Reviewerly/exHarmony huggingface dataset:
Analysis of Modern Release Engineering Topics : – A Large-Scale Study using StackOverflow –

Release engineers are continuously required to de-liver high-quality software products to the end-user. As a result, modern software companies are proposing new changes in their delivery process that adapt to new technologies such as continuous deployment and Infrastructure-as-Code. However, developers and release engineers still find these practices challenging, and resort to question and answer websites such as StackOverflow to find answers. This paper presents the results of our empirical study on release engineering questions in StackOverflow, to understand the modern release engineering topics of interest and their difficulty. Using topic modeling techniques, we find that (i) developers discuss on a broader range of 38 release engineering topics covering all the six phases of modern release engineering, (ii) the topics Merge Conflict, Branching & Remote Upstream are more popular, while topics Code review, Web deployment, MobileApp Debugging & Deployment, Continuous Deployment are less popular yet more complicated, (iii)-Particularly, the release engineering topic "security" is both popular and difficult according to data collected from StackOverflow.
Vessels

Deep learning systems on the cloud are increasingly targeted by attacks that attempt to steal sensitive data. Intel SGX has been proven effective to protect the confidentiality and integrity of such data during computation. However, state-of-the-art SGX systems still suffer from substantial performance overhead induced by the limited physical memory of SGX. This limitation significantly undermines the usability of deep learning systems due to their memory-intensive characteristics.
Attentive Prototype Few-Shot Learning with Capsule Network-Based Embedding

Few-shot learning, namely recognizing novel categories with a very small amount of training examples, is a challenging area of machine learning research. Traditional deep learning methods require massive training data to tune the huge number of parameters, which is often impractical and prone to over-fitting. In this work, we further research on the well-known few-shot learning method known as prototypical networks for better performance. Our contributions include (1) a new embedding structure to encode relative spatial relationships between features by applying a capsule network; (2) a new triplet loss designated to enhance the semantic feature embedding where similar samples are close to each other while dissimilar samples are farther apart; and (3) an effective non-parametric classifier termed attentive prototypes in place of the simple prototypes in current few-shot learning. The proposed attentive prototype aggregates all of the instances in a support class which are weighted by their importance, defined by the reconstruction error for a given query. The reconstruction error allows the classification posterior probability to be estimated, which corresponds to the classification confidence score. Extensive experiments on three benchmark datasets demonstrate that our approach is effective for the few-shot classification task.
Multiple imputation for analysis of incomplete data in distributed health data networks

Distributed health data networks (DHDNs) leverage data from multiple sources or sites such as electronic health records (EHRs) from multiple healthcare systems and have drawn increasing interests in recent years, as they do not require sharing of subject-level data and hence lower the hurdles for collaboration between institutions considerably. However, DHDNs face a number of challenges in data analysis, particularly in the presence of missing data. The current state-of-the-art methods for handling incomplete data require pooling data into a central repository before analysis, which is not feasible in DHDNs. In this paper, we address the missing data problem in distributed environments such as DHDNs that has not been investigated previously. We develop communication-efficient distributed multiple imputation methods for incomplete data that are horizontally partitioned. Since subject-level data are not shared or transferred outside of each site in the proposed methods, they enhance protection of patient privacy and have the potential to strengthen public trust in analysis of sensitive health data. We investigate, through extensive simulation studies, the performance of these methods. Our methods are applied to the analysis of an acute stroke dataset collected from multiple hospitals, mimicking a DHDN where health data are horizontally partitioned across hospitals and subject-level data cannot be shared or sent to a central data repository.
Quantum reference frame transformations as symmetries and the paradox of the third particle

In a quantum world, reference frames are ultimately quantum systems too -- but what does it mean to "jump into the perspective of a quantum particle"? In this work, we show that quantum reference frame (QRF) transformations appear naturally as symmetries of simple physical systems. This allows us to rederive and generalize known QRF transformations within an alternative, operationally transparent framework, and to shed new light on their structure and interpretation. We give an explicit description of the observables that are measurable by agents constrained by such quantum symmetries, and apply our results to a puzzle known as the `paradox of the third particle'. We argue that it can be reduced to the question of how to relationally embed fewer into more particles, and give a thorough physical and algebraic analysis of this question. This leads us to a generalization of the partial trace (`relational trace') which arguably resolves the paradox, and it uncovers important structures of constraint quantization within a simple quantum information setting, such as relational observables which are key in this resolution. While we restrict our attention to finite Abelian groups for transparency and mathematical rigor, the intuitive physical appeal of our results makes us expect that they remain valid in more general situations.
Replay Attacks and Defenses Against Cross-shard Consensus in Sharded Distributed Ledgers

