Reviewerly/exHarmony huggingface dataset:
Visualization Principle in Teaching English Vocabulary to Secondary School Pupils

The research objective includes analysing theoretical foundations of visual aids usage at secondary school English lessons. The paper covers the following issues: the psychological and pedagogical basis of the visualization principle, the role of visual aids in teaching foreign vocabulary. The study focuses on the problem of visual aids usage at different stages of vocabulary work: introducing new lexical units, consolidation of newly acquired vocabulary, active usage of newly acquired words. Scientific originality of the research lies in the fact that the authors propose comprehensive methodology of using visual aids at secondary school English lessons, justify efficiency of visual aids when teaching English vocabulary to secondary school pupils. The research findings are as follows: the methodological conditions promoting efficiency of the English vocabulary acquisition are revealed, the visualization principle is defined as a multifunctional teaching means to stimulate pupils’ intellectual cognitive activity, to raise their motivation for foreign language learning, to form a positive attitude to the academic discipline, and as a leading approach to promote development of language and communication skills.
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction

We study the problem of minimizing a relatively-smooth convex function using stochastic Bregman gradient methods. We first prove the convergence of Bregman Stochastic Gradient Descent (BSGD) to a region that depends on the noise (magnitude of the gradients) at the optimum. In particular, BSGD with a constant step-size converges to the exact minimizer when this noise is zero (\emph{interpolation} setting, in which the data is fit perfectly). Otherwise, when the objective has a finite sum structure, we show that variance reduction can be used to counter the effect of noise. In particular, fast convergence to the exact minimizer can be obtained under additional regularity assumptions on the Bregman reference function. We illustrate the effectiveness of our approach on two key applications of relative smoothness: tomographic reconstruction with Poisson noise and statistical preconditioning for distributed optimization.
Embedded real-time people detection and tracking with time-of-flight camera

People recognition is a relevant subset of the generic image based recognition task with many possible application areas such as security, surveillance, human-robot interaction or recently the social security in a pandemic context. In this work we present a light-weight recognition pipeline for time-of-flight cameras based on deep learning techniques tailored to this specific type of camera with registered infrared and depth images. By combining the maturity of the 2D image based recognition techniques with the custom depth sensing we achieved effective solutions for a number of relevant industrial applications. In particular, our focus was on automatic door-control and people counting applications.
Image = Structure + Few Colors

Topology plays an important role in computer vision by capturing the structure of the objects. Nevertheless, its potential applications have not been sufficiently developed yet. In this paper, we combine the topological properties of an image with hierarchical approaches to build a topology preserving irregular image pyramid (TIIP). The TIIP algorithm uses combinatorial maps as data structure which implicitly capture the structure of the image in terms of the critical points. Thus, we can achieve a compact representation of an image, preserving the structure and topology of its critical points (maxima, the minima and the saddles). The parallel algorithmic complexity of building the pyramid is \(O(\log d)\) where d is the diameter of the largest object. We achieve promising results for image reconstruction using only a few color values and the structure of the image, although preserving fine details including the texture of the image.
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning

There has been significant recent interest in devising verification techniques for learning-enabled controllers (LECs) that manage safety-critical systems. Given the opacity and lack of interpretability of the neural policies that govern the behavior of such controllers, many existing approaches enforce safety properties through shield, a dynamic monitoring-and-repairing mechanism that ensures a LEC does not emit actions that would violate desired safety conditions. These methods, however, have been shown to have significant scalability limitations because verification costs grow as problem dimensionality and objective complexity increase. In this paper, we propose a new automated verification pipeline capable of synthesizing high-quality safe controllers even when the problem domain involves hundreds of dimensions, or when the desired objective involves stochastic perturbations, liveness considerations, and other complex non-functional properties. Our key insight involves separating safety verification from neural controller training, and using pre-computed verified safety shields to constrain the training process. Experimental results over a range of high-dimensional benchmarks demonstrate the effectiveness of our approach in a range of stochastic linear time-invariant and time-variant systems.
Chat communication in library user services

The chat communication is a new type of user distance service. The authors examine the technology applications in different organizations and libraries, in particular. Intelligent information systems are used for designing virtual assistants and chatbots. The authors emphasize that, as for today, the scientific classification for chatbots is lacking. They attempt to categorize virtual talkbots. They discuss iPavlov artificial intellect library. The most interesting Russian developments are Telegram chatbots of Russian State Library for Young Adults, Directorate of Cultural Centers of Moscow, and that of St. Petersburg State Library for the Blind and Visually Impaired. RNPLS&amp;T has implemented jivo system of chat communication. The reasons for choosing this system and operation results are discussed. The authors conclude that chat communication meets the principles and improves efficiency of user services.
Blockchain-based technologies

