OpenRaiser/Intern-Atlas huggingface dataset:
Biometric Recognition System (Algorithm)
Venue: arXiv cs.CV 2018
Year: 2018

Fingerprints are the most widely deployed form of biometric identification. No two individuals share the same fingerprint because they have unique biometric identifiers. This paper presents an efficient fingerprint verification algorithm which improves matching accuracy. Fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to singular point detection and minutiae extraction. Singular point is the point of maximum curvature. It is determined by the normal of each fingerprint ridge, and then following them inward towards the centre. The local ridge features known as minutiae is extracted using cross-number method to find ridge endings and ridge bifurcations. The proposed algorithm chooses a radius and draws a circle with core point as centre, making fingerprint images rotationally invariant and uniform. The radius can be varied according to the accuracy depending on the particular application. Morphological techniques such as clean, spur and H-break is employed to remove noise, followed by removing spurious minutiae. Templates are created based on feature vector extraction and databases are made for verification and identification for the fingerprint images taken from Fingerprint Verification Competition (FVC2002). Minimum Euclidean distance is calculated between saved template and the test fingerprint image template and compared with the set threshold for matching decision. For the performance evaluation of the proposed algorithm various measures, equal error rate (EER), Dmin at EER, accuracy and threshold are evaluated and plotted. The measures demonstrate that the proposed algorithm is more effective and robust.
Efficient transfer learning and online adaptation with latent variable models for continuous control
Venue: arXiv cs.LG 2018
Year: 2018

Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.
Zero Initialization of modified Gated Recurrent Encoder-Decoder Network for Short Term Load Forecasting
Venue: arXiv cs.LG 2018
Year: 2018

Single layer Feedforward Neural Network(FNN) is used many a time as a last layer in models such as seq2seq or could be a simple RNN network. The importance of such layer is to transform the output to our required dimensions. When it comes to weights and biases initialization, there is no such specific technique that could speed up the learning process. We could depend on deep network initialization techniques such as Xavier or He initialization. But such initialization fails to show much improvement in learning speed or accuracy. In this paper we propose Zero Initialization (ZI) for weights of a single layer network. We first test this technique with on a simple RNN network and compare the results against Xavier, He and Identity initialization. As a final test we implement it on a seq2seq network. It was found that ZI considerably reduces the number of epochs used and improve the accuracy. The developed model has been applied for short-term load forecasting using the load data of Australian Energy Market. The model is able to forecast the day ahead load accurately with error of 0.94%.
ICSREF: A Framework for Automated Reverse Engineering of Industrial Control Systems Binaries
Venue: arXiv cs.CR 2018
Year: 2018

The security of Industrial Control Systems (ICS) has been attracting increased attention over the past years, following the discovery of real threats targeting industrial environments. Despite this attention, automation of the reverse engineering process of ICS binaries for programmable logic controllers remains an open problem, mainly due to the use of proprietary compilers by ICS vendors. Such automation could be a double-edged sword; on the one hand it could accelerate digital forensic investigations and incident response actions, while on the other hand it could enable dynamic generation of malicious ICS payloads. In this work, we propose a structured methodology that automates the reverse engineering process for ICS binaries taking into account their unique domain-specific characteristics. We apply this methodology to develop the modular Industrial Control Systems Reverse Engineering Framework (ICSREF), and instantiate ICSREF modules for reversing binaries compiled with CODESYS, a widely used software stack and compiler for PLCs. To evaluate our framework we create a database of samples by collecting real PLC binaries from public code repositories, as well as developing binaries in-house. Our results demonstrate that ICSREF can successfully handle diverse PLC binaries from varied industry sectors, irrespective of the programming language used. Furthermore, we deploy ICSREF on a commercial smartphone which orchestrates and launches a completely automated process-aware attack against a chemical process testbed. This example of dynamic payload generation showcases how ICSREF can enable sophisticated attacks without any prior knowledge.
Security Vulnerability of FDD Massive MIMO Systems in Downlink Training Phase
Venue: arXiv cs.CR 2018
Year: 2018

