Reviewerly/exHarmony huggingface dataset:
A Method to Estimate Software Strategic Indicators in Software Development: An Industrial Application

Exploiting software development related data from software-development intensive organizations to support tactical and strategic decision making is a challenge. Combining data-driven approaches with expert knowledge has been highlighted as a sensible approach for leading software-development intensive organizations to rightful decision-making improvements. However, most of the existing proposals lack of important aspects that hinders their industrial uptake such as: customization guidelines to fit the proposals to other contexts and/or automatic or semi-automatic data collection support for putting them forward in a real organization. As a result, existing proposals are rarely used in the industrial context. Support software-development intensive organizations with guidance and tools for exploiting software development related data and expert knowledge to improve their decision making. We have developed a novel method called SESSI (Specification and Estimation of Software Strategic Indicators) that was articulated from industrial experiences with Nokia, Bittium, Softeam and iTTi in the context of Q-Rapids European project following a design science approach. As part of the industrial summative evaluation, we performed the first case study focused on the application of the method. We detail the phases and steps of the SESSI method and illustrate its application in the development of ModelioNG, a software product of Modeliosoft development firm. The application of the SESSI method in the context of ModelioNG case study has provided us with useful feedback to improve the method and has evidenced that applying the method was feasible in this context.
A RISC-V Based Coprocessor Accelerator Technology Research for Convolution Neural Networks

Abstract The advancements of neural networks have significantly improved the accuracy of a variety of intelligent applications, such as image processing, voice recognition and so on. However, one of the challenges is to accelerate the speed of inference with the networks designed to be deeper. In this paper, we first realize the algorithm involved in software. Then, combined with the expanded characteristic of RISC-V architecture, the acceleration of convolution operation taking up the largest proportion of computation in CNN is realized by coprocessor expansion mode on open-source Hummingbird E203 processor. Through tests, convolution coprocessor improves the performance of CNN while ensuring the accuracy in function. Finally, the Cifar-10 image classification, a common benchmark in machine learning, is used to verify the feasibility in functions and comparatively analysis the pure software-based and coprocessor-based implementations.
Amazigh isolated word speech recognition system using the Adaptive Orthogonal Transform Method

This work presents a method for the automatic recognition of amazigh isolated word speech based on the orthogonal adaptive transformation by creating an adaptive operator according to the analyzed signals that extracts the characteristics of each of them to obtain a vector of minimum dimensional information characteristics that will allow the identification of voice signals with high certainty and we will make a comparison with other approaches used for speech recognition system such as principal component analysis, empirical modal decomposition and discrete wavelet transform. The experimental results show the importance of the creation of the adaptive operator which gives an added value to our approach.
Navier–Stokes-Based Image Inpainting for Restoration of Missing Data Due to Clouds

One of the challenges in the utilization of optical satellite images is cloud occlusions. This missing data due to clouds leads to issues in image interpretation and its utilization in different applications. For images, where sparse clouds are present, single image-based restoration is possible; however, cloud dominated images require multi-temporal images for removing clouds. Standard image interpolation-based technique for single image uses only the neighboring pixels information for restoration. The image restoration method based on image inpainting explores the local information of the entire image and propagates the same into the missing gaps. In this paper, the Navier–Stokes-based inpainting algorithm is being used for the restoration. The algorithm propagates the smoothness of the image via partial differential equations(PDEs) and at the same time preserves the edges at the boundary/features of the image. It is analyzed on different satellite images covering different types of terrains and the results show improved statistical characteristics for the restored images.
Modeling Heterogeneous Edges to Represent Networks with Graph Auto-Encoder

In the real world, networks often contain multiple relationships among nodes, manifested as the heterogeneity of the edges in the networks. We convert the heterogeneous networks into multiple views by using each view to describe a specific type of relationship between nodes, so that we can leverage the collaboration of multiple views to learn the representation of networks with heterogeneous edges. Given this, we propose a regularized graph auto-encoders (RGAE) model, committed to utilizing abundant information in multiple views to learn robust network representations. More specifically, RGAE designs shared and private graph auto-encoders as main components to capture high-order nonlinear structure information of the networks. Besides, two loss functions serve as regularization to extract consistent and unique information, respectively. Concrete experimental results on realistic datasets indicate that our model outperforms state-of-the-art baselines in practical applications.
A Survey on Applications of Neuro-Fuzzy Models