Blockchain-based technologies can be understood as a distributed network of computers, ideally organised in a decentralised way, mutually agreeing on a common state while tolerating failures (incl.malicious behaviour) to some extent.
Knowledge Sharing Practice and Its Associated Factors Among Healthcare Providers at University of Gondar Comprehensive Specialized Hospital, North West Ethiopian: Cross-sectional Study

Abstract Background In healthcare, information and knowledge needs of health care providers arise in the patient care process. However, the right information and knowledge at the right time and place to the right person is not reached so far yet. Thus, leads limited knowledge sharing practice among health providers in Ethiopia. Objectives The objectives of the study were to assess knowledge sharing practice and associated factors among healthcare providers at University of Gondar hospital. Methods Institutional based cross-sectional study design was conducted through stratified simple random sampling technique among 423 samples from February 24 up to March 27, 2020. Pretested and self-administered questionnaire was used. Epi info version 7 and stata version 15 software were used for data entry, processing and analysis respectively. Descriptive statistics and multivariable logistic regression model were applied to describe the study objects and to assess knowledge sharing practice and its associated factors by considering P value ≤ 0.05. Results 423 respondents were participated with 100% response rate. From the total respondents, 222 of 423 (52.48%) of respondents were male. More than two third (66.90%) of respondents were degree and diploma holders. Nearly three fourth of 423 (74.70%) healthcare providers were under the age group of 21–30 years. The level of knowledge sharing practice among the respondents was 65.01% (95% CI: 60.46–69.56). In multivariable logistic regression model awareness AOR = 2.44, 95% CI= [1.32–4.50], willingness AOR = 1.96, 95% CI= [1.10–3.53], loss of knowledge power AOR = 0.192, 95% CI= [.12-.32], availability of health information resource AOR = 2.00, 95% CI= [1.56–5.38] and opportunity AOR = 2.91, 95% CI= [1.71–4.95] were significantly associated with knowledge sharing practice. Conclusions Knowledge sharing practice of healthcare providers is higher as compared with most studies conducted in Ethiopia. However, it needs further opportunity, resource allocation and supportive leadership.
A Quantile-based Approach for Hyperparameter Transfer Learning

Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance objectives of the same algorithm across multiple datasets. In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}. The main idea is to regress the mapping from hyperparameter to objective quantiles with a semi-parametric Gaussian Copula distribution, which provides robustness against different scales or outliers that can occur in different tasks. We introduce two methods to leverage this mapping: a Thompson sampling strategy as well as a Gaussian Copula process using such quantile estimate as a prior. We show that these strategies can combine the estimation of multiple objectives such as latency and accuracy, steering the hyperparameters optimization toward faster predictions for the same level of accuracy. Extensive experiments demonstrate significant improvements over state-of-the-art methods for both hyperparameter optimization and neural architecture search.
Intrusive tech-enabled surveillance and ‘National Security’ secrecy: mounting concerns of mass snooping amid informational asymmetry

Abstract The onset of technological innovations such as Big Data and Analytics is changing the ways in which law enforcement agencies police and investigate crimes in India. Pervasive technologies like smartphones, closed-circuit cameras, etc, coupled with such innovations have augmented abilities of law enforcement to identify, monitor and predict suspicious individuals and activities. However, the expansive adoption of intrusive technologies for law enforcement challenge the statutory and the constitutional limits on mass surveillance. Also, the government has not been forthcoming in disclosing details about the extent and the ambition of such surveillance technologies. The secrecy has resulted in an informational asymmetry between the people and the government which raises profound concerns of mistrust, transparency and public-accountability. This article examines the feasibility of the ‘national security’ argument that law enforcement agencies normatively use to justify such secrecy of their surveillance powers. They argue that such disclosures may negatively affect their investigational strategies and render them susceptible to circumvention, such that it may be a risk to ‘national security’. The article analyses such ‘national security’ secrecy claims that currently exist without any proper surveillance law and external oversight. The article seeks to recommend meaningful approaches to limit the over-broad ‘national security’ secrecy claims and initiate an informed public conversation on expansive tech-enabled surveillance practices of the Indian government.
Summarising Historical Text in Modern Languages

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language.This is a fundamentally important routine to historians and digital humanities researchers but has never been automated.We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese.Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms.We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.* Chenghua Lin is the corresponding author. DE №34 StoryJhre Königl.Majest.befinden sich noch vnweit Thorn / ... / dahero zur Erledigung Hoffnung gemacht werden will.(Their Royal Majesties are still not far from Torn, ... , therefore completion of the
The Application of Blockchain in Supply Chain Industry

This research focuses on analyzing the state of the art of the blockchain technology and in publicizing the potential applications and implementations that the blockchain can deliver to the supply chain. For this, it will be explained in a simple way technology and its operation in the supply chain and case studies of real companies that are currently betting on this technological revolution, capable of improving the efficiency of any process logistic.
ANALYSIS OF THE ABILITY OF LECTURERS IN THE USE OF ONLINE LEARNING MEDIA IN THE FACULTY OF TARBIYAH AND KEGURUAN (FTK) UIN AR-RANIRY BANDA ACEH

