Reviewerly/exHarmony huggingface dataset:
Using Natural Language Processing and Graph Mining to Explore Inter- Related Requirements in Software Artefacts

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts thereby mitigating the fault propagation to later phases. The requirements documented in natural language (NL) are prone to contain faults because of different vocabularies among stakeholders. This research employs various NL processing with semantic analysis (SA) and mining solutions from graph theory to NL requirements to develop inter-related requirements (IRRs) that can help identify requirements that may need similar fixes. Additionally, our approach aims at aiding requirements' engineers with fault-prone regions both pre and post inspection. Pre-inspection, our approach using IRRs help removing redundant and extraneous faults within related requirements while post-inspection, it aids engineers analyse the impact of a change in one requirement on another related requirements. So, this research aims at developing a graph of inter-related requirements using natural language processing and semantic analysis approaches on a given requirements document that can be used to aid various decisions pre and post-inspections.
Learning to Learn Kernels with Variational Random Features

We introduce kernels with random Fourier features in the meta-learning framework for few-shot learning. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTM-based inference network can effectively integrate the context information of previous tasks with task-specific information, generating informative and adaptive features. The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks. Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better, or at least competitive, performance compared to existing meta-learning alternatives.
Segmentation of CT Brain Stroke Image using Marker Controlled Watershed

In this paper, an algorithm is proposed to detect and segment ischemic stroke from CT brain images. Firstly, our proposed method starts by a preprocessing step contains skull bone striping and text removal from CT images, then the images are enhanced using median filter and histogram equalization. Next the watershed segmentation and Marker Controlled watershed methods is applied to detect the ischemic stroke. The experimental results show that Marker controlled watershed is better than the watershed due to over segmentation caused by the noise of the CT image. The over segmentation problem was resolved and succeeded to detect and segment the infarcted regions in the CT Ischemic stroke image that will help the non-radiologists identify the stroke visually.
High Accuracy Text Detection using ResNet as Feature Extractor

Traditional OCR system cannot handle scene texts. Text detection in natural scene images is much more difficult than the recognition of text in scanned document images because of its complicating background. Popular existing methods for characters segmentation are CTPN, EAST and PixelLink. However, they are not very capable of dealing with the small and densely character in large image, and connected characters. To cope with these problems, we adopted above popular character segmentation networks CTPN or EAST as the main structure and proposed using ResNet as the feature extraction network due to its excellent sensitivity of tiny features among those existing methods. The experimental result shows that the feature extraction network can affect the precision of locating text significantly. In the experiment with ICDAR dataset, the effect of deeper depth and larger width of ResNet on EAST is notable.
Context-Aware Sliding Window for Sentiment Classification

Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods.
Comparison of Similarity Measurement Metrics on Medical Image Data

Similarity measurement plays an important role to solve many pattern recognition problems such as classification, clustering and particularly the content based image retrieval problems. In this paper, a detailed comparison of 10 similarity measures has been presented for medical image retrieval. Different types of medical images from databases such as NEMA, OASIS and EXACT09 are used to evaluate the performance. Features of all the medical images are extracted using Log-Gabor wavelet. The effectiveness of all the distance metrics are investigated and tested on more than 800 medical images from the databases. It is observed from the experimental results that the retrieval performance can be improved by the distance measures like Bray- Curtis and Canberra distance metric as compared to other existing distance based approach.
Circuit Schematics of a Capacitive Proximity Sensor

The purpose of this paper consists in development of hardware, ensuring artificial tactile sensing of an anthropomorphic robot. Through analysis, performed to that end, it was discovered, that the majority of capacitive sensors, installed as well on the grippers of the anthropomorphic robots, as on robots of other types are implemented in terms of microelectromechamical systems (MEMS) technology. Obstacle detection and surface type recognition using the solutions, discussed in the related work, requires much closer proximity, than with the capacitive sensor, developed by us. We present a circuit schematics implementation of the sensor and charted graphical dependencies of the output voltage from distance for various objects, tracked by the sensor and made of different materials. We present experiment outcomes, obtained on examples of water and aqueous salt solution. We discovered, that the output voltage when using solid materials has similar values, but the respective values were quite different in the case of water or aqueous salt solution.
Use of a Weather Forecast Model to Identify Suitable Sites for New Wind Power Plants in Karnataka

The wind speed forecast by a weather forecast model (with a resolution of 3 km) has been used to identify regions with high wind speed in Karnataka.The correlation between daily wind speed predicted by the model and the measured wind speed at three stations in Karnataka has been shown to be high.Based on this model, new locations have been suggested for the installation of new wind power plants in Karnataka.
A Method of Plant Root Image Restoration Based on GAN

