OpenRaiser/Intern-Atlas huggingface dataset:
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
Venue: arXiv cs.CV 2017
Year: 2017

Traditional works have shown that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Make full use of these multi-scale information can improve the image restoration performance. However, the current proposed deep learning based restoration methods do not take the multi-scale information into account. In this paper, we propose a dilated convolution based inception module to learn multi-scale information and design a deep network for single image super-resolution. Different dilated convolution learns different scale feature, then the inception module concatenates all these features to fuse multi-scale information. In order to increase the reception field of our network to catch more contextual information, we cascade multiple inception modules to constitute a deep network to conduct single image super-resolution. With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance. Experimental results show that our proposed method outperforms many state-of-the-art single image super-resolution methods.
Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos
Venue: arXiv cs.CV 2017
Year: 2017

Current state-of-the-art human action recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame. In this work we address the problem of action localisation and instance segmentation in which multiple concurrent actions of the same class may be segmented out of an image sequence. We cast the action tube extraction as an energy maximisation problem in which configurations of region proposals in each frame are assigned a cost and the best action tubes are selected via two passes of dynamic programming. One pass associates region proposals in space and time for each action category, and another pass is used to solve for the tube's temporal extent and to enforce a smooth label sequence through the video. In addition, by taking advantage of recent work on action foreground-background segmentation, we are able to associate each tube with class-specific segmentations. We demonstrate the performance of our algorithm on the challenging LIRIS-HARL dataset and achieve a new state-of-the-art result which is 14.3 times better than previous methods.
A New Approach to Time-Optimal Path Parameterization based on Reachability Analysis
Venue: arXiv cs.RO 2017
Year: 2017

Time-Optimal Path Parameterization (TOPP) is a well-studied problem in robotics and has a wide range of applications. There are two main families of methods to address TOPP: Numerical Integration (NI) and Convex Optimization (CO). NI-based methods are fast but difficult to implement and suffer from robustness issues, while CO-based approaches are more robust but at the same time significantly slower. Here we propose a new approach to TOPP based on Reachability Analysis (RA). The key insight is to recursively compute reachable and controllable sets at discretized positions on the path by solving small Linear Programs (LPs). The resulting algorithm is faster than NI-based methods and as robust as CO-based ones (100% success rate), as confirmed by extensive numerical evaluations. Moreover, the proposed approach offers unique additional benefits: Admissible Velocity Propagation and robustness to parametric uncertainty can be derived from it in a simple and natural way.
Towards Good Practices for Deep 3D Hand Pose Estimation
Venue: arXiv cs.CV 2017
Year: 2017

3D hand pose estimation from single depth image is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional random forest based methods is not so apparent. To exploit the good practice and promote the performance for hand pose estimation, we propose a tree-structured Region Ensemble Network (REN) for directly 3D coordinate regression. It first partitions the last convolution outputs of ConvNet into several grid regions. The results from separate fully-connected (FC) regressors on each regions are then integrated by another FC layer to perform the estimation. By exploitation of several training strategies including data augmentation and smooth $L_1$ loss, proposed REN can significantly improve the performance of ConvNet to localize hand joints. The experimental results demonstrate that our approach achieves the best performance among state-of-the-art algorithms on three public hand pose datasets. We also experiment our methods on fingertip detection and human pose datasets and obtain state-of-the-art accuracy.
A GPU Based Memory Optimized Parallel Method For FFT Implementation
Venue: arXiv cs.DC 2017
Year: 2017

