Reviewerly/exHarmony huggingface dataset:
Effect of VR exercise for anti-aging

Developing cost-effective exercise programs for anti-aging is a national priority. As the world’s aging population grows, many scientists are looking for ways to prevent cognitive decline and performance in daily living. It is a factor that determines the independent life of the elderly and is directly related to the deterioration of the quality of life. Although this study could not suggest a specific exercise program, VR training is expected to be a positive exercise method for improving cognitive ability and ADL, suggesting that it can improve or accelerate cognitive function and ADL. Despite the fact that there is a suitable system for exercise programs for the elderly such as VR at a time we face a super-aged society due to the development of medical technology and science technology, surprisingly, it was only used for cognitive impairment rehabilitation for dementia and stroke patients. As the current situation around the world, the development of anti-aging health promotion programs using VR programs is urgently needed at a time when the COVID-19 pandemic era is prolonged. If large-scale additional studies continue in the future, it is thought that it will provide a direction for anti-aging research based on VR exercise intervention.
Analysis of Arbitrary Content on Blockchain-Based Systems using BigQuery

Blockchain-based systems have gained immense popularity as enablers of independent asset transfers and smart contract functionality. They have also, since as early as the first Bitcoin blocks, been used for storing arbitrary contents such as texts and images. On-chain data storage functionality is useful for a variety of legitimate use cases. It does, however, also pose a systematic risk. If abused, for example by posting illegal contents on a public blockchain, data storage functionality can lead to legal consequences for operators and users that need to store and distribute the blockchain, thereby threatening the operational availability of entire blockchain ecosystems. In this paper, we develop and apply a cloud-based approach for quickly discovering and classifying content on public blockchains. Our method can be adapted to different blockchain systems and offers insights into content-related usage patterns and potential cases of abuse. We apply our method on the two most prominent public blockchain systems - Bitcoin and Ethereum - and discuss our results. To the best of our knowledge, the presented study is the first to systematically analyze non-financial content stored on the Ethereum blockchain and the first to present a side-by-side comparison between different blockchains in terms of the quality and quantity of stored data.
Technip Energies gets FEED contract for Encina[StQuote]s commercial PFCC unit

One of the most promising renewable energy sources (RES) is photovoltaic energy system. It generates power by using a photovoltaic (PV) module. The solar illumination and temperature value are the main parameters for the PV module because a small change can affect the entire power generation. The PV module is based on the double diode equation for extracting unknown parameter values and tracking maximum power point using basic analytical methods (Newton Raphson and Lambert W-function), particle swarm optimization (PSO) and artificial neural network (ANN) algorithms are performed. Then, a deep neural network (DNN) algorithm is proposed in this work for alleviating the disadvantages of the existing methods. Particularly, the DNN algorithm is proposed for reducing the mismatching power loss. Firstly, two-parameter values retrieve from a specific location at NASA open-access source for the year 2020. Then, the parameter values optimization through two analytical methods such as Newton Raphson (NR) and Lambert-W function (W-function) methods. Based on these analytical methods, the characteristic curve is executed. It is compared one among one and also the analytical methods evaluates with soft computing algorithms. The soft computing algorithm represents particle swarm optimization and the artificial neural network. Both the analytical methods and the soft computing algorithms compare for the extraction of the unknown parameter value for the PV module. Then, the existing algorithm is compared with advanced soft computing algorithms. In that deep neural network, the algorithm is implemented for the maximum power point tracking (MPPT) process. The DNN algorithm contains two inputs, three hidden layers and one output. The input neuron is defined with the voltage and current value and the hidden layer contains the sigmoid activation function. The result evaluates in terms of percentage of error for maximum power point (PEmpp).
Building an enhanced sentiment classification framework based on natural language processing

Sentiment classification is one of the major tasks of natural language processing (NLP) and has gained much attention by researchers and businesses in recent years. However, the semantics of the social networking language is becoming increasingly complex and unpredictable, affecting the accuracy of the associated NLP systems. In this paper, we propose a hybrid sentiment analysis (SA) framework that classifies the opinions of Vietnamese reviews into one of two types: positive or negative. The special feature of the proposed framework is that it is built on a combination of three different text representation models that focus on analyzing social media network language characteristics. Our system achieved an accuracy score of 81.54% on the test set, which is better than other strategies. Based on the experimental results, this work proves that the choice of text representation model determines the performance of the system.
Feature Extraction-Based Phishing URL Detection Using Machine Learning Techniques

