OpenRaiser/Intern-Atlas huggingface dataset:
Improving Reliability of Service Function Chains with Combined VNF Migrations and Replications
Venue: arXiv cs.NI 2017
Year: 2017

The Network Function Virtualization (NFV) paradigm is enabling flexibility, programmability and implementation of traditional network functions into generic hardware, in form of Virtual Network Functions (VNFs). To provide services, the VNFs are commonly concatenated in a certain ordered sequence, known as Service Function Chains (SFCs). SFCs are usually required to meeting a certain level of reliability. This creates the need to place the VNFs while optimizing reliability jointly with other objectives, such as network and server load balancing. Traditional migration and replication mechanisms, commonly used for Virtual Machines (VM) in data centers, can be used to improve SFC reliability. We study how to improve service reliability using jointly replications and migrations, considering the chaining problem inherent in NFV. While replications provide reliability, performing migrations to more reliable servers decreases the resource overhead. A Linear Programming (LP) model is presented to study the impact of active-active configurations on the network and server resources. Additionally, to provide a fast recovery from server failures, we consider N-to-N configurations in NFV networks and study its impact on server resources. The results show that replications do not only improve reliability, but can also be used to achieving a better server and network load balancing, and when used jointly with migrations can improve resource utilization without degrading reliability.
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
Venue: arXiv cs.GT 2017
Year: 2017

Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, they suffer from limitations in computational and radio resources, which calls for fair efficient resource management in the MEC servers. The problem is however challenging due to the ultra-high density, distributed nature, and intrinsic randomness of next generation wireless networks. In this article, we focus on the application of game theory and reinforcement learning for efficient distributed resource management in MEC, in particular, for computation offloading. We briefly review the cutting-edge research and discuss future challenges. Furthermore, we develop a game-theoretical model for energy-efficient distributed edge server activation and study several learning techniques. Numerical results are provided to illustrate the performance of these distributed learning techniques. Also, open research issues in the context of resource management in MEC servers are discussed.
Interactive Complexity: Software Metrics from an Ecosystem Perspective
Venue: arXiv cs.SE 2017
Year: 2017

With even the most trivial of applications now being written on top of millions of lines code of libraries, API's, and programming languages, much of the complexity that used to exist when designing software has been abstracted away to allow programmers to focus on primarily business logic. With each application relying so heavily on the ecosystem it was designed to run in, whether that is limited to a local system or includes dependencies on machines connected by networks, measuring the complexity of these systems can no longer be done simply by observing the code internal to the application; we also need to account for its external interactions. This is especially important when considering issues of security, which becomes more vital as our healthcare, financial, and automobiles rely on complicated software systems. We propose Interactive Complexity, which provide a quantitative measure of how intertwined parts of the system are. Some of the most well-known software complexity metrics out there are the metrics in the CK-metric suite; these metrics are designed for use in measuring object oriented systems, but we believe they can be adapted to help measure the interaction of software systems. Our experimental results show strong correlations between the number of bugs fixed in a release and the value of some of these metrics in systems of sufficient scale.
Demystifying AlphaGo Zero as AlphaGo GAN
Venue: arXiv cs.LG 2017
Year: 2017

The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a qualitative analysis to indicate that AlphaGo Zero can be understood as a specially structured GAN system which is expected to possess an inherent good convergence property. Thus we deduct the success of AlphaGo Zero may not be a sign of a new generation of AI.
Warped-Linear Models for Time Series Classification
Venue: arXiv cs.LG 2017
Year: 2017

This article proposes and studies warped-linear models for time series classification. The proposed models are time-warp invariant analogues of linear models. Their construction is in line with time series averaging and extensions of k-means and learning vector quantization to dynamic time warping (DTW) spaces. The main theoretical result is that warped-linear models correspond to polyhedral classifiers in Euclidean spaces. This result simplifies the analysis of time-warp invariant models by reducing to max-linear functions. We exploit this relationship and derive solutions to the label-dependency problem and the problem of learning warped-linear models. Empirical results on time series classification suggest that warped-linear functions better trade solution quality against computation time than nearest-neighbor and prototype-based methods.
Correlated Eigenvalues Optical Communications
Venue: arXiv cs.IT 2017
Year: 2017