FFT (fast Fourier transform) plays a very important role in many fields, such as digital signal processing, digital image processing and so on. However, in application, FFT becomes a factor of affecting the processing efficiency, especially in remote sensing, which large amounts of data need to be processed with FFT. So shortening the FFT computation time is particularly important. GPU (Graphics Processing Unit) has been used in many common areas and its acceleration effect is very obvious compared with CPU (Central Processing Unit) platform. In this paper, we present a new parallel method to execute FFT on GPU. Based on GPU storage system and hardware processing pipeline, we improve the way of data storage. We divided the data into parts reasonably according the size of data to make full use of the characteristics of the GPU. We propose the memory optimized method based on share memory and texture memory to reduce the number of global memory access to achieve better efficiency. The results show that the GPU-based memory optimized FFT implementation not only can increase over 100% than FFTW library in CPU platform, but also can improve over 30% than CUFFT library in GPU platform.
Composing Distributed Representations of Relational Patterns
Venue: arXiv cs.CL 2017
Year: 2017

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In order to address semantic modeling of relational patterns, this paper constructs a new dataset that provides multiple similarity ratings for every pair of relational patterns on the existing dataset. In addition, we conduct a comparative study of different encoders including additive composition, RNN, LSTM, and GRU for composing distributed representations of relational patterns. We also present Gated Additive Composition, which is an enhancement of additive composition with the gating mechanism. Experiments show that the new dataset does not only enable detailed analyses of the different encoders, but also provides a gauge to predict successes of distributed representations of relational patterns in the relation classification task.
MatchZoo: A Toolkit for Deep Text Matching
Venue: arXiv cs.IR 2017
Year: 2017

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics. In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interaction-focused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo.
Seminar Users in the Arabic Twitter Sphere
Venue: arXiv cs.SI 2017
Year: 2017

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.
An Online Learning Approach to Buying and Selling Demand Response
Venue: arXiv cs.SY 2017
Year: 2017

We adopt the perspective of an aggregator, which seeks to coordinate its purchase of demand reductions from a fixed group of residential electricity customers, with its sale of the aggregate demand reduction in a two-settlement wholesale energy market. The aggregator procures reductions in demand by offering its customers a uniform price for reductions in consumption relative to their predetermined baselines. Prior to its realization of the aggregate demand reduction, the aggregator must also determine how much energy to sell into the two-settlement energy market. In the day-ahead market, the aggregator commits to a forward contract, which calls for the delivery of energy in the real-time market. The underlying aggregate demand curve, which relates the aggregate demand reduction to the aggregator's offered price, is assumed to be affine and subject to unobservable, random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the aggregator, we investigate the extent to which the aggregator might dynamically adapt its offered prices and forward contracts to maximize its expected profit over a time window of $T$ days. Specifically, we design a dynamic pricing and contract offering policy that resolves the aggregator's need to learn the unknown demand model with its desire to maximize its cumulative expected profit over time. In particular, the proposed pricing policy is proven to incur a regret over $T$ days that is no greater than $O(\log(T)\sqrt{T})$.
Compact Model Representation for 3D Reconstruction
Venue: arXiv cs.CV 2017
Year: 2017

3D reconstruction from 2D images is a central problem in computer vision. Recent works have been focusing on reconstruction directly from a single image. It is well known however that only one image cannot provide enough information for such a reconstruction. A prior knowledge that has been entertained are 3D CAD models due to its online ubiquity. A fundamental question is how to compactly represent millions of CAD models while allowing generalization to new unseen objects with fine-scaled geometry. We introduce an approach to compactly represent a 3D mesh. Our method first selects a 3D model from a graph structure by using a novel free-form deformation FFD 3D-2D registration, and then the selected 3D model is refined to best fit the image silhouette. We perform a comprehensive quantitative and qualitative analysis that demonstrates impressive dense and realistic 3D reconstruction from single images.
Detecting Topological Changes in Dynamic Community Networks
Venue: arXiv cs.SI 2017
Year: 2017