Artificial intelligence techniques such as neuro-fuzzy have been successfully applied in a wide variety of uses to be mentioned as economics, engineering, social science, and business. In order to show the implementations of neuro-fuzzy in engineering the most recent researches in the area of neuro-fuzzy are covered in this paper. As many researchers have effectively utilized neuro-fuzzy in engineering applications, detailed studies are provided in this work for stimulating future researches.
A Components Based Framework for Quantum Key Distribution Networks

The difficulties related to operating basic quantum communication technologies and the inherent limitations of their direct transmission distances, be these be a consequence of absorption or optical aperture, leads to the fact that current generations of Quantum Key Distribution (QKD) systems are essentially designed to work in a stand-alone mode on a single link. This limits their utility and the potential impact on the market. To avoid these barriers, it is necessary to advance towards systems built for a network. QKD systems have to perform as network devices, and ideally on an equal footing with other telecom devices, without requiring any specialized or ad hoc tuning. To achieve this “zero-touch” integration, it is necessary to build a software ecosystem. This software should take a bare QKD system and provide all the information, so that the network can manage and the applications use the system. Here we present a SW architecture, based on components with a well-defined functionality, to build the SW ecosystem needed to deploy QKD systems in networks. The components support traditional and Software Defined Networks (SDN), as well as separated or integrated (sharing infrastructure) networks to improve their industrialization.
Efficient Computerized Lung Cancer Detection Using Bag of Words

The automatic detection of diseases in the medical field is growing very fast nowadays. It is widely being accepted as this system can reduce the burden on doctors. Among the available examination of diseases, attention on lung cancers is required more as these play a major role in increasing the mortality rate in the present day. Though many computerized cancer detection techniques were proposed earlier, those techniques gets failed in managing the better accuracy rate due to their combinations of filtering techniques, segmentation techniques, and classifiers. An MLP-BPNN(Multi-Layered Perceptron Back Propagation Neural Network based on SIFT(Scale Invariant Feature Transform) feature extraction along with Bag of Words(BOW) is proposed which gives the better accuracy rate of 89% when compared to any other Cancer detection technique proposed earlier. Lung images of 300 are collected from the Rajiv Gandhi Cancer Institute and Research Centre, Delhi as a dataset out of which 100 images are used for testing and 200 images are used for training.
Enabling Decision and Objective Space Exploration for Interactive Multi-Objective Refactoring

Due to the conflicting nature of quality measures, there are always multiple refactoring options to fix quality issues. Thus, interaction with developers is critical to inject their preferences. While several interactive techniques have been proposed, developers still need to examine large numbers of possible refactorings, which makes the interaction time-consuming. Furthermore, existing interactive tools are limited to the ”objective space” to show developers the impacts of refactorings on quality attributes. However, the “decision space” is also important since developers may want to focus on specific code locations. In this paper, we propose an interactive approach that enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Developers may be interested in looking at refactoring strategies that can improve a specific quality attribute, such as extendibility (objective space), but such strategies may be related to different code locations (decision space). A plethora of solutions is generated at first using multi-objective search that tries to find the possible trade-offs between quality objectives. Then, an unsupervised learning algorithm clusters the trade-off solutions based on their quality metrics, and another clustering algorithm is applied within each cluster of the objective space to identify solutions related to different code locations. The objective and decision spaces can now be explored more efficiently by the developer, who can give feedback on a smaller number of solutions. This feedback is then used to generate constraints for the optimization process, to focus on the developer's regions of interest in both the decision and objective spaces. A manual validation of selected refactoring solutions by developers confirms that our approach outperforms state of the art refactoring techniques.
Real-Valued Neural Networks for Complex-Valued Impairment Compensation Using Digital Up-Conversion

