Reviewerly/exHarmony huggingface dataset:
A Comparison Between Two Approaches to Optimize Weights of Connections in Artificial Neural Networks

Artificial neural networks (ANNs) have been used for estimation in numerous areas.Raising the accuracy of ANNs is always one of the important challenges, which is generally defined as a non-linear optimization problem.The aim of this optimization is to find better values for the weights of the connections and biases in ANN because they seriously affect the efficiency.This study uses two approaches to do such optimization in an ANN.For this aim, we create a feed-forward backpropagation ANN using the functions of MATLAB's deep learning toolbox.To improve its accuracy, in the first approach, we use the Levenberg -Marquardt algorithm (LMA) for training, which is available in MATLAB's deep learning toolbox.In the second approach, we optimize the values of weights and biases of ANN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), available in MATLAB's global optimization toolbox.Then, we assess the accuracy of estimation for the trained ANNs.In this way, for the first time in the literature, we compare these methods for the optimization of an ANN.The used data sets are also available in MATLAB.Based on the acquired results, in some data sets, training with LMA, and for some others training with PSO cause the best results, however, training with LMA is faster, significantly.Although the used approaches and the obtained conclusions are beneficial for researchers that work in this field, they have some limitations.For instance, since only the functions and data sets from MATLAB are used, it can only serve as an example for researchers.
Cybersecurity, Internal Controls, and Brand Capital

In this paper, we examine whether and how firms’ brand capital is hurt by cybersecurity breaches. Our difference-in-differences analysis based on matched pairs indicates that firms suffering cybersecurity breaches are associated with more canceled trademarks, fewer trademark registrations, and fewer trademark citations, all pointing to weakened brand capital. We also show that cybersecurity breaches are associated with more negative news sentiments and poorer operating performance, suggesting that the loss of clients’ trust serves as the underlying mechanism. Additional analyses show that brand capital is hurt more by cybersecurity breaches when firms engage in more internet advertising activity, when firms are headquartered in states that enact notification laws, or when firms have poorer internal controls. We also find an intriguing pattern that cyberattacks on a firm also hurt its peers’ brand capital, a finding that supports a contagion effect.
AI Enabled Data Quality Monitoring with Hydra

Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how “off-the-shelf” technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.
HTTPS-Only: Upgrading all connections to https in Web Browsers

The number of websites that support encrypted and secure https connections has increased rapidly in recent years.Despite major gains in the proportion of websites supporting https, the web contains millions of legacy http links that point to insecure versions of websites.Worse, numerous websites often use http connections by default, even though they already support https.Establishing a connection using http rather than https has the downside that http transfers data in cleartext, granting an attacker the ability to eavesdrop, or even tamper with the transmitted data.To date, however, no web browser has attempted to remedy this problem by favouring secure connections by default.We present HTTPS-Only, an approach which first tries to establish a secure connection to a website using https and only allows a fallback to http if a secure connection cannot be established.Our approach also silently upgrades all insecure http subresource requests (image, stylesheet, script) within a secure website to use the secure https protocol instead.Our measurements indicate that our approach can upgrade the majority of connections to https and therefore suggests that browser vendors have an opportunity to evolve their current connection model.
A Graph Attention Network Model for GMV Forecast on Online Shopping Festival

In this paper, we present a novel Graph Attention Network based framework for GMV (Gross Merchandise Volume) forecast on online festival, called GAT-GF. Based on the well-designed retailer-customer graph and retailer-retailer graph, we employ a graph neural network based encoder cooperated with multi-head attention and self attention mechanism to comprehensively capture complicated structure between consumers and retailers, followed by a two-way regression decoder for effective predition. Extensive experiments on real promotion datasets demonstrate the superiority of GAT-GF.
Visual MAV Tracker with Adaptive Search Region

Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion. A new model was developed by combining compact and adaptive search region (SR). The model can accurately and robustly track MAVs with a fast computation speed. A compact SR, which is slightly larger than a target MAV, is less likely to include a distracting background than a large SR; thus, it can accurately track the MAV. Moreover, the compact SR reduces the computation time because tracking can be conducted with a relatively shallow network. An optimal SR to MAV size ratio was obtained in this study. However, this optimal compact SR causes frequent tracking failures in the presence of the dynamic MAV motion. An adaptive SR is proposed to address this problem; it adaptively changes the location and size of the SR based on the size, location, and velocity of the MAV in the SR. The compact SR without adaptive strategy tracks the MAV with an accuracy of 0.613 and a robustness of 0.086, whereas the compact and adaptive SR has an accuracy of 0.811 and a robustness of 1.0. Moreover, online tracking is accomplished within approximately 400 frames per second, which is significantly faster than the real-time speed.
Imbalanced Dataset Optimization with New Resampling Techniques

Imbalanced datasets, which are very common in many application fields, represent a formidable problem for most of the machine learning algorithms. On the other hand, such algorithms are being extensively applied in many areas, showing promising results and outperforming other approaches. Therefore, many techniques have been developed to re-balance datasets in order to improve machine learning applicability. In this paper we present two algorithms, called G1No (Generative resampling 1-nearest Neighbour) and G1No Gourmet, which compensate dataset imbalance by generating synthetic samples, and compare them with two state-of-the-art re-balancing algorithms, namely the Synthetic Minority Oversampling (SMOTE) and the ADAptive SYNthetic sampling (ADASYN). The experiments, carried on a realistic malware traffic dataset, namely, the MTA-KDD’19, show that G1No outperforms the other algorithms and is even able to improve the quality of the original dataset.
Feature selection for improving Indian spoken language identification in utterance duration mismatch condition

In spoken language identification (SLID) systems, the test data may be of a sufficiently shorter duration than training data, known as duration mismatch condition. Duration normalized features are used to identify a spoken language for nine Indian languages in duration mismatch conditions. Random forest-based importance vectors of 1582 OpenSMILE features are calculated for each utterance in different duration datasets. The feature importance vectors are normalized across each dataset and later across different duration datasets. The optimal number of duration normalized features is selected to maximize SLID system accuracy. Three classifiers, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), and their fusion, weights optimized using logistic regression, are used. The speech material comprised utterances, each of 30 sec, extracted from the All India Radio dataset with nine Indian languages. Seven new datasets of smaller utterance durations were generated by carefully splitting each utterance. Experimental results showed that 150 most important duration normalized features were optimal with a relative increase in 18-80% accuracy for mismatch conditions. The accuracy decreased with increased duration mismatch.
Temperature variation impact on estimating costs and most critical components in a cloud data centre

Cooling plays a very important role in data centre availability by mitigating the overheating of Information Technology (IT) equipment. While many existing works evaluated the performance of the cooling sub-systems in data centres, only a few studies have considered the important relationship between cooling and IT sub-systems. This work provides efficient models (using Stochastic Petri Nets (SPNs)) to represent a cooling sub-system and analyse the impact of its failures in terms of service downtime and financial cost. We provide an efficient model, diminishing the state explosion problem when modelling DC availability. The study also identifies the components that are the most critical with respect to service availability through the use of sensitivity analysis. Results show that the adoption of a redundant architecture reduces the annual costs related to downtime by as much as 70%. The chiller is observed as the main component that affects service availability and operation costs.
Character Segmentation and Skew Correction for Handwritten Devanagari Scripts: A Friends Technique

Optical Character Recognition (OCR) technology allows a computer to “read” text (both typed and handwritten) the way a human brain does.Significant research efforts have been put in the area of Optical Character Segmentation (OCR) of typewritten text in various languages, however very few efforts have been put on the segmentation and skew correction of handwritten text written in Devanagari which is a scripting language of Hindi. This paper aims a novel technique for segmentation and skew correction of hand written Devanagari text. It shows the accuracy of 91% and takes less than one second to segment a particular handwritten word.
A Novel Gaze Input System Based on Iris Tracking With Webcam Mounted Eyeglasses

