OpenRaiser/Intern-Atlas huggingface dataset:
Saliency-Enhanced Robust Visual Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Discrete correlation filter (DCF) based trackers have shown considerable success in visual object tracking. These trackers often make use of low to mid level features such as histogram of gradients (HoG) and mid-layer activations from convolution neural networks (CNNs). We argue that including semantically higher level information to the tracked features may provide further robustness to challenging cases such as viewpoint changes. Deep salient object detection is one example of such high level features, as it make use of semantic information to highlight the important regions in the given scene. In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses. This combination is performed with an adaptive weight on the saliency based filter responses, which is automatically selected according to the temporal consistency of visual saliency. We show that our method consistently improves a baseline DCF based tracker especially in challenging cases and performs superior to the state-of-the-art. Our improved tracker operates at 9.3 fps, introducing a small computational burden over the baseline which operates at 11 fps.
Online Learning: A Comprehensive Survey
Venue: arXiv cs.LG 2018
Year: 2018

Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time. The goal of online learning is to ensure that the online learner would make a sequence of accurate predictions (or correct decisions) given the knowledge of correct answers to previous prediction or learning tasks and possibly additional information. This is in contrast to many traditional batch learning or offline machine learning algorithms that are often designed to train a model in batch from a given collection of training data instances. This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the learning type and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) supervised online learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.
Adaptive online scheduling of tasks with anytime property on heterogeneous resources
Venue: arXiv cs.DC 2018
Year: 2018

An acceptable response time of a server is an important aspect in many client-server applications; this is evident in situations in which the server is overloaded by many computationally intensive requests. In this work, we consider that the requests, or in this case tasks, generated by the clients are instances of optimization problems solved by anytime algorithms, i.e. the quality of the solution increases with the processing time of a task. These tasks are submitted to the server which schedules them to the available computational resources where the tasks are processed. To tackle the overload problem, we propose a scheduling algorithm which combines traditional scheduling approaches with a quality control heuristic which adjusts the requested quality of the solutions and thus changes the processing time of the tasks. Two efficient quality control heuristics are introduced: the first heuristic sets a global quality for all tasks, whereas the second heuristic sets the quality for each task independently. Moreover, in practice, the relationship between the processing time and the quality is not known a priori. Because it is crucial for scheduling algorithms to know at least the estimation of these relationships, we propose a general procedure for estimating these relationships using information obtained from the already executed tasks. Finally, the performance of the proposed scheduling algorithm is demonstrated on a real-world problem from the domain of personnel rostering with very good results.
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to other state-of-the-art in lifelong learning without forgetting.
Practical Issues of Action-conditioned Next Image Prediction
Venue: arXiv cs.CV 2018
Year: 2018

The problem of action-conditioned image prediction is to predict the expected next frame given the current camera frame the robot observes and an action selected by the robot. We provide the first comparison of two recent popular models, especially for image prediction on cars. Our major finding is that action tiling encoding is the most important factor leading to the remarkable performance of the CDNA model. We present a light-weight model by action tiling encoding which has a single-decoder feedforward architecture same as [action_video_prediction_honglak]. On a real driving dataset, the CDNA model achieves ${0.3986} \times 10^{-3}$ MSE and ${0.9846}$ Structure SIMilarity (SSIM) with a network size of about {\bfseries ${12.6}$ million} parameters. With a small network of fewer than {\bfseries ${1}$ million} parameters, our new model achieves a comparable performance to CDNA at ${0.3613} \times 10^{-3}$ MSE and ${0.9633}$ SSIM. Our model requires less memory, is more computationally efficient and is advantageous to be used inside self-driving vehicles.
Algorithmic Bidding for Virtual Trading in Electricity Markets
Venue: arXiv cs.GT 2018
Year: 2018

We consider the problem of optimal bidding for virtual trading in two-settlement electricity markets. A virtual trader aims to arbitrage on the differences between day-ahead and real-time market prices; both prices, however, are random and unknown to market participants. An online learning algorithm is proposed to maximize the cumulative payoff over a finite number of trading sessions by allocating the trader's budget among his bids for K options in each session. It is shown that the proposed algorithm converges, with an almost optimal convergence rate, to the global optimal corresponding to the case when the underlying price distribution is known. The proposed algorithm is also generalized for trading strategies with a risk measure. By using both cumulative payoff and Sharpe ratio as performance metrics, evaluations were performed based on historical data spanning ten year period of NYISO and PJM markets. It was shown that the proposed strategy outperforms standard benchmarks and the S&P 500 index over the same period.
D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication
Venue: arXiv cs.DC 2018
Year: 2018

