Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Process Mining in Agile Software Development Methodologies: A Systematic Mapping Study

The structure and the way in which organizations manage their projects have evolved. Agile software development has emerged as an alternative to manage projects management processes more efficiently. Process mining allows the analysis of project historical information and proposing improvements for agile processes. A systematic mapping study (SMS) was conducted to classify the proposed approaches in agile development methodologies that uses process mining. A total of 502 studies were identified, and finally 6 studies were selected and analyzed according to distinct aspects. Conference proceedings is the most common venue. There is a concentration of approaches published that comes from Asia and Europe. Disco tool is the most frequently used tool. Meanwhile, the process discovery being the most relevant process mining type used by researchers in this research area. There are two evaluation methods reported as being used: case study and running example, where Scrum is the most frequently methodology used. To the best of our knowledge, this is the first research that has been conducted to generate a SMS in this research area.
Recovering 6 D Object Pose from RGB Indoor Image based on Two-stage Detection Network with Multitask Loss

Object pose estimation from an RGB image has recently become a common problem owing to its widespread application. The advent of convolutional neural networks has impelled significant progress in object detection. However, most available methods do not involve a category-level pose estimation approach. This study presents an end-to-end 6D category-level pose estimation based on a two-stage bounding-box recognition backbone architecture. Our network directly outputs the 6D pose without requiring multiple stages or additional post-processing such as a Perspective-n-Point (PnP). The two-stage CNN architecture and our loss function render multitask joint training effective and efficient. We improve the pose estimation accuracy by replacing fully connected layers with fully convolutional layers. Fully convolutional networks require fewer parameters and are less susceptible to overfitting. Moreover, we transform the pose estimation problem into classification and regression tasks using our network; these are called Pose-cls and Pose-reg, respectively. We also present qualitative and quantitative results on real data from the SUN RGB-D dataset. The experiments demonstrate the effectiveness of our algorithms compared to other state-of-the-art methods.
Influence of Destination Attributes Importance to Tourists’ Satisfaction-Gujarat Tourism

Destination is a bundle of different tourism products ranging from attractions, accommodation, amenities to accessibility. When it comes to tourists’ satisfaction, certain questions arise such as: whether to consider overall satisfaction or satisfaction from specific destination attribute or both? Does experience of tourists from a destination matters only for their satisfaction? Do tourists have expectations before visiting the destination? Do the expectations of the tourists really influence their satisfaction? It has been researched that, before visiting a particular destination, tourists have several destination images or different expectations regarding destination attributes. When it comes to satisfaction, tourists consider the kind of services and the quality of services they are getting (Mohammad & Som, 2010).
Practical Cryptanalysis of kary C ∗

Recently, an article by Felke appeared in Cryptography and Communications discussing the security of biquadratic C∗ and a further generalization, k-ary C∗. The article derives lower bounds for the complexity of an algebraic attack, directly inverting the public key, under an assumption that the first-fall degree is a good approximation of the solving degree, an assumption that the paper notes requires “greater justification and clarification. ” In this work, we provide a practical attack breaking all k-ary C∗ schemes. The attack is based on differential techniques and requires nothing but the ability to evaluate the public key and solve linear systems. In particular, the attack breaks the parameters provided in CryptoChallenge 11 by constructing and solving linear systems of moderate size in a few minutes.
Generational Differences Related to Linguistic and Discourse Features of WhatsApp Users of Texting

The aim of this study is twofold: 1) to find out what aspects of linguistic and discourse features are contained in WhatsApp conversations 2) to investigate WhatsApp used by different generations. WhatsApp analysis tertiary level students, Indonesia was conducted to analyse the language elements used in WhatsApp groups. WhatsApp chat collected through mobile devices is fully analysed and then categorized into linguistic and discourse features. The results shown in this study are that there are several differences between the WhatsApp language used by generation X and generation Y in linguistic and discourse features. WhatsApp's message to Generation X is more likely to use letters that are omitted, logograms, and initialism, while Generation Y uses non-standard spelling, shortening, and pictograms. The features of discourse in both generations produce the same number of mixed languages. Whereas generation Y uses fewer letters for emphasis in conversation while they using WhatsApp than generation X.
A model for diffracting elements to reduce traffic noise