Abstract Due to the high cost of eye-tracking systems based on pupillary corneal reflections, efforts to develop a webcam-based eye-tracking system have increased to provide an affordable alternative to disabled people in recent years. However, due to the camera specification and location, ambient light changes and positional changes of the users, the gazing point of the eyes has not yet been determined precisely by such a system. Therefore, only 8 different gaze directions or up to 10 gaze regions could be detected in the previous webcam-based human–computer interaction studies. In this study, a novel gaze input system has been proposed to make the best use of the limited performance of webcam-based eye tracking and offer an economical alternative for disabled people. To reduce the impact of head movements, the webcam has been mounted to an ordinary glasses frame and positioned in front of the eye. For estimation of the gaze regions, a feature-based method (Hough transformation) was used by considering the circular shape of the iris and the contrast between the iris and sclera. The central coordinates of the iris image captured by the webcam were given to the k-nearest neighbor classifier. We performed a series of experiments with 20 subjects to determine the performance of the system and to investigate the effect of ambient light on the system’s accuracy. The 23 regions that were gazed at by subjects were determined with an average accuracy of 99.54%. When the ambient light level was reduced by half, the accuracy decreased to 94.74%. As a result, it has been found that the proposed prototype allows more accurate recognition of a larger number of regions on the screen than previous webcam-based systems. It has been observed that system performance decreases if the ambient light is reduced byhalf.
Honest leave‐one‐out cross‐validation for estimating post‐tuning generalization error

Many machine learning models have tuning parameters to be determined by the training data, and cross‐validation (CV) is perhaps the most commonly used method for selecting tuning parameters. This work concerns the problem of estimating the generalization error of a CV‐tuned predictive model. We propose to use an honest leave‐one‐out cross‐validation framework to produce a nearly unbiased estimator of the post‐tuning generalization error. By using the kernel support vector machine and the kernel logistic regression as examples, we demonstrate that the honest leave‐one‐out cross‐validation has very competitive performance even when competing with the state‐of‐the‐art .632+ estimator.
Context-Aware Candidates for Image Cropping

Image cropping aims to enhance the aesthetic quality of a given image by removing unwanted areas. Existing image cropping methods can be divided into two groups: candidate-based and candidate-free methods. For candidate-based methods, dense predefined candidate boxes can indeed cover good boxes, but most candidates with low aesthetic quality may disturb the following judgment and lead to an undesirable result. For candidate-free methods, the cropping box is directly acquired according to certain prior knowledge. However, the effect of only one box is not stable enough due to the subjectivity of image cropping. In order to combine the advantages of the above methods and overcome these shortcomings, we need fewer but more representative candidate boxes. To this end, we propose FCRNet, a fully convolutional regression network, which predicts several context-aware cropping boxes in an ensemble manner as candidates. A multi-task loss is employed to supervise the generation of candidates. Unlike previous candidate-based works, FCRNet outputs a small number of context-aware candidates without any predefined box and the final result is selected from these candidates by an aesthetic evaluation network or even manual selection. Extensive experiments show the superiority of our context-aware candidates based method over the state-of-the-art approaches.
Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset

High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry.
Interactive Virtual Reality Touring System: A Case Study of Shulin Ji’an Temple in Taiwan

Most current tour guiding methods for Taiwanese temples employ graphic webpage frameworks combined with captioned pictures for introduction. This type of tour guiding lacks interactive presence. In addition, the audience may not be able to focus on browsing webpages or learn essential information from the introduction. This study adopted the Delphi method to evaluate the current developed system. This system was aimed at designing VR-based interaction that differs from conventional tour guiding methods to aid users in viewing the display space from their viewpoints. Users cannot only control camera view angles but also select the paths and guiding information as if they were walking in the temple. The analysis results revealed that, in general, the users perceived VR tour guiding as convenient and easy to use. The display and content of the tour guiding system presented clear information to the users, aiding them in gaining further understanding of the introduced item. Finally, the study results can serve as a reference for design research on VR applications in tour guiding.
NEUROTECHNOLOGIES IN PROFESSIONAL EDUCATION: REFLECTION OF THEIR POSSIBILITIES

