Reviewerly/exHarmony huggingface dataset:
Deep just-in-time defect prediction: how far are we?

Defect prediction aims to automatically identify potential defective code with minimal human intervention and has been widely studied in the literature. Just-in-Time (JIT) defect prediction focuses on program changes rather than whole programs, and has been widely adopted in continuous testing. CC2Vec, state-of-the-art JIT defect prediction tool, first constructs a hierarchical attention network (HAN) to learn distributed vector representations of both code additions and deletions, and then concatenates them with two other embedding vectors representing commit messages and overall code changes extracted by the existing DeepJIT approach to train a model for predicting whether a given commit is defective. Although CC2Vec has been shown to be the state of the art for JIT defect prediction, it was only evaluated on a limited dataset and not compared with all representative baselines. Therefore, to further investigate the efficacy and limitations of CC2Vec, this paper performs an extensive study of CC2Vec on a large-scale dataset with over 310,370 changes (8.3 X larger than the original CC2Vec dataset). More specifically, we also empirically compare CC2Vec against DeepJIT and representative traditional JIT defect prediction techniques. The experimental results show that CC2Vec cannot consistently outperform DeepJIT, and neither of them can consistently outperform traditional JIT defect prediction. We also investigate the impact of individual traditional defect prediction features and find that the added-line-number feature outperforms other traditional features. Inspired by this finding, we construct a simplistic JIT defect prediction approach which simply adopts the added-line-number feature with the logistic regression classifier. Surprisingly, such a simplistic approach can outperform CC2Vec and DeepJIT in defect prediction, and can be 81k X/120k X faster in training/testing. Furthermore, the paper also provides various practical guidelines for advancing JIT defect prediction in the near future.
Face Forensics in the Wild

On existing public benchmarks, face forgery detection techniques have achieved great success. However, when used in multi-person videos, which often contain many people active in the scene with only a small subset having been manipulated, their performance remains far from being satisfactory. To take face forgery detection to a new level, we construct a novel large-scale dataset, called FFIW <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10K</inf> , which comprises 10,000 high-quality forgery videos, with an average of three human faces in each frame. The manipulation procedure is fully automatic, controlled by a domain-adversarial quality assessment network, making our dataset highly scalable with low human cost. In addition, we propose a novel algorithm to tackle the task of multi-person face forgery detection. Supervised by only video-level label, the algorithm explores multiple instance learning and learns to automatically attend to tampered faces. Our algorithm outperforms representative approaches for both forgery classification and localization on FFIW <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10K</inf> , and also shows high generalization ability on existing benchmarks. We hope that our dataset and study will help the community to explore this new field in more depth.
RangeIoUDet: Range Image based Real-Time 3D Object Detector Optimized by Intersection over Union

Real-time and high-performance 3D object detection is an attractive research direction in autonomous driving. Recent studies prefer point based or voxel based convolution for achieving high performance. However, these methods suffer from the unsatisfied efficiency or complex customized convolution, making them unsuitable for applications with real-time requirements. In this paper, we present an efficient and effective 3D object detection framework, named RangeIoUDet that uses the range image as input. Benefiting from the dense representation of the range image, RangeIoUDet is entirely constructed based on 2D convolution, making it possible to have a fast inference speed. This model learns pointwise features from the range image, which is then passed to a region proposal network for predicting 3D bounding boxes. We optimize the pointwise feature and the 3D box via the point-based IoU and box-based IoU supervision, respectively. The point-based IoU supervision is proposed to make the network better learn the implicit 3D information encoded in the range image. The 3D Hybrid GIoU loss is introduced to generate high-quality boxes while providing an accurate quality evaluation. Through the point-based IoU and the box-based IoU, RangeIoUDet outperforms all single-stage models on the KITTI dataset, while running at 45 FPS for inference. Experiments on the self-built dataset further prove its effectiveness on different LIDAR sensors and object categories.
The coloniality of collaboration: sources of epistemic obedience in data-intensive astronomy in Chile

