Reviewerly/exHarmony huggingface dataset:
A Static Hand Gesture and Face Recognition System for Blind People

This paper presents a recognition system, which can be helpful for a blind person. Hand gesture recognition system and face recognition system has been implemented in this paper using which various tasks can be performed. Dynamic images are being taken from a dynamic video and is being processed according to certain algorithms. In the Hand gesture system Skin color detection has been done in YCbCr color space and to discover hand convex defect character point of hand is used where different features like fingertips, angle between fingers are being extracted. According to gesture Recognized, various tasks can be performed like turning on the fan or lights. While in face recognition, Haar Cascade Classifiers and LBPH recognizer are being used for face detection and recognition respectively. With the help of OpenCV, The research has been implemented. Various hand gestures and human faces have been detected and identified using this system. The hand gesture was recognized with an accuracy of 95.2% was achieved and facial recognition was done with an accuracy of 92%.
Integrated robot planning, path following, and obstacle avoidance in two and three dimensions: Wheeled robots, underwater vehicles, and multicopters

We propose an innovative, integrated solution to path planning, path following, and obstacle avoidance that is suitable both for 2D and 3D navigation. The proposed method takes as input a generic curve connecting a start and a goal position, and is able to find a corresponding path from start to goal in a maze-like environment even in the absence of global information, it guarantees convergence to the path with kinematic control, and finally avoids locally sensed obstacles without becoming trapped in deadlocks. This is achieved by computing a closed-form expression in which the control variables are a continuous function of the input curve, the robot’s state, and the distance of all the locally sensed obstacles. Specifically, we introduce a novel formalism for describing the path in two and three dimensions, as well as a computationally efficient method for path deformation (based only on local sensor readings) that is able to find a path to the goal even when such path cannot be produced through continuous deformations of the original. The article provides formal proofs of all the properties above, as well as simulated results in a simulated environment with a wheeled robot, an underwater vehicle, and a multicopter.
Incorporation of principal component analysis, geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district, NW Iran

Delineation of mineralization-related geochemical anomalies of stream sediment data is an essential stage in regional geochemical exploration. In this study, principal component analysis (PCA) was applied to 12 selected elements to acquire a multi-element geochemical signature associated with Cu-Au mineralization in Feizabad district, NE Iran. The spatial distribution of enhanced multi-element geochemical signature of the second component (PC2) was modeled by different geostatistical procedures including variogram calculation, ordinary kriging (OK) and inverse distance weighting (IDW) interpolation techniques. Concentration-area (C-A) fractal and U-spatial statistics models were then applied to the continuous-value interpolated models for delineation of geochemical anomalies. Quantitative comparison of results based on the known mineral occurrences in the study area was carried out using normalized density index and success-rate curves. All generated models represent a high positive relation with known Cu (±Au) deposits in the study area, although, comparison of the results revealed that the OK-based U-spatial statistics model was superior to the rest of models. Besides, the low, moderate and high-intensity anomalies are spatially associated with geological-structural features in the study area.
Privacy Module for Distributed Electronic Health Records(EHRs) Using the Blockchain

Currently, storing patient's sensitive data by medical healthcare into Electronic Healthcare Records (EHR) has evolved immensely. Specifically, distributed healthcare records have brought ease to how hospitals and other different third parties access the sensitive medical health information of patients for various uses leading to generation of big data. Big data in healthcare is important as it can be used in the prediction of outcome of diseases, prevention of co-morbidities fatality and saving the cost of medical treatment. However, this has also made it easy for security breaches and privacy violations during the data collection process. In this paper, we propose a platform which uses the blockchain technology for privacy preservation during collection, management and distribution of EHR data. The aim of this paper is to ensure the total privacy, integrity and access control of distributed electronic health records to the data owners during its distribution on the blockchain. Simulated results demonstrate our proposed system establishes total transparency and ensures perfect privacy within the distributed network of sharing EHRs in the medical setting using the blockchain.
Transformation towards agile software product line engineering in large companies: A literature review

