Reviewerly/exHarmony huggingface dataset:
Augmented reality for cultural heritage: a new dimension for the perceptual knowledge

Abstract The paper presents an innovative way of conceiving the museum exhibit particularly useful in those territorial contexts in which it is possible to simultaneously show the place of discovery of the exhibit n (contextualizing it through the virtual reproduction of the historical scenario) and the find itself adding to this the possibility of narrating one’s own history and characteristics. The entire meta-project is designed for the Pietraroja site in the province of Benevento which would acquire considerable cultural interest thanks to the potential of augmented and mixed reality. The main idea is to use this new technology to involve the user (observer) by rebuilding the environment and the peculiarities of the finds. The perception of the augmented elements will be possible by design a specific application for smartphone that everyone gets in its pocket.
Incremental Flow Analysis through Computational Dependency Reification

Static analyses are used to gain more confidence in changes made by developers.To be of most use, such analyses must deliver feedback fast.Therefore, incremental static analyses update previous results rather than entirely recompute them.This reduces the analysis time upon a program change, and makes the analysis well-suited for environments where the code base is frequently updated, such as in IDEs and CI pipelines.In this work, we present a general approach to render a modular static analysis for highly dynamic programs incremental, by exploiting dependencies between intermediate analysis results.Modular analyses divide a program in interdependent parts that are analysed in isolation.The dependencies between these parts stem, for example, from the use of shared variables within the program.Our incrementalisation approach leverages the modularity of the analysis together with the dependencies that it reifies to compute and bound the impact of changes.This way, only the affected parts of the result need to be reanalysed, and unnecessary recomputations are avoided.We apply our approach to both a function-modular and a thread-modular analysis and evaluate it by comparing an incremental update of an existing result to a full reanalysis.We find reductions of the analysis time from 6% to 99% on 14 out of 16 benchmark programs, and on most programs the impact on precision is limited.On 7 of the programs, reanalysis time is reduced by more than 75%, showing that our approach results in fast incremental updates.
Traffic Sign Detection Using YOLOv3

Advanced driving assistance systems (ADASs) are increasingly being installed in modern vehicles because they make driving safer and more comfortable. With the implementation of cameras in the vehicle, the range of possible ADASs increases. One of such systems is the one aimed for traffic sign recognition, which alerts the driver about different road conditions such as excess of the speed limit or traffic ban. In this paper, a solution for detecting a specific set of 11 traffic signs typical for most European countries is presented. The algorithm used for detecting traffic signs is You Only Look Once (YOLO) v3, where the model parameters are trained on a train set acquired from the newly created dataset. The rest of the dataset images are used for creating a test set. The dataset is derived from the video signals that were capturing traffic with a front view camera mounted inside the vehicle, in the city of Osijek in different weather conditions (sunny, cloudy, rain, night). The dataset images are extracted from 28 different video sequences, which resulted in 5567 images with the total number of 6751 annotated traffic signs. The proposed solution for detecting a specific set of traffic signs achieves high performance when tested on the test set created from the proposed dataset.
Entity Relative Position Representation Based Multi-head Selection for Joint Entity and Relation Extraction

Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, the Multi-Head Selection (MHS) framework is efficient in extracting entities and relations simultaneously. However, the method is weak for its limited performance. In this paper, we propose several effective insights to address this problem. First, we propose an entity-specific Relative Position Representation (eRPR) to allow the model to fully leverage the distance information between entities and context tokens. Second, we introduce an auxiliary Global Relation Classification (GRC) to enhance the learning of local contextual features. Moreover, we improve the semantic representation by adopting a pre-trained language model BERT as the feature encoder. Finally, these new keypoints are closely integrated with the multi-head selection framework and optimized jointly. Extensive experiments on two benchmark datasets demonstrate that our approach overwhelmingly outperforms previous works in terms of all evaluation metrics, achieving significant improvements for relation F1 by $$+2.40\%$$ on CoNLL04 and $$+1.90\%$$ on ACE05, respectively.
Define UX Design