There is a fundamental limit on the capacity of fibre optical communication system (Shannon Limit). This limit can be potentially overcome via using Nonlinear Frequency Division Multiplexing. Dealing with noises in these systems is one of the most critical parts in implementing a practical system. In this paper, we discover and characterize the correlations among the NFT channels. It is demonstrated that the correlation is universal (i.e., independent of types of system noises) and can be exploited to maximize transmission throughput. We propose and experimentally confirm a noise model showing that end-to-end noise can be modeled as the accumulation of noise associated with each segment of optical communication which can be dealt with independently. Also, each point noise can be further decomposed into different components, some of which are more significant (and even dominating) than others. Hence, one can further approximate and simplify the noise model by focusing on the significant component.
On the Relations of Correlation Filter Based Trackers and Struck
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, two types of trackers, namely correlation filter based tracker (CF tracker) and structured output tracker (Struck), have exhibited the state-of-the-art performance. However, there seems to be lack of analytic work on their relations in the computer vision community. In this paper, we investigate two state-of-the-art CF trackers, i.e., spatial regularization discriminative correlation filter (SRDCF) and correlation filter with limited boundaries (CFLB), and Struck, and reveal their relations. Specifically, after extending the CFLB to its multiple channel version we prove the relation between SRDCF and CFLB on the condition that the spatial regularization factor of SRDCF is replaced by the masking matrix of CFLB. We also prove the asymptotical approximate relation between SRDCF and Struck on the conditions that the spatial regularization factor of SRDCF is replaced by an indicator function of object bounding box, the weights of SRDCF in its loss item are replaced by those of Struck, the linear kernel is employed by Struck, and the search region tends to infinity. Extensive experiments on public benchmarks OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we explain how detailed differences among SRDCF, CFLB, and Struck would give rise to slightly different performances on visual sequences
Spectral Element Methods for Liquid Metal Reactors Applications
Venue: arXiv cs.CE 2017
Year: 2017

Funded by the U.S. Department of Energy, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop an integrated multiphysics simulation capability for the design and analysis of future generations of nuclear power plants. NEAMS embraces a multiresolution hierarchy designing the code suite structure to ultimately span the full range of length and time scales present in relevant reactor design and safety analyses. Advanced reactors, such as liquid metal reactors, rely on innovative component designs to meet cost and safety targets. In order to span a wider design range, advanced modeling and simulation capabilities that rely on minimal assumptions play an important role in optimizing the design. Over the past several years the NEAMS program has developed the integrated multiphysics code suite (thermal-hydraulics, structural analysis and neutronics) SHARP aimed at streamlining the prototyping of such components. For the simulation of fluid flow and heat transfer, SHARP focuses on the high-fidelity end, aiming primarily at turbulence-resolving techniques such large eddy simulation (LES) and direct numerical simulation (DNS). The computational fluid dynamics code (CFD) selected for SHARP is Nek5000, a state-of-the-art highly scalable tool employing the spectral element method (SEM). In this manuscript, to be published in a Von karman institute lecture series monograph on liquid metal reactors, we review the method and its implementation in Nek5000. We also examine several applications. We note that Nek5000 is also regularly employed for intermediate-fidelity approaches such as Reynolds-averaged Navier-Stokes (RANS) and for reduced-order models employing momentum sources or porous media, especially when coupled to neutronics modeling.
Smartphone App Usage Prediction Using Points of Interest
Venue: arXiv cs.CY 2017
Year: 2017

In this paper we present the first population-level, city-scale analysis of application usage on smartphones. Using deep packet inspection at the network operator level, we obtained a geo-tagged dataset with more than 6 million unique devices that launched more than 10,000 unique applications across the city of Shanghai over one week. We develop a technique that leverages transfer learning to predict which applications are most popular and estimate the whole usage distribution based on the Point of Interest (POI) information of that particular location. We demonstrate that our technique has an 83.0% hitrate in successfully identifying the top five popular applications, and a 0.15 RMSE when estimating usage with just 10% sampled sparse data. It outperforms by about 25.7% over the existing state-of-the-art approaches. Our findings pave the way for predicting which apps are relevant to a user given their current location, and which applications are popular where. The implications of our findings are broad: it enables a range of systems to benefit from such timely predictions, including operating systems, network operators, appstores, advertisers, and service providers.
Generative Adversarial Network for Abstractive Text Summarization
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner. Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.
On the robustness of hybrid control systems to measurement noise and actuator disturbances
Venue: arXiv cs.SY 2017
Year: 2017

Robustness of hybrid control systems to measurement noise, actuator disturbances, and more generally perturbations, is analyzed. The relationship between the robustness of a hybrid control system and of its implementations is emphasized. Firstly, a formal definition of implementation of a hybrid control system is provided, based on the uniqueness of the solutions. Then, two examples are analyzed in detail, showing how the previously developed robustness property fails to guarantee that the implementations, necessarily used in control practice, are also robust. A new concept of strong robustness is proposed, which guarantees that at least jumping-first and flowing-first implementations are robust when the hybrid control system is strongly robust. In addition, we provide a sufficient condition for strong robustness based on the previously developed hybrid relaxation results.
Novel Adaptive Genetic Algorithm Sample Consensus
Venue: arXiv cs.NE 2017
Year: 2017

