Reviewerly/exHarmony huggingface dataset:
An Exploratory Study on the Refactoring of Unit Test Files in Android Applications

An essential activity of software maintenance is the refactoring of source code. Refactoring operations enable developers to take necessary actions to correct bad programming practices (i.e., smells) in the source code of both production and test files. With unit testing being a vital and fundamental part of ensuring the quality of a system, developers must address smelly test code. In this paper, we empirically explore the impact and relationship between refactoring operations and test smells in 250 open-source Android applications (apps). Our experiments showed that the type of refactoring operations performed by developers on test files differ from those performed on non-test files. Further, results around test smells show a co-occurrence between certain smell types and refactorings, and how refactorings are utilized to eliminate smells. Findings from this study will not only further our knowledge of refactoring operations on test files, but will also help developers in understanding the possible ways on how to maintain their apps.
Low-cost ultrasonic based object detection and collision avoidance method for autonomous robots

Abstract This work focuses on the development of an effective collision avoidance algorithm that detects and avoids obstacles autonomously in the vicinity of a potential collision by using a single ultrasonic sensor and controlling the movement of the vehicle. The objectives are to minimise the deviation from the vehicle’s original path and also the development of an algorithm utilising one of the cheapest sensors available for very lost cost systems. For instance, in a scenario where the main ranging sensor malfunctions, a backup low cost sensor is required for safe navigation of the vehicle while keeping the deviation to a minimum. The developed algorithm utilises only one ultrasonic sensor and approximates the front shape of the detected object by sweeping the sensor mounted on top of the unmanned vehicle. In this proposed approach, the sensor is rotated for shape approximation and edge detection instead of moving the robot around the encountered obstacle. It has been tested in various indoor situations using different shapes of objects, stationary objects, moving objects, and soft or irregularly shaped objects. The results show that the algorithm provides satisfactory outcomes by entirely avoiding obstacles and rerouting the vehicle with a minimal deviation.
Object Detection Techniques: A Comparison

Computer vision is one of the technologies that aim at digitally perceiving the real world at a higher level through digital images and videos. Object detection, a subset to computer vision is one of the prominent techniques in this area of research. Object detection is basically an algorithm based on either machine learning or deep learning approaches employed for classification of elements in diverse classes and localization in the image. This paper provides a comparison among the three prominent approaches to achieve object detection. R-CNN, Fast R-CNN, YOLO are the techniques in the trend which facilitates the developer in accomplishing the task of detecting an object in the image. These techniques train and compute the parameters of the model in reduced hence increase performance as compared to the traditional object detection techniques.
A Novel Feature Extraction System for Cursive Word Vocabulary Recognition using Local Features Descriptors and Gabor Filter

The problems which arise in the system of automatic recognition of the handwritten Arabic script shows that the morphology complexity of the Arabic script and its cursivity remains a very vast subject of research. This research subject has known in recent years a great progress especially in the automation of postal mail sorting, check processing and form processing. Generally, there are two main types of approaches namely the analytical approach and the global approach. In this paper, we have exploited the global approach, which is based on a single description of the image of the word, seen as an indivisible entity. Our work is devoted to offline handwritten Arabic word recognition based on the global limited vocabulary approach using the IFN/ENIT database, which contains handwritten Arabic words featuring handwritten Tunisian city/village names. In the feature extraction phase, we studied three types of descriptors; the oriented gradient histogram, the local binary pattern and the Gabor filter for feature extraction. We evaluate the performance of our models by calculating some common metrics. Among the performances of three descriptors, the model based on the Gabor filter achieves an extreme precision of 99.90%.
SINVAD

The testing of Deep Neural Networks (DNNs) has become increasingly important as DNNs are widely adopted by safety critical systems. While many test adequacy criteria have been suggested, automated test input generation for many types of DNNs remains a challenge because the raw input space is too large to randomly sample or to navigate and search for plausible inputs. Consequently, current testing techniques for DNNs depend on small local perturbations to existing inputs, based on the metamorphic testing principle. We propose new ways to search not over the entire image space, but rather over a plausible input space that resembles the true training distribution. This space is constructed using Variational Autoencoders (VAEs), and navigated through their latent vector space. We show that this space helps efficiently produce test inputs that can reveal information about the robustness of DNNs when dealing with realistic tests, opening the field to meaningful exploration through the space of highly structured images.
GraphShield: Dynamic Large Graphs for Secure Queries with Forward Privacy

