OpenRaiser/Intern-Atlas huggingface dataset:
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Images of handwritten digits are different from natural images as the orientation of a digit, as well as similarity of features of different digits, makes confusion. On the other hand, deep convolutional neural networks are achieving huge success in computer vision problems, especially in image classification. BDNet is a densely connected deep convolutional neural network model used to classify (recognize) Bengali handwritten numeral digits. It is end-to-end trained using ISI Bengali handwritten numeral dataset. During training, untraditional data preprocessing and augmentation techniques are used so that the trained model works on a different dataset. The model has achieved the test accuracy of 99.775%(baseline was 99.40%) on the test dataset of ISI Bengali handwritten numerals. So, the BDNet model gives 62.5% error reduction compared to previous state-of-the-art models. Here we have also created a dataset of 1000 images of Bengali handwritten numerals to test the trained model, and it giving promising results. Codes, trained model and our own dataset are available at: {https://github.com/Sufianlab/BDNet}.
Robust Multi-Modal Sensor Fusion: An Adversarial Approach
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, multi-modal fusion has attracted a lot of research interest, both in academia, and in industry. Multimodal fusion entails the combination of information from a set of different types of sensors. Exploiting complementary information from different sensors, we show that target detection and classification problems can greatly benefit from this fusion approach and result in a performance increase. To achieve this gain, the information fusion from various sensors is shown to require some principled strategy to ensure that additional information is constructively used, and has a positive impact on performance. We subsequently demonstrate the viability of the proposed fusion approach by weakening the strong dependence on the functionality of all sensors, hence introducing additional flexibility in our solution and lifting the severe limitation in unconstrained surveillance settings with potential environmental impact. Our proposed data driven approach to multimodal fusion, exploits selected optimal features from an estimated latent space of data across all modalities. This hidden space is learned via a generative network conditioned on individual sensor modalities. The hidden space, as an intrinsic structure, is then exploited in detecting damaged sensors, and in subsequently safeguarding the performance of the fused sensor system. Experimental results show that such an approach can achieve automatic system robustness against noisy/damaged sensors.
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Venue: arXiv cs.LG 2020
Year: 2020

Strong theoretical guarantees of robustness can be given for ensembles of classifiers generated by input randomization. Specifically, an $\ell_2$ bounded adversary cannot alter the ensemble prediction generated by an additive isotropic Gaussian noise, where the radius for the adversary depends on both the variance of the distribution as well as the ensemble margin at the point of interest. We build on and considerably expand this work across broad classes of distributions. In particular, we offer adversarial robustness guarantees and associated algorithms for the discrete case where the adversary is $\ell_0$ bounded. Moreover, we exemplify how the guarantees can be tightened with specific assumptions about the function class of the classifier such as a decision tree. We empirically illustrate these results with and without functional restrictions across image and molecule datasets.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Venue: arXiv cs.LG 2020
Year: 2020

Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks.
Decremental Optimization of Dominating Sets Under the Reconfiguration Framework
Venue: arXiv cs.DM 2020
Year: 2020

Given a dominating set, how much smaller a dominating set can we find through elementary operations? Here, we proceed by iterative vertex addition and removal while maintaining the property that the set forms a dominating set of bounded size. This can be seen as the optimization variant of the dominating set reconfiguration problem, where two dominating sets are given and the question is merely whether they can be reached one from another through elementary operations. We show that this problem is PSPACE-complete, even if the input graph is a bipartite graph, a split graph, or has bounded pathwidth. On the positive side, we give linear-time algorithms for cographs, trees and interval graphs. We also study the parameterized complexity of this problem. More precisely, we show that the problem is W[2]-hard when parameterized by the upper bound on the size of an intermediary dominating set. On the other hand, we give fixed-parameter algorithms with respect to the minimum size of a vertex cover, or $d+s$ where $d$ is the degeneracy and $s$ is the upper bound of output solution.
Multitask Learning for Network Traffic Classification
Venue: arXiv cs.LG 2020
Year: 2020

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach.
Efficient Exploration via State Marginal Matching
Venue: arXiv cs.LG 2020
Year: 2020

Exploration is critical to a reinforcement learning agent's performance in its given environment. Prior exploration methods are often based on using heuristic auxiliary predictions to guide policy behavior, lacking a mathematically-grounded objective with clear properties. In contrast, we recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution. The target distribution is a uniform distribution in most cases, but can incorporate prior knowledge if available. In effect, SMM amortizes the cost of learning to explore in a given environment. The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective. Using this formalism, we further demonstrate that prior work approximately maximizes the SMM objective, offering an explanation for the success of these methods. On both simulated and real-world tasks, we demonstrate that agents that directly optimize the SMM objective explore faster and adapt more quickly to new tasks as compared to prior exploration methods.
A New Approach for Optimizing Highly Nonlinear Problems Based on the Observer Effect Concept
Venue: arXiv cs.NE 2020
Year: 2020

A lot of real-world engineering problems represent dynamicity with nests of nonlinearities due to highly complex network of exponential functions or large number of differential equations interacting together. Such search spaces are provided with multiple convex regions peaked with diverse nonlinear slopes and in non-homogenous ways. To find global optima, a new meta-heuristic algorithm is proposed based on Observer Effect concepts for controlling memory usage per localities without pursuing Tabu-like cut-off approaches. Observer effect in physics (or psychology) regards bias in measurement (or perception) due to the interference of instrument (or knowledge). Performance analysis of the proposed algorithms is sought in two real-world engineering applications, i.e., Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as their characteristics. In addition, the effect of version improvement has been assessed. The performance comparison with other optimizers in the same context suggests that proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.
Matroidal Approximations of Independence Systems
Venue: arXiv cs.DM 2020
Year: 2020

Milgrom (2017) has proposed a heuristic for determining a maximum weight basis of an independence system ${\mathcal I}$ given that we want an approximation guarantee only for sets in a prescribed ${\mathcal O}\subseteq {\mathcal I}$. This ${\mathcal O}$ reflects prior knowledge of the designer about the location of the optimal basis. The heuristic is based on finding an `inner matroid', one contained in the independence system. We show that even in the case ${\mