OpenRaiser/Intern-Atlas huggingface dataset:
A Real-time Robotic Grasp Approach with Oriented Anchor Box
Venue: arXiv cs.RO 2018
Year: 2018

Grasp is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust and real-time robotic grasp approach with fully convolutional neural network. The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors. Because the orientation of detected grasps is significant, which determines the rotation angle configuration of the gripper, we propose the Orientation Anchor Box Mechanism to regress grasp angle based on predefined assumption instead of classification or regression without any priors. With oriented anchor boxes, the grasps can be predicted more accurately and efficiently. Besides, to accelerate the network training and further improve the performance of angle regression, Angle Matching is proposed during training instead of Jaccard Index Matching. Five-fold cross validation results demonstrate that our proposed algorithm achieves an accuracy of 98.8% and 97.8% in image-wise split and object-wise split respectively, and the speed of our detection algorithm is 67 FPS with GTX 1080Ti, outperforming all the current state-of-the-art grasp detection algorithms on Cornell Dataset both in speed and accuracy. Robotic experiments demonstrate the robustness and generalization ability in unseen objects and real-world environment, with the average success rate of 90.0% and 84.2% of familiar things and unseen things respectively on Baxter robot platform.
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Venue: arXiv cs.AI 2018
Year: 2018

Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is important to detect VRUs and to predict their intentions. In this article a holistic approach for detecting intentions of VRUs by cooperative methods is presented. The intention detection consists of basic movement primitive prediction, e.g. standing, moving, turning, and a forecast of the future trajectory. Vehicles equipped with sensors, data processing systems and communication abilities, referred to as intelligent vehicles, acquire and maintain a local model of their surrounding traffic environment, e.g. crossing cyclists. Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e.g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties. The resulting model allows to extend the perceptual horizon of the individual agent beyond their own sensory capabilities, enabling a longer forecast horizon. Concealments, implausibilities and inconsistencies are resolved by the collective intelligence of cooperating agents. Novel techniques of signal processing and modelling in combination with analytical and learning based approaches of pattern and activity recognition are used for detection, as well as intention prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge fusion, takes place on the level of perception and intention recognition. Based on the requirements of the cooperative approach for the communication a new strategy for an ad hoc network is proposed.
A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images
Venue: arXiv cs.CV 2018
Year: 2018

This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation. We present a two-stage method for lesion segmentation with optimised training method and ensemble post-process. Our method achieves state-of-the-art performance on lesion segmentation and we win the first place in ISIC 2018 task1.
The $ε$-error Capacity of Symmetric PIR with Byzantine Adversaries
Venue: arXiv cs.IT 2018
Year: 2018

The capacity of symmetric private information retrieval with $K$ messages, $N$ servers (out of which any $T$ may collude), and an omniscient Byzantine adversary (who can corrupt any $B$ answers) is shown to be $1 - \frac{T+2B}{N}$ [1], under the requirement of zero probability of error. In this work, we show that by weakening the adversary slightly (either providing secret low rate channels between the servers and the user, or limiting the observation of the adversary), and allowing vanishing probability of error, the capacity increases to $1 - \frac{T+B}{N}$.
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
Venue: arXiv cs.CL 2018
Year: 2018

Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot well identify whether a sentence is semantically rational. The methods based on the language model do not measure the sentence by rationality but by commonness. The methods based on the similarity with human written sentences will fail if human-written references are not available. In this paper, we propose a novel model called Sememe-Word-Matching Neural Network (SWM-NN) to tackle semantic rationality evaluation by taking advantage of sememe knowledge base HowNet. The advantage is that our model can utilize a proper combination of sememes to represent the fine-grained semantic meanings of a word within the specific contexts. We use the fine-grained semantic representation to help the model learn the semantic dependency among words. To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset. Experimental results on this dataset show that the proposed model outperforms the competitive baselines with a 5.4\% improvement in accuracy.
A consistent approach for fluid-structure-contact interaction based on a porous flow model for rough surface contact
Venue: arXiv cs.CE 2018
Year: 2018

Simulation approaches for fluid-structure-contact interaction, especially if requested to be consistent even down to the real contact scenarios, belong to the most challenging and still unsolved problems in computational mechanics. The main challenges are twofold - one is to have a correct physical model for this scenario, and the other one is to have a numerical method that is capable of working and being consistent down to a zero gap. And when analyzing such challenging setups of fluid-structure interaction that include contact of submersed solid components it gets obvious that the influence of surface roughness effects is essential for a physical consistent modeling of such configurations. To capture this system behavior, we present a continuum mechanical model which is able to include the effects of the surface microstructure in a fluid-structure-contact interaction framework. An averaged representation for the mixture of fluid and solid on the rough surfaces, which is of major interest for the macroscopic response of such a system, is introduced therein. The inherent coupling of the macroscopic fluid flow and the flow inside the rough surfaces, the stress exchange of all contacting solid bodies involved, and the interaction between fluid and solid is included in the construction of the model. Although the physical model is not restricted to finite element based methods, a numerical approach with its core based on the Cut Finite Element Method (CutFEM), enabling topological changes of the fluid domain to solve the presented model numerically, is introduced. Such a CutFEM based approach is able to deal with the numerical challenges mentioned above. Different test cases give a perspective towards the potential capabilities of the presented physical model and numerical approach.
Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
Venue: arXiv cs.SY 2018
Year: 2018