The increasing amount of graph-structured data catalyzes analytics over graph databases using semantic queries. Motivated by the ubiquity of commercial cloud platforms, data owners are willing to store their graph databases remotely. However, data privacy has emerged as a widespread concern since the cloud platforms are not fully trusted. One viable solution is to encrypt sensitive data before outsourcing, which inevitably hinders data retrieval. To enable queries over encrypted data, searchable symmetric encryption (SSE) has been introduced. Yet, the most well-studied class of SSE schemes focuses on retrieving textual files given keywords, which cannot be applied to graph databases directly. This paper extends our preliminary work (FC′17) and proposes GraphShield, a structured encryption scheme for graphs. Beyond shortest distance queries, GraphShield can support other classic graph-based queries (e.g., maximum flow) and more complicated analytics (e.g., PageRank). Technically, we incorporate a suite of (efficient) cryptographic primitives and tailor some extra secure protocols for facilitating graph analytics. Our scheme also allows updates on the encrypted graph with forward privacy guaranteed. We formalize the security model and prove the adaptive security with reasonable leakage. Finally, we implement our scheme on various real-world datasets, and the experiment results demonstrate its practicality and scalability.
Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

Abstract Sentiment analysis is the process of extracting the opinions of customers from online reviews. In general, customers express their reviews in natural language. It becomes a complex task when applying sentiment analysis on those reviews. In earlier stages, word‐level features with various feature weighting methods such as Bag of Words, TF‐IDF, and Word2Vec were applied for sentiment analysis and deep learning networks are not explored much. We considered phrase level and sentence level features instead of applying word‐level features for sentiment analysis and also enhanced by applying various deep learning techniques. In this article, we have proposed a hybrid convolutional bidirectional recurrent neural network model (CBRNN) by combining two‐layer convolutional neural network (CNN) with a bidirectional gated recurrent unit (BGRU). In the proposed CBRNN model, the CNN layer extracts the rich set of phrase‐level features and BGRU captures the chronological features through long term dependency in a multi‐layered sentence. The proposed approach was evaluated on two benchmark datasets and compared with various baselines. The experimental results show that the proposed hybrid model provides better results than any other models with an F 1 score of 87.62% and 77.4% on IMDB and Polarity datasets,respectively. Our CBRNN model outperforms the state of the art by 2%‐4% on these two datasets. It is also observed that, the time taken for training is slightly higher than the existing approaches with the substantial improvement in the performance.
People Detection System Using YOLOv3 Algorithm

In crowd security systems, precise real-time detection of people in images or videos can be very challenging especially in complex and dense crowds whereby some individuals could possibly be partly or entirely occluded for varying lengths of time. Thus, this paper presents a large Convolutional Neural Network (CNN) that is trained using a single step model, You Only Look Once version 3 (YOLOv3) on Google Colaboratory to process the images within a database and to accurately locate people within the images. YOLOv3 splits the image up into regions and predicts bounding boxes and predicts the probabilities for each region. These bounding boxes are weighted by the projected probabilities and finally, the model is able to make its detection based on the final weights. This model will be using a customised dataset from Google's Open Images with 500 high resolution images. Once trained, the neural network able to successfully generate the test data and achieve a mean average precision (mAP) of 78.3% and a final average loss of 0.6 on top of confidently detecting the people within the images.
Adversarial Attack Against Urban Scene Segmentation for Autonomous Vehicles

Understanding the surrounding environment is crucial for autonomous vehicles to make correct driving decisions. In particular, urban scene segmentation is a significant integral module commonly equipped in the perception system of autonomous vehicles to understand the real scene like a human. Any missegmentation of the driving scenario can potentially result in uncontrollable consequences such as serious accidents or the exception of the perception system. In this article, we investigate the vulnerability of the popular scene segmentation models designed with the backbones of deep neural networks (DNNs), which have been shown to be sensitive to adversarial attacks. Specifically, we propose an iterative projected gradient-based attack method that can effectively fool several DNN-based segmentation models with a remarkably higher attacking successful rate, and much smaller adversarial perturbations. Moreover, we also develop an adversarial training algorithm with min-max optimization style to enrich the robustness of the scene segmentation models. Extensive experiments on the Cityscape benchmark dataset consisting of large-scale urban scene images for autonomous vehicles demonstrate the effectiveness of our proposed attack method, as well as the benefit of the adversarial training scheme for the scene segmentation models.
DNC: A Deep Neural Network-based Clustering-oriented Network Embedding Algorithm

