OpenRaiser/Intern-Atlas huggingface dataset:
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
Venue: arXiv cs.LG 2020
Year: 2020

Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are gaining popularity. One of the most important categories of data-driven approaches relies on a predefined or learned health indicator to characterize the equipment condition up to the present time and make inference on how it is likely to evolve in the future. In these approaches, health indicator forecasting that constructs the health indicator curve over the lifespan using partially observed measurements (i.e., health indicator values within an initial period) plays a key role. Existing health indicator forecasting algorithms, such as the functional Empirical Bayesian approach, the regression-based formulation, a naive scenario matching based on the nearest neighbor, have certain limitations. In this paper, we propose a new `generative + scenario matching' algorithm for health indicator forecasting. The key idea behind the proposed approach is to first non-parametrically fit the underlying health indicator curve with a continuous Gaussian Process using a sample of run-to-failure health indicator curves. The proposed approach then generates a rich set of random curves from the learned distribution, attempting to obtain all possible variations of the target health condition evolution process over the system's lifespan. The health indicator extrapolation for a piece of functioning equipment is inferred as the generated curve that has the highest matching level within the observed period. Our experimental results show the superiority of our algorithm over the other state-of-the-art methods.
Citing is earlier than Cited?
Venue: arXiv cs.DL 2020
Year: 2020

Generally, it is common that cited papers are earlier than citing papers. But we found three different cases, with more undiscovered. In this letter, we attempted to explain the reasons. However, negative time lag between citing and cited papers may mislead us when we study the characteristics of science.
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level
Venue: arXiv cs.CV 2020
Year: 2020

Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spatial attention networks are known to learn this relationship and focus its gaze on salient objects in the image. Thus, we propose to utilize spatial attention networks for image-level visual-textual fusion preserving local (word) and global (phrase) information to refine region proposals with an in-network Region Proposal Network (RPN) and detect single or multiple regions for a phrase query. We focus only on the phrase query - ground truth pair (referring expression) for a model independent of the constraints of the datasets i.e. additional attributes, context etc. For such referring expression dataset ReferIt game, our Multi-region Attention-assisted Grounding network (MAGNet) achieves over 12\% improvement over the state-of-the-art. Without the context from image captions and attribute information in Flickr30k Entities, we still achieve competitive results compared to the state-of-the-art.
A Vehicle Transmission Scheduling Scheme for Supporting Vehicle Trust Management
Venue: arXiv cs.NI 2020
Year: 2020

Recent developments in advanced sensors, wireless communications and intelligent vehicle control technologies have enabled vehicles to detect traffic anomalies on the road and then notify surrounding vehicles to improve traffic safety. However, due to the high-speed movement of vehicles and the frequent topological changes between vehicles, it is difficult for vehicles to evaluate the credibility of received messages. Quite a lot of research effort has been carried out to establish various trustworthiness platforms. These studies mostly focus on how to enhance the accuracy of credibility evaluation, overlooking that the transmission performance may affect the quality of vehicle messages. In this paper, we aim to support the improvement of credibility evaluation in vehicle networks by enhancing the transmission experience of vehicles. The proposed solution utilizes the vehicle's trajectory information, detection range and a roadside unit (RSU) coverage to form a controlled number of detection zones, which guarantees that events can be detected and reported while limiting the number of transmission vehicles. Furthermore, our scheme takes account of vehicle credibility and interference ranges when selecting reporting vehicles, supporting the timely and reliable delivery of vehicles' event reports when accidents occur. Our ns2 evaluation shows that our scheme can greatly reduce delay and loss rates of vehicle messages to help existing studies on accurate vehicle credibility evaluation.
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation
Venue: arXiv cs.CV 2020
Year: 2020

