Reviewerly/exHarmony huggingface dataset:
Zones of Pain: Visualising the Relationship Between Software Architecture and Defects

Substantial development time is devoted to software maintenance and testing. As development resources are usually finite, there is a risk that some components receive insufficient effort for thorough testing. Architectural complexity (e.g. tight coupling) can make effective testing particularly challenging. Software components with high architectural complexity are more likely be defect–prone. The aim of this study is to investigate the relationship between established architectural attributes and defect–proneness. We used the architectural attributes: abstractness, instability and distance to measure the architectural complexity of software components. We investigated the ability of these attributes to discriminate between defective and non-defective components on four open source systems. We visualised defect–proneness by plotting architectural complexity and defectiveness using Martin’s ‘Zones of Pain’. Our results show that architecture has an inconsistent impact on defect–proneness. Some architecturally complex components seem immune to defects in some projects. In other projects architecturally complex components significantly suffer from defects. Where architectural complexity does increase defect–proneness the impact is strong. We recommend practitioners monitor the architectural complexity of their software components over time by visualising potential defect–proneness using Martin’s Zones of Pain.
THE ROLE OF SOFT SKILLS IN ENHANCING THE IMPACT OF INTERNAL MARKETING ON CUSTOMER SATISFACTION AN APPLIED STUDY OF A SAMPLE OF PRIVATE BANK MANAGERS IN BAGHDAD

This study examined the effect of elements of internal marketing activities on customer satisfaction through an exploratory study of banking services in Baghdad City.This study aims to determine the effect of internal communication, training and internal market research on customer satisfaction, represented by quality of service, loyalty and market share.And the role that soft skills play in strengthening this relationship, as the research attempted to provide a comprehensive theoretical framework for the most important presented by the book and researchers on the variables of research, data was collected using a questionnaire-based survey consisting of 36 questions and distributed to (223) managers of private banks in the city of Baghdad .The research attempts to answer a set of questions.The body of the research problem (is there a correlation and influence between the dimensions of internal marketing and customer satisfaction, and between soft skills and customer satisfaction, do soft skills reinforce positively and morally the relationship between internal marketing and customer satisfaction), in order to analyze the data that has been Obtained from questionnaires, quantitative methods such as mean, standard deviation, coefficient of variation, simple correlation coefficient, and structured equation modeling were used.The results showed that the elements of internal marketing activities have a major impact on customer satisfaction.Finally, the study recommends that management should improve internal marketing activities by applying the activities appropriate to the company's business to obtain customer satisfaction
A Novel Approach to Tennis Game Analysis and Emulation using Computer Vision

Continuous practice, analysis and game experience are essential to tennis players hoping to improve their game. While practice for tennis players is being made available through thousands of academies around the world, game analysis and game experience are lacking. In this paper, we propose a method of obtaining a set of statistics quantifying a tennis player's game from a video using an optimized version of a state-of-the-art computer vision algorithm to detect the position of the ball in a frame in real-time and infer the state of the game through a series of such detections. We also propose a system that is capable of virtually simulating a player's game in real-time using a graphics processing unit. This virtual simulation can be extended to real life with the use of three ball shooting machines, a camera, and a processing unit. We are able to obtain the statistics with an accuracy of 70% on a test set of two videos consisting of tennis rallies.
Multiple Spaces Deep Hashing for Image Retrieval

Deep learning has been widely applied in image retrieval due to its promising performance improvement. Typically, deep hashing has attracted more and more attention. At first, it learns hash coding in deep neural networks and maps features into hash spaces. For more discrimination features in the hash space, a loss function is used to make similar pictures closer, and dissimilar pictures further apart. However, the effect of using the loss function to approximate the similar image distance in the hash space is not particularly good, so we introduce such constrain in Euclidean space. We add a contrastive loss function to the Euclidean space. It further makes similar pictures closer together, and the distance between dissimilar images is farther. Comprehensive empirical evidences show that our method produces better image retrieval performance with different weight precisions than existing methods.
A Fuzzy Approach for Texture-based Segmentation

In this paper, we propose a fuzzy approach for texture-based segmentation, where both the obtained regions and the texture features are fuzzy. This way, we have considered the imprecision related to the texture concept, as well as the imprecision associated with the boundaries between regions in the images. With regard to the texture descriptors, we propose to define them on the basis of the perceptual properties of texture. Since these properties are imprecise by nature, they are modelled by means of fuzzy sets defined on the domain of some of the most representative measures of each property. With regard to the segmentation technique, a fuzzy path-based segmentation is proposed. In this approach, fuzzy connectivity is used to measure the relationship between any pair of pixels. Thus, given a set of seed points, fuzzy regions are obtained on the basis of the connectivity between each seed point and the rest of pixels in the image. Finally, this proposal is applied to obtain fuzzy segmentations from real images.
Is Complexity of Re-test a Reason Why Some Refactorings Are Buggy? an Empirical Perspective

In this short paper, we explore one simple, yet unexplored question about the relationship between refactoring and bugs. Is the complexity of re-testing code immediately after refactoring a reason why some refactorings are buggy? To facilitate our analysis, we use a set of over four thousand refactorings mined from three open-source systems and decomposed into the four test categories of van Deursen and Moonen. Preliminary results showed that, compared with non-buggy classes, buggy classes had been subjected to more refactorings where a large re-test commitment was required; extent of re-test may therefore be a significant factor in determining whether refactoring creates bugs. Our finding supports that of Bavota et al. - that more and better testing after certain refactoring practices could reduce the harm that refactorings cause.
Modulation Classifier for Primary User Detection

Spectrum scarcity issue is being addressed by using dynamic spectrum sharing of cognitive radio. Primary user detection is the primary task in cognitive radio. Modulation Classification can be applied as the vital principle in this article to identify the particular primary user radio type. A novel method to classify the modulation type has been suggested in this paper by using the neural network algorithm. The entire system is implemented using a Software Defined Radio platform (NIUSRP). The system will classify the type of modulation and which is used to detect the presence of the primary user for the cognitive radio application. In this paper, three different modulation type has been implemented in USRP SDR in real-time. The primary purpose of implementing the modulation classification algorithm is to identify the existence of a signal at a given location at a given frequency band at the given time.
Region Proposal for Line Insulator Based on the Improved Selective Search Algorithm

The region proposal algorithm for line insulators is a critical step in line insulator detection. The selective search algorithm provides a rich candidate area for line insulators. However, the selective search algorithm uses SIFT descriptor to extract texture features. It causes a large time overhead. In response to this problem, this paper proposes an improved algorithm. The algorithm use the Harr-Like algorithm to form a feature value map. Then, the formed feature value map is mapped to a fixed-size texture vector HBSN (Harr-Like Based on SPP-Net) by using Spatial Pyramid Pooling Network. In the process of the formation of feature maps and the construction of feature vectors, the integral image is used for acceleration. The integral image can only traverses the pixels in the original image once, forming an integral image of the feature values corresponding to the original image. When calculating the texture vector of different regions in the original image, it only needs to index the corresponding position of the eigenvalue integral image, which overcomes the problem of repeated calculation of texture features in the overlapping region by selective search and achieves fast calculation of texture similarity in different regions. Experiments show that the improved selective search algorithm for line insulator has a calculation speed increase of 8.21% compared with the selective search.
Designs from the Narrow-Sense Primitive BCH Codes $$\mathcal {C}_{(q, q^m-1, \delta _3, 1)}$$

The interplay between coding theory and t-designs has been a very interesting topic for researchers. It is well known that the supports of all codewords with a fixed weight in a code may form a t-design under certain conditions. In this paper, we will determine explicitly the parameters of many infinite families of 2-designs from the extended of the primitive BCH codes $$\mathcal {C}_{(q, q^m-1, \delta _3, 1)}$$ with $$\delta _3=(q-1)q^{m-1}-1-q^{\lfloor (m+1)/2\rfloor }$$ and odd prime q given by Li et al. [18] and point out that their duals hold 2-designs.
Application of ELM-MapReduce Technique in Stock Market Forecasting

Forecasting is a technique commonly used in the study of time series to forecast a variable response for a specified period of time, such as monthly earnings, stock performance, or unemployment figures. Forecasting is historical data behavior to determine the direction of future trends. Therefore, many machine learning algorithms are used in the past few years. In this study, a summary of an extreme learning machine with MapReduce technique (ELM_MapReduce) is presented. This technique is based on the concept of processing large amount of historical data and application of extreme learning machine to achieve fast learning speed. As stock market data is large set of historical data that need time to process, MapReduce method is used to handle such limitations. The technique shows the advantages and disadvantages of using MapReduce method in ELM and can be used in different areas of research.
Pattern-Based Approach to Modelling and Verifying System Security

