Reviewerly/exHarmony huggingface dataset:
Classification of Non-Functional Requirements Using Fuzzy Similarity KNN Based on ISO / IEC 25010

In developing software, one of the important role is non-functional requirements. They are often forgotten by the developer, so that it causes adverse effects. For this reason, a non-functional requirement classification method is needed to make it easier for software developers to classify non-functional requirements from requirement document. Most of studies in this area are focusing on single label classification. However, a non-functional requirement sentence can be classified to more than one class. Therefore, a classification technique that support multi-labels was needed. This research proposes a model for classifying non-functional requirements into quality aspects based on ISO/IEe 25010. It uses Fuzzy Similarity KNN (FSKNN) for building the sentence-based classification model. Six difference dataset which is contain 1432 sentence is used in the test. Best accuracy, precision and recall value from classification using FSKNN respectively is 42.8%, 68.1%, and 55.9%.
A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm

The deep "Convolutional Neural Networks (CNNs)" gained a grand success on a broad of computer vision tasks. However, CNN structures training consumes a massive computing resources amount. The researchers in this field are concerned on designing CNN structures to maximize the performance and accuracy. The main design methods are human hand-crafted fixed model structures and automatic generated models. We proposed an automatic CNN structure design approach based on genetic algorithm that concerned with generating light weight CNN structures. We also introduce a chromosome novel representation for the structure of CNN. Unlike existing approaches, the proposed methodology is designed to work on limited computing assets with achieving high accuracy. It utilizes advanced training methods to decrease the overhead on the computing resources that are involved in the process. Our experimental results denote the proposed model effectiveness over the related work methods.
Combining External Sentiment Knowledge for Emotion Cause Detection

Emotion cause detection (ECD) that aims to extract the trigger event of a certain emotion explicitly expressed in text has become a hot topic in natural language processing. However, the performance of existing models all suffers from inadequate sentiment information fusion and the limited size of corpora. In this paper, we propose a novel model to combine external sentiment knowledge for ECD task, namely ExSenti-ECD, to try to solve these problems. First, in order to fully fuse sentiment information, we utilize a sentiment-specific embedding method to encode external sentiment knowledge contained in emotional text into word vectors. Meanwhile a new sentiment polarity corpus is merged from multiple corpora. Then, a pre-training method is adopted to mitigate the impact of the limitation of annotated data for ECD task instead of simply expanding samples. Furthermore, we apply attention mechanism to take emotional context into consideration based on the observation that the context around emotion keywords can provide emotion cause clues. Experimental results show that our model greatly outperforms the state-of-the-art baseline models.
Constructing the (Healthy) Neoliberal Citizen: Using the Walkthrough Method “Do” Critical Health Communication Research

Introduction. Since the launch of Web 2.0, we have witnessed a trend towards digitizing healthcare tools for use by both patients and providers. Clinical trials focus on the ways that digital health technologies result in better outcomes for patients, increase access to healthcare and reduce costs. Critical approaches which explore how these technologies result in changes in patient embodiment, power relations and the patient-provider relationship are badly needed. Objective. To provide an instruction case example of how Light, Burgess and Duguay’s (2018) walkthrough method can be mobilized to study apps to address critical health communication (CHC) research questions. Methods. We apply the walkthrough method to the BEACON Rx Platform. In doing, we conduct a detailed technical walkthrough and evaluate the environment of expected use to answer the following questions: How does the platform shape (and how is it shaped by) understandings of what it means to be healthy? Who are its ideal users? How does this impact its environment of expected use? Conclusions. This paper demonstrates the potential contributions of the walkthrough method to critical health communication research, namely how it enables a detailed consideration of how an app’s technical architecture and environment of expected use are embedded with symbolic representations on what it means to be healthy and what practices should be engaged in to maintain “good” health. It also demonstrated that, despite the rhetoric that digital health technologies democratize healthcare, the BEACON Rx platform is a risk monitoring tool by its very design.
Mobile Robot Path Planning Using Genetic Algorithm in a Static Environment

