Reviewerly/exHarmony huggingface dataset:
ETHERST: Ethereum-Based Public Key Infrastructure Identity Management with a Reward-and-Punishment Mechanism

Public Key Infrastructure (PKI) is the fundamental of secure digital communications. It provides a secure means to authenticate identities over the Internet. Symmetric or asymmetric encryption schemes are widely used in identity authentication in any kind of PKI. The conventional PKI has several drawbacks due to the centralized and non-transparent design. Several recent research works utilize blockchain technology to overcome the limitations of conventional implementations of PKI. Blockchain-based PKI integrates blockchain technology with PKI to form a new type of decentralized PKI (DPKI). Several works utilize the currency property in blockchains to implement the reward-and-punishment mechanism. In this paper, we propose a smart contract-based PKI which utilizes the Ethereum smart contract to build a new type of blockchain-based PKI with the reward-and-punishment mechanism using ERC-20 tokens. It has several advantages over previous implementations of similar research that use Ethereum’s main currency—Ether.
The impact of human resource management practices on person– job fit: the mediating role of human resources agility at Jordanian airlines companies

This study aims to analyze the impact of the human resources management practices on adapting person- job fit, with the existence of human resource agility as a modified variable in Jordanian airlines, The researcher relies on the descriptive and analytical approach, The study population consist of all employees of the Jordanian airline companies, within the upper and middle administrative levels, whose number is (185) individuals. Due to the study small population number, the researcher adopted the entire study population as a sample for the study and in the comprehensive survey method. The results showed that human resource management practices has a statistically significant effect on person- job fit, human resource management practices effect at the agility of human resources, and agility of human resources effect at the person - job fit in the Jordanian airline companies. The results showed that human resource agility improves the impact of human resource management practices on person- job fit. The study recommended that Jordanian airlines maintain the continuity of interest in human resource agility and alignment of the individual with his job, in addition to human resource management practices in all its dimensions and the use of tools to ensure that the level of interest in them does not decline or decline.
Blockchain Oracle-Based Privacy Preservation and Reliable Identification for Vehicles

Vehicle identity authentication is an important research topic in the field of intelligent transportation. Existing vehicle identity authentication schemes solve the privacy and authentication problem using encryption, but they cannot detect whether a vehicle has malicious behavior while identifying the vehicle. To solve the above problems, a novel vehicle reliable identification scheme based on blockchain oracle is proposed in this paper. The scheme includes a trusted vehicle registration model, a data source reliability model, and an off-blockchain data aggregation model. Specifically, the trusted vehicle registration model divides the registered vehicles into ordinary vehicles and new vehicles. Ordinary vehicles use decentralized oracle technology to combine the on-blockchain smart contract with the off-blockchain real world, while new vehicles are constrained by an additional punishment mechanism. The data source reliability model uses an indicator voting and a resource quantification protocol to ensure the reliability of third-party data sources, and uses (t,n) threshold signature and elliptic curve cryptography (ECC) to guarantee privacy when accessing the information of vehicles and drivers from third-party data sources. The off-blockchain data aggregation model uses multi-attribute analytic hierarchy process to aggregate the third-party data of vehicles and drivers. We implement the scheme in the Solidity Remix integrated development environment and Python environment. The results show that the scheme can effectively guarantee the privacy of vehicles and drivers, and also can achieve credibility, reliability, and fairness.
A Novel Approach to Get Awareness in Saudi Arabia Regarding Phishing Attacks

Phishing attacks are nowadays becoming one of the most predominant cyber-attacks in the world with reports stating a dramatic increase during the COVID-19 pandemic. These types of attacks target the user's often easily exposable knowledge and awareness thereby often resulting in a high success rate. The danger is heightened when the goal of these types of attacks is driven by financial gain, or the obtaining of personal information and critically important data. Our proposed research aims to measure the phishing attack awareness level of Saudi internet users. To collect this data, a survey was designed to measure user's information and awareness about phishing. The result demonstrates the knowledge and the phishing attack awareness level of internet users in Saudi Arabia. Moreover, we have analyzed the collected data with a discussion of the outcomes leading to a final conclusion of our research and the future work that can be done within this area.
A Novel Underwater Image Synthesis Method Based on a Pixel-Level Self-Supervised Training Strategy

