Reviewerly/exHarmony huggingface dataset:
Dynamics of entanglement and population inversion of two qubits in a hybrid nonlinear system

An analytical solution is obtained when the Kerr medium and Stark shift are considered as nonlinear interaction terms to the system containing two-qubit and two-mode electromagnetic field from the parametric amplifier. Dynamics of the population inversion, cavity–qubit and qubit–qubit entanglements are analyzed under the unitary cavity–qubit interaction, the Kerr medium and the Stark shift. The population inversion of a qubit presents periodic revivals and collapses. The results show that the entanglement and the population inversion as well as the inversion have the same stable intervals, that is, the collapse intervals. It is found that the Kerr medium and the Stark shift may lead to reduction of the periods and the amplitudes of the population inversion and the cavity–qubit/qubit–qubit entanglement. The deteriorated qubit–qubit/cavity–qubit entanglement and the population inversion, due to the Kerr medium, may be increased by increasing the Stark shift and vice versa.
Embedded Virtualization Computing Platform Security Architecture Based on Trusted Computing

With the application of virtualization and multi-core processor in embedded system, the computing capacity of embedded system has been improved comprehensively, but it is also faced with malicious attacks against virtualization technology. First, it was analyzed the security requirements of each layer of embedded virtualization computing platform. Aiming at the security requirements, it was proposed the security architecture of embedded virtualization computing platform based on trusted computing module. It was designed the hardware trusted root on the hardware layer, the virtualization trusted root on the virtual machine manager layer, trusted computing component and security function component on guest operation system layer. Based on the trusted roots, it was built the static extension of the trusted chain on the platform. This security architecture can improve the active security protection capability of embedded virtualization computing platform.
The Regulation of Social Media Influencers: An Introduction

Social media influencers have become increasingly important in the last years. Influencers, that is, individuals with a large number of followers on social media, filter information, advertise products and services, offer advice, and promote political opinions with a significant impact on a broad audience. Nevertheless, their actions have remained largely unregulated and overlooked in the literature. This paper offers an exploratory analysis of this phenomenon and delves into the regulatory challenges resulting from the activity of social media influencers. It fills an important gap in the academic literature by providing an interdisciplinary analysis of a phenomenon with growing societal relevance. Given their large audiences, influencers are very effective not only at creating online engagement for the companies that employ their services, but also at manipulating their followers’ opinions and transactional behavior, as the latter do not always distinguish between genuine and sponsored marketing advice. Although advertising regulations include endorsements made on Instagram or Youtube by ‘prosumers’, influencers appear to disregard these regulatory limits and national regulators have experienced difficulties in safeguarding their enforcement. In this context, average social media users are at an ever-greater risk of falling prey to inconspicuous commercial interests. This paper also introduces an interdisciplinary project that brings together insights from media studies, law, communication science and empirical legal studies, dealing with themes such as social media, influencer marketing, free speech, gig work, platform governance, and consumer protection.
The Digital Information Competence of an Individual: Historical Aspect

The article is devoted to analyses of the digital information competence. The article presents the positions of scientists on this problem. The components of the specialists' information competence are distinguished. In the article, the relevance of new information technologies is described in the unified educational and information space. The reasons for expanding the digital literacy and the appeal to the digital competence are determined. It is noted that the responsibility as a component of the digital competence requires the knowledge of the rules in the digital world. Four types of the digital competences are defined: information and media competence; communicative, technical, consumer competences. Particular attention is paid to the motivational component, which is related to the security of modern information and communication technologies for studying youth.
A sustainable approach to music education: towards a cultural ecology in the digital age

The paper explains the future perspectives of musical education in the digital age in the aspect of leading trends of economic, political and cultural development of the modern world such as Sustainable development. Nowadays, extremely fast increase of technological progress including digital tools in music offers to transform traditional functions of the most musical professions threaten to change not only the place of music in human life, but a cultural ecology in general including social interaction. Using a sustainable approach allows to shift the point of view from the applied problems in music such as making sounds to the core of the profession of musician as providing social and cultural values as universal category for any human activity include scientific progress. Looking from this side allows to describe the meaning of music as an essential part of cultural ecology and to point out new trends for the development of digital tools in music as a mean to improve creative capacity that is necessary on the way to the purposes of sustainable development in any profession field.
Corporate Social Responsibility and the Role of Rural Women in Sustainable Agricultural Development in Sub-Saharan Africa: Evidence From the Niger Delta in Nigeria