Analog distortion compensation based on digital signal processing methods are widely applied for transmitter or receiver impairments in various digital communication systems. Recently, several neural network methods were developed for distortion compensation. However, while digital communication systems are typically complex-valued, neural networks are mostly designed to work with real-valued inputs. Thus, adaptations of the network architecture or input data should be applied. In this article a method for using a single-input real-valued neural network for digital communication-based complex-valued signals without any modifications to the neural network is proposed. The method transforms the complex-valued signal to a real-valued one by taking the real component of a complex frequency offset applied through digital up-conversion, without affecting the distortion, therefore allowing standard neural network-based functionality with significant reduction in size. The method is tested with a multi-layer perceptron and gated recurrent unit architectures applied over a generic Wiener-Hammerstein model for the case of equalization of a coherent optical system. A reduction of about 7% and 26% in network size is shown for the gated recurrent unit-based and multi-layer perceptron-based architectures respectively, without any significant change in performance. This size reduction capability shows the high potential in applying the proposed method in neural network-based equalization and pre-distortion operations.
A Novel Brain Storm Optimization Algorithm Driven by Sine-Cosine Search Mechanism

In this paper, a hybrid approach which combines brain storm optimization algorithm (BSO) with sine-cosine search mechanism is proposed. BSO is a newly proposed meta-heuristic algorithm which takes inspiration from the brainstorming behavior. It can obtain good results in solving some optimization problems. However, its performance is still raw, especially in dealing with complex and multi-modal problems. Thus, in this paper, we introduce a powerful search mechanism taken from sine cosine algorithm (SCA) which has considerable capacity to enhance the search ability of BSO. The proposed algorithm is tested by CEC'17 benchmark functions and the result indicates that this hybridization has considerable merit.
A Framework for Monitoring Indoor Navigational Hazards and Safety of Elderly

Providing environmental and navigational safety to occupants in workplace, public venues, home and other environments is of a critical concern and has multiple applications for various end users. A framework, for monitoring movement, pose and behavior of elderly people for safe navigation in indoor environments is presented in this work. This framework, at the intersection of different disciplines has multiple functionalities. First, it provides a definition of human behavior in terms of joint point movements associated with Activities of Daily Living (ADL), while discussing two most common forms of human motions during stair navigation – upstairs and downstairs; second, it presents a two-fold approach – context-based and motion-based for accurate detection of these movements amongst other human motions associated with ADL; third, it consists of a methodology to study the Big Data associated with these movements and finally it also includes a rather comprehensive case study where the performance of multiple learning models are evaluated to identify the best learning model for development of this framework.
Constructing Causal Networks Through Regressions: A Tutorial

Background: Significant progress has been made in the practice of conducting causal analysis using network models. Despite this progress, there is limited evidence that hospital risk managers are using these analytical models. Objective: This article introduces the causal network, its related concepts, and methods of analysis. The article demonstrates how hospital risk managers can use existing regression software to construct a causal network and identify root causes of an adverse event. Methods: Causal networks depict cause and effect in a set of variables. In this context, causes are strong correlations that meet 3 additional criteria: (1) causes occur prior to effects, (2) there is an articulated mechanism for how causes lead to effects, and (3) the association between cause and effect is not spurious, meaning the association persists even after other variables are statistically controlled for (a method of analysis called counterfactual). A causal network can be constructed through repeated use of least absolute shrinkage and selection operator (LASSO) regression. In the proposed regressions, the response variable is any variable in the data. The independent variables are variables that occur prior to the response variable. By design, the statistically significant coefficients in the time-constrained LASSO regressions identify “direct” causes of the response variables. When direct causes of all variables are identified, then the entire network model, including root causes, has been specified. In the final step, the parameters of the network model (ie, strength of causal associations) are estimated by fitting the network structure to the available data. We demonstrate these concepts through fitting a network model to simulated data for causes of excessive boarding in emergency departments. Results: The network (involving 12 causes, over 4 periods, and 1 sentinel event) was accurately recovered from the simulated case reports. The recovered network did not differ from the original network used to simulate the data in any of the 156 possible links. The recovered network allowed the identification of root and direct causes. It showed that hospital occupancy rate, and not emergency department efficiency, was root cause of excessive emergency department boarding. Discussion: Causal networks can provide insights into root, and direct, causes of an adverse event. These models provide empirical tests of causes of adverse events. We encourage the use of these methods by hospital risk managers.
Testing Convolutional Neural Network using Adversarial Attacks on Potential Critical Pixels