With the rapid development of deep neural networks, underwater vision plays an increasingly important role in the underwater robotic operation. However, the scarce underwater datasets greatly limit the performance of deep learning on underwater visual tasks, further hindering the applications of underwater operation. To solve this problem, we propose an underwater image synthesis method, which can directly convert the natural light image into the synthetic underwater image end-to-end. Particularly, a pixel-level self-supervised training strategy is designed to maximize the structural similarity between the synthesized and real images, through training the real underwater images. Finally, extensive experiments are carried out, and the obtained results demonstrate the effectiveness and superiority of our methods by quantitative and qualitative comparisons. The proposed underwater image synthesis method offers a valuable sight for underwater vision and manipulating control.
Toward an eclectic and malleable multiagent educational assistant

Abstract Conversational agents are systems capable of processing and responding to human language. They have evolved over the years from a means to pass the Turing Test to chatbots that fulfill a utilitarian purpose. Closed-domain chatbots are specialized in a specific knowledge base and are often used in an attempt to assist users in an educational context. Existing open-source, educational assistant chatbots are narrow in immediate functionality, thus limiting the content and services that can be provided to students and educators. To address this limitation, a novel multiagent framework is proposed, providing diverse capabilities and component flexibility to better meet varied educational requirements. The version presented in this experiment can not only answer questions in different styles but is also able to provide content summaries and links. The solution is tested by presenting participants with a lesson containing information related to coronavirus disease 2019 (COVID-19), followed by engagement with the chatbot system, a subsequent evaluation of its responses, and a quiz to quantify its pedagogical efficacy. COVID-19 was chosen as the knowledge base due to its current relevance in society. The chatbot framework's knowledge base is comprised of two data sets containing facts related to the virus, one which is used to provide longer, frequently asked questions-type responses, and another used to provide short answers. The resulting participant evaluations indicate a preference for more informative responses in the experimental context and showcase the benefit of the framework's malleability in not only fulfilling but discovering varying needs in educational assistance.
Watching people decide: decision prediction using heatmaps of reading of a decision-support document

Introduction: Previous studies show that reading behavior varies with the readers’ Levels of Expertise (LoE) in a task area. Except for LoE, other factors like acquired information plays a role in this process. In the area of health policymaking, people read supporting documents to inform their decisions. This leads to a natural question: could it be possible to predict the decisions based on the reading pattern of the supporting document on top of their LoE? Method: We collected eye tracker data from a group of people with various LoE. We used the heatmaps as the primary pattern of reading. These were prepared using the average fixation duration of the individuals. First, we performed a hierarchical cluster analysis with the pairwise correlation matrix between the heatmaps, to see whether heatmaps as A single feature were effective to reach our goal. In the second step, we made an ensemble of the features of the reading patterns from the heatmaps and pupillometric features, and LoE, with the decision made by the participants as an outcome, using AdaBoost regressor. In this decision-making task, one could choose one among expensive, prudent, and midway. Result: The first analysis reveals to us that there are a minority number of individuals who read less than the majority group. This minority group tends to make decisions in the extremities. The result of AdaBoost-regressor, shows us 1. the LoE is a stronger feature than the patterns of reading to predict the decision to be taken. 2. the pupillometric features are weaker feature than the reading patterns from the heatmaps for our task. Conclusion: Reading patterns could be useful for forecasting a decision, given the LoE of the individual. Heatmaps can be used as both qualitative and quantitative measures for reading patterns.
Compound Channel Capacities under Energy Constraints and Application

Compound channel models offer a simple and straightforward way of analyzing the stability of decoder design under model variations. With this work we provide a coding theorem for a large class of practically relevant compound channel models. We give explicit formulas for the cases of the Gaussian classical-quantum compound channels with unknown noise, unknown phase and unknown attenuation. We show analytically how the classical compound channel capacity formula motivates nontrivial choices of the displacement parameter of the Kennedy receiver. Our work demonstrates the value of the compound channel model as a method for the design of receivers in quantum communication.
Image Aesthetic Assessment Based on Emotion-Assisted Multi-Task Learning Network

Image emotion recognition and image aesthetic assessment are recent research hotspots in user perception of image content. However, for the study of image aesthetics and image emotion, the vast majority of studies are separated from the relationship between the two, respectively. But in fact, there is a potential and explicit connection between image aesthetics and image emotion. In this paper, we use a multi-task convolutional neural network. To achieve the purpose of this experiment, for the first time, we propose a strictly labeled dataset, which contains multi-person annotated aesthetic scores and eight types of emotion distributions. With the help of this dataset, on the basis of aesthetic assessment based on scene and object branch, the method of an emotion-assisted multi-task learning network is adopted to improve the performance of aesthetic assessment. The experimental results show that our network structure achieves excellent performance in many indexes of image aesthetic assessment when comparing several state-of-the-art aesthetic assessment algorithms and convolution neural networks with strong performance.
Measuring Carbonate Rock Strength using Spectroscopy across the Optical and Thermal Region

