Reviewerly/exHarmony huggingface dataset:
On the performance of method-level bug prediction: A negative result

Bug prediction is aimed at identifying software artifacts that are more likely to be defective in the future. Most approaches defined so far target the prediction of bugs at class/file level. Nevertheless, past research has provided evidence that this granularity is too coarse-grained for its use in practice. As a consequence, researchers have started proposing defect prediction models targeting a finer granularity (particularly method-level granularity), providing promising evidence that it is possible to operate at this level. Particularly, models mixing product and process metrics provided the best results. We present a study in which we first replicate previous research on method-level bug-prediction, by using different systems and timespans. Afterwards, based on the limitations of existing research, we (1) re-evaluate method-level bug prediction models more realistically and (2) analyze whether alternative features based on textual aspects, code smells, and developer-related factors can be exploited to improve method-level bug prediction abilities. Key results of our study include that (1) the performance of the previously proposed models, tested using the same strategy but on different systems/timespans, is confirmed; but, (2) when evaluated with a more practical strategy, all the models show a dramatic drop in performance, with results close to that of a random classifier. Finally, we find that (3) the contribution of alternative features within such models is limited and unable to improve the prediction capabilities significantly. As a consequence, our replication and negative results indicate that method-level bug prediction is still an open challenge.
Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems

The solutions proposed in this article are based on our experience with fuzzy systems (FSs), their interpretability, and population-based algorithms (PBAs). They provide a consistent approach to the design of interpretable FSs. First, PBAs can make a useful tool for selecting both parameters and the FS structure. In practice, such structure is usually chosen by trial and error. Second, in this article, we propose a new multipopulation PBA, which uses a variety of search formulas. This helps to eliminate the problem of incorrect PBA selection. Third, we propose interesting solutions for the interpretability of FSs with trapezoidal membership functions. These functions are well suited for modeling ranges of linguistic variables. We are particularly interested in providing them with original interpretability criteria, which are used by a PBA to design FSs. Furthermore, we offer an original way of setting up trapezoidal functions, which prevents them from overlapping with each other. Fourth, we believe that interpretability can be achieved through a capable extension of fuzzy rules. That is the reason why we used weights and dedicated operators for their processing. In this article, a different extension of the rules base is proposed. It involves adding relation operators (ROs) (e.g., “>” and “>=”), which can be used to model linguistic phrases, e.g. “electrical voltage less than or equal to high.” An additional task of the PBA is the automatic selection of an RO, which facilitates the extraction of knowledge from the data. The approach proposed in this article was tested using well-known classification benchmarks.
Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting

Rivet detection is usually the first step for almost all surface and rivet inspection methods in aircraft skins. With 3D laser scanners, one can rapidly obtain the precise 3D information, i.e. point cloud, of the surface and rivets. Subsequently, rivet detection can be converted to a multiple-structure fitting problem from 3D point clouds. However, robust structure fitting from scanned 3D point cloud remains an open problem due to its challenging nature, such as noise and outliers, irregular sampling density and missing scanning. To reduce the fitting variability, this paper presents an automated density-aware multiple-structure fitting algorithm to perform rivet detection based on a 3D point cloud. The key observation is that the local density of points belonging to the rivet contour is relatively higher. We hereby formulate rivet detection as a multiple structure fitting problem with a density-based significance measure. By considering the local distribution characteristics, we first perform adaptive density enhancement on the basic local density. Subsequently, we detect the potential circle hypotheses and thereby extract rivet contours. By performing the mode-seeking algorithm on hypergraphs, all the circle structures can be obtained simultaneously. Overall, the proposed extraction algorithm is able to efficiently and effectively detect rivets from the raw scanned point clouds. We also demonstrate that the proposed algorithm achieves significant superiority over several state-of-the-art model fitting methods on the real scanned point cloud via experimental results. Moreover, we give the application of our algorithm on rivet flush inspection, showing that our method can assist in the rapid measurement of riveting quality.
Superpixel Embedding Network

