OpenRaiser/Intern-Atlas huggingface dataset:
Egocentric Hand Track and Object-based Human Action Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective. In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly, without further use of visual features. Initially, we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions. We employ object detection and region tracking techniques to locate hands and capture their movements. Prior knowledge about egocentric views facilitates hand identification between left and right. With regard to detection and tracking, we contribute a pipeline that successfully operates on unseen egocentric videos to find the camera wearer's hands and associate them through time. Moreover, we emphasize on the value of scene information for action recognition. We acknowledge that the presence of objects is significant for the execution of actions by humans and in general for the description of a scene. To acquire this information, we utilize object detection for specific classes that are relevant to the actions we want to recognize. Our experiments are targeted on videos of kitchen activities from the Epic-Kitchens dataset. We model action recognition as a sequence learning problem of the detected spatial positions in the frames. Our results show that explicit hand and object detections with no other visual information can be relied upon to classify hand-related human actions. Testing against methods fully dependent on visual features, signals that for actions where hand motions are conceptually important, a region-of-interest-based description of a video contains equally expressive information with comparable classification performance.
Characterizing Attention Cascades in WhatsApp Groups
Venue: arXiv cs.SI 2019
Year: 2019

An important political and social phenomena discussed in several countries, like India and Brazil, is the use of WhatsApp to spread false or misleading content. However, little is known about the information dissemination process in WhatsApp groups. Attention affects the dissemination of information in WhatsApp groups, determining what topics or subjects are more attractive to participants of a group. In this paper, we characterize and analyze how attention propagates among the participants of a WhatsApp group. An attention cascade begins when a user asserts a topic in a message to the group, which could include written text, photos, or links to articles online. Others then propagate the information by responding to it. We analyzed attention cascades in more than 1.7 million messages posted in 120 groups over one year. Our analysis focused on the structural and temporal evolution of attention cascades as well as on the behavior of users that participate in them. We found specific characteristics in cascades associated with groups that discuss political subjects and false information. For instance, we observe that cascades with false information tend to be deeper, reach more users, and last longer in political groups than in non-political groups.
Web data mining for public health purposes
Venue: arXiv cs.IR 2019
Year: 2019

For a long time, public health events, such as disease incidence or vaccination activity, have been monitored to keep track of the health status of the population, allowing to evaluate the effect of public health initiatives and to decide where resources for improving public health are best spent. This thesis investigates the use of web data mining for public health monitoring, and makes contributions in the following two areas: New approaches for predicting public health events from web mined data, and novel applications of web mined data for public health monitoring.
Self-supervised Learning for Video Correspondence Flow
Venue: arXiv cs.CV 2019
Year: 2019

The objective of this paper is self-supervised learning of feature embeddings that are suitable for matching correspondences along the videos, which we term correspondence flow. By leveraging the natural spatial-temporal coherence in videos, we propose to train a ``pointer'' that reconstructs a target frame by copying pixels from a reference frame. We make the following contributions: First, we introduce a simple information bottleneck that forces the model to learn robust features for correspondence matching, and prevent it from learning trivial solutions, \eg matching based on low-level colour information. Second, to tackle the challenges from tracker drifting, due to complex object deformations, illumination changes and occlusions, we propose to train a recursive model over long temporal windows with scheduled sampling and cycle consistency. Third, we achieve state-of-the-art performance on DAVIS 2017 video segmentation and JHMDB keypoint tracking tasks, outperforming all previous self-supervised learning approaches by a significant margin. Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation.
Designing a robust controller for a missile autopilot based on Loop shaping approach
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, a robust autopilot is designed for a missile autopilot, such that the system stability is guaranteed in low altitude and short-range conditions. First, using the v-gap metric, the system is linearzed around the equilibrium point. Then, the robust $H_\infty$ loop shaping controller is built for the linear model. The proposed approach does not utilize the gain scheduling method, and guarantees the system stability throughout the flight envelope. Particle Swarm Optimization (PSO) algorithm is used along with the control approach to reduce the complicated tuning process of the weight functions. The weighting functions are optimized throughout the evolutionary algorithm to maximize the stability margin. From the simulations, it is proved that the stability margins achieved guarantees the stability of interceptor throughout the whole flight envelope.
A Fuzzy Inference System for the Identification
Venue: arXiv cs.LG 2019
Year: 2019