We present a family of replay attacks against sharded distributed ledgers targeting cross-shard consensus protocols, such as the recently proposed Chainspace and Omniledger. They allow an attacker, with network access only, to double-spend or lock resources with minimal efforts. The attacker can act independently without colluding with any nodes, and succeed even if all nodes are honest; most of the attacks can also exhibit themselves as faults under periods of asynchrony. These attacks are effective against both shard-led and client-led cross-shard consensus approaches. We present Byzcuit-a new cross-shard consensus protocol that is immune to those attacks. We implement a prototype of Byzcuit and evaluate it on a real cloud-based testbed, showing that our defenses impact performance minimally, and overall performance surpasses previous works.
SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation

3D hand pose estimation based on RGB images has been studied for a long time. Most of the studies, however, have performed frame-by-frame estimation based on independent static images. In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework, which leads to the necessity of a large-scale dataset with sequential RGB hand images. We propose a novel method that generates a synthetic dataset that mimics natural human hand movements by re-engineering annotations of an extant static hand pose dataset into pose-flows. With the generated dataset, we train a newly proposed recurrent framework, exploiting visuo-temporal features from sequential synthetic hand images and emphasizing smoothness of estimations with temporal consistency constraints. Our novel training strategy of detaching the recurrent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust estimations. Utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in our experiments on hand pose estimation benchmarks.
An Autoencoder Framework With Attention Mechanism for Cross-Domain Recommendation

In recent years, the recommender system has been widely used in online platforms, which can extract useful information from giant volumes of data and recommend suitable items to the user according to user preferences. However, the recommender system usually suffers from sparsity and cold-start problems. Cross-domain recommendation, as a particular example of transfer learning, has been used to solve the aforementioned problems. However, many existing cross-domain recommendation approaches are based on matrix factorization, which can only learn the shallow and linear characteristics of users and items. Therefore, in this article, we propose a novel autoencoder framework with an attention mechanism (AAM) for cross-domain recommendation, which can transfer and fuse information between different domains and make a more accurate rating prediction. The main idea of the proposed framework lies in utilizing autoencoder, multilayer perceptron, and self-attention to extract user and item features, learn the user and item-latent factors, and fuse the user-latent factors from different domains, respectively. In addition, to learn the affinity of the user-latent factors between different domains in a multiaspect level, we also strengthen the self-attention mechanism by using multihead self-attention and propose AAM++. Experiments conducted on two real-world datasets empirically demonstrate that our proposed methods outperform the state-of-the-art methods in cross-domain recommendation and AAM++ performs better than AAM on sparse and large-scale datasets.
Vehicle Registration and Information Management using Blockchain based Distributed Ledger from Bangladesh Perspective

Blockchain (BC) technology has gathered tremendous attention to the academia and industry due to its transparency and security by design without involving the third party. Academia and industry have adapted Blockchain with the future smart network, and they have been utilizing it in several application areas, including crypto-currency, IoT, supply chain management, smart contracts, and other major distributed applications. Alongside the growth of the BC, researchers have recognized distributed ledger as a potential for information management across the shareable platforms. However, most of the organizations still use centralized management systems that are slower and risks-associated. Therefore, to address this issue, we propose a BC-based distributed ledger framework that is capable of maintaining a vehicle registration and information management system across platforms. Our proposed framework addresses the issues in the traditional paper-based management system and ensures the process to be faster and more transparent without the direct involvement of a third party. As the Government must have the supervision capability, we introduce the system with a hybrid architecture that not only makes the user experience better but also transparent at the same time.
Structural residual learning for single image rain removal

