OpenRaiser/Intern-Atlas huggingface dataset:
Next Stop "NoOps": Enabling Cross-System Diagnostics Through Graph-based Composition of Logs and Metrics
Venue: arXiv cs.DC 2018
Year: 2018

Performing diagnostics in IT systems is an increasingly complicated task, and it is not doable in satisfactory time by even the most skillful operators. Systems and their architecture change very rapidly in response to business and user demand. Many organizations see value in the maintenance and management model of NoOps that stands for No Operations. One of the implementations of this model is a system that is maintained automatically without any human intervention. The path to NoOps involves not only precise and fast diagnostics but also reusing as much knowledge as possible after the system is reconfigured or changed. The biggest challenge is to leverage knowledge on one IT system and reuse this knowledge for diagnostics of another, different system. We propose a framework of weighted graphs which can transfer knowledge, and perform high-quality diagnostics of IT systems. We encode all possible data in a graph representation of a system state and automatically calculate weights of these graphs. Then, thanks to the evaluation of similarity between graphs, we transfer knowledge about failures from one system to another and use it for diagnostics. We successfully evaluate the proposed approach on Spark, Hadoop, Kafka and Cassandra systems.
Response Time Bounds for Typed DAG Parallel Tasks on Heterogeneous Multi-cores
Venue: arXiv cs.DC 2018
Year: 2018

Heterogeneous multi-cores utilize the strength of different architectures for executing particular types of workload, and usually offer higher performance and energy efficiency. In this paper, we study the worst-case response time (WCRT) analysis of \emph{typed} scheduling of parallel DAG tasks on heterogeneous multi-cores, where the workload of each vertex in the DAG is only allowed to execute on a particular type of cores. The only known WCRT bound for this problem is grossly pessimistic and suffers the \emph{non-self-sustainability} problem. In this paper, we propose two new WCRT bounds. The first new bound has the same time complexity as the existing bound, but is more precise and solves its \emph{non-self-sustainability} problem. The second new bound explores more detailed task graph structure information to greatly improve the precision, but is computationally more expensive. We prove that the problem of computing the second bound is strongly NP-hard if the number of types in the system is a variable, and develop an efficient algorithm which has polynomial time complexity if the number of types is a constant. Experiments with randomly generated workload show that our proposed new methods are significantly more precise than the existing bound while having good scalability.
Improved Online Wilson Score Interval Method for Community Answer Quality Ranking
Venue: arXiv cs.SI 2018
Year: 2018

In this paper, a fast and easy-to-deploy method with a strong interpretability for community answer quality ranking is proposed. This method is improved based on the Wilson score interval method [Wilson, 1927], which retains its advantages and simultaneously improve the degree of satisfaction with the ranking of the high-quality answers. The improved answer quality score considers both Wilson score interval and the spotlight index, the latter of which will be introduced in the article. This method could significantly improve the ranking of the best answers with high attention in diverse scenarios.
Internet Protocol Version 6: Dead or Alive?
Venue: arXiv cs.NI 2018
Year: 2018

Internet Protocol (IP) is the narrow waist of multilayered Internet protocol stack which defines the rules for data sent across networks. IPv4 is the fourth version of IP and first commercially available for deployment set by ARPANET in 1983 which is a 32 bit long address and can support up to 232 devices. In April 2017, all Regional Internet Registries (RIRs) confirmed that IPv4 addresses are exhausted and cannot be allocated anymore implying any new organization requesting a block of Internet addresses will be allocated IPv6. This creates troubles of interoperability, migration and deployment, and therefore organizations hesitated to use IPv6 borrowing IPv4 addresses from other big organizations instead. Currently, when IPv4 is not available, and IPv6 is not adopted for around 20 years, the question arises whether IPv6 will still be accepted by the computer society or will it have an end of life soon with alternate better protocol such as ID based networks taking its place. This paper claims that IPv6 has lost its deployment window and can be safely skipped when new ID based protocols are available which not only have simple interoperability, deployment and migration guidelines but also provide advanced features as compared to IPv6. The paper provides answers to these questions with a comprehensive comparison of IPv6 with its available alternatives and reasons of IPv6 failures in its adoption. Finally, the paper declares IPv6 as a dead protocol and suggests to use newer available protocols in future.
Joint Optimization of Application Specific Routing in an Anycast Network
Venue: arXiv cs.NI 2018
Year: 2018