Genetic algorithm, developed by Goldberg, has been used to solve optimization problems, and in modeling systems where randomness is involved. This paper presents a review of the path planning optimization problem, and an algorithm for robot path planning in a static environment, using genetic algorithm as a tool. The purpose of the algorithm is to find the fastest route in a given number of steps, while avoiding obstacles in the space. The algorithm's performance is enhanced by automatically omitting all suggested routes that cross the boundaries of the environment. For optimization and search problems, genetic algorithms are used as a search tool in computing to find exact or an approximate solution. The algorithm is tested with number of steps less, equal, and more than the sufficient steps needed. The results show how the number of steps in different environments set the best path to be followed.
Evaluating Competence Measures for Dynamic Regressor Selection

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to estimate the target value of a given test pattern. This competence is usually quantified using the performance of the regressors in local regions of the feature space around the test pattern. However, choosing the best measure to calculate the level of competence correctly is not straightforward. The literature of dynamic classifier selection presents a wide variety of competence measures, which cannot be used or adapted for DRS. In this paper, we review eight measures used with regression problems, and adapt them to test the performance of the DRS algorithms found in the literature. Such measures are extracted from a local region of the feature space around the test pattern, called region of competence, therefore competence measures. To better compare the competence measures, we perform a set of comprehensive experiments of 15 regression datasets. Three DRS systems were compared against individual regressor and static systems that use the Mean and the Median to combine the outputs of the regressors from the ensemble. The DRS systems were assessed varying the competence measures. Our results show that DRS systems outperform individual regressors and static systems but the choice of the competence measure is problem-dependent.
Learning Similarity: Feature-Aligning Network for Few-shot Action Recognition

Deep learning structures have achieved impressive results in action recognition. However, most of deep models require extensive training on large scale datasets. Besides, Insufficient data can easily lead to overfitting. In this work, we propose a conceptually simple, flexible, and general approach for few-shot action recognition, where a model must learn to reliably classify an example having seen only few previous instances which belongs to the same class with it. Our method, called the Feature-Aligning Network (FAN), can be trained with a small amount of data. By applying "alignment" on representations from a pair of videos, we use a CNN to get an incorporating feature and learn a nonlinear similarity metric of it. In this way, FAN mainly focuses on capturing similarities and differences in the same type of feature maps between two feature map sets. We conduct standard few-shot classification experiments on UCF11, UCF101 and HMDB51 datasets, showing the ability of our model that it can quickly learn from few examples. Moreover, FAN is also applicable to action similarity labeling task, which is not only competitive, but also far simpler and more efficient than other approaches.
AbsoluteSecure: A Tri-Layered Data Security System

Data has been touted as the new oil. This attests to the level of importance it has garnered over the years. With increased proliferation of and advancements in technology, data is bound to play more prominent role. The need for data security, therefore, cannot be overstated. Existing systems for securing data often rely on one or combination of biometric, cryptography, and steganography. In this paper, we propose AbsoluteSecure, a data security system that combines the three techniques to enhance the security of data. The system is implemented using C#. To evaluate its performance, experiments are performed to assess its usability and security. Specifically, on usability, its capacity to successfully enroll a new user’s fingerprint and authenticate an enrolled user are evaluated. On the other hand, to ascertain its security, we measure how much it can detect and deny access to unauthorized users, both at the authentication and usage levels. The results of the experiments show that AbsoluteSecure can ensure the confidentiality, integrity, and availability of data.
Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil

Lithium (Li) is defined as an alkaline metal which does not exist in nature in its free form. Moreover, it has properties that make it possible to be applied in several manners, such as industrial use, especially in the ceramic and glass industries, as well as the battery industry which has had an increase in this element consumption. It is crucial to use less expensive and faster techniques, compared to classical and intrusive methods, to identify new Li deposits. Remote sensing as proved to be a powerful tool to identify areas with potential for Li exploration. The objective of this work is to apply several images processing techniques, such as RGB band combinations, Band Ratios and Principal Components Analysis (PCA) to identify potential areas for Li prospection in the pegmatite district of São João Del Rei, located in the south of the state of Minas Gerais, Brazil. In these areas, two study zones were defined: the zone A, with approximately 323 km<sup>2</sup> in the pegmatite district of São João Del Rei and the zone B with approximately 90 km<sup>2</sup> in the pegmatite district of Araçuaí. The results of the techniques applied in this study are very promising since, in addition to ease and low cost, these techniques can be applied to several locations. This approach is highly valuable for the Li mining industry.
Resource-Constrained Replication Strategies for Hierarchical and Heterogeneous Tasks

