Reviewerly/exHarmony huggingface dataset:
What can we expect from the EU legal framework in a pandemic outbreak

This work was supported by Eusko Jaurlaritza [grant number Ayudas a grupos de investigacion IT-1066-16]; H2020 Science with and for Society [grant number GRANT AGREEMENT NUMBER — 788039 — PANELFIT].
Organized Online Learning of Students in Universities and Schools: Didactics and Methodology

This article discusses the urgent aspects of online learning didactics. They are comprised of, for instance, organization and methods of organized learning, main principles of actions aimed at practical orientation; their objective is combination of the methods of distance learning, formation of efficient skills of students with social mediation, analysis of reasons of errors and their correction, pursuance of success in relevant areas. Organized online learning also provides opportunity for students to use all advantages of digital technologies: flexibility, individual distribution of learning duration, compatibility with other forms of occupation. In addition to digital know-how, organization of learning and independent work of distant students can develop self-discipline, sustainability of acquired skills. Students can use various digital sources of information available under various conditions in addition to that organized under the management of distance learning teacher.
Secured Question Paper Management System

The examination is one of the essential parts of the student’s life. After learning from an institution, the students must attend and pass various types of examinations to get their certificates. In this process, sometimes students try to pass the examination by answering the leaked questions. From the beginning of making the question papers to delivering them to the students is now at stake in the existing question management system due to question leakage. So, the traditional way of managing the question papers needs to be changed so that it can stop the leakage of question papers. To secure the question papers and making a trustworthy, physical existence free, and affordable a system is proposed in this paper. To design this model, we have used the concept of Blockchain, Digital Signature, Randomization, OTAC.
Impact of Trade Openness on Foreign Direct Investment in Sub Saharan Africa Countries

No country in the world that is stay isolated and become developed without influence of other countries in one way or the other. Countries in the world are working hard through various economic policies in order to attract more Foreign Direct Investment (FDI). Therefore any attempt to find out the influence of FDI is paramount important. The paper seek to look into the impact of trade openness on Foreign Direct Investment in Sub-Sahara African Countries for the period of 2000 to 2017. The study employed panel data analysis, including the panel unit root test, panel co integration test and fully modified least square method (FMOLS). The findings reveal that, all the variables were cointegration of order one and having a long run relationship. More so the results show that trade openness was positive and statistically significant in influencing FDI in the region, while corruption was negative and statistically significant in influencing FDI in the region. The findings suggest that, the existing trade policies should be amend by governments through various legislative arms of government to give room for inflow of capital and new technological advancement from developed countries where these FDI comes from to those countries under study. Also Governments in the region should create more polices that is used to reduce the level of corruption in the system through improvement of the existing institutions that fight corruption to a standstill.
L2PF - Learning to Prune Faster

Various applications in the field of autonomous driving are based on convolutional neural networks (CNNs), especially for processing camera data. The optimization of such CNNs is a major challenge in continuous development. Newly learned features must be brought into vehicles as quickly as possible, and as such, it is not feasible to spend redundant GPU hours during compression. In this context, we present Learning to Prune Faster which details a multi-task, try-and-learn method, discretely learning redundant filters of the CNN and a continuous action of how long the layers have to be fine-tuned. This allows us to significantly speed up the convergence process of learning how to find an embedded-friendly filter-wise pruned CNN. For ResNet20, we have achieved a compression ratio of 3.84 $$\times $$ with minimal accuracy degradation. Compared to the state-of-the-art pruning method, we reduced the GPU hours by 1.71 $$\times $$ .
PyART: Python API Recommendation in Real-Time

This is the research artifact of the paper titled 'PyART: Python API Recommendation in Real-Time'. PyART is a real-time API recommendation tool for Python, which includes two main functions: data-flow analysis and real-time API recommendation for both incomplete and complete Python code context. Compared to classical tools, PyART has two important particularities: it is able to work on real-time recommendation scenario, and it provides data-flow analysis and API recommendation for dynamic language. Classical tools often fail to make static analysis in real-time recommendation scenario, due to the incompletion of syntax. And the dynamic features of Python language also bring challenges to type inference and API recommendation. Different from classical tools, PyART derives optimistic data-flow that is neither sound nor complete but sufficient for API recommendation and cost-effective to collect, and provides real-time API recommendations based on novel candidate collection, context analysis and feature learning techniques. The artifact evaluation experiments of PyART include three main aspects: data-flow analysis, intra-project API recommendation and across-project API recommendation. We assume users of the artifact is able to use Linux Ubuntu Operating System.
Actual aspects of digitalization of Russian education