Phishing URLs are one of the major contributors in cybercrimes. Internet users are trapped by phishing URLs that are difficult to distinguish from legitimate URLs. The purpose of this research is to identify phishing URLs based on address bar features along with behavioral features. We have considered and performed cross-validation technique on multiple machine learning algorithms and derived that decision tree algorithm works effectively in classifying phishing URLs based on the certain identified features. The dataset used in this research has 9581 data points and 41 features. Finally, we are able to achieve 97% accuracy in finding phishing URLs which is relatively better accuracy than other existing detection models.
THE DEVELOPMENT OF BUSINESS INTELLIGENCE DASHBOARD FOR MONITORING QUALITY IN HIGHER EDUCATION INSTITUTIONS IN SAUDI ARABIA INCLUDING SENTIMENT ANALYSIS FROM SOCIAL MEDIA

Information about the paper titled "THE DEVELOPMENT OF BUSINESS INTELLIGENCE DASHBOARD FOR MONITORING QUALITY IN HIGHER EDUCATION INSTITUTIONS IN SAUDI ARABIA INCLUDING SENTIMENT ANALYSIS FROM SOCIAL MEDIA" at IATED Digital Library
Mask Guided Unsupervised Face Frontalization using 3D Morphable Model from Single-View Images

Face frontalization can be used for improving face recognition accuracy under large poses. Existing deep learning based methods have made great progress in face frontalization by using a large amount of front-profile image pairs for training. However, only a few public datasets provide such front-profile image pairs which are collected from constrained conditions. To address the problem of lacking training data, we propose a face frontalization framework combined with 3D Morphable Model (3DMM) that only adopts front images for training. Profile images with real facial textures are synthesized through 3DMM to form the front-profile image pairs from front face images. Furthermore, our proposed framework adopts masked background images to guide the generating position of front faces and maintain the background from inputs with dual discriminators. Experiment results demonstrate that our proposed framework can generate face images with photorealistic frontal view and maintain the consistency of face identity and retain the color tone as well as the background from input images using masked background images. Extensive experiments on Labeled Faces in the Wild (LFW) datasets show that our proposed framework achieves favorable performance on face recognition.
Validation of GGMplus and CHGeo2004 Using Observed Deflection of the Vertical Data from QDaedalus and CODIAC Systems in the Mountainous Terrain of the Surses Region, Switzerland

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Validation of GGMplus and CHGeo2004 Using Observed Deflection of the Vertical Data from QDaedalus and CODIAC Systems in the Mountainous Terrain of the Surses Region, SwitzerlandAuthorsMugeAlbayrakiDDanielWilliSébastienGuillaumeIvan DarioHerrera PinzoniDLukasMüllerUrsMartiChristianHirtSee all authors Muge AlbayrakiDCorresponding Author• Submitting AuthorLa Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD)iDhttps://orcid.org/0000-0001-6705-4044view email addressThe email was not providedcopy email addressDaniel WilliFederal Office of Topography swisstopoview email addressThe email was not providedcopy email addressSébastien GuillaumeLa Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD)view email addressThe email was not providedcopy email addressIvan Dario Herrera PinzoniDETH Swiss Federal Institute of Technology ZurichiDhttps://orcid.org/0000-0002-9793-0831view email addressThe email was not providedcopy email addressLukas MüllerETH Swiss Federal Institute of Technology Zurichview email addressThe email was not providedcopy email addressUrs MartiFederal Office of Topography swisstopoview email addressThe email was not providedcopy email addressChristian HirtState Surveying Agency of Lower Saxony (LGLN)Technical University of Munichview email addressThe email was not providedcopy email address
Protecting user privacy in location-based services over road networks

In location-based services (LBS), the users with mobile devices share information such as their locations and queries to obtain desired services. Additionally, LBS providers are often able to track or record the movements of their users with great spatial and temporal accuracy. However, user location information can be misused by LBS providers or attackers, which creates privacy problems for users. This raises concerns about privacy breaches; therefore, many approaches have been proposed to protect user privacy. Thus, in previous works, the authors have proposed approaches to maintain the privacy of mobile users in the Euclidean space. However, it is difficult to apply current methods of Euclidean space to the environment of the road networks due to the topological characteristics of the road networks. In this paper, we propose a new approach to preserve user privacy in LBS over road networks. Additionally, in this paper, we provide new methods for selecting segments and users on the road network to face the attacks. Moreover, to evaluate our work, we compared it with other works such as MCC, BATCH, V-DCA, D-TC, and GCA approaches. After comparing these works, our work offers a good balance between quality of service and privacy.
Local sum uncertainty relations for angular momentum operators of bipartite permutation symmetric systems