Security is one of the most important problems in the engineering of online service-oriented systems. The current best practice in security design is a pattern-oriented approach. A large number of security design patterns have been identified, categorised and documented in the literature. The design of a security solution for a system starts with identification of security requirements and selection of appropriate security design patterns; these are then composed together. It is crucial to verify that the composition of security design patterns is valid in the sense that it preserves the features, semantics and soundness of the patterns and correct in the sense that the security requirements are met by the design. This paper proposes a methodology that employs the algebraic specification language SOFIA to specify security design patterns and their compositions. The specifications are then translated into the Alloy formalism and their validity and correctness are verified using the Alloy model checker. A tool that translates SOFIA into Alloy is presented. A case study with the method and the tool is also reported.
Issues in Gaussian Model-Based Clustering

In this chapter, we discuss some issues arising when Gaussian Mixture Models (GMMs) are applied for clustering purposes and introduce the most commonly known strategies to solve them. Specifically, we discuss issues related to model-based clustering in high-dimensional spaces and to departures from the nominal model due to skew or contaminated data. Applications to several case studies will be presented.
Improving Fine-grained Text Sentiment Transfer for Diverse Review Generation

In this paper, we focus on the task of generating reviews of diverse sentiment intensity via fine-grained text sentiment transfer. This task aims to revise an input review to satisfy a given sentiment intensity, while preserving the original semantic content. The challenge is to achieve fine-grained control of the sentiment intensity when generating reviews, different from conventional coarse-grained sentiment transfer task that only reverses the sentiment polarity of text. To tackle this problem, we propose a new text sentiment transfer model which encodes the original views by semantic attention and sentiment attention separately, then incorporates a numeric sentiment intensity value for decoding to finely control the sentiment diversity of the output reviews. Moreover, we formalize the optimization problem as to minimize the negative expected reward as well as the Kullback-Leibler divergence between the distributions of the original and generated reviews. Inspired by the existing Seq2SentiSeq approach, we train the model using a cycle reinforcement learning algorithm but introducing a more precise reward for sentiment transformation to include the sentiment difference of the backward generated reviews from the original ones. Experimental results show that our model outperforms existing methods by a large margin in both automatic evaluation and human evaluation.
Bargaining Chip

Negotiation is one of the basic forms of interpersonal communication, especially important in contemporary business. Although we assume that only humans can engage in negotiations, there are many negotiation support systems and software agents designed to assist human negotiators before and during the process. They were developed to overcome human cognitive biases and limited ability to handle information. They can support human negotiators, but also be used in place of humans to carry out negotiations on their own. The aim of this chapter is to provide an overview of different types of electronic negotiation, most recent advances in that area, some challenges in electronic negotiation, and interaction between humans and agents in negotiation.
A generic framework for the implementation of a secure quantum teleportation infrastructure

A generic framework for the implementation of secure quantum teleportation infrastructure is proposed and presented to enhance data transmission security for cloud and data centres operating on OSI layer 2 over the Wide Area Network. Quantum Teleportation (QT) and its mechanisms promises unconditional security through the use of unconventional methods. Currently there is a lack of generic framework with models and methods to guide the implementation of QT infrastructures to enable secure transmission of information. Design Science Research was undertaken to design and develop the artefact. The proposed generic secure QT framework artefact forms a step towards realizing an abstract and low-level architecture developments to deploy a full-scale quantum teleportation infrastructure rollout.
A Structured Approach to Risk Assessment of Machine Learning Applications

The accelerating development of better tools and techniques in Machine Learning (ML) is continually lowering the threshold for participation in this field, fuelling increased interest in applying ML techniques to problems related to condition monitoring and asset management. But not all models are good models, and care is required to ensure that the many pitfalls of ML modelling are avoided. In both the consumer/domestic and industrial spaces, recent failures of systems involving ML have highlighted the need for regulatory frameworks for assurance of such systems. In this paper, we identify two key aspects of assurance of data-driven systems: assurance of the modelling process and; assurance of the model itself, and describe how practitioners can take steps to ensure that their data-driven models will work as expected.
Efficient Decoder Reduction for a Variety of Encoder-Decoder Problems