There is a great demand for noise reducing measures to ful ll noise legislation and to reduce the annoyance of tra c noise. Silent pavements and noise barriers are well-known, but are there other alternatives? Recently, a new sound di racting element was developed; a concrete block with cavities that is placed on ground level in close proximity of the road. These di racting element de ect tyre-road noise in an upward direction, creating a zone of noise reduction behind the elements. It can therefore act as a complement to existing noise-reducing measures and can be optimized for maximum noise reduction. Based on a nite element model, it is possible to assess the performance of the di racting elements. However, these models are impractically large for optimization purposes. We therefore developed a very fast, semi-numerical model, which makes optimization of the geometry feasible. In this paper we present this so-called piston-model and report the validation of its correctness by comparison with a nite element model. In an accompanying paper of J. Hooghwer , a measurement setup and actual experimental results for the di racting elements will be presented, showing that this innovation can increase noise reduction up to 4 dB.
Singing Voice Separation using Generative Adversarial Networks

In this paper, we propose a novel approach extending Wasserstein generative adversarial networks (GANs) [3] to separate singing voice from the mixture signal. We used the mixture signal as a condition to generate singing voices and applied the U-net style network for the stable training of the model. Experiments with the DSD100 dataset show the promising results with the potential of using the GANs for music source separation.
Event Cameras, Contrast Maximization and Reward Functions: An Analysis

Event cameras asynchronously report timestamped changes in pixel intensity and offer advantages over conventional raster scan cameras in terms of low-latency, low redundancy sensing and high dynamic range. In recent years, much of research in event based vision has been focused on performing tasks such as optic flow estimation, moving object segmentation, feature tracking, camera rotation estimation and more, through contrast maximization. In contrast maximization, events are warped along motion trajectories whose parameters depend on the quantity being estimated, to some time t_ref. The parameters are then scored by some reward function of the accumulated events at t_ref. The versatility of this approach has lead to a flurry of research in recent years, but no in-depth study of the reward chosen during optimization has yet been made. In this work we examine the choice of reward used in contrast maximization, propose a classification of different rewards and show how a reward can be constructed that is more robust to noise and aperture uncertainty. We validate our work experimentally by predicting optical flow and comparing to ground-truth data.
Supplementary material : Cross-Domain 3 D Equivariant Image Embeddings

Figure 1. Network architectures used in this work. Rectangles indicate data dimensions (width x height x channels). Red arrow: spherical convolutional residual bottleneck layer; dashed arrow: global average pooling; blue arrow: residual bottleneck layer; black arrow: convolutional layer. Nodes with yellow and green backgrounds are the target embeddings for pose and synthesis, respectively. 1. Architecture details
CLASSIFICATION USING DEEP LEARNING-BASED ENSEMBLE AVERAGING Technical Report

In our submission to the DCASE 2019 Task 1a, we have explored the use of four different deep learning based neural networks architectures: Vgg12, ResNet50, AclNet, and AclSincNet. In order to improve performance, these four network architectures were pretrained with Audioset data, and then fine-tuned over the development set for the task. The outputs produced by these networks, due to the diversity of feature front-end and of architecture differences, proved to be complementary when fused together. The ensemble of these models’ outputs improved from best single model accuracy of 77.9% to 83.0% on the validation set, trained with the challenge default’s development split.
Innovative 3 d Localization Technique Based On Antenna Radiation Pattern

Localization without considering antenna radiation pattern gives a significant error which can be minimized . we propose a novel technique of localization based on antenna radiation pattern for finding 3D location of sensor node in a wireless sensor network by using radiation pattern of antenna with received signal strength indication. Proposed localization algorithm is developed by considering target node equipped with half wave dipole antenna which has omnidirectional radiation pattern. The results show that proposed algorithm estimates 3D location of sensor node in a sensor network with low average error (<0.495m). Keywords-Localization, Dipole antenna, Received Signal Strength Indication (RSSI). --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 22-03-2018 Date of acceptance:07-04-2018 -------------------------------------------------------------------------------------------------------------------------------------
Sentiment Analysis Model for Polarity Classification Based On Movie Reviews Using Lexicon Based Technique

