Reviewerly/exHarmony huggingface dataset:
Multimodal Wireless Situational Awareness-Based Tourism Service Scene

Community platforms featuring user sharing and self-expression in social media generate big data on tourism resources, which, if fully utilized in a smart tourism system driven by high-tech and new technologies, will bring new life to the field of smart tourism research and will play an important role in the development of Internet+ tourism. However, tourism data in social media has the following characteristics: diversity, redundancy, heterogeneity, and intelligence. To address the characteristics of tourism data in social media, this thesis focuses on the following challenges: it is difficult to efficiently obtain tourism visualization information (text and images) in social media; it is difficult to effectively utilize tourism multimodal heterogeneous information; it is difficult to properly retrieve multimedia entity information of tourism attractions; and it is difficult to reasonably construct tourism personalized recommendation models. In this paper, an image search reordering method based on a hybrid feature graph model is proposed to realize the rapid acquisition of high-quality Internet images from the web using hybrid visual features and graph models, thus providing data security for the analysis of social media-based tourism images. To address the shortcomings of current search engines for image retrieval, visual information is used to bridge the problem of semantic gap between text-based search and images. To address the limitation of single visual features, we use latent semantic analysis to fuse multiple visual features to obtain hybrid features, which not only combine multiple single features but also preserve the potential relationship between these features. To address the shortcomings of the reordering methods based on classification and clustering, a reordering framework based on the graph model is used to reorder the images and finally complete the image search reordering based on the hybrid feature graph model. This method can obtain image information in social media with high efficiency and quality and then prepare for the subsequent work of tourism image analysis mining and personalized recommendation.
Reducing the manual length setting error of a passive Gough-Stewart platform for surgical template fabrication using a digital measurement system

Abstract As recently demonstrated, a passive Gough-Stewart platform (a.k.a. hexapod) can be used to create a personalized surgical template to achieve minimally invasive access to the cochlea. The legs of the hexapod are manually adjusted to the desired length, which must be read off an analog scale. Previous experiments have shown that manual length setting of the hexapod’s legs is error-prone because of the imprecise readability of the analog scale. The objective of this study is to determine if integration of a linear encoder and digitally displaying the measured length help reduce the length setting error. Two experiments were conducted where users set the leg length manually. In both experiments, the users were asked to set the leg length to 20 nominal values using the whole setting range from 0 mm to 10 mm. In the first experiment, users had to rely only on the analog scale; in the second experiment, the electronic display additionally showed the user the actual leg length. Results show that the mean length setting error without using the digital display and only relying on the analog scale was (0.036 ± 0.020) mm (max: 0.107 mm) in contrast to (0.001 ± 0.000) mm (max: 0.002 mm) for the experiment with the integrated digital measurement system. The results support integration of digital length measurement systems as a promising tool to increase the accuracy of surgical template fabrication and thereby patients’ safety. Future studies must be conducted to evaluate if integration of a linear encoder in each of the six legs is feasible.
ChartMaster: An End-to-End Method to Recognize, Redraw, and Redesign Chart

Chart is one kind of ubiquitous information, which is widely utilized and easy for people to understand. Due to there are so many different kinds and different styles of charts, it is not an easy task for a computer to recognize a chart, as well as to redraw the chart or redesign it. This study proposes a three-stage method to chart recognition: analyze the classification of charts, analyze the structure of charts, and analyze the content of charts. When classifying charts, we choose ResNet-50. When recognizing the structure and content of charts, we use different deep frameworks to extract key points based on different types of charts. Besides, we also introduce two datasets, UCCD and UCID, to train deep models to classify and recognize charts. Finally, we utilize some traditional geometric methods to obtain an original table of a chart, so we can redraw it.
Fake or Genuine? Contextualised Text Representation for Fake Review Detection

Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models.
Connecting the Dots: Engaging Wider Forms of Openness for the Mutual Benefit of Musicians and Musicologists

