OpenRaiser/Intern-Atlas huggingface dataset:
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus
Venue: arXiv cs.CL 2019
Year: 2019

We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on various GEC dataset, includi ng a low resource track of the shared task at Building Educational Applications 2019 (BEA 2019). As a result, we achieved an F_0.5 score of 28.31 points with the test data of the low resource track.
Variance Reduction in Actor Critic Methods (ACM)
Venue: arXiv cs.LG 2019
Year: 2019

After presenting Actor Critic Methods (ACM), we show ACM are control variate estimators. Using the projection theorem, we prove that the Q and Advantage Actor Critic (A2C) methods are optimal in the sense of the $L^2$ norm for the control variate estimators spanned by functions conditioned by the current state and action. This straightforward application of Pythagoras theorem provides a theoretical justification of the strong performance of QAC and AAC most often referred to as A2C methods in deep policy gradient methods. This enables us to derive a new formulation for Advantage Actor Critic methods that has lower variance and improves the traditional A2C method.
Multisensory Learning Framework for Robot Drumming
Venue: arXiv cs.RO 2019
Year: 2019

The hype about sensorimotor learning is currently reaching high fever, thanks to the latest advancement in deep learning. In this paper, we present an open-source framework for collecting large-scale, time-synchronised synthetic data from highly disparate sensory modalities, such as audio, video, and proprioception, for learning robot manipulation tasks. We demonstrate the learning of non-linear sensorimotor mappings for a humanoid drumming robot that generates novel motion sequences from desired audio data using cross-modal correspondences. We evaluate our system through the quality of its cross-modal retrieval, for generating suitable motion sequences to match desired unseen audio or video sequences.
Using Model Checking to Formally Verify Rendezvous Algorithms for Robots with Lights in Euclidean Space
Venue: arXiv cs.DC 2019
Year: 2019

The paper details the first successful attempt at using model-checking techniques to verify the correctness of distributed algorithms for robots evolving in a \emph{continuous} environment. The study focuses on the problem of rendezvous of two robots with lights. There exist many different rendezvous algorithms that aim at finding the minimal number of colors needed to solve rendezvous in various synchrony models (e.g., FSYNC, SSYNC, ASYNC). While these rendezvous algorithms are typically very simple, their analysis and proof of correctness tend to be extremely complex, tedious, and error-prone as impossibility results are based on subtle interactions between robots activation schedules. The paper presents a generic verification model written for the SPIN model-checker. In particular, we explain the subtle design decisions that allow to keep the search space finite and tractable, as well as prove several important theorems that support them. As a sanity check, we use the model to verify several known rendezvous algorithms in six different models of synchrony. In each case, we find that the results obtained from the model-checker are consistent with the results known in the literature. The model-checker outputs a counter-example execution in every case that is known to fail. In the course of developing and proving the validity of the model, we identified several fundamental theorems, including the ability for a well chosen algorithm and ASYNC scheduler to produce an emerging property of memory in a system of oblivious mobile robots, and why it is not a problem for luminous rendezvous algorithms.
Convolutional Dictionary Learning in Hierarchical Networks
Venue: arXiv cs.LG 2019
Year: 2019

Filter banks are a popular tool for the analysis of piecewise smooth signals such as natural images. Motivated by the empirically observed properties of scale and detail coefficients of images in the wavelet domain, we propose a hierarchical deep generative model of piecewise smooth signals that is a recursion across scales: the low pass scale coefficients at one layer are obtained by filtering the scale coefficients at the next layer, and adding a high pass detail innovation obtained by filtering a sparse vector. This recursion describes a linear dynamic system that is a non-Gaussian Markov process across scales and is closely related to multilayer-convolutional sparse coding (ML-CSC) generative model for deep networks, except that our model allows for deeper architectures, and combines sparse and non-sparse signal representations. We propose an alternating minimization algorithm for learning the filters in this hierarchical model given observations at layer zero, e.g., natural images. The algorithm alternates between a coefficient-estimation step and a filter update step. The coefficient update step performs sparse (detail) and smooth (scale) coding and, when unfolded, leads to a deep neural network. We use MNIST to demonstrate the representation capabilities of the model, and its derived features (coefficients) for classification.
A Simple Design of IRS-NOMA Transmission
Venue: arXiv cs.IT 2019
Year: 2019