Odor identification is an important area in a wide range of industries like cosmetics, food, beverages and medical diagnosis among others. Odor detection could be done through an array of gas sensors conformed as an electronic nose where a data acquisition module converts sensor signals to a standard output to be analyzed. To facilitate odors detection a system is required for the identification. This paper presents the results of an automated odor identification process implemented by a fuzzy system and an electronic nose. First, an electronic nose prototype is manufactured to detect organic compounds vapor using an array of five tin dioxide gas sensors, an arduino uno board is used as a data acquisition section. Second, an intelligent module with a fuzzy system is considered for the identification of the signals received by the electronic nose. This solution proposes a system to identify odors by using a personal computer. Results show an acceptable precision.
Anti-Confusing: Region-Aware Network for Human Pose Estimation
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation. Specifically, the proposed method addresses three key aspects, i.e., data augmentation, feature learning and prediction fusion, respectively. First, we propose Parsing-based Data Augmentation (PDA) to generate abundant data that synthesizes confusing textures. Second, we not only propose a Feature Pyramid Stem (FPS) to learn stronger low-level features in lower stage; but also incorporate an Effective Region Extraction (ERE) module to excavate better target-specific features. Third, we introduce Cascade Voting Fusion (CVF) to explicitly exclude the inferior predictions and fuse the rest effective predictions for the final pose estimation. Extensive experimental results on two popular benchmarks, i.e. MPII and LSP, demonstrate the effectiveness of our method against the state-of-the-art competitors. Especially on easily-confusable joints, our method makes significant improvement.
Robust Cooperative Formation Control of Fixed-Wing Unmanned Aerial Vehicles
Venue: arXiv cs.SY 2019
Year: 2019

Robust cooperative formation control is investigated in this paper for fixed-wing unmanned aerial vehicles in close formation flight to save energy. A novel cooperative control method is developed. The concept of virtual structure is employed to resolve the difficulty in designing virtual leaders for a large number of UAVs in formation flight. To improve the transient performance, desired trajectories are passed through a group of cooperative filters to generate smooth reference signals, namely the states of the virtual leaders. Model uncertainties due to aerodynamic couplings among UAVs are estimated and compensated using uncertainty and disturbance observers. The entire design, therefore, contains three major components: cooperative filters for motion planning, baseline cooperative control, and uncertainty and disturbance observation. The proposed formation controller could at least secure ultimate bounded control performance for formation tracking. If certain conditions are satisfied, asymptotic formation tracking control could be obtained. Major contributions of this paper lie in two aspects: 1) the difficulty in designing virtual leaders is resolved in terms of the virtual structure concept; 2) a robust cooperative controller is proposed for close formation flight of a large number of UAVs suffering from aerodynamic couplings in between. The efficiency of the proposed design will be demonstrated using numerical simulations of five UAVs in close formation flight.
Secure Integration of Electric Vehicles with the Power Grid
Venue: arXiv cs.SY 2019
Year: 2019

This paper focuses on the secure integration of distributed energy resources (DERs), especially pluggable electric vehicles (EVs), with the power grid. We consider the vehicle-to-grid (V2G) system where EVs are connected to the power grid through an aggregator. In this paper, we propose a novel Cyber-Physical Anomaly Detection Engine that monitors system behavior and detects anomalies almost instantaneously. This detection engine ensures that the critical power grid component (viz.,aggregator)remains secure by monitoring(a)cyber messages for various state changes and data constraints along with (b)power data on the V2G cyber network using power measurements from sensors on the physical/power distribution network. Since the V2G system is time-sensitive, the anomaly detection engine also monitors the timing requirements of the protocol messages to enhance the safety of the aggregator. To the best of our knowledge, this is the first piece of work that combines(a)the EV charging/discharging protocols, the(b)cyber network and(c)power measurements from physical network to detect intrusions in the EV to power grid system.
Lifting 2d Human Pose to 3d : A Weakly Supervised Approach
Venue: arXiv cs.CV 2019
Year: 2019