While it is encouraging to see renewed attention to 'openness' in academia, that debate (and its interpretation of the F.A.I.R. principles) is often rather narrowly defined. This paper addresses openness in a broad sense, asking not so much whether a project is open, but how open and to whom. I illustrate these ideas through examples of my own ongoing projects which to seek to make the most of a potential symbiosis between academic and wider musical communities. Specifically, I discuss how these communities can both benefit from – and even work together on building – highly accessible and interoperable corpora of scores and analyses when ambitious openness is factored into decision making from the outset.
Color Image Landscape Photo Hand-Painted Effect Evaluation with PFA Algorithm

With the continuous development of society and economy, diversified presentation and publicity of landscape are becoming more and more popular among the public, such as using color pictures to display multimedia publicity videos or APP, WeChat public accounts, and other new media. There are various ways of expression. Against the limitations of color image landscape, this article introduced the PFA algorithm, during the experiment of hand-painted landscape pictures, the effect of color hand-painted images is evaluated by using the coordination degree of natural target detection scene, and the corresponding image evaluation index values are obtained for effective analysis, and the prediction model is established. The simulation results show that the PFA algorithm is effective and can support the evaluation of the hand-painted effect of color landscape photos.
Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Cloud computing systems revolutionized the Internet, and web systems in particular. Quality of service is the basis of resource configuration management in the cloud. Load balancing mechanisms are implemented in order to reduce costs and increase the quality of service. The usage of those methods with adaptive intelligent algorithms can deliver the highest quality of service. In this article, the method of load distribution using neural networks to estimate service times is presented. The discussed and conducted research and experiments include many approaches, among others, application of a single artificial neuron, different structures of the neural networks, and different inputs for the networks. The results of the experiments let us choose a solution that enables effective load distribution in the cloud. The best solution is also compared with other intelligent approaches and distribution methods often used in production systems.
Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error

Accidentally clicking on a link is a type of human error known as a slip in which a user unintentionally performs an unintended task. The risk magnitude is the probability of occurrences of such error with a possible substantial effect to which even experienced individuals are susceptible. Phishing attacks take advantage of slip-based human error by attacking psychological aspects of the users that lead to unintentionally clicking on phishing links. Such actions may lead to installing tracking software, downloading malware or viruses, or stealing private, sensitive information, to list a few. Therefore, a system is needed that detects whether a click on a link is intentional or unintentional and, if unintentional, can then prevent it. This paper proposes a micro-behavioral accidental click detection system (ACDS) to prevent slip-based human error. A within-subject-based experiment was conducted with 20 participants to test the potential of the proposed system. The results reveal the statistical significance between the two cases of intentional vs. unintentional clicks using a smartphone. Random tree, random forest, and support vector machine classifiers were used, exhibiting 82.6%, 87.2%, and 91.6% accuracy in detecting unintentional clicks, respectively.
Improving Security and Fairness in Federated Learning Systems

The ever-increasing use of Artificial Intelligence applications has made apparent that the quality of the training datasets affects the performance of the models. To this end, Federated Learning aims to engage multiple entities to contribute to the learning process with locally maintained data, without requiring them to share the actual datasets. Since the parameter server does not have access to the actual training datasets, it becomes challenging to offer rewards to users by directly inspecting the dataset quality. Instead, this paper focuses on ways to strengthen user engagement by offering “fair” rewards, proportional to the model improvement (in terms of accuracy) they offer. Furthermore, to enable objective judgment of the quality of contribution, we devise a point system to record user performance assisted by blockchain technologies. More precisely, we have developed a verification algorithm that evaluates the performance of users’ contributions by comparing the resulting accuracy of the global model against a verification dataset and we demonstrate how this metric can be used to offer security improvements in a Federated Learning process. Further on, we implement the solution in a simulation environment in order to assess the feasibility and collect baseline results using datasets of varying quality.
“My battery is low and it's getting dark”