Superpixel segmentation is a fundamental computer vision technique that finds application in a multitude of high level computer vision tasks. Most state-of-the-art superpixel segmentation methods are unsupervised in nature and thus cannot fully utilize frequently occurring texture patterns or incorporate multiscale context. In this paper, we show that superpixel segmentation can be improved by leveraging the superior modeling power of deep convolutional autoencoders in a fully unsupervised manner. We pose the superpixel segmentation problem as one of manifold learning where pixels that belong to similar texture patterns are assigned near identical embedding vectors. The proposed deep network is able to learn image-wide and dataset-wide feature patterns and the relationships between them. This knowledge is used to segment and group pixels in a way that is consistent with a more global definition of pattern coherence. Experiments demonstrate that the superpixels obtained from the embeddings learned by the proposed method outperform the state-of-theart superpixel segmentation methods for boundary precision and recall values. Additionally, we find that semantic edges obtained from the superpixel embeddings to be significantly better than the contemporary unsupervised approaches.
A semi-supervised learning approach towards automatic wireless technology recognition

Radio spectrum has become a scarce commodity due to the advent of several non-collaborative radio technologies that share the same spectrum. Recognizing a radio technology that accesses the spectrum is fundamental to define spectrum management policies to mitigate interference. State-of-the-art approaches for technology recognition using machine learning are based on supervised learning, which requires an extensive labeled data set to perform well. However, if the technologies and their environment are entirely unknown, the labeling task becomes time-consuming and challenging. In this work, we present a Semisupervised Learning (SSL) approach for technology recognition that exploits the capabilities of modern Software Defined Radios (SDRs) to build large unlabeled data sets of IQ samples but requires only a few of them to be labeled to start the learning process. The proposed approach is implemented using a Deep Autoencoder, and the comparison is carried out against a Supervised Learning (SL) approach using Deep Neural Network (DNN). Using the DARPA Colosseum test bed, we created an IQ sample data set of 16 unknown radio technologies and obtain a classification accuracy of > 97% using the entire labeled data set using both approaches. However, the proposed SSL approach achieves a classification accuracy of $\geq$ 70% while using only 10% of the labeled data. This performance is equivalent to 4.6x times better classification accuracy than the DNN using the same reduced labeled data set. More importantly, the proposed approach is more robust than the DNN under corrupted input, e.g., noisy signals, which gives us to 2x and 3x better accuracy at Signal-to-Noise Ratio (SNR) of -5 dB and 0 dB, respectively.
Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

A human gesture is a non-verbal form of communication and is critical in human-robot interactions.Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions.Hand gesture recognition allows a convenient and usable interface between devices and users.Hand gestures can be used for various fields which makes it be able to be implemented for communication and further.Hand gesture recognition is not only useful for people who are hearingimpaired or disabled but also for the people who experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more.In this paper, an approach for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed.The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square.Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.
Video quality assessment using space–time slice mappings

We develop a full-reference (FR) video quality assessment framework that integrates analysis of space–time slices (STSs) with frame-based image quality measurement (IQA) to form a high-performance video quality predictor. The approach first arranges the reference and test video sequences into a space–time slice representation. To more comprehensively characterize space–time distortions, a collection of distortion-aware maps are computed on each reference–test video pair. These reference-distorted maps are then processed using a standard image quality model, such as peak signal-to-noise ratio (PSNR) or Structural Similarity (SSIM). A simple learned pooling strategy is used to combine the multiple IQA outputs to generate a final video quality score. This leads to an algorithm called Space–TimeSlice PSNR (STS-PSNR), which we thoroughly tested on three publicly available video quality assessment databases and found it to deliver significantly elevated performance relative to state-of-the-art video quality models. Source code for STS-PSNR is freely available at: http://live.ece.utexas.edu/research/Quality/STS-PSNR_release.zip.
A Deep Framework for Facial Emotion Recognition using Light Field Images

