Reviewerly/exHarmony huggingface dataset:
Implementation of Real-Time Virtual Dressing Room Using Microsoft Kinect SDK and Supervised Learning

Presently the usage of Electronic Commerce has been growing at a rapid space and many customers choose the online shopping option available, to avoid waiting in a long queue. Before buying an item specially clothing item, jewellery, or accessories the online shoppers have a desire to try them on. Female customers are worried about the security issue in the trial room of shopping complex. Using virtual dressing room people may virtually try and choose clothing, jewellery, accessories, etc. at their home without any security issues. In the proposed work augmented reality technology has been used to give an efficient trial experience to the customer by implementing virtual dressing room using Microsoft Kinect Software Development Kit (SDK), where Two-Dimensional (2D) model of an apparel is superimposed on to the user’s image and it appears that the customers are wearing the actual dress and the results are satisfactory. In addition to this, work has been done using supervised learning algorithm and the same is in process. The proposed work eliminates the security issues as the privacy of woman customer is the major concern and they may feel like they are in trial room of shopping complex.
An Extrinsic Depth Camera Calibration Method for Narrow Field of View Color Camera

This work designs and implements a calibration method between a narrow field of view camera and a depth camera in an endoscope-like scenario. An endoscopy-like scenario has a limited specular reflective surface, a camera with a narrow field of view. Instead of pushing the accuracy of the target marker with low-resolution data, we design a new loss function, which utilizes all of the three dimensions points of the checkerboard measured with the depth camera, and calculates the distance between projected 3D positions onto 2D image surface and the color image. The final re-projected error is improved to be less than 1 millimeters on average.
High-resolution catalog of the the Maurienne Swarm (French Alps) based on template matching and double-different relocation

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Since 2017, the Maurienne Valley (French Alps) has been affected by an episode of seismic unrest. In this study we focused on the seismic swarm that occurred in 2017 and 2018, which was characterized by 8 events with ML &amp;gt; 3 and a maximum magnitude of 3.7. The goal was to extend the existing SISmalp catalog, and also to provide accurate locations and magnitude estimations.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;The employed data was recorded by a local seismic network composed of 6 broadband stations. The use of template matching allowed us to detect more than 70000 events, increasing the detection rate by more than ten times compared to the original catalog. We obtained high resolution locations applying a double difference relocation method, providing as input differential times calculated by cross-correlating templates with their respective detections. Finally, we estimated magnitudes using template-family-based linear regression analysis, in order to include even the weakest events. The seismic locations will be discussed in the tectonic and geological setting of the Maurienne Valley.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
A New Approach of Deep Learning-Based Tamil Vowels Prediction Using Segmentation and U-Net Architecture

In this chapter, 500 different images of Tamil vowels that are hand written (அஆஇஈஉஊஎஏஐஒஓஔஃ) interprets that the Tamil alphabets model has trained about 75% accuracy with proposed U-net model algorithm. The introduction of various segmentation proportions was discussed for English and Tamil language text identification was explained. In this work, the selection of image is split into four segments and read the data during training itself. Thus, the Tamil and English font prediction accuracy of the model was improved about 85% using U-net architecture was explained.
A fast and robust real-time surveillance video stitching method

Abstract Real-time video stitching can build a wider field of view for surveillance, which faces a compromise between stitching speed and visual quality. A fast and robust real-time surveillance video stitching method is proposed to deal with the ghosting effect caused by moving objects and misalignments caused by background change or slight camera shift through automatic updating. By stitching key frames, parameters such as pix mapping table, stitching seams and blending weights are calculated, and most of subsequent frames are directly blended with CUDA acceleration based on the pre-calculated stitching parameters. Fast and effective algorithms are designed to detect the change of stitching seam and background during the whole stitching process, which determines whether to update the stitching seam or recalculate stitching parameters. Experiments show that this method can robustly and automatically solve the ghosting and misalignments to improve visual quality and achieve satisfactory real-time performance.
Research on Multilevel Robust Information Decision Optimization Model of Data Communication Network