The Mars Exploration Rover Opportunity operated on Mars from 2004 until it was disabled by a dust storm in 2018. Its demise was declared in February 2019 after months of unsuccessful recontact attempts by scientists at the National Aeronautics and Space Administration (NASA). This announcement sparked a global outpouring of grief that demonstrated people understood and related to the robot in a notably human-like manner. In short, it had been given a collectively understood persona. This paper presents a study of 100 digital postcards created by users on a NASA website that demonstrate the ways in which people expressed love, grief, hope, and thanks for Opportunity’s fourteen years of operation on another planet. In presenting this case study, the paper argues that certain personas are collective achievements. This is especially likely to occur for robots and other inanimate objects which have no centrally controlled or developed persona. The paper is situated within existing persona studies literature to extend and stretch the definition of persona studies and therefore expand the field in productive ways to incorporate the study of non-human personas.
Comparative assessment of evapotranspiration in Bhima sub-basin using spatial analysis for normal and ENSO years

Evapotranspiration (ET) estimation is important for hydrological modelling and water management for irrigation. The present study estimates the reference evapotranspiration using FAO Penman-Monteith (FAO P-M) method and SWAT hydrological model, and its spatial variation during ENSO events during 1996 to 2013. The spatial variation of crop coefficient and actual evapotranspiration (ETa) is also analyzed. The results from these methods are compared for various El Niño-Southern Oscillation (ENSO) events and normal years. MODIS NDVI data was used to generate crop coefficients which were further used for generation of ETa.The results show that the ET0 estimated using FAO P-M is less during the pre-monsoon period than ET0 estimated using SWAT model. ET0values from FAO P-M show decreasing trends while those by SWAT show increasing trends. Also, ET0 shows higher values during post monsoon period of El Niño years as compared to La Niña and normal years.
Efficient PBFT: A Novel and Efficient Approach to the PBFT Consensus Algorithm

Blockchain technology, which is built upon the distributed and decentralised network, suffers from mutual trust among the nodes, and hence, it employs a consensus algorithm to achieve democracy. Consensus algorithm is one of the core elements of blockchain architecture, and a strong, reliable and efficient consensus algorithm is very much required for a blockchain network in order to prevent the intrusion of malicious nodes. A good consensus algorithm drives the blockchain network in the right direction by keeping the malicious transactions off the chain. Thus, in this article, a novel consensus algorithm referred to as efficient practical Byzantine fault tolerance (E-PBFT) is proposed for a private blockchain. E-PBFT finds the winner miner for mining the candidate blocks in an efficient manner which reduces the number of messages exchanged between the nodes. Further, in order to give additional security, a hidden winner miner concept is also included in E-PBFT which hides the winner miner from the adversaries, so that the adversaries will never come to know who is mining the transactions. The theoretical proof for the proposed algorithm is also given whose results are quite convincing when compared to classical PBFT algorithm. The proposed E-PBFT will definitely strengthen the blockchain network by quickly reaching the consensus and by hiding the winner miner, thereby giving additional security.Rao, Vishakh Shenoy, Amodh Ranjal Kiran, M.
Image Denoising using Adaptive NL Means Filtering with Method Noise Thresholding

Background/Objectives: Image denoising is an important step in image processing applications.Usually noise is added to the original image during transmission, acquisition and storage process and is considered as noisy image.For precise analysis and extraction of image features, the noisy image is denoised without losing the original image details.This study aims to introduce a novel denoising method to obtain denoised image(s) such that it has fewer artifacts and is more efficient at higher noise levels.Method: The proposed novel denoising method introduces Adaptive Non Local Means (ANL) along with Method Noise Thresholding (MNT) technique to improve the image quality of the denoised image.Method Noise (MN) image obtained by taking the difference of image details between noisy image and pre-filtered mage.Recovered value from the MN through thresholding includes some of the important components of the original image.These values computed added to pre-filtered image to recover image features of the original image.Findings: The standard image, denoised with noise standard (σ =10) using bior6.8wavelet when filtered using existing Gaussian Bilateral Filter along with Method-Noise Thresholding filtering technique and Wiener Filter along with Residual Thresholding show improvement in quality of the denoised image in terms of PSNR and ISSN values as compared to the proposed filtering technique.The proposed filter technique results in higher PSNR and ISSN values (PSNR =33.80 and SSIN =0.9994).Novelty: It is known that ANLM results in improved denoised parameters compared with NLM filter; however, when MNT is blended with ANLM shows further improvement in quality of denoised image.Hence, in the proposed method, MNT is incorporated along with ANLM for improvement in denoising process.Image Quality Index (IQI) of the different standard images using ANLMT filtering technique is also studied.
Is the EU human rights legal framework able to cope with discriminatory AI?

