OpenRaiser/Intern-Atlas huggingface dataset:
Using JSON-LD to Compose Different IoT and Cloud Services
Venue: arXiv cs.SE 2018
Year: 2018

Internet of things and cloud computing are in the widespread use today, and often work together to accomplish complex business task and use cases. This paper propose the framework and its practical implementation to compose different things as services and cloud services. The ontology based approach and JSON-LD was used to semantically annotate both types of services, and enable the mechanism to semi-automatically compose these services. The use case and proof-of-concept application that use the proposed theoretical approach is also described in this work.
Fast Motion Planning for High-DOF Robot Systems Using Hierarchical System Identification
Venue: arXiv cs.RO 2018
Year: 2018

We present an efficient algorithm for motion planning and control of a robot system with a high number of degrees-of-freedom. These include high-DOF soft robots or an articulated robot interacting with a deformable environment. Our approach takes into account dynamics constraints and present a novel technique to accelerate the forward dynamic computation using a data-driven method. We precompute the forward dynamic function of the robot system on a hierarchical adaptive grid. Furthermore, we exploit the properties of underactuated robot systems and perform these computations for a few DOFs. We provide error bounds for our approximate forward dynamics computation and use our approach for optimization-based motion planning and reinforcement-learning-based feedback control. Our formulation is used for motion planning of two high DOF robot systems: a high-DOF line-actuated elastic robot arm and an underwater swimming robot operating in water. As compared to prior techniques based on exact dynamic function computation, we observe one to two orders of magnitude improvement in performance.
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we first tackle the problem of simultaneous pixel-level localization and image-level classification with only image-level labels for fully convolutional network training. We investigate the global pooling method which plays a vital role in this task. Classical global max pooling and average pooling methods are hard to indicate the precise regions of objects. Therefore, we revisit the global weighted average pooling (GWAP) method for this task and propose the class-agnostic GWAP module and the class-specific GWAP module in this paper. We evaluate the classification and pixel-level localization ability on the ILSVRC benchmark dataset. Experimental results show that the proposed GWAP module can better capture the regions of the foreground objects. We further explore the knowledge transfer between the image classification task and the region-based object detection task. We propose a multi-task framework that combines our class-specific GWAP module with R-FCN. The framework is trained with few ground truth bounding boxes and large-scale image-level labels. We evaluate this framework on PASCAL VOC dataset. Experimental results show that this framework can use the data with only image-level labels to improve the generalization of the object detection model.
How do you correct run-on sentences it's not as easy as it seems
Venue: arXiv cs.CL 2018
Year: 2018

Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences that outperform leading methods for related tasks, punctuation restoration and whole-sentence grammatical error correction. Due to the limited annotated data for this error, we experiment with artificially generating training data from clean newswire text. Our findings suggest artificial training data is viable for this task. We discuss implications for correcting run-ons and other types of mistakes that have low coverage in error-annotated corpora.
Generating GraphQL-Wrappers for REST(-like) APIs
Venue: arXiv cs.SE 2018
Year: 2018

GraphQL is a query language and thereupon-based paradigm for implementing web Application Programming Interfaces (APIs) for client-server interactions. Using GraphQL, clients define precise, nested data-requirements in typed queries, which are resolved by servers against (possibly multiple) backend systems, like databases, object storages, or other APIs. Clients receive only the data they care about, in a single request. However, providers of existing REST(-like) APIs need to implement additional GraphQL interfaces to enable these advantages. We here assess the feasibility of automatically generating GraphQL wrappers for existing REST(-like) APIs. A wrapper, upon receiving GraphQL queries, translates them to requests against the target API. We discuss the challenges for creating such wrappers, including dealing with data sanitation, authentication, or handling nested queries. We furthermore present a prototypical implementation of OASGraph. OASGraph takes as input an OpenAPI Specification (OAS) describing an existing REST(-like) web API and generates a GraphQL wrapper for it. We evaluate OASGraph by running it, as well as an existing open source alternative, against 959 publicly available OAS. This experiment shows that OASGraph outperforms the existing alternative and is able to create a GraphQL wrapper for 89.5% of the APIs -- however, with limitations in many cases. A subsequent analysis of errors and warnings produced by OASGraph shows that missing or ambiguous information in the assessed OAS hinders creating complete wrappers. Finally, we present a use case of the IBM Watson Language Translator API that shows that small changes to an OAS allow OASGraph to generate more idiomatic and more expressive GraphQL wrappers.
The Impact of Correlated Blocking on Millimeter-Wave Personal Networks
Venue: arXiv cs.IT 2018
Year: 2018