Root is one of the most important organs for plants to obtain water and nutrients so that its morphological research is critique for identifying plant growth conditions. Aiming at breakthrough of barriers and obtaining accurate root phenotype data based on the original plant root image, a method of Arabidopsis thaliana root image restoration based on GAN (generative adversarial network) was proposed in this paper. Firstly, a second generation Kinect camera is used to capture the matched data set for training the GAN, which includes high-resolution images of some objects and their matched fuzzy and distort images, and high-resolution images of Arabidopsis’ roots and their images in the biogel. Secondly, a GAN with attention mechanism is constructed and trained. The network mainly consists of two parts: the generator and the discriminator with attention mechanism. It is multi-layer convolution network, except that the generator adopts a de-convolution structure to carry out the super-resolution reconstruction. The generator is responsible for converting a fuzzy image into high-resolution image, and the discriminator is used to distinguish whether the inputted image is derived from the prepared dataset or generated by the generator. With the progress of network training, the generator is getting better and better at generating images, the same is true for the effect of the discriminator discriminating the image, that is, the better mapping relationship between the blurred or partially missing image and the high resolution complete image is established. Finally, import the root image of the Arabidopsis planted in the biogel into the trained network and the repaired and restored root image can be obtained. Compared with the original image, the restored one has more accurate details and accordingly more accurate root morphology parameters are computed. The experiment results showed that the proposed method can be used to achieve the super-resolution reconstruction and complete the incomplete or blur Arabidopsis root images.
A New Baseline for Automated Hyper-Parameter Optimization

Finding the optimal hyper-parameters values for a given problem is essential for most machine learning algorithms. In this paper, we propose a novel hyper-parameter optimization algorithm that is very simple to implement and still competitive with the state-of-the-art L-SHADE variant of Differential Evolution. While the most common method for hyper-parameter optimization is a combination of grid and manual search, random search has recently shown itself to be more effective and has been proposed as a baseline against which to measure other methods. In this paper, we compare three optimization algorithms, namely, the state-of-the-art L-SHADE algorithm, the random search algorithm, and our novel and simple adaptive random search algorithm. We find that our simple adaptive random search strategy is capable of finding parameters that achieve results comparable to the state-of-the-art L-SHADE algorithm, both of which achieve significantly better performance than random search when optimizing the hyper-parameters of the state-of-the-art XGBoost algorithm for 11 datasets. Because of the significant performance increase of our simple algorithm when compared to random search, we propose this as the new go-to method for tuning the hyper-parameters of machine learning algorithms when desiring a simple-to-implement algorithm, and also propose to use this algorithm as a new baseline against which other strategies should be measured.
A Benefit Optimization Approach to the Evaluation of Classification Algorithms

We address the problem of binary classification when applied to non-communicable diseases. In such problems the data are typically skewed towards samples of healthy subjects. Because of this, traditional performance metrics (such as accuracy) are not suitable. Furthermore, classifiers are typically trained with the assumption that the benefit or cost associated with decision outcomes are the same. In the case of non-communicable diseases this is not necessarily the case since it is more important to err on the side of treatment of the disease rather on the side of over-diagnosis. In this paper we consider the use of benefits/costs for evaluation of classifiers and we also propose how the Logistic Regression cost function can be modified to account for these benefits and costs for better training to achieve the desired goal. We then illustrate the advantage of the approach for the case of identifying diabetes and breast cancer.
Bitcoin Price Prediction Combining Data and Text Mining

The idea implemented in this paper is the creation of a machine learning model, which utilizes past prices of Bitcoin, Google trends data and some custom features, which were created by text mining on tweets about Bitcoin. The aim of this study was to predict future Bitcoin prices. For this purpose, we compared a Deep Neural Network, and specifically, a network with LSTM layers, a Gradient Boosting Regression model and an XGBoost model. According to our results, the Deep Neural Network performed better, with a significant root mean square error of 0.999% on test data.
Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming

Current mainstream super-resolution algorithms based on deep learning use a deep convolution neural network (CNN) framework to realize end-to-end learning from low-resolution (LR) image to high-resolution (HR) images, and have achieved good image restoration effects. However, as the number of layers in the network is increased, better results are not necessarily obtained, and there will be problems such as slow training convergence, mismatched sample blocks, and unstable image restoration results. We propose a preclassified deep-learning algorithm (MGEP-SRCNN) using Multilabel Gene Expression Programming (MGEP), which screens out a sample sub-bank with high relevance to the target image before image block extraction, preclassifies samples in a multilabel framework, and then performs nonlinear mapping and image reconstruction. The algorithm is verified through standard images, and better objective image quality is obtained. The restoration effect under different magnification conditions is also better.
Red-faced ROUGE: Examining the Suitability of ROUGE for Opinion Summary Evaluation.

One of the most common metrics to automatically evaluate opinion summaries is ROUGE, a metric developed for text summarisation. ROUGE counts the overlap of word or word units between a candidate summary against reference summaries. This formulation treats all words in the reference summary equally.In opinion summaries, however, not all words in the reference are equally important. Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries. We investigate the suitability of ROUGE for evaluating opin-ion summaries of online reviews. Using three simulation-based experiments, we evaluate the behaviour of ROUGE for opinion summarisation on the ability to match aspect and polarity. We show that ROUGE cannot distinguish opinion summaries of similar or opposite polarities for the same aspect. Moreover,ROUGE scores have significant variance under different configuration settings. As a result, we present three recommendations for future work that uses ROUGE to evaluate opinion summarisation.
Exploring research data management planning challenges in practice

Abstract Research data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.
Speed control of brushless de motor using Ant Colony Optimization

Abstract DC motor has as a key aspect of industrial applications. Thus, due to their high performance, BLDC motors are preferred as a small horsepower motor. However, it is hard to acquire the good controlling performance with traditional tuning approaches in order to solve the speed control. This paper provides an approach of determining the optimum control parameters of PID for the BLDC speed control using the Ant Colony Optimization (ACO), which is an intelligent algorithm based on feeding behavior of the swarm. The efficiency and validity of the design method based on ACO are shown in the Simulation outcomes.
Computer Vision Based Asset Surveillance for Smart Buildings

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.
Using Prezi Online Software to Improve Teaching Listening Skill

Mastering the listening skill is considered to be difficult by L2 learners because lack of practice opportunities and impracticality of text-books for enhancing the listening skill. One of the relatively recent methods in teaching the listening skill is using the teaching media. This study aimed to investigate the effectiveness of a different method, use of Prezi online software as a learning-teaching material to improve the listening skill of the students in a German language department in Indonesia. This mixed-methods study last for six months and employed 26 participants. This study used the theory by Richey and Klein (2009) which explains that there are three stages in the teaching material development model; namely design, production, and evaluation. In the design phase, the students’ needs for listening learning were identified. The need which stood out was the need for more attractive teaching media. Next, in the production stage, a product was created based on the students’ needs. At this stage, learning media with Prezi online software were developed. In the last phase, the specifications of the product were evaluated. The results of the study showed that Prezi online software was well-accepted and very well-applied by the students. The students preferred this method to books. Moreover, it was observed that using this method improves the students’ listening skill who study the German language. Pedagogical implications are discussed.
Two-Way Handshake User Authentication Scheme for e-Banking System

The e-commerce system that involves many fraudulent activities as of today needs enormous research to be done to raise the level of security. The security even if provided in many aspects needs it to be reviewed and authenticated. In most of the existing systems like SSL, OTP generation to the mobile number or mail id, or any such systems proves that there are some drawbacks. Such drawbacks can be overcome by using multiple biometric entities for authentication even failing one authentication proves to be a spooler. Such a foolproof system is proposed that involves three biometric traits and the security measures are analyzed.
Significance of Electronic Word of Mouth (e-WOM) in Opinion Formation

In the realm of interconnected digital world, social ranking systems are readily used in different sections of society, for several reasons.The private and public sectors both are making use of social ranking systems as a tool to engineer human behavior, and crafting a digitally stimulated social control.Online reviews and ratings are one of the significant marketing strategies of online sellers to steer out consumers' opinion and ultimately their purchasing decisions.Buyers usually go through these reviews and ratings while purchasing online product or hiring online services.Online consumer reviews, recommendations for product and services, and peer viewpoints play a significant role in the customer's opinion formation.Different online forums of product reviews, ratings and recommendations differ in their objectives, functions, and characteristics.This paper focuses upon a systematic literature review and comparative study of the influence the positive and negative reviews and ratings of the products, automobile services, movies, restaurants, products and services on OLX & eBay, etc. have on opinion formation.Moreover, how these reviews influence others opinions of buying and using the products, services and apps will be analyzed.
Improvement of the Device Descriptions of ECHONET Lite by adding version specific information

The Device Descriptions of ECHONET Lite is a machine readable JSON format data of each device information. We improved it to handle version specific information by (A) describing version information, (B) inserting version information into device description object and property object and (c) enumerating multiple specifications of device description objects and property objects. We confirmed the effectiveness of this improvement by developing a sample application, APPENDIX viewer.
Social Anchor: Privacy-Friendly Attribute Aggregation From Social Networks

In the last decade or so, we have experienced a tremendous proliferation and popularity of different Social Networks (SNs), resulting more and more user attributes being stored in such SNs. These attributes represent a valuable asset and many innovative online services are offered in exchange of such attributes. This particular phenomenon has allured these social networks to act as Identity Providers (IdPs). However, the current setting unnecessarily imposes a restriction: a user can only release attributes from one single IdP in a single session, thereby, limiting the user to aggregate attributes from multiple IdPs within the same session. In addition, our analysis suggests that the manner by which attributes are released from these SNs is extremely privacy-invasive and a user has very limited control to exercise her privacy during this process. In this article, we present Social Anchor, a system for attribute aggregation from social networks in a privacy-friendly fashion. Our proposed Social Anchor system effectively addresses both of these serious issues. Apart from the proposal, we have implemented Social Anchor following a set of security and privacy requirements. We have also examined the associated trust issues using a formal trust analysis model. Besides, we have presented a formal analysis of its protocols using a state-of-the-art formal analysis tool called AVISPA to ensure the security of Social Anchor. Finally, we have provided a performance analysis of Social Anchor.