OpenRaiser/Intern-Atlas huggingface dataset:
A First Look into the Structural Properties and Resilience of Blockchain Overlays
Venue: arXiv cs.NI 2021
Year: 2021

Blockchain (BC) systems are highly distributed peer-to-peer networks that offer an alternative to centralized services and promise robustness to coordinated attacks. However, the resilience and overall security of a BC system rests heavily on the structural properties of its underlying peer-to-peer overlay. Despite their success, BC overlay networks' critical design aspects, connectivity properties and network-layer inter-dependencies are still poorly understood. In this work, we set out to fill this gap and study the most important overlay network structural properties and robustness to targeted attacks of seven distinct BC networks. In particular, we probe and crawl these BC networks every two hours to gather information about all their available peers, over a duration of 28 days. We analyze 335 network snapshots per BC network, for a total of 2345 snapshots. We construct, at frequent intervals, connectivity graphs for each BC network, consisting of all potential connections between peers. We analyze the structural graph properties of these networks and compare them across the seven BC networks. We also study how these properties associate with the resilience of each network to partitioning attacks, i.e., when peers are selected, attacked and taken offline, using different selection strategies driven by the aforementioned structural properties. In fact, we show that by targeting fewer than 10 highly-connected peers, major BCs such as Bitcoin can be partitioned into disjoint, i.e., disconnected, components. Finally, we uncover a hidden interconnection between different BC networks, where certain peers participate in more than one BC network, which has serious implications for the robustness of the overall BC network ecosystem.
Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection
Venue: arXiv cs.CV 2021
Year: 2021

Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used. A major challenge in such approaches is the limited amount of data that arises, for example, when more specialized and rarer vehicles such as agricultural machinery or construction vehicles are to be detected. This lack of data contrasts with the enormous data hunger of deep learning methods in general and object recognition in particular. In this article, we address this issue in the context of the detection of road vehicles in aerial images. To overcome the lack of annotated data, we propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance. In cases of very limited or even no real-world images, we observe an improvement in average precision of up to 0.70 points. We address the remaining performance gap to real-world datasets by analyzing the effect of the image composition of background and objects and give insights into the importance of background.
Serverless Predictions: 2021-2030
Venue: arXiv cs.DC 2021
Year: 2021

Within the next 10 years, advances on resource disaggregation will enable full transparency for most Cloud applications: to run unmodified single-machine applications over effectively unlimited remote computing resources. In this article, we present five serverless predictions for the next decade that will realize this vision of transparency -- equivalent to Tim Wagner's Serverless SuperComputer or AnyScale's Infinite Laptop proposals.
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Venue: arXiv cs.CL 2021
Year: 2021

Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters. Despite its significantly large volume, social media content is often too noisy for direct use in any application. Therefore, it is important to filter, categorize, and concisely summarize the available content to facilitate effective consumption and decision-making. To address such issues automatic classification systems have been developed using supervised modeling approaches, thanks to the earlier efforts on creating labeled datasets. However, existing datasets are limited in different aspects (e.g., size, contains duplicates) and less suitable to support more advanced and data-hungry deep learning models. In this paper, we present a new large-scale dataset with ~77K human-labeled tweets, sampled from a pool of ~24 million tweets across 19 disaster events that happened between 2016 and 2019. Moreover, we propose a data collection and sampling pipeline, which is important for social media data sampling for human annotation. We report multiclass classification results using classic and deep learning (fastText and transformer) based models to set the ground for future studies. The dataset and associated resources are publicly available. https://crisisnlp.qcri.org/humaid_dataset.html
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks
Venue: arXiv cs.LG 2021
Year: 2021

Deep residual network architectures have been shown to achieve superior accuracy over classical feed-forward networks, yet their success is still not fully understood. Focusing on massively over-parameterized, fully connected residual networks with ReLU activation through their respective neural tangent kernels (ResNTK), we provide here a spectral analysis of these kernels. Specifically, we show that, much like NTK for fully connected networks (FC-NTK), for input distributed uniformly on the hypersphere $\mathbb{S}^{d-1}$, the eigenfunctions of ResNTK are the spherical harmonics and the eigenvalues decay polynomially with frequency $k$ as $k^{-d}$. These in turn imply that the set of functions in their Reproducing Kernel Hilbert Space are identical to those of FC-NTK, and consequently also to those of the Laplace kernel. We further show, by drawing on the analogy to the Laplace kernel, that depending on the choice of a hyper-parameter that balances between the skip and residual connections ResNTK can either become spiky with depth, as with FC-NTK, or maintain a stable shape.
Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking
Venue: arXiv cs.CV 2021
Year: 2021

