OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic MIMO Symbol Detection with Expectation Consistency Approximate Inference
Venue: arXiv cs.IT 2019
Year: 2019

In this paper we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the Expectation Consistency (EC) framework, which describes the approximate inference problem as an optimization over a non-convex function. EC generalizes algorithms such as Belief Propagation and Expectation Propagation. For the MIMO symbol detection problem, we discuss feasible methods to find stationary points of the EC function and explore their tradeoffs between accuracy and speed of convergence. The accuracy is studied, first in terms of input-output mutual information and show that the proposed EC MIMO detector greatly improves state-of-the-art methods, with a complexity order cubic in the number of transmitting antennas. Second, these gains are corroborated by combining the probabilistic output of the EC detector with a low-density parity-check (LDPC) channel code.
On the estimation of the Wasserstein distance in generative models
Venue: arXiv cs.LG 2019
Year: 2019

Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets. GANs are notoriuos for training difficulties and their dependence on arbitrary hyperparameters. One recent improvement in GAN literature is to use the Wasserstein distance as loss function leading to Wasserstein Generative Adversarial Networks (WGANs). Using this as a basis, we show various ways in which the Wasserstein distance is estimated for the task of generative modelling. Additionally, the secrets in training such models are shown and summarized at the end of this work. Where applicable, we extend current works to different algorithms, different cost functions, and different regularization schemes to improve generative models.
Object Parsing in Sequences Using CoordConv Gated Recurrent Networks
Venue: arXiv cs.CV 2019
Year: 2019

We present a monocular object parsing framework for consistent keypoint localization by capturing temporal correlation on sequential data. In this paper, we propose a novel recurrent network based architecture to model long-range dependencies between intermediate features which are highly useful in tasks like keypoint localization and tracking. We leverage the expressiveness of the popular stacked hourglass architecture and augment it by adopting memory units between intermediate layers of the network with weights shared across stages for video frames. We observe that this weight sharing scheme not only enables us to frame hourglass architecture as a recurrent network but also prove to be highly effective in producing increasingly refined estimates for sequential tasks. Furthermore, we propose a new memory cell, we call CoordConvGRU which learns to selectively preserve spatio-temporal correlation and showcase our results on the keypoint localization task. The experiments show that our approach is able to model the motion dynamics between the frames and significantly outperforms the baseline hourglass network. Even though our network is trained on a synthetically rendered dataset, we observe that with minimal fine tuning on 300 real images we are able to achieve performance at par with various state-of-the-art methods trained with the same level of supervisory inputs. By using a simpler architecture than other methods enables us to run it in real time on a standard GPU which is desirable for such applications. Finally, we make our architectures and 524 annotated sequences of cars from KITTI dataset publicly available.
New Failure Rate Model for Iterative Software Development Life Cycle Process
Venue: arXiv cs.SE 2019
Year: 2019

Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid Particle Swarm Optimization and Gravitational Search Algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33 % of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU
Venue: arXiv cs.CL 2019
Year: 2019

We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.
Optimising Optimisers with Push GP
Venue: arXiv cs.NE 2019
Year: 2019

This work uses Push GP to automatically design both local and population-based optimisers for continuous-valued problems. The optimisers are trained on a single function optimisation landscape, using random transformations to discourage overfitting. They are then tested for generality on larger versions of the same problem, and on other continuous-valued problems. In most cases, the optimisers generalise well to the larger problems. Surprisingly, some of them also generalise very well to previously unseen problems, outperforming existing general purpose optimisers such as CMA-ES. Analysis of the behaviour of the evolved optimisers indicates a range of interesting optimisation strategies that are not found within conventional optimisers, suggesting that this approach could be useful for discovering novel and effective forms of optimisation in an automated manner.
Performance Analysis and Design of Non-orthogonal Multiple Access for Wireless Communications
Venue: arXiv cs.IT 2019
Year: 2019

In this thesis, we study performance analysis and resource allocation designs for non-orthogonal multiple access (NOMA) in wireless communication systems. In contrast to conventional orthogonal multiple access (OMA) schemes, NOMA allows multiple users to share the same degree of freedom via superposition coding and the successive interference cancelation (SIC) decoding. Inspired by the solid foundations from the information theory perspective, NOMA has rekindled the interests of researchers as a benefit of the recent advancement in signal processing and silicon technologies. However, comprehensive performance analysis on NOMA and practical resource allocation designs to exploit potential gains of NOMA in terms of spectral and energy efficiency have not been fully studied and investigated in the literature. This thesis attempts to address these problems by providing a unified performance analysis and a systematic resource allocation design for NOMA in wireless communication systems.
An efficient floating point multiplier design for high speed applications using Karatsuba algorithm and Urdhva-Tiryagbhyam algorithm
Venue: arXiv cs.AR 2019
Year: 2019