Random sample consensus (RANSAC) is a successful algorithm in model fitting applications. It is vital to have strong exploration phase when there are an enormous amount of outliers within the dataset. Achieving a proper model is guaranteed by pure exploration strategy of RANSAC. However, finding the optimum result requires exploitation. GASAC is an evolutionary paradigm to add exploitation capability to the algorithm. Although GASAC improves the results of RANSAC, it has a fixed strategy for balancing between exploration and exploitation. In this paper, a new paradigm is proposed based on genetic algorithm with an adaptive strategy. We utilize an adaptive genetic operator to select high fitness individuals as parents and mutate low fitness ones. In the mutation phase, a training method is used to gradually learn which gene is the best replacement for the mutated gene. The proposed method adaptively balance between exploration and exploitation by learning about genes. During the final Iterations, the algorithm draws on this information to improve the final results. The proposed method is extensively evaluated on two set of experiments. In all tests, our method outperformed the other methods in terms of both the number of inliers found and the speed of the algorithm.
How to Measure Sessions of Mobile Device Use? Quantification, Evaluation, and Applications
Venue: arXiv cs.CY 2017
Year: 2017

Research on mobile phone use often starts with a question of "How much time users spend on using their phones?". The question involves an equal-length measure that captures the duration of mobile phone use but does not tackle the other temporal characteristics of user behavior, such as frequency, timing, and sequence. In the study, we proposed a variable-length measure called "session" to uncover the unmeasured temporal characteristics. We use an open source data to demonstrate how to quantify sessions, aggregate the sessions to higher units of analysis within and across users, evaluate the results, and apply the measure for theoretical or practical purposes.
MAVOT: Memory-Augmented Video Object Tracking
Venue: arXiv cs.CV 2017
Year: 2017

We introduce a one-shot learning approach for video object tracking. The proposed algorithm requires seeing the object to be tracked only once, and employs an external memory to store and remember the evolving features of the foreground object as well as backgrounds over time during tracking. With the relevant memory retrieved and updated in each tracking, our tracking model is capable of maintaining long-term memory of the object, and thus can naturally deal with hard tracking scenarios including partial and total occlusion, motion changes and large scale and shape variations. In our experiments we use the ImageNet ILSVRC2015 video detection dataset to train and use the VOT-2016 benchmark to test and compare our Memory-Augmented Video Object Tracking (MAVOT) model. From the results, we conclude that given its oneshot property and simplicity in design, MAVOT is an attractive approach in visual tracking because it shows good performance on VOT-2016 benchmark and is among the top 5 performers in accuracy and robustness in occlusion, motion changes and empty target.
Effective Matrix Methods in Commutative Domains
Venue: arXiv cs.SC 2017
Year: 2017

Effective matrix methods for solving standard linear algebra problems in a commutative domains are discussed. Two of them are new. There are a methods for computing adjoined matrices and solving system of linear equations in a commutative domains.
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications.
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Venue: arXiv cs.SI 2017
Year: 2017

Singular Value Decomposition (SVD) is a popular approach in various network applications, such as link prediction and network parameter characterization. Incremental SVD approaches are proposed to process newly changed nodes and edges in dynamic networks. However, incremental SVD approaches suffer from serious error accumulation inevitably due to approximation on incremental updates. SVD restart is an effective approach to reset the aggregated error, but when to restart SVD for dynamic networks is not addressed in literature. In this paper, we propose TIMERS, Theoretically Instructed Maximum-Error-bounded Restart of SVD, a novel approach which optimally sets the restart time in order to reduce error accumulation in time. Specifically, we monitor the margin between reconstruction loss of incremental updates and the minimum loss in SVD model. To reduce the complexity of monitoring, we theoretically develop a lower bound of SVD minimum loss for dynamic networks and use the bound to replace the minimum loss in monitoring. By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold.We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks. We conduct extensive experiments on several synthetic and real dynamic networks. The experimental results demonstrate that our proposed method significantly outperforms the existing methods by reducing 27% to 42% in terms of the maximum error for dynamic network reconstruction when fixing the number of restarts. Our method reduces the number of restarts by 25% to 50% when fixing the maximum error tolerated.
Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments
Venue: arXiv cs.RO 2017
Year: 2017

Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This paper takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot-object and object-object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multi-body dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects pose and in the contact dynamics. The work enhances the state validity checker, the control sampler and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate and quality of the solution path.
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
Venue: arXiv cs.LG 2017
Year: 2017

This paper proposes a new algorithm for controlling classification results by generating a small additive perturbation without changing the classifier network. Our work is inspired by existing works generating adversarial perturbation that worsens classification performance. In contrast to the existing methods, our work aims to generate perturbations that can enhance overall classification performance. To solve this performance enhancement problem, we newly propose a perturbation generation network (PGN) influenced by the adversarial learning strategy. In our problem, the information in a large external dataset is summarized by a small additive perturbation, which helps to improve the performance of the classifier trained with the target dataset. In addition to this performance enhancement problem, we show that the proposed PGN can be adopted to solve the classical adversarial problem without utilizing the information on the target classifier. The mentioned characteristics of our method are verified through extensive experiments on publicly available visual datasets.