Reviewerly/exHarmony huggingface dataset:
Trusted Blockchain Oracle Scheme Based on Aggregate Signature

With the development of blockchain technology, more and more applications need out-of-chain data.Thus, blockchain oracles have become an important bridge for transferring data on and off the chain.This paper studies the mainstream blockchain oracles scheme, summarizes the shortcomings of the existing schemes and proposes a new blockchain oracle scheme based on BLS (Bohen-Lynn-Shacham) aggregation signature to ensure that off-chain data can be transferred into the blockchain in a trusted and reliable way.Specifically, the scheme uses multiple blockchain oracles to avoid the single point of failure or even a small number of malicious oracles, and improve the credibility of data.At the same time, it not only uses BLS aggregate signature to reduce the storage cost and communication overhead, but also uses commitment mechanisms to ensure the reliability and authenticity of the data.Besides, the simulation results show that the scheme can meet the practical application requirements.
Sensitivity Context-Aware PrivacyPreserving Sentiment Analysis

Sentiment analysis has been a prominent research domain under data analytics whose outcomes have immensely contributed to business growth. Consumers being an integral part of any business, their data along with feedback are exposed and are prone to privacy breach. Although many researchers have contributed extensively towards privacy preservation,some plentiful gaps and challenges still exist. This paper proposes an approach Sensitivity Context-Awareness (SCA) based privacy preserving sentiment analysis to preserve the privacy of consumer data which is acquired with their consent. Experiments are conducted on synthesized dataset about healthcare services and sentiments including positive, negative, and neutral are obtained while preserving the consumer's privacy.
Hybrid Real-Time Fall Detection System Based on Deep Learning and Multi-sensor Fusion

As China's aging population increases, falls have become a common health problem among the elderly. Therefore, fall detection has become an important research direction relating to elderly health problems. In this article, we propose a low-cost smart home fall detecting and monitoring system based on the YCL(YOLO V3 combined with the LiteFlowNet) algorithm and multi-sensor fusion. On the one hand, the behavior of the elderly is monitored by using the YCL algorithm to detect fall activities. On the other hand, a multi-sensor fusion algorithm is applied based on the embedded sensors of a smartphone-i.e., acceleration and air pressure sensors-to detect fall actions. The outputs of these two methods are fused to generate a final result, which is improved in terms of the performance and accuracy of fall detection. Once the fall action is confirmed, the smartphone will immediately send an alert SMS to the emergency contact to seek medical assistance. Our experimental results validate that by combining the two methods, the false alarm rate is reduced, and the fall detection can be achieved even in the blind zone of the camera.
Fuzzy modeling and control of a class of non‐differentiable multi‐input multi‐output nonlinear systems

Abstract This paper presents a new approach in modeling and control of multi‐input multi‐output (MIMO) systems that have non‐differentiable operating points. A circle criterion is introduced at the non‐differentiable operating points to divide the entire operating region into two parts. Takagi‐Sugeno fuzzy models are developed in each part, and a switching framework is introduced to model the operating region. Accordingly, a sliding mode controller (SMC) is developed. The proposed modeling and controller are implemented on the benchmark quadruple tank process (QTP). It is demonstrated that the proposed modeling and controller design provides simplicity and universality in a set of nonlinear systems; it is robust with respect to various internal and external disturbances and model uncertainties which allows for accurate regulation and tracking; and it converges in finite time, is capable of controlling nonlinear systems with coupling dynamic, and enables fuzzy modeling of continuous‐time systems without imposing differentiability.
The Current Challenges of Further Education in ICT with the Example of the Czech Republic

Schools have an irreplaceable role in preparing the young generation for educational, professional, and social challenges, however, this effort fails if teachers themselves are not equipped with sufficient ICT competencies and do not work on their constant development. The presented quantitative study analyzed how secondary school teachers understand and actively use Information and Communication Technologies (ICT) in their teaching practice and what barriers they encounter in terms of the further education in ICT provided by accredited institutions. In a sample of 1846 Czech teachers who completed an online survey, results showed they lacked both understanding and active use of new technologies, rarely participated in further education, and were unsatisfied with the courses provided to them. Their main barriers to further education were the lack of sufficient technology available in school, insufficient knowledge and skills in ICT, and the lack of support from school management. Unlike in other studies, time was not identified as an important barrier. Possible legislative, educational, and school management consequences regarding the further education in ICT based on the barriers in attending the courses and identified age and gender differences are discussed.
Fitting quantum noise models to tomography data

