Reviewerly/exHarmony huggingface dataset:
Augmented Reality on Museums: the Ecomuseu Virtual Guide

Studies point that smartphones show an important role in the improvement of museum visitors experience. Mobile devices have become the main tool available for travellers, which enables the diffusion of augmented reality solutions through this channel. Based on that, a virtual guide prototype was developed to enhance the museum visitors experience inside a famous attraction from Foz do Iguaçu, the Ecomuseu, constructed with the objective to show the work of Itaipu’s hydroelectric power plant and the history regarding the region occupied by its reservoir. The developed application inserts virtual elements in different locations of the museum. Whenever such elements are “captured”, they tell the visitor information about what is being exhibited so the perception of the visitor and knowledge absorption can be improved. To validate the proposed solution, we tested the app with 15 different users. Due to the COVID-19 pandemic, we adapted the app and performed the evaluation over the virtual tour of the same museum, which is available online. For the sake of our knowledge, this is the first time an augmented reality application is evaluated over a virtual representation of a museum. Despite the difficulties imposed by the current context, the feedback from the tested users was very positive.
QUANTUM MACHINE LEARNING FOR HEP DETECTOR SIMULATIONS

Quantum Machine Learning (qML) is one of the most promising and very intuitive applications onnear-term quantum devices which possess the potential to combat computing resource challengesfaster than traditional computers. Classical Machine Learning (ML) is taking up a significant role inparticle physics to speed up detector simulations. Generative Adversarial Networks (GANs) haveproven to achieve a similar level of accuracy compared to Monte Carlo-based simulations whiledecreasing the computation time by orders of magnitude. In this research we are moving on and applyquantum computing to GAN-based detector simulations.Given the limitations of current quantum hardware in terms of number of qubits, connectivity, andnoise, we perform initial tests with a simplified GAN model running on quantum simulators. Themodel is a classical-quantum hybrid ansatz. It consists of a quantum generator, defined as aparameterised circuit based on single and two qubit gates, combined with a classical discriminator.Our initial qGAN prototype focuses on a one-dimensional toy-distribution, representing the energydeposited in a detector by a single particle. It employs three qubits and achieves high physics accuracythanks to hyper-parameter optimisation. Furthermore, we study the influence of real hardware noisefor the qML GAN training. A second qGAN is developed to simulate 2D images with a 64-pixelresolution, representing the energy deposition patterns in the detector. Different quantum ansatzes arestudied. We obtained the best results using a tree-tensor-network architecture with six qubits.Additionally, we discuss challenges and potential benefits of quantum computing as well as our plansfor future developments.
Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.
SOURCE LOCATION MECHANISM OF MICROSEISMIC MONITORING USING P-S WAVES AND ITS EFFECT ANALYSIS

Seismic source location is the most fundamental and most important problem in microseismic monitoring. However, only P wave has been mostly applied in the existing microseismic monitoring networks, with low location accuracy and poor stability of location result for the microseismic events occurring beyond monitoring networks. The seismic source location was implemented using P wave and S wave in this study to expand the effective monitoring area of a microseismic monitoring network and improve its location accuracy for microseismic events nearby the monitoring network. Then, the seismic source location mechanism using P-S wave was revealed through theoretical derivation and analysis. Subsequently, the program development and numerical simulation were combined to analyze and compare systematically the location effects of differently distributed monitoring networks, those consisting of different quantities of sensors, and those with S wave contained in some sensors under two circumstances: combination of P wave and S wave and single use of P wave. Results demonstrate that adding S wave in the plane enhances the accuracy control in the radius direction of the monitoring network. After S wave is included, the location accuracy within a certain area beyond the monitoring network is improved considerably, the effective monitoring area of the whole network is expanded, and the unstable location zones using only P wave are eliminated. The location results of differently distributed monitoring networks and the influence laws of the quantity of sensors constituting the networks on the location results are acquired. This study provides evidence for microseismic monitoring to realize accurate and stable location within a larger range. Keywords: seismic source location, P wave and S wave, mechanism, location effect
Wireless Network Access and Emotion Recognition of Online English Translation Teaching System from the Perspective of Artificial Intelligence

The research is aimed at verifying the application effect of the online automatic evaluation system in English translation teaching and at understanding the satisfaction of students with different feedback methods. The research uses three classes of human resource management majors in Xi’an Technological University as the research object and uses questionnaire survey and comparative experiment methods to compare and analyse the three feedback methods: teacher feedback, online automatic feedback, and teacher feedback combined with online automatic feedback. The research answers the following three questions: (1) will the three feedback methods affect students’ English translation performance? (2) Which of the three feedback methods will improve students’ English performance better? (3) What about students’ satisfaction with current feedback methods? The research results show that the significance value between the control group (CG) and the experimental group 1 (EG1) is 0.029, that between CG and the experimental group 2 (EG2) is 0.432, and that between EG1 and EG2 is 0.001. There are obvious differences in the posttest scores of the three groups of students. EG2 has the largest average posttest score, which is 9.8182; there is no obvious difference in posttest translation scores between CG and EG2. It indicates that “teacher feedback + online automatic feedback” and teacher feedback have the equivalent effect on improving students’ translation. The results of the questionnaire survey show that students have the highest degree of recognition of “teacher feedback + online automatic feedback.” The research is helpful for teachers to better understand the shortcomings in the translation teaching process, so that they can take effective measures against these problems in the follow-up teaching process to improve their teaching effect.
Tuning of control parameters of the Whale Optimization Algorithm using fuzzy inference system