Estimating 3d human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from the single view. Recent deep learning based methods show promising results by using supervised learning on 3d pose annotated datasets. However, the lack of large-scale 3d annotated training data captured under in-the-wild settings makes the 3d pose estimation difficult for in-the-wild poses. Few approaches have utilized training images from both 3d and 2d pose datasets in a weakly-supervised manner for learning 3d poses in unconstrained settings. In this paper, we propose a method which can effectively predict 3d human pose from 2d pose using a deep neural network trained in a weakly-supervised manner on a combination of ground-truth 3d pose and ground-truth 2d pose. Our method uses re-projection error minimization as a constraint to predict the 3d locations of body joints, and this is crucial for training on data where the 3d ground-truth is not present. Since minimizing re-projection error alone may not guarantee an accurate 3d pose, we also use additional geometric constraints on skeleton pose to regularize the pose in 3d. We demonstrate the superior generalization ability of our method by cross-dataset validation on a challenging 3d benchmark dataset MPI-INF-3DHP containing in the wild 3d poses.
Browser Fingerprinting: A survey
Venue: arXiv cs.CR 2019
Year: 2019

With this paper, we survey the research performed in the domain of browser fingerprinting, while providing an accessible entry point to newcomers in the field. We explain how this technique works and where it stems from. We analyze the related work in detail to understand the composition of modern fingerprints and see how this technique is currently used online. We systematize existing defense solutions into different categories and detail the current challenges yet to overcome.
MobiKa - Low-Cost Mobile Robot for Human-Robot Interaction
Venue: arXiv cs.RO 2019
Year: 2019

One way to allow elderly people to stay longer in their homes is to use of service robots to support them with everyday tasks. With this goal, we design, develop and evaluate a low-cost mobile robot to communicate with elderly people. The main idea is to create an affordable communication assistant robot which is optimized for multimodal Human-Robot Interaction (HRI). Our robot can navigate autonomously through dynamic environments using a new algorithm to calculate poses for approaching persons. The robot was tested in a real life scenario in an elderly care home.
Event-triggered Online Proactive Network Association to Mobile Edge Computing for IoT
Venue: arXiv cs.NI 2019
Year: 2019

Ultra-low latency communication for mobile machines emerges as a critical technology in Internet of Things (IoT). Proactive network association has been suggested to support ultra-low latency communication with the assistance of mobile edge computing. To resolve system dynamics and uncertainty, in this paper, an online proactive network association is proposed to minimize average task delay while considering time-average energy consumption constraints. Under distributed computing and networking environments, we formulate an event-triggered proactive network association model by semi-Markov task states and independent identically distributed (i.i.d.) random events. Then we facilitate the mobility-aware anticipatory network association to predictively consider handover effects caused by the mobility. Based on the Markov decision processes (MDP) and Lyapunov optimization, the two-stage online proactive network association (TOPNA) decision algorithm is proposed without the knowledge nor distribution of random events. Simulation results exhibit the effectiveness of the proposed algorithm.
Three-Stage Subspace Clustering Framework with Graph-Based Transformation and Optimization
Venue: arXiv cs.LG 2019
Year: 2019

Subspace clustering (SC) refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Based on spectral clustering, state-of-the-art approaches solve SC problem within a two-stage framework. In the first stage, data representation techniques are applied to draw an affinity matrix from the original data. In the second stage, spectral clustering is directly applied to the affinity matrix so that data can be grouped into different subspaces. However, the affinity matrix obtained in the first stage usually fails to reveal the authentic relationship between data points, which leads to inaccurate clustering results. In this paper, we propose a universal Three-Stage Subspace Clustering framework (3S-SC). Graph-Based Transformation and Optimization (GBTO) is added between data representation and spectral clustering. The affinity matrix is obtained in the first stage, then it goes through the second stage, where the proposed GBTO is applied to generate a reconstructed affinity matrix with more authentic similarity between data points. Spectral clustering is applied after GBTO, which is the third stage. We verify our 3S-SC framework with GBTO through theoretical analysis. Experiments on both synthetic data and the real-world data sets of handwritten digits and human faces demonstrate the universality of the proposed 3S-SC framework in improving the connectivity and accuracy of SC methods based on $\ell_0$, $\ell_1$, $\ell_2$ or nuclear norm regularization.
A Right-of-Way Based Strategy to Implement Safe and Efficient Driving at Non-Signalized Intersections for Automated Vehicles
Venue: arXiv cs.RO 2019
Year: 2019