Abstract We seek to define new empirical relations between the spectral signature of rock surfaces and their geomechanical characteristics. By this means, proxy tools can be developed to classify the quality of rock masses in situations where access is restricted or impossible. In rock mechanics research, remote sensing technologies are mainly used for mapping surface geometry and rock discontinuities. However, the characterization of intact rock’s mechanical properties via remote sensing is a technique that has not yet been developed. In this study, spectral information is implemented using optical and thermal spectral regions. This technique enables identifying and recognizing objects based on the spectral absorption features of specific chemical attributes. In general, spectral acquisition of rocks can be done using stationary or moving (airborne) platforms. Assuming the chemical and physical attributes of rocks correlate to the material physical and chemical properties we find the spectral-based model for assessing rock strength remotely. As part of a preliminary proof-of-concept stage, we collected multiple cylindrical samples of different carbonate formations from several rock outcrops in Israel. We used point and imaging hyperspectral remote sensors to measure the surfaces of the samples in the visible and infrared regions. We determined the uniaxial compressive strength of the rock samples and analyzed them, to generate a model describing the strength of the samples, solely from spectral readings at both ranges. In addition, we conducted in-situ measurements of rock strength using a Schmidt hammer and rock surface spectra in the visible region on rock outcrops. The results of this pioneering study show that the correlation between carbonated rock strength and its spectral signatures enabled the remote assessment of the carbonate rock strength and led the way to an airborne remote sensing application. We continue to extend the database and further refine the analytical method, using this to establish a solid spectral-based model to depict rock strength remotely through exploiting the hyperspectral technology from stationary and airborne platforms.
Multi-modal Sign Language Recognition with Enhanced Spatiotemporal Representation

Sign language recognition (SLR) has become increasingly popular in recent years in computer vision. It is essential to extract discriminative spatiotemporal features to model the spatial and temporal evolutions of different signs. Also, local gesture and facial expression representations contribute to distinguishing signs with similar motion patterns but different meanings. In this paper, we propose a multi-modal sign language recognition framework, in the RGB representation model, we design the adaptive spatiotemporal attention modules to fulfill the visual cue definition in signing videos, and the adapter is designed for constructing the auxiliary task, which jointly learning with the SLR task to enhances the performance of the model. Given a signing video, a spatiotemporal attention-based Pseudo-3D Residual Networks (STA P3D ResNet) is used to learn spatiotemporal features mainly from the areas of interest and the key frames. After feature extraction, the attention-based Bidirectional Long Short-Term Memory Networks (Att-BLSTM) is utilized to select the significant motions. Meanwhile, from the skeletal data, we can obtain the texture image by color encoding and construct the spatial relation features, which are high level representations of human posture. The learnt skeleton-based features from skeletal data fused with the attention-aware video features to further provide more informative spatiotemporal information for SLR. Experiments are carried out on two large scale sign language datasets. And the experimental results demonstrate the effectiveness of our proposed method.
Accuracy and Size Trade-off of a Cartesian Genetic Programming Flow for Logic Optimization

Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Traditional approaches for logic synthesis have been in the spotlight so far. However, due to advances in machine learning and their high performance in solving specific problems, such algorithms appear as an attractive option to improve electronic design tools. In our work, we explore Cartesian Genetic Programming for logic optimization of exact or approximate combinational circuits. The proposed CGP flow receives input from the circuit description in the format of AND-Inverter Graphs and its expected behavior as a truth-table. The CGP may improve solutions found by other techniques used for bootstrapping the evolutionary process or initialize the search from random (unbiased) individuals seeking optimal circuits. We propose two different evaluation methods for the CGP: to minimize the number of AIG nodes or optimize the circuit accuracy. We obtain at least 22.6% superior results when considering the ratio between accuracy and size for the benchmarks used, compared with the teams from the IWLS 2020 contest that obtained the best accuracy and size results. It is noteworthy that any logic synthesis approach based on AIGs can easily incorporate the proposed flow. The results obtained show that their usage may achieve improved logic circuits.
2021 High-Dynamic Range (HDR) Progress Report