In order to cope with the complex and changeable electromagnetic environment, we need a strategy to reduce the risk of system transmission and improve the information response ability. This paper proposes a new integration method which is suitable for the multi-level robust decision information. This method is combined with the hierarchical structure of the data communication network and based on the analysis of dynamic complex network communication environment. This method can not only solve the problem of information interaction and cooperation between levels in the current environment, and the closed feedback loop also has the characteristics of the autonomous evolution.
The Eye Tracking and Gaze Estimation System by Low Cost Wearable Devices

This study develop a wearable eye tracking and gaze estimation low cost devices. This devices use infrared camera and design by integration of elastic mechanism adaptable. The use of cheap endoscope camera for mobile and 3D print technique for building up the devices, which results in be a low cost solution. This device can effectively extract and estimate pupil ellipse from few camera-captured samples of an eye and compute the corresponding 3D eye model. And this device use multiple point's calibration method to solve the related polynomial formula for future angle-to-gaze mapping. This wearable device is a low-cost which can be used for virtual reality and auxiliary equipment.
Face Recognition Using Computational Efficient Algorithms

Research in pattern recognition and biometrics have received considerable attention in recent years among computer vision communities, but have remained very problematic in actual applications. This paper performed a detail and direct comparison of two algorithm, i.e. Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD), for face recognition. PCA project the image onto the first few principal component, where foremost principal component is a direction that maximizes the variance of projected image and last principal component captures least amount of variance among images. Even when the light and facial expressions are greatly varied, FLD creates a group of images of the same class and separately of different class. The eigen vectors of face datasets are used as a significant features for classification. Here six facial databases (FEI face database, Face94’ Essex face database, CASIA, FERRET database and NITH face database) have been taken for comparison of performance. These six databases provide a comprehensive dataset for testing the performance of the PCA and FLD algorithm.
Economic Evaluation of Cryptocurrency Investment

This study proposes a method for constructing cryptocurrency portfolios using forecast models. We predict returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determine the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure. Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates. This is due to high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.
Secure Electronic Banking Authentication - Survey

The electronic banking sector is overgrowing due to the increasing demand for banking services, and many countries are turning to the digital transformation of banking services, because of the rising numbers of smart phone users and computer users with the increase in the demand for electronic banks, cyber-attacks such as impersonation and deception appear with which the technical and security aspects of the bank and the user should be paid attention from user/bank side. In this survey, we give an overview of the authentication methods used in electronic banks and their application domains. We review the new user authentication techniques, making it easier for the manager to choose the appropriate techniques for his organization by understanding the way it works, advantages, and disadvantages. The administrator can integrate these mechanisms in a manner that is appropriate for his security plan in the banking system. The paper is organized as follows: section I, explains an introduction to electronic banks and their applications, section II, displays the recent technologies used, the third section includes analysis of the techniques, presentation of advantages and disadvantages.
A Deep Manifold Representation for Information Discovery

Information discovery plays a vital role in the success of various machine learning and data-driven tasks. In essence, it is a process of exploring useful knowledge from input data sources. In this paper, a deep manifold representation method is proposed for information discovery, consisting of multistage manifold filters, a hashing transform and a histogram operation. Specifically, a manifold method, locality preserving projections (LPP) is utilized for constructing multistage filter banks in different layers, followed by a hashing transform and a histogram operation to generate the final manifold representation. In the proposed method, not only is the intrinsic local structure revealed by LPP, but also the abstract representation from different levels is explored by multistage filters, leading to better data representation for information discovery. To demonstrate the effectiveness of the proposed strategy, we conduct experiments on two visual analysis and recognition tasks. Experimental results show that the proposed method is superior to the related methods.
A modified numerical model for DNI prediction under clear sky: Case study of the south of Tunisia

Having enough knowledge of the basic solar components, including Direct Normal Irradiation (DNI), Diffuse Horizontal Irradiance (DHI) and Global Horizontal Irradiation (GHI), is greatly significant to concentrated solar power (CSP) projects. (DNI), the amount of direct irradiance received on a plane normal to the sun, is of remarkable usefulness to CSP technologies around the world. However, in Tunisia, the field of solar energy generation faces twofold challenges. First, the unique type of solar concentrating collectors works only on global horizontal irradiation (GHI) which is the geometric sum of DNI and DHI. Second, these collectors are available in a limited number of locations.Regarding this additional review of hourly DNI, our approach to the real time assessment of the clear sky DNI gives a good estimation in the worst case the mean absolute error equal to 40 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for hourly values in comparison to the measured data.
Balancing Body based and Discrete Interaction for Usability and Presence in Virtual Reality