The current trend of socio-economic development is digitalization, which affects all spheres of the economy and society, including education. The article examines the features of digitalization of the Russian education system, notes the contradictory nature of the changes especially related to the introduction of digital technologies. The risks of introducing distance education, including for consumers of educational services, are identified. The author emphasizes the lack of elaboration of legal norms for effective regulation of the process of digitalization of Russian education. The scientific validity and economic feasibility of the implemented format of digitalization of Russian education and possible consequences of its transformation are evaluated.
All AR-Board: Seamless AR Marker Integration into Board Games

Although Augmented Reality (AR) technologies have experienced very rapid development over the past few years, a number of issues still prevent them from facilitating a seamless, social, multiplayer experience. The potential for augmented board games to support interactive, animated stories and games for multiple participants has been previously explored, but primarily using expensive and largely unavailable hardware. This paper examines challenges and potential solutions for creating a multiplayer AR-based board game with affordable, readily available technology.
Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.
Hate Speech Detection in Indonesian Twitter using Contextual Embedding Approach

Hate speech develops along with the rapid development of social media. Hate speech is often issued due to a lack of public awareness of the difference between criticism and statements that might contribute to this crime. Therefore, it is very important to do early detection of sentences that will be written before causing a criminal act due to public ignorance. In this paper, we use the advancement of deep neural networks to predict whether a sentence contains a hate speech and abusive tone. We demonstrate the robustness of different word and contextual embedding to represent the semantic of hate speech words. In addition, we use a document embedding representation via a recurrent neural networks with gated recurrent unit as the main architecture to provide richer representation. Compared to syntactic representation of the previous approach, the contextual embedding in our model proved to give a significant boost on the performance by a significant margin.
Analysis of association among various attributes in medical data of heart patients by using data mining methods

ABSTRACTLarge quantities of information about patients have been collected in clinical databases. The data collection is started for the use of doctors but actually these information are a good input for data mining. However, the collected data contains much valuable information when it is treated as a single set of data. From the data many useful knowledge can be extracted. Most of the hospitals are nowadays keep the data of every visited patient. This data can be used to extract new knowledge associated with medical ﬁeld. The association rule mining, which is one of the popular data mining techniques, can be applied on clinical databases to extract novel and potential knowledge. In this paper, analysis of results obtained from application of association rule mining on a database consisting of details of 10000 patients is presented.
Neural-Guided Inductive Synthesis of Functional Programs on List Manipulation by Offline Supervised Learning

Synthesizing intended programs from user-specified input-output examples, also known as Programming by Examples (PBE), is a challenging problem in program synthesis, and has been applied to a wide range of domains. A key challenge in PBE is to efficiently discover a user-intended program in the search space that can be exponentially large. In this work, we propose a method for automatic synthesis of functional programs on list manipulation, by using offline-trained Recurrent Neural Network (RNN) models to guide the program search. We adopt miniKanren, an embedded domain-specific language for flexible relational programming, as an underlying top-down deductive search engine of candidate programs that are consistent with input-output examples. Our approach targets an easy and effective integration of deep learning techniques in making better PBE systems and combines two technical ideas on generating diverse training dataset and designing rich feature embeddings of probable subproblems for synthesis generated by deductive search. The offline-learned model is then used in PBE to guide the top-down deductive search with specific strategies. To practically manipulate data structures of lists, our method synthesizes functional programs with popular higher-order combinators including map, foldl and foldr. We have implemented our method and evaluated it with challenging program synthesis tasks on list manipulation. The experiments show promising results on the performance of our method compared to related state-of-the-art inductive synthesizers.
Hearing Deliberately: Sensible Communication and Perception in Walden