Generalization Performance of Deep Learning models trained using Empirical Risk Minimization can be improved significantly by using Data Augmentation strategies such as simple transformations, or using Mixed Samples. We attempt to empirically analyze the impact of such strategies on the transfer of generalization between teacher and student models in a distillation setup. We observe that if a teacher is trained using any of the mixed sample augmentation strategies, such as MixUp or CutMix, the student model distilled from it is impaired in its generalization capabilities. We hypothesize that such strategies limit a model's capability to learn example-specific features, leading to a loss in quality of the supervision signal during distillation. We present a novel Class-Discrimination metric to quantitatively measure this dichotomy in performance and link it to the discriminative capacity induced by the different strategies on a network's latent space.
A generalized expression for filling congruent circles in a circle
Venue: arXiv cs.CG 2020
Year: 2020

The paper reports a generalized expression for filling the congruent circles (of radius r) in a circle (of radius R). First, a generalized expression for the biggest circle (r) inscribed in the nth part of the bigger circle (R) was developed. Further, it was extended as n such circles (r) touching each other and the bigger circle (R). To fill the bigger circle (R), the exercise was further repeated by considering the bigger circle radius as R-2r, R-4r and so on. In the process, a generalized expression was deduced for the total no. of such circles (r) which could be inscribed in this way of filling the bigger circle (R). The approach does not claim the closest packing always though it could be helpful for practical purposes.
Compressive analysis and the Future of Privacy
Venue: arXiv cs.CY 2020
Year: 2020

Compressive analysis is the name given to the family of techniques that map raw data to their smaller representation. Largely, this includes data compression, data encoding, data encryption, and hashing. In this paper, we analyse the prospects of such technologies in realising customisable individual privacy. We enlist the dire needs to establish privacy preserving frameworks and policies and how can individuals achieve a trade-off between the comfort of an intuitive digital service ensemble and their privacy. We examine the current technologies being implemented, and suggest the crucial advantages of compressive analysis.
Hardware-Software Contracts for Secure Speculation
Venue: arXiv cs.CR 2020
Year: 2020

Since the discovery of Spectre, a large number of hardware mechanisms for secure speculation has been proposed. Intuitively, more defensive mechanisms are less efficient but can securely execute a larger class of programs, while more permissive mechanisms may offer more performance but require more defensive programming. Unfortunately, there are no hardware-software contracts that would turn this intuition into a basis for principled co-design. In this paper, we put forward a framework for specifying such contracts, and we demonstrate its expressiveness and flexibility. On the hardware side, we use the framework to provide the first formalization and comparison of the security guarantees provided by a representative class of mechanisms for secure speculation. On the software side, we use the framework to characterize program properties that guarantee secure co-design in two scenarios traditionally investigated in isolation: (1) ensuring that a benign program does not leak information while computing on confidential data, and (2) ensuring that a potentially malicious program cannot read outside of its designated sandbox. Finally, we show how the properties corresponding to both scenarios can be checked based on existing tools for software verification, and we use them to validate our findings on executable code.
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors
Venue: arXiv cs.AI 2020
Year: 2020

Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes and lead to unsatisfying prediction performance. We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors. The online behaviors come from the log of activities when students use the online learning management system. The offline behaviors derive from the check-in records of the library. Our main observations are two folds. Significantly different from good students, STAR barely have regular and clear study routines. We devised a multi-scale bag-of-regularity method to extract the regularity of learning behaviors that is robust to sparse data. Second, friends of STAR are more likely to be at risk. We constructed a co-occurrence network to approximate the underlying social network and encode the social homophily as features through network embedding. To validate the proposed algorithm, extensive experiments have been conducted among an Asian university with 15,503 undergraduate students. The results indicate EPARS outperforms baselines by 14.62% ~ 38.22% in predicting STAR.
Instance segmentation of buildings using keypoints
Venue: arXiv cs.CV 2020
Year: 2020

Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.
Guarded Deep Learning using Scenario-Based Modeling
Venue: arXiv cs.SE 2020
Year: 2020

Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways when deployed. One approach for allowing safer deployment of DNN models calls for augmenting them with hand-crafted override rules, which serve to override decisions made by the DNN model when certain criteria are met. Here, we propose to bring together DNNs and the well-studied scenario-based modeling paradigm, by expressing these override rules as simple and intuitive scenarios. This approach can lead to override rules that are comprehensible to humans, but are also sufficiently expressive and powerful to increase the overall safety of the model. We describe how to extend and apply scenario-based modeling to this new setting, and demonstrate our proposed technique on multiple DNN models.
Scalene: Scripting-Language Aware Profiling for Python
Venue: arXiv cs.PL 2020
Year: 2020

Existing profilers for scripting languages (a.k.a. "glue" languages) like Python suffer from numerous problems that drastically limit their usefulness. They impose order-of-magnitude overheads, report information at too coarse a granularity, or fail in the face of threads. Worse, past profilers---essentially variants of their counterparts for C---are oblivious to the fact that optimizing code in scripting languages requires information about code spanning the divide between the scripting language and libraries written in compiled languages. This paper introduces scripting-language aware profiling, and presents Scalene, an implementation of scripting-language aware profiling for Python. Scalene employs a combination of sampling, inference, and disassembly of byte-codes to efficiently and precisely attribute execution time and memory usage to either Python, which developers can optimize, or library code, which they cannot. It includes a novel sampling memory allocator that reports line-level memory consumption and trends with low overhead, helping developers reduce footprints and identify leaks. Finally, it introduces a new metric, copy volume, to help developers root out insidious copying costs across the Python/library boundary, which can drastically degrade performance. Scalene works for single or multi-threaded Python code, is precise, reporting detailed information at the line granularity, while imposing modest overheads (26%--53%).
A Robust Attentional Framework for License Plate Recognition in the Wild
Venue: arXiv cs.CV 2020
Year: 2020

Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, eg, shooting in frontal and horizontal view-angles and under good lighting conditions. However, their performance drops significantly in an unconstrained environment that features rotation, distortion, occlusion, blurring, shading or extreme dark or bright conditions. In this work, we propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. On one hand, the CycleGAN based plate generation engine alleviates the exhausting human annotation work. Massive amount of training data can be obtained with a more balanced character distribution and various shooting conditions, which helps to boost the recognition accuracy to a large extent. On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly. Without using any heuristics rule or post-processing, our method achieves the state-of-the-art performance on four public datasets, which demonstrates the generality and robustness of our framework. Moreover, we released a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China. The dataset can be available from: https://github.com/wangpengnorman/CLPD_dataset.
Self-Supervised Dynamic Networks for Covariate Shift Robustness
Venue: arXiv cs.CV 2020
Year: 2020

As supervised learning still dominates most AI applications, test-time performance is often unexpected. Specifically, a shift of the input covariates, caused by typical nuisances like background-noise, illumination variations or transcription errors, can lead to a significant decrease in prediction accuracy. Recently, it was shown that incorporating self-supervision can significantly improve covariate shift robustness. In this work, we propose Self-Supervised Dynamic Networks (SSDN): an input-dependent mechanism, inspired by dynamic networks, that allows a self-supervised network to predict the weights of the main network, and thus directly handle covariate shifts at test-time. We present the conceptual and empirical advantages of the proposed method on the problem of image classification under different covariate shifts, and show that it significantly outperforms comparable methods.
Steganography GAN: Cracking Steganography with Cycle Generative Adversarial Networks
Venue: arXiv cs.CR 2020
Year: 2020

For as long as humans have participated in the act of communication, concealing information in those communicative mediums has manifested into an art of its own. Crytographic messages, through written language or images, are a means of concealment, usually reserved for highly sensitive or compromising information. Specifically, the field of Cryptography is the construction and analysis of protocols that prevent third parties from understanding private messages. Steganography is related to Cryptography in that the goal is to obscure information using some method or algorithm, but the most important difference is that the information and the method of concealing information within Steganography both involve images--more precisely, the embedding of one image or piece of information into another image. Ever since the creation of covert communication methods, steps have been taken to crack cryptography and steganography algorithms. The desire for this rises from both human curiosity and the need to counteract adverse uses, such as encoding harmful media in inconspicuous media (phishing attack). In this paper, we succeed in cracking the Least Significant Bit (LSB) steganography algorithm using Cycle Generative Adversarial Networks (CycleGANs) and Bayesian Optimization and compare the use of CycleGANs against Convolutional Autoencoders. The results of our experiments highlight the promising nature of CycleGANs in cracking steganography and open several possible avenues of research.
A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings from the City of Austin, TX
Venue: arXiv cs.CY 2020
Year: 2020