This study aims to describe lecturers' ability to use online learning media at the Tarbiyah and Keguruan Faculty of UIN Ar-Raniry Banda Aceh in the learning process. This study used a quantitative descriptive research design. Research subjects are lecturers who actively teach in the odd semester of 2020/2021. Meanwhile, 42 people were randomly selected as samples. Data collection was carried out using questionnaires and interviews. Data analysis used descriptive statistical analysis. The results showed that lecturers' ability with indicators of skills in using online learning media obtained an average value of 79.29%, which means that, on average, the lecturers were able to use online learning media well. The ability of lecturers with indicators of skills in managing online learning is obtained an average value of 67.56%, which means that the lecturer can manage online learning well.
Fast Hierarchical Games for Image Explanations

As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible. The current lack of interpretability often undermines the deployment of accurate machine learning tools in sensitive settings. In this work, we present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients-Hierarchical Shap (h-Shap)-that resolves some of the limitations of current approaches. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. Under certain distributional assumptions, such as those common in multiple instance learning, h-Shap retrieves the exact Shapley coefficients with an exponential improvement in computational complexity. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem, showing that h-Shap outperforms the state-of-the-art in both accuracy and runtime. Code and experiments are made publicly available.
Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

Abstract This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.
Cloud service negotiation framework for real-time E-commerce application using game theory decision system

A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement.
Phishing Mitigation Techniques: A Literature Survey

Email is a channel of communication which is considered to be a confidential medium of communication for exchange of information among individuals and organisations. The confidentiality consideration about e-mail is no longer the case as attackers send malicious emails to users to deceive them into disclosing their private personal information such as username, password, and bank card details, etc. In search of a solution to combat phishing cybercrime attacks, different approaches have been developed. However, the traditional exiting solutions have been limited in assisting email users to identify phishing emails from legitimate ones. This paper reveals the different email and website phishing solutions in phishing attack detection. It first provides a literature analysis of different existing phishing mitigation approaches. It then provides a discussion on the limitations of the techniques, before concluding with an exploration into how phishing detection can be improved.
College English Assistant Teaching System Based on Artificial Intelligence

Abstract With the development of information explosion and education reform, traditional education is facing great challenges. It is imperative to reform teaching methods and means. As a new teaching method, artificial intelligence technology has been widely used in college English teaching. The system receives information, processes it, and feeds back the results, that is, man-machine conversation. With the rapid development of network and multimedia technology, Teachers should combine network and multimedia technology to improve the diversity and interest of English teaching, so as to improve the quality of English teaching. This paper studies the present situation of English teaching at home and abroad, finds out the advantages and disadvantages of English teaching resources in China through literature retrieval, interview and questionnaire survey. Under the guidance of constructionist teaching theory, humanistic education theory and developmental evaluation theory, this paper analyzes the advantages and disadvantages of English teaching resources in China based on the development characteristics of contemporary college students and the contents of current college English courses. According to the teaching principle of “student-centered, process oriented, communication oriented, people-oriented”, this paper analyzes the advantages and disadvantages of domestic English teaching resources The results show that: from the perspective of the purpose of College English learning, to improve students’ comprehensive ability and quality, the proportion is the highest, accounting for 26%.
An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering

In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.
Response of quantum spin networks to attacks

Abstract We investigate the ground states of spin models defined on networks that we imprint (e.g., non-complex random networks like Erdos–Renyi, or complex networks like Watts–Strogatz, and Barabasi–Albert), and their response to decohering processes which we model with network attacks. We quantify the complexity of these ground states, and their response to the attacks, by calculating distributions of network measures of an emergent network whose link weights are the pairwise mutual information between spins. We focus on attacks which projectively measure spins. We find that the emergent networks in the ground state do not satisfy the usual criteria for complexity, and their average properties are captured well by a single dimensionless parameter in the Hamiltonian. While the response of classical networks to attacks is well-studied, where classical complex networks are known to be more robust to random attacks than random networks, we find counter-intuitive results for our quantum networks. We find that the ground states for Hamiltonians defined on different classes of imprinted networks respond similarly to all our attacks, and the attacks rescale the average properties of the emergent network by a constant factor. Mean field theory explains these results for relatively dense networks, but we also find the simple rescaling behavior away from the regime of validity of mean field theory. Our calculations indicate that complex spin networks are not more robust to projective measurement attacks, and presumably also other quantum attacks, than non-complex spin networks, in contrast to the classical case. Understanding the response of the spin networks to decoherence and attacks will have applications in understanding the physics of open quantum systems, and in designing robust complex quantum systems—possibly even a robust quantum internet in the long run—that is maximally resistant to decoherence.