OpenRaiser/Intern-Atlas huggingface dataset:
Modelling Quantum Circuits with UML
Venue: arXiv cs.SE 2021
Year: 2021

None of the quantum computing applications imagined will ever become a reality without quantum software. Quantum programmes have, to date, been coded with ad hoc techniques. Researchers in the field of quantum software engineering are, therefore, now demanding more systematic techniques and methods with which to produce software with sufficient quality. One of the challenges and lessons learned from classic software engineering is the need for high-level, abstract and technology-independent representations with which to design software before it is coded. This paper specifically addresses this challenge for quantum software design. Since UML is a well-proven modelling language that has been widely employed by industry for some time, we propose a UML extension for the representation of quantum algorithms. Our proposal comprises the definition of a UML profile based on various stereotypes that can be applied to the existing UML activity diagrams in order to represent quantum circuits. The advantage of this representation is that UML quantum circuits can be interrelated with other UML elements and diagrams, which will make it possible to represent various concerns and viewpoints of the so-called hybrid information systems. This will consequently enable classical and quantum aspects to be modelled together in integrated designs in a technological-agnostic manner that is already supported by a considerable number of existing software design tools.
Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression
Venue: arXiv cs.RO 2021
Year: 2021

We develop an online probabilistic metric-semantic mapping approach for mobile robot teams relying on streaming RGB-D observations. The generated maps contain full continuous distributional information about the geometric surfaces and semantic labels (e.g., chair, table, wall). Our approach is based on online Gaussian Process (GP) training and inference, and avoids the complexity of GP classification by regressing a truncated signed distance function (TSDF) of the regions occupied by different semantic classes. Online regression is enabled through a sparse pseudo-point approximation of the GP posterior. To scale to large environments, we further consider spatial domain partitioning via an octree data structure with overlapping leaves. An extension to the multi-robot setting is developed by having each robot execute its own online measurement update and then combine its posterior parameters via local weighted geometric averaging with those of its neighbors. This yields a distributed information processing architecture in which the GP map estimates of all robots converge to a common map of the environment while relying only on local one-hop communication. Our experiments demonstrate the effectiveness of the probabilistic metric-semantic mapping technique in 2-D and 3-D environments in both single and multi-robot settings.
Towards Active Learning Based Smart Assistant for Manufacturing
Venue: arXiv cs.AI 2021
Year: 2021

A general approach for building a smart assistant that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps is presented. We develop a methodology to build such a system. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing. The system provides means for knowledge acquisition, gathering data from users. We envision active learning can be used to get data labels where labeled data is scarce.
Repopulating Street Scenes
Venue: arXiv cs.CV 2021
Year: 2021

We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving, and composing scenes to achieve a certain ambiance, such as empty streets in the early morning. At a technical level, our work has three primary contributions: (1) a method for clearing images of objects, (2) a method for estimating sun direction from a single image, and (3) a way to compose objects in scenes that respects scene geometry and illumination. Each component is learned from data with minimal ground truth annotations, by making creative use of large-numbers of short image bursts of street scenes. We demonstrate convincing results on a range of street scenes and illustrate potential applications.
cuConv: A CUDA Implementation of Convolution for CNN Inference
Venue: arXiv cs.DC 2021
Year: 2021

Convolutions are the core operation of deep learning applications based on Convolutional Neural Networks (CNNs). Current GPU architectures are highly efficient for training and deploying deep CNNs, and hence, these are largely used in production for this purpose. State-of-the-art implementations, however, present a lack of efficiency for some commonly used network configurations. In this paper we propose a GPU-based implementation of the convolution operation for CNN inference that favors coalesced accesses, without requiring prior data transformations. Our experiments demonstrate that our proposal yields notable performance improvements in a range of common CNN forward propagation convolution configurations, with speedups of up to 2.29x with respect to the best implementation of convolution in cuDNN, hence covering a relevant region in currently existing approaches.
Is segmentation uncertainty useful?
Venue: arXiv cs.CV 2021
Year: 2021

Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are designed to capture different aspects of segmentation uncertainty and ambiguity, these modelling differences are rarely discussed in the context of applications of uncertainty. We consider two common use cases of segmentation uncertainty, namely assessment of segmentation quality and active learning. We consider four established strategies for probabilistic segmentation, discuss their modelling capabilities, and investigate their performance in these two tasks. We find that for all models and both tasks, returned uncertainty correlates positively with segmentation error, but does not prove to be useful for active learning.
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB
Venue: arXiv cs.LG 2021
Year: 2021

RocksDB is a general-purpose embedded key-value store used in multiple different settings. Its versatility comes at the cost of complex tuning configurations. This paper investigates maximizing the throughput of RocksDB IO operations by auto-tuning ten parameters of varying ranges. Off-the-shelf optimizers struggle with high-dimensional problem spaces and require a large number of training samples. We propose two techniques to tackle this problem: multi-task modeling and dimensionality reduction through a manual grouping of parameters. By incorporating adjacent optimization in the model, the model converged faster and found complicated settings that other tuners could not find. This approach had an additional computational complexity overhead, which we mitigated by manually assigning parameters to each sub-goal through our knowledge of RocksDB. The model is then incorporated in a standard Bayesian Optimization loop to find parameters that maximize RocksDB's IO throughput. Our method achieved x1.3 improvement when benchmarked against a simulation of Facebook's social graph traffic, and converged in ten optimization steps compared to other state-of-the-art methods that required fifty steps.
On the relative power of algebraic approximations of graph isomorphism
Venue: arXiv cs.LO 2021
Year: 2021