The purpose of this study is to understand how academics and practitioners alike define and discuss user experience (UX). There are many academic and practitioner definitions of UX design, but there is not presently a universally accepted definition either theoretically or practically. How will colleges and universities prepare students for this nebulous professional field?
Resilient Mølmer-Sørensen gate with cavity QED

We propose a simple scheme for implementing resilient two-qubit entangling gates in universal cavity QED systems. The technique employs suitably designed laser pulse with modulated amplitude and phase to decrease residual qubit-cavity entanglement that reduces the fidelity of the desired entangling operation. Numerical simulations suggest that the optimized gates are robust against errors in gate time, frequency detuning, atom-field coupling constant and pulse amplitude. Moreover, the optimized gates provide increased robustness to cavity decay as well as strong suppression of atomic spontaneous emissions. With modest experimental parameters the scheme could enable a high gate fidelity exceeding 99% in realistic scenarios.
Speculative trading in Bitcoin: A Brazilian market evidence

Abstract This paper tests the speculative trading in Bitcoin Brazilian market using daily data for Bitcoin spanning the 2011–2018 sample period. Two points are examined: the first is based in stylized facts about price dynamics on Brazilian market and the second implies in testing the hypothesis that speculative trading in Bitcon is resposible for its unusual volatility level. We check the existence of speculative trading in the Brazilian market based on three different experiments, broadly relevant to both market participants and academic researchers. Our results are summarized as follows: (i) The Bitcoin's bull on Brazilian market presented short duration, given the volatility exposed into period, showing signs of asymmetry in marketinformation and uncertainty; (ii) It was observed the different trajectories between volatilityand return, reflecting the high degree of speculation of the market; (iii). It was proved the ex-istence of speculative trading in Bitcoin's Brazilian market through three different experiments
Extended anomaly detection and breakdown prediction in LINAC 4’s RF power source output

Linear accelerators are complex machines that can face significant periods of downtime due to anomalies and the subsequent failure of one or more components. The need for reliable linear accelerator operations (LINAC) is critical to the spread of this method in the medical environment. At CERN, where LINACs are used for fundamental research, similar problems are encountered, such as the appearance of jitter in plasma sources (2 MHz RF generators), which can have a significant effect on subsequent beam quality in the accelerator. The SmartLINAC project was created to increase LINACs' reliability by early detection and prediction of anomalies in its operations, down to the component level. This article shows how anomalies were first discovered and goes deep into understanding the nature of the data. The research adds new elements to anomaly detection approaches used to record jitter periods on 2MHz RF generators.
Generation of Synthetic Data with Conditional Generative Adversarial Networks

Abstract The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal.
A real-time explainable traffic collision inference framework based on probabilistic graph theory

Millions of motor vehicle collisions occur each year and lots of them result in heavy fatalities. Although some promising works are proposed, they have the following problems: (1) most of existing methods depend on feature regression, but ignore the causal relationship among them; (2) the vision-based techniques cost enormous resources to process the large scale of video data; (3) the lack of considering real-time traffic environment leads to an unsatisfied performance. To tackle these problems, we propose a real-time explainable collision inference framework through social media analysis. First, we design and extract various kinds of real-time traffic features from the social media. Then, we propose an effective algorithm to discover the causal relationships among the adopted features, which are denoted by probabilistic graphs. Finally, we employ the probabilistic graphs with the top-k BDeu score to calculate the probability of one collision occurring with nearly linear time complexity. Extensive experiments show that our framework achieves 0.752, 0.747, and 0.750 in precision, recall, and F1-measure. Extensive results show that our proposal has good scalability and has a good chance to solve other emergency event inference.
Robust Regression for Automatic Fusion Plasma Analysis Based on Generative Modeling

The first step to realize an automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are routinely obtained. However, it has been difficult to construct algorithms that fit all the data without overfitting and underfitting. In this paper, we show that this difficulty originates from the lack of knowledge of the probability distribution that the measurement data follow. We demonstrate the use of a machine learning technique to estimate the data distribution and to construct an optimal generative model. We show that the fitting algorithm based on the generative modeling outperforms classical heuristic methods in terms of the stability as well as the accuracy.
Mobile application with augmented reality as a tool to reinforce learning in pre-Inca cultures