Deep Neural Networks (DNNs) have achieved impressive success in the domain of Euclidean data such as image. However, designing deep neural network to cluster nodes especially in social networks is still a challenging task. Moreover, recent advanced methods for node clustering have focused on learning node embedding, upon which classic clustering algorithms like K-means are applied. Nevertheless, the resulting node embeddings are customarily task-agnostic. This results in the fact that the performance of clustering is difficult to guarantee. To effectively mitigate the problem, in this paper, we propose a novel clustering-oriented node embedding method named Deep Node Clustering (DNC) for non-attributed network data by resorting to deep neural networks. We first present a preprocessing method via adopting a random surfing model to capture graph structural information directly. Subsequently, we propose to learn a deep clustering network, which could jointly learn node embeddings and cluster assignments. Extensive experiments on three real-world network datasets for node clustering are conducted, which demonstrate that the proposed DNC substantially outperforms the state-of-the-art node clustering methods.
Adapting requirements models to varying environments

The engineering of high-quality software requirements generally relies on properties and assumptions about the environment in which the software-to-be has to operate. Such properties and assumptions, referred to as environment conditions in this paper, are highly subject to change over time or from one software variant to another. As a consequence, the requirements engineered for a specific set of environment conditions may no longer be adequate, complete and consistent for another set.
A Study of Potential Code Borrowing and License Violations in Java Projects on GitHub

With an ever-increasing amount of open-source software, the popularity of services like GitHub that facilitate code reuse, and common misconceptions about the licensing of open-source software, the problem of license violations in the code is getting more and more prominent. In this study, we compile an extensive corpus of popular Java projects from GitHub, search it for code clones, and perform an original analysis of possible code borrowing and license violations on the level of code fragments. We chose Java as a language because of its popularity in industry, where the plagiarism problem is especially relevant because of possible legal action. We analyze and discuss distribution of 94 different discovered and manually evaluated licenses in files and projects, differences in the licensing of files, distribution of potential code borrowing between licenses, various types of possible license violations, most violated licenses, etc. Studying possible license violations in specific blocks of code, we have discovered that 29.6% of them might be involved in potential code borrowing and 9.4% of them could potentially violate original licenses.
Semantic-Preserving Image Compression

Video traffic comprises a large majority of the total traffic on the internet today. Uncompressed visual data requires a very large data rate; lossy compression techniques are employed in order to keep the data-rate manageable. Increasingly, a significant amount of visual data being generated is consumed by analytics (such as classification, detection, etc.) residing in the cloud. Image and video compression can produce visual artifacts, especially at lower data-rates, which can result in a significant drop in performance on such analytic tasks. Moreover, standard image and video compression techniques aim to optimize perceptual quality for human consumption by allocating more bits to perceptually significant features of the scene. However, these features may not necessarily be the most suitable ones for semantic tasks. We present here an approach to compress visual data in order to maximize performance on a given analytic task. We train a deep auto-encoder using a multi-task loss to learn the relevant embeddings. An approximate differentiable model of the quantizer is used during training which helps boost the accuracy during inference. We apply our approach on an image classification problem and show that for a given level of compression, it achieves higher classification accuracy than that obtained by performing classification on images compressed using JPEG. Our approach also outperforms the relevant state-of-the-art approach by a significant margin.
Emerging and Changing Tasks in the Development Process for Machine Learning Systems

Integrating machine learning components in software systems is a task more and more companies are confronted with. However, there is not much knowledge today on how the software development process needs to change, when such components are integrated into a software system. We performed an interview study with 16 participants, focusing on emerging and changing task. The results uncover a set of 25 tasks associated to different software development phases, such as requirements engineering or deployment. We are just starting to understand the implications of using machine-learning components on the software development process. This study allows some first insights into how widespread the required process changes are.
The Challenges and Opportunities of Artificial Intelligence for Trustworthy Robots and Autonomous Systems