Floating point multiplication is a crucial operation in high power computing applications such as image processing, signal processing etc. And also multiplication is the most time and power consuming operation. This paper proposes an efficient method for IEEE 754 floating point multiplication which gives a better implementation in terms of delay and power. A combination of Karatsuba algorithm and Urdhva-Tiryagbhyam algorithm (Vedic Mathematics) is used to implement unsigned binary multiplier for mantissa multiplication. The multiplier is implemented using Verilog HDL, targeted on Spartan-3E and Virtex-4 FPGA.
A Blueprint for Interoperable Blockchains
Venue: arXiv cs.DB 2019
Year: 2019

Research in blockchain systems has mainly focused on improving security and bridging the performance gaps between blockchains and databases. Despite many promising results, we observe a worrying trend that the blockchain landscape is fragmented in which many systems exist in silos. Apart from a handful of general-purpose blockchains, such as Ethereum or Hyperledger Fabric, there are hundreds of others designed for specific applications and typically do not talk to each other. In this paper, we describe our vision of interoperable blockchains. We argue that supporting interaction among different blockchains requires overcoming challenges that go beyond data standardization. The underlying problem is to allow smart contracts running in different blockchains to communicate. We discuss three open problems: access control, general cross-chain transactions, and cross-chain communication. We describe partial solutions to some of these problems in the literature. Finally, we propose a novel design to overcome these challenges.
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
Venue: arXiv cs.CL 2019
Year: 2019

The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.
Stochastic Bandits with Delayed Composite Anonymous Feedback
Venue: arXiv cs.LG 2019
Year: 2019

We explore a novel setting of the Multi-Armed Bandit (MAB) problem inspired from real world applications which we call bandits with "stochastic delayed composite anonymous feedback (SDCAF)". In SDCAF, the rewards on pulling arms are stochastic with respect to time but spread over a fixed number of time steps in the future after pulling the arm. The complexity of this problem stems from the anonymous feedback to the player and the stochastic generation of the reward. Due to the aggregated nature of the rewards, the player is unable to associate the reward to a particular time step from the past. We present two algorithms for this more complicated setting of SDCAF using phase based extensions of the UCB algorithm. We perform regret analysis to show sub-linear theoretical guarantees on both the algorithms.
Ternary Quantized Polar Code Decoders: Analysis and Design
Venue: arXiv cs.IT 2019
Year: 2019

The performance of short polar codes under successive cancellation (SC) and SC list (SCL) decoding is analyzed for the case where the decoder messages are coarsely quantized. This setting is of particular interest for applications requiring low-complexity energy-efficient transceivers (e.g., internet-of-things or wireless sensor networks). We focus on the extreme case where the decoder messages are quantized with 3 levels. We show how under SCL decoding quantized log-likelihood ratios lead to a large inaccuracy in the calculation of path metrics, resulting in considerable performance losses with respect to an unquantized SCL decoder. We then introduce two novel techniques which improve the performance of SCL decoding with coarse quantization. The first technique consists of a modification of the final decision step of SCL decoding, where the selected codeword is the one maximizing the maximum-likelihood decoding metric within the final list. The second technique relies on statistical knowledge about the reliability of the bit estimates, obtained through a suitably modified density evolution analysis, to improve the list construction phase, yielding a higher probability of having the transmitted codeword in the list. The effectiveness of the two techniques is demonstrated through simulations.
Using Image Priors to Improve Scene Understanding
Venue: arXiv cs.CV 2019
Year: 2019

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. Existing algorithms treat each image in isolation, but autonomous vehicles often revisit the same locations or maintain information from the immediate past. We propose a simple yet effective method for leveraging these image priors to improve semantic segmentation of images from sequential driving datasets. We examine several methods to fuse these temporal scene priors, and introduce a prior fusion network that is able to learn how to transfer this information. The prior fusion model improves the accuracy over the non-prior baseline from 69.1% to 73.3% for dynamic classes, and from 88.2% to 89.1% for static classes. Compared to models such as FCN-8, our prior method achieves the same accuracy with 5 times fewer parameters. We used a simple encoder decoder backbone, but this general prior fusion method could be applied to more complex semantic segmentation backbones. We also discuss how structured representations of scenes in the form of a scene graph could be leveraged as priors to further improve scene understanding.
ROMark: A Robust Watermarking System Using Adversarial Training
Venue: arXiv cs.CV 2019
Year: 2019

The availability and easy access to digital communication increase the risk of copyrighted material piracy. In order to detect illegal use or distribution of data, digital watermarking has been proposed as a suitable tool. It protects the copyright of digital content by embedding imperceptible information into the data in the presence of an adversary. The goal of the adversary is to remove the copyrighted content of the data. Therefore, an efficient watermarking framework must be robust to multiple image-processing operations known as attacks that can alter embedded copyright information. Another line of research \textit{adversarial machine learning} also tackles with similar problems to guarantee robustness to imperceptible perturbations of the input. In this work, we propose to apply robust optimization from adversarial machine learning to improve the robustness of a CNN-based watermarking framework. Our experimental results on the COCO dataset show that the robustness of a watermarking framework can be improved by utilizing robust optimization in training.
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
Venue: arXiv cs.LG 2019
Year: 2019