Peru is one of the countries with the most rich and varied culture in the world, which can be made known by making use of augmented reality for better understanding and learning at school. This document shows the development of an application following the mobile d methodology, for the creation of 3D models regarding pre-Inca cultures and determines the influence it would have on the learning of students at the primary level. Using the quasi-experimental design, the sample consists of 84 students from the third grade divided into 2 classrooms (control group and experimental group). The results show an increase of 33.07% in grades, 49.51% in satisfaction, 42.85% in motivation and a reduction of 34.05% in evaluation time with respect to the post-test, applied to the experimental group.
Deep neural network oriented evolutionary parametric eye modeling

Comprehensive and accurate eye modeling is crucial to a variety of applications, including human-computer interaction, assistive technologies, and medical diagnosis. However, most studies focus on the localization of one or two components of eyes, such as pupil or iris, lacking a comprehensive eye model. We propose to model an eye image by a set of parametric curves. The set of curves are plotted on an eye image to form a Contour-Eye image. A deep neural network is trained to evaluate the fitness of the Contour-Eye image. Then an evolutionary process is conducted to search the best fitting curve set, guided by the trained deep neural network. Finally, an accurate eye model with optimized parametric curves is obtained. For the algorithm evaluation, a finely annotated eye dataset denoted as FAED-50 is established by us, which contains 2,498 eye images from 50 subjects. The experimental results on the FAED-50 and the relabeled CASIA datasets and comparison with the state-of-the-art methods demonstrate the effectiveness and accuracy of the proposed parametric model.
WITHDRAWN: Suspect detection system – An architecture based on surveillance visual analytics using IoT

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter

With the wide popularity of social media, it’s becoming more convenient for people to express their opinions online. To better understand what the public think about a topic, sentiment classification techniques have been widely used to estimate the overall orientation of opinions in post contents. However, users might have various degrees of influence depending on their participation in discussions on different topics. In this paper, we address the issues of combining sentiment classification and link analysis techniques for extracting stances of the public from social media. Since social media posts are usually very short, word embedding models are first used to learn different word usages in various contexts. Then, deep learning methods such as Long Short-Term Memory (LSTM) are used to learn the long-distance context dependency among words for better estimation of sentiments. Third, we consider the major user participation in popular social media by adjusting the users weights to reflect their relative influence in user-post interaction graphs. Finally, we combine post sentiments and user influences into a total opinion score for extracting public stances. In the experiments, we evaluated the performance of our proposed approach for tweets about the 2016 U.S. Presidential Election. The best performance of sentiment classification can be observed with an F-measure of 72.97% for LSTM classifiers. This shows the effectiveness of deep learning methods in learning word usage in social media contexts. The experimental results on stance extraction showed the best performance of 0.68% Mean Absolute Error (MAE) in aggregating public stances on election candidates. This shows the potential of combining tweet sentiments and user participation structures for extracting the aggregate stances of the public on popular topics. Further investigation is needed to verify the performance in different social media sources.
Certain Investigations on Leveraging Blockchain Technology for Developing Electronic Health Records

This paper exhibits a Blockchain-based design for our current Electronic Health Records (EHR) frameworks. Electronic Health Records commonly comprise extremely penetrating and precarious records associated with patients. It needs to track all the events that occurred in the records to achieve data integrity for secure transactions. To solve these problems, we proposed two smart agreements, classified contracts, and user record associated contracts. The classified contract involves organizing the records in a distributed ledger format with secured interventions of Doctors and healthcare providers. The user record associated contracts validate the transactions requested by the concerned miners. We evaluate the proposed architecture by realizing the Ethereum framework, which includes Remix environment, Metamask wallet, and Solidity language. Compared to conventional EHR frameworks, the suggested structure with the employment of the Blockchain has improved efficiency and security for storing electronic health records.
Smart workload migration on external cloud service providers to minimize delay, running time, and transfer cost