We consider downlink channel training of a frequency division duplex (FDD) massive multiple-input-multiple-output (MIMO) system when a multi-antenna jammer is present in the network. The jammer intends to degrade mean square error (MSE) of the downlink channel training by designing an attack based on second-order statistics of its channel. The channels are assumed to be spatially correlated. First, a closed-form expression for the channel estimation MSE is derived and then the jammer determines the conditions under which the MSE is maximized. Numerical results demonstrate that the proposed jamming can severely increase the estimation MSE even if the optimal training signals with a large number of pilot symbols are used by the legitimate system.
Monocular and Stereo Cues for Landing Zone Evaluation for Micro UAVs
Venue: arXiv cs.RO 2018
Year: 2018

Autonomous and safe landing is important for unmanned aerial vehicles. We present a monocular and stereo image based method for fast and accurate landing zone evaluation for UAVs in various scenarios. Many existing methods rely on Lidar or depth sensor to provide accurate and dense surface reconstruction. We utilize stereo images to evaluate the slope and monocular images to compute homography error. By combining them together, our approach works for both rigid and non-rigid dynamic surfaces. Experiments on many outdoor scenes such as water, grass and roofs, demonstrate the robustness and effectiveness of our approach.
Reliable Identification of Redundant Kernels for Convolutional Neural Network Compression
Venue: arXiv cs.NE 2018
Year: 2018

To compress deep convolutional neural networks (CNNs) with large memory footprint and long inference time, this paper proposes a novel pruning criterion using layer-wised Ln-norm of feature maps. Different from existing pruning criteria, which are mainly based on L1-norm of convolution kernels, the proposed method utilizes Ln-norm of output feature maps after non-linear activations, where n is a variable, increasing from 1 at the first convolution layer to inf at the last convolution layer. With the ability of accurately identifying unimportant convolution kernels, the proposed method achieves a good balance between model size and inference accuracy. The experiments on ImageNet and the successful application in railway surveillance system show that the proposed method outperforms existing kernel-norm-based methods and is generally applicable to any deep neural network with convolution operations.
Learning Non-Uniform Hypergraph for Multi-Object Tracking
Venue: arXiv cs.CV 2018
Year: 2018

The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them less effective in handling complex scenarios. In this work, we present a new near-online MOT algorithm based on non-uniform hypergraph, which can model different degrees of dependencies among tracklets in a unified objective. The nodes in the hypergraph correspond to the tracklets and the hyperedges with different degrees encode various kinds of dependencies among them. Specifically, instead of setting the weights of hyperedges with different degrees empirically, they are learned automatically using the structural support vector machine algorithm (SSVM). Several experiments are carried out on various challenging datasets (i.e., PETS09, ParkingLot sequence, SubwayFace, and MOT16 benchmark), to demonstrate that our method achieves favorable performance against the state-of-the-art MOT methods.
Gender and Research Publishing in India: Uniformly high inequality?
Venue: arXiv cs.DL 2018
Year: 2018

Women's access to academic careers has been historically limited by discrimination and cultural constraints. Comprehensive information about gender inequality within disciplines is needed to understand the problem and target remedial action. India is the fifth largest research producer but has a low international index of gender inequality and so is an important case. This study assesses gender inequalities in Indian journal article publishing in 2017 for 186 research fields. It also seeks overall gender differences in interests across academia by comparing the terms used in 27,710 articles with an Indian male or female first author. The data show that there are at least 1.5 male first authors per female first author in each of 26 broad fields and 2.8 male first authors per female first author overall. Compared to the USA, India has a much lower share of female first authors but smaller variations in gender differences between broad fields. Dentistry, Economics and Maths are all more female in India, but Veterinary is much less female than in the USA. There is a tendency for males to research thing-oriented topics and for females to research helping people and some life science topics. More initiatives to promote gender equality in science are needed to address the overall imbalance, but care should be taken to avoid creating the larger between-field gender differences found in the USA.
An individualized super Gaussian single microphone Speech Enhancement for hearing aid users with smartphone as an assistive device
Venue: arXiv cs.SD 2018
Year: 2018

In this letter, we derive a new super Gaussian Joint Maximum a Posteriori based single microphone speech enhancement gain function. The developed Speech Enhancement method is implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids. We introduce a tradeoff parameter in the derived gain function that allows the smartphone user to customize their listening preference, by controlling the amount of noise suppression and speech distortion in real-time based on their level of hearing comfort perceived in noisy real world acoustic environment. Objective quality and intelligibility measures show the effectiveness of the proposed method in comparison to benchmark techniques considered in this paper. Subjective results reflect the usefulness of the developed Speech Enhancement application in real-world noisy conditions at signal to noise ratio levels of 0 dB and 5 dB.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the base-learners as multiple versions of "ground truths". Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semi-supervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization
Venue: arXiv cs.LG 2018
Year: 2018

Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable. In this work, we propose a model agnostic method for estimating the uncertainty of a model?s predictions based on reconstruction error, using it in control and exploration. As our experiments show, this uncertainty estimation can be used to improve control performance on a wide variety of environments by choosing predictions of which the model is confident. It can also be used for active learning to explore more efficiently the environment by planning for trajectories with high uncertainty, allowing faster model learning.
IoTC2: A Formal Method Approach for Detecting Conflicts in Large Scale IoT Systems
Venue: arXiv cs.CR 2018
Year: 2018

Internet of Things (IoT) has become a common paradigm for different domains such as health care, transportation infrastructure, smart home, smart shopping, and e-commerce. With its interoperable functionality, it is now possible to connect all domains of IoT together for providing competent services to the users. Because numerous IoT devices can connect and communicate at the same time, there can be events that trigger conflicting actions to an actuator or an environmental feature. However, there have been very few research efforts made to detect conflicting situation in IoT system using formal method. This paper provides a formal method approach, IoT Confict Checker (IoTC2), to ensure safety of controller and actuators' behavior with respect to conflicts. Any policy violation results in detection of the conflicts. We defined the safety policies for controller, actions, and triggering events and implemented the those with Prolog to prove the logical completeness and soundness. In addition to that, we have implemented the detection policies in Matlab Simulink Environment with its built-in Model Verification blocks. We created smart home environment in Simulink and showed how the conflicts affect actions and corresponding features. We have also experimented the scalability, efficiency, and accuracy of our method in the simulated environment.
Real-time Closed Loop Neural Decoding on a Neuromorphic Chip
Venue: arXiv cs.ET 2018
Year: 2018

This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of approximately 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of approximately 85% of those obtained by hand-controlled joystick . These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.
Supervised Deep Kriging for Single-Image Super-Resolution
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel single-image super-resolution approach based on the geostatistical method of kriging. Kriging is a zero-bias minimum-variance estimator that performs spatial interpolation based on a weighted average of known observations. Rather than solving for the kriging weights via the traditional method of inverting covariance matrices, we propose a supervised form in which we learn a deep network to generate said weights. We combine the kriging weight generation and kriging process into a joint network that can be learned end-to-end. Our network achieves competitive super-resolution results as other state-of-the-art methods. In addition, since the super-resolution process follows a known statistical framework, we are able to estimate bias and variance, something which is rarely possible for other deep networks.
Counting symbol switches in synchronizing automata
Venue: arXiv cs.FL 2018
Year: 2018

Instead of looking at the lengths of synchronizing words as in Černý's conjecture, we look at the switch count of such words, that is, we only count the switches from one letter to another. Where the synchronizing words of the Černý automata $\mathcal{C}_n$ have switch count linear in $n$, we wonder whether synchronizing automata exist for which every synchronizing word has quadratic switch count. The answer is positive: we prove that switch count has the same complexity as synchronizing word length. We give some series of synchronizing automata yielding quadratic switch count, the best one reaching $\frac{2}{3} n^2 + O(n)$ as switch count. We investigate all binary automata on at most 9 states and determine the maximal possible switch count. For all $3\leq n\leq 9$, a strictly higher switch count can be reached by allowing more symbols. This behaviour differs from length, where for every $n$, no automata are known with higher synchronization length than $\mathcal{C}_n$, which has only two symbols. It is not clear if this pattern extends to larger $n$. For $n\geq 12$, our best construction only has two symbols.
Yaps: Python Frontend to Stan
Venue: arXiv cs.PL 2018
Year: 2018

Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models. Unfortunately, existing embeddings of Stan in Python use multi-line strings. That approach forces users to switch between two different language styles, with no support for syntax highlighting or simple error reporting within the Stan code. This paper tackles the question of whether Stan could use Python syntax while retaining its self-contained semantics. The answer is yes, that can be accomplished by reinterpreting the Python syntax. This paper introduces Yaps, a new frontend to Stan based on reinterpreted Python. We tested Yaps on over a thousand Stan models and made it available open-source.
Duelling Bandits with Weak Regret in Adversarial Environments
Venue: arXiv cs.LG 2018
Year: 2018

Research on the multi-armed bandit problem has studied the trade-off of exploration and exploitation in depth. However, there are numerous applications where the cardinal absolute-valued feedback model (e.g. ratings from one to five) is not suitable. This has motivated the formulation of the duelling bandits problem, where the learner picks a pair of actions and observes a noisy binary feedback, indicating a relative preference between the two. There exist a multitude of different settings and interpretations of the problem for two reasons. First, due to the absence of a total order of actions, there is no natural definition of the best action. Existing work either explicitly assumes the existence of a linear order, or uses a custom definition for the winner. Second, there are multiple reasonable notions of regret to measure the learner's performance. Most prior work has been focussing on the $\textit{strong regret}$, which averages the quality of the two actions picked. This work focusses on the $\textit{weak regret}$, which is based on the quality of the better of the two actions selected. Weak regret is the more appropriate performance measure when the pair's inferior action has no significant detrimental effect on the pair's quality. We study the duelling bandits problem in the adversarial setting. We provide an algorithm which has theoretical guarantees in both the utility-based setting, which implies a total order, and the unrestricted setting. For the latter, we work with the $\textit{Borda winner}$, finding the action maximising the probability of winning against an action sampled uniformly at random. The thesis concludes with experimental results based on both real-world data and synthetic data, showing the algorithm's performance and limitations.
Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input
Venue: arXiv cs.SY 2018
Year: 2018

We present an algorithm that quickly finds falsifying inputs for hybrid systems, i.e., inputs that steer the system towards violation of a given temporal logic requirement. Our method is based on a probabilistically directed search of an increasingly fine grained spatial and temporal discretization of the input space. A key feature is that it adapts to the difficulty of a problem at hand, specifically to the local complexity of each input segment, as needed for falsification. In experiments with standard benchmarks, our approach consistently outperforms existing techniques by a significant margin. In recognition of the way it works and to distinguish it from previous work, we describe our method as a "Las Vegas tree search".
Ruin Theory for Dynamic Spectrum Allocation in LTE-U Networks
Venue: arXiv cs.NI 2018
Year: 2018

LTE in the unlicensed band (LTE-U) is a promising solution to overcome the scarcity of the wireless spectrum. However, to reap the benefits of LTE-U, it is essential to maintain its effective coexistence with WiFi systems. Such a coexistence, hence, constitutes a major challenge for LTE-U deployment. In this paper, the problem of unlicensed spectrum sharing among WiFi and LTE-U system is studied. In particular, a fair time sharing model based on \emph{ruin theory} is proposed to share redundant spectral resources from the unlicensed band with LTE-U without jeopardizing the performance of the WiFi system. Fairness among both WiFi and LTE-U is maintained by applying the concept of the probability of ruin. In particular, the probability of ruin is used to perform efficient duty-cycle allocation in LTE-U, so as to provide fairness to the WiFi system and maintain certain WiFi performance. Simulation results show that the proposed ruin-based algorithm provides better fairness to the WiFi system as compared to equal duty-cycle sharing among WiFi and LTE-U.