Low productivity among female farmers when compared with their male counterparts is considered an outcome of limited access to agricultural land and inputs. The objective of this investigation was to assess the impact of multinational oil companies’ (MOCs’) CSR on rural women access to modern agricultural inputs in the Niger Delta, Nigeria. A total of 700 rural female farmers were sampled across the region. Results from the use of a logit model indicated that CSR recorded significant success in agricultural development generally, but has undermined equality. This implies that if a woman’s agricultural productivity is continuously hindered by unequal access to agricultural resources (or opportunities) and widespread inequality will limit poverty reduction efforts in Nigeria. The results also showed that women depended on CSR of MOCs for policy dialogue and advocacy for women’s access to agricultural land and inputs. Supporting agricultural initiatives that focus on empowering women would boost food security in sub-Saharan Africa.
Multi-point Stabilization Approach to the Optimal Control Problem with Uncertainties

The article is studying the solution of the optimal control problem on the implementation. The problem here is that to implement the received optimal control a stabilization system is needed. But construction of such system makes changes to the mathematical model of the control object, so that the received control is not more optimal for that object. The paper introduces the concept of feasibility of control systems. An approach based on multi-point stabilization to receive feasible solutions of the optimal control problem is proposed. According to the approach, stabilization system synthesis is solved firstly. The synthesis problem can be solved by any known analytical or technical method. In the paper a symbolic regression method is used for the numerical solution of the synthesis problem. As far as the solution of synthesis problem is received, the differential equations of the model as one-parameter mappings acquire the contraction property, which reduces small model uncertainties. Then positions of stable points in the state space are found such that when switching from one point to another in some time interval the control object will move from initial conditions to terminal one with an optimal value of a quality criterion. In the computational experiment the efficiency of the proposed approach comparative to the direct approach is shown in the presence of uncertainties.
Visual Analysis of COVID-19 Trends

The declaration of COVID-19 as a pandemic has urged health and government authorities to investigate and control the outbreak.Among the various techniques that support the decision making, visualization represents a powerful tool to provide insightful information into every facet of the pandemic.In this effort, we provide a visual anaytic tool that facilitates the investigation of COVID-19 data including time series daily reports and mobility trends.The tool provides several visual layouts and enables users to gain insight into the impact of preventive and precautionary measures in combating the pandemic.
Fuzzy trust based collaborative filtering analysis for mobile user preferences

With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.
A Framework for Assessing and Refining the Quality of R2RML mappings

"Uplift" mapping execution applies a set of mapping definitions to transform non-RDF data sources to RDF. During the uplift mapping process, mapping definitions are iteratively refined until they conform with the user's expressed requirements. The W3C standard R2RML is one language which allows for specifying the mappings needed to generate RDF datasets from relational databases. Many approaches have been proposed to assess the quality of the generated RDF datasets, even though the root cause of several of those quality violations are found in mappings. In this paper, we present a framework for assessing and refining the quality of the definitions used to transform non-RDF data to RDF. This paper also provides an overview of an implementation of the proposed quality assessment framework for the R2RML mapping language, which uses the W3C standard Shapes Constraint Language (SHACL). We also provide a demonstration of the proposed framework and its implementation through a walkthrough use case.
Defining a no-reference image quality assessment by means of the self-affine analysis

In this paper we propose a novel Blind Image Quality Assessment via Self-Affine Analysis (BIQSAA) method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. BIQSAA decomposes a distorted image into a set of wavelet planes ωλ, ϕ of different spatial frequencies λ and spatial orientations ϕ, and it transforms these wavelet planes into one-dimension vector Ω using a Hilbert scanning. From the vector Ω there were obtained their wavelet coefficient fluctuations estimated by the inverse of the Hurst exponent in decibels, whose scaling-law or fractal behavior was obtained by applying Fractal Geometry or Self-Affine Analysis. The scaling exponents calculated for the coefficient fluctuation behavior of Image Lena at 24bpp, at 1.375bpp, and at 0.50bpp were H24bpp = 0.0395, H1.375bpp = 0.0551, and H0.50bpp = 0.0612, respectively. Our experiments show that BIQSAA algorithm improves in 14.36% the Human Visual System correlation, respect to the four state-of-the-art No-Reference Image Quality Assessments.
Movie Recommender System using Single Value Decomposition and K-means Clustering