Abstract Almost all companies struggle with software systems that are getting increasingly complex. Therefore, in particular large companies often use software product lines, which provide an efficient way to manage software reuse as well as the high complexity. However, software product lines seem to be too slow to react to changes. Agile development approaches promise to meet this demand. At the same time, integrating an agile approach is not always easy. Consequently, there is a need for a transformation model that supports the agile transformation without harming the benefits of software product lines. In this paper, we present the results of a literature review regarding agile transformation in large software companies. Our results summarize the insights from 85 papers and present a list of 20 tasks and tasks that are recommended by several researchers to be considered during the transformation. On the basis of these results, we create an agile transformation model—the so‐called “agile hamburger”—that contains these tasks and preserves software product lines. Since the model is rather coarse grained and generic, we also support the adaption to different teams by presenting an assessment model tailored to the demands of the automotive domain.
Efficiency analysis of a filtering algorithm for discrete-time linear stochastic systems with polynomial measurements

A filtering algorithm is presented for discrete-time linear stochastic systems with polynomial measurements. The techniques to evaluate its efficiency are proposed. The algorithm performance is demonstrated by solving two navigation data processing problems, map-aided navigation using geophysical fields and single-beacon navigation for an autonomous underwater vehicle. The designed polynomial filter is compared to the extended Kalman filter, which is commonly used in practice. The simulation results reveal advantages of the polynomial algorithm. The obtained conclusions are reported.
Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier

In this modern age where the internet is growing rapidly, the existence of the internet can make it easier for tourist to find any information. In the field of tourism hotel, internet is very helpful in promotion of hotel. Tourists usually tell the experience during the hotel by writing reviews on the internet. Hence many hotel's reviews are found on the internet. The impact on hotel owners is that they can take advantage of reviews on the internet to improve and evaluate their hotels. With the availability of reviews on the internet with large numbers, tourists can't understand all the reviews they read whether they contain positive or negative opinions. It takes a sentiment analysis to quickly detect if the reviews is a positive or negative reviews. This study provides a solution by classifying positive opinion reviews and negative opinions using the Multinomial Naïve Bayes Classifier method and comparing models using preprocessing, feature extraction and feature selection. The best experimental results using preprocessing and feature selection with 10 fold cross validation have an average F1-Score more than 91%.
Triple-translation GAN with multi-layer sparse representation for face image synthesis

Face image synthesis with facial feature and identity preserving is one of the key challenges in computer vision. Recently, outstanding performances in image translation and synthesis have been reported in CycleGAN. However, for the task of face image synthesis, there are still several remaining issues (e.g., poor-visual-quality facial feature, changed face identity, unstable model optimization). In order to solve the above issues, in this paper we propose a novel model of triple translation GAN (TTGAN) with multi-layer sparse representation. We design a multi-layer sparse representation model, in which L1-norm representation constraint is integrated into the image generation to enhance the ability of identity preserving and the robustness of the generated facial images to reconstruction error. Moreover, in order to improve the stability of the model optimization, we propose a triple translation consistence loss, including a designed third image translation from a reconstructed original input to a desired output. The face synthesis experimental results on the benchmark face databases clearly shows the superior performance over the competing methods.
A Lightweight Auditing Service for Shared Data with Secure User Revocation in Cloud Storage

As data sharing has become one of the most popular services offered by cloud storage, designing public auditing mechanisms for integrity of shared data becomes more important. Two problems which arise in shared data auditing include preserving users identity and collusion resistant revocation of users. When data stored at the cloud is shared among a group of users, different users may modify and sign different data blocks which leaks signer identities to the public verifier. Also, when a user is revoked from the group, signatures generated by this user should be re-signed by the cloud server using re-signature keys. In addition, collusion of cloud server and the revoked user should leak no information about the private key of other users. In this paper, by employing a new proxy re-signature scheme, we propose a public shared data auditing mechanism that provides identity privacy and collusion resistant user revocation, simultaneously. The proposed protocol requires only lightweight computations at the user side for signing data blocks in real-time online phase. Moreover, our protocol supports large dynamic group of users, batch auditing and dynamic data operations. Experimental results demonstrate excellent efficiency of our scheme in comparison to the state of the art.
Classification complexity assessment for hyper-parameter optimization