The presence of noise is currently one of the main obstacles to achieving large-scale quantum computation. Strategies to characterise and understand noise processes in quantum hardware are a critical part of mitigating it, especially as the overhead of full error correction and fault-tolerance is beyond the reach of current hardware. Non-Markovian effects are a particularly unfavourable type of noise, being both harder to analyse using standard techniques and more difficult to control using error correction. In this work we develop a set of efficient algorithms, based on the rigorous mathematical theory of Markovian master equations, to analyse and evaluate unknown noise processes. In the case of dynamics consistent with Markovian evolution, our algorithm outputs the best-fit Lindbladian, i.e., the generator of a memoryless quantum channel which best approximates the tomographic data to within the given precision. In the case of non-Markovian dynamics, our algorithm returns a quantitative and operationally meaningful measure of non-Markovianity in terms of isotropic noise addition. We provide a Python implementation of all our algorithms, and benchmark these on a range of 1- and 2-qubit examples of synthesised noisy tomography data, generated using the Cirq platform. The numerical results show that our algorithms succeed both in extracting a full description of the best-fit Lindbladian to the measured dynamics, and in computing accurate values of non-Markovianity that match analytical calculations.
Spatial Imagination With Semantic Cognition for Mobile Robots

The imagination of the surrounding environment based on the experience and semantic cognition has great potential to extend the limited observations to leverage the ability for mapping, collision avoidance and path planning. This paper provides a training-based algorithm for mobile robots to perform spatial imagination based on semantic cognition and evaluates the proposed method for the mapping task. We utilize a photo-realistic simulation environment, Habitat, for training and evaluation. The trained model is composed of Resent-18 as encoder and U-net as the backbone. We demonstrate that the algorithm can perform imagination for unseen parts of the object universally, by recalling the images and experience and compare our approach with traditional semantic mapping methods. It is found that our approach will improve the efficiency and accuracy of semantic mapping.
Dehazing of Satellite Images using Adaptive Black Widow Optimization-based framework

Haze is a common atmospheric disturbance that adversely affects the quality of optical data, thus often restricting their usability. Since these effects are inherent in the process of spaceborne Earth sensing, it is important to develop effective methods to remove them. This work proposes a novel method for de-hazing satellite imagery and outdoor camera images. It is developed by modifying the transmission map used in Dark Channel Prior (DCP) method. A Weighted Variance Guided Filter (WVGF) is introduced for enhancing the image quality, which included a two-stage image decomposition and fusion process. The method also optimally combines the radiance and transmission components along with an additional stage modelling a fusion-based transparency function. A final guided filter-based image refinement scheme is incorporated to improve the processed image quality. The optimal tuning of the image-dependent parameters at various stages is achieved using the newly proposed Adaptive Black Widow Optimization (ABWO) algorithm, which makes the proposed de-hazing scheme fully automatic. Qualitative and quantitative performance analyses, and the results are compared with other state-of-the-art methods. The experimental results reveal that the proposed method performs better as compared with others, independent of the haze density, without losing the natural look of the scene.
Improved Generalization for Secure Personal Data Publishing Using Deviation

The human being leads to an improved lifestyle because of better healthcare facilities, which is possible by practicing “sharing.” Sharing of minimal, necessary Electronic Health Records (EHRs) with legitimate grounds have made it easier for researchers and scientists to apply statistics, develop medicines, and improve healthcare facilities. However, it invites many security and privacy concerns. The data may contain personally identifiable attributes; publishing such data will cause problems. Several studies have been proposed to maintain a balance between privacy and utility, but they were not up-to-the-mark. We propose an improved technique for anonymizing EHR, quasi-identifiers to retain data privacy, and maintains utility while keeping it anonymous. The proposed technique performs anonymization by assigning data into classes, adding uncertainty to it, based on the deviation. Based on experiments, it is concluded that the proposed scheme performs better in terms of privacy, utility and is better to its predecessor while making it difficult to trace back.
A game of information security investment considering security insurance and complementary information assets

Abstract Considering information security insurance, this paper investigates an information security investment game between two firms with complementary information assets. Each firm's information security investment and expected profit in Nash equilibrium (i.e., firms make decisions individually) and social optimum (i.e., firms make decisions jointly) are analyzed through rigorous theoretical analyses and numerical examples. We find that making decisions jointly will make the two firms as a whole obtain more profits than when they make decisions alone, whereas this does not mean that each firm will benefit from the joint decision‐making process. Our results show that a firm yields a smaller expected profit in the joint decision game than the individual decision game under some conditions. In addition, the impacts of a higher insurance price and a higher investment efficiency on a firm's information security investment and expected profit are explored. The results indicate that, for a single firm, a higher insurance price does not necessarily result in smaller profit, and a higher investment efficiency does not always lead to larger profit. Then we design a compensation‐based contract to coordinate the two firms’ information security investments when they make decisions individually. The contract will make the two firms achieve social optimum and ensure that each firm yields more profits than firms without the contract. Finally, we extend our research by setting the insurance amount as a decision variable to verify the above analyses.
Survey of the Development of Quantum Cryptography and Its Applications