Data collaborations have gained currency over the last decade as a means for data-and skills-poor actors to thrive as a fourth paradigm takes hold in the sciences.Against this backdrop, this article traces the emergence of a collaborative subject position that strives to establish reciprocal and technical-oriented collaborations so as to catch up with the ongoing changes in research.Combining insights from the modernity/coloniality group, political theory and science and technology studies, the article argues that this positionality engenders epistemic obedience by bracketing off critical questions regarding with whom and for whom knowledge is generated.In particular, a dis-embedding of the data producers, the erosion of local ties, and a data conformism are identified as fresh sources of obedience impinging upon the capacity to conduct research attuned to the needs and visions of the local context.A discursive-material analysis of interviews and field notes stemming from the case of astronomy data in Chile is conducted, examining the vision of local actors aiming to gain proximity to the mega observatories producing vast volumes of data in the Atacama Desert.Given that these observatories are predominantly under the control of organisations from the United States and Europe, the adoption of a collaborative stance is now seen as the best means to ensure skills and technology transfer to local research teams.Delving into the epistemological dimension of data colonialism, this article warns that an increased emphasis on collaboration runs the risk of reproducing planetary hierarchies in times of data-intensive research.
Quantifying Explainers of Graph Neural Networks in Computational Pathology

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities’ notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashion. To this end, we propose herein a set of novel quantitative metrics based on statistics of class separability using pathologically measurable concepts to characterize graph explainers. We employ the proposed metrics to evaluate three types of graph explainers, namely the layer-wise relevance propagation, gradient-based saliency, and graph pruning approaches, to explain Cell-Graph representations for Breast Cancer Subtyping. The proposed metrics are also applicable in other domains by using domain-specific intuitive concepts. We validate the qualitative and quantitative findings on the BRACS dataset, a large cohort of breast cancer RoIs, by expert pathologists. The code, data, and models can be accessed here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks. Despite constant updating of the methods of avoiding such cyber-attacks, the ultimate outcome is currently inadequate. On the other hand, phishing emails have increased exponentially in recent years, which suggests a need for more effective and advanced methods to counter them. Numerous methods have been established to filter phishing emails, but the problem still needs a complete solution. To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails. This study provides an analysis of the numerous state-of-the-art NLP strategies currently in use to identify phishing emails at various stages of the attack, with an emphasis on ML strategies. These approaches are subjected to a comparative assessment and analysis. This gives a sense of the problem, its immediate solution space, and the expected future research directions.
Fractional-Order Terminal Sliding-Mode Control Using Self-Evolving Recurrent Chebyshev Fuzzy Neural Network for MEMS Gyroscope

To maintain the vibrations of the gyroscope proof mass, a trajectory tracking control system using a neural network estimator is proposed. The proposed control system incorporates a fractional controller based on the terminal sliding-mode and a recurrent Chebyshev fuzzy neural network using a self-evolving mechanism. The fractional-order terminal sliding-mode control can guarantee the tracking error exponential stable, and a self-evolving recurrent Chebyshev fuzzy neural network (SERCFNN) is introduced to relax the requirement of nonlinear functional certainty. In addition, the SERCFNN develops the advantages of the self-evolving fuzzy neural network (SEFNN), recurrent fuzzy neural network (RFNN), and Chebyshev function network (CFN). The SEFNN can adaptively update the dynamic structure through generating and adjusting fuzzy logic rules. The RFNN improves the performance for coping with a temporal problem with the capability to store prior information. The CFN is capable to enlarge the dimensionality of the input variables. Moreover, the asymptotic stability of the proposed control system can be proved by the Lyapunov stability theory. The effectiveness and superiority of the performance are exhibited with simulation studies and comprehensive comparisons.
Fusion of Handcrafted and Deep Features for Forgery Detection in Digital Images