The introduction of neurotechnologies into all spheres of social and professional activity is one of the modern digital society trends. The experience of using neurotechnologies, highlighted in the psychological and pedagogical literature, shows both positive and negative effects of their use for educational purposes. The purpose of the article is to analyze the possibilities of using neurotechnologies in vocational education, to present the virtual workshops as the organizational form of their implementation. The methodology of the study . The methodological basis for the study was the competence-based approach implemented in the competence model of the subject of educational and professional activity E. F. Zeer, and the theory of personalization by V. A. Petrovsky, underlying the concept of personalized education. The research methods are theoretical and methodological analysis of the research subject based on the study and logical generalization of psychological and pedagogical literature, as well as generalization of the practical application experience of neuroeducational technologies in vocational education. The integration of neurotechnologies into traditional forms and methods of teaching significantly enriches the possibilities for the formation of competencies, demanded in high-tech industries, and also allows you to master professional educational programs along personalized educational trajectories. Neuroeducational technologies include: practice-oriented cases; accelerated form of training; gamification; web quests; virtual training simulators; immersive learning. Virtual workshops focused on modeling high-tech production processes is the organizational form of the implementation of the neuroeducational technologies. The purpose of virtual workshops is the formation of social and professional competencies that ensure multifunctionality and professional mobility. Virtual workshops provide an opportunity to master professional educational programs according to individual (personalized) educational trajectories. The introduction of virtual workshops allows: to create conditions as close as possible to real ones, to improve the quality of education; significantly expands the professional and educational space, allows you to combine the efforts of educational organizations and employers to solve the problems of training qualified personnel for the regional economy. Practical significance. Neurotechnology has not yet been put into practice in professional education. This is largely due to the complexity of devices that allow you to simulate high-tech production. But virtual workshops are already being used in innovative sites for highly qualified personnel.
Workshop on Online and Adaptative Recommender Systems (OARS)

Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. In addition, there have been many advances that make online and adaptive recommender systems (OARS) feasible, scalable, and more sophisticated. This workshop aims to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems and improve user experiences by better modeling and responding to user intent.
Expert Recommendation for Answering Questions on Social Media

In this paper, we propose a method for recommending experts to appropriately answer questions based on social activity analysis on social media. By analyzing various social activities performed on social media, the user’s interests are identified. Through the human relation analysis of the users of a particular interest field and by considering the response speed and answer quality of the user, we determine the influence of a user. An expert group is matched by analyzing the content of queries by a user and using a hierarchical structure of words. For a user question, the accuracy of an expert recommendation is enhanced by incorporating the question content and sublevel words based on the hierarchical structure of words. Various evaluations have demonstrated that the performance of the proposed method is superior to existing methods.
Legal Basis for Introducing Restrictions on Human Rights and Freedoms during the First Wave of the COVID-19 Pandemic

In this article, we attempt to present the legal grounds for introducing restrictions on human rights during the COVID-19 pandemic from a comparative legal perspective. We refer to the findings of a research project completed in 2020, trying to synthesize them and confront them with existing theoretical models. We strive to capture general patterns in the legal basis for states’ actions in response to global threats such as the COVID-19 pandemic. Comparative legal research contributes to the creation of universal solutions, which, taking into account the specificity of the system, can then be applied in local conditions.
The Problem of Automated Facial Recognition Technologies in Brazil: Social Countermovements and the New Frontiers of Fundamental Rights

This article analyzes the characteristics of automated facial recognition technologies and their response by civil society organizations in Brazil. We analyze two arguments in this debate: the endemic bias argument, which seeks to correct unjust and potentially racist consequences, and the endemic oppression argument, which identifies a set of facilitators of systematic violation of fundamental rights. We present the concept of countermovements to explain the possibilities of legal contestation of the dissemination of facial recognition and explain how the argument about recognition can move from the logic of bias to that of oppression, with the possibility of changing the regulation to ban this technology in certain uses.