Thus, thanks to the newly added features, the payment habits of the cardholder were better learned by the model and the success of the model was increased.
Comparing individual and collective management of referential choices in dialogue

Past research shows that when a discourse referent is mentioned repeatedly, it is usually introduced with a full noun phrase and maintained with a reduced form such as a pronoun. Is this also the case in dialogue, where the same referent may be introduced by one person and maintained by another person? An experiment was conducted in which participants either told entire stories to each other or told stories together, thus enabling us to contrast situations in which characters were introduced and maintained by the same person (control condition) and situations in which the introduction and the maintaining of each character were performed by different people (alternating condition). Story complexity was also manipulated through the introduction of one or two characters in each story. We found that participants were less likely to use reduced forms to maintain referents in the alternating condition. The use of reduced forms also depended on the context in which the referent was maintained (in particular, first or second mention of a character) and on story complexity. These results shed light on how the pressure to signal understanding to one's conversational partner affects referential choices throughout the interaction.
Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization

Neural models have become successful at producing abstractive summaries that are human-readable and fluent. However, these models have two critical shortcomings: they often don't respect the facts that are either included in the source article or are known to humans as commonsense knowledge, and they don't produce coherent summaries when the source article is long. In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings. First, we incorporate entity-level knowledge from the Wikidata knowledge graph into the encoder-decoder architecture. Injecting structural world knowledge from Wikidata helps our abstractive summarization model to be more fact-aware. Second, we utilize the ideas used in Transformer-XL language model in our proposed encoder-decoder architecture. This helps our model with producing coherent summaries even when the source article is long. We test our model on CNN/Daily Mail summarization dataset and show improvements on ROUGE scores over the baseline Transformer model. We also include model predictions for which our model accurately conveys the facts, while the baseline Transformer model doesn't.
Minimax Sample Complexity for Turn-based Stochastic Game

The empirical success of multi-agent reinforcement learning is encouraging, while few theoretical guarantees have been revealed. In this work, we prove that the plug-in solver approach, probably the most natural reinforcement learning algorithm, achieves minimax sample complexity for turn-based stochastic game (TBSG). Specifically, we perform planning in an empirical TBSG by utilizing a `simulator' that allows sampling from arbitrary state-action pair. We show that the empirical Nash equilibrium strategy is an approximate Nash equilibrium strategy in the true TBSG and give both problem-dependent and problem-independent bound. We develop reward perturbation techniques to tackle the non-stationarity in the game and Taylor-expansion-type analysis to improve the dependence on approximation error. With these novel techniques, we prove the minimax sample complexity of turn-based stochastic game.