The study of time-varying (dynamic) networks (graphs) is of fundamental importance for computer network analytics. Several methods have been proposed to detect the effect of significant structural changes in a time series of graphs. The main contribution of this work is a detailed analysis of a dynamic community graph model. This model is formed by adding new vertices, and randomly attaching them to the existing nodes. It is a dynamic extension of the well-known stochastic blockmodel. The goal of the work is to detect the time at which the graph dynamics switches from a normal evolution -- where balanced communities grow at the same rate -- to an abnormal behavior -- where communities start merging. In order to circumvent the problem of decomposing each graph into communities, we use a metric to quantify changes in the graph topology as a function of time. The detection of anomalies becomes one of testing the hypothesis that the graph is undergoing a significant structural change. In addition the the theoretical analysis of the test statistic, we perform Monte Carlo simulations of our dynamic graph model to demonstrate that our test can detect changes in graph topology.
Grant-Free Massive NOMA: Outage Probability and Throughput
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider a massive uncoordinated non-orthogonal multiple access (NOMA) scheme where devices have strict latency requirements and no retransmission opportunities are available. Each device chooses a pilot sequence from a predetermined set as its signature and transmits its selected pilot and data simultaneously. A collision occurs when two or more devices choose the same pilot sequence. Collisions are treated as interference to the remaining received signals. We consider successive joint decoding (SJD) and successive interference cancellation (SIC) under a Rayleigh fading and path loss channel model. We first derive the expression for the outage probability for the case where devices transmit at the same fixed rate. Then, we derive the expression for the maximum achievable throughput for the case where devices transmit with rateless codes. Thus, their code rate is adaptive to the system conditions, i.e., load, received powers, and interference. Numerical results verify the accuracy of our analytical expressions. For low data rate transmissions, results show that SIC performs close to that of SJD in terms of outage probability for packet arrival rates up to 10 packets per slot. However, SJD can achieve almost double the throughput of SIC and is, thus, far more superior.
Exploiting Interference for Secrecy Wireless Information and Power Transfer
Venue: arXiv cs.IT 2017
Year: 2017

Radio-frequency (RF) signals enabled wireless information and power transfer (WIPT) is a cost-effective technique to achieve two-way communications and at the same time provide energy supplies for low-power wireless devices. However, the information transmission in WIPT is vulnerable to the eavesdropping by the energy receivers (ERs). To achieve secrecy communications with information nodes (INs) while satisfying the energy transfer requirement of ERs, an efficient solution is to exploit a dual use of the energy signals also as useful interference or artificial noise (AN) to interfere with the ERs, thus preventing against their potential information eavesdropping. Towards this end, this article provides an overview on the joint design of energy and information signals to achieve energy-efficient and secure WIPT under various practical setups, including simultaneous wireless information and power transfer (SWIPT), wireless powered cooperative relaying and jamming, and wireless powered communication networks (WPCN). We also present some research directions that are worth pursuing in the future.
Next Generation Cloud Computing: New Trends and Research Directions
Venue: arXiv cs.DC 2017
Year: 2017

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.
Verifying Policy Enforcers
Venue: arXiv cs.SE 2017
Year: 2017

Policy enforcers are sophisticated runtime components that can prevent failures by enforcing the correct behavior of the software. While a single enforcer can be easily designed focusing only on the behavior of the application that must be monitored, the effect of multiple enforcers that enforce different policies might be hard to predict. So far, mechanisms to resolve interferences between enforcers have been based on priority mechanisms and heuristics. Although these methods provide a mechanism to take decisions when multiple enforcers try to affect the execution at a same time, they do not guarantee the lack of interference on the global behavior of the system. In this paper we present a verification strategy that can be exploited to discover interferences between sets of enforcers and thus safely identify a-priori the enforcers that can co-exist at run-time. In our evaluation, we experimented our verification method with several policy enforcers for Android and discovered some incompatibilities.
A Wait-free Multi-word Atomic (1,N) Register for Large-scale Data Sharing on Multi-core Machines
Venue: arXiv cs.DC 2017
Year: 2017