We show that violation of variance based local sum uncertainty relation (LSUR) for angular momentum operators of a bipartite system, proposed by Hofmann and Takeuchi~[Phys.Rev.A {\bf 68}, 032103 (2003)], reflects entanglement in the equal bipartitions of an $N$-qubit symmetric state with even qubits. We establish the one-to-one connection with the violation of LSUR with negativity of covariance matrix [Phys. Lett. A, {\bf 364}, 203 (2007)] of the two-qubit reduced system of a permutation symmetric $N$-qubit state.
Without more theory, psychology will be a headless rider

Abstract We argue that Yarkoni's proposed solutions to the generalizability crisis are half-measures because he does not recognize that the crisis arises from investigators' underappreciation of the roles of theory in experimental research. Rather than embracing qualitative analysis, the research community should make an effort to develop better theories and work toward consistently incorporating theoretical results into experimental practice.
Microscopic fringe projection profilometry system calibration based on double telecentric lenses

Calibration methods for microscopic fringe projection profilometry (MFPP) system usually require complicated and time consuming system calibration. This paper proposes a convenient MFPP model and its calibration method. Compared to the conventional structured light model, the proposed model contains a telecentric imaging unit and a telecentric projection unit, and a precisely controlled moving platform was used to ensure that the optical axis of the camera is perpendicular to the reference plane. Instead of establishing a one-to-one mapping between the camera image and projector image to obtain the system parameters, the proposed model only requires to detect the width of the horizontal and vertical fringes owing to the orthographic projection characteristic of the telecentric projector, which is simpler and more convenient. The experimental results demonstrate the feasibility and high measurement accuracy of the proposed method, which shows great potential for the practical application for the MFPP measurement.
Imbalanced Learning for Robust Moving Object Classification in Video Surveillance Applications

In the context of video surveillance applications in outdoor scenes, the moving object classification still remains an active area of research, due to the complexity and the diversity of the real-world constraints. In this context, the class imbalance object distribution is an important factor that can hinder the classification performance and particularly regarding the minority classes. In this paper, our main contribution is to enhance the classification of the moving objects when learning from imbalanced data. Thus, we propose an adequate learning framework for moving object classification fitting imbalanced scenarios. Three series of experiments which were led on a challenging dataset have proved that the proposed algorithm improved efficiently the classification of moving object in the presence of asymmetric class distribution. The reported enhancement regarding the minority class reaches 116% in terms of F-score when compared with standard learning algorithms.
Classroom Attention Estimation Method Based on Mining Facial Landmarks of Students

Classroom attention estimation aims to capture the multi-modal semantic information contained in the teaching situation and analyze the level of concentration and participation of students in the classroom. However, it is a challenge to mine different modal information in non-experimental real teaching scenes to construct a unified attention mode. In order to advance these researches, this paper proposes a new method of automatically estimating attention through facial feature points. This method uses face detection and face alignment algorithms to capture 68 landmarks on student faces in classroom videos, and introduces face reference information to constrain landmarks and extract feature sets. The purpose is to reduce the sensitivity of the attention model to differences in different face information. The automatic evaluation module uses machine learning algorithms to train the classifier to estimate the individual student's attention level. In a large number of experiments conducted on multiple real classroom video data, our three-level attention classifier achieves an accuracy of 82.5%, which can achieve better results than other studies in the field of student participation analysis. The results show that the method based on facial landmark mining can more accurately predict the individual student's classroom attention level, and can be used as a non-intrusive automatic analysis method for real classroom multimedia data analysis.
Lightfield displays: a window into the metaverse

Headset-based AR/VR offers an immersive dive into these new digital worlds, but to many it still feels cumbersome and unfamiliar. As a result, mass adoption is still relatively slow. Lightfield displays offer a naturally immersive, “window-like” 3D look into the Metaverse while leaving users’ faces unencumbered. They can be readily deployed on familiar terminals, from smartphones / tablets to laptops or automotive displays. Better still, this method ensures compatibility with much of the existing digital content ecosystem, hence democratizing access to the Metaverse and potentially accelerating its deployment. In this talk, I will review our efforts at Leia to commercialize Lightfield-based mobile devices and our take on how to steadily ramp consumer adoption of the Metaverse.
Extraction of Kidney Anatomy Based on a 3D U-ResNet with Overlap-Tile Strategy