Since its introduction to the consumer market in 2015, high-dynamic range, better known by its acronym “HDR,” has established itself as a foundational component when looking at the aspects defining today’s image fidelity. This three-letter acronym “HDR” has become synonymous with brighter, more colorful pixels also known as (aka) “better pixels” enabled by a larger color palette that can better utilize the perceptual capability of human vision. SMPTE played a key role in enabling the HDR ecosystem with the standardization of an electro-optical transfer function (EOTF) based on the contrast sensitivity of the human visual system (HVS). This standard, ST 2084:2014, or more commonly “PQ” (for perceptual quantizer) establishes a practical maximum luminance range of 0–10,000 cd/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for entertainment purposes much like 20 Hz to 20 kHz defines a practical frequency range for audio reproduction. Note that candelas/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is a technical term for the perceived luminous intensity of light weighted by the HVS spectral response and is often colloquially referred to as “nits”—a term believed to come from the Latin word nitēre, “to shine.” <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Application of cloud technology in digital cultural heritage: an analysis of public culture cloud platforms in China

Purpose The purpose of this paper is to investigate the significant advantages of cloud technology in digital cultural heritage construction by analyzing public culture cloud platforms in China. The authors hope to provide references for other countries and regions on the applications of cloud computing techniques in digital cultural construction. Design/methodology/approach The primary research methods involved interview and case analysis. A comprehensive understanding of cloud technology and China’s culture cloud platforms were gained through research into extensive amounts of literature. Analyzing 21 culture cloud platforms offers a general understanding of culture clouds, while the Hunan Public Culture Cloud acts as a representative sample that gives detailed insight. Findings This paper explores the considerable advantages of cloud computing in digital cultural construction from four aspects: integration of decentralized heterogeneous resources, coordination and cooperation, accurately matching user needs and promotion of balanced service development. Originality/value Existing studies fall short of comprehensive investigations of culture cloud platforms and in-depth analysis of the advantages of cloud technology applications. This paper uses the construction of public culture cloud platforms in China as the research object. Further, this paper compares the construction status of different culture cloud platforms.
Margin-based discriminant embedding guided sparse matrix regression for image supervised feature selection

Matrix regression uses matrix data as input and directly selects the features from matrix data by employing several couples of left and right regression matrices. However, the existing matrix regression methods do not consider the relationship between different classes of data and cannot get discriminant left/right regression matrix, which results in poor classifications. In this paper, a margin-based discriminant embedding sparse matrix regression (MDESMR) model for image supervised feature selection is proposed. For each matrix data, a margin is first defined as the difference between two types of distances determined by the left/right regression matrix. Maximizing the average margin for all training matrix data can get the nonlinear discriminant embedding. Thus, a nonlinear embedding and its linear approximation can be obtained simultaneously. An alternative iterative optimization algorithm for solving the proposed model is also designed and the corresponding closed-form solutions in each iteration are found. Some experiments on several datasets demonstrate the superiority of our method.
A path planning method based on improved particle swarm optimization algorithm

In order to improve the efficiency and reduce energy consumption of unmanned sailing boats for water quality inspection and sampling, particle swarm optimization was used for global path planning. The traditional particle swarm optimization algorithm has the problem of premature convergence because it is easy to fall into the local optimal solution. To solve this problem, an improved particle swarm optimization algorithm is proposed. Based on the traditional particle swarm optimization algorithm, the crossover and mutation operations in genetic algorithm are introduced. Firstly, the particle swarm was initialized and the fitness of the particle was calculated, and the individual optimal value and local optimal value were updated. Then crossover and mutation operations are introduced to update the particle velocity and position according to the results. Finally, the convergence of the algorithm is judged according to the decision criteria. The simulation results show that compared with the traditional particle swarm optimization algorithm, this algorithm can effectively avoid the algorithm falling into the local optimal solution, and greatly shorten the overall path length and reduce path planning time.
A New Blind Video Quality Metric for Assessing Different Turbulence Mitigation Algorithms

Although many algorithms have been proposed to mitigate air turbulence in optical videos, there do not seem to be consistent blind video quality assessment metrics that can reliably assess different approaches. Blind video quality assessment metrics are necessary because many videos containing air turbulence do not have ground truth. In this paper, a simple and intuitive blind video quality assessment metric is proposed. This metric can reliably and consistently assess various turbulent mitigation algorithms for optical videos. Experimental results using more than 10 videos in the literature show that the proposed metrics correlate well with human subjective evaluations. Compared with an existing blind video metric and two other blind image quality metrics, the proposed metrics performed consistently better.