Due to its potential to support high data rates at low latency with reasonable interference isolation, millimeter-wave (mmWave) communications has emerged as a promising solution for wireless personal-area networks (WPAN) and an enabler for emerging applications such as high-resolution untethered virtual reality. At mmWave, signals are prone to blockage by objects in the environment, including human bodies. Most mmWave systems utilize directional antennas in order to overcome the significant path loss. In this paper, we consider the effects of blockage and antenna directivity on the performance of a mmWave WPAN. Similar to related work, we assume that the interferers are in arbitrary locations and the blockages are drawn from a random point process. However, unlike related work that assumes independent blocking, we carefully account for the possibility of correlated blocking, which arises when two interferers are close to each other and therefore an obstruction that blocks the first interferer may likely block the second interferer. Closed form expressions for the blockage correlation coefficient and the distribution of the SINR are provided for the case of two dominant interferers and a fixed number of blockages drawn from a binomial point process. Finally, the effects of antenna directivity and the spatial randomness of the interferers are taken into account, resulting in SINR curves that fully account for correlated blocking, which are compared against curves that neglect correlation. The results provide insight into the validity of the commonly held assumption of independent blocking and the improved accuracy that can be obtained when the blocking correlation is taken into account.
Geometric Multi-Model Fitting by Deep Reinforcement Learning
Venue: arXiv cs.CV 2018
Year: 2018

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.
RPNet: an End-to-End Network for Relative Camera Pose Estimation
Venue: arXiv cs.CV 2018
Year: 2018

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation.
Shift-based Primitives for Efficient Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

We propose a collection of three shift-based primitives for building efficient compact CNN-based networks. These three primitives (channel shift, address shift, shortcut shift) can reduce the inference time on GPU while maintains the prediction accuracy. These shift-based primitives only moves the pointer but avoids memory copy, thus very fast. For example, the channel shift operation is 12.7x faster compared to channel shuffle in ShuffleNet but achieves the same accuracy. The address shift and channel shift can be merged into the point-wise group convolution and invokes only a single kernel call, taking little time to perform spatial convolution and channel shift. Shortcut shift requires no time to realize residual connection through allocating space in advance. We blend these shift-based primitives with point-wise group convolution and built two inference-efficient CNN architectures named AddressNet and Enhanced AddressNet. Experiments on CIFAR100 and ImageNet datasets show that our models are faster and achieve comparable or better accuracy.
Co-simulation of Continuous Systems: A Tutorial
Venue: arXiv cs.CE 2018
Year: 2018

Co-simulation consists of the theory and techniques to enable global simulation of a coupled system via the composition of simulators. Despite the large number of applications and growing interest in the challenges, the field remains fragmented into multiple application domains, with limited sharing of knowledge. This tutorial aims at introducing co-simulation of continuous systems, targeted at researchers new to the field.
Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers
Venue: arXiv cs.SI 2018
Year: 2018

Extensive research on social media usage during emergencies has shown its value to provide life-saving information, if a mechanism is in place to filter and prioritize messages. Existing ranking systems can provide a baseline for selecting which updates or alerts to push to emergency responders. However, prior research has not investigated in depth how many and how often should these updates be generated, considering a given bound on the workload for a user due to the limited budget of attention in this stressful work environment. This paper presents a novel problem and a model to quantify the relationship between the performance metrics of ranking systems (e.g., recall, NDCG) and the bounds on the user workload. We then synthesize an alert-based ranking system that enforces these bounds to avoid overwhelming end-users. We propose a Pareto optimal algorithm for ranking selection that adaptively determines the preference of top-k ranking and user workload over time. We demonstrate the applicability of this approach for Emergency Operation Centers (EOCs) by performing an evaluation based on real world data from six crisis events. We analyze the trade-off between recall and workload recommendation across periodic and realtime settings. Our experiments demonstrate that the proposed ranking selection approach can improve the efficiency of monitoring social media requests while optimizing the need for user attention.
Towards Language Agnostic Universal Representations
Venue: arXiv cs.CL 2018
Year: 2018