Discussion on quantum cryptography is largely focused on quantum key distribution. This leads to the terms occasionally being used interchangeably. However, quantum cryptography is a wide field of study encompassing more than just key distribution. This paper surveys many areas of research in quantum cryptography and how they have developed both theoretically and experimentally. This serves as both a summary of the landscape and provides a basis to construct a trajectory of the future in the field. The paper concludes that many of these areas of study suffer similar drawbacks when being realized and that some require more theoretical advancement. In response to these drawbacks, device independent protocols have gained traction in the field.
Motion Guided Attention Fusion to Recognize Interactions from Videos

We present a dual-pathway approach for recognizing fine-grained interactions from videos. We build on the success of prior dual-stream approaches, but make a distinction between the static and dynamic representations of objects and their interactions explicit by introducing separate motion and object detection pathways. Then, using our new Motion-Guided Attention Fusion module, we fuse the bottom-up features in the motion pathway with features captured from object detections to learn the temporal aspects of an action. We show that our approach can generalize across appearance effectively and recognize actions where an actor interacts with previously unseen objects. We validate our approach using the compositional action recognition task from the Something-Something-v2 dataset where we outperform existing state-of-the-art methods. We also show that our method can generalize well to real world tasks by showing state-of-the-art performance on recognizing humans assembling various IKEA furniture on the IKEA-ASM dataset.
Saccade Landing Point Prediction Based on Fine-Grained Learning Method

The landing point of a saccade defines the new fixation region, the new region of interest. We asked whether it was possible to predict the saccade landing point early in this very fast eye movement. This work proposes a new algorithm based on LSTM networks and a fine-grained loss function for saccade landing point prediction in real-world scenarios. Predicting the landing point is a critical milestone toward reducing the problems caused by display-update latency in gaze-contingent systems that make real-time changes in the display based on eye tracking. Saccadic eye movements are some of the fastest human neuro-motor activities with angular velocities of up to 1,000°/s. We present a comprehensive analysis of the performance of our method using a database with almost 220,000 saccades from 75 participants captured during natural viewing of videos. We include a comparison with state-of-the-art saccade landing point prediction algorithms. The results obtained using our proposed method outperformed existing approaches with improvements of up to 50% error reduction. Finally, we analyzed some factors that affected prediction errors including duration, length, age, and user intrinsic characteristics.
Deep Dynamic Scene Deblurring for Unconstrained Dual-Lens Cameras

Dual-lens (DL) cameras capture depth information, and hence enable several important vision applications. Most present-day DL cameras employ unconstrained settings in the two views in order to support extended functionalities. But a natural hindrance to their working is the ubiquitous motion blur encountered due to camera motion, object motion, or both. However, there exists <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> a single work for the prospective unconstrained DL cameras that addresses this problem (so called dynamic scene deblurring). Due to the unconstrained settings, degradations in the two views need not be the same, and consequently, naive deblurring approaches produce inconsistent left-right views and disrupt scene-consistent disparities. In this paper, we address this problem using Deep Learning and make three important contributions. First, we address the root cause of view-inconsistency in standard deblurring architectures using a Coherent Fusion Module. Second, we address an inherent problem in unconstrained DL deblurring that disrupts scene-consistent disparities by introducing a memory-efficient Adaptive Scale-space Approach. This signal processing formulation allows accommodation of different image-scales in the same network <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> increasing the number of parameters. Finally, we propose a module to address the Space-variant and Image-dependent nature of dynamic scene blur. We experimentally show that our proposed techniques have substantial practical merit.
QuTI! Quantifying Text-Image Consistency in Multimodal Documents

The World Wide Web and social media platforms have become popular sources for news and information. Typically, multimodal information, e.g., image and text is used to convey information more effectively and to attract attention. While in most cases image content is decorative or depicts additional information, it has also been leveraged to spread misinformation and rumors in recent years. In this paper, we present a web-based demo application that automatically quantifies the cross-modal relations of entities~(persons, locations, and events) in image and text. The applications are manifold. For example, the system can help users to explore multimodal articles more efficiently, or can assist human assessors and fact-checking efforts in the verification of the credibility of news stories, tweets, or other multimodal documents.
Information Technology and Gender Economic Inclusion in Sub-Saharan Africa