We compare the capabilities of two approaches to approximating graph isomorphism using linear algebraic methods: the \emph{invertible map tests} (introduced by Dawar and Holm) and proof systems with algebraic rules, namely \emph{polynomial calculus}, \emph{monomial calculus} and \emph{Nullstellensatz calculus}. In the case of fields of characteristic zero, these variants are all essentially equivalent to the the Weisfeiler-Leman algorithms. In positive characteristic we show that the invertible map method can simulate the monomial calculus and identify a potential way to extend this to the monomial calculus.
Systematic Mapping Protocol: Reasoning Algorithms on Feature Model
Venue: arXiv cs.SE 2021
Year: 2021

Context: The importance of the feature modeling for the software product lines considering the modeling and management of the variability. Objective: Define a protocol to conduct a systematic mapping study to summarize and synthesize the evidence on reasoning algorithms for feature modeling. Method: Application the protocol to conduct a systematic mapping study according the guidelines of K. Petersen. Results: A validated protocol to conduct a systematic mapping study. Conclusions: Initial findings show that a more detailed review for the different reasoning algorithms for feature modeling is needed.
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
Venue: arXiv cs.CV 2021
Year: 2021

Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic. We propose pyramid reasoning modules (PRMs) to model the anatomical correlation between anchor and target volumes. In practice, the proposed module first computes a correlation matrix between target and anchor computerized tomography (CT) volumes. Then, this matrix is used to transform the feature representations of both anchor volume and its segmentation mask. Finally, OrganNet learns to fuse the representations from various inputs and predicts segmentation results for target volume. Extensive experiments show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task. Moreover, even when compared with fully-supervised segmentation models, OrganNet is still able to produce satisfying segmentation results.
Differentiable Network Adaption with Elastic Search Space
Venue: arXiv cs.CV 2021
Year: 2021

In this paper we propose a novel network adaption method called Differentiable Network Adaption (DNA), which can adapt an existing network to a specific computation budget by adjusting the width and depth in a differentiable manner. The gradient-based optimization allows DNA to achieve an automatic optimization of width and depth rather than previous heuristic methods that heavily rely on human priors. Moreover, we propose a new elastic search space that can flexibly condense or expand during the optimization process, allowing the network optimization of width and depth in a bi-direction manner. By DNA, we successfully achieve network architecture optimization by condensing and expanding in both width and depth dimensions. Extensive experiments on ImageNet demonstrate that DNA can adapt the existing network to meet different targeted computation requirements with better performance than previous methods. What's more, DNA can further improve the performance of high-accuracy networks obtained by state-of-the-art neural architecture search methods such as EfficientNet and MobileNet-v3.
The Development and Validation of the Technology-Supported Reflection Inventory
Venue: arXiv cs.HC 2021
Year: 2021

Reflection is an often addressed design goal in Human-Computer Interaction (HCI) research. An increasing number of artefacts for reflection have been developed in recent years. However, evaluating if and how an interactive technology helps a user reflect is still complex. This makes it difficult to compare artefacts (or prototypes) for reflection, impeding future design efforts. To address this issue, we developed the \emph{Technology-Supported Reflection Inventory} (TSRI), which is a scale that evaluates how effectively a system supports reflection. We first created a list of possible scale items based on past work in defining reflection. The items were then reviewed by experts. Next, we performed exploratory factor analysis to reduce the scale to its final length of nine items. Subsequently, we confirmed test-retest validity of our instrument, as well as its construct validity. The TSRI enables researchers and practitioners to compare prototypes designed to support reflection.
Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art
Venue: arXiv cs.NI 2021
Year: 2021

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises can not afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives.
A new line-symmetric mobile infinity-pod
Venue: arXiv cs.RO 2021
Year: 2021

We construct parallel manipulators with one degree of freedom and admitting infinitely many legs lying on a curve of degree ten and genus six. Our technique relies upon a duality between the spaces parametrizing all the possible legs and all the possible configurations of a manipulator. Before describing our construction, we show how this duality helps explaining several known phenomena regarding mobility of parallel manipulators.
Decoding Orders for Securing Untrusted NOMA
Venue: arXiv cs.IT 2021
Year: 2021

This letter focuses on exploring a new decoding order to resolve the secrecy issue among untrusted nonorthogonal multiple access users. In this context, firstly, we identify the total number of possible decoding orders analytically. Then, we propose a decoding order strategy ensuring positive secrecy rate for all users, and obtain the number of feasible secure decoding orders based on this proposed strategy numerically. Thereafter, we present a suboptimal policy to avoid the computational complexity involved in finding the best secure decoding order. Numerical results illustrate that the suboptimal solution provides a performance gain of about 137% over therelevant benchmark.
Multilayer Graph Clustering with Optimized Node Embedding
Venue: arXiv cs.LG 2021
Year: 2021

