Reviewerly/exHarmony huggingface dataset:
Media Competence in Spanish Secondary School Students. Assessing Instrumental and Critical Thinking Skills in Digital Contexts

The ways in which young people communicate have changed in line with the impact of technologies. This change has been accompanied by growing differences between the young, and their "liquid" experience, and adults with their "solid" experience, and these shapes the state of the question that defines young people as digital natives. This work analyses Spanish adolescents' level of media competence. The sample consisted of 672 students attending secondary schools in 10 provinces in Spain. The average age of the participants was 14. The study aims to show that, although today's adolescents are described as the digital generation, and there is a widely held belief that they are digital natives, their level of media competence is low and there is need for fomenting greater literacy skills in this field. The ad hoc questionnaire yielded results that indicate an overall medium level of media competence, but a skills shortage in the dimensions relating to critical thinking, such as in reception and audience, production and programming processes, and ideology and values. The conclusions question the supposed superior competence of digital natives in media. This study underlines the need for a clear definition of the concept of media competence from a convergence perspective.
A survey of energy-efficient motion planning for wheeled mobile robots

Purpose As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots. Design/methodology/approach The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness. Findings According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented. Research limitations/implications EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study. Practical implications The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios. Originality/value This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.
Robust Maximum Mixture Correntropy Criterion-Based Semi-Supervised ELM With Variable Center

The recent correntropy criterion based semi-supervised random neural network extreme learning machine (RC-SSELM) achieved outstanding performance in dealing with datasets with large outliers and non-Gaussian noises. To further improve the effectiveness and flexibility of the algorithm in combating large and complex outliers, we explore a more effective semi-supervised ELM data learning algorithm with the robust maximum mixture correntropy criterion (MMCC) based optimization scheme in this brief. Meanwhile, the generalized correntropy criterion kernel function with the variable kernel center is applied to MMCC, and the resultant novel semi-supervised learning algorithm is abbreviated as MC-SSELM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vc</sup> . The fixed-point iteration learning algorithm is adopted for the output weight optimization of MC-SSELM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vc</sup> . Experiment conducted on many benchmark datasets are given to show the effectiveness of MC-SSELM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vc</sup> and comparisons to several state-of-the-art semi-supervised learning algorithms are provided for the superiority demonstration.
A Double Residual Compression Algorithm for Efficient Distributed Learning

Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchronization between the master and worker nodes becomes the major obstacle for efficient learning as the number of workers and the dimension of the model increase. In this paper, we propose DORE, a DOuble REsidual compression stochastic gradient descent algorithm, to reduce over $95\%$ of the overall communication such that the obstacle can be immensely mitigated. Our theoretical analyses demonstrate that the proposed strategy has superior convergence properties for both strongly convex and nonconvex objective functions. The experimental results validate that DORE achieves the best communication efficiency while maintaining similar model accuracy and convergence speed in comparison with start-of-the-art baselines.
Controlled Cavity-Free, Single-Photon Emission and Bipartite Entanglement of Near-Field-Excited Quantum Emitters

We report theoretical statistics of 1- and 2-qubit (bipartite) systems, namely, photon antibunching and entanglement, of near-field excited quantum emitters. The sub-diffraction focusing of a plasmonic waveguide is shown to generate enough power over a sufficiently small region (<50 × 50 nm2) to strongly drive quantum emitters. This enables ultrafast (10–14 s) single-photon emission as well as creates entangled states between two emitters when performing a controlled-NOT operation. A comparative analysis of silicon and near-zero index materials demonstrates advantages and uncovers challenges of embedding quantum emitters for single-photon emission and for bipartite entanglement. The use of a movable plasmonic waveguide, in lieu of stationary nanostructures, allows high-speed rasterization between sets of qubits and enables spatially flexible data storage and quantum information processing. Furthermore, the sub-diffraction focusing of the waveguide is shown to achieve cavity-free dynamic entanglement. This greatly reduces fabrication constraints and increases the speed and scalability of nanophotonic quantum devices.
Exploring the impact of 360° movie cuts in users’ attention

Virtual Reality (VR) has grown since the first devices for personal use became available on the market. However, the production of cinematographic content in this new medium is still in an early exploratory phase. The main reason is that cinematographic language in VR is still under development, and we still need to learn how to tell stories effectively. A key element in traditional film editing is the use of different cutting techniques, in order to transition seamlessly from one sequence to another. A fundamental aspect of these techniques is the placement and control over the camera. However, VR content creators do not have full control of the camera. Instead, users in VR can freely explore the 360° of the scene around them, which potentially leads to very different experiences. While this is desirable in certain applications such as VR games, it may hinder the experience in narrative VR. In this work, we perform a systematic analysis of users’ viewing behavior across cut boundaries while watching professionally edited, narrative 360° videos. We extend previous metrics for quantifying user behavior in order to support more complex and realistic footage, and we introduce two new metrics that allow us to measure users’ exploration in a variety of different complex scenarios. From this analysis, (i) we confirm that previous insights derived for simple content hold for professionally edited content, and (ii) we derive new insights that could potentially influence VR content creation, informing creators about the impact of different cuts in the audience’s behavior.
From Athens to the Blockchain: Oracles for Digital Democracy