Content authentication of digital images has captured the attention of forensic experts and security researchers due to a multi-fold increase in the dissemination of multimedia data through the open and vulnerable Internet. Shrewd attackers successfully devise novel ways to challenge state-of-art forensic tools used for forgery detection in digital images. Feature engineering approaches have yielded up to 97% accuracy on benchmarked datasets. Deep learning approaches have shown promising results in various image classification problems but cannot find hidden patterns in digital images, which can reliably detect image forgeries. State-of-art accuracy of deep learning approaches for forgery detection is up to 98% on benchmarked datasets. The objective of the proposed approach is to further escalate the detection accuracy, pushing it near 100%. In this paper, a synergy of handcrafted features based on color characteristics and deep features using the image’s luminance channel is employed to mine patterns responsible for accurate forgery detection. In the first Stream, 648-D Markov-based features are computed from the quaternion discrete cosine transform of the image. In the second Stream, the luminance channel of YCbCr colorspace is used to extract the Local Binary Pattern of the image. Further, local binary feature maps are fed to the pre-trained ResNet-18 model to obtain a 512-D feature vector called ’ResFeats’ from the last layer of the model’s convolutional base portion. The handcrafted features from Stream I and ResFeats from Stream II are combined to form an 1160-D feature vector. Further, classification is performed using a shallow neural network, and the method is tested on CASIA v1 and CASIA v2 datasets. The accuracy of the proposed fusion-based approach is 99.3% on benchmark datasets.
Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN

Current state-of-the-art object tracking methods have largely benefited from the public availability of numerous benchmark datasets. However, the focus has been on open-air imagery and much less on underwater visual data. Inherent underwater distortions, such as color loss, poor contrast, and underexposure, caused by attenuation of light, refraction, and scattering, greatly affect the visual quality of underwater data, and as such, existing open-air trackers perform less efficiently on such data. To help bridge this gap, this article proposes a first comprehensive underwater object tracking (UOT100) benchmark dataset to facilitate the development of tracking algorithms well-suited for underwater environments. The proposed dataset consists of 104 underwater video sequences and more than 74 000 annotated frames derived from both natural and artificial underwater videos, with great varieties of distortions. We benchmark the performance of 20 state-of-the-art object tracking algorithms and further introduce a cascaded residual network for underwater image enhancement model to improve tracking accuracy and success rate of trackers. Our experimental results demonstrate the shortcomings of existing tracking algorithms on underwater data and how our generative adversarial network (GAN)-based enhancement model can be used to improve tracking performance. We also evaluate the visual quality of our model's output against existing GAN-based methods using well-accepted quality metrics and demonstrate that our model yields better visual data.
TryOnGAN

Given a pair of images---target person and garment on another person---we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data training. We designed a pose conditioned StyleGAN2 architecture with a clothing segmentation branch that is trained on images of people wearing garments. Once trained, we propose a new layered latent space interpolation method that allows us to preserve and synthesize skin color and target body shape while transferring the garment from a different person. We demonstrate results on high resolution 512 × 512 images, and extensively compare to state of the art in try-on on both latent space generated and real images.
Combining context-relevant features with multi-stage attention network for short text classification

Short text classification is a challenging task in natural language processing. Existing traditional methods using external knowledge to deal with the sparsity and ambiguity of short texts have achieved good results, but accuracy still needs to be improved because they ignore the context-relevant features. Deep learning methods based on RNN or CNN are hence becoming more and more popular in short text classification. However, RNN based methods cannot perform well in the parallelization which causes the lower efficiency, while CNN based methods ignore sequences and relationships between words, which causes the poorer effectiveness. Motivated by this, we propose a novel short text classification approach combining Context-Relevant Features with multi-stage Attention model based on Temporal Convolutional Network (TCN) and CNN, called CRFA. In our approach, we firstly use Probase as external knowledge to enrich the semantic representation for the solution to the data sparsity and ambiguity of short texts. Secondly, we design a multi-stage attention model based on TCN and CNN, where TCN is introduced to improve the parallelization of the proposed model for higher efficiency, and discriminative features are obtained at each stage through the fusion of attention and different-level CNN for a higher accuracy. Specifically, TCN is adopted to capture context-related features at word and concept levels, and meanwhile, in order to measure the importance of features, Word-level TCN (WTCN) based attention, Concept-level TCN (CTCN) based attention and different-level CNN are used at each stage to focus on the information of more important features. Finally, experimental studies demonstrate the effectiveness and efficiency of our approach in the short text classification compared to several well-known short text classification approaches based on CNN and RNN.
Automation and artificial intelligence in hospitality and tourism