Encoder-decoder networks have become the standard solution for a variety of segmentation tasks. Many of these approaches use a symmetrical design where both the encoder as well as the decoder are approximately of the same computational complexity. However, symmetrical properties of encoder-decoder networks are not necessarily optimal. This work proposes an elegant and generic method to reduce the decoder complexity in encoder-decoder networks by scaling the number of feature channels in the decoder. The popular network U-Net is used as an example for how to adapt existing models with symmetrical properties. The effect of the decoder size is investigated on three data sets with varying complexity, namely, the ISIC, Cityscapes, and SUN RDB-D data sets. We show that a reduction in decoder channels shows no statistically differing results while at the same time providing a decoder requiring up to 99% fewer FLOPs, (± 90% fewer FLOPs is attainable for all investigated problems). In addition, results show that the number of parameters in the decoder of two models which already have smaller decoders can be further optimised depending on the problem. The proposed solution is a simple method and can easily be implemented in other encoder-decoder models. Empirical results also show that a reduction in parameters may even lead to improved performance, which is likely due to fewer parameters, reducing overfitting effects.
A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns

A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining. The experimental study presented in this paper faces this new proposal regarding an ensemble of one of the most used algorithms within supervised descriptive rule discovery. Results presents a set of patterns with a major interpretability and precision for the new proposal which could be interesting for experts in real-world applications.
Tuning of CNN Architecture by CSA for EMNIST Data

Convolutional neural network is the deep learning model which has several hidden layers in contrast to feed-forward neural network. Modeling of CNN layers depends upon the dataset and very challenging task as several trials are required to select the CNN parameters. In our work, we presented an optimal solution to tune the hyperparameters of CNN architecture by clonal search algorithm (CSA). This is tested on a challenging dataset of EMNIST, which is enhanced from ML benchmark dataset of NIST. With the proposed algorithm, it is possible to get the accuracy up to 98.7%.
Affect of Data Filter on Performance of Latent Semantic Analysis based Research Paper Recommender System

Latent Semantic Analysis uses Singular Value Decomposition (SVD) to effectively retrieve relevant information from the information corpus. However, LSA has a high computational cost. In order to address this aspect, it is proposed to filter only those words carrying high semantic importance. The aim is to improve the execution time of semantic space construction and dimensionality reduction. We present how the use of data filter can effectively meet the proposed goals in comparison to baseline method of performing recommendations. The proposed system was assessed over a dataset of 80 articles (Titles and Abstracts). The results of the experiments show that the proposed system performed better in terms of elapsed time with an average precision of 85.54% (78.64% for baseline method) and an average recall of 92.96% (89.70% for baseline method).
SSNet: Saliency-SelectNet for Vehicle Re-Identification

Vehicle re-identification of cross-region surveillance video is one of the current research hotspots, helping to analyze the trajectory and the travel rules of vehicles. In this paper, a feature select vehicle re-identification method without using the license plate is proposed. The method is based on a convolutional neural network (CNN) and is divided into three parts. In the first part, salient features are used in series with the original image as input for training in CNN, greatly enhancing the propagation of robust features. In the second part, the feature map selection mechanism selects and sends the features with low similarity to the next convolutional layer, reducing the propagation of irrelevant features in the network. Finally, the distance metrics between image features are compared and sorted in ascending order, as which of the same ID should be small. Experiments on datasets VRID-1 show that the proposed SSNet significantly outperforms the state-of-the-art methods for vehicle re-identification.
Investigating the impact of power decomposition models on the solar irradiance downscaling accuracy in a tropical island

Accurate solar resource assessment is crucial in formulating successful performance models that evaluate the financial viability of solar energy systems. In the absence of reliable ground-based solar radiation measurement data, satellite images have been extensively used to produce solar maps with spatial resolutions of a few kilometers. Spatial resolutions of less than one km have also been achieved through various spatial interpolation techniques that downscale the solar irradiance. These methods usually consist of several steps that account for the anisotropy of the diffuse radiation and the impact of topography on the incident irradiance. In this study, we use a downscaling methodology to reduce the original resolution of satellite-based solar radiation map for the island of Mauritius from 3km×3km to the more refined resolution of 90m×90m. The impact of three solar irradiance decomposition models, namely, Skarveit & Olsen, Bugler and Perez models on the accuracy of the downscaling methodology is also investigated. Results show that the Bugler decomposition model is most accurate and that the northern part of the island is more conducive to PV system installations.