OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Text in the Wild with Deep Character Embedding Network
Venue: arXiv cs.CV 2019
Year: 2019

Most text detection methods hypothesize texts are horizontal or multi-oriented and thus define quadrangles as the basic detection unit. However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches. In this paper, we propose a deep character embedding network (CENet) which simultaneously predicts the bounding boxes of characters and their embedding vectors, thus making text detection a simple clustering task in the character embedding space. The proposed method does not require strong assumptions of forming a straight line on general text detection, which provides flexibility on arbitrarily curved or perspectively distorted text. For character detection task, a dense prediction subnetwork is designed to obtain the confidence score and bounding boxes of characters. For character embedding task, a subnet is trained with contrastive loss to project detected characters into embedding space. The two tasks share a backbone CNN from which the multi-scale feature maps are extracted. The final text regions can be easily achieved by a thresholding process on character confidence and embedding distance of character pairs. We evaluated our method on ICDAR13, ICDAR15, MSRA-TD500, and Total-Text. The proposed method achieves state-of-the-art or comparable performance on all these datasets, and shows substantial improvement in the irregular-text datasets, i.e. Total-Text.
Learning Generalizable Physical Dynamics of 3D Rigid Objects
Venue: arXiv cs.CV 2019
Year: 2019

Humans have a remarkable ability to predict the effect of physical interactions on the dynamics of objects. Endowing machines with this ability would allow important applications in areas like robotics and autonomous vehicles. In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force. Different from previous work, our approach is capable of generalizing to unseen object shapes - an important requirement for real-world applications. To achieve this, we represent object shape as a 3D point cloud that is used as input to a neural network, making our approach agnostic to appearance variation. The design of our network is informed by an understanding of physical laws. We train our model with data from a physics engine that simulates the dynamics of a large number of shapes. Experiments show that we can accurately predict the resting position and total rotation for unseen object geometries.
Linear colour segmentation revisited
Venue: arXiv cs.CV 2019
Year: 2019

In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard's DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.
Multi-Label Adversarial Perturbations
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating adversarial perturbations in multi-class classification problems, many real-world applications fall into the multi-label setting in which one instance could be associated with more than one label. For example, a spammer may generate adversarial spams with malicious advertising while maintaining the other labels such as topic labels unchanged. To analyze the vulnerability and robustness of multi-label learning models, we investigate the generation of multi-label adversarial perturbations. This is a challenging task due to the uncertain number of positive labels associated with one instance, as well as the fact that multiple labels are usually not mutually exclusive with each other. To bridge this gap, in this paper, we propose a general attacking framework targeting on multi-label classification problem and conduct a premier analysis on the perturbations for deep neural networks. Leveraging the ranking relationships among labels, we further design a ranking-based framework to attack multi-label ranking algorithms. We specify the connection between the two proposed frameworks and separately design two specific methods grounded on each of them to generate targeted multi-label perturbations. Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies. Several interesting findings including an unpolished defensive strategy, which could potentially enhance the interpretability and robustness of multi-label deep learning models, are further presented and discussed at the end.
Instance-Based Classification through Hypothesis Testing
Venue: arXiv cs.LG 2019
Year: 2019

Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address the issue of statistical significance. In this paper, we formulate the binary classification problem as a two-sample testing problem. More precisely, our classification model is a generic framework that is composed of two steps. In the first step, the distance between the test instance and each training instance is calculated to derive two distance sets. In the second step, the two-sample test is performed under the null hypothesis that the two sets of distances are drawn from the same cumulative distribution. After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value. Essentially, the presented classification method can be regarded as an instance-based classifier based on hypothesis testing. The experimental results on 40 real data sets show that our method is able to achieve the same level performance as the state-of-the-art classifiers and has significantly better performance than existing testing-based classifiers. Furthermore, we can handle outlying instances and control the false discovery rate of test instances assigned to each class under the same framework.
3DCAM: A Low Overhead Crosstalk Avoidance Mechanism for TSV-Based 3D ICs
Venue: arXiv cs.ET 2019
Year: 2019