Purpose This study aims to develop a framework to identify and prioritize the key factors in automation and artificial intelligence (AI) implementation in the hospitality and tourism industry. Design/Methodology/Approach This paper used the analytic hierarchy process, a multi-criteria decision-making method, to prioritize the factors influencing automation and AI implementation. This paper developed a model with five criteria (human knowledge, services, robotics applications, internal environment and institutional environment) and 23 sub-criteria obtained from previous studies. This paper designed a questionnaire in the form of pairwise comparisons based on the proposed hierarchical structure. This paper used a nine-point ranking scale to show the relative significance of each variable in the hierarchy and tested the model among staff from 35 five-star hotels and top-rated tourism agencies in the United Arab Emirates. Findings Human knowledge, services and robotics applications were the most significant factors influencing automation and AI implementation. Practitioners and researchers in the hospitality and tourism industry could apply the proposed framework to develop sustainable strategies for implementing and managing automation and AI. The proposed framework may also be useful in future studies examining AI implementation in the hospitality and tourism industry. Originality/Value This paper developed a framework for policymakers that identifies and could help to overcome some of the challenges in implementing automation and AI in the hospitality and tourism sector around the world. The results provide an agenda for future research in this area.
Quantified language connectedness in schizophrenia-spectrum disorders

Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.
On-Demand Generation of Entangled Photon Pairs in the Telecom C-Band with InAs Quantum Dots

Entangled photons are an integral part in quantum optics experiments and a key resource in quantum imaging, quantum communication, and photonic quantum information processing. Making this resource available on-demand has been an ongoing scientific challenge with enormous progress in recent years. Of particular interest is the potential to transmit quantum information over long distances, making photons the only reliable flying qubit. Entangled photons at the telecom C-band could be directly launched into single-mode optical fibers, enabling worldwide quantum communication via existing telecommunication infrastructure. However, the on-demand generation of entangled photons at this desired wavelength window has been elusive. Here, we show a photon pair generation efficiency of 69.9 ± 3.6% in the telecom C-band by an InAs/GaAs semiconductor quantum dot on a metamorphic buffer layer. Using a robust phonon-assisted two-photon excitation scheme we measure a maximum concurrence of 91.4 ± 3.8% and a peak fidelity to the Φ+ state of 95.2 ± 1.1%, verifying on-demand generation of strongly entangled photon pairs and marking an important milestone for interfacing quantum light sources with our classical fiber networks.
Multi-label text classification via joint learning from label embedding and label correlation

For the multi-label text classification problems with many classes, many existing multi-label classification algorithms become infeasible or suffer an unaffordable cost. Some researches hence perform the Label Space Dimension Reduction(LSDR) to solve this problem, but a number of methods ignore the sequence information of texts and the label correlation in the original label space, and treat the label as a meaningless multi-hot vector. In this paper, we put forward a multi-label text classification algorithm LELC(joint learning from Label Embedding and Label Correlation) based on the multi-layer attention and label correlation to solve the issue of multi-label text classification with a large number of class labels. Specifically, we firstly extract features through Bidirectional Gated Recurrent Unit Network(Bi-GRU), multi-layer attention and linear layers. Bi-GRU will capture the content information and sequence information of the text at the same time, and the attention mechanism can help us select the valid features related to labels. Then, we use matrix factorization to perform LSDR, and consider label correlation of the original label space in this process, which allows us to implicitly encode the latent space and simplify the model learning. Finally, Deep Canonical Correlation Analysis(CCA) technology is exploited to couple features and the latent space in an end-to-end pattern, so that these two can influence each other to learn the mapping of feature space to latent space. Experiments on 11 real-world datasets show the comparability between our proposed model and the state-of-the-art methods.