OpenRaiser/Intern-Atlas huggingface dataset:
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition
Venue: arXiv cs.CV 2017
Year: 2017

It remains a challenge to efficiently extract spatialtemporal information from skeleton sequences for 3D human action recognition. Although most recent action recognition methods are based on Recurrent Neural Networks which present outstanding performance, one of the shortcomings of these methods is the tendency to overemphasize the temporal information. Since 3D convolutional neural network(3D CNN) is a powerful tool to simultaneously learn features from both spatial and temporal dimensions through capturing the correlations between three dimensional signals, this paper proposes a novel two-stream model using 3D CNN. To our best knowledge, this is the first application of 3D CNN in skeleton-based action recognition. Our method consists of three stages. First, skeleton joints are mapped into a 3D coordinate space and then encoding the spatial and temporal information, respectively. Second, 3D CNN models are seperately adopted to extract deep features from two streams. Third, to enhance the ability of deep features to capture global relationships, we extend every stream into multitemporal version. Extensive experiments on the SmartHome dataset and the large-scale NTU RGB-D dataset demonstrate that our method outperforms most of RNN-based methods, which verify the complementary property between spatial and temporal information and the robustness to noise.
Black-Box Attacks against RNN based Malware Detection Algorithms
Venue: arXiv cs.LG 2017
Year: 2017

Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples. These works mainly focused on the detection algorithms which use features with fixed dimension, while some researchers have begun to use recurrent neural networks (RNN) to detect malware based on sequential API features. This paper proposes a novel algorithm to generate sequential adversarial examples, which are used to attack a RNN based malware detection system. It is usually hard for malicious attackers to know the exact structures and weights of the victim RNN. A substitute RNN is trained to approximate the victim RNN. Then we propose a generative RNN to output sequential adversarial examples from the original sequential malware inputs. Experimental results showed that RNN based malware detection algorithms fail to detect most of the generated malicious adversarial examples, which means the proposed model is able to effectively bypass the detection algorithms.
Capacity Outer Bound and Degrees of Freedom of Wiener Phase Noise Channels with Oversampling
Venue: arXiv cs.IT 2017
Year: 2017

The discrete-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. A novel outer bound on the capacity with an average input power constraint is derived as a function of the oversampling factor. This outer bound yields the degrees of freedom for the scenario in which the oversampling factor grows with the transmit power $P$ as $P^α$. The result shows, perhaps surprisingly, that the largest pre-log that can be attained with phase modulation at high signal-to-noise ratio is at most $1/4$.
Deep Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate.
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing. Previous work typically used a combination of low-level features like color histograms, in conjunction with simple models such as SVMs. Instead, we propose to learn shot detection end-to-end, from pixels to final shot boundaries. For training such a model, we rely on our insight that all shot boundaries are generated. Thus, we create a dataset with one million frames and automatically generated transitions such as cuts, dissolves and fades. In order to efficiently analyze hours of videos, we propose a Convolutional Neural Network (CNN) which is fully convolutional in time, thus allowing to use a large temporal context without the need to repeatedly processing frames. With this architecture our method obtains state-of-the-art results while running at an unprecedented speed of more than 120x real-time.
Sensitivity Properties of Intermittent Control
Venue: arXiv cs.SY 2017
Year: 2017

The sensitivity properties of intermittent control are analysed and the conditions for a limit cycle derived theoretically and verified by simulation.
Verification of a lazy cache coherence protocol against a weak memory model
Venue: arXiv cs.LO 2017
Year: 2017

In this paper we verify a modern lazy cache coherence protocol, TSO-CC, against the memory consistency model it was designed for, TSO. We achieve this by first showing a weak simulation relation between TSO-CC (with a fixed number of processors) and a novel finite-state operational model which exhibits the laziness of TSO-CC and satisfies TSO. We then extend this by an existing parameterisation technique, allowing verification for an unlimited number of processors. The approach is executed entirely within a model checker, no external tool is required and very little in-depth knowledge of formal verification methods is required of the verifier.
Accelerating Discrete Wavelet Transforms on GPUs
Venue: arXiv cs.CV 2017
Year: 2017