When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in,even if the math lessons were only taught in one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion. We learn these representations by taking inspiration from linguistics and formalizing Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970). We demonstrate the capabilities of these representations by showing that the models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.
Evolution of Threats in the Global Risk Network
Venue: arXiv cs.SI 2018
Year: 2018

With a steadily growing population and rapid advancements in technology, the global economy is increasing in size and complexity. This growth exacerbates global vulnerabilities and may lead to unforeseen consequences such as global pandemics fueled by air travel, cyberspace attacks, and cascading failures caused by the weakest link in a supply chain. Hence, a quantitative understanding of the mechanisms driving global network vulnerabilities is urgently needed. Developing methods for efficiently monitoring evolution of the global economy is essential to such understanding. Each year the World Economic Forum publishes an authoritative report on the state of the global economy and identifies risks that are likely to be active, impactful or contagious. Using a Cascading Alternating Renewal Process approach to model the dynamics of the global risk network, we are able to answer critical questions regarding the evolution of this network. To fully trace the evolution of the network we analyze the asymptotic state of risks (risk levels which would be reached in the long term if the risks were left unabated) given a snapshot in time, this elucidates the various challenges faced by the world community at each point in time. We also investigate the influence exerted by each risk on others. Results presented here are obtained through either quantitative analysis or computational simulations.
Harvesting Time-Series Data from Service-Based Systems Hosted in MANETs
Venue: arXiv cs.DC 2018
Year: 2018

We are concerned with reliably harvesting data collected from service-based systems hosted on a mobile ad hoc network (MANET). More specifically, we are concerned with time-bounded and time-sensitive time-series monitoring data describing the state of the network and system. The data are harvested in order to perform an analysis, usually one that requires a global view of the data taken from distributed sites. For example, network- and application-state data are typically analysed in order to make operational and maintenance decisions. MANETs are a challenging environment in which to harvest monitoring data, due to the inherently unstable and unpredictable connectivity between nodes, and the overhead of transferring data in a wireless medium. These limitations must be overcome to support time-series analysis of perishable and time-critical data. We present an epidemic, delay tolerant, and intelligent method to efficiently and effectively transfer time-series data between the mobile nodes of MANETs. The method establishes a network-wide synchronization overlay to transfer increments of the data over intermediate nodes in periodic cycles. The data are then accessible from local stores at the nodes. We implemented the method in Java~EE and present evaluation on a run-time dependence discovery method for Web Service applications hosted on MANETs, and comparison to other four methods demonstrating that our method performs significantly better in both data availability and network overhead.
From Megabits to CPU~Ticks: Enriching a Demand Trace in the Age of MEC
Venue: arXiv cs.NI 2018
Year: 2018

All the content consumed by mobile users, be it a web page or a live stream, undergoes some processing along the way; as an example, web pages and videos are transcoded to fit each device's screen. The recent multi-access edge computing (MEC) paradigm envisions performing such processing within the cellular network, as opposed to resorting to a cloud server on the Internet. Designing a MEC network, i.e., placing and dimensioning the computational facilities therein, requires information on how much computational power is required to produce the contents needed by the users. However, real-world demand traces only contain information on how much data is downloaded. In this paper, we demonstrate how to {\em enrich} demand traces with information about the computational power needed to process the different types of content, and we show the substantial benefit that can be obtained from using such enriched traces for the design of MEC-based networks.
Generalized Low-Rank Optimization for Topological Cooperation in Ultra-Dense Networks
Venue: arXiv cs.IT 2018
Year: 2018