Recent developments in the field of Networking have provided opportunities for networks to efficiently cater application specific needs of a user. In this context, a routing path is not only dependent upon the network states but also is calculated in the best interest of an application using the network. These advanced routing algorithms can exploit application state data to enhance advanced network services such as anycast, edge cloud computing and cyber physical systems (CPS). In this work, we aim to design such a routing algorithm where the router decisions are based upon convex optimization techniques.
Weak nodes detection in urban transport systems: Planning for resilience in Singapore
Venue: arXiv cs.SI 2018
Year: 2018

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.
Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation
Venue: arXiv cs.SY 2018
Year: 2018

This work addresses the problem of path tracking control of a suspended load using a tilt-rotor UAV. The main challenge in controlling this kind of system arises from the dynamic behavior imposed by the load, which is usually coupled to the UAV by means of a rope, adding unactuated degrees of freedom to the whole system. Furthermore, to perform the load transportation it is often needed the knowledge of the load position to accomplish the task. Since available sensors are commonly embedded in the mobile platform, information on the load position may not be directly available. To solve this problem in this work, initially, the kinematics of the multi-body mechanical system are formulated from the load's perspective, from which a detailed dynamic model is derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear state-space representation of the system, affine in the inputs, with the load's position and orientation directly represented by state variables. A zonotopic state estimator is proposed to solve the problem of estimating the load position and orientation, which is formulated based on sensors located at the aircraft, with different sampling times, and unknown-but-bounded measurement noise. To solve the path tracking problem, a discrete-time mixed $\mathcal{H}_2/\mathcal{H}_\infty$ controller with pole-placement constraints is designed with guaranteed time-response properties and robust to unmodeled dynamics, parametric uncertainties, and external disturbances. Results from numerical experiments, performed in a platform based on the Gazebo simulator and on a Computer Aided Design (CAD) model of the system, are presented to corroborate the performance of the zonotopic state estimator along with the designed controller.
3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present a new approach to visual servoing for robotics, referred to as 3D Move to See (3DMTS), based on the principle of finding the next best view using a 3D camera array and a robotic manipulator to obtain multiple samples of the scene from different perspectives. The method uses semantic vision and an objective function applied to each perspective to sample a gradient representing the direction of the next best view. The method is demonstrated within simulation and on a real robotic platform containing a custom 3D camera array for the challenging scenario of robotic harvesting in a highly occluded and unstructured environment. It was shown on a real robotic platform that by moving the end effector using the gradient of an objective function leads to a locally optimal view of the object of interest, even amongst occlusions. The overall performance of the 3DMTS method obtained a mean increase in target size by 29.3% compared to a baseline method using a single RGB-D camera, which obtained 9.17%. The results demonstrate qualitatively and quantitatively that the 3DMTS method performed better in most scenarios, and yielded three times the target size compared to the baseline method. The increased target size in the final view will improve the detection of key features of the object of interest for further manipulation, such as grasping and harvesting.
Automatic Rule Learning for Autonomous Driving Using Semantic Memory
Venue: arXiv cs.LG 2018
Year: 2018

This paper presents a novel approach for automatic rule learning applicable to an autonomous driving system using real driving data.
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image. Specifically, a two-phase approach is developed. We firstly utilize a generator with two branches for the extraction of explicit and implicit depth information respectively. During the training process, an adversarial scheme is also employed to further improve the performance. The implicit and explicit depth information with the estimated 2D joints generated by a widely used estimator, in the second step, are together fed into a deep 3D pose regressor for the final pose generation. Our method achieves MPJPE of 58.68mm on the ECCV2018 3D Human Pose Estimation Challenge.
Printing-while-moving: a new paradigm for large-scale robotic 3D Printing
Venue: arXiv cs.RO 2018
Year: 2018