Oracles were trusted sources of knowledge for public deliberation in classical Athens. Very much like expert and technical knowledge, divine advice was embedded in the deliberation and decision-making process of the democratic Assembly. While the idea of religious divination is completely out of place in our contemporary democracies, oracles made a technological comeback with modern computer science and cryptography and, more recently, the emergence of the blockchain as a “trust machine”. This paper reviews the role of oracles in Athenian democracy and, stemming from the renewed use of the term in computer sciences and cryptography, analyses the case of oracles in the nascent blockchain ecosystem. The paper also proposes a sociotechnical approach to the use of distributed oracles as informational devices to assist deliberative processes in digital democracy settings, and considers the limits that such an approach may face.
Local nearest neighbour classification with applications to semi-supervised learning

We derive a new asymptotic expansion for the global excess risk of a local-$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the test point. This expansion elucidates conditions under which the dominant contribution to the excess risk comes from the decision boundary of the optimal Bayes classifier, but we also show that if these conditions are not satisfied, then the dominant contribution may arise from the tails of the marginal distribution of the features. Moreover, we prove that, provided the $d$-dimensional marginal distribution of the features has a finite $\rho $th moment for some $\rho >4$ (as well as other regularity conditions), a local choice of $k$ can yield a rate of convergence of the excess risk of $O(n^{-4/(d+4)})$, where $n$ is the sample size, whereas for the standard $k$-nearest neighbour classifier, our theory would require $d\geq 5$ and $\rho >4d/(d-4)$ finite moments to achieve this rate. These results motivate a new $k$-nearest neighbour classifier for semi-supervised learning problems, where the unlabelled data are used to obtain an estimate of the marginal feature density, and fewer neighbours are used for classification when this density estimate is small. Our worst-case rates are complemented by a minimax lower bound, which reveals that the local, semi-supervised $k$-nearest neighbour classifier attains the minimax optimal rate over our classes for the excess risk, up to a subpolynomial factor in $n$. These theoretical improvements over the standard $k$-nearest neighbour classifier are also illustrated through a simulation study.
Bitrate Adaptation for Video Streaming Services in Edge Caching Systems

With the evolution of 5G networks, mobile edge computing (MEC) is being considered as a key enabler for realizing significant improvements in heterogeneous video streaming services. This is because MEC provides storage and computation resources for adaptive bitrate (ABR)-video streaming services to transcode the original video into lower bitrate variants at the edge caching server proactively, thereby facilitating the heterogeneous demands of users. In this paper, we propose a caching and processing framework that jointly considers the popularity and retention rate of video streams to maximize their video bitrate. The problem is formulated as an integer linear program (ILP), which that is challenging because of its NP-hardness. Our algorithm is called online iterative greedy-base adaptation (OIGA); it is built based on the greedy approach with strict constraints for storage size and computing capacity of the cache server. The simulation results show that our proposed solution adapts well to the change in video popularity and retention rate for a maximal video bitrate.
Label-Leaks: Membership Inference Attack with Label.

Machine learning (ML) has made tremendous progress during the past decade and ML models have been deployed in many real-world applications. However, recent research has shown that ML models are vulnerable to attacks against their underlying training data. One major attack in this field is membership inference the goal of which is to determine whether a data sample is part of the training set of a target machine learning model. So far, most of the membership inference attacks against ML classifiers leverage the posteriors returned by the target model as their input. However, empirical results show that these attacks can be easily mitigated if the target model only returns the predicted label instead of posteriors. In this paper, we perform a systematic investigation of membership inference attack when the target model only provides the predicted label. We name our attack label-only membership inference attack. We focus on two adversarial settings and propose different attacks, namely transfer-based attack and perturbation based attack. The transfer-based attack follows the intuition that if a locally established shadow model is similar enough to the target model, then the adversary can leverage the shadow model's information to predict a target sample's membership. The perturbation-based attack relies on adversarial perturbation techniques to modify the target sample to a different class and uses the magnitude of the perturbation to judge whether it is a member or not. This is based on the intuition that a member sample is harder to be perturbed to a different class than a non-member sample. Extensive experiments over 6 different datasets demonstrate that both of our attacks achieve strong performance. This further demonstrates the severity of membership privacy risks of machine learning models.
Asynchronous Byzantine Agreement with Subquadratic Communication