Summary With limited resources, overutilization is a challenging issue in the emerging system of smart devices, resulting in higher costs, poor performance and low prices due to service level agreement (SLA) violations. These factors extremely annoy the customers as well as providers. External resources are hired to overcome the resources scalability challenges; however, external resources cause higher delay, running time, and transfer cost. The lift in delay explicitly means more cost and more customers dissatisfaction. The delay and transfer cost increases with geographical distance and overutilization. In this article, a mechanism is proposed to make efficient migration decisions to external cloud service providers (CSPs) to minimize the delay, running time, and transfer cost by searching an optimum data center (DC), where the resources may be taken with optimum conditions. The Cloud Analyst is extended to simulate the proposed framework. Results were calculated for three different phenomena, where hiring the resources from underutilized DC decreases the running time to 0.237 ms; however, the response time and transfer cost increases to 499 ms and 0.228 $, respectively; similarly, getting the resources of nearest DC drops the delay time and transfer cost to 80 ms and 0.065 $ respectively; however, the execution time increases to 0.500 ms. The proposed framework optimized the delay time, execution time and transfer cost to 50 ms, 0.237 ms, and 0.065 $, respectively.
A Community Discovery and TrustRank based Approach for Spammer Ranking

Driven by profit incentives, spammers deliberately post fake reviews to promote or demote the reputation of online stores or products. As review spamming behaviors spread fast in e-commerce domain, spammers usually collaborate to write fake reviews to effect target products. Previous research mainly focuses on fake review detection and spammer detection using supervised machine learning techniques. In order to solve the problem of lack of supervised learning data for spammer detection, we propose a reviewer suspiciousness ranking algorithm based on community discovery and TrustRank. First, we use overlapping community discovery technology to find highly suspicious user communities as candidate spammer groups. Then we realize an improved algorithm CommunityRank based on initial value of suspiciousness of reviewer in candidate groups. Experiment results show that CommunityRank performs better than baseline methods in ranking of spammers and can effectively improve performance of spammer ranking in unsupervised environment.
Retaining Image Feature Matching Performance Under Low Light Conditions

Poor image quality in low light images may result in a reduced number of feature matching between images. In this paper, we investigate the performance of feature extraction algorithms in low light environments. To find an optimal setting to retain feature matching performance in low light images, we look into the effect of changing feature acceptance threshold for feature detector and adding pre-processing in the form of Low Light Image Enhancement (LLIE) prior to feature detection. We observe that even in low light images, feature matching using traditional hand-crafted feature detectors still performs reasonably well by lowering the threshold parameter. We also show that applying LLIE algorithms can improve feature matching even further when paired with the right feature extraction algorithm.
Virtual look around: interaction quality evaluation for virtual tour in multiple platforms

In the last few years, Virtual Reality (VR) has become readily available through different devices, from desktop to head-mounted displays (HMD). For instance, virtual tour experiences became popular by 360° panoramic photographs and video clips on online social media, so people could visit a remote location without being exposed to crowded transportation or places. Also, a virtual tour demonstrates considerable potential as a tool for escapism and even remote teaching in the current pandemic situation. However, we lack studies that evaluate the interaction quality in such virtual tours in different immersion levels. Since this kind of research is important to allow users to balance cost-benefit decisions better, the present paper aims to compare the interaction quality in a virtual tour website developed in WebXR through different devices. To achieve our objective, we developed a virtual tour based on 360° pictures using WebXR and React 360 and carried out a user study with 41 undergraduate students using four different devices: a laptop computer, a smartphone, a Google Cardboard headset, and a Samsung GearVR HMD. We evaluated users' attitudes by adapting the Suitability Evaluation Questionnaire (SEQ), and users' performance by measuring time to fulfill a set of tasks. The key findings from our research include that (i) the overall self-reported experience using Google Cardboard is worse than using other devices, (ii) the users' performance is quite similar between the platforms, and (iii) there is evidence of unexpected cybersickness symptoms in tests with the smartphone.