In large-scale cloud computing systems, a task is often divided into multiple subtasks which can be executed in parallel in different machines. As a result, the task completion time is constrained by the completion time of the slowest subtask. To reduce the task completion time, the strategy of replicating the straggling subtasks has been employed in cloud computing frameworks such as MapReduce and Hadoop. Analyzing mathematically the performance of such replication strategies has recently received great attention. However, most of the analytical work focuses on the case where the completion times of the subtasks are identically distributed. This assumption may not hold in practice due to the modularization and encapsulation of the computation of a task, resulting in different service requirements for different subtasks. In this paper, we consider the case where the completion times of the subtasks of a task are drawn from heterogeneous/empirical distributions. Furthermore, we consider the case where jobs consist of hierarchical tasks that are required to be executed in a specific order described by a task precedence graph. We propose a novel framework to investigate how to allocate replication resources among the subtasks such that the overall task completion time is minimized. Specifically, we devise a Lagrange multiplier-based method and a water-filling-like algorithm for integer programs. We show via analysis and simulations the optimality and efficiency of our proposed algorithms, and explore the tradeoff between cost and latency from introducing replications in a task graph.
Evaluating the performance of support vector machines (SVMs) and random forest (RF) in Li-pegmatite mapping: preliminary results

Machine learning algorithms (MLAs) have gained great importance in remote sensing-based applications, and also in mineral prospectivity mapping. Studies show that MLAs can outperform classical classification techniques. So, MLAs can be useful in the exploration of strategical raw materials like lithium (Li), which is used in consumer electronics and in the green-power industry. The study area of this work is the Fregeneda-Almendra region (between Spain and Portugal), where Li occurs in pegmatites. However, their smaller exposition can be regarded as a problem to the application of remote sensing methods. To overcome this, Support Vector Machine (SVM) and Random Forest (RF) algorithms were applied to. This study aims at: (i) comparing the performance accuracy in lithological mapping achieved by SVM and by RF; (ii) evaluating the sensitivity of both classifiers to class imbalance and; (iii) compare the results achieved with previously obtained results. For these, the same Level 1-C Sentinel-2 images (October 2017) were used. SVM showed slightly better accuracy, but RF was able to correctly classify a larger number of mapped Li-bearing pegmatites. The performance of the models was not equal for all classes, having all underperformed in some classes. Also, RF was affected by class imbalanced, while SVM prove to be more insensitive. The potential of this kind of approach in Li-exploration was confirmed since both algorithms correctly identified the presence of Li-bearing pegmatites in the three open-pit mines where they outcrop as well in areas where Li-pegmatites were mapped. Also, some of the areas classified as Li-bearing pegmatites are corroborated by the interest areas delimited in previous studies.
An improved flux density distribution model for a flat heliostat (iHFLCAL) compared with HFLCAL

The simulation of the flux spot reflected by a flat heliostat is one of the fundamental problems in the central receiver system. In this paper, we propose an improved model based on Gaussian distribution assumption to more faithfully depict the flux density distribution on the receiver reflected by a flat heliostat, which is also the basis for study of the focusing heliostat. First, an imaginary flux density distribution is modeled by an elliptical Gaussian function in the image plane coordinate system. The relationship between the standard deviations of the Gaussian function and the heliostat length and width is revealed. Shading and blocking effects are carefully considered and addressed. Then, this distribution is mapped to the receiver plane through oblique parallel projection along the reflection direction of the heliostat based on the law of energy conservation and calculus. A state-of-the-art GPU-based ray tracing simulation method is adopted, and satisfactory consistency between the proposed model and the ray tracing result is found. The experiments and comparisons demonstrate that the proposed model is as efficient as but more accurate than the related Gaussian models.
Lifting 2d Human Pose to 3d : A Weakly Supervised Approach