E-scooter-sharing and e-bike-sharing systems are accommodating and easing the increased traffic in dense cities and are expanding considerably. However, these new micro-mobility transportation modes raise numerous operational and safety concerns. This study analyzes e-scooter and dockless e-bike sharing system user behavior. We investigate how average trip speed change depending on the day of the week and the time of the day. We used a dataset from the city of Austin, TX from December 2018 to May 2019. Our results generally show that the trip average speed for e-bikes ranges between 3.01 and 3.44 m/s, which is higher than that for e-scooters (2.19 to 2.78 m/s). Results also show a similar usage pattern for the average speed of e-bikes and e-scooters throughout the days of the week and a different usage pattern for the average speed of e-bikes and e-scooters over the hours of the day. We found that users tend to ride e-bikes and e-scooters with a slower average speed for recreational purposes compared to when they are ridden for commuting purposes. This study is a building block in this field, which serves as a first of its kind, and sheds the light of significant new understanding of this emerging class of shared-road users.
An Algorithm for Fuzzification of WordNets, Supported by a Mathematical Proof
Venue: arXiv cs.AI 2020
Year: 2020

WordNet-like Lexical Databases (WLDs) group English words into sets of synonyms called "synsets." Although the standard WLDs are being used in many successful Text-Mining applications, they have the limitation that word-senses are considered to represent the meaning associated to their corresponding synsets, to the same degree, which is not generally true. In order to overcome this limitation, several fuzzy versions of synsets have been proposed. A common trait of these studies is that, to the best of our knowledge, they do not aim to produce fuzzified versions of the existing WLD's, but build new WLDs from scratch, which has limited the attention received from the Text-Mining community, many of whose resources and applications are based on the existing WLDs. In this study, we present an algorithm for constructing fuzzy versions of WLDs of any language, given a corpus of documents and a word-sense disambiguation (WSD) system for that language. Then, using the Open-American-National-Corpus and UKB WSD as algorithm inputs, we construct and publish online the fuzzified version of English WordNet (FWN). We also propose a theoretical (mathematical) proof of the validity of its results.
Dual Policy Distillation
Venue: arXiv cs.LG 2020
Year: 2020

Policy distillation, which transfers a teacher policy to a student policy has achieved great success in challenging tasks of deep reinforcement learning. This teacher-student framework requires a well-trained teacher model which is computationally expensive. Moreover, the performance of the student model could be limited by the teacher model if the teacher model is not optimal. In the light of collaborative learning, we study the feasibility of involving joint intellectual efforts from diverse perspectives of student models. In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning. The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms, since it is unclear whether the knowledge distilled from an imperfect and noisy peer learner would be helpful. To address the challenge, we theoretically justify that distilling knowledge from a peer learner will lead to policy improvement and propose a disadvantageous distillation strategy based on the theoretical results. The conducted experiments on several continuous control tasks show that the proposed framework achieves superior performance with a learning-based agent and function approximation without the use of expensive teacher models.
Contextualisation of Data Flow Diagrams for security analysis
Venue: arXiv cs.CR 2020
Year: 2020

Data flow diagrams (DFDs) are popular for sketching systems for subsequent threat modelling. Their limited semantics make reasoning about them difficult, but enriching them endangers their simplicity and subsequent ease of take up. We present an approach for reasoning about tainted data flows in design-level DFDs by putting them in context with other complementary usability and requirements models. We illustrate our approach using a pilot study, where tainted data flows were identified without any augmentations to either the DFD or its complementary models.