Reviewerly/exHarmony huggingface dataset:
Memory and resource leak defects and their repairs in Java projects

Despite huge software engineering efforts and programming language support, resource and memory leaks are still a troublesome issue, even in memory-managed languages such as Java. Understanding the properties of leak-inducing defects, how the leaks manifest, and how they are repaired is an essential prerequisite for designing better approaches for avoidance, diagnosis, and repair of leak-related bugs. We conduct a detailed empirical study on 491 issues from 15 large open-source Java projects. The study proposes taxonomies for the leak types, for the defects causing them, and for the repair actions. We investigate, under several aspects, the distributions within each taxonomy and the relationships between them. We find that manual code inspection and manual runtime detection are still the main methods for leak detection. We find that most of the errors manifest on error-free execution paths, and developers repair the leak defects in a shorter time than non-leak defects. We also identify 13 recurring code transformations in the repair patches. Based on our findings, we draw a variety of implications on how developers can avoid, detect, isolate and repair leak-related bugs.
Reference Language based Unsupervised Neural Machine Translation

Exploiting a common language as an auxiliary for better translation has a long tradition in machine translation and lets supervised learning-based machine translation enjoy the enhancement delivered by the well-used pivot language in the absence of a source language to target language parallel corpus.The rise of unsupervised neural machine translation (UNMT) almost completely relieves the parallel corpus curse, though UNMT is still subject to unsatisfactory performance due to the vagueness of the clues available for its core back-translation training.Further enriching the idea of pivot translation by extending the use of parallel corpora beyond the source-target paradigm, we propose a new reference language-based framework for UNMT, RUNMT, in which the reference language only shares a parallel corpus with the source, but this corpus still indicates a signal clear enough to help the reconstruction training of UNMT through a proposed reference agreement mechanism.Experimental results show that our methods improve the quality of UNMT over that of a strong baseline that uses only one auxiliary language, demonstrating the usefulness of the proposed reference language-based UNMT and establishing a good start for the community.
MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention

This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities -text, audio and video -in a multimodal video.Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities.We examine the usefulness and challenges of deriving information from the audio modality and present a sequence-to-sequence trimodal hierarchical attention-based model that overcomes these challenges by letting the model pay more attention to the text modality.MAST outperforms the current state of the art model (video-text) by 2.51 points in terms of Content F1 score and 1.00 points in terms of Rouge-L score on the How2 dataset for multimodal language understanding.
On decomposing a deep neural network into modules

Deep learning is being incorporated in many modern software systems. Deep learning approaches train a deep neural network (DNN) model using training examples, and then use the DNN model for prediction. While the structure of a DNN model as layers is observable, the model is treated in its entirety as a monolithic component. To change the logic implemented by the model, e.g. to add/remove logic that recognizes inputs belonging to a certain class, or to replace the logic with an alternative, the training examples need to be changed and the DNN needs to be retrained using the new set of examples. We argue that decomposing a DNN into DNN modules— akin to decomposing a monolithic software code into modules—can bring the benefits of modularity to deep learning. In this work, we develop a methodology for decomposing DNNs for multi-class problems into DNN modules. For four canonical problems, namely MNIST, EMNIST, FMNIST, and KMNIST, we demonstrate that such decomposition enables reuse of DNN modules to create different DNNs, enables replacement of one DNN module in a DNN with another without needing to retrain. The DNN models formed by composing DNN modules are at least as good as traditional monolithic DNNs in terms of test accuracy for our problems.
API method recommendation via explicit matching of functionality verb phrases

Due to the lexical gap between functionality descriptions and user queries, documentation-based API retrieval often produces poor results.Verb phrases and their phrase patterns are essential in both describing API functionalities and interpreting user queries. Thus we hypothesize that API retrieval can be facilitated by explicitly recognizing and matching between the fine-grained structures of functionality descriptions and user queries. To verify this hypothesis, we conducted a large-scale empirical study on the functionality descriptions of 14,733 JDK and Android API methods. We identified 356 different functionality verbs from the descriptions, which were grouped into 87 functionality categories, and we extracted 523 phrase patterns from the verb phrases of the descriptions. Building on these findings, we propose an API method recommendation approach based on explicit matching of functionality verb phrases in functionality descriptions and user queries, called PreMA. Our evaluation shows that PreMA can accurately recognize the functionality categories (92.8%) and phrase patterns (90.4%) of functionality description sentences; and when used for API retrieval tasks, PreMA can help participants complete their tasks more accurately and with fewer retries compared to a baseline approach.
Coupling loss and self-used privileged information guided multi-view transfer learning

Transfer learning builds models for the target domain by leveraging the information from another related source domain, in which the distributions of two domains are usually quite distinct. Real-world data are often characterized by multiple representations known as multi-view features. In the multi-view transfer learning field, existing methods aim to address the following two issues. Firstly, due to the distributional difference between the two domains, the classifier trained on the source domain may underperform on the target domain. Moreover, the lack of data from the target domain generally occurs in the training phase. Secondly, how to fully exploit the relations among multiple features is challenging when such multi-view representations emerge in the source and target domains. In this paper, we propose a new coupling loss and self-used privileged information guided multi-view transfer learning method (MVTL-CP). The first issue is addressed by utilizing the weighted labeled data from the source domain to learn a precise classifier for the target domain. Following the consensus and complementarity principles, we tackle the second issue by making the best use of multiple views. Furthermore, we analyze the consistency between views and the generalization capability of MVTL-CP. Comprehensive experiments confirm the effectiveness of our proposed model.
Efficient first-order methods for convex minimization: a constructive approach

We describe a novel constructive technique for devising efficient first-order methods for a wide range of large-scale convex minimization settings, including smooth, non-smooth, and strongly convex minimization. The technique builds upon a certain variant of the conjugate gradient method to construct a family of methods such that a) all methods in the family share the same worst-case guarantee as the base conjugate gradient method, and b) the family includes a fixed-step first-order method. We demonstrate the effectiveness of the approach by deriving optimal methods for the smooth and non-smooth cases, including new methods that forego knowledge of the problem parameters at the cost of a one-dimensional line search per iteration, and a universal method for the union of these classes that requires a three-dimensional search per iteration. In the strongly convex case, we show how numerical tools can be used to perform the construction, and show that the resulting method offers an improved worst-case bound compared to Nesterov's celebrated fast gradient method.
3D Image Segmentation With Sparse Annotation by Self-Training and Internal Registration

Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose a self-training framework that alternates between two steps consisting of assigning pseudo annotations to unlabeled voxels and updating the 3D segmentation network by employing both the labeled and pseudo labeled voxels. To produce pseudo labels more accurately, we benefit from both propagation of labels (or pseudo-labels) between adjacent slices and 3D processing of voxels. More precisely, a 2D registration-based method is proposed to gradually propagate labels between consecutive 2D slices and a 3D U-Net is employed to utilize volumetric information. Ablation studies on benchmarks show that cooperation between the 2D registration and the 3D segmentation provides accurate pseudo-labels that enable the segmentation network to be trained effectively when for each training sample only even one segmented slice by an expert is available. Our method is assessed on the CHAOS and Visceral datasets to segment abdominal organs. Results demonstrate that despite utilizing just one segmented slice for each 3D image (that is weaker supervision in comparison with the compared weakly supervised methods) can result in higher performance and also achieve closer results to the fully supervised manner.
Spatiotemporal data analysis with chronological networks

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet.
Better-than-chance classification for signal detection

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona-fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that probability of detecting differences between two distributions is lower for accuracy based statistics. We examine several candidate causes for the low power of accuracy tests. These causes include: the discrete nature of the accuracy test statistic, the type of signal accuracy tests are designed to detect, their inefficient use of the data, and their regularization. When the purposes of the analysis is not signal detection, but rather, the evaluation of a particular classifier, we suggest several improvements to increase power. In particular, to replace V-fold cross validation with the Leave-One-Out Bootstrap.
Enhancing multi‐tenancy security in the cloud computing using hybrid ECC‐based data encryption approach

In this study, elliptic curve cryptography (ECC) is elected for tenant authentication, data encryption, and data decryption due to its minimum key size. The proposed ECC-based authentication approach allows the authorised person to access private data; it protects different related attacks effectively. To develop more secure data encryption, the authors have combined a nature-inspired optimisation, such as a moth search algorithm (MSA) with ECC, to select the correct and optimal value of the elliptic curve. The proposed encryption and decryption approach combines DNA encoding with the ECC encryption algorithm. The mechanism of DNA encoded ECC provided multi-level security with less computational power. The security analysis of the proposed method has been provided to prove its effectiveness against certain attacks, such as denial-of-service attack, impersonation attack, reply attack, plaintext attack and chosen-ciphertext attack. The experimental result is evaluated based on encryption time, decryption time, throughput and key size of the security model. The average execution time of the proposed encryption and decryption is only 83.153 and 86.076 s, respectively. From the evaluation, it is clearly determined that the proposed technique provides two-layer security with minimum key size and less storage space.
Coded Computing for Distributed Graph Analytics

Many distributed computing systems have been developed recently for implementing graph based algorithms such as PageRank over large-scale graph-structured datasets such as social networks. Performance of these systems significantly suffers from communication bottleneck as a large number of messages are exchanged among servers at each step of the computation. Motivated by graph based MapReduce, we propose a coded computing framework that leverages computation redundancy to alleviate the communication bottleneck in distributed graph processing. As a key contribution of this work, we develop a novel coding scheme that systematically injects structured redundancy in the computation phase to enable coded multicasting opportunities during message exchange between servers, reducing the communication load substantially in large-scale graph processing. For theoretical analysis, we consider random graph models, and focus on schemes in which subgraph allocation and Reduce allocation are only dependent on vertex ID while the Shuffle design varies with graph connectivity. Specifically, we prove that our proposed scheme enables an (asymptotically) inverse-linear trade-off between computation load and average communication load for two popular random graph models - Erdös-Rényi model, and power law model. Particularly, for a given computation load r, (i.e. when each graph vertex is carefully stored at r servers), the proposed scheme slashes the average communication load by (nearly) a multiplicative factor of r. Furthermore, for the Erdös-Rényi model, we prove that our proposed scheme is optimal asymptotically as the graph size increases by providing an information-theoretic converse. To illustrate the benefits of our scheme in practice, we implement PageRank over Amazon EC2, using artificial as well as real-world datasets, demonstrating gains of up to 50.8% in comparison to the conventional PageRank implementation. Additionally, we specialize our coded scheme and extend our theoretical results to two other random graph models - random bi-partite model, and stochastic block model. Our specialized schemes asymptotically enable inverse-linear trade-offs between computation and communication loads in distributed graph processing for these popular random graph models as well. We complement the achievability results with converse bounds for both of these models.
Approximate Quantum Error Correction Revisited: Introducing the Alpha-Bit

We establish that, in an appropriate limit, qubits of communication should be regarded as composite resources, decomposing cleanly into independent correlation and transmission components. Because qubits of communication can establish ebits of entanglement, qubits are more powerful resources than ebits. We identify a new communications resource, the zero-bit, which is precisely half the gap between them, replacing classical bits by zero-bits makes teleportation asymptotically reversible. The decomposition of a qubit into an ebit and two zero-bits has wide-ranging consequences including applications to state merging, the quantum channel capacity, entanglement distillation, quantum identification and remote state preparation. The source of these results is the theory of approximate quantum error correction. The action of a quantum channel is reversible if and only if no information is leaked to the environment, a characterization that is useful even in approximate form. However, different notions of approximation lead to qualitatively different forms of quantum error correction in the limit of large dimension. We study the effect of a constraint on the dimension of the reference system when considering information leakage. While the resulting condition fails to ensure that the entire input can be corrected, it does ensure that all subspaces of dimension matching that of the reference are correctable. The size of the reference can be characterized by a parameter $\alpha$, we call the associated resource an $\alpha$-bit. Changing $\alpha$ interpolates between standard quantum error correction and quantum identification, a form of equality testing for quantum states. We develop the theory of $\alpha$-bits, including the applications above, and determine the $\alpha$-bit capacity of general quantum channels, finding single-letter formulas for the entanglement-assisted and amortised variants.
Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms

Today we are living in a digital world where most of the activities performed are online. Fraud transactions are ever growing since the growth of ecommerce applications. Millions of transactions are happening around every second everyday giving us the benefit of enjoying financial services through credit and debit cards. Fraud transactions are allowing illegal users to misuse the money of genuine users causing them financial loss. Accessibility of credit card transactions data, techniques used by the frauds, identifying scams in the bulk data which is getting produced very quickly, imbalanced data are some of the major challenges involved in detecting fraudulent credit card transactions. Hence, we need powerful techniques to identify fraudulent transactions. The current paper deals with various machine learning techniques such as Artificial Neural Network (ANN), Decision Trees, Support Vector Machine (SVM), Logistic Regression and Random Forest to detect fraudulent transactions. Performance analysis of these techniques is done using Accuracy, Precision and False alarm rate metrics. Dataset used to carry out the experiment is taken from Kaggle data repository. The experiment shows that Radom Forest could achieve an accuracy of 99.21%, Decision Tree 98.47%. Logistic Regression 95.55%, SVM 95.16% and ANN 99.92%.
Mask R-CNN Powerline Detector: A Deep Learning approach with applications to a UAV

This paper explores the use of a UAV for power-line inspection using a Deep Learning algorithm for detection. Unmanned Aerial Vehicles (UAVs) are versatile with the potential to revolutionize multiple business domains, with benefits being discovered both inside and outside the world of utility and energy industries. They can reduce operational costs, improve worker safety, asset reliability, and decision making, also having semiautonomous flight capability. Utility and Energy companies have successfully deployed UAVs in inspections. Power-line companies usually perform regular visual inspections to check the status of their transmission lines mainly using helicopters equipped with external gimbals housing infrared and an ultraviolet camera to detect hot spots and corona discharges. This solution is quite expensive and dangerous for the crew. To resolve this, a powerline-detection segmentation algorithm based on transfer learning and an improved mask regional convolutional neural network (Mask RCNN), Mask RCNN Powerline Detector is proposed and is deployed on an UAV. For this Draganfly XP-4 was used as the UAV platform. The power-line detection system used a Deep Learning Resnet50 architecture as the backbone network, combined with the Feature Pyramid Network (FPN) architecture for feature extraction. The Region Proposal Network (RPN) was trained end-to-end to create region proposals for each feature map. This paper will present the development of the MRPD system, integration, and testing of the UAV.
Training Interpretable Convolutional Neural Networks by Differentiating Class-Specific Filters

Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is due to the filter-class entanglement – an intricate many-to-many correspondence between filters and classes. Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model. In contrast, we focus on alleviating filter-class entanglement during training. Inspired by cellular differentiation, we propose a novel strategy to train interpretable CNNs by encouraging class-specific filters, among which each filter responds to only one (or few) class. Concretely, we design a learnable sparse Class-Specific Gate (CSG) structure to assign each filter with one (or few) class in a flexible way. The gate allows a filter's activation to pass only when the input samples come from the specific class. Extensive experiments demonstrate the fabulous performance of our method in generating a sparse and highly class-related representation of the input, which leads to stronger interpretability. Moreover, comparing with the standard training strategy, our model displays benefits in applications like object localization and adversarial sample detection. Code link: https://github.com/hyliang96/CSGCNN .