The Whale Optimization Algorithm (WOA) is a recent approach to the swarm intelligence field that can be explored in many global optimization applications. This paper proposes a new mechanism to tune the control parameters that influence the hunting process in the WOA to improve its convergence rate. This schema adjustment is made by a fuzzy inference system that uses the normalized fitness value of each whale and the hunting mechanism control parameters of WOA. The method proposed was tested and compared with the conventional WOA and another version that uses a fuzzy inference system as input information on the ratio of the current iteration number and the maximum number of iterations. For performance analysis of the method proposed, all optimizers were evaluated with twenty-three benchmark optimization functions in the continuous domain. The algorithms were also implemented in the identification process of two real control system that are a boiler system and water supply network. For identification process, it is used the value of MSE (mean squared error) to available each algorithm. The simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional WOA and it is competitive in relation to another fuzzy version adopted in the WOA design.
Coverage of deformable contour shapes with minimal multi-camera system

Perception over time is a critical problem in those cases where deformable objects are manipulated. The goal of this study is to cover the contour of an object along a deformation process and according to a prescribed coverage objective, in terms of visibility and resolution. This task is carried out by a set of limited field-of-view cameras. We propose novel methods for guaranteeing feasibility of the coverage objectives, which include the computation of the maximum visibility and resolution of the contour. Then, we introduce the coverage objectives in an offline constrained optimization problem to compute a priori the minimum number of cameras that achieve the coverage requirements. Finally, we propose an online technique that provides optimized configurations faster than the offline one, even when the object’s reference deformation is unknown. We report experimental results in which our method achieves 100% of the coverage in simulation and in a real task.
Orientated Silhouette Matching for Single-Shot Ship Instance Segmentation

Object detection and semantic segmentation have achieved remarkable performance propelled by deep convolutional neural networks. However, neither of them can well parse and deal with swarms of rotating ships in remote sensing images. In this article, we pay more attention to the instance-level segmentation task, which recognizes objects more effectively and straightly. We propose a new network architecture, called orientated silhouette matching network, employing multiscale features and instance-level masks to enable single-shot and anchor-box-free instance segmentation. To be specific, we propose a novel-orientated polar template mask with orientated mask IoU to better match the ship silhouette. We also design a multiscale feature propagation and fusion module to improve the precision of detection. To further improve the performance, our network adopts Res2Net and Soft-NMS. Extensive experiments on the open datasets, namely Airbus Ship, demonstrate that our method improves the average precision by 14.2 and 10.0 percentage points on Res2Net101, compared with PolarMask and YOLACT. The source code will be open source after the reviewing process.
Analysis and Improvement of Blockchain-Based Multilevel Privacy-Preserving Location Sharing Scheme for Telecare Medical Information Systems

Patient location sharing is an important part of modern smart healthcare and mobile medical services. Blockchain has many attractive properties and is suitable for managing patient locations in telecare medical information systems (TMIS). Recently, Ji et al. proposed a blockchain-based multilevel privacy-preserving location sharing (BMPLS) scheme for TMIS. In this paper, we show that Ji et al.’s BMPLS scheme does not achieve confidentiality and multilevel privacy-preserving. An adversary outside the system can use an ordinary personal computer to completely break the system within a dozen hours and obtain the location of any patient at any time. The adversary inside the system can use an ordinary personal computer to obtain the location of the designated patient within tens of seconds. Using salting technology, we propose an improved BMPLS scheme to fix our attacks. We also optimized the BMLS scheme to make it correct and executable. The security analysis shows that the improved BMPLS scheme achieves decentralization, untamperability, confidentiality, multilevel privacy-preserving, retrievability, and verifiability. The simulation shows that the improved BMPLS scheme is practical, the computational overhead of the location record phase is within 10 ms, and the computational overheads of the location sharing and location extraction phases are both within 30 ms.
Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network

Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon.Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction.Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes.Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition.However, in infrared, the textural information is lost.We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach.This approach includes four scientific contributions.The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community.The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images.The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images.This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space.To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ.The qualitative evaluation shows that our method generates more realistic faces.The quantitative one demonstrates that the proposed TV-CycleGAN gives the best improvement on face recognition rates.Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database.It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively.It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database.It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics.Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper.
A Novel Online Teaching Effect Evaluation Model Based on Visual Question Answering