We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fidelity term to the layers of a given multilayer graph, and a regularization on the (single-layer) graph induced by the embedding. The fidelity term uses the contrastive loss to properly aggregate the observed layers into a representative embedding. The regularization pushes for a sparse and community-aware graph, and it is based on a measure of graph sparsification called "effective resistance", coupled with a penalization of the first few eigenvalues of the representative graph Laplacian matrix to favor the formation of communities. The proposed optimization problem is nonconvex but fully differentiable, and thus can be solved via the descent gradient method. Experiments show that our method leads to a significant improvement w.r.t. state-of-the-art multilayer graph clustering algorithms.
Deep Gaussian Processes for Few-Shot Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples, in order to segment novel query images. A common approach is to model each class with a single prototype. While conceptually simple, these methods suffer when the target appearance distribution is multi-modal or not linearly separable in feature space. To tackle this issue, we propose a few-shot learner formulation based on Gaussian process (GP) regression. Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space. The GP provides a principled way of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. We further exploit the end-to-end learning capabilities of our approach to learn the output space of the GP learner, ensuring a richer encoding of the segmentation mask. We perform comprehensive experimental analysis of our few-shot learner formulation. Our approach sets a new state-of-the-art for 5-shot segmentation, with mIoU scores of 68.1 and 49.8 on PASCAL-5i and COCO-20i, respectively
Binarized Neural Networks for Resource-Constrained On-Device Gait Identification
Venue: arXiv cs.LG 2021
Year: 2021

User authentication through gait analysis is a promising application of discriminative neural networks -- particularly due to the ubiquity of the primary sources of gait accelerometry, in-pocket cellphones. However, conventional machine learning models are often too large and computationally expensive to enable inference on low-resource mobile devices. We propose that binarized neural networks can act as robust discriminators, maintaining both an acceptable level of accuracy while also dramatically decreasing memory requirements, thereby enabling on-device inference. To this end, we propose BiPedalNet, a compact CNN that nearly matches the state-of-the-art on the Padova gait dataset, with only 1/32 of the memory overhead.
TUSQ: Targeted High-Utility Sequence Querying
Venue: arXiv cs.DB 2021
Year: 2021

Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds suffix remain utility and terminated descendants utility as well as a vertical Last Instance Table structure. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.
Cooperative Beam Hopping for Accurate Positioning in Ultra-Dense LEO Satellite Networks
Venue: arXiv cs.NI 2021
Year: 2021

In ultra-dense LEO satellite networks, conventional communication-oriented beam pattern design cannot provide multiple favorable signals from different satellites simultaneously, and thus leads to poor positioning performance. To tackle this issue, in this paper, we propose a novel cooperative beam hopping (BH) framework to adaptively tune beam layouts suitable for multi-satellite coordinated positioning. On this basis, a joint user association, BH design and power allocation optimization problem is formulated to minimize average Cramér-Rao lower bound (CRLB). An efficient flexible BH control algorithm (FBHCA) is then proposed to solve the problem. Finally, a thorough experimental platform is built following the Third Generation Partnership Project (3GPP) defined non-terrestrial network (NTN) simulation parameters to validate the performance gain of the devised algorithm. The numerical results demonstrate that FBHCA can significantly improve CRLB performance over the benchmark scheme.
Spatial Content Alignment For Pose Transfer
Venue: arXiv cs.CV 2021
Year: 2021

Due to unreliable geometric matching and content misalignment, most conventional pose transfer algorithms fail to generate fine-trained person images. In this paper, we propose a novel framework Spatial Content Alignment GAN (SCAGAN) which aims to enhance the content consistency of garment textures and the details of human characteristics. We first alleviate the spatial misalignment by transferring the edge content to the target pose in advance. Secondly, we introduce a new Content-Style DeBlk which can progressively synthesize photo-realistic person images based on the appearance features of the source image, the target pose heatmap and the prior transferred content in edge domain. We compare the proposed framework with several state-of-the-art methods to show its superiority in quantitative and qualitative analysis. Moreover, detailed ablation study results demonstrate the efficacy of our contributions. Codes are publicly available at github.com/rocketappslab/SCA-GAN.
Exploring Plausible Patches Using Source Code Embeddings in JavaScript
Venue: arXiv cs.SE 2021
Year: 2021

Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the so-called Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle. The latter, however, might not give a reliable result, because of the imperfections in such oracles; one of which is usually the test suite. Although (re-) running the test suite is right under one's nose, in real life applications the problem of over- and underfitting often occurs, resulting in inadequate patches. Efforts that have been made to tackle with this problem include patch filtering, test suite expansion, careful patch producing and many more. Most approaches to date use post-filtering relying either on test execution traces or make use of some similarity concept measured on the generated patches. Our goal is to investigate the nature of these similarity-based approaches. To do so, we trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it. These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program. We analyzed these similarity lists and found that plain document embeddings may lead to misclassification - it fails to capture nuanced code semantics. Nevertheless, in some cases it also provided useful information, thus helping to better understand the area of Automated Program Repair.