A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barrier, they mostly rely on recognizing spatio-temporal patterns, without giving insights on how the performance are tied with the system features and the sensor deployment. In this paper, we try to fill this gap and investigate the limitations and performance limits of fault localization using Phasor Measurement Units (PMUs), in the low measurements regime, i.e., when the system is unobservable with the measurements available. Our main contribution is to show how one can leverage the scarce measurements to localize different type of distribution line faults (three-phase, single-phase to ground, ...) at the level of sub-graph, rather than with the resolution of a line. We show that the resolution we obtain is strongly tied with the graph clustering notion in network science.
Reducing Uncertainty of Schema Matching via Crowdsourcing with Accuracy Rates
Venue: arXiv cs.DB 2018
Year: 2018

Schema matching is a central challenge for data integration systems. Inspired by the popularity and the success of crowdsourcing platforms, we explore the use of crowdsourcing to reduce the uncertainty of schema matching. Since crowdsourcing platforms are most effective for simple questions, we assume that each Correspondence Correctness Question (CCQ) asks the crowd to decide whether a given correspondence should exist in the correct matching. Furthermore, members of a crowd may sometimes return incorrect answers with different probabilities. Accuracy rates of individual crowd workers are probabilities of returning correct answers which can be attributes of CCQs as well as evaluations of individual workers. We prove that uncertainty reduction equals to entropy of answers minus entropy of crowds and show how to obtain lower and upper bounds for it. We propose frameworks and efficient algorithms to dynamically manage the CCQs to maximize the uncertainty reduction within a limited budget of questions. We develop two novel approaches, namely `Single CCQ' and `Multiple CCQ', which adaptively select, publish and manage questions. We verify the value of our solutions with simulation and real implementation.
Neural-Augmented Static Analysis of Android Communication
Venue: arXiv cs.PL 2018
Year: 2018

We address the problem of discovering communication links between applications in the popular Android mobile operating system, an important problem for security and privacy in Android. Any scalable static analysis in this complex setting is bound to produce an excessive amount of false-positives, rendering it impractical. To improve precision, we propose to augment static analysis with a trained neural-network model that estimates the probability that a communication link truly exists. We describe a neural-network architecture that encodes abstractions of communicating objects in two applications and estimates the probability with which a link indeed exists. At the heart of our architecture are type-directed encoders (TDE), a general framework for elegantly constructing encoders of a compound data type by recursively composing encoders for its constituent types. We evaluate our approach on a large corpus of Android applications, and demonstrate that it achieves very high accuracy. Further, we conduct thorough interpretability studies to understand the internals of the learned neural networks.
Poisoning Attacks to Graph-Based Recommender Systems
Venue: arXiv cs.IR 2018
Year: 2018

Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacker desires. However, these poisoning attacks are either agnostic to recommendation algorithms or optimized to recommender systems that are not graph-based. Like association-rule-based and matrix-factorization-based recommender systems, graph-based recommender system is also deployed in practice, e.g., eBay, Huawei App Store. However, how to design optimized poisoning attacks for graph-based recommender systems is still an open problem. In this work, we perform a systematic study on poisoning attacks to graph-based recommender systems. Due to limited resources and to avoid detection, we assume the number of fake users that can be injected into the system is bounded. The key challenge is how to assign rating scores to the fake users such that the target item is recommended to as many normal users as possible. To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users. We also propose techniques to solve the optimization problem. We evaluate our attacks and compare them with existing attacks under white-box (recommendation algorithm and its parameters are known), gray-box (recommendation algorithm is known but its parameters are unknown), and black-box (recommendation algorithm is unknown) settings using two real-world datasets. Our results show that our attack is effective and outperforms existing attacks for graph-based recommender systems. For instance, when 1% fake users are injected, our attack can make a target item recommended to 580 times more normal users in certain scenarios.
Magnetically Guided Capsule Endoscopy
Venue: arXiv cs.CV 2018
Year: 2018

The following research undertakes a historical review of this technology with specific highlighting of its advancement in medical diagnostics as well as the therapeutic functionality of wireless capsule endoscopy. Without restriction to the gastrointestinal tract alone, the review will additionally investigate the developments in the technology of micro-robots guided through the magnetic power and are capable of navigating through multiple forms of air and fluid filled lumina as well as cavities within the body. All these capabilities are of use in the utilization of minimally invasive medicine.
On-Demand TDMA for Energy Efficient Data Collection with LoRa and Wake-up Receiver
Venue: arXiv cs.NI 2018
Year: 2018