Achieving the best performance with many machine learning methods depends critically on model hyper-parameter optimization. However, this optimization which requires strong expertise is often a “black magic” especially on deep learning models. Currently, even some widely used classic methods such as random search, grid search and manual search have obtained success to some extent, whose evaluation on hyper-parameter optimization problems is still computationally expensive and unpractical. They have to face the same challenge about how to choose the initial set of trials from random hyper-parameter permutation and combination. In this paper, to develop these methods, we present a simple and efficient framework for improving the efficiency and accuracy of hyper-parameter optimization by combining classification complexity and hyper-parameter optimization. Through this framework, it can quickly choose an initial set of hyper-parameters for a new coming classification task, thus reducing the number of trials in terms of hyper-parameter space. Results of six real-world datasets on three representative deep learning models demonstrate that the initial hyper-parameters set which is provided by our framework can make good performance, while plays a significant and efficient role on hyper-parameter optimization.
Robust ellipse fitting via alternating direction method of multipliers

The edge point errors, especially outliers, introduced in the edge detection step, will cause severe performance degradation in ellipse fitting. To address this problem, we adopt the ℓp-norm with p < 2 in the direct least square fitting method to achieve outlier resistance, and develop a robust ellipse fitting approach using the alternating direction method of multipliers (ADMM). Especially, to solve the formulated nonconvex and nonlinear problem, we decouple the ellipse parameter vector in the nonlinear ℓp-norm objective function from the nonconvex quadratic constraint via introducing auxiliary variables, and estimate the ellipse parameter vector and auxiliary variables alternately via the derived numerical methods. Simulation and experimental examples are presented to demonstrate the robustness of the proposed approach.
Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.
A Resources-Efficient Configurable Accelerator for Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have become one of the most popular approaches to many visual processing tasks. The majority of existing works on the accelerating DCNNs focus on high performance while neglecting the hardware resource utilization, like on-chip memory and DSP. In this paper, we propose a resources-efficient and configurable DCNN accelerator. A four-level processing-element (PE)-array-based structure is presented to realize high parallelism calculation of convolutional operation, and a new storage pattern named hybrid stationary (HS) is proposed to take full advantage of the used on-chip memory footprint and limited off-chip memory bandwidth. Moreover, roofline model is adopted to explore the design space of the given hardware resources. The proposed architecture achieves 113 G-ops/s at 100 MHz and consumes 784 DSP48 modules and 211.5 Block RAM modules on ZYNQ-7 ZC706 evaluation board. To the best of our knowledge, the proposed accelerator is the only implemented system on FGPA platform that can achieve multiple advantages: high-performanced, configurable, and efficient in power and resources utilization. It shows significant utilization improvement compared with the other available architectures.
A Systematic Mapping Study of Diversity in Software Engineering: A Perspective from the Agile Methodologies

Diversity is being discussed intensively by different knowledge areas. Some studies show that diversity builds better teams, delivers better results, and more. Cognitive diversity is linked to better outcomes, and studies show that cognitive diversity is influenced by identity diversity (e.g., gender, race, age, etc.), mainly when tasks are related to problem-solving and prediction. The discussions about diversity in Software Engineering are increasing as well. There is a known lack of representativeness from some groups when we talk about identity diversity as, for example, gender and race. To support diversity in Software Engineering is valuable and it is especially relevant once we are dealing with problem-solving. In this work, we are interested in understanding how the subject is being conducted in Software Engineering-related research and more specifically, in Agile Methodologies. For that we conducted a Systematic Mapping, so we can have an overview of the research area through classification and counting contributions related to the subject. The outcome is an inventory of papers on how Diversity is being discussed in Software Engineering and Agile Methodologies. We list the most common publication venues, the frequency publication through the years, the main areas in software engineering that are interested in the subject and finally, a first discussion on how Agile Methodologies and their intrinsic characteristics can support better deliveries from more diverse development teams.
LINEX Support Vector Machine for Large-Scale Classification

Traditional soft margin support vector machine usually uses hinge loss to build a classifier with the ''maximum-margin'' principle.However, C-SVM depends on support vectors causing the loss of data information.Then, least square support vector machine is proposed with square loss (l 2 -loss).It establishes equality constraints instead of inequalities and considering all the instances.However, the square loss is still not the perfect one, since it gives equivalent punishment to the instances at both sides of the center plane.It does not match the reality considering the instances between two center planes deserve heavier penalty than the others.To this end, we propose a novel SVM method with the adoption of the asymmetry LINEX (linearexponential) loss, which we called it LINEX-SVM.The LINEX loss gives different treatments to instances based on the importance of each point.It gives a heavier penalty to the points between two center planes while drawing light penalty to the points outside of the corresponding center planes.The comprehensive experiments have been implemented to validate the effectiveness of the LINEX-SVM.
Quantum dynamical speedup in correlated noisy channels