Work package 2 (WP2) aims to develop libraries for energy-efficient inter-process communication and data sharing on the EXCESS platforms. The Deliverable D2.4 reports on the final prototype of programming abstractions for energy-efficient inter- process communication. Section 1 is the updated overview of the prototype of programming abstraction and devised power/energy models. The Section 2-6 contain the latest results of the four studies: i) GreenBST, a energy-efficient and concurrent search tree (cf. Section 2) ii) Customization methodology for implementation of streaming aggregation in embedded systems (cf. Section 3) iii) Energy Model on CPU for Lock-free Data-structures in Dynamic Environments (cf. Section 4.10) iv) A General and Validated Energy Complexity Model for Multithreaded Algorithms (cf. Section 5)
Abstraction, Composition and Contracts: A Sheaf Theoretic Approach
Venue: arXiv cs.LO 2018
Year: 2018

Complex systems of systems (SoS) are characterized by multiple interconnected subsystems. Typically, each subsystem is designed and analyzed using methodologies and formalisms that are specific to the particular subsystem model of computation considered --- Petri nets, continuous time ODEs, nondeterministic automata, to name a few. When interconnecting subsystems, a designer needs to choose, based on the specific subsystems models, a common abstraction framework to analyze the composition. In this paper we introduce a new framework for abstraction, composition and analysis of SoS that builds on results and methods developed in sheaf theory, category theory and topos theory. In particular, we will be modeling behaviors of systems using sheaves, leverage category theoretic methods to define wiring diagrams and formalize composition and, by establishing a connection with topos theory, define a formal (intuitionistic/constructive) logic with a sound sheaf semantics
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
Venue: arXiv cs.CV 2018
Year: 2018

Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. In this paper, we aim to extensively review the latest trends and advances in the tracking algorithms and evaluate the robustness of trackers in the presence of noise. The first part of this work comprises a comprehensive survey of recently proposed tracking algorithms. We broadly categorize trackers into correlation filter based trackers and the others as non-correlation filter trackers. Each category is further classified into various types of trackers based on the architecture of the tracking mechanism. In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise. Multiple levels of additive noise are added to the Object Tracking Benchmark (OTB) 2015, and the precision and success rates of the tracking algorithms are evaluated. Some algorithms suffered more performance degradation than others, which brings to light a previously unexplored aspect of the tracking algorithms. The relative rank of the algorithms based on their performance on benchmark datasets may change in the presence of noise. Our study concludes that no single tracker is able to achieve the same efficiency in the presence of noise as under noise-free conditions; thus, there is a need to include a parameter for robustness to noise when evaluating newly proposed tracking algorithms.
The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved
Venue: arXiv cs.CR 2018
Year: 2018

The future of Internet of Things (IoT) is already upon us. IoT applications have been widely used in many field of social production and social living such as healthcare, energy and industrial automation. While enjoying the convenience and efficiency that IoT brings to us, new threats from IoT also have emerged. There are increasing research works to ease these threats, but many problems remain open. To better understand the essential reasons of new threats and the challenges in current research, this survey first proposes the concept of "IoT features". Then, the security and privacy effects of eight IoT new features were discussed including the threats they cause, existing solutions and challenges yet to be solved. To help researchers follow the up-to-date works in this field, this paper finally illustrates the developing trend of IoT security research and reveals how IoT features affect existing security research by investigating most existing research works related to IoT security from 2013 to 2017.
Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices
Venue: arXiv cs.IT 2018
Year: 2018

One of the key issues in the acquisition of sparse data by means of compressed sensing (CS) is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution.
Piecewise Flat Embedding for Image Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation. Based on the theory of sparse signal recovery, piecewise flat embedding with diverse channels attempts to recover a piecewise constant image representation with sparse region boundaries and sparse cluster value scattering. The resultant piecewise flat embedding exhibits interesting properties such as suppressing slowly varying signals, and offers an image representation with higher region identifiability which is desirable for image segmentation or high-level semantic analysis tasks. We formulate our embedding as a variant of the Laplacian Eigenmap embedding with an $L_{1,p} (0<p\leq1)$ regularization term to promote sparse solutions. First, we devise a two-stage numerical algorithm based on Bregman iterations to compute $L_{1,1}$-regularized piecewise flat embeddings. We further generalize this algorithm through iterative reweighting to solve the general $L_{1,p}$-regularized problem. To demonstrate its efficacy, we integrate PFE into two existing image segmentation frameworks, segmentation based on clustering and hierarchical segmentation based on contour detection. Experiments on four major benchmark datasets, BSDS500, MSRC, Stanford Background Dataset, and PASCAL Context, show that segmentation algorithms incorporating our embedding achieve significantly improved results.
Triplet-based Deep Similarity Learning for Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
Venue: arXiv cs.NE 2018
Year: 2018