Unmanned aerial vehicle (UAV)-based tracking is attracting increasing attention and developing rapidly in applications such as agriculture, aviation, navigation, transportation and public security. Recently, discriminative correlation filters (DCF)-based trackers have stood out in UAV tracking community for their high efficiency and appealing robustness on a single CPU. However, due to limited onboard computation resources and other challenges the efficiency and accuracy of existing DCF-based approaches is still not satisfying. In this paper, we explore using segmentation by the GrabCut to improve the wildly adopted discriminative scale estimation in DCF-based trackers, which, as a mater of fact, greatly impacts the precision and accuracy of the trackers since accumulated scale error degrades the appearance model as online updating goes on. Meanwhile, inspired by residue representation, we exploit the residue nature inherent to videos and propose residue-aware correlation filters that show better convergence properties in filter learning. Extensive experiments are conducted on four UAV benchmarks, namely, UAV123@10fps, DTB70, UAVDT and Vistrone2018 (VisDrone2018-test-dev). The results show that our method achieves state-of-the-art performance.
Self-stabilizing Multivalued Consensus in Asynchronous Crash-prone Systems
Venue: arXiv cs.DC 2021
Year: 2021

The problem of multivalued consensus is fundamental in the area of fault-tolerant distributed computing since it abstracts a very broad set of agreement problems in which processes have to uniformly decide on a specific value v in V, where |V| >1. Existing solutions (that tolerate process failures) reduce the multivalued consensus problem to the one of binary consensus, e.g., Mostefaoui-Raynal-Tronel and Zhang-Chen. Our study aims at the design of an even more reliable solution. We do so through the lenses of self-stabilization -- a very strong notion of fault-tolerance. In addition to node and communication failures, self-stabilizing algorithms can recover after the occurrence of arbitrary transient-faults; these faults represent any violation of the assumptions according to which the system was designed to operate (as long as the algorithm code stays intact). This work proposes the first (to the best of our knowledge) self-stabilizing algorithm for multivalued consensus for asynchronous message-passing systems prone to process failures and arbitrary transient-faults. Our solution is also the first (to the best of our knowledge) to support wait-freedom. Moreover, using piggybacking techniques, our solution can invoke n binary consensus objects concurrently. Thus, the proposed self-stabilizing wait-free solution can terminate using fewer resources than earlier non-self-stabilizing solutions by Mostefaoui, Raynal, and Tronel, which uses an unbounded number of binary consensus objects, or Zhang and Chen, which is not wait-free.
Image Composition Assessment with Saliency-augmented Multi-pattern Pooling
Venue: arXiv cs.CV 2021
Year: 2021

Image composition assessment is crucial in aesthetic assessment, which aims to assess the overall composition quality of a given image. However, to the best of our knowledge, there is neither dataset nor method specifically proposed for this task. In this paper, we contribute the first composition assessment dataset CADB with composition scores for each image provided by multiple professional raters. Besides, we propose a composition assessment network SAMP-Net with a novel Saliency-Augmented Multi-pattern Pooling (SAMP) module, which analyses visual layout from the perspectives of multiple composition patterns. We also leverage composition-relevant attributes to further boost the performance, and extend Earth Mover's Distance (EMD) loss to weighted EMD loss to eliminate the content bias. The experimental results show that our SAMP-Net can perform more favorably than previous aesthetic assessment approaches.
TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption
Venue: arXiv cs.CR 2021
Year: 2021

Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains. These algorithms often rely on sensitive and private data such as medical and financial records. Therefore, it is vital to draw further attention regarding privacy threats and corresponding defensive techniques applied to machine learning models. In this paper, we present TenSEAL, an open-source library for Privacy-Preserving Machine Learning using Homomorphic Encryption that can be easily integrated within popular machine learning frameworks. We benchmark our implementation using MNIST and show that an encrypted convolutional neural network can be evaluated in less than a second, using less than half a megabyte of communication.
Towards Optimal Use of Exception Handling Information for Function Detection
Venue: arXiv cs.CR 2021
Year: 2021

Function entry detection is critical for security of binary code. Conventional methods heavily rely on patterns, inevitably missing true functions and introducing errors. Recently, call frames have been used in exception-handling for function start detection. However, existing methods have two problems. First, they combine call frames with heuristic-based approaches, which often brings error and uncertain benefits. Second, they trust the fidelity of call frames, without handling the errors that are introduced by call frames. In this paper, we first study the coverage and accuracy of existing approaches in detecting function starts using call frames. We found that recursive disassembly with call frames can maximize coverage, and using extra heuristic-based approaches does not improve coverage and actually hurts accuracy. Second, we unveil call-frame errors and develop the first approach to fix them, making their use more reliable.
Graph Reordering for Cache-Efficient Near Neighbor Search
Venue: arXiv cs.DS 2021
Year: 2021

Graph search is one of the most successful algorithmic trends in near neighbor search. Several of the most popular and empirically successful algorithms are, at their core, a simple walk along a pruned near neighbor graph. Such algorithms consistently perform at the top of industrial speed benchmarks for applications such as embedding search. However, graph traversal applications often suffer from poor memory access patterns, and near neighbor search is no exception to this rule. Our measurements show that popular search indices such as the hierarchical navigable small-world graph (HNSW) can have poor cache miss performance. To address this problem, we apply graph reordering algorithms to near neighbor graphs. Graph reordering is a memory layout optimization that groups commonly-accessed nodes together in memory. We present exhaustive experiments applying several reordering algorithms to a leading graph-based near neighbor method based on the HNSW index. We find that reordering improves the query time by up to 40%, and we demonstrate that the time needed to reorder the graph is negligible compared to the time required to construct the index.
A Unified Model for Fingerprint Authentication and Presentation Attack Detection
Venue: arXiv cs.CV 2021
Year: 2021

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this paper, we reformulate the workings of a typical fingerprint recognition system. In particular, we posit that both spoof detection and fingerprint recognition are correlated tasks. Therefore, rather than performing the two tasks separately, we propose a joint model for spoof detection and matching to simultaneously perform both tasks without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain an authentication accuracy (1:1 matching) of TAR = 100% @ FAR = 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LiveDet 2015 dataset, both maintaining the performance of stand-alone methods. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50% and 40%, respectively; a significant advantage for recognition systems running on resource-constrained devices and communication channels. The project page for our work is available at https://www.bit.ly/ijcb2021-unified .
EXPATS: A Toolkit for Explainable Automated Text Scoring
Venue: arXiv cs.CL 2021
Year: 2021

Automated text scoring (ATS) tasks, such as automated essay scoring and readability assessment, are important educational applications of natural language processing. Due to their interpretability of models and predictions, traditional machine learning (ML) algorithms based on handcrafted features are still in wide use for ATS tasks. Practitioners often need to experiment with a variety of models (including deep and traditional ML ones), features, and training objectives (regression and classification), although modern deep learning frameworks such as PyTorch require deep ML expertise to fully utilize. In this paper, we present EXPATS, an open-source framework to allow its users to develop and experiment with different ATS models quickly by offering flexible components, an easy-to-use configuration system, and the command-line interface. The toolkit also provides seamless integration with the Language Interpretability Tool (LIT) so that one can interpret and visualize models and their predictions. We also describe two case studies where we build ATS models quickly with minimal engineering efforts. The toolkit is available at \url{https://github.com/octanove/expats}.
DJXPerf: Identifying Memory Inefficiencies via Object-centric Profiling for Java
Venue: arXiv cs.PF 2021
Year: 2021

Java is the "go-to" programming language choice for developing scalable enterprise cloud applications. In such systems, even a few percent CPU time savings can offer a significant competitive advantage and cost saving. Although performance tools abound in Java, those that focus on the data locality in the memory hierarchy are rare. In this paper, we present DJXPerf, a lightweight, object-centric memory profiler for Java, which associates memory-hierarchy performance metrics (e.g., cache/TLB misses) with Java objects. DJXPerf uses statistical sampling of hardware performance monitoring counters to attribute metrics to not only source code locations but also Java objects. DJXPerf presents Java object allocation contexts combined with their usage contexts and presents them ordered by the poor locality behaviors. DJXPerf's performance measurement, object attribution, and presentation techniques guide optimizing object allocation, layout, and access patterns. DJXPerf incurs only ~8% runtime overhead and ~5% memory overhead on average, requiring no modifications to hardware, OS, Java virtual machine, or application source code, which makes it attractive to use in production. Guided by DJXPerf, we study and optimize a number of Java and Scala programs, including well-known benchmarks and real-world applications, and demonstrate significant speedups.
Contour Proposal Networks for Biomedical Instance Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available.
FatNet: A Feature-attentive Network for 3D Point Cloud Processing
Venue: arXiv cs.CV 2021
Year: 2021

The application of deep learning to 3D point clouds is challenging due to its lack of order. Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we propose three improvements to the task of point cloud analysis. First, we introduce a novel feature-attentive neural network layer, a FAT layer, that combines both global point-based features and local edge-based features in order to generate better embeddings. Second, we find that applying the same attention mechanism across two different forms of feature map aggregation, max pooling and average pooling, gives better performance than either alone. Third, we observe that residual feature reuse in this setting propagates information more effectively between the layers, and makes the network easier to train. Our architecture achieves state-of-the-art results on the task of point cloud classification, as demonstrated on the ModelNet40 dataset, and an extremely competitive performance on the ShapeNet part segmentation challenge.
Automated User Experience Testing through Multi-Dimensional Performance Impact Analysis
Venue: arXiv cs.SE 2021
Year: 2021

Although there are many automated software testing suites, they usually focus on unit, system, and interface testing. However, especially software updates such as new security features have the potential to diminish user experience. In this paper, we propose a novel automated user experience testing methodology that learns how code changes impact the time unit and system tests take, and extrapolate user experience changes based on this information. Such a tool can be integrated into existing continuous integration pipelines, and it provides software teams immediate user experience feedback. We construct a feature set from lexical, layout, and syntactic characteristics of the code, and using Abstract Syntax Tree-Based Embeddings, we can calculate the approximate semantic distance to feed into a machine learning algorithm. In our experiments, we use several regression methods to estimate the time impact of software updates. Our open-source tool achieved 3.7% mean absolute error rate with a random forest regressor.
Secure Software Engineering in the Financial Services: A Practitioners' Perspective
Venue: arXiv cs.SE 2021
Year: 2021

Secure software engineering is a fundamental activity in modern software development. However, while the field of security research has been advancing quite fast, in practice, there is still a vast knowledge gap between the security experts and the software development teams. After all, we cannot expect developers and other software practitioners to be security experts. Understanding how software development teams incorporate security in their processes and the challenges they face is a step towards reducing this gap. In this paper, we study how financial services companies ensure the security of their software systems. To that aim, we performed a qualitative study based on semi-structured interviews with 16 software practitioners from 11 different financial companies in three continents. Our results shed light on the security considerations that practitioners take during the different phases of their software development processes, the different security practices that software teams make use of to ensure the security of their software systems, the improvements that practitioners perceive as important in existing state-of-the-practice security tools, the different knowledge-sharing and learning practices that developers use to learn more about software security, and the challenges that software practitioners currently face when it comes to secure their systems.
Learning to Coordinate via Multiple Graph Neural Networks
Venue: arXiv cs.MA 2021
Year: 2021

The collaboration between agents has gradually become an important topic in multi-agent systems. The key is how to efficiently solve the credit assignment problems. This paper introduces MGAN for collaborative multi-agent reinforcement learning, a new algorithm that combines graph convolutional networks and value-decomposition methods. MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation of attention between all agents. We show the amazing ability of the graph network in representation learning by visualizing the output of the graph network, and therefore improve interpretability for the actions of each agent in the multi-agent system.