Abstract: Bag-of-words (BOW) is now the most popular way to model text in statistical machine learning approaches in sentiment analysis. However, the performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. The propose model called dual sentiment analysis (DSA) address the problem for sentiment classification. Firstly propose a novel data expansion technique by creating a sentiment-reversed review for each training and test review. On this base, propose a dual training algorithm to make use of original and reversed training reviews in pairs for learning a sentiment classifier, and a dual prediction algorithm to classify the test reviews by considering two sides of one review. It extend the DSA framework from polarity (positive-negative) classification to 3-class (positive negative- neutral) classification, by taking the neutral reviews into consideration. By dual sentiment analysis propose approach is to analysis sentiment as well as its automatic rating count. This can be calculated by using user review on the basis of positive, negative and neutral response. Then calculate all review and display the result analysis.
Large-Scale and Interpretable Collaborative Filtering for Educational Data

Large-scale and interpretable educational data analysis is a key enabler of the next generation of education, and a variety of statistical models for such data and corresponding machine learning algorithms have been proposed in the literature. In this work, we introduce an interpretable multidimensional IRT model and propose an efficient algorithm that is highly scalable and parallelizable. Our approach provides improved human interpretability and greater scalability. We also provide experimental results on a real-world large-scale data set to demonstrate that our algorithm achieves as good prediction performance as the state of the arts. Further, leveraging the interpretability of our model, we offer an efficient and systematic method of identifying wrongly annotated tagging information.
Unemployment , rural-urban migration and environmental regulation 1

This paper develops a general equilibrium model that incorporates specific features pertaining to developing countries: a large informal sector and rural-urban migration. A calibrated version of the model is used to study the effects of energy tax changes as well as a reduction in energy subsidies on labor market outcomes. The results indicate that the incidence of energy taxes is partly shifted on to the rural sector through rural-urban migration. The results thus highlight the importance of modeling the features relevant for developing countries and the economic general equilibrium effects in assessing the impact of environmental taxation in those countries.
FAULT DETECTION AND DIAGNOSIS METHODS IN POWER GENERATION PLANTS-THE INDIAN POWER GENERATION SECTOR PERSPECTIVE : AN INTRODUCTORY REVIEW 3

India is one of the largest consumers of energy in the world. However, more than 70 percent of its primary energy needs are being met through imports, mainly in the form of crude oil and natural gas. Power generation in the country uses mainly two types of energy sources: conventional and non-conventional energy sources. The use of non-conventional energy sources are increasing since last two decades for power generation because of its inherent advantages of transportation and certainty of availability. However, the conventional energy pollutes the atmosphere to a great extent.
Deep Coupling of Random Ferns

The purpose of this study is to design a new lightweight explainable deep model instead of deep neural networks (DNN) because of its high memory and processing resource requirement as well as black-box training although DNN is a powerful algorithm for classification and regression problems. This study propose a non-neural network style deep model based on combination of deep coupling random ferns (DCRF). In proposed DCRF, each neuron of a layer is replaced with the Fern and each layer consists of several type of Ferns. The proposed method showed a higher uniform performance in terms of the number of parameters and operations without a loss of accuracy compared to a few related studies including a DNN based model compression algorithm.
Designing for multi-agent collaboration: a shared mental model perspective

With the growing use of intelligent agents, it is important to know how to design artificial agents that would be capable of working in mixed teams of humans and artificial agents. In this thesis, formal models of teamwork and concepts successful in explaining effective teamwork in human teams are used to design artificial agents to enable the agents to be effective team members. For artificial agents to be effective teammates, they need to reason explicitly with concepts that achieve effective teamwork. To give artificial agents this reasoning ability, it is intuitive to explore teamwork concepts that work well with human teams. After all, humans will be an integral part of the human-agent system, and considering concepts that work well with human teams can provide useful insights when designing
' s personal copy Distributed energy-efficient estimation in spatially correlated wireless sensor networks q

We study the problem of maximizing lifetime in sensor networks that are deployed for estimating an unknown parameter or process. Sensors take measurements and transport them in multi-hop fashion to a fusion center (FC) for Maximum Likelihood (ML) estimation of this process. A prime distinguishing feature of wireless sensor networks is the spatial correlation among measurements of nearby sensors. We investigate the way spatial correlation shapes the tradeoff between estimation quality and energy efficiency, according to which more measurement data improve estimation quality, but they are more energy-costly to transport. If the effect of spatial correlation is understood, then a given estimation quality can be achieved with minimum data redundancy and energy consumption. We study the dynamic control of the sensor sampling rates and the routes to the FC. Sensor attributes such as spatial correlation, measurement accuracy and energy reserve as well as the quality of wireless links, collectively affect the sampling rates and routes to the FC. Further, due to spatial correlation, sensor sampling rates alter the joint probability density functions (p.d.f’s) for sensor readings and thus they affect the average estimation error. We show that the optimization problem can be decomposed into smaller ones, where each sensor autonomously takes its sampling rate and next-hop forwarding decisions, and we propose an iterative, low-overhead primal–dual algorithm. Our work yields interesting insights on the fundamental tradeoff between network lifetime and estimation quality, it provides a clear intuition on when spatial correlation is beneficial for the tradeoff above, and it provides a solution with distributed sensor coordination. 2014 Elsevier B.V. All rights reserved.
Un ) Stable Manifold Computation via Iterative Forward-Backward Runge-Kutta Type Methods