Building and Construction have recently become an exciting application ground for robotics. In particular, rapid progress in materials formulation and in robotics technology has made robotic 3D Printing of concrete a promising technique for in-situ construction. Yet, scalability remains an important hurdle to widespread adoption: the printing systems (gantry- based or arm-based) are often much larger than the structure to be printed, hence cumbersome. Recently, a mobile printing system - a manipulator mounted on a mobile base - was proposed to alleviate this issue: such a system, by moving its base, can potentially print a structure larger than itself. However, the proposed system could only print while being stationary, imposing thereby a limit on the size of structures that can be printed in a single take. Here, we develop a system that implements the printing-while-moving paradigm, which enables printing single-piece structures of arbitrary sizes with a single robot. This development requires solving motion planning, localization, and motion control problems that are specific to mobile 3D Printing. We report our framework to address those problems, and demonstrate, for the first time, a printing-while-moving experiment, wherein a 210 cm x 45 cm x 10 cm concrete structure is printed by a robot arm that has a reach of 87 cm.
SCC: Automatic Classification of Code Snippets
Venue: arXiv cs.SE 2018
Year: 2018

Determining the programming language of a source code file has been considered in the research community; it has been shown that Machine Learning (ML) and Natural Language Processing (NLP) algorithms can be effective in identifying the programming language of source code files. However, determining the programming language of a code snippet or a few lines of source code is still a challenging task. Online forums such as Stack Overflow and code repositories such as GitHub contain a large number of code snippets. In this paper, we describe Source Code Classification (SCC), a classifier that can identify the programming language of code snippets written in 21 different programming languages. A Multinomial Naive Bayes (MNB) classifier is employed which is trained using Stack Overflow posts. It is shown to achieve an accuracy of 75% which is higher than that with Programming Languages Identification (PLI a proprietary online classifier of snippets) whose accuracy is only 55.5%. The average score for precision, recall and the F1 score with the proposed tool are 0.76, 0.75 and 0.75, respectively. In addition, it can distinguish between code snippets from a family of programming languages such as C, C++ and C#, and can also identify the programming language version such as C# 3.0, C# 4.0 and C# 5.0.
FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation
Venue: arXiv cs.SI 2018
Year: 2018

Graph representation learning aims at transforming graph data into meaningful low-dimensional vectors to facilitate the employment of machine learning and data mining algorithms designed for general data. Most current graph representation learning approaches are transductive, which means that they require all the nodes in the graph are known when learning graph representations and these approaches cannot naturally generalize to unseen nodes. In this paper, we present a Fast Inductive Graph Representation Learning framework (FI-GRL) to learn nodes' low-dimensional representations. Our approach can obtain accurate representations for seen nodes with provable theoretical guarantees and can easily generalize to unseen nodes. Specifically, in order to explicitly decouple nodes' relations expressed by the graph, we transform nodes into a randomized subspace spanned by a random projection matrix. This stage is guaranteed to preserve the projection-cost of the normalized random walk matrix which is highly related to the normalized cut of the graph. Then feature extraction is achieved by conducting singular value decomposition on the obtained matrix sketch. By leveraging the property of projection-cost preservation on the matrix sketch, the obtained representation result is nearly optimal. To deal with unseen nodes, we utilize folding-in technique to learn their meaningful representations. Empirically, when the amount of seen nodes are larger than that of unseen nodes, FI-GRL always achieves excellent results. Our algorithm is fast, simple to implement and theoretically guaranteed. Extensive experiments on real datasets demonstrate the superiority of our algorithm on both efficacy and efficiency over both macroscopic level (clustering) and microscopic level (structural hole detection) applications.
Polyline Drawings with Topological Constraints
Venue: arXiv cs.CG 2018
Year: 2018

Let $G$ be a simple topological graph and let $Γ$ be a polyline drawing of $G$. We say that $Γ$ \emph{partially preserves the topology} of $G$ if it has the same external boundary, the same rotation system, and the same set of crossings as $G$. Drawing $Γ$ fully preserves the topology of $G$ if the planarization of $G$ and the planarization of $Γ$ have the same planar embedding. We show that if the set of crossing-free edges of $G$ forms a connected spanning subgraph, then $G$ admits a polyline drawing that partially preserves its topology and that has curve complexity at most three (i.e., at most three bends per edge). If, however, the set of crossing-free edges of $G$ is not a connected spanning subgraph, the curve complexity may be $Ω(\sqrt{n})$. Concerning drawings that fully preserve the topology, we show that if $G$ has skewness $k$, it admits one such drawing with curve complexity at most $2k$; for skewness-1 graphs, the curve complexity can be reduced to one, which is a tight bound. We also consider optimal $2$-plane graphs and discuss trade-offs between curve complexity and crossing angle resolution of drawings that fully preserve the topology.
Human activity recognition based on time series analysis using U-Net
Venue: arXiv cs.LG 2018
Year: 2018

Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method. This method faces the multi-class window problem which mistakenly labels different classes of sampling points within a window as a class. In this paper, a HAR algorithm based on U-Net is proposed to perform activity labeling and prediction at each sampling point. The activity data of the triaxial accelerometer is mapped into an image with the single pixel column and multi-channel which is input into the U-Net network for training and recognition. Our proposal can complete the pixel-level gesture recognition function. The method does not need manual feature extraction and can effectively identify short-term behaviors in long-term activity sequences. We collected the Sanitation dataset and tested the proposed scheme with four open data sets. The experimental results show that compared with Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully Convolutional Networks (FCN) methods, our proposal has the highest accuracy and F1-socre in each dataset, and has stable performance and high robustness. At the same time, after the U-Net has finished training, our proposal can achieve fast enough recognition speed.
Dynamic Environment Mapping for Augmented Reality Applications on Mobile Devices
Venue: arXiv cs.CV 2018
Year: 2018

Augmented Reality is a topic of foremost interest nowadays. Its main goal is to seamlessly blend virtual content in real-world scenes. Due to the lack of computational power in mobile devices, rendering a virtual object with high-quality, coherent appearance and in real-time, remains an area of active research. In this work, we present a novel pipeline that allows for coupled environment acquisition and virtual object rendering on a mobile device equipped with a depth sensor. While keeping human interaction to a minimum, our system can scan a real scene and project it onto a two-dimensional environment map containing RGB+Depth data. Furthermore, we define a set of criteria that allows for an adaptive update of the environment map to account for dynamic changes in the scene. Then, under the assumption of diffuse surfaces and distant illumination, our method exploits an analytic expression for the irradiance in terms of spherical harmonic coefficients, which leads to a very efficient rendering algorithm. We show that all the processes in our pipeline can be executed while maintaining an average frame rate of 31Hz on a mobile device.
SERoCS: Safe and Efficient Robot Collaborative Systems for Next Generation Intelligent Industrial Co-Robots
Venue: arXiv cs.RO 2018
Year: 2018

Human-robot collaborations have been recognized as an essential component for future factories. It remains challenging to properly design the behavior of those co-robots. Those robots operate in dynamic uncertain environment with limited computation capacity. The design objective is to maximize their task efficiency while guaranteeing safety. This paper discusses a set of design principles of a safe and efficient robot collaboration system (SERoCS) for the next generation co-robots, which consists of robust cognition algorithms for environment monitoring, efficient task planning algorithms for reference generations, and safe motion planning and control algorithms for safe human-robot interactions. The proposed SERoCS will address the design challenges and significantly expand the skill sets of the co-robots to allow them to work safely and efficiently with their human counterparts. The development of SERoCS will create a significant advancement toward adoption of co-robots in various industries. The experiments validate the effectiveness of SERoCS.
onlineSPARC: a Programming Environment for Answer Set Programming
Venue: arXiv cs.AI 2018
Year: 2018

Recent progress in logic programming (e.g., the development of the Answer Set Programming paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online answer set programming environment with a self contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.
Smart grid modeling and simulation - Comparing GridLAB-D and RAPSim via two Case studies
Venue: arXiv cs.SY 2018
Year: 2018

One of the most important tools for the development of the smart grid is simulation. Therefore, analyzing, designing, modeling, and simulating the smart grid will allow to explore future scenarios and support decision making for the grid's development. In this paper, we compare two open source simulation tools for the smart grid, GridLAB-Distribution (GridLAB-D) and Renewable Alternative Power systems Simulation (RAPSim). The comparison is based on the implementation of two case studies related to a power flow problem and the integration of renewable energy resources to the grid. Results show that even for very simple case studies, specific properties such as weather simulation or load modeling are influencing the results in a way that they are not reproducible with a different simulator.
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
Venue: arXiv cs.CV 2018
Year: 2018

Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding. In many cases, however, data is only available in pixel form. We present a method for generating images directly in a high-level domain (e.g. brush strokes), without the need for real pairwise data. Specifically, we train a "canvas" network to imitate the mapping of high-level constructs to pixels, followed by a high-level "drawing" network which is optimized through this mapping towards solving a desired image recreation or translation task. We successfully discover sequential vector representations of symbols, large sketches, and 3D objects, utilizing only pixel data. We display applications of our method in image segmentation, and present several ablation studies comparing various configurations.