We present a multi-word atomic (1,N) register for multi-core machines exploiting Read-Modify-Write (RMW) instructions to coordinate the writer and the readers in a wait-free manner. Our proposal, called Anonymous Readers Counting (ARC), enables large-scale data sharing by admitting up to $2^{32}-2$ concurrent readers on off-the-shelf 64-bits machines, as opposed to the most advanced RMW-based approach which is limited to 58 readers. Further, ARC avoids multiple copies of the register content when accessing it---this affects classical register's algorithms based on atomic read/write operations on single words. Thus it allows for higher scalability with respect to the register size. Moreover, ARC explicitly reduces improves performance via a proper limitation of RMW instructions in case of read operations, and by supporting constant time for read operations and amortized constant time for write operations. A proof of correctness of our register algorithm is also provided, together with experimental data for a comparison with literature proposals. Beyond assessing ARC on physical platforms, we carry out as well an experimentation on virtualized infrastructures, which shows the resilience of wait-free synchronization as provided by ARC with respect to CPU-steal times, proper of more modern paradigms such as cloud computing.
Data clustering using stochastic block models
Venue: arXiv cs.SI 2017
Year: 2017

It has been shown that community detection algorithms work better for clustering tasks than other, more popular methods, such as k-means. In fact, network analysis based methods often outperform more widely used methods and do not suffer from some of the drawbacks we notice elsewhere e.g. the number of clusters k usually has to be known in advance. However, stochastic block models which are known to perform well for community detection, have not yet been tested for this task. We discuss why these models cannot be directly applied to this problem and test the performance of a generalization of stochastic block models which work on weighted graphs and compare them to other clustering techniques.
Analysing Errors of Open Information Extraction Systems
Venue: arXiv cs.CL 2017
Year: 2017

We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation of OIE systems.
MFDL: A Multicarrier Fresnel Penetration Model based Device-Free Localization System leveraging Commodity Wi-Fi Cards
Venue: arXiv cs.NI 2017
Year: 2017

Device-free localization plays an important role in many ubiquitous applications. Among the different technologies proposed, Wi-Fi based technology using commercial devices has attracted much attention due to its low cost, ease of deployment, and high potential for accurate localization. Existing solutions use either fingerprints that require labor-intensive radio-map survey and updates, or models constructed from empirical studies with dense deployment of Wi-Fi transceivers. In this work, we explore the Fresnel Zone Theory in physics and propose a generic Fresnel Penetration Model (FPM), which reveals the linear relationship between specific Fresnel zones and multicarrier Fresnel phase difference, along with the Fresnel phase offset caused by static multipath environments. We validate FPM in both outdoor and complex indoor environments. Furthermore, we design a multicarrier FPM based device-free localization system (MFDL), which overcomes a number of practical challenges, particularly the Fresnel phase difference estimation and phase offset calibration in multipath-rich indoor environments. Extensive experimental results show that compared with the state-of-the-art work (LiFS), our MFDL system achieves better localization accuracy with much fewer number of Wi-Fi transceivers. Specifically, using only three transceivers, the median localization error of MFDL is as low as 45$cm$ in an outdoor environment of 36$m^2$, and 55$cm$ in indoor settings of 25$m^2$. Increasing the number of transceivers to four allows us to achieve 75$cm$ median localization error in a 72$m^2$ indoor area, compared with the 1.1$m$ median localization error achieved by LiFS using 11 transceivers in a 70$m^2$ area.
A Loosely-Coupled Approach for Metric Scale Estimation in Monocular Vision-Inertial Systems
Venue: arXiv cs.RO 2017
Year: 2017

In monocular vision systems, lack of knowledge about metric distances caused by the inherent scale ambiguity can be a strong limitation for some applications. We offer a method for fusing inertial measurements with monocular odometry or tracking to estimate metric distances in inertial-monocular systems and to increase the rate of pose estimates. As we performed the fusion in a loosely-coupled manner, each input block can be easily replaced with one's preference, which makes our method quite flexible. We experimented our method using the ORB-SLAM algorithm for the monocular tracking input and Euler forward integration to process the inertial measurements. We chose sets of data recorded on UAVs to design a suitable system for flying robots.