This letter proposes a simple design of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) transmission, which can ensure that more users are served on each orthogonal spatial direction than spatial division multiple access (SDMA). In particular, by employing IRS, the directions of users' channel vectors can be effectively aligned, which facilitates the implementation of NOMA. Both analytical and simulation results are provided to demonstrate the performance of the proposed IRS-NOMA scheme and also study the impact of hardware impairments on IRS-NOMA.
Detection of Real-world Driving-induced Affective State Using Physiological Signals and Multi-view Multi-task Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and increase safety, comfort and well-being. Recent advances in affective computing have enabled the detection of such states. This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety. In this work, we propose a multiview multi-task machine learning method for the detection of driver's affective states using physiological signals. The proposed approach is able to account for inter-drive variability in physiological responses while enabling interpretability of the learned models, a factor that is especially important in systems deployed in the real world. We evaluate the models on three different datasets containing real-world driving experiences. Our results indicate that accounting for drive-specific differences significantly improves model performance.
Deep Temporal Analysis for Non-Acted Body Affect Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Affective computing is a field of great interest in many computer vision applications, including video surveillance, behaviour analysis, and human-robot interaction. Most of the existing literature has addressed this field by analysing different sets of face features. However, in the last decade, several studies have shown how body movements can play a key role even in emotion recognition. The majority of these experiments on the body are performed by trained actors whose aim is to simulate emotional reactions. These unnatural expressions differ from the more challenging genuine emotions, thus invalidating the obtained results. In this paper, a solution for basic non-acted emotion recognition based on 3D skeleton and Deep Neural Networks (DNNs) is provided. The proposed work introduces three majors contributions. First, unlike the current state-of-the-art in non-acted body affect recognition, where only static or global body features are considered, in this work also temporal local movements performed by subjects in each frame are examined. Second, an original set of global and time-dependent features for body movement description is provided. Third, to the best of out knowledge, this is the first attempt to use deep learning methods for non-acted body affect recognition. Due to the novelty of the topic, only the UCLIC dataset is currently considered the benchmark for comparative tests. On the latter, the proposed method outperforms all the competitors.
Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulator
Venue: arXiv cs.NI 2019
Year: 2019

Autonomous vehicles, on the ground and in the air, are the next big evolution in human mobility. While autonomous driving in highway scenarios is already possible using only the vehicles sensors, the complex scenarios of big cities with all its different traffic participants is still a vision. Cellular Vehicle-to-Everything (C-V2X) communication is a necessary enabler of this vision and and an emerging field of interest in today's research. However, to the best of our knowledge open source simulators essential for open research do not exist yet. In this work we present our open source C-V2X mode 4 simulator based on the discrete-event network simulator ns-3. To analyze the performance of C-V2X mode 4 using our simulator, we created a worst case scenario and the 3GPP reference Manhattan grid scenario using the microscopic traffic simulator SUMO. We also added the WINNER+ B1 channel model to ns-3, as this is also used by 3GPP. Our results show, that C-V2X is scalable to 250 vehicles within a worst case scenario on a playground of 100 m x 100 m, with respect to the LTE rel. 14 V2X requirements. For the more realistic Manhattan grid scenario, the performance is better, as to be expected. We also analyzed the Packet Inter-Reception time with an outcome of max. 100 ms for more than 99 % of all transmissions. In addition, we investigated the impact of the Resource Reservation Period and the Resource Reselection Probability on the system's Packet Reception Ratio.
Multimodal Age and Gender Classification Using Ear and Profile Face Images
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present multimodal deep neural network frameworks for age and gender classification, which take input a profile face image as well as an ear image. Our main objective is to enhance the accuracy of soft biometric trait extraction from profile face images by additionally utilizing a promising biometric modality: ear appearance. For this purpose, we provided end-to-end multimodal deep learning frameworks. We explored different multimodal strategies by employing data, feature, and score level fusion. To increase representation and discrimination capability of the deep neural networks, we benefited from domain adaptation and employed center loss besides softmax loss. We conducted extensive experiments on the UND-F, UND-J2, and FERET datasets. Experimental results indicated that profile face images contain a rich source of information for age and gender classification. We found that the presented multimodal system achieves very high age and gender classification accuracies. Moreover, we attained superior results compared to the state-of-the-art profile face image or ear image-based age and gender classification methods.
Generalized Deletion Propagation on Counting Conjunctive Query Answers
Venue: arXiv cs.DB 2019
Year: 2019