Estimating 3d human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from the single view. Recent deep learning based methods show promising results by using supervised learning on 3d pose annotated datasets. However, the lack of large-scale 3d annotated training data captured under in-the-wild settings makes the 3d pose estimation difficult for in-the-wild poses. Few approaches have utilized training images from both 3d and 2d pose datasets in a weakly-supervised manner for learning 3d poses in unconstrained settings. In this paper, we propose a method which can effectively predict 3d human pose from 2d pose using a deep neural network trained in a weakly-supervised manner on a combination of ground-truth 3d pose and ground-truth 2d pose. Our method uses re-projection error minimization as a constraint to predict the 3d locations of body joints, and this is crucial for training on data where the 3d ground-truth is not present. Since minimizing re-projection error alone may not guarantee an accurate 3d pose, we also use additional geometric constraints on skeleton pose to regularize the pose in 3d. We demonstrate the superior generalization ability of our method by cross-dataset validation on a challenging 3d benchmark dataset MPI-INF-3DHP containing in the wild 3d poses.
A Perception-Inspired Deep Learning Framework for Predicting Perceptual Texture Similarity

Similarity learning plays a fundamental role in the fields of multimedia retrieval and pattern recognition. Prediction of perceptual similarity is a challenging task as in most cases we lack human labeled ground-truth data and robust models to mimic human visual perception. Although in the literature, some studies have been dedicated to similarity learning, they mainly focus on the evaluation of whether or not two images are similar, rather than prediction of perceptual similarity which is consistent with human perception. Inspired by the human visual perception mechanism, we here propose a novel framework in order to predict perceptual similarity between two texture images. Our proposed framework is built on the top of Convolutional Neural Networks (CNNs). The proposed framework considers both powerful features and perceptual characteristics of contours extracted from the images. The similarity value is computed by aggregating resemblances between the corresponding convolutional layer activations of the two texture maps. Experimental results show that the predicted similarity values are consistent with the human-perceived similarity data.
FERNN: A Fast and Evolving Recurrent Neural Network Model for Streaming Data Classification

With the recent explosion of data navigating in motion, there is a growing research interest for analyzing streaming data, and consequently, there are several recent works on data stream analytics. However, exploring the potentials of traditional recurrent neural network (RNN) in the context of streaming data classification is still a little investigated area. In this paper, we propose a novel variant of RNN, termed as FERNN, which features single-pass learning capability along with self-evolution property. The online learning capability makes FERNN fit for working on streaming data, whereas the self-organizing property makes the model adaptive to the rapidly changing environment. FERNN utilizes hyperplane activation in the hidden layer, which not only reduces the network parameters to a significant extent, but also triggers the model to work by default as per teacher forcing mechanism so that it automatically handles the vanishing/exploding gradient issues in traditional RNN learning based on back-propagation-through-time policy. Moreover, unlike the majority of the existing autonomous learning models, FERNN is free from normal distribution assumption for streaming data, making it more flexible. The efficacy of FERNN is evaluated in terms of classifying six publicly available data streams, under the prequential test-then-train protocol. Experimental results show encouraging performance of FERNN attaining state-of-the-art classification accuracy with fairly reduced computation cost.
Self-adaptive dual-strategy differential evolution algorithm

Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the "DE/best/2" mutation operator with better global exploration ability and "DE/rand/2" mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems' dimension.
A Survey on the Impacts of Quantum Computers on Information Security

Quantum computers differ from traditional computers because unlike traditional computers that use and process information in bits (0 or 1), the unit of information in quantum computers is the quantum bit, or qubit, that can represent additional states beyond ones and zeros at the same time. The additional states called superimposition and entanglement facilitate phenomenal processing speed of quantum computers. The emergence of quantum computers has raised many concerns in various areas of information security. This paper employs a literature survey methodology to elucidate the positive and negative impacts of quantum computers on information security. This paper further addresses the concern that quantum computers will negatively impact information security by examining all recommended information security and privacy controls of the NIST SP 800-53 Rev. 5 catalog of controls. Furthermore, the paper briefly outlines the recent work towards quantum-resistant standards.