Trust is essential in designing autonomous and semiautonomous Robots and Autonomous Systems (RAS), because of the “No trust, no use” concept. RAS should provide high quality services, with four key properties that make them trustworthy: they must be (i) robust with regards to any system health related issues, (ii) safe for any matters in their surrounding environments, (iii) secure against any threats from cyber spaces, and (iv) trusted for human-machine interaction. This article thoroughly analyses the challenges in implementing the trustworthy RAS in respects of the four properties, and addresses the power of AI in improving the trustworthiness of RAS. While we focus on the benefits that AI brings to human, we should realize the potential risks that could be caused by AI. This article introduces for the first time the set of key aspects of human-centered AI for RAS, which can serve as a cornerstone for implementing trustworthy RAS by design in the future.
Learn and Link: Learning Critical Regions for Efficient Planning

This paper presents a new approach to learning for motion planning (MP) where critical regions of an environment are learned from a given set of motion plans and used to improve performance on new environments and problem instances. We introduce a new suite of sampling-based motion planners, Learn and Link. Our planners leverage critical regions to overcome the limitations of uniform sampling, while still maintaining guarantees of correctness inherent to sampling-based algorithms. We also show that convolutional neural networks (CNNs) can be used to identify critical regions for motion planning problems. We evaluate Learn and Link against planners from the Open Motion Planning Library (OMPL) using an extensive suite of experiments on challenging motion planning problems. We show that our approach requires far less planning time than existing sampling-based planners.
GitterCom

Team communication is essential for the development of modern software systems. For distributed software development teams, such as those found in many open source projects, this communication usually takes place using electronic tools. Among these, modern chat platforms such as Gitter are becoming the de facto choice for many software projects due to their advanced features geared towards software development and effective team communication. Gitter channels contain numerous messages exchanged by developers regarding the state of the project, issues and features of the system, team logistics, etc. These messages can contain important information to researchers studying open source software systems, developers new to a particular project and trying to get familiar with the software, etc. Therefore, uncovering what developers are communicating about through Gitter is an essential first step towards successfully understanding and leveraging this information.
Automated identification of leukocyte subsets improves standardization of database-guided expert-supervised diagnostic orientation in acute leukemia: a EuroFlow study

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide toward the relevant classification panel and final diagnosis. In this study, we designed and validated an algorithm for automated (database-supported) gating and identification (AGI tool) of cell subsets within samples stained with ALOT. A reference database of normal peripheral blood (PB, n = 41) and bone marrow (BM; n = 45) samples analyzed with the ALOT was constructed, and served as a reference for the AGI tool to automatically identify normal cells. Populations not unequivocally identified as normal cells were labeled as checks and were classified by an expert. Additional normal BM (n = 25) and PB (n = 43) and leukemic samples (n = 109), analyzed in parallel by experts and the AGI tool, were used to evaluate the AGI tool. Analysis of normal PB and BM samples showed low percentages of checks (<3% in PB, <10% in BM), with variations between different laboratories. Manual analysis and AGI analysis of normal and leukemic samples showed high levels of correlation between cell numbers (r2 > 0.95 for all cell types in PB and r2 > 0.75 in BM) and resulted in highly concordant classification of leukemic cells by our previously published automated database-guided expert-supervised orientation tool for immunophenotypic diagnosis and classification of acute leukemia (Compass tool). Similar data were obtained using alternative, commercially available tubes, confirming the robustness of the developed tools. The AGI tool represents an innovative step in minimizing human intervention and requirements in expertise, toward a “sample-in and result-out” approach which may result in more objective and reproducible data analysis and diagnostics. The AGI tool may improve quality of immunophenotyping in individual laboratories, since high percentages of checks in normal samples are an alert on the quality of the internal procedures.
The Production Quality Control Process, Enhanced with Augmented Reality Glasses and the New Generation Computing Support System

The world is accelerating the digitisation of many branches of the economy. In production, it is often called Industry 4.0 - the term invented by Prof. Wahlster in Germany. One branch of ‘Production Digitisation’ is Augmented Reality (AR)- a technology that gives a very convenient and natural human-machine interface. Users are presented with the information requested, right in front of their eyes, and can communicate with the system through simple gestures and speech. The key advantage of AR is having one’s handsfree so that the user can perform his/her regular tasks, unhindered. In the first part of this paper, the concept of using AR glasses, in the quality control process, is presented. The present authors describe a proprietary support system. A specific example is then given in a real production environment on a real production process, comparing the old way with the new AR way of conducting test procedures. An evaluation of its usability is characterised using important factors that can affect the final outcome or features of the product and the manner in which it is applied in real life. In concluding, the authors focus on its advantages, the disadvantages, the technological imperfections, and the limitations and go on to deliberate on the conclusions and further research topics.