The maximal evolution speed of a quantum system can be represented by quantum speed limit time (QSLT).We investigate QSLT of a two-qubit system passing through a correlated channel (amplitude damping, phase damping, and depolarizing).By adjusting the correlation parameter of channel and the initial entanglement,a method to accelerate the evolution speed of the system for some specific channels is proposed.It is shown that, in amplitude damping channel and depolarizing channel,QSLT may be shortened in some cases by increasing correlation parameter of the channel and initial entanglement, which are in sharp contrast to phase damping channel.In particular, under depolarizing channels, the transition from no-speedup evolution to speedup evolution for the system can be realized by changing correlation strength of the channel.
Get a grip

A key assumption conventionally made by flexible head-mounted eye-tracking systems is often invalid: The eye center does not remain stationary w.r.t. the eye camera due to slippage. For instance, eye-tracker slippage might happen due to head acceleration or explicit adjustments by the user. As a result, gaze estimation accuracy can be significantly reduced. In this work, we propose Grip, a novel gaze estimation method capable of instantaneously compensating for eye-tracker slippage without additional hardware requirements such as glints or stereo eye camera setups. Grip was evaluated using previously collected data from a large scale unconstrained pervasive eye-tracking study. Our results indicate significant slippage compensation potential, decreasing average participant median angular offset by more than 43% w.r.t. a non-slippage-robust gaze estimation method. A reference implementation of Grip was integrated into EyeRecToo, an open-source hardware-agnostic eye-tracking software, thus making it readily accessible for multiple eye trackers (Available at: www.ti.uni-tuebingen.de/perception).
A Comparative Evaluation of Word Embeddings Techniques for Twitter Sentiment Analysis

The writer's opinion is important information, which is becoming increasingly desirable with the increasing volume of user-generated content on the Web. That's why made sentiment analysis an important tool for extracting this information, which can usually be positive, negative or neutral. To target sentiment classification problem, the standard approach used is the binary classification, considering the sentiment (or the polarity), positive or negative. The classification result depends on the text representation and then the extracted features used to train the classifier. Word embeddings techniques have emerged as a prospect for generating word representation for different text mining tasks, especially sentiment analysis. In this paper, we focus on the comparison of three commonly used word embeddings techniques (Word2vec, Fasttext and Glove) on Twitter datasets for Sentiment Analysis, employing six popular machine learning algorithms, namely, GaussianNB, LinearSVC, NuSVC, LogisticRegression, SGD and RandomForest. We find that Fasttext representation used with NuSVC, which is a type of SVM classifier, outperforms the other combinations in accuracy.
Solving the Tree-Structured Task Allocation Problem via Group Multirole Assignment

Task allocation is a critical phase of project management. Tree-type structures are frequently used constraints to obtain a pertinent task allocation. They can illustrate where one task may require numerous agents and when an agent can be assigned to different tasks (roles). The process of task allocation is made more complex when administrators need to satisfy sequential and fixed branch relationships between/among tasks (roles). This paper formalizes the tree-structured task allocation problem (TSTAP) with group multirole assignment (GMRA) and proves necessary conditions, the necessary and sufficient condition, as well as sufficient conditions, of TSTAP. The formalization makes it easy to find a solution with the IBM ILOG CPLEX optimization package (CPLEX). The necessary conditions improve the CPLEX solution by eliminating infeasible cases. The necessary and sufficient condition describes the solution space of TSTAP completely. Another exciting result is that the sufficient conditions can not only improve the CPLEX solution by describing a practical approximate solution space but also help decision-makers and human resource officers organize a team in order to successfully assign tasks. The proposed approach is verified by simulation experiments with respect to a real-world problem. The experimental results present the practicability of the proposed solutions in this paper. This paper was motivated by general cooperative projects whose tasks have tree-structured relationships. This can make the problem of successful multitask assignment extremely challenging. The traditional method of assignment such as the KM algorithm can no longer solve this problem. To solve the assignment problem with tree-structured relationships, an efficient many-to-many assignment with constraints is required. The proposed approach provides theoretical and technical foundations for efficient assignment of TSTA, which can not only provide a viable and effective assignment scheme for TSTA problems but also help human resource officers to formulate reasonable plans according to the relationships between/among tasks.