To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed. However, the performance of these deep learning methods largely relies on the covering range of rain shapes contained in the pre-collected training rainy-clean image pairs. This makes them easily trapped into the overfitting-to-the-training-samples issue and cannot finely generalize to practical rainy images with complex and diverse rain streaks. Against this generalization issue, this study proposes a new network architecture by enforcing the output residual of the network possess intrinsic rain structures. Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages. Such a general regularization function naturally leads to both its better training accuracy and testing generalization capability even for those non-seen rain configurations. Such superiority is comprehensively substantiated by experiments implemented on synthetic and real datasets both visually and quantitatively as compared with current state-of-the-art methods.
Feature Representation Matters: End-to-End Learning for Reference-Based Image Super-Resolution

In this paper, we are aiming for a general reference-based super-resolution setting: it does not require the low-resolution image and the high-resolution reference image to be well aligned or with a similar texture. Instead, we only intend to transfer the relevant textures from reference images to the output super-resolution image. To this end, we engaged neural texture transfer to swap texture features between the low-resolution image and the high-resolution reference image. We identified the importance of designing a super-resolution task-specific features rather than classification oriented features for neural texture transfer, making the feature extractor more compatible with the image synthesis task. We develop an end-to-end training framework for the reference-based super-resolution task, where the feature encoding network prior to matching and swapping is jointly trained with the image synthesis network. We also discovered that learning the high-frequency residual is an effective way for the reference-based super-resolution task. Without bells and whistles, the proposed method E2ENT $$^2$$ achieved better performance than state-of-the method (i.e., SRNTT with five loss functions) with only two basic loss functions. Extensive experimental results on several datasets demonstrate that the proposed method E2ENT $$^2$$ can achieve superior performance to existing best models both quantitatively and qualitatively.
Talking about public good for the second quantum revolution: analysing quantum technology narratives in the context of national strategies

Abstract The ‘second quantum revolution’ has been the subject of substantial speculation, investment by public and private sectors, and media hype. We investigate some of this hype in the form of three national strategies for quantum technology. In the course of analysing these strategies, we ask: how can we ensure new quantum technologies benefit the societies they are used in and are a part of ? To help clarify this question, we posit a public good test for quantum research requiring diversity in research agendas, social orders, and research-society networks.
DSPNet: A low computational-cost network for human pose estimation

Existing human pose estimation methods usually have a high computational load, which is very unfavorable for resource-limited equipment. To address this issue, we propose a low computational-cost deep supervision pyramid network called DSPNet. Firstly, we design a lightweight up-sampling unit instead of transposed convolution as a decoder for the network. In the case of decreased computation, it has brought an increase in prediction accuracy. Secondly, we present a novel deep supervision pyramid architecture to improve the multi-scale obtaining ability of MSRA SimpleBaseline while not bringing any increase in the number of parameters. The experimental results on both MPII and COCO pose estimation benchmarks illustrate that DSPNet achieves almost equivalent state-of-the-art results with a low computational load. Especially, the computational cost of DSPNet is 12.7% of SimpleBaseline and the estimation accuracy is improved by 0.9 points when both methods use the same backbone network (EfficientNet) on MPII validation set. The code of the proposed method is availabe at https://github.com/sumaliqinghua/DSPNet.
Deep Neural Network Classifier for Variable Stars with Novelty Detection Capability

Abstract Common variable star classifiers are built with the singular goal of producing the correct class labels, leaving much of the multi-task capability of deep neural networks unexplored. We present a periodic light curve classifier that combines a recurrent neural network autoencoder for unsupervised feature extraction and a dual-purpose estimation network for supervised classification and novelty detection. The estimation network optimizes a Gaussian mixture model in the reduced-dimension feature space, where each Gaussian component corresponds to a variable class. An estimation network with a basic structure of a single hidden layer attains a cross-validation classification accuracy of ∼99%, which is on par with the conventional workhorses, random forest classifiers. With the addition of photometric features, the network is capable of detecting previously unseen types of variability with precision 0.90, recall 0.96, and an F 1 score of 0.93. The simultaneous training of the autoencoder and estimation network is found to be mutually beneficial, resulting in faster autoencoder convergence, as well as superior classification and novelty detection performance. The estimation network also delivers adequate results even when optimized with pre-trained autoencoder features, suggesting that it can readily extend existing classifiers to provide added novelty detection capabilities.
Quantum interference enables constant-time quantum information processing