Understanding the communication complexity of Byzantine agreement (BA) is a fundamental problem in distributed computing. In particular, for protocols involving a large number of parties (as in, e.g., the context of blockchain protocols), it is important to understand the dependence of the communication on the number of parties n. Although adaptively secure BA protocols with $$o(n^2)$$ communication are known in the synchronous and partially synchronous settings, no such protocols are known in the fully asynchronous case. We show asynchronous BA protocols with (expected) subquadratic communication complexity tolerating an adaptive adversary who can corrupt $$f< (1-\epsilon )n/3$$ of the parties (for any $$\epsilon >0$$ ). One protocol assumes initial setup done by a trusted dealer, after which an unbounded number of BA executions can be run; alternately, we can achieve subquadratic amortized communication with no prior setup. We also show that some form of setup is needed for (non-amortized) subquadratic BA tolerating $$\varTheta (n)$$ corrupted parties. As a contribution of independent interest, we show a secure-computation protocol in the same threat model that has $$o(n^2)$$ communication when computing no-input functionalities with short output (e.g., coin tossing).
Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.
Topic Sentiment Analysis in Online Learning Community from College Students

Abstract Purpose Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’ learning situation, which provides the necessary theoretical basis for following revision of teaching plans. To improve the accuracy of topic-sentiment analysis, a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models. Methodology/approach We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularity-levels. The proposed method comprised data preprocessing, topic detection, sentiment analysis, and visualization. Findings The proposed model can effectively perceive students’ sentiment tendencies on different topics, which provides powerful practical reference for improving the quality of information services in teaching practice. Research limitations The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach, without considering the intensity of students’ sentiments and their evolutionary trends. Practical implications The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint, which can be utilized as a reference for enhancing the accuracy of learning content recommendation, and evaluating the quality of their services. Originality/value The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics, which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.
Ensemble learning for human activity recognition

This paper describes a activity recognition method for Sussex-Huawei Locomotion (SHL) Challenge 2020 by team TDU_BSA. The use of ensemble learning, which combines the outputs of multiple classifiers to produce a single estimation result, improved the accuracy of activity recognition. The ensemble model consists of CNN models and a gradient-boosting model. The objective of SHL Challenge 2020 is that the users of SHL test-set are two different from SHL training-set, and the phone location of SHL test-set is not known to the SHL's participants. Therefore, estimating phone location and the user improved accuracy. SHL test-set's phone location was estimated to be Hips. The user can be estimated from SHL validation-set. The ensemble model was made with all SHL training-set (Only Hips) and 70% of SHL validation-set (Only Hips). In the submission phase, the best F-measure obtained for last 30% SHL validation-set was 84.8%.
The distributed ledger technology as a measure to minimize risks of poor-quality pharmaceuticals circulation

In the modern world, millions of people suffer from fake and poor-quality medical products entering the market. Violation of the rules of transportation of drugs makes them ineffective and even dangerous. The relationship between the various parts of the supply chain, production and regulation of drugs is too hard and has many problems. Distributed ledger technology is a distributed database, the properties of which allow us to track the entire path of medical products from the manufacturer to consumer, to improve the current model of the supply chain, to transform the pharmaceutical industry and prevent falsified drugs reach the market.The aim of the article is to analyze the distributed ledger technology as an innovative means of poor-quality pharmaceuticals prevention to reach the market as well as their forehanded detection.Content analysis of web sites of companies developing distributed ledger technology solutions had been performed. Five examples found with a google search engine by keywords "distributed ledger technology", "blockchain", "pharmaceuticals" and "supply chain" were examined. Analysis of relative scientific publications had been made. With the help of generalization and systematization methods, services provided by these companies were analyzed. The visual model of the supply chain was created with Microsoft Visio software.The analysis results contain a principle scheme of distributed ledger technology implementation to achieve the objectives. The analysis of present-day pharmaceuticals supply chain structure and the distributed ledger technology capacities to improve pharmaceutical companies has been carried out and presented. Furthermore, the article allows getting acquainted with today's projects released to the market as well as the prognosis of the distributed ledger technology in pharmaceutical industry enhancement in the future.
High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence

Machine learning systems are vulnerable to adversarial attack. By applying to the input object a small, carefully-designed perturbation, a classifier can be tricked into making an incorrect prediction. This phenomenon has drawn wide interest, with many attempts made to explain it. However, a complete understanding is yet to emerge. In this paper we adopt a slightly different perspective, still relevant to classification. We consider retrieval, where the output is a set of objects most similar to a user-supplied query object, corresponding to the set of k-nearest neighbors. We investigate the effect of adversarial perturbation on the ranking of objects with respect to a query. Through theoretical analysis, supported by experiments, we demonstrate that as the intrinsic dimensionality of the data domain rises, the amount of perturbation required to subvert neighborhood rankings diminishes, and the vulnerability to adversarial attack rises. We examine two modes of perturbation of the query: either `closer' to the target point, or `farther' from it. We also consider two perspectives: `query-centric', examining the effect of perturbation on the query's own neighborhood ranking, and `target-centric', considering the ranking of the query point in the target's neighborhood set. All four cases correspond to practical scenarios involving classification and retrieval.