Light field cameras capture the intensity of light rays coming from multiple directions, thus allowing a set of 2D images, named sub-aperture (SA) images, to be rendered. These images correspond to observations of the scene from slightly different angles. The rich spatio-angular information obtained using these cameras is exploited in this paper, for the first time, in the context of facial emotion recognition. A deep learning spatio-angular fusion framework is adopted which is able to model both the intra-view/spatial and inter-view/angular information, using a VGG-16 convolutional neural network and a long short-term memory (LSTM) recurrent network. The proposed solution, based on the adopted deep spatio-angular fusion framework, creates two view sequences, horizontal and vertical, with selected SA images, for which VGG-Face descriptions are extracted. The resulting descriptions are fed to two LSTM networks, with the aim of independently learning horizontal and vertical classification models. The softmax classifier scores obtained for the horizontal and vertical descriptors are then fused to obtain the final emotion recognition labels. A comprehensive set of experiments has been conducted on the IST-EURECOM light field face database using two assessment protocols. The adopted framework achieves superior emotion recognition performance when compared with state-of-the-art benchmarking methods.
Neural-like Methods and Hardware Structures for Real-time Data Encryption and Decryption

Combined method of cryptographic data protection based on the autoassociative neural network using “model of successive geometric transformations” paradigm and XOR masking has been described. Improved methods of scalar product calculation with regards to hardware implementation have been presented. Structure of neural-like hardware means for real-time data encryption-decryption has been developed.
Adaptively Weighted Multiview Proximity Learning for Clustering

Recently, the proximity-based methods have achieved great success for multiview clustering. Nevertheless, most existing proximity-based methods take the predefined proximity matrices as input and their performance relies heavily on the quality of the predefined proximity matrices. A few multiview proximity learning (MVPL) methods have been proposed to tackle this problem but there are still some limitations, such as only emphasizing the intraview relation but overlooking the inter-view correlation, or not taking the weight differences of different views into account when considering the inter-view correlation. These limitations affect the quality of the learned proximity matrices and therefore influence the clustering performance. With the aim of breaking through these limitations simultaneously, a novel proximity learning method, called adaptively weighted MVPL (AWMVPL), is proposed. In the proposed method, both the intraview relation and the inter-view correlation are considered. Besides, when considering the inter-view correlation, the weights of different views are learned in a self-weighted scheme. Furthermore, through an adaptively weighted scheme, the information of the learned view-specific proximity matrices is integrated into a view-common cluster indicator matrix which outputs the final clustering result. Extensive experiments are conducted on several synthetic and real-world datasets to demonstrate the effectiveness and superiority of our method compared with the existing methods.
AusAEM: Australia’s airborne electromagnetic continental-scale acquisition program

The AusAEM airborne electromagnetic (AEM) survey is one of the main components of the Exploring for the Future program. This Australian Government initiative is aimed at enhancing the geoscientific information available, to support resource exploration and to showcase Australia as a destination for investment opportunities. Regional geophysical mapping programs are the basis for informed mineral exploration and have enabled the building of a continental-scale inventory of Australia's potential resource endowment. They also provide elements of the scientific backing for decision-making and answering a range of different geoscientific questions. The latest of these airborne initiatives AusAEM is composed of a series of wide line-spaced studies targeting acquisition of data for regional mapping in area of new frontiers; it is gathering data and unveiling information on extents never previously attempted by AEM surveys.
Design of a Smart Sole with Advanced Fall Detection Algorithm

Fall has become the second leading cause of unintentional injury, death, after road traffic injuries, for the elderly in Europe.This proportion will increase in the next decades and become more than ever a real public health issue.France was selected by the World Health Organization to be the first country to implement a program that reduces the coverage of the dependence.Commercial automatic fall detection devices can help seniors get back on their feet faster by reducing the time of emergency procedure.Many seniors do not take advantage of this potentially life-saving technology mainly because of intrusiveness constraints.After having reminded the context and the challenges of fall detection systems, this paper presents an original device which is unobtrusive, comfortable and very effective.The hardware architecture embedded into the sole and a new fall detection algorithm based on acceleration and time thresholds are presented.The algorithm introduces a new concept of differential acceleration to eliminate some drawbacks of current systems.Tests were carried out under real life conditions by 6 young participants for different ADLs.The data were analyzed blindly.We compared the detected falls and found a 100% sensibility and more than 93% sensitivity for all participants and scenarios.
Few-Shot Structured Domain Adaptation for Virtual-to-Real Scene Parsing