The challenges introduced by AI for the EU anti-discrimination legal framework have been a widely discussed topic among the doctrine. In the light of the 20th anniversary of the EU Charter of Fundamental Rights, the Commission released a regulatory proposal to tackle AI. This paper seeks to determine whether the proposal successfully addresses the existent pitfalls of the EU framework. First, this paper explores the functioning of AI systems that employ machine learning techniques and determines how discrimination takes place. Second, the article examines intellectual property rights as one of the main barriers for accountability and redressal of violations committed by an AI system. Third, the state of the discussion concerning the pitfalls of the existent EU approach towards non-discrimination is addressed. The available academic literature suggests that discriminatory outputs produced by an AI will amount to indirect discrimination in most scenarios. In this sense, cases of indirect proxy discrimination will likely pass the proportionality test, therefore justifying the discriminatory output. The last section of this article studies the Commission’s regulatory proposal. Although the document seems to effectively tackle discrimination caused by biased training data sets, this paper concludes that intellectual property rights and proxy discrimination still constitute significant barriers for the enforcement of anti-discrimination law.
Smart Identity Management System by Face Detection Using Multitasking Convolution Network

Identity management system in most academic and office environments is presently achieved primarily by a manual method where the user has to input their attendance into the system. The manual method sometimes results in human error and makes the process less efficient and time-consuming. The proposed system highlights the implementation and design of a smart face identification-based management system while taking into account both the background luminosity and distance. This system detects and recognizes the person and marks their attendance with the timestamp. In this methodology, the face is initially resized to 3 different sizes of 256, 384, and 512 pixels for multiscale testing. The overall outcome size descriptor is the overall mean for these characteristic vectors, and the deep convolution neural network calculates 22 facial features in 128 distinct embeddings in 22-deep network layers. The pose of the 2D face from −15 to +15° provides identification with 98% accuracy in low computation time. Another feature of the proposed system is that it is able to accurately perform identification with an accuracy of 99.92% from a distance of 5 m under optimal light conditions. The accuracy is also dependent on the light intensity where it varies from 96% to 99% under 100 to 1000 lumen/m2, respectively. The presented model not only improves accuracy and identity under realistic conditions but also reduces computation time.
Mixture of Species Sampling Models

We introduce mixtures of species sampling sequences (mSSS) and discuss how these sequences are related to various types of Bayesian models. As a particular case, we recover species sampling sequences with general (not necessarily diffuse) base measures. These models include some “spike-and-slab” non-parametric priors recently introduced to provide sparsity. Furthermore, we show how mSSS arise while considering hierarchical species sampling random probabilities (e.g., the hierarchical Dirichlet process). Extending previous results, we prove that mSSS are obtained by assigning the values of an exchangeable sequence to the classes of a latent exchangeable random partition. Using this representation, we give an explicit expression of the Exchangeable Partition Probability Function of the partition generated by an mSSS. Some special cases are discussed in detail—in particular, species sampling sequences with general base measures and a mixture of species sampling sequences with Gibbs-type latent partition. Finally, we give explicit expressions of the predictive distributions of an mSSS.
Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models

Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.
Remote Learning Environment: Transition to Remote Education

The study identifies the peculiarities of e-learning in the transitional period in secondary vocational and higher education in self-isolation mode, caused by COVID-19. In this research, the authors aim to determine the capabilities of online learning tools and Information and Communication Technologies (ICT) implemented in the academic process for all levels of Higher Education: Bachelors / Masters Degree Programs.