Network densification is a natural way to support dense mobile applications under stringent requirements, such as ultra-low latency, ultra-high data rate, and massive connecting devices. Severe interference in ultra-dense networks poses a key bottleneck. Sharing channel state information (CSI) and messages across transmitters can potentially alleviate interferences and improve system performance. Most existing works on interference coordination require significant CSI signaling overhead and are impractical in ultra-dense networks. This paper investigate topological cooperation to manage interferences in message sharing based only on network connectivity information. In particular, we propose a generalized low-rank optimization approach to maximize achievable degrees-of-freedom (DoFs). To tackle the challenges of poor structure and non-convex rank function, we develop Riemannian optimization algorithms to solve a sequence of complex fixed rank subproblems through a rank growth strategy. By exploiting the non-compact Stiefel manifold formed by the set of complex full column rank matrices, we develop Riemannian optimization algorithms to solve the complex fixed-rank optimization problem by applying the semidefinite lifting technique and Burer-Monteiro factorization approach. Numerical results demonstrate the computational efficiency and higher DoFs achieved by the proposed algorithms.
Learning for Video Super-Resolution through HR Optical Flow Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Video super-resolution (SR) aims to generate a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts. The generation of accurate correspondence plays a significant role in video SR. It is demonstrated by traditional video SR methods that simultaneous SR of both images and optical flows can provide accurate correspondences and better SR results. However, LR optical flows are used in existing deep learning based methods for correspondence generation. In this paper, we propose an end-to-end trainable video SR framework to super-resolve both images and optical flows. Specifically, we first propose an optical flow reconstruction network (OFRnet) to infer HR optical flows in a coarse-to-fine manner. Then, motion compensation is performed according to the HR optical flows. Finally, compensated LR inputs are fed to a super-resolution network (SRnet) to generate the SR results. Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance. Comparative results on the Vid4 and DAVIS-10 datasets show that our framework achieves the state-of-the-art performance.
Accelerate CU Partition in HEVC using Large-Scale Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

High efficiency video coding (HEVC) suffers high encoding computational complexity, partly attributed to the rate-distortion optimization quad-tree search in CU partition decision. Therefore, we propose a novel two-stage CU partition decision approach in HEVC intra-mode. In the proposed approach, CNN-based algorithm is designed to decide CU partition mode precisely in three depths. In order to alleviate computational complexity further, an auxiliary earl-termination mechanism is also proposed to filter obvious homogeneous CUs out of the subsequent CNN-based algorithm. Experimental results show that the proposed approach achieves about 37% encoding time saving on average and insignificant BD-Bitrate rise compared with the original HEVC encoder.
Comparing Video Based Shoulder Surfing with Live Simulation
Venue: arXiv cs.HC 2018
Year: 2018

We analyze the claims that video recreations of shoulder surfing attacks offer a suitable alternative and a baseline, as compared to evaluation in a live setting. We recreated a subset of the factors of a prior video-simulation experiment conducted by Aviv et al. (ACSAC 2017), and model the same scenario using live participants ($n=36$) instead (i.e., the victim and attacker were both present). The live experiment confirmed that for Android's graphical patterns video simulation is consistent with the live setting for attacker success rates. However, both 4- and 6-digit PINs demonstrate statistically significant differences in attacker performance, with live attackers performing as much 1.9x better than in the video simulation. The security benefits gained from removing feedback lines in Android's graphical patterns are also greatly diminished in the live setting, particularly under multiple attacker observations, but overall, the data suggests that video recreations can provide a suitable baseline measure for attacker success rate. However, we caution that researchers should consider that these baselines may greatly underestimate the threat of an attacker in live settings.
Textually Enriched Neural Module Networks for Visual Question Answering
Venue: arXiv cs.CL 2018
Year: 2018

Problems at the intersection of language and vision, like visual question answering, have recently been gaining a lot of attention in the field of multi-modal machine learning as computer vision research moves beyond traditional recognition tasks. There has been recent success in visual question answering using deep neural network models which use the linguistic structure of the questions to dynamically instantiate network layouts. In the process of converting the question to a network layout, the question is simplified, which results in loss of information in the model. In this paper, we enrich the image information with textual data using image captions and external knowledge bases to generate more coherent answers. We achieve 57.1% overall accuracy on the test-dev open-ended questions from the visual question answering (VQA 1.0) real image dataset.