I present numerical methods for the computation of stable and unstable manifolds in autonomous dynamical systems. Through differentiation of the Lyapunov-Perron operator in [1], we find that the stable and unstable manifolds are boundary value problems on the original set of differential equation. This allows us to create a forward-backward approach for manifold computation, where we iteratively integrate one set of variables forward in time, and one set of variables backward in time. Error and stability of these methods is discussed.
A multi-phase authentication system for data protection with the assistance of graphical password and fingerprint authentication system against shoulder surfing attacks

In today’s world of quick growing technologies, security play’s a prominent role in the protection of people’s vital information from varied system attacks. So as a solution to these problems there should be some form of authenticated protection. Most of the system user prefer textual password in the application of security and privacy for their computer system. However there will always be a risk of human’s selecting a bad or small password or either inputting them in an insecure manner which is said to be “the weakest process” in the authentication chain. Rather than selecting a powerful character set, user tend to select the password which is easier, less complicated, short, or a type of password which is easy for memorization. With new web technologies and mobile application pilling up, individuals can easily access their application anywhere with various devices such as mobiles. This transformation brings nice convince but also increase the risk of exposing password to shoulder surfing attacks. The observation by the attacker can be done directly with naked eyes or indirectly by using some recording devices to collect’s individual’s confidential data. 3016 Shums Tabrez and Jagadeesh Sai D We introduced a multiple phase authentication system where the phase 1 is graphical authentication system called as Pass Matrix, In PassMatrix password which is formed graphically in which, we select only single pass-square block per pass-image for a sequence of n images. The number of images (i.e., n) is user-defined. In Pass Matrix, users choose one pass-square block per image for a sequence of n images rather than n pass-squares blocks or points in one image. Then we next tend to add another phase of protection with the help of fingerprint authentication where the users need to match the correct finger details for authentication. Here we are considering features such as minutiae intersection and minutiae Endpoint. Once both authentication is done we proceed to the final phase where user is capable of uploading the file in cloud storage which is encrypted and can only access if he enters the right key that is been send to his registered email id. If an individual enters thrice the wrong key then a file will be downloaded which will be fake and also the user who is trying to access the file with trial and error method will be blocked ,which in term defines the better solution for the protection of user data. As a result we found that the proposed system has a better resistive solution to shoulder surfing attacks while maintaining usability.
PCA Regularized Nonrigid Registration for PET / MRI Attenuation Correction

An open issue in the use of hybrid PET / MRI scanners is the attenuation correction of the PET image. In order to solve this problem, we propose to perform a nonrigid registration of an atlas CT image with the MRI. The registered atlas CT contains the information about the tissue densities necessary for the attenuation correction. In multi-modal, nonrigid image registration, the correspondence between the intensity values is not known a priori. Statistical, multi-modal distance measures determine this correspondence during the registration solely from the intensity distributions. Without the incorporation of prior knowledge this may lead to wrong results, such as the alignment of the skull with brain tissue, or the skin with fat surrounding the skull. Therefore, we propose a novel, PCA-based regularization of the nonrigid registration. This limits the possible registration results to morphologically plausible deformations. The model is constructed such that it is invariant to global translations in the registration. Thus, the registration is less dependent on the initial, rigid preregistration. Results are presented on a database of 18 CT datasets for the training of a PCA deformation model. MR images of the same patients have been rigidly registered with the corresponding CT datasets, which are used as ground truth for the tests. The evaluation is performed using a leave-one-out cross-validation by registering an atlas onto the total of 38 MRI datasets and comparing the deformed atlas with the ground truth CT of the patient. Results indicate a better performance of the proposed approach compared to the standard. On average, the mean squared error is decreased by 18% and the sensitivity for correct soft tissue and bone alignment is increased by 4%.