OpenRaiser/Intern-Atlas huggingface dataset:
On Timely Channel Coding with Hybrid ARQ
Venue: arXiv cs.IT 2019
Year: 2019

A status updating communication system is examined, in which a transmitter communicates with a receiver over a noisy channel. The goal is to realize timely delivery of fresh data over time, which is assessed by an age-of-information (AoI) metric. Channel coding is used to combat the channel errors, and feedback is sent to acknowledge updates' reception. In case decoding is unsuccessful, a hybrid ARQ protocol is employed, in which incremental redundancy (IR) bits are transmitted to enhance the decoding ability. This continues for some amount of time in case decoding remains unsuccessful, after which a new (fresh) status update is transmitted instead. In case decoding is successful, the transmitter has the option to idly wait for a certain amount of time before sending a new update. A general problem is formulated that optimizes the codeword and IR lengths for each update, and the waiting times, such that the long term average AoI is minimized. Stationary deterministic policies are investigated, in which the codeword and IR lengths are fixed for each update, and the waiting time is a deterministic function of the AoI. The optimal waiting policy is then derived, and is shown to have a threshold structure, in which the transmitter sends a new update only if the AoI grows above a certain threshold that is a function of the codeword and IR lengths. Choosing the codeword and IR lengths is discussed in the context of binary symmetric channels.
On the representation of speech and music
Venue: arXiv cs.SD 2019
Year: 2019

In most automatic speech recognition (ASR) systems, the audio signal is processed to produce a time series of sensor measurements (e.g., filterbank outputs). This time series encodes semantic information in a speaker-dependent way. An earlier paper showed how to use the sequence of sensor measurements to derive an "inner" time series that is unaffected by any previous invertible transformation of the sensor measurements. The current paper considers two or more speakers, who mimic one another in the following sense: when they say the same words, they produce sensor states that are invertibly mapped onto one another. It follows that the inner time series of their utterances must be the same when they say the same words. In other words, the inner time series encodes their speech in a manner that is speaker-independent. Consequently, the ASR training process can be simplified by collecting and labelling the inner time series of the utterances of just one speaker, instead of training on the sensor time series of the utterances of a large variety of speakers. A similar argument suggests that the inner time series of music is instrument-independent. This is demonstrated in experiments on monophonic electronic music.
Advancements in Image Classification using Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet model. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.
On Coverage Probability in Uplink NOMA With Instantaneous Signal Power-Based User Ranking
Venue: arXiv cs.IT 2019
Year: 2019

In uplink non-orthogonal multiple access (NOMA) networks, the order of decoding at the base station (BS) depends on the received instantaneous signal powers (ISPs). Therefore, the signal-to-interference-plus-noise ratio (SINR) coverage probability for an uplink NOMA user should be calculated using the ISP-based ranking of the users. In the existing literature, however, mean signal power (MSP)-based ranking is used to determine the decoding order and calculate the SINR coverage probability. Although this approximation provides tractable results, it is not accurate. In this letter, we derive the coverage probability for ISP-based ranking, and we show that MSP-based ranking underestimates the SINR coverage probability.
Weakly Labeling the Antarctic: The Penguin Colony Case
Venue: arXiv cs.CV 2019
Year: 2019

Antarctic penguins are important ecological indicators -- especially in the face of climate change. In this work, we present a deep learning based model for semantic segmentation of Adélie penguin colonies in high-resolution satellite imagery. To train our segmentation models, we take advantage of the Penguin Colony Dataset: a unique dataset with 2044 georeferenced cropped images from 193 Adélie penguin colonies in Antarctica. In the face of a scarcity of pixel-level annotation masks, we propose a weakly-supervised framework to effectively learn a segmentation model from weak labels. We use a classification network to filter out data unsuitable for the segmentation network. This segmentation network is trained with a specific loss function, based on the average activation, to effectively learn from the data with the weakly-annotated labels. Our experiments show that adding weakly-annotated training examples significantly improves segmentation performance, increasing the mean Intersection-over-Union from 42.3 to 60.0% on the Penguin Colony Dataset.
Data-Efficient Mutual Information Neural Estimator
Venue: arXiv cs.LG 2019
Year: 2019

Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (Belghazi et al. 2018), focused on estimating tight variational lower bounds of MI using neural networks, but assumed unlimited supply of samples to prevent overfitting. In real world applications, data is not always available at a surplus. In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE), by developing a relaxed predictive MI lower bound that can be estimated at higher data efficiency by orders of magnitudes. The predictive MI lower bound also enables us to develop a new meta-learning approach using task augmentation, Meta-DEMINE, to improve generalization of the network and further boost estimation accuracy empirically. With improved data-efficiency, our estimators enables statistical testing of dependency at practical dataset sizes. We demonstrate the effectiveness of our estimators on synthetic benchmarks and a real world fMRI data, with application of inter-subject correlation analysis.
SMT-based Constraint Answer Set Solver EZSMT+
Venue: arXiv cs.LO 2019
Year: 2019

Constraint answer set programming integrates answer set programming with constraint processing. System EZSMT+ is a constraint answer set programming tool that utilizes satisfiability modulo theory solvers for search. Its theoretical foundation lies on generalizations of Niemela's characterization of answer sets of a logic program via so called level rankings.
A New Hybrid Control Architecture to Attenuate Large Horizontal Wind Disturbance for a Small-Scale Unmanned Helicopter
Venue: arXiv cs.SY 2019
Year: 2019

This paper presents a novel method to attenuate large horizontal wind disturbance for a small-scale unmanned autonomous helicopter combining wind tunnel-based experimental data and a backstepping algorithm. Large horizontal wind disturbance is harmful to autonomous helicopters, especially to small ones because of their low inertia and the high cross-coupling effects among the multiple inputs. In order to achieve more accurate and faster attenuation of large wind disturbance, a new hybrid control architecture is proposed to take advantage of the direct force/moment compensation based on the wind tunnel experimental data. In this architecture, large horizontal wind disturbance is treated as an additional input to the control system instead of a small perturbation around the equilibrium state. A backstepping algorithm is then designed to guarantee the stable convergence of the hilicopter to the desired position. The proposed technique is finally evaluated in simulation on the platform, HIROBO Eagle, compared with a traditional wind velocity compensation method.
General Method for Prime-point Cyclic Convolution over the Real Field
Venue: arXiv cs.AI 2019
Year: 2019

A general and fast method is conceived for computing the cyclic convolution of n points, where n is a prime number. This method fully exploits the internal structure of the cyclic matrix, and hence leads to significant reduction of the multiplication complexity in terms of CPU time by 50%, as compared with Winograd's algorithm. In this paper, we only consider the real and complex fields due to their most important applications, but in general, the idea behind this method can be extended to any finite field of interest. Clearly, it is well-known that the discrete Fourier transform (DFT) can be expressed in terms of cyclic convolution, so it can be utilized to compute the DFT when the block length is a prime.
Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack
Venue: arXiv cs.CR 2019
Year: 2019

"Feint Attack", as a new type of APT attack, has become the focus of attention. It adopts a multi-stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused huge losses inadvertently. However, to our knowledge, all previous works use common methods such as Causal-Correlation or Cased-based to detect outdated multi-stage attacks. Few attentions have been paid to detect the "Feint Attack", because the difficulty of detection lies in the diversification of the concept of "Feint Attack" and the lack of professional datasets, many detection methods ignore the semantic relationship in the attack. Aiming at the existing challenge, this paper explores a new method to solve the problem. In the attack scenario, the fuzzy clustering method based on attribute similarity is used to mine multi-stage attack chains. Then we use a few-shot deep learning algorithm (SMOTE&CNN-SVM) and bidirectional Recurrent Neural Network model (Bi-RNN) to obtain the "Feint Attack" chains. "Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain. So we used Bi-RNN coding to obtain the hidden feature of "Feint Attack" chain. In the end, our method achieved the goal to detect the "Feint Attack" accurately by using the LLDoS1.0 and LLDoS2.0 of DARPA2000 and CICIDS2017 of Canadian Institute for Cybersecurity.
Limits of Deepfake Detection: A Robust Estimation Viewpoint
Venue: arXiv cs.LG 2019
Year: 2019

Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximation for the low error regime. Lastly, relationships between error probability and epidemic thresholds for spreading processes in networks are established.
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
Venue: arXiv cs.NI 2019
Year: 2019

Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination. Reinforcement learning (RL) has been introduced to design autonomous packet routing policies with local information of stochastic packet arrival and service. However, the curse of dimensionality of RL prohibits the more comprehensive representation of dynamic network states, thus limiting its potential benefit. In this paper, we propose a novel packet routing framework based on \emph{multi-agent} deep reinforcement learning (DRL) in which each router possess an \emph{independent} LSTM recurrent neural network for training and decision making in a \emph{fully distributed} environment. The LSTM recurrent neural network extracts routing features from rich information regarding backlogged packets and past actions, and effectively approximates the value function of Q-learning. We further allow each route to communicate periodically with direct neighbors so that a broader view of network state can be incorporated. Experimental results manifest that our multi-agent DRL policy can strike the delicate balance between congestion-aware and shortest routes, and significantly reduce the packet delivery time in general network topologies compared with its counterparts.
Detecting Sybil Attacks in Vehicular Ad Hoc Networks
Venue: arXiv cs.NI 2019
Year: 2019

Ad hoc networks is vulnerable to numerous number of attacks due to its infrastructure-less nature, one of these attacks is the Sybil attack. Sybil attack is a severe attack on vehicular ad hoc networks (VANET) in which the intruder maliciously claims or steals multiple identities and use these identities to disturb the functionality of the VANET network by disseminating false identities. Many solutions have been proposed in order to defense the VANET network against the Sybil attack. In this research a hybrid algorithm is proposed, by combining footprint and privacy-preserving detection of abuses of pseudonyms (P2DAP) methods. The hybrid detection algorithm is implemented using the ns2 simulator. The proposed algorithm is working as follows, P2DAP acting better than footprint when the number of vehicles increases. On the other hand, the footprint algorithm acting better when the speed of vehicles increases. The hybrid algorithm depends on encryption, authentication and on the trajectory of the vehicle. The scenarios will be generated using SUMO and MOVE tools.
Reversible Data Hiding in JPEG Images with Multi-objective Optimization
Venue: arXiv cs.MM 2019
Year: 2019

Among various methods of reversible data hiding (RDH) in JPEG images, the consideration in designing is only the image quality, but the image quality and the file size expansion are equally important in JPEG images. Based on this situation, we propose a RDH scheme in JPEG images considering both the image quality and the file size expansion while designing the algorithm. The multi-objective optimization strategy is utilized to realize the balance of the two objectives. Specifically, the cover is divided into several non-overlapping signals firstly, and after that, the embedding costs of signals are calculated using the knowledge of the JPEG compression. Next, the optimized combination of signals for embedding data is gained by the multi-objective optimization. Experimental results show the better performance of our proposed RDH compared with state-of-the-art RDH in JPEG images.
Jcave: A 3D Interactive Game to Assist Home Physiotherapy Rehabilitation
Venue: arXiv cs.HC 2019
Year: 2019

The purpose of this paper is to investigate the applicability of applying gamification approach on the physiotherapy rehabilitation. A new developing game called JCave was designed and developed for the prove of concept. The propose game target the children from six to twelve years of age who need physical therapy in their upper limbs. JCave is a finite and multilevel single-player 3D video game. The player's role is to collect jewels from a cave and increase his/her score by performing physical therapy exercises. The game uses Xbox360 Kinect as a motion capture camera to observe gestures and track the child. Automatic gesture recognition algorithms are implemented for elbow flexion-extension exercises and shoulder flexion, which are the active range of motion (AROM) exercises for both the right and left arms. The JCave game is implemented using Unity3D and Blender to design 3D model objects.
Investigation of Multifractal Properties of Additive Data Stream
Venue: arXiv cs.NI 2019
Year: 2019

The work presents results of a numerical study of fractal characteristics of multifractal stream at addition of stream, which does not have multifractal properties. They showed that the generalized Hurst exponent of total stream tends to one of original multifractal stream with increase in signal/noise ratio.
The multifractal load balancing method
Venue: arXiv cs.NI 2019
Year: 2019

The load-balancing system, built on the basis of a subsystem load balancer and subsystem control and monitoring that closely interact with each other was propose in work. This system is presented as a queuing system with priority service discipline. In the described queuing system parallel processing flow applications occur in the multiple serving devices and successive junction of them into unified stream is done. The method of multifractal load balancing is submited on the basis of the developed system of load balancing.
Fast and Efficient Zero-Learning Image Fusion
Venue: arXiv cs.CV 2019
Year: 2019