This article takes up Thoreau's concerns in Walden with the stakes of standardized speech, hearing, and auditory communication. I reframe Thoreau's frequent preoccupations with hearing and sound through the histories and sensory epistemologies of deafness, which can highlight the threads that lace through Thoreau's work about comprehending nonnormative perception in the midst of an American cultural landscape increasingly preoccupied with demanding particular types of auditory and verbal behaviors. Following scholars like Christopher Krentz and Rebecca Sanchez, I "deafen" Thoreau by putting his work in dialogue with the alternative sensory, linguistic, and embodied histories and phenomenologies given to us by deaf experience. Thoreau frames sound and hearing, alongside broader notions about communication and perception, as fluid and individually variant processes rather than standard sensory givens. Thoreau's deeply subjective way of thinking through the body and its processes of perception and self-expression can highlight how the standardizing impulses of modernity work against more fluid human types of "multimodality" and the "heterogeneity" of sensing and communicating. The subjectivity and particularity that characterize Thoreau's explorations of nonnormative hearing and communicating ultimately prompt a deeper critique of standard sensory experience as a prerequisite for social legibility and interaction.
Research on Key Technologies of big data analysis in smart irrigation area

Abstract Several key data analysis technologies involved in irrigation area construction are discussed. A closed-loop large data management model for irrigation area is proposed, which includes data collection, data sharing, data management, data development and utilization. Five types of analysis, including statistical analysis, professional calculation, planning optimization, system simulation and intelligent algorithm, are compared, and an abnormal water intake behavior early warning, water supply and demand forecast, water-saving irrigation area evaluation and installation are constructed. Four typical application scenarios, such as intelligent research and judgment of abnormal operation, have certain reference value for promoting the construction of intelligent irrigation area.
On optimal linear codes of dimension 4

In coding theory, the problem of finding the shortest linear codes for a fixed set of parameters is central. Given the dimension $k$, the minimum weight $d$, and the order $q$ of the finite field $\bF_q$ over which the code is defined, the function $n_q(k, d)$ specifies the smallest length $n$ for which an $[n, k, d]_q$ code exists. The problem of determining the values of this function is known as the problem of optimal linear codes. Using the geometric methods through projective geometry, we determine $n_q(4,d)$ for some values of $d$ by constructing new codes and by proving the nonexistence of linear codes with certain parameters.
Analyzing Moroccan Tweets to Extract Sentiments Related to the Coronavirus Pandemic: A New Classification Approach

At the end of 2019, the world has known the covid-19 crisis that negatively affected the health, economic, social, and psychological status of people. Since the beginning of this crisis, users express their ideas, opinions, and sentiments about the coronavirus on all social networks such as Facebook, Twitter, Instagram, etc. For example, until May 8th, 2020, the number of tweets published on Twitter is equal to 628,809,016. In this paper, our proposed method analyzes and classifies covid-19 tweets published in morocco for extracting sentiments. Our approach uses the advantages of new proposed tweets features using a dictionary-based approach and a Python library for developing a new recommendation approach. As Experiments, Our proposed approach outperforms the well-known machine learning classifiers. We find also that based on the epidemiological situation in morocco, the sentiments of Moroccan users changed.
Demonstrating User Authentication via Electrical Muscle Stimulation

We propose a novel modality for active biometric authentication: electrical muscle stimulation (EMS). To explore this, we engineered an interactive system, which we call ElectricAuth, that stimulates the user’s forearm muscles with a sequence of electrical impulses (i.e., EMS challenge) and measures the user’s involuntary finger movements (i.e., response to the challenge). ElectricAuth leverages EMS’s intersubject variability, where the same electrical stimulation results in different movements in different users because everybody’s physiology is unique (e.g., differences in bone and muscular structure, skin resistance and composition, etc.). As such, ElectricAuth allows users to login without memorizing passwords or PINs. ElectricAuth’s challenge-response structure makes it secure against data breaches and replay attacks, a major vulnerability facing today’s biometrics such as facial recognition and fingerprints. Furthermore, ElectricAuth never reuses the same challenge twice in authentications – in just one second of stimulation it encodes one of 68M possible challenges.
A New Task Scheduling Algorithm based on Water Wave Optimization for Cloud Computing

Nowadays cloud computing provides many benefits for organizations.Businesses can ensure reliable calamity recovery and backup solutions without the spat of tuning them up on a physical machine.For many companies, exploiting complex calamity recovery plans can be an expensive guarantee, and backing up data is time exhaustion.The cloud itself is built in such a way that the data stored more than one time in servers, so that if any server fails, the data is backed up immediately.The capability of accessing data readily is available after handling the failure.However, still, cloud computing resources face many problems such as scheduling problems.This paper tackles the resource scheduling problem and presents a new efficient algorithm, called Improved Water Wave Optimization (IWWO), to address such a problem.The main idea is the enhancement/improvement of the Water Wave Optimization (WWO) algorithm by using reinforcement learning to overcome the local optimality of the conventional WWO during the searching process.The proposed IWWO is implemented in the CloudSim toolkit and evaluated by considering a real data set and a randomly generated data set.The results are compared with the results of the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm.The obtained results show that the IWWO can solve the resource scheduling with minimum schedule length and a high balance degree.
The evaluation of quality teaching methods in research at Czech Republic.