Low-power and long-range communication tech- nologies such as LoRa are becoming popular in IoT applications due to their ability to cover kilometers range with milliwatt of power consumption. One of the major drawbacks of LoRa is the data latency and the traffic congestion when the number of devices in the network increases. Especially, the latency arises due to the extreme duty cycling of LoRa end-nodes for reducing the overall energy consumption. To overcome this drawback, we propose a heterogeneous network architecture and an energy-efficient On-demand TDMA communication scheme improving both the device lifetime and the data latency of standard LoRa networks. We combine the capabilities of micro- watt wake-up receivers to achieve ultra-low power states and pure asynchronous communication together with the long-range connectivity of LoRa. Experimental results show a data reliability of 100% and a round-trip latency on the order of milliseconds with end devices dissipating less than 46 mJ when active and 1.83 μW during periods of inactivity, lasting up to 3 years on a 1200 mAh Lithium battery.
A Cryptographic Escrow for Treaty Declarations and Step-by-Step Verification
Venue: arXiv cs.CR 2018
Year: 2018

The verification of arms-control and disarmament agreements requires states to provide declarations, including information on sensitive military sites and assets. There are important cases, however, where negotiations of these agreements are impeded because states are reluctant to provide any such data, because of concerns about prematurely handing over militarily significant information. To address this challenge, we present a cryptographic escrow that allows a state to make a complete declaration of sites and assets at the outset and commit to its content, but only reveal the sensitive information therein sequentially. Combined with an inspection regime, our escrow allows for step-by-step verification of the correctness and completeness of the initial declaration so that the information release and inspections keep pace with parallel diplomatic and political processes. We apply this approach to the possible denuclearization of North Korea. Such approach can be applied, however, to any agreement requiring the sharing of sensitive information.
Security and Privacy Enhancement for Outsourced Biometric Identification
Venue: arXiv cs.CR 2018
Year: 2018

A lot of research has been focused on secure outsourcing of biometric identification in the context of cloud computing. In such schemes, both the encrypted biometric database and the identification process are outsourced to the cloud. The ultimate goal is to protect the security and privacy of the biometric database and the query templates. Security analysis shows that previous schemes suffer from the enrolment attack and unnecessarily expose more information than needed. In this paper, we propose a new secure outsourcing scheme aims at enhancing the security from these two aspects. First, besides all the attacks discussed in previous schemes, our proposed scheme is also secure against the enrolment attack. Second, we model the identification process as a fixed radius similarity query problem instead of the kNN search problem. Such a modelling is able to reduce the exposed information thus enhancing the privacy of the biometric database. Our comprehensive security and complexity analysis show that our scheme is able to enhance the security and privacy of the biometric database and query templates while maintaining the same computational savings from outsourcing.
An Approach to Handle Big Data Warehouse Evolution
Venue: arXiv cs.DB 2018
Year: 2018

One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used for several decades to achieve the same goal, they could be leveraged also to provide analysis of data stored in Big Data systems. The problem of adapting data warehouse data and schemata to changes in these requirements as well as data sources has been studied by many researchers worldwide. However, innovative methods must be developed also to support evolution of data warehouses that are used to analyze data stored in Big Data systems. In this paper, we propose a data warehouse architecture that allows to perform different kinds of analytical tasks, including OLAP-like analysis, on big data loaded from multiple heterogeneous data sources with different latency and is capable of processing changes in data sources as well as evolving analysis requirements. The operation of the architecture is highly based on the metadata that are outlined in the paper.
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
Venue: arXiv cs.RO 2018
Year: 2018

Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed---rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.
Poster Abstract: LPWA-MAC - a Low Power Wide Area network MAC protocol for cyber-physical systems
Venue: arXiv cs.SY 2018
Year: 2018

Low-Power Wide-Area Networks (LPWANs) are being successfully used for the monitoring of large-scale systems that are delay-tolerant and which have low-bandwidth requirements. The next step would be instrumenting these for the control of Cyber-Physical Systems (CPSs) distributed over large areas which require more bandwidth, bounded delays and higher reliability or at least more rigorous guarantees therein. This paper presents LPWA-MAC, a novel Low Power Wide-Area network MAC protocol, that ensures bounded end-to-end delays, high channel utility and supports many of the different traffic patterns and data-rates typical of CPS.
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
Venue: arXiv cs.LG 2018
Year: 2018

While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in memory. In this paper, we introduce a natural and expressive way to tackle these problems, by incorporating GPs in sum-product networks (SPNs), a recently proposed tractable probabilistic model allowing exact and efficient inference. In particular, by using GPs as leaves of an SPN we obtain a novel flexible prior over functions, which implicitly represents an exponentially large mixture of local GPs. Exact and efficient posterior inference in this model can be done in a natural interplay of the inference mechanisms in GPs and SPNs. Thereby, each GP is -- similarly as in a mixture of experts approach -- responsible only for a subset of data points, which effectively reduces inference cost in a divide and conquer fashion. We show that integrating GPs into the SPN framework leads to a promising probabilistic regression model which is: (1) computational and memory efficient, (2) allows efficient and exact posterior inference, (3) is flexible enough to mix different kernel functions, and (4) naturally accounts for non-stationarities in time series. In a variate of experiments, we show that the SPN-GP model can learn input dependent parameters and hyper-parameters and is on par with or outperforms the traditional GPs as well as state of the art approximations on real-world data.
Combined Reinforcement Learning via Abstract Representations
Venue: arXiv cs.LG 2018
Year: 2018

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.