Evolutionary deep intelligence synthesizes highly efficient deep neural networks architectures over successive generations. Inspired by the nature versus nurture debate, we propose a study to examine the role of external factors on the network synthesis process by varying the availability of simulated environmental resources. Experimental results were obtained for networks synthesized via asexual evolutionary synthesis (1-parent) and sexual evolutionary synthesis (2-parent, 3-parent, and 5-parent) using a 10% subset of the MNIST dataset. Results show that a lower environmental factor model resulted in a more gradual loss in performance accuracy and decrease in storage size. This potentially allows significantly reduced storage size with minimal to no drop in performance accuracy, and the best networks were synthesized using the lowest environmental factor models.
Opacity of nondeterministic transition systems: A (bi)simulation relation approach
Venue: arXiv cs.LO 2018
Year: 2018

In this paper, we propose several opacity-preserving (bi)simulation relations for general nondeterministic transition systems (NTS) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient construction to compute such relations. In addition, we use a two-way observer method to verify opacity of nondeterministic finite transition systems (NFTSs). As a result, although the verification of opacity for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to an NFTS, the (lack of) opacity of the NTS can be easily verified over the NFTS which is decidable.
When Textbook RSA is Used to Protect the Privacy of Hundreds of Millions of Users
Venue: arXiv cs.CR 2018
Year: 2018

We evaluate Tencent's QQ Browser, a popular mobile browser in China with hundreds of millions of users---including 16 million overseas, with respect to the threat model of a man-in-the-middle attacker with state actor capabilities. This is motivated by information in the Snowden revelations suggesting that another Chinese mobile browser, UC Browser, was being used to track users by Western nation-state adversaries. Among the many issues we found in QQ Browser that are presented in this paper, the use of "textbook RSA"---that is, RSA implemented as shown in textbooks, with no padding---is particularly interesting because it affords us the opportunity to contextualize existing research in breaking textbook RSA. We also present a novel attack on QQ Browser's use of textbook RSA that is distinguished from previous research by its simplicity. We emphasize that although QQ Browser's cryptography and our attacks on it are very simple, the impact is serious. Thus, research into how to break very poor cryptography (such as textbook RSA) has both pedagogical value and real-world impact.
Mean-field Games for Bio-inspired Collective Decision-making in Dynamical Networks
Venue: arXiv cs.SY 2018
Year: 2018

Given a large number of homogeneous players that are distributed across three possible states, we consider the problem in which these players have to control their transition rates, while minimizing a cost. The optimal transition rates are based on the players' knowledge of their current state and of the distribution of all the other players, and this introduces mean-field terms in the running and the terminal cost. The first contribution involves a mean-field game model that brings together macroscopic and microscopic dynamics. We obtain the mean-field equilibrium associated with this model, by solving the corresponding initial-terminal value problem. We perform an asymptotic analysis to obtain a stationary equilibrium for the system. The second contribution involves the study of the microscopic dynamics of the system for a finite number of players that interact in a structured environment modeled by an interaction topology. The third contribution is the specialization of the model to describe honeybee swarms, virus propagation, and cascading failures in interconnected smart-grids. A numerical analysis is conducted which involves two types of cyber-attacks. We simulate in which ways failures propagate across the interconnected smart grids and the impact on the grids frequencies. We reframe our analysis within the context of Lyapunov's linearisation method and stability theory of nonlinear systems and Kuramoto coupled oscillators model.
Generalized Master-Slave-Splitting Method and Application to Transmission-Distribution Coordinated Energy Management
Venue: arXiv cs.SY 2018
Year: 2018

Transmission-Distribution coordinated energy management (TDCEM) is recognized as a promising solution to the challenge of high DER penetration, but there is a lack of a distributed computation method that universally and effectively works for the TDCEM. To bridge this gap, a generalized mas-ter-slave-splitting (G-MSS) method is presented in this paper. This method is based on a general-purpose transmis-sion-distribution coordination model called G-TDCM, which thus enables the G-MSS to be applicable to most of the central functions of the TDCEM. In this G-MSS method, a basic heter-ogenous decomposition (HGD) algorithm is first derived from the HGD of the coupling constraints in the optimality conditions of the G-TDCM. Its optimality and convergence properties are then proved. Further, inspired by the conditions for conver-gence, a modified HGD algorithm that utilizes the subsystem's response function is developed and thus converges faster. The distributed G-MSS method is then demonstrated to successfully solve a series of central functions, e.g. power flow, contingency analysis, voltage stability assessment, economic dispatch and optimal power flow, of the TDCEM. The severe issues of over-voltage and erroneous assessment of the system security that are caused by DERs are thus resolved by the G-MSS method with modest computation cost.
Pros and Cons of GAN Evaluation Measures
Venue: arXiv cs.CV 2018
Year: 2018

Generative models, in particular generative adversarial networks (GANs), have received significant attention recently. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs remains a daunting task. While several measures have been introduced, as of yet, there is no consensus as to which measure best captures strengths and limitations of models and should be used for fair model comparison. As in other areas of computer vision and machine learning, it is critical to settle on one or few good measures to steer the progress in this field. In this paper, I review and critically discuss more than 24 quantitative and 5 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models. I also provide a set of 7 desiderata followed by an evaluation of whether a given measure or a family of measures is compatible with them.