Abstract The recommender system is technique and tool to filter the massive overloaded information for suggesting most useful information to the user in a personalized manner. In the period of “Big Data”, the researcher experiences many problems to process big data accurately and efficiently. In this work, we introduce an efficient model using Single Value Decomposition (SVD) as a method for dimensions reduction and K-means clustering as classification method. Our proposed method and its corresponding results have been evaluated and compare results with other existing methods using metrics like standard deviation (SD), mean absolute error (MAE) root mean square error (RMSE), t-value, dunn index, average similarity and computational time using two publicly available datasets using Flixter dataset and MovieLens dataset. The result demonstrates that our proposed method is able to outperform other existing methods.
Considering Human Perception and Memory in Interactive Multimedia Retrieval Evaluations

Experimental evaluations dealing with visual known-item search tasks, where real users look for previously observed and memorized scenes in a given video collection, represent a challenging methodological problem. Playing a searched “known” scene to users prior to the task start may not be sufficient in terms of scene memorization for re-identification (i.e., the search need may not necessarily be successfully “implanted”). On the other hand, enabling users to observe a known scene played in a loop may lead to unrealistic situations where users can exploit very specific details that would not remain in their memory in a common case. To address these issues, we present a proof-of-concept implementation of a new visual known-item search task presentation methodology that relies on a recently introduced deep saliency estimation method to limit the amount of revealed visual video contents. A filtering process predicts and subsequently removes information which in an unconstrained setting would likely not leave a lasting impression in the memory of a human observer. The proposed presentation setting is compliant with a realistic assumption that users perceive and memorize only a limited amount of information, and at the same time allows to play the known scene in the loop for verification purposes. The new setting also serves as a search clue equalizer, limiting the rich set of present exploitable content features in video and thus unifies the perceived information by different users. The performed evaluation demonstrates the feasibility of such a task presentation by showing that retrieval is still possible based on query videos processed by the proposed method. We postulate that such information incomplete tasks constitute the necessary next step to challenge and assess interactive multimedia retrieval systems participating at visual known-item search evaluation campaigns.
Price Discovery in Bitcoin Futures: Evidence From BitMEX

BitMEX is the largest unregulated bitcoin derivatives exchange, listing contracts suitable for leverage trading and hedging. Using minute-by-minute data, we examine its price discovery and hedging effectiveness. We find that BitMEX derivatives lead prices on major bitcoin spot exchanges. Bid-ask spreads, inter-exchange spreads and relative trading volumes are important determinants of price discovery. Further analysis shows that BitMEX derivatives have positive net spillover effects, are informationally more efficient than bitcoin spot prices, and serve as effective hedges against spot price volatility. Our evidence suggests that regulators prioritise investigation of the legitimacy of BitMEX and its contracts.
Reasoning About Ignorance and Beliefs

When building artificial agents that have to make decisions, understanding what follows from what they know or believe is mandatory, but it is also important to understand what happens when those agents ignore some facts, where ignoring a fact is interpreted to stand for not knowing/not being aware of something. This becomes especially relevant when such agents ignore their ignorance, since this hinders their ability of seeking the information they are missing. Given this fact, it might prove useful to clarify in which circumstances ignorance is present and what might cause an agent to ignore that he/she is ignoring. This paper is an attempt at exploring those facts. In the paper, the relationship between ignorance and beliefs is analysed. In particular, three doxastic effects are discussed, showing that they can be seen as a cause of ignorance. The effects are formalized in a bi-modal formal language for knowledge and belief and it is shown how ignorance follows directly from those effects. Moreover, it is shown that negative introspection is the culprit of the passage between simply ignoring a fact and ignoring someone’s ignorance about that fact. Those results could prove useful when artificial agents are designed, since modellers would be aware of which conditions are mandatory to avoid deep forms of ignorance; this means that those artificial agents would be able to infer which information they are ignoring and they could employ this fact to seek it and fill the gaps in their knowledge/belief base.
Assessing journals in library and information studies based on altmetrical indicators