We propose a real-time image fusion method using pre-trained neural networks. Our method generates a single image containing features from multiple sources. We first decompose images into a base layer representing large scale intensity variations, and a detail layer containing small scale changes. We use visual saliency to fuse the base layers, and deep feature maps extracted from a pre-trained neural network to fuse the detail layers. We conduct ablation studies to analyze our method's parameters such as decomposition filters, weight construction methods, and network depth and architecture. Then, we validate its effectiveness and speed on thermal, medical, and multi-focus fusion. We also apply it to multiple image inputs such as multi-exposure sequences. The experimental results demonstrate that our technique achieves state-of-the-art performance in visual quality, objective assessment, and runtime efficiency.
Unified Adversarial Invariance
Venue: arXiv cs.LG 2019
Year: 2019

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions. Invariance to nuisance is achieved by learning a split representation of data through competitive training between the prediction task and a reconstruction task coupled with disentanglement, whereas that to biasing factors is brought about by penalizing the network if the latent embedding contains any information about them. We describe an adversarial instantiation of this framework and provide analysis of its working. Our model outperforms previous works at inducing invariance to nuisance factors without using any labeled information about such variables, and achieves state-of-the-art performance at learning independence to biasing factors in fairness settings.
Practical Algebraic Attack on DAGS
Venue: arXiv cs.CR 2019
Year: 2019

DAGS scheme is a key encapsulation mechanism (KEM) based on quasi-dyadic alternant codes that was submitted to NIST standardization process for a quantum resistant public key algorithm. Recently an algebraic attack was devised by Barelli and Couvreur (Asiacrypt 2018) that efficiently recovers the private key. It shows that DAGS can be totally cryptanalysed by solving a system of bilinear polynomial equations. However, some sets of DAGS parameters were not broken in practice. In this paper we improve the algebraic attack by showing that the original approach was not optimal in terms of the ratio of the number of equations to the number of variables. Contrary to the common belief that reducing at any cost the number of variables in a polynomial system is always beneficial, we actually observed that, provided that the ratio is increased and up to a threshold, the solving can be heavily improved by adding variables to the polynomial system. This enables us to recover the private keys in a few seconds. Furthermore, our experimentations also show that the maximum degree reached during the computation of the Gröbner basis is an important parameter that explains the efficiency of the attack. Finally, the authors of DAGS updated the parameters to take into account the algebraic cryptanalysis of Barelli and Couvreur. In the present article, we propose a hybrid approach that performs an exhaustive search on some variables and computes a Gröbner basis on the polynomial system involving the remaining variables. We then show that the updated set of parameters corresponding to 128-bit security can be broken with 2^83 operations.
Sound texture synthesis using convolutional neural networks
Venue: arXiv cs.SD 2019
Year: 2019

The following article introduces a new parametric synthesis algorithm for sound textures inspired by existing methods used for visual textures. Using a 2D Convolutional Neural Network (CNN), a sound signal is modified until the temporal cross-correlations of the feature maps of its log-spectrogram resemble those of a target texture. We show that the resulting synthesized sound signal is both different from the original and of high quality, while being able to reproduce singular events appearing in the original. This process is performed in the time domain, discarding the harmful phase recovery step which usually concludes synthesis performed in the time-frequency domain. It is also straightforward and flexible, as it does not require any fine tuning between several losses when synthesizing diverse sound textures. A way of extending the synthesis in order to produce a sound of any length is also presented, after which synthesized spectrograms and sound signals are showcased. We also discuss on the choice of CNN, on border effects in our synthesized signals and on possible ways of modifying the algorithm in order to improve its current long computation time.
Finding Optimal Longest Paths by Dynamic Programming in Parallel
Venue: arXiv cs.DS 2019
Year: 2019

We propose an exact algorithm for solving the longest simple path problem between two given vertices in undirected weighted graphs. By using graph partitioning and dynamic programming, we obtain an algorithm that is significantly faster than other state-of-the-art methods. This enables us to solve instances that were previously unsolved and solve hard instances significantly faster. Lastly, we present a scalable parallelization which yields the first efficient parallel algorithm for the problem.