Three Dimensional Integrated Circuits (3D IC) offer lower power consumption, higher performance, higher bandwidth, and scalability over the conventional two dimensional ICs. Through-Silicon Via (TSV) is one of the fabrication mechanisms that connects stacked dies to each other. The large size of TSVs and the proximity between them lead to undesirable coupling capacitance. This interference causes mutual influences between adjacent TSVs and produces crosstalk noise. Furthermore, this effect threats the reliability of data during traversal between layers. This paper proposes a mechanism that efficiently reduces crosstalk noise between TSVs with lower area overhead as compared to previous works. This mechanism revolves around the fact that retaining TSV value in current state can reduce coupling in some cases. To evaluate the mechanism, gem5 simulator is used for data extraction and several benchmarks are taken from the SPEC2006 suite. The simulation results show that the proposed mechanism reduces crosstalk noise with only 30% imposed TSV overhead while delay decreased up to 25.7% as compared to a recent related work.
An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
Venue: arXiv cs.NE 2019
Year: 2019

Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal experiment, which selects individuals in manner of a new discontinuing non-dominated sorting and crowding distance, to produce the initial population. And a new pruning strategy based on clustering is proposed to self-adaptively prunes individuals with similar features and poor performance in non-dominated sorting and crowding distance, or to individuals are far away from the Pareto Front according to the degree of intra-class aggregation of clustering results. The new pruning strategy makes population to converge to the Pareto Front more easily and maintain the distribution of population. OTNSGA-II and NSGA-II are compared on various types of test functions to verify the improvement of OTNSGA-II in terms of distribution and convergency.
The Price of Free Illegal Live Streaming Services
Venue: arXiv cs.CR 2019
Year: 2019

As Internet streaming of live content has gained on traditional cable TV viewership, we have also seen significant growth of free live streaming services which illegally provide free access to copyrighted content over the Internet. Some of these services draw millions of viewers each month. Moreover, this viewership has continued to increase, despite the consistent coupling of this free content with deceptive advertisements and user-hostile tracking. In this paper, we explore the ecosystem of free illegal live streaming services by collecting and examining the behavior of a large corpus of illegal sports streaming websites. We explore and quantify evidence of user tracking via third-party HTTP requests, cookies, and fingerprinting techniques on more than $27,303$ unique video streams provided by $467$ unique illegal live streaming domains. We compare the behavior of illegal live streaming services with legitimate services and find that the illegal services go to much greater lengths to track users than most legitimate services, and use more obscure tracking services. Similarly, we find that moderated sites that aggregate links to illegal live streaming content fail to moderate out sites that go to significant lengths to track users. In addition, we perform several case studies which highlight deceptive behavior and modern techniques used by some domains to avoid detection, monetize traffic, or otherwise exploit their viewers. Overall, we find that despite recent improvements in mechanisms for detecting malicious browser extensions, ad-blocking, and browser warnings, users of free illegal live streaming services are still exposed to deceptive ads, malicious browser extensions, scams, and extensive tracking. We conclude with insights into the ecosystem and recommendations for addressing the challenges highlighted by this study.
Causality for General LTL-definable Properties
Venue: arXiv cs.FL 2019
Year: 2019

In this paper we provide a notion of causality for the violation of general Linear Temporal Logic (LTL) properties. The current work is a natural extension of the previously proposed approach handling causality in the context of LTL-definable safety properties. The major difference is that now, counterexamples of general LTL properties are not merely finite traces, but infinite lasso-shaped traces. We analyze such infinite counterexamples and identify the relevant ordered occurrences of causal events, obtained by unfolding the looping part of the lasso shaped counterexample sufficiently many times. The focus is on LTL properties from practical considerations: the current results are to be implemented in QuantUM, a tool for causality checking, that exploits explicit state LTL model checking.
Efficient Race Detection with Futures
Venue: arXiv cs.DS 2019
Year: 2019

This paper addresses the problem of provably efficient and practically good on-the-fly determinacy race detection in task parallel programs that use futures. Prior works determinacy race detection have mostly focused on either task parallel programs that follow a series-parallel dependence structure or ones with unrestricted use of futures that generate arbitrary dependences. In this work, we consider a restricted use of futures and show that it can be race detected more efficiently than general use of futures. Specifically, we present two algorithms: MultiBags and MultiBags+. MultiBags targets programs that use futures in a restricted fashion and runs in time $O(T_1 α(m,n))$, where $T_1$ is the sequential running time of the program, $α$ is the inverse Ackermann's function, $m$ is the total number of memory accesses, $n$ is the dynamic count of places at which parallelism is created. Since $α$ is a very slowly growing function (upper bounded by $4$ for all practical purposes), it can be treated as a close-to-constant overhead. MultiBags+ an extension of MultiBags that target programs with general use of futures. It runs in time $O((T_1+k^2)α(m,n))$ where $T_1$, $α$, $m$ and $n$ are defined as before, and $k$ is the number of future operations in the computation. We implemented both algorithms and empirically demonstrate their efficiency.
XOS: An Application-Defined Operating System for Data Center Servers
Venue: arXiv cs.OS 2019
Year: 2019

Rapid growth of datacenter (DC) scale, urgency of cost control, increasing workload diversity, and huge software investment protection place unprecedented demands on the operating system (OS) efficiency, scalability, performance isolation, and backward-compatibility. The traditional OSes are not built to work with deep-hierarchy software stacks, large numbers of cores, tail latency guarantee, and increasingly rich variety of applications seen in modern DCs, and thus they struggle to meet the demands of such workloads. This paper presents XOS, an application-defined OS for modern DC servers. Our design moves resource management out of the OS kernel, supports customizable kernel subsystems in user space, and enables elastic partitioning of hardware resources. Specifically, XOS leverages modern hardware support for virtualization to move resource management functionality out of the conventional kernel and into user space, which lets applications achieve near bare-metal performance. We implement XOS on top of Linux to provide backward compatibility. XOS speeds up a set of DC workloads by up to 1.6X over our baseline Linux on a 24-core server, and outperforms the state-of-the-art Dune by up to 3.3X in terms of virtual memory management. In addition, XOS demonstrates good scalability and strong performance isolation.
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games
Venue: arXiv cs.LG 2019
Year: 2019

We propose local symplectic surgery, a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms cannot guarantee convergence to local Nash equilibria due to the existence of non-Nash stationary points. By taking advantage of the differential structure of the game, we construct an algorithm for which the local Nash equilibria are the only attracting fixed points. We also show that the algorithm exhibits no oscillatory behaviors in neighborhoods of equilibria and show that it has the same per-iteration complexity as other recently proposed algorithms. We conclude by validating the algorithm on two numerical examples: a toy example with multiple Nash equilibria and a non-Nash equilibrium, and the training of a small generative adversarial network (GAN).
HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting
Venue: arXiv cs.LG 2019
Year: 2019

Breakthroughs in the fields of deep learning and mobile system-on-chips are radically changing the way we use our smartphones. However, deep neural networks inference is still a challenging task for edge AI devices due to the computational overhead on mobile CPUs and a severe drain on the batteries. In this paper, we present a deep neural network inference engine named HG-Caffe, which supports GPUs with half precision. HG-Caffe provides up to 20 times speedup with GPUs compared to the original implementations. In addition to the speedup, the peak memory usage is also reduced to about 80%. With HG-Caffe, more innovative and fascinating mobile applications will be turned into reality.
A Network-based Multimodal Data Fusion Approach for Characterizing Dynamic Multimodal Physiological Patterns
Venue: arXiv cs.LG 2019
Year: 2019

Characterizing the dynamic interactive patterns of complex systems helps gain in-depth understanding of how components interrelate with each other while performing certain functions as a whole. In this study, we present a novel multimodal data fusion approach to construct a complex network, which models the interactions of biological subsystems in the human body under emotional states through physiological responses. Joint recurrence plot and temporal network metrics are employed to integrate the multimodal information at the signal level. A benchmark public dataset of is used for evaluating our model.
Guigle: A GUI Search Engine for Android Apps
Venue: arXiv cs.SE 2019
Year: 2019