The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transform on graphics processing units (GPUs). However, all of them only dealt with lifting and convolution computation schemes. In this paper, we show that corresponding horizontal and vertical lifting parts of the lifting scheme can be merged into non-separable lifting units, which halves the number of steps. We also discuss an optimization strategy leading to a reduction in the number of arithmetic operations. The schemes were assessed using the OpenCL and pixel shaders. The proposed non-separable lifting scheme outperforms the existing schemes in many cases, irrespective of its higher complexity.
Optimization in large graphs: Toward a better future?
Venue: arXiv cs.DS 2017
Year: 2017

Finding groups of connected individuals in large graphs with tens of thousands or more nodes has received considerable attention in academic research. In this paper, we analyze three main issues with respect to the recent influx of papers on community detection in (large) graphs, highlight the specific problems with the current research avenues, and propose a first step towards a better approach. First, in spite of the strong interest in community detection, a strong conceptual and theoretical foundation of connectedness in large graphs is missing. Yet, it is crucial to be able to determine the specific feats that we aim to analyze in large networks, to avoid a purely black-or-white view. Second, in literature commonly employed (meta)heuristic frameworks are applied for the large graph problems. Currently, it is, however, unclear whether these techniques are even viable options, and what the added value of the constituting parts is. Additionally, the manner in which different algorithms are compared is also ambiguous. Finally, no analyses of the impact of data parameters on the reported clusters is done. Nonetheless, it would be interesting to evaluate which characteristics lead to which type of communities and what their effect is on computational difficulty.
A Colonel Blotto Game for Interdependence-Aware Cyber-Physical Systems Security in Smart Cities
Venue: arXiv cs.GT 2017
Year: 2017

Smart cities must integrate a number of interdependent cyber-physical systems that operate in a coordinated manner to improve the well-being of the city's residents. A cyber-physical system (CPS) is a system of computational elements controlling physical entities. Large-scale CPSs are more vulnerable to attacks due to the cyber-physical interdependencies that can lead to cascading failures which can have a significant detrimental effect on a city. In this paper, a novel approach is proposed for analyzing the problem of allocating security resources, such as firewalls and anti-malware, over the various cyber components of an interdependent CPS to protect the system against imminent attacks. The problem is formulated as a Colonel Blotto game in which the attacker seeks to allocate its resources to compromise the CPS, while the defender chooses how to distribute its resources to defend against potential attacks. To evaluate the effects of defense and attack, various CPS factors are considered including human-CPS interactions as well as physical and topological characteristics of a CPS such as flow and capacity of interconnections and minimum path algorithms. Results show that, for the case in which the attacker is not aware of the CPS interdependencies, the defender can have a higher payoff, compared to the case in which the attacker has complete information. The results also show that, in the case of more symmetric nodes, due to interdependencies, the defender achieves its highest payoff at the equilibrium compared to the case with independent, asymmetric nodes.
Distributed Precoding Systems in Multi-Gateway Multibeam Satellites: Regularization and Coarse Beamforming
Venue: arXiv cs.IT 2017
Year: 2017

This paper deals with the problem of beamforming design in a multibeam satellite, which is shared by different groups of terminals -clusters-, each served by an Earth station or gateway. Each gateway precodes the symbols addressed to its respective users; the design follows an MMSE criterion, and a regularization factor judiciously chosen allows to account for the presence of mutually interfering clusters, extending more classical results applicable to one centralized station. More importantly, channel statistics can be used instead of instantaneous channel state information, avoiding the exchange of information among gateways through backhaul links. The on-board satellite beamforming weights are designed to exploit the degrees of freedom of the satellite antennas to minimize the noise impact and the interference to some specific users. On-ground beamforming results are provided as a reference to compare the joint performance of MMSE precoders and on-board beamforming network. A non-adaptive design complements the results and makes them more amenable to practical use by designing a coarse beamforming network.
Classification of Aerial Photogrammetric 3D Point Clouds
Venue: arXiv cs.CV 2017
Year: 2017