The evaluation of the quality of teaching and the evaluation of education at universities, methods of evaluating teaching and assessment are one of the key criteria for university evaluation. At present, a project of the ESF project of the University of West Bohemia in Pilsen was realized and one of its activities is focused on "Improving Educational Activities and Modern Educational Trends" and on the subsequent implementation and reflection of student education with new teaching methods. The questionary survey was conducted in 2019 with 616 respondents from the University of West Bohemia in Pilsen, who is the only one public higher education institution in the Pilsen Region. The results analyze in detail the views of the students - taking into account the extension of pedagogical competencies within the framework of the career order. The aim of the project is a comprehensive system of interconnection of the education of academic staff in the area of increasing pedagogical competencies with introduction of new teaching methods into practice and taking into account the extension of pedagogical competencies within the framework of the career order. The results of a survey focused on evaluating the quality of teaching methods confirmed a number of mostly known means of activating teaching, interactive teaching, communication, discussion, the role of the personality of the lecturer and the instructor. These activation approaches need to be continued.
Pedagogical aspects of students' digital competence development

The purpose of the research was to study the pedagogical aspects of students’ digital competence development and the degree of teachers’ encouragement to develop their own professional digital competence. The study is based on quantitative and qualitative methodology and contains an online structured interview of 132 teachers of Ukraine who took an online course on the development of digital competencies. Results. University teachers demonstrated different beliefs and effectiveness in the use of ICT for pedagogical purposes. Although a few teachers seamlessly integrated ICT into the learning process, most were more tool-oriented and supported a teacher-rather than student-centered approach when designing online courses. In a pandemic, teachers are massively interested in the development of digital competence as part of professional development. At the same time, the strategy of the university and the practice of functioning of the university testify to the lack of a course for the implementation of absolute digital pedagogy.
Privacy Beyond Possession: Solving the Access Conundrum in Digital Dollars

The advent of a retail central bank digital currency (CBDC) could reshape the US payments system. A retail CBDC would be a digital representation of the US dollar in the form of an account or token that is widely accessible to the general public. It would be a third form of US fiat money that is created and issued by the Federal Reserve and complementary to physical cash. CBDC proposals have suggested a myriad of retail CBDC design models with an overwhelming interest in a retail CBDC that either implements a centralized ledger system or some form of a distributed ledger system to process payments. The technology of a retail CBDC would enable instantaneous payments for consumers and greater transparency for government officials. Additionally, CBDC proponents are championing retail CBDC as a tool to promote financial inclusion. However, antiquated US privacy protections may be inadequate to safeguard against the potential risks to individual privacy within digital payments and consequently undermine financial inclusion. A retail CBDC system that is under the control of the Federal Reserve could bolster regulatory compliance and oversight but also exacerbate workarounds by government entities in the current US privacy framework that are concerning for individual privacy in the age of big data and dataveillance. A proper privacy framework governing retail CBDC records would alleviate risks to privacy and enhance public trust in a retail CBDC system. Refining the Privacy Act of 1974 or creating a new regulatory framework that is informed by both the Privacy Act and the impact of innovative data analytics would help balance the inherent tension between privacy and transparency of user identity and transactions within a retail CBDC system under the control of the Federal Reserve.
Constructing Cycles in Isogeny Graphs of Supersingular Elliptic Curves

Abstract Loops and cycles play an important role in computing endomorphism rings of supersingular elliptic curves and related cryptosystems. For a supersingular elliptic curve E defined over 𝔽 p 2 , if an imaginary quadratic order O can be embedded in End( E ) and a prime L splits into two principal ideals in O , we construct loops or cycles in the supersingular L -isogeny graph at the vertices which are next to j ( E ) in the supersingular ℓ-isogeny graph where ℓ is a prime different from L . Next, we discuss the lengths of these cycles especially for j ( E ) = 1728 and 0. Finally, we also determine an upper bound on primes p for which there are unexpected 2-cycles if ℓ doesn’t split in O .