The process of developing a mobile application typically starts with the ideation and conceptualization of its user interface. This concept is then translated into a set of mock-ups to help determine how well the user interface embodies the intended features of the app. After the creation of mock-ups developers then translate it into an app that runs in a mobile device. In this paper we propose an approach, called GUIGLE, that aims to facilitate the process of conceptualizing the user interface of an app through GUI search. GUIGLE indexes GUI images and metadata extracted using automated dynamic analysis on a large corpus of apps extracted from Google Play. To perform a search, our approach uses information from text displayed on a screen, user interface components, the app name, and screen color palettes to retrieve relevant screens given a query. Furthermore, we provide a lightweight query language that allows for intuitive search of screens. We evaluate GUIGLE with real users and found that, on average, 68.8% of returned screens were relevant to the specified query. Additionally, users found the various different features of GUIGLE useful, indicating that our search engine provides an intuitive user experience. Finally, users agree that the information presented by GUIGLE is useful in conceptualizing the design of new screens for applications.
SFQmap: A Technology Mapping Tool for Single Flux Quantum Logic Circuits
Venue: arXiv cs.ET 2019
Year: 2019

Single flux quantum (SFQ) logic is a promising candidate to replace the CMOS logic for high speed and low power applications due to its superiority in providing high performance and energy efficient circuits. However, developing effective Electronic Design Automation (EDA) tools, which cater to special characteristics and requirements of SFQ circuits such as depth minimization and path balancing, are essential to automate the whole process of designing large SFQ circuits. In this paper, a novel technology mapping tool, called SFQmap, is presented, which provides optimization methods for minimizing first the circuit depth and path balancing overhead and then the worst-case stage delay of mapped SFQ circuits. Compared with the state-of-the-art technology mappers, SFQmap reduces the depth and path balancing overhead by an average of 14% and 31%, respectively.
A Model for Learned Bloom Filters, and Optimizing by Sandwiching
Venue: arXiv cs.LG 2019
Year: 2019

Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach.
Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding
Venue: arXiv cs.AI 2019
Year: 2019

The design of reward functions in reinforcement learning is a human skill that comes with experience. Unfortunately, there is not any methodology in the literature that could guide a human to design the reward function or to allow a human to transfer the skills developed in designing reward functions to another human and in a systematic manner. In this paper, we use Systematic Instructional Design, an approach in human education, to engineer a machine education methodology to design reward functions for reinforcement learning. We demonstrate the methodology in designing a hierarchical genetic reinforcement learner that adopts a neural network representation to evolve a swarm controller for an agent shepherding a boids-based swarm. The results reveal that the methodology is able to guide the design of hierarchical reinforcement learners, with each model in the hierarchy learning incrementally through a multi-part reward function. The hierarchy acts as a decision fusion function that combines the individual behaviours and skills learnt by each instruction to create a smart shepherd to control the swarm.
Online Social Media Recommendation over Streams
Venue: arXiv cs.IR 2019
Year: 2019

As one of the most popular services over online communities, the social recommendation has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is social item recommendation over high speed social media streams. Existing streaming recommendation techniques are not effective for handling social users with diverse interests. Meanwhile, approaches for recommending items to a particular user are not efficient when applied to a huge number of users over high speed streams. In this paper, we propose a novel framework for the social recommendation over streaming environments. Specifically, we first propose a novel Bi-Layer Hidden Markov Model (BiHMM) that adaptively captures the behaviors of social users and their interactions with influential official accounts to predict their long-term and short-term interests. Then, we design a new probabilistic entity matching scheme for effectively identifying the relevance score of a streaming item to a user. Following that, we propose a novel indexing scheme called {\Tree} for improving the efficiency of our solution. Extensive experiments are conducted to prove the high performance of our approach in terms of the recommendation quality and time cost.