A structured domain adaptation (SDA) model for virtual-to-real scene parsing, learning to predict visual structure labels in real-world target scenes via mitigating the statistical discrepancy between large scale labeled virtual source and unlabeled real-world target images. But different from the source images drawn from urban simulation platforms, the target images could be expansive and difficult to collect at scale in real-world scenes. Besides, the trend of urbanization constantly changes the visual appearances of target scenes, which encourages SDA models to quickly adapt to new target scenes by merely given very few target images for training. To address the concerns, we attempt to achieve the virtual-to-real scene parsing from a new perspective inspired by few-shot learning. Instead of using a large amount of unlabeled target data used in existing SDA models, our few-shot SDA model takes a few of target real images with semantic labels in each scene, which collaborates with virtual source domain to train a virtual-to-real scene parser. Specifically, our framework is a two-stage adversarial network which contains a scene parser and two discriminators. Based on the data pairing method, our framework can handle the problem of scarce target data well and make full use of the limited semantic labels. We evaluate our method on two suites of virtual-to-real scene parsing setups. The experimental results show that our method exceeds the state-of-the-art SDA model by 7.1% in mIoU on SYNTHIA-to-CITYSCAPES and 4.03% in mIoU on GTA5-to-CITYSCAPES in the case of 1-shot.
Soft large margin clustering for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) methods usually perform feature matching between domains by considering the domain shift. However, the cluster structure of data, which is one focus in traditional unsupervised learning, is not considered in those methods. In this paper, we attempt to explore such cluster structure in UDA. Specifically, a general transfer learning framework called Clustering for Domain Adaptation (DAC) has been proposed. DAC explores the cluster structure of target data with the help of source data. It seeks a domain-invariant classifier by simultaneously reducing the distribution shifts between domains and exploring the cluster structure for target instances. The optimization of DAC adopts the ADMM strategy, in which each iteration generates a closed-form solution. Empirical results demonstrate the effectiveness of DAC over several real datasets.
Enhancing Human Face Recognition with an Interpretable Neural Network

The purpose of this work is to determine if the ability to interpret a convolutional neural network (CNN) architecture can enhance human performance, pertaining to face recognition. We are interested in distinguishing between the faces of two similar-looking actresses of Indian origin, who have only a few discriminating features. This recognition task proved challenging for humans who were not previously familiar with the actresses (novices) as they performed only just better than random. When asked to perform the same task, humans who were more familiar with the actresses (experts) performed significantly better. We attempted the same task with a Siamese CNN which performed as well as the experts. We therefore became interested in applying any new knowledge obtained from the CNN to aid in improving the distinguishing abilities of other novices. This was accomplished by generating activation maps from the CNN. The maps showed what parts of the input face images created the highest activations in the last convolutional layer of the network. Using "fooling" techniques, we also investigated what spatial locations on the face were most responsible for confusing one actress for the other. Empirically, the cheekbones and foreheads were determined to be the strongest differentiating features between the actresses. By providing this information verbally to a new set of novices, we successfully raised the human recognition rates by 11%. For this work, we therefore successfully increased human understanding pertaining to facial recognition via post-hoc interpretability of a CNN.
Locality and Compositionality in Zero-Shot Learning

In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on different datasets (e.g. ImageNet) is performed. The results of our experiment show how locality, in terms of small parts of the input, and compositionality, i.e. how well can the learned representations be expressed as a function of a smaller vocabulary, are both deeply related to generalization and motivate the focus on more local-aware models in future research directions for representation learning.
Phishing URL Detection Via Capsule-Based Neural Network

As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering and can not detect newly emerging phishing attacks well enough, which makes developing an efficient and effective phishing detection method an urgent need. In this work, we propose a novel phishing website detection approach by detecting the Uniform Resource Locator (URL) of a website, which is proved to be an effective and efficient detection approach. To be specific, our novel capsule-based neural network mainly includes several parallel branches wherein one convolutional layer extracts shallow features from URLs and the subsequent two capsule layers generate accurate feature representations of URLs from the shallow features and discriminate the legitimacy of URLs. The final output of our approach is obtained by averaging the outputs of all branches. Extensive experiments on a validated dataset collected from the Internet demonstrate that our approach can achieve competitive performance against other state-of-the-art detection methods while maintaining a tolerable time overhead.