Robotics has proved to be an indispensable tool in many industrial as well as social applications, such as warehouse automation, manufacturing, disaster robotics, etc. In most of these scenarios, damage to the agent while accomplishing mission-critical tasks can result in failure. To enable robotic adaptation in such situations, the agent needs to adopt policies which are robust to a diverse set of damages and must do so with minimum computational complexity. We thus propose a damage aware control architecture which diagnoses the damage prior to gait selection while also incorporating domain randomization in the damage space for learning a robust policy. To implement damage awareness, we have used a Long Short Term Memory based supervised learning network which diagnoses the damage and predicts the type of damage. The main novelty of this approach is that only a single policy is trained to adapt against a wide variety of damages and the diagnosis is done in a single trial at the time of damage.
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
Venue: arXiv cs.LG 2019
Year: 2019

We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise. These simple environments allow us to derive bounds on the estimator variance in terms of the environment and noise parameters. We compare the predictions of our bounds to the empirical variance in simulation experiments.
An empirical study of pretrained representations for few-shot classification
Venue: arXiv cs.LG 2019
Year: 2019

Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we systematically investigate which models provide the best representations for a few-shot image classification task when pretrained on the Imagenet dataset. We test their representations when used as the starting point for different few-shot classification algorithms. We observe that models trained on a supervised classification task have higher performance than models trained in an unsupervised manner even when transferred to out-of-distribution datasets. Models trained with adversarial robustness transfer better, while having slightly lower accuracy than supervised models.
Face Manifold: Manifold Learning for Synthetic Face Generation
Venue: arXiv cs.CV 2019
Year: 2019

Face is one of the most important things for communication with the world around us. It also forms our identity and expressions. Estimating the face structure is a fundamental task in computer vision with applications in different areas such as face recognition and medical surgeries. Recently, deep learning techniques achieved significant results for 3D face reconstruction from flat images. The main challenge of such techniques is a vital need for large 3D face datasets. Usually, this challenge is handled by synthetic face generation. However, synthetic datasets suffer from the existence of non-possible faces. Here, we propose a face manifold learning method for synthetic diverse face dataset generation. First, the face structure is divided into the shape and expression groups. Then, a fully convolutional autoencoder network is exploited to deal with the non-possible faces, and, simultaneously, preserving the dataset diversity. Simulation results show that the proposed method is capable of denoising highly corrupted faces. The diversity of the generated dataset is evaluated qualitatively and quantitatively and compared to the existing methods. Experiments show that our manifold learning method outperforms the state of the art methods significantly.
D2D-LSTM based Prediction of the D2D Diffusion Path in Mobile Social Networks
Venue: arXiv cs.SI 2019
Year: 2019

Recently, how to expand data transmission to reduce cell data and repeated cell transmission has received more and more research attention. In mobile social networks, content popularity prediction has always been an important part of traffic offloading and expanding data dissemination. However, current mainstream content popularity prediction methods only use the number of downloads and shares or the distribution of user interests, which do not consider important time and geographic location information in mobile social networks, and all of data is from OSN which is not same as MSN. In this work, we propose D2D Long Short-Term Memory (D2D-LSTM), a deep neural network based on LSTM, which is designed to predict a complete D2D diffusion path. Our work is the first attempt in the world to use real data of MSN to predict diffusion path with deep neural networks which conforms to the D2D structure. Compared to linear sequence networks, only learn users' social features without time distribution or GPS distribution and files' content features, our model can predict the propagation path more accurately (up to 85.858\%) and can reach convergence faster (less than 100 steps) because of the neural network that conforms to the D2D structure and combines user social features and files features. Moreover, we can simulate generating a D2D propagation tree. After experiment and comparison, it is found to be very similar to the ground-truth trees. Finally, we define a user prototype refinement that can more accurately describe the propagation sharing habits of a prototype user (including content preferences, time preferences, and geographic location preferences), and experimentally validate the predictions when the user prototype is added to 1000 classes, it is almost identical to the 50 categories.
SEUSS: Rapid serverless deployment using environment snapshots
Venue: arXiv cs.OS 2019
Year: 2019

Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.
Incremental learning for the detection and classification of GAN-generated images
Venue: arXiv cs.CV 2019
Year: 2019

Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones. In particular, human face generation achieved a stunning level of realism, opening new opportunities for the creative industry but, at the same time, new scary scenarios where such content can be maliciously misused. Therefore, it is essential to develop innovative methodologies to automatically tell apart real from computer generated multimedia, possibly able to follow the evolution and continuous improvement of data in terms of quality and realism. In the last few years, several deep learning-based solutions have been proposed for this problem, mostly based on Convolutional Neural Networks (CNNs). Although results are good in controlled conditions, it is not clear how such proposals can adapt to real-world scenarios, where learning needs to continuously evolve as new types of generated data appear. In this work, we tackle this problem by proposing an approach based on incremental learning for the detection and classification of GAN-generated images. Experiments on a dataset comprising images generated by several GAN-based architectures show that the proposed method is able to correctly perform discrimination when new GANs are presented to the network