As the major goal of virtual reality (VR) is to provide enriched experience, motion based interface involving many parts of the body, if not the whole, is often employed. On the other hand, in general, the more body parts involved and active the interaction becomes, the less usable and more tiring the user can feel, and the more space the system can require. This in turn can even reversely affect the total experience in the negative way. In this paper, we explore whether it might be possible to convey the rich active VR experience while maintaining some minimum level of usability, by considering the extent of the of body involvement in the VR interaction. We compare six different styles of interaction in terms of their usability and experience in a simple virtual tennis application: one that uses the whole body (both upper and lower body), one that uses only the discrete input interaction device, and four others that use varied degrees of the body and device. Our experiment has shown that compared to the full body interaction, part body interaction showed comparable level of immersion, presence and active experience, while incurring a similarly low level of fatigue of the full interaction device based interaction. Constraining the active VR interface to the upper body without sacrificing the level of experience this way has an added advantage of less operating space. We believe that this investigation can serve as a guideline for VR interaction design that employs body based action interfaces.
SOAR: synthesis for open-source API refactoring

The manual refactoring between APIs is a tedious and error prone task. We introduce Synthesis for Open-Source API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code that is required to invoke those APIs.
On the diversity and frequency of code related to mathematical formulas in real-world Java projects

In this paper, the term formula code refers to fragments of source code that implement a mathematical formula. We present empirical studies that analyze the diversity and frequency of formula code in open-source-software projects. In an exploratory study, we investigated what kinds of formulas are implemented in real-world Java projects and derived syntactical patterns and constraints. We refined these patterns for sum and product formulas to automatically detect formula code in software archives and to reconstruct the implemented formula in mathematical notation. In a quantitative study of a large sample of engineered Java projects on GitHub we analyzed the frequency of formula code and estimated that one of 700 lines of code in this sample implements a sum or product formula. For a sample of scientific-computing projects, we found that one of 100 lines of code implements a sum or product formula. To assess the need for tool support, we investigated the helpfulness of comments for program understanding in a sample of formula-code fragments and performed an online survey. Our findings provide first insights into the characteristics of formula code, that can motivate further studies on the role of formula code in software projects and the design of formula-related tools.
Recency-Weighted Acceleration for Continuous Control Through Deep Reinforcement Learning

Model-free reinforcement learning algorithms have been successfully applied to continuous control tasks. However, these algorithms suffer from severe instability and high sample complexity. Inspired by Averaged-DQN, this paper proposes a recency-weighted target estimator for actor-critic settings, which will construct a target estimator with more weight placed on recently learned value functions, obtaining a more stable and accurate value estimator. Besides, delaying policy updates with more flexible control is adopted to reduce per-update error because of value function errors. Furthermore, to improve the performance of prioritized experience replay (PER) for continuous control tasks, Phased-PER is proposed to accelerate training in different periods. Experimental results are given to demonstrate that using the same hyper-parameters and architecture the proposed algorithm is more robust and achieves better performance, surpassing the existing methods on a range of continuous control benchmark tasks.
Development of NodeJS based Backend System with Multiple Storefronts for Batik Online Store

Inibatikita is an online clothing store company which sells its products through multiple online stores, each of which is associated with a partner influencer. Currently, there exist no ecommerce solution which can accommodate this business process. This research aims to develop a system which facilitates this business process while addressing scalability for up to 50000 users. After the requirements are gathered, a system analysis is performed to compare existing ecommerce solutions with a custom solution. Post analysis, system design takes place and is swiftly followed by development and testing; finishing with deployment of the system. The results of system analysis indicate that existing ecommerce solutions aren't able to accommodate a multiple store approach, while a custom NodeJS solution is capable of doing so. Development and testing then proves that a NodeJS solution is able to fulfill the business process while accommodating 50000 users through indexing and caching. The results show that a custom NodeJS solution addresses the business requirements and scalability of inibatikita, when existing solutions could not.
Nonlinear Matrix Factorization with BatchNorm