Non-signalized intersection is a typical and common scenario for connected and automated vehicles (CAVs). How to balance safety and efficiency remains difficult for researchers. To improve the original Responsibility Sensitive Safety (RSS) driving strategy on the non-signalized intersection, we propose a new strategy in this paper, based on right-of-way assignment (RWA). The performances of RSS strategy, cooperative driving strategy, and RWA based strategy are tested and compared. Testing results indicate that our strategy yields better traffic efficiency than RSS strategy, but not satisfying as the cooperative driving strategy due to the limited range of communication and the lack of long-term planning. However, our new strategy requires much fewer communication costs among vehicles.
Positioning and Location-Aware Communications for Modern Railways with 5G New Radio
Venue: arXiv cs.NI 2019
Year: 2019

Providing high-capacity radio connectivity for high-speed trains (HSTs) is one of the most important use cases of emerging 5G New Radio (NR) networks. In this article, we show that 5G NR technology can also facilitate high-accuracy continuous localization and tracking of HSTs. Furthermore, we describe and demonstrate how the NR network can utilize the continuous location information for efficient beam-management and beamforming, as well as for downlink Doppler precompensation in the single-frequency network context. Additionally, with particular focus on millimeter wave networks, novel concepts for low-latency intercarrier interference (ICI) estimation and compensation, due to residual Doppler and oscillator phase noise, are described and demonstrated. The provided numerical results at 30 GHz operating band show that sub-meter positioning and sub-degree beam-direction accuracies can be obtained with very high probabilities in the order of 95-99%. The results also show that the described Doppler precompensation and ICI estimation and cancellation methods substantially improve the throughput of the single-frequency HST network.
Exploration of TCP Parameters for Enhanced Performance in a Datacenter Environment
Venue: arXiv cs.NI 2019
Year: 2019

TCP parameters in most of the operating systems are optimized for generic home and office environments having low latencies in their internal networks. However, this arrangement invariably results in a compromized network performance when same parameters are straight away slotted into a datacenter environment. We identify key TCP parameters that have a major impact on network performance based on the study and comparative analysis of home and datacenter environments. We discuss certain criteria for the modification of TCP parameters and support our analysis with simulation results that validate the performance improvement in case of datacenter environment.
RLE edit distance in near optimal time
Venue: arXiv cs.DS 2019
Year: 2019

We show that the edit distance between two run-length encoded strings of compressed lengths $m$ and $n$ respectively, can be computed in $\mathcal{O}(mn\log(mn))$ time. This improves the previous record by a factor of $\mathcal{O}(n/\log(mn))$. The running time of our algorithm is within subpolynomial factors of being optimal, subject to the standard SETH-hardness assumption. This effectively closes a line of algorithmic research first started in 1993.
Dataspace architecture and manage its components class projection
Venue: arXiv cs.DB 2019
Year: 2019

Big Data technology is described. Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. There is constructed dataspace architecture. Dataspace has focused solely - and passionately - on providing unparalleled expertise in business intelligence and data warehousing strategy and implementation. Dataspaces are an abstraction in data management that aims to overcome some of the problems encountered in data integration system. In our case it is block vector for heterogeneous data representation. Traditionally, data integration and data exchange systems have aimed to offer many of the purported services of dataspace systems. Dataspaces can be viewed as a next step in the evolution of data integration architectures, but are distinct from current data integration systems in the following way. Data integration systems require semantic integration before any services can be provided. Hence, although there is not a single schema to which all the data conforms and the data resides in a multitude of host systems, the data integration system knows the precise relationships between the terms used in each schema. As a result, significant up-front effort is required in order to set up a data integration system. For realization of data integration from different sources we used SQL Server Integration Services, SSIS. For developing the portal as an architectural pattern there is used pattern Model-View-Controller (MVC). There is specifics debug operation data space as a complex system. The query translator in Backus/Naur Form is give.
Palindromic Ziv-Lempel and Crochemore Factorizations of $m$-Bonacci Infinite Words
Venue: arXiv cs.DM 2019
Year: 2019

We introduce a variation of the Ziv-Lempel and Crochemore factorizations of words by requiring each factor to be a palindrome. We compute these factorizations for the Fibonacci word, and more generally, for all $m$-bonacci words.
Adaptive filter ordering in Spark
Venue: arXiv cs.DB 2019
Year: 2019

This report describes a technical methodology to render the Apache Spark execution engine adaptive. It presents the engineering solutions, which specifically target to adaptively reorder predicates in data streams with evolving statistics. The system extension developed is available as an open-source prototype. Indicative experimental results show its overhead and sensitivity to tuning parameters.