In this paper we present our approach for the KiTS21 Challenge. The goal is to automatically segment kidneys, (renal) tumors and (renal) cysts based on 3D computed tomography (CT) images of the abdomen. The challenge provided public training 300 cases for this purpose. To solve this problem, we used a 3D U-ResNet with pre- and postprocessing and data augmentation. The preprocessing includes the overlap-tile strategy by preparing the input patches, while a rule-based postprocessing was applied to remove false-positive artefacts. Our model achieved 0.812 average dice, 0.694 average surface dice and 0.7 tumor dice. This led to the 12.5th position in the KiTS21 challenge.
Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet

In this work, we introduce a spatially transformed DenseNet architecture for transformation invariant classification of cancer tissue. Our architecture increases the accuracy of the base DenseNet architecture while adding the ability to operate in a transformation invariant way while simultaneously being simpler than other models that try to provide some form of invariance.
Sentiment Analysis of Tweets Related to COVID-19

Since the advent of COVID-19, lockdowns have been imposed all over the world. Hence, people have took to the internet to voice their opinions and frustrations regarding this issue. Social media has become more relevant after lockdown has been imposed as people are confined to their homes and have no other means to stay connected with family and friends. Hence, twitter will be very insightful information medium to collect information on public opinion about COVID-19. This chapter analyzes sentiment on COVID-19 data and the community’s general sentiment towards it and try to gauge the how the mentality of people has varied throughout this lockdown. This will be done using Tweets related to COVID-19 collected through several hashtags such as #covid19 and #coronavirus. This data will then be preprocessed using common techniques like Tokenization. We will find the sentiments of the various words in the tweets using a pre-trained model which has many words with attached polarity and subjectivity and then be deciding average sentiment values (Positive, neutral, sad) for each tweet. Based on this, an analysis will be performed about how people feel about COVID-19 which will include word cloud, sentiment analysis, etc.
Handcrafting: Improving Automated Masking in Hardware with Manual Optimizations

Masking is an important countermeasure against side-channel attacks, but its secure implementation is known to be error-prone. The automated verification and generation of masked designs is therefore an important theoretical and practical challenge. In a recent work, Knichel et al. proposed a tool for the automated generation of masked hardware implementations satisfying strong security properties (e.g., glitch-freeness and composability). In this paper, we study the possibility to improve their results based on manual performance optimizations for the AES algorithm. Our main conclusion is that as the target architecture becomes more serial, such a handcrafted approach gains interest. For example, we reach latency reductions by a factor six for 8-bit architectures. We conclude the paper by discussing the extent to which such optimizations could be integrated in the tool of Knichel et al. As a bonus, we adapt a composition-based verification tool to check that our implementations are robust against glitches & transitions, and confirm the security order of exemplary implementations with preliminary leakage assessment.
Intelligent Video Detection Algorithm for Partial Discharge in Electrical Equipment

For effective detection of electrical equipment for partial discharge phenomenon, this article, taking the research of intelligent video detection algorithm, this paper discusses mainly the significance of equipment partial discharge detection algorithm, after introduce intelligent video detection algorithm, simultaneously discharge detection system design, analysis, the basic principle of the detection system, finally combining case to verify this algorithm system testing result, confirmed this system is valid. The method presented in this paper can be used to establish an intelligent video detection algorithm system for partial discharge of electrical equipment effectively. The system is verified to be able to accurately detect and identify whether partial discharge exists in the equipment, and the recognition accuracy is 97%–99%, which meets the technical requirements.
A Taxonomy of Approaches and Methods for Software Effort Estimation

Accurate effort estimation in software products is all-important and indispensable. So far, different taxonomies have been proposed in the literature of software cost estimation. Each taxonomy has specific principles and factors and is utilized in particular applications. In recent years, there has been significant progress in this field of research. Many papers have adopted different strategies to the problem of software cost estimation; each one proposes a relative taxonomy concerning a specific approach. In this paper, we introduce a novel taxonomy based on techniques presented in the field of software cost estimation.
Indian Sign Language Detection Using YOLOv3

Communication is one of the greatest gifts mankind has ever got. People with communication disabilities find it hard to communicate in a way most people do. Deaf people can communicate or express their thoughts through sign language. In this paper, an attempt has been made to enable deaf people to input the message in sign language that the person is trying to convey. The experimental setup consists of two stages, namely dataset creation and object detection. In the dataset creation phase, the image dataset is created for each Indian sign language (ISL) alphabet. In the object detection phase, the model is trained on the dataset created using the You Only Look Once version-3 (YOLOv3) algorithm, and recognition of ISL alphabets is performed using the trained model. YOLOv3 convolutional neural Network (CNN) uses darknet-53 as a backbone network. CNN is responsible for feature extraction. The effectiveness of the proposed method is validated on a test dataset containing 50 images. The proposed system achieves an average accuracy of 82%.