OpenRaiser/Intern-Atlas huggingface dataset:
TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection
Venue: arXiv cs.CV 2020
Year: 2020

Arbitrary-shaped text detection is a challenging task due to the complex geometric layouts of texts such as large aspect ratios, various scales, random rotations and curve shapes. Most state-of-the-art methods solve this problem from bottom-up perspectives, seeking to model a text instance of complex geometric layouts with simple local units (e.g., local boxes or pixels) and generate detections with heuristic post-processings. In this work, we propose an arbitrary-shaped text detection method, namely TextRay, which conducts top-down contour-based geometric modeling and geometric parameter learning within a single-shot anchor-free framework. The geometric modeling is carried out under polar system with a bidirectional mapping scheme between shape space and parameter space, encoding complex geometric layouts into unified representations. For effective learning of the representations, we design a central-weighted training strategy and a content loss which builds propagation paths between geometric encodings and visual content. TextRay outputs simple polygon detections at one pass with only one NMS post-processing. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed approach. The code is available at https://github.com/LianaWang/TextRay.
Study on State-of-the-art Cloud Services Integration Capabilities with Autonomous Ground Vehicles
Venue: arXiv cs.CY 2020
Year: 2020

Computing and intelligence are substantial requirements for the accurate performance of autonomous ground vehicles (AGVs). In this context, the use of cloud services in addition to onboard computers enhances computing and intelligence capabilities of AGVs. In addition, the vast amount of data processed in a cloud system contributes to overall performance and capabilities of the onboard system. This research study entails a qualitative analysis to gather insights on the applicability of the leading cloud service providers in AGV operations. These services include Google Cloud, Microsoft Azure, Amazon AWS, and IBM Cloud. The study begins with a brief review of AGV technical requirements that are necessary to determine the rationale for identifying the most suitable cloud service. The qualitative analysis studies and addresses the applicability of the cloud service over the proposed generalized AGV's architecture integration, performance, and manageability. Our findings conclude that a generalized AGV architecture can be supported by state-of-the-art cloud service, but there should be a clear line of separation between the primary and secondary computing needs. Moreover, our results show significant lags while using cloud services and preventing their use in real-time AGV operation.
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
Venue: arXiv cs.CL 2020
Year: 2020

Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.
Scheduling activities with time-dependent durations and resource consumptions
Venue: arXiv cs.DS 2020
Year: 2020

In this paper we study time-dependent scheduling problems where activities consume a resource with limited availability. Activity durations as well as resource consumptions are assumed to be time-dependent and the resource can be replenished between activities. Because of the interaction of time-dependent activity durations and resource consumptions, scheduling policies based on starting all activities as early as possible may fail due to unnecessarily high resource consumptions. We propose a dynamic discretization discovery algorithm that generates a partially time-expanded network during the search. We propose preloading techniques allowing to significantly reduce the computational effort if the approach is embedded in an iterative solution procedure that frequently evaluates activity sequences that start with the same activities. We evaluate our approaches on a case of routing a fleet of electric vehicles in which vehicles can recharge batteries during the route.
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
Venue: arXiv cs.CV 2020
Year: 2020

Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the development of those data-driven approaches. To facilitate the research of this area, we present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks and also an effective learning framework for its hierarchical segmentation task. The dataset was generated via the photogrammetric processing on unmanned aerial vehicle (UAV) images of the National University of Singapore (NUS) campus, and has been point-wisely annotated with both hierarchical and instance-based labels. Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies. To solve this problem, a two-stage method including multi-task (MT) learning and hierarchical ensemble (HE) with consistency consideration is proposed. Experimental results demonstrate the superiority of the proposed method and potential advantages of our hierarchical annotations. In addition, we benchmark results of semantic and instance segmentation, which is accessible online at https://3d.dataset.site with the dataset and all source codes.
Event Extraction Based on Deep Learning in Food Hazard Arabic Texts
Venue: arXiv cs.SI 2020
Year: 2020

Social Media websites have disseminated digital devices to the public, making information sharing easier and faster. Exchanging textual data is the most popular communication among social media users. It has become a necessity for treatment. Event extraction on the other hand indicates an understanding of events across social media posts streams. Event extraction helps to take faster corrective action in natural disasters, and may save lives. The main objective of the task is to develop specific model to detect and extract the events (incidents) identified in the digital text. We proposed here a model based on deep recurrent networks to extract the events from social media feeds.
Changes, States, and Events: The Thread from Staticity to Dynamism in the Conceptual Modeling of Systems
Venue: arXiv cs.SE 2020
Year: 2020