Altmetrical indicators are characterized in brief; their advantages, disadvantages and applicability in assessing periodicals are examined. The authors attempt to define the significance of library journals based on the alternative metrics of views and loadings. Essential data are obtained from Russian Science Citation Index database. The selection of publications under examination is substantiated. The low-cited publications and those RSCI provide insufficient data on, were not considered. The comparison is made; the mostly cited, viewed and loaded journals are identified for the years 2013-2017. The key study tasks are to identify citation, view and loading level for each journal; to reveal correlation between loadings and citations and to evaluate its closeness. The main advantages of altmetrical indicators are: responsiveness, open access, wide coverage of sources. The reasons are discussed why the high-cited publications correspond to the limited number of loadings. This correspondence enables to suggest that the higher the number of loadings, the more significant it is. The authors suggest using number of loadings to reveal the most authoritative publications when developing library collections. They also suggest that the role of altmetrics will be increasing.
SETA: Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

While end-to-end encryption brings security and privacy to the end-users, it makes legacy solutions such as Deep Packet Inspection ineffective. Despite the recent work in machine learning-based encrypted traffic classification, these new techniques would require, if they were to be deployed in real enterprise-scale networks, an enhanced flow sampling due to sheer volume of data being traversed. In this paper, we propose a holistic architecture that can cope with encryption and multi-Gbps line rate with sampling and sketching flow statistics, which allows network operators to both accurately estimate the flow size distribution and identify the nature of VPN-obfuscated traffic. With over 6000 video traffic traces, we show that it is possible to achieve 99% accuracy for service provider classification even with sampled possibly inaccurate data.
Performance Evaluation of One-Class Classifiers (OCC) for Damage Detection in Structural Health Monitoring

Structural Health Monitoring (SHM) has become an area of continuous research with the ever-increasing demand for the safety of civil structures. The damage in civil structures can be detected using multimodal data from sensors, which presents instances of both damaged and undamaged data. The availability of damaged data in real life is difficult to obtain from a healthy structure and hence the problem of damage detection needs to be attempted using normal healthy data, and it becomes synonymous with the anomaly or novelty detection. One-Class classifiers work on the principle that the abundance of healthy data can be used to model an envelope of conditions which, if violated by any data instance, can be termed as damage or outlier detection. We have attempted an array of classifiers on a benchmark structure dataset (IASC-ASCE) both from supervised and unsupervised machine learning domain and propose a comparison between their success rates in determining damage in civil structures. We used classical techniques such as One-Class Support Vector Machines (OC-SVM), One-Class Isolation Forest (OC-IF), One-Class K-means clustering (OC-KMC), One-Class K-nearest neighbors (OC-KNN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), One-Class Principal Component Analysis (OC-PCA), Local Outlier Factor (LOF) and One-Class Gaussian Distribution (OC-GD). These techniques were tested on the IASC (International Association for Structural Control)–ASCE (American Society of Civil Engineers) SHM benchmark problem for a range of noise levels and range of force intensities to cover wide variations in the generated dataset using MATLAB based simulation. Our study helps us conclude that OC-SVM, Isolation Forest, and OC-PCA are the most robust algorithms for the anomaly detection task.
A Vehicle Reidentification Algorithm Based on Double-Channel Symmetrical CNN

It has become a challenging research topic to accurately identify the vehicles in the past from the mass monitoring data. The challenge is that the vehicle in the image has a large attitude, angle of view, light, and other changes, and these complex changes will seriously affect the vehicle recognition performance. In recent years, the convolutional neural network (CNN) has achieved great success in the field of vehicle reidentification. However, due to the small amount of vehicle annotation in the dataset of vehicle reidentification, the existing CNN model is not fully utilized in the training process, which affects the ability to identify the deep learning model. In order to solve the above problems, a double-channel symmetric CNN vehicle recognition algorithm is proposed by improving the network structure. In this method, two samples are taken as input at the same time, in which each sample has complementary characteristics. In this case, with limited training samples, the combination of inputs will be more diversified, and the training process of the CNN model will be more abundant. Experiments show that the recognition accuracy of the proposed algorithm is better than other existing methods, which further verifies the effectiveness of the proposed algorithm in this study.