We present a powerful method to extract per-point semantic class labels from aerialphotogrammetry data. Labeling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.
Timed k-Tail: Automatic Inference of Timed Automata
Venue: arXiv cs.SE 2017
Year: 2017

Accurate and up-to-date models describing the be- havior of software systems are seldom available in practice. To address this issue, software engineers may use specification mining techniques, which can automatically derive models that capture the behavior of the system under analysis. So far, most specification mining techniques focused on the functional behavior of the systems, with specific emphasis on models that represent the ordering of operations, such as tempo- ral rules and finite state models. Although useful, these models are inherently partial. For instance, they miss the timing behavior, which is extremely relevant for many classes of systems and com- ponents, such as shared libraries and user-driven applications. Mining specifications that include both the functional and the timing aspects can improve the applicability of many testing and analysis solutions. This paper addresses this challenge by presenting the Timed k-Tail (TkT) specification mining technique that can mine timed automata from program traces. Since timed automata can effectively represent the interplay between the functional and the timing behavior of a system, TkT could be exploited in those contexts where time-related information is relevant. Our empirical evaluation shows that TkT can efficiently and effectively mine accurate models. The mined models have been used to identify executions with anomalous timing. The evaluation shows that most of the anomalous executions have been correctly identified while producing few false positives.
Second-Order Word Embeddings from Nearest Neighbor Topological Features
Venue: arXiv cs.CL 2017
Year: 2017

We introduce second-order vector representations of words, induced from nearest neighborhood topological features in pre-trained contextual word embeddings. We then analyze the effects of using second-order embeddings as input features in two deep natural language processing models, for named entity recognition and recognizing textual entailment, as well as a linear model for paraphrase recognition. Surprisingly, we find that nearest neighbor information alone is sufficient to capture most of the performance benefits derived from using pre-trained word embeddings. Furthermore, second-order embeddings are able to handle highly heterogeneous data better than first-order representations, though at the cost of some specificity. Additionally, augmenting contextual embeddings with second-order information further improves model performance in some cases. Due to variance in the random initializations of word embeddings, utilizing nearest neighbor features from multiple first-order embedding samples can also contribute to downstream performance gains. Finally, we identify intriguing characteristics of second-order embedding spaces for further research, including much higher density and different semantic interpretations of cosine similarity.
Combinatorial n-fold Integer Programming and Applications
Venue: arXiv cs.DS 2017
Year: 2017

Many fundamental NP-hard problems can be formulated as integer linear programs (ILPs). A famous algorithm by Lenstra solves ILPs in time that is exponential only in the dimension of the program, and polynomial in the size of the ILP. That algorithm became a ubiquitous tool in the design of fixed-parameter algorithms for NP-hard problems, where one wishes to isolate the hardness of a problemby some parameter. However, in many cases using Lenstra's algorithm has two drawbacks: First, the run time of the resulting algorithms is often doubly-exponential in the parameter, and second, an ILP formulation in small dimension cannot easily express problems involving many different costs. Inspired by the work of Hemmecke, Onn and Romanchuk [Math. Prog. 2013], we develop a single-exponential algorithm for so-called combinatorial n-fold integer programs, which are remarkably similar to prior ILP formulations for various problems, but unlike them, also allow variable dimension. We then apply our algorithm to a few representative problems like Closest String, Swap Bribery, Weighted Set Multicover, and obtain exponential speedups in the dependence on the respective parameters, the input size, or both. Unlike Lenstra's algorithm, which is essentially a bounded search tree algorithm, our result uses the technique of augmenting steps. At its heart is a deep result stating that in combinatorial n-fold IPs, existence of an augmenting step implies existence of a \local" augmenting step, which can be found using dynamic programming. Our results provide an important insight into many problems by showing that they exhibit this phenomenon, and highlights the importance of augmentation techniques.
Towards Near Real-Time BGP Deep Analysis: A Big-Data Approach
Venue: arXiv cs.NI 2017
Year: 2017