This paper examines the concept of change in conceptual modeling. Change is inherent in the nature of things and has increasingly become a focus of much interest and investigation. Change can be modeled as a transition between two states of a finite state machine (FSM). This change represents an exploratory starting point in this paper. Accordingly, a sample FSM that models a car s transmission system is re-expressed in terms of a new modeling methodology called thinging machine (TM) modeling. Recasting the car-transmission model involves developing (1) an S model that captures the static aspects, (2) a D model that identifies states, and (3) a B model that specifies the behavior. The analysis progresses as follows. - S represents an atemporal diagrammatic description that embeds underlying compositions (static changes) from which the roots of system behavior can be traced. - S is broken down into multiple subsystems that correspond to static states (ordered constitutive components). - Introducing time into static states converts these states into events, and the behavior (B) model is constructed based on the chronology of these events. The analysis shows that FSM states are static (atemporal) changes that introduce temporal events as carriers of behavior. This result enhances the semantics of the concepts of change, states, and events in modeling and shows how to specify a system s behavior through its static description.
Trust Infrastructures for Virtual Asset Service Providers
Venue: arXiv cs.CR 2020
Year: 2020

Virtual asset service providers (VASPs) currently face a number of challenges, both from the technological and the regulatory perspectives. In the context of virtual asset transfers one key issue is the need for VASPs to securely exchange customer information to comply to the Travel Rule. We discuss a VASP information sharing network as one form of a trust infrastructure for VASP-to-VASP interactions. Related to this is the need for a trusted identity infrastructure for VASPs that would permit other entities to quickly ascertain the legal business status of a VASP. For regulated wallets, an attestation infrastructure may provide VASPs and insurance providers with better visibility into the state of wallets based on trusted hardware. Finally, for customers of VASPs there is a need for seamless integration between the VASP services with the existing consumer identity management infrastructure, providing a user-friendly experience for transferring virtual assets to other users.
Online Graph Completion: Multivariate Signal Recovery in Computer Vision
Venue: arXiv cs.CV 2020
Year: 2020

The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.
Experimental Analysis of Legendre Decomposition in Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

In this technical report, we analyze Legendre decomposition for non-negative tensor in theory and application. In theory, the properties of dual parameters and dually flat manifold in Legendre decomposition are reviewed, and the process of tensor projection and parameter updating is analyzed. In application, a series of verification experiments and clustering experiments with parameters on submanifold were carried out, hoping to find an effective lower dimensional representation of the input tensor. The experimental results show that the parameters on submanifold have no ability to be directly used as low-rank representations. Combined with analysis, we connect Legendre decomposition with neural networks and low-rank representation applications, and put forward some promising prospects.
Molecular dynamics simulation of crack growth in mono-crystal nickel with voids and inclusions
Venue: arXiv cs.CE 2020
Year: 2020

In this study, the crack propagation of the pre-cracked mono-crystal nickel with the voids and inclusions has been investigated by molecular dynamics simulations. Different sizes of voids, inclusions and materials of inclusions are used to fully study the effect of the voids and inclusions during the crack propagation process. The dislocations evolution, stress distribution and crack length are analyzed as the associated mechanical properties. The results indicate that the voids and inclusions can change the path of crack propagation of the pre-cracked mono-crystal nickel. Moreover, the results show that the voids and inclusions can lead a better resistance to plastic deformation of the mono-crystal and the inclusions can make the system more difficult to fracture.
Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection. Existing image analysis approaches tend to place bounding boxes around the defected region which is not adequate both for structural analysis and pre-fabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep learning models (FCN, U-Net and Mask R-CNN) for corrosion detection, which perform better in terms of accuracy and time and require a smaller number of annotated samples compared to other deep models, e.g. CNN. However, the final images derived are still not sufficiently accurate for structural analysis and pre-fabrication. Thus, we adopt a novel data projection scheme that fuses the results of color segmentation, yielding accurate but over-segmented contours of a region, with a processed area of the deep masks, resulting in high-confidence corroded pixels.
Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach
Venue: arXiv cs.HC 2020
Year: 2020