It is an open question how fast information processing can be performed and whether quantum effects can speed up the best existing solutions. Signal extraction, analysis, and compression in diagnostics, astronomy, chemistry, and broadcasting build on the discrete Fourier transform. It is implemented with the fast Fourier transform (FFT) algorithm that assumes a periodic input of specific lengths, which rarely holds true. A lesser-known transform, the Kravchuk-Fourier (KT), allows one to operate on finite strings of arbitrary length. It is of high demand in digital image processing and computer vision but features a prohibitive runtime. Here, we report a one-step computation of a fractional quantum KT. The quantum d-nary (qudit) architecture we use comprises only one gate and offers processing time independent of the input size. The gate may use a multiphoton Hong-Ou-Mandel effect. Existing quantum technologies may scale it up toward diverse applications.
A Comparative Study of Opinion Summarization Techniques

In the Web 3.0 platforms, enormous amount of information is shared whereby individuals express their thoughts and opinions and learn from others' experiences. Many e-commerce websites provide service of posting opinionated reviews to allow consumers post their opinions using free text. Examples of these e-commerce websites include eBay, Amazon, and Yahoo shopping. Summarizing text is taken as an interesting task of Natural Language Processing (NLP). The proposed work presents a comparative study of different techniques used for opinion summarization. It covers both abstractive and extractive approaches where summary of sentences is achieved by considering aspects. This article highlights the gaps in the previous study by proposing a novel graph-based technique for generating abstractive summary of duplicate sentences. The method discusses the details by constructing graphs, ensuring the sentence correctness using some constraints, and finally scoring the sentences individually by fusing sentiments using SentiWordNet. Extractive approach uses the principle of principal component analysis (PCA). The work includes the application of PCA in summarization of text by reducing the number of dimensions in data (aspects) and relatively finding the summary of the reviews on ranking the most relevant ones, according to the prime aspects without any loss of information respective of a particular domain. The analysis is conducted on the standard Opinosis data set and comparison is made between both of the techniques to discuss which method generates more coherent and complete summary.
Leveraging Quantum Annealing for Election Forecasting

Accurate, reliable sampling from fully-connected graphs with arbitrary correlations is a difficult problem. Such sampling requires knowledge of the probabilities of observing every possible state of a graph. As graph size grows, the number of model states becomes intractably large and efficient computation requires full sampling be replaced with heuristics and algorithms that are only approximations of full sampling. This work investigates the potential impact of adiabatic quantum computation for sampling purposes, building on recent successes training Boltzmann machines using a quantum device. We investigate the use case of quantum computation to train Boltzmann machines for predicting the 2016 Presidential election.
Community expectations for research artifacts and evaluation processes

Background. Artifact evaluation has been introduced into the software engineering and programming languages research community with a pilot at ESEC/FSE 2011 and has since then enjoyed a healthy adoption throughout the conference landscape. Objective. In this qualitative study, we examine the expectations of the community toward research artifacts and their evaluation processes. Method. We conducted a survey including all members of artifact evaluation committees of major conferences in the software engineering and programming language field since the first pilot and compared the answers to expectations set by calls for artifacts and reviewing guidelines. Results. While we find that some expectations exceed the ones expressed in calls and reviewing guidelines, there is no consensus on quality thresholds for artifacts in general. We observe very specific quality expectations for specific artifact types for review and later usage, but also a lack of their communication in calls. We also find problematic inconsistencies in the terminology used to express artifact evaluation's most important purpose – replicability. Conclusion. We derive several actionable suggestions which can help to mature artifact evaluation in the inspected community and also to aid its introduction into other communities in computer science.
SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches

Sketch-based image retrieval (SBIR) has been a popular research topic in recent years. Existing works concentrate on mapping the visual information of sketches and images to a semantic space at the object level. In this paper, for the first time, we study the fine-grained scene-level SBIR problem which aims at retrieving scene images satisfying the user’s specific requirements via a freehand scene sketch. We propose a graph embedding based method to learn the similarity measurement between images and scene sketches, which models the multi-modal information, including the size and appearance of objects as well as their layout information, in an effective manner. To evaluate our approach, we collect a dataset based on SketchyCOCO and extend the dataset using Coco-stuff. Comprehensive experiments demonstrate the significant potential of the proposed approach on the application of fine-grained scene-level image retrieval.