&lt;p&gt;The paper proposes a novel visual question answering (VQA)-based online teaching effect evaluation model. Based on the text interaction between teacher and students, we give a guide-attention (GA) model to discover the directive clues. Combining the self-attention (SA) models, we reweight the vital feature to locate the critical information on the whiteboard and students&amp;rsquo; faces and further recognize their content and facial expressions. Three branches of information are encoded into the feature vectors to be fed into a bidirectional GRU network. With the real labels of the students’ answers annotated by two teachers and the predicted labels from the text and facial expression feedback, we train the chained network. Experiment reports a couple of competitive performance in the 2-class and 5-class tasks on the self-collected dataset, respectively.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
IOTA Based Anomaly Detection Machine learning in Mobile Sensing

In this proposed method, iMCS can detect and prevent fake sensing activities of mobile users using machine learning techniques.Our iMCS solution uses behavioral analysis based on participants' reliability scores to detect variation in behavior of users and introduces a new role in a distributed system of MCS architecture to validate the collected data.To evaluate the incentive based on the participant's sensory data and data quality, to properly distribute profit among the participants, we employ the Shapley Value approach.The evaluation results demonstrate that our method is effective in both quality estimations and incentive sharing.
Online Evaluation System of College Physical Education Students Based on Big Data and Artificial Intelligence Technology

The era of big data in education has come. The popularity of online learning space and MOOC and other online learning platforms makes it possible to acquire more and more teaching and learning data. The decision-making of education and teaching based on big data has become the norm. With the popularization of big data (BD) and artificial intelligence (AI) technology, college sports also need to adapt to this change. The era of BD brings new development ideas. The purpose of this paper is to provide better technical suggestions and means for the introduction of BD into physical education by studying the online evaluation system of college physical education students based on BD and AI technology. By using the basic way of system development, tennis, for example, put forward the demand for hitting numerical analysis, combined with today's advanced motion capture system, around the motion capture and realization process is discussed, so as to build a system of strokes scoring system, will provide the motion capture data compared with the scoring system and then realize the function to the student movement scores. The results show that the system can effectively evaluate the students' tennis moves, saving 70% of the time compared with the traditional method.
Spruce Counting Based on Lightweight Mask R-CNN with UAV Images

To achieve rapid and accurate counting of seedlings on mobile terminals such as Unmanned Aerial Vehicle (UAV), we propose a lightweight spruce counting model. Given the difficulties of spruce adhesion and complex environment interference, we adopt the Mask R-CNN as the basic model, which performs instance-level segmentation of the target. To successfully apply the basic model to the mobile terminal applications, we modify the Mask R-CNN model in terms of the light-weighted as follows: the feature extraction network is changed to MobileNetV1 network; NMS is changed to Fast NMS. At the implementation level, we expand the 403 spruce images taken by UAV to the 1612 images, where 1440 images are selected as the training set and 172 images are selected as the test set. We evaluate the lightweight Mask R-CNN model. Experimental results indicate that the Mean Counting Accuracy (MCA) is 95%, the Mean Absolute Error (MAE) is 8.02, the Mean Square Error (MSE) is 181.55, the Average Counting Time (ACT) is 1.514 s, and the Model Size (MS) is 90Mb. We compare the lightweight Mask R-CNN model with the counting effects of the Mask R-CNN model, the SSD+MobileNetV1 counting model, the FCN+Hough circle counting model, and the FCN+Slice counting model. ACT of the lightweight Mask R-CNN model is 0.876 s, 0.359 s, 1.691 s, and 2.443 s faster than the other four models, respectively. In terms of MCA, the lightweight Mask R-CNN model is similar to the Mask R-CNN model. It is 4.2%, 5.2%, and 9.3% higher than the SSD+MobileNetV1 counting model, the FCN+Slice counting model, and the FCN+Hough circle counting model, respectively. Experimental results demonstrate that the lightweight Mask R-CNN model achieves high accuracy and real-time performance, and makes a valuable exploration for the deployment of automatic seedling counting on the mobile terminal.
Kripto Para Ödemelerini Kabul Eden Perakendecilerin Motivasyonları ve Kripto Para Kabulünün Perakende Müşteri Deneyimi Üzerindeki Etkileri

The level of technology use of humanity has increased, while the interest in digital assets has increased with the effect of the pandemic period. This increasing interest has led to curiosity about what kind of consumer experience cryptocurrencies will offer when used in retail shopping. This study aims to reveal the advantages and disadvantages of using cryptocurrencies in shopping based on the experiences of businesses that accept payments with cryptocurrencies. Interviews were conducted with 9 businesses in Turkey that accept payments with crypto money using the in-depth interview technique in this study. As a result of the content analysis carried out with the NVivo analysis program for the findings, a research model was proposed. The results of this research point out that crypto currencies are preferred by consumers and retailers due to the fact that they are a fast, safe and cheap global payment tool. However, it has been found that there are also consumers who are totally unaware of the existence of payments with crypto currencies. Some consumers do not prefer payments with crypto currencies either because of low technology usage level or lack of trust to the system. However, when necessary legal sub-regulations are made and everything is digitized, money will also become digital in the common opinion. Unlike the studies in the literature, as a result of this study, it is predicted that payments with crypto money will be one of the alternative payment tools of the future, after the cryptocurrencies are positioned on a legal platform by the official authorities.