We investigate the computational complexity of minimizing the source side-effect in order to remove a given number of tuples from the output of a conjunctive query. In particular, given a multi-relational database $D$, a conjunctive query $Q$, and a positive integer $k$ as input, the goal is to find a minimum subset of input tuples to remove from D that would eliminate at least $k$ output tuples from $Q(D)$. This problem generalizes the well-studied deletion propagation problem in databases. In addition, it encapsulates the notion of intervention for aggregate queries used in data analysis with applications to explaining interesting observations on the output. We show a dichotomy in the complexity of this problem for the class of full conjunctive queries without self-joins by giving a characterization on the structure of $Q$ that makes the problem either polynomial-time solvable or NP-hard. Our proof of this dichotomy result already gives an exact algorithm in the easy cases; we complement this by giving an approximation algorithm for the hard cases of the problem.
Spectral Visualization Sharpening
Venue: arXiv cs.GR 2019
Year: 2019

In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations.
Mixed-Supervised Dual-Network for Medical Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning based medical image segmentation models usually require large datasets with high-quality dense segmentations to train, which are very time-consuming and expensive to prepare. One way to tackle this challenge is by using the mixed-supervised learning framework, in which only a part of data is densely annotated with segmentation label and the rest is weakly labeled with bounding boxes. The model is trained jointly in a multi-task learning setting. In this paper, we propose Mixed-Supervised Dual-Network (MSDN), a novel architecture which consists of two separate networks for the detection and segmentation tasks respectively, and a series of connection modules between the layers of the two networks. These connection modules are used to transfer useful information from the auxiliary detection task to help the segmentation task. We propose to use a recent technique called "Squeeze and Excitation" in the connection module to boost the transfer. We conduct experiments on two medical image segmentation datasets. The proposed MSDN model outperforms multiple baselines.
Motion-Aware Feature for Improved Video Anomaly Detection
Venue: arXiv cs.CV 2019
Year: 2019

Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feature. This feature alone can achieve competitive performance with previous state-of-the-art methods, and when combined with them, can achieve significant performance improvements. Furthermore, we incorporate temporal context into the Multiple Instance Learning (MIL) ranking model by using an attention block. The learned attention weights can help to differentiate between anomalous and normal video segments better. With the proposed motion-aware feature and the temporal MIL ranking model, we outperform previous approaches by a large margin on both anomaly detection and anomalous action recognition tasks in the UCF Crime dataset.
Receding Horizon Optimization for Energy-Efficient UAV Communication
Venue: arXiv cs.IT 2019
Year: 2019

In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV's energy efficiency (EE), which is defined as the achievable rate among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method.
A Forensic Audit of the Tor Browser Bundle
Venue: arXiv cs.CR 2019
Year: 2019

The increasing use of encrypted data within file storage and in network communications leaves investigators with many challenges. One of the most challenging is the Tor protocol, as its main focus is to protect the privacy of the user, in both its local footprint within a host and over a network connection. The Tor browser, though, can leave behind digital artefacts which can be used by an investigator. This paper outlines an experimental methodology and provides results for evidence trails which can be used within real-life investigations.
StableNet: Semi-Online, Multi-Scale Deep Video Stabilization
Venue: arXiv cs.CV 2019
Year: 2019

Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions. In this paper we propose a data-driven online video stabilization method along with a paired dataset for deep learning. The network processes each unsteady frame progressively in a multi-scale manner, from low resolution to high resolution, and then outputs an affine transformation to stabilize the frame. Different from conventional methods which require explicit feature tracking or optical flow estimation, the underlying stabilization process is learned implicitly from the training data, and the stabilization process can be done online. Since there are limited public video stabilization datasets available, we synthesized unstable videos with different extent of shake that simulate real-life camera movement. Experiments show that our method is able to outperform other stabilization methods in several unstable samples while remaining comparable in general. Also, our method is tested on complex contents and found robust enough to dampen these samples to some extent even it was not explicitly trained in the contents.
Face Recognition for Motorcycle Engine Ignition with Messaging System
Venue: arXiv cs.CY 2019
Year: 2019

In this current world where technology is growing up day by day and scientific researchers are presenting new era of discoveries, the need for security is also increasing in all areas. At present, the vehicle usage is basic necessity for everyone. Simultaneously, protecting the vehicle against theft is also very important. Traditional vehicle security system depends on many sensors and cost is also high. When the vehicle is stolen, no more response or alternative could be available to help the owner of the vehicle to find it back. The main goal of this paper is to protect the vehicle from any unauthorized access, using fast, easy-to-use, clear, reliable and economical face recognition technique. An efficient automotive security system is implemented for anti-theft using an embedded system for starting the engine by the use of face recognition and integrated with Global Positioning System (GPS) and Global System for Mobile Communication (GSM). This proposed work is an attempt to design and develop a smart anti-theft system that uses Face recognition, GPS and GSM system to prevent theft and to determine the exact location of vehicle.
Privacy Parameter Variation Using RAPPOR on a Malware Dataset
Venue: arXiv cs.CR 2019
Year: 2019

Stricter data protection regulations and the poor application of privacy protection techniques have resulted in a requirement for data-driven companies to adopt new methods of analysing sensitive user data. The RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) method adds parameterised noise, which must be carefully selected to maintain adequate privacy without losing analytical value. This paper applies RAPPOR privacy parameter variations against a public dataset containing a list of running Android applications data. The dataset is filtered and sampled into small (10,000); medium (100,000); and large (1,200,000) sample sizes while applying RAPPOR with ? = 10; 1.0; and 0.1 (respectively low; medium; high privacy guarantees). Also, in order to observe detailed variations within high to medium privacy guarantees (? = 0.5 to 1.0), a second experiment is conducted by progressively.
Inconsistency Proofs for ASP: The ASP-DRUPE Format
Venue: arXiv cs.LO 2019
Year: 2019

Answer Set Programming (ASP) solvers are highly-tuned and complex procedures that implicitly solve the consistency problem, i.e., deciding whether a logic program admits an answer set. Verifying whether a claimed answer set is formally a correct answer set of the program can be decided in polynomial time for (normal) programs. However, it is far from immediate to verify whether a program that is claimed to be inconsistent, indeed does not admit any answer sets. In this paper, we address this problem and develop the new proof format ASP-DRUPE for propositional, disjunctive logic programs, including weight and choice rules. ASP-DRUPE is based on the Reverse Unit Propagation (RUP) format designed for Boolean satisfiability. We establish correctness of ASP-DRUPE and discuss how to integrate it into modern ASP solvers. Later, we provide an implementation of ASP-DRUPE into the wasp solver for normal logic programs. This work is under consideration for acceptance in TPLP.
Open DNN Box by Power Side-Channel Attack
Venue: arXiv cs.CR 2019
Year: 2019

Deep neural networks are becoming popular and important assets of many AI companies. However, recent studies indicate that they are also vulnerable to adversarial attacks. Adversarial attacks can be either white-box or black-box. The white-box attacks assume full knowledge of the models while the black-box ones assume none. In general, revealing more internal information can enable much more powerful and efficient attacks. However, in most real-world applications, the internal information of embedded AI devices is unavailable, i.e., they are black-box. Therefore, in this work, we propose a side-channel information based technique to reveal the internal information of black-box models. Specifically, we have made the following contributions: (1) we are the first to use side-channel information to reveal internal network architecture in embedded devices; (2) we are the first to construct models for internal parameter estimation; and (3) we validate our methods on real-world devices and applications. The experimental results show that our method can achieve 96.50\% accuracy on average. Such results suggest that we should pay strong attention to the security problem of many AI applications, and further propose corresponding defensive strategies in the future.