With the exponential increase of information in the high-tech era, how to quickly find information that is valuable to oneself has become a major problem in people's lives. As the main force to solve this problem, the recommendation system can predict the user's rating of the item, and recommend the item with a higher rating to the user. However, due to the increasing amount of various information, the problem of data sparsity has become increasingly prominent, which greatly affects the scoring accuracy and recommendation quality of the recommendation system. In order to solve this problem, this paper integrates Nonlinear Matrix Factorization and batch normalization model BatchNorm to improve the recommendation quality under the condition of data sparsity and increase the training speed. Experiments prove that the algorithm model in this paper has a better recommendation effect.
Microarchitectural Isolation Guarantees Through Execution Based Signatures

Trusted Execution Environments (TEEs) aim to provide integrity and confidentiality guarantees to certain computations irrespective of the state of the rest of the system. That is, they protect Trusted Applications (TAs) even if the Operating System or the hypervisor are compromised. The TEE runs in parallel with the OS and leverages a set of hardware and software components to create such an isolated environment. However, this isolation can be broken by exploiting microarchitectural side-channels. The state of the shared components of multi-core processors depends on the actual processes being executed, and as a result, some information is leaked from one process to any other running in the same processor. This leakage completely breaks the confidentiality guarantees that TEEs promise. The only way to completely avoid the leakage is to avoid the share of resources, but this is nearly impossible to achieve without a huge degradation in the performance of the processor. Assuming that it is possible that the leakage exists, and the attacker only can get information from the observable microarchitectural state, we propose to monitor the hardware resources to detect the microarchitectural state changes caused by the attacks. To this end, we have implemented a hardware module that compares at runtime pre-stored microarchitectural execution signatures of each enclave, with the actual execution trace, and triggers an alarm when it detects significant variation.
Evaluating the Utility of Human-Machine User Interfaces Using Balanced Score Cards

Evaluating the utility of Human-Machine Systems’ User Interfaces is not trivial. Several evaluation methods can be used to investigate if the behaviour of the user interface complies with best practices of Human-Machine Interface Design. Even when is possible to agree on which methods to use to conduct the evaluation, defining the utility requires evaluating the interface under analysis toward the company’s goals, or mission. This paper investigates how the utility, perceived by end users of interfaces, can be captured by a research instrument, as well as be represented by a structured approach based on Usability evaluation and Balanced Score Cards methodology. This is an alternative demarche for accessing the Usability of a Software System, and the main goal is helping designers and administrators to maintain and improve their systems.
Unpaired Abstract-to-Conclusion Text Style Transfer using CycleGANs

The availability of paired examples greatly facilitates the task of style transfer by allowing the use of supervised learning. However, our scenario does not enjoy such a condition. We focus on style transfer for academic writing, and examine the possibility of performing style transfer between sentences from the abstract and conclusion sections of a scientific article in the Natural Language Processing field, in both directions. We assume a latent correlation between the abstract and conclusion styles, and construct an unpaired data set. We propose the use of a version of CydeGAN based on transformers to perform the task. Our approach is shown to realize differences in tense or word usage which are characteristic of the different sections.
The Quality of Programs Conceived by Object Enhanced Time Petri Nets

Software developers use metrics to estimate the software quality before the development stage to impact early decision making at the requirement stage, development quantification, operation, and software maintenance. These metrics give knowledge of certain attributes of the software system and thus help to evaluate it objectively and give opportunities for software improvement and making plans for modifications. The application development is supposed to be performed using the OETPN based modeling method. The newly proposed metrics are: Structural Complexity (number of arcs and the number of modules), Hierarchical Complexity (if one OETPN module is created by another), Unstructuredness of OETPN Models (which is based on the number of modules, cycles and decision places), Branching Complexity (which is computed according to the reachability graph), Decision Vertex (which is computed based on the decision node of the execution graph), and the Net Complexity (the summation of all the previous complexities).