BGP (Border Gateway Protocol) serves as the primary routing protocol for the Internet, enabling Autonomous Systems (individual network operators) to exchange network reachability information. Alongside significant on-going research and development efforts, there is a practical need to understand the nature of events that occur on the Internet. Network operators are acutely aware of security-related incidents such as 'Prefix Hijacking' as well as the impact of network instabilities that ripple through the Internet. Recent research focused on the study of BGP anomalies (both network/prefix instability and security-related incidents) has been based on the analysis of historical logs. Further analysis to understand the nature of these anomalous events is not always sufficient to be able to differentiate malicious activities, such as prefix- or sub-prefix- hijacking, from those events caused by inadvertent misconfigurations. In addition, such techniques are challenged by a lack of sufficient resources to store and process data feeds in real-time from multiple BGP Vantage Points (VPs). In this paper, we present a BGP Deep-analysis application developed using the PNDA (Platform for Network Data Analytics) 'Big-Data' platform. PNDA provides a highly scalable environment that enables the ingestion and processing of 'live' BGP feeds from many vantage points in a schema-agnostic manner. The Apache Spark-based application, in conjunction with PNDA's distributed processing capabilities, is able to perform high-level insights as well as near-to-real-time statistical analysis
Non-orthogonal Multiple Access for High-reliable and Low-latency V2X Communications
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we propose a non-orthogonal multiple access scheme to reduce the latency and to improve the packet reception probability. In the proposed scheme, the BS performs the semi-persistent scheduling to optimize the time scheduling and allocate frequency resources in a non-orthogonal manner while the vehicles autonomously perform distributed power control. We formulate the centralized scheduling and resource allocation problem as equivalent to a multi-dimensional stable roommate matching problem, in which the users and time/frequency resources are considered as disjoint sets of players to be matched with each other. We then develop a novel rotation matching algorithm, which converges to a q-exchange stable matching after a limited number of iterations. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the latency and reliability.
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Venue: arXiv cs.RO 2017
Year: 2017

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation.
Properties of Normalization for a math based intermediate representation
Venue: arXiv cs.PL 2017
Year: 2017

The Normalization transformation plays a key role in the compilation of Diderot programs. The transformations are complicated and it would be easy for a bug to go undetected. To increase our confidence in normalization part of the compiler we provide a formal analysis on the rewriting system. We proof that the rewrite system is type preserving, value preserving (for tensor-valued expressions), and terminating.
HTTP adaptive streaming with indoors-outdoors detection in mobile networks
Venue: arXiv cs.MM 2017
Year: 2017

In mobile networks, users may lose coverage when entering a building due to the high signal attenuation at windows and walls. Under such conditions, services with minimum bit-rate requirements, such as video streaming, often show poor Quality-of-Experience (QoE). We will present a Bayesian detector that combines measurements from two Smartphone sensors to decide if a user is inside a building or not. Based on this coverage classification, we will propose an HTTP adaptive streaming (HAS) algorithm to increase playback stability at a high average bitrate. Measurements in a typical office building show high accuracy for the presented detector and superior QoE for the proposed HAS algorithm.
Parallel Matrix-Free Implementation of Frequency-Domain Finite Difference Methods for Cluster Computing
Venue: arXiv cs.CE 2017
Year: 2017

Full-wave 3D electromagnetic simulations of complex planar devices, multilayer interconnects, and chip packages are presented for wide-band frequency-domain analysis using the finite difference integration technique developed in the PETSc software package. Initial reordering of the index assignment to the unknowns makes the resulting system matrix diagonally dominant. The rearrangement also facilitates the decomposition of large domain into slices for passing the mesh information to different machines. Matrix-free methods are then exploited to minimize the number of element-wise multiplications and memory requirements in the construction of the system of linear equations. Besides, the recipes provide extreme ease of modifications in the kernel of the code. The applicability of different Krylov subspace solvers is investigated. The accuracy is checked through comparisons with CST MICROWAVE STUDIO transient solver results. The parallel execution of the compiled code on specific number of processors in multi-core distributed-memory architectures demonstrate high scalability of the computational algorithm.