This study investigates how ICT affects gender economic inclusion via gender parity education channels. We examine the issue using data from 49 countries in sub–Saharan Africa for the period 2004–2018 divided into: (i) 42 countries for the period 2004–2014; and (ii) 49 countries for the period 2008-2018. Given the overwhelming evidence of negative net effects in the first sample, an extended analysis is used to establish thresholds of ICT penetration that nullify the established net negative effects. We found that in order to enhance female labor force participation, the following ICT thresholds are worthwhile for the secondary education channel: 165 mobile phone penetration per 100 people, 21.471 internet penetration per 100 people and 3.475 fixed broadband subscriptions per 100 people. For the same outcome of inducing a positive effect on female labor force participation, a 31.966 internet penetration per 100 people threshold, is required for the mechanism of tertiary school education. These computed thresholds have economic meaning and policy relevance because they are within the established ICT policy ranges. In the second sample, a mobile phone penetration threshold of 122.20 per 100 people is needed for the tertiary education channel to positively affect female labor force participation.
A Bayesian nonparametric estimation to entropy

A Bayesian nonparametric estimator to entropy is proposed. The derivation of the new estimator relies on using the Dirichlet process and adapting the well-known frequentist estimators of Vasicek (Journal of Royal Statistical Society B 38 (1976) 54–59) and Ebrahimi, Pflughoeft and Soofi (Statistics & Probability Letters 20 (1994) 225–234). Several theoretical properties, such as consistency, of the proposed estimator are obtained. The quality of the proposed estimator has been investigated through several examples, in which it exhibits excellent performance.
Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process

Hybrid semiparametric models integrate physics-based (“white-box”, parametric) and data-driven (“black-box”, non-parametric) submodels. Black-box models are often implemented using artificial neural networks (ANNs). In this work, we investigate the fitness of symbolic regression (SR) for black-box modelling. The main advantage of this approach is that a trained hybrid model can be expressed in closed form as an algebraic equation. We examine and test the idea on a simple example, namely the v-shape bending of a metal sheet, where an analytical solution for the stamping force is readily available. We explore unconstrained and hybrid symbolic regression modelling to show that hybrid SR models, where the regression tree is partly fixed according to a-priori knowledge, perform much better than purely data-driven SR models based on unconstrained regression trees. Furthermore, the generation of algebraic equations by this method is much more repeatable, which makes the approach applicable to process knowledge discovery.
A Novel Formulation of Trace Ratio Linear Discriminant Analysis

The linear discriminant analysis (LDA) method needs to be transformed into another form to acquire an approximate closed-form solution, which could lead to the error between the approximate solution and the true value. Furthermore, the sensitivity of dimensionality reduction (DR) methods to subspace dimensionality cannot be eliminated. In this article, a new formulation of trace ratio LDA (TRLDA) is proposed, which has an optimal solution of LDA. When solving the projection matrix, the TRLDA method given by us is transformed into a quadratic problem with regard to the Stiefel manifold. In addition, we propose a new trace difference problem named optimal dimensionality linear discriminant analysis (ODLDA) to determine the optimal subspace dimension. The nonmonotonicity of ODLDA guarantees the existence of optimal subspace dimensionality. Both the two approaches have achieved efficient DR on several data sets.
Naive Bayes Algorithm Implementation Based on Particle Swarm Optimization in Analyzing the Defect Product

Abstract In the era of progressively more competitive industrial competition, especially in the manufacturing world, it is always required to develop the quality or quality of products and productivity. Each company is compete to win market share. One of the strategies carried out by the company is improving the quality of products and the production process conducted by the company. In the industrial world, product quality and productivity are the keys for success of the production process. Therefore, the purpose of this study is to analyze data for defective products at PT Mane Indonesia with the Particle Swarm Optimization (PSO) and Naïve Bayes Classifier method. The accuracy results using the Naïve Bayes algorithm get a value of 84.38% and an AUC value of 0.953. The results of the PSO-based Naïve Bayes algorithm get a value of 88.62% and AUC value of 0.945. Based on the research which has been performed by using Naïve Bayes based on PSO, it developed a contribution rate of 5,02% in predicting the defected products.
Trust Factors of Social Engineering Attacks on Social Networking Services

Social engineering is a hacking technique that steals personal or confidential information by taking advantage of human psychological weaknesses. This paper investigates the trust factors of social engineering attacks on SNSs, by conducting an experiment with 35 participants using a pseudo-social-networking-service application facing a social engineering attack. The results indicate that factors such as the profile details of the attacker and the content of the post do not affect trust, and only a presentation of a negative response to the post affects trust.