Convolutional neural networks (CNNs) are known to be vulnerable to adversarial attacks. Well-crafted perturbations to the inputs can mislead a state-of-the-art CNN to make wrong decisions. Therefore, there is a pressing need for the development of methods that can test or detect the vulnerability of CNNs. In this study, we propose an adversarial attack method, called Dual Iterative Fusion (DIF) with potential critical pixels, for CNN testing to reveal the vulnerability of CNNs. DIF modifies as few as 5 pixels out of 32x32 images in this study and achieves faster, less noticeable, and more targeted attacks to a CNN. Testing CNNs with DIF, we observed that some classes are more vulnerable than the others within many classical CNNs for image classification. In other words, some classes are susceptible to misclassification due to adversarial attacks. For example, in VGG19 trained with CIFAR10 data set, the vulnerable class is "Cat". The successfully-targeted attack rate of class "Cat" in VGG19 is obviously higher than the others, 57.01% versus 25%. In the ResNet18, the vulnerable class is "Plane", with a successfully-targeted attack rate of 37.08% while the other classes are lower than 12%. These classes should be considered as vulnerabilities in the CNNs, and are pinpointed by generating test images using DIF. The issues can be mitigated through retraining the CNNs with the adversarial images generated by DIF, and the misclassification rate of the vulnerable classes declines at most from 61.67% to 6.37% after the retraining.
Predicting Twitter Engagement With Deep Language Models

Twitter has become one of the main information sharing platforms for millions of users world-wide. Numerous tweets are created daily, many with highly time sensitive content such as breaking news, new multimedia content or personal updates. Consequently, accurately recommending relevant tweets to users in a timely manner is a highly important and challenging problem. The 2020 ACM RecSys Challenge is aimed at benchmarking leading recommendation models for this task. The challenge is based on a large and recent dataset of over 200M tweet engagements released by Twitter with content in over 50 languages. In this work we present our approach where we leverage recent advances in deep language modeling and attention architectures, to combine information from extracted features, user engagement history and target tweet content. We first fine-tune leading multilingual language models M-BERT and XLM-R for Twitter data. Embeddings from these models are used to extract tweet and user history representations. We then combine all components together and jointly train them to maximize engagement prediction accuracy. Our approach achieves highly competitive performance placing 2’nd on the final private leaderboard. Full code is available here: https://github.com/layer6ai-labs/RecSys2020.
Towards Sketch-based User Interaction with Integrated Software Development Environments

Powerful software tools, such as software development environments, often have complex graphical user interfaces (GUIs) that are not intuitive to handle, especially when performing complex, multi-step operations. We hypothesize that sketching could be a more intuitive way of expressing user intentions than navigating nested menus or memorizing keyboard shortcuts to accomplish complex operations. Enabling this vision requires software capable of both allowing the user to sketch anywhere on a GUI, and interpreting those sketches as specific commands to be performed within the integrated development environment (IDE). In this paper, we report on preliminary results of an elicitation study performed to gather insights into how developers would use a sketch-based interface.
On the Number of Equivalence Classes of Boolean and Invertible Boolean Functions

The number U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> of equivalence classes of Boolean functions of n variables and the number V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> of equivalence classes of vectorial Boolean functions of n variables under the action of four groups of transformations are considered. The four groups are the group S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ' of permutations of variables, the group G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> of permutations and complementations, the linear group GL(n, 2) and the affine group AGL(n, 2). Harrison obtained cycle indexes for these groups and the expressions for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> and V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> in terms of the corresponding cycle index. He also tabulated the numbers U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> and the cycle indexes for n≤6 for S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ' and Gn, and for n≤5 for GL(n, 2) and AGL(n, 2). This bound was only recently slightly exceeded. Fripertinger implemented computation of cycle indexes for GL(n, q) and AGL(n, q); if q = 2 this implementation works for about n 5≤21. By introducing appropriate precomputed tables, we reduced the cycle index computation to evaluation of a sum over partitions of n for all the four groups. Using this more efficient procedure, we obtained values of U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> and the explicit cycle index expressions for larger values of n.