Millions of people have enrolled and enrol (especially in the Covid-19 pandemic world) in MOOCs. However, the retention rate of learners is notoriously low. The majority of the research work on this issue focuses on predicting the dropout rate, but very few use explainable learning patterns as part of this analysis. However, visual representation of learning patterns could provide deeper insights into learners' behaviour across different courses, whilst numerical analyses can -- and arguably, should -- be used to confirm the latter. Thus, this paper proposes and compares different granularity visualisations for learning patterns (based on clickstream data) for both course completers and non-completers. In the large-scale MOOCs we analysed, across various domains, our fine-grained, fish-eye visualisation approach showed that non-completers are more likely to jump forward in their learning sessions, often on a 'catch-up' path, whilst completers exhibit linear behaviour. For coarser, bird-eye granularity visualisation, we observed learners' transition between types of learning activity, obtaining typed transition graphs. The results, backed up by statistical significance analysis and machine learning, provide insights for course instructors to maintain engagement of learners by adapting the course design to not just 'dry' predicted values, but explainable, visually viable paths extracted.
Defending Adversarial Examples via DNN Bottleneck Reinforcement
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks. Typical DNN classifiers encode the input image into a compressed latent representation more suitable for inference. This information bottleneck makes a trade-off between the image-specific structure and class-specific information in an image. By reinforcing the former while maintaining the latter, any redundant information, be it adversarial or not, should be removed from the latent representation. Hence, this paper proposes to jointly train an auto-encoder (AE) sharing the same encoding weights with the visual classifier. In order to reinforce the information bottleneck, we introduce the multi-scale low-pass objective and multi-scale high-frequency communication for better frequency steering in the network. Unlike existing approaches, our scheme is the first reforming defense per se which keeps the classifier structure untouched without appending any pre-processing head and is trained with clean images only. Extensive experiments on MNIST, CIFAR-10 and ImageNet demonstrate the strong defense of our method against various adversarial attacks.
Learning to Detect Anomalous Wireless Links in IoT Networks
Venue: arXiv cs.NI 2020
Year: 2020

After decades of research, the Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health. As a massive number of IoT devices are deployed, they naturally incur great operational costs to ensure intended operations. To effectively handle such intended operations in massive IoT networks, automatic detection of malfunctioning, namely anomaly detection, becomes a critical but challenging task. In this paper, motivated by a real-world experimental IoT deployment, we introduce four types of wireless network anomalies that are identified at the link layer. We study the performance of threshold- and machine learning (ML)-based classifiers to automatically detect these anomalies. We examine the relative performance of three supervised and three unsupervised ML techniques on both non-encoded and encoded (autoencoder) feature representations. Our results demonstrate that; i) selected supervised approaches are able to detect anomalies with F1 scores of above 0.98, while unsupervised ones are also capable of detecting the said anomalies with F1 scores of, on average, 0.90, and ii) OC-SVM outperforms all the other unsupervised ML approaches reaching at F1 scores of 0.99 for SuddenD, 0.95 for SuddenR, 0.93 for InstaD and 0.95 for SlowD.
Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic
Venue: arXiv cs.CY 2020
Year: 2020

COVID-19 has given rise to malicious content online, including online abuse and hate toward British MPs. In order to understand and contextualise the level of abuse MPs receive, we consider how ministers use social media to communicate about the crisis, and the citizen engagement that this generates. The focus of the paper is on a large-scale, mixed methods study of abusive and antagonistic responses to UK politicians during the pandemic from early February to late May 2020. We find that pressing subjects such as financial concerns attract high levels of engagement, but not necessarily abusive dialogue. Rather, criticising authorities appears to attract higher levels of abuse. In particular, those who carry the flame for subjects like racism and inequality, may be accused of virtue signalling or receive higher abuse levels due to the topics they are required by their role to address. This work contributes to the wider understanding of abusive language online, in particular that which is directed at public officials.
Text Classification based on Multi-granularity Attention Hybrid Neural Network
Venue: arXiv cs.CL 2020
Year: 2020

Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet). RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation. ConvNets do not have memory capability and it has to model sequential data as un-ordered features. Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation. As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks. However, few investigation explores the reconciliation of these seemingly incompatible architectures. To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN). The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models. In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention. The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics. We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts.
Scytale -- An Evolutionary Cryptosystem
Venue: arXiv cs.CR 2020
Year: 2020

With the advent of quantum computing, and other advancements in computation and processing capabilities of modern systems, there arises a need to develop new trapdoor functions that will serve as the foundation for a new generation of encryption schemes. This paper explores the possibility of one such potential trapdoor function using concepts stemming from Reversible Cellular Automata (RCA) -- specifically, the Critter's Rule set up in a Margolus Neighborhood. The proposed block encryption algorithm discusses how sensitive data can be manipulated and converted efficiently into a two dimensional sequence of bits, that can be iteratively evolved using the rules of the RCA and a private key to achieve a desirable level of encryption within a reasonable runtime. The performance benchmark and analysis results exemplify how well the proposed encryption algorithm stands against different forms of attacks.
Feature importance in mobile malware detection
Venue: arXiv cs.CR 2020
Year: 2020

The topic of mobile malware detection on the Android platform has attracted significant attention over the last several years. However, while much research has been conducted toward mobile malware detection techniques, little attention has been devoted to feature selection and feature importance. That is, which app feature matters more when it comes to machine learning classification. After succinctly surveying all major, dated from 2012 to 2020, datasets used by state-of-the-art malware detection works in the literature, we analyse a critical mass of apps from the most contemporary and prevailing datasets, namely Drebin, VirusShare, and AndroZoo. Next, we rank the importance of app classification features pertaining to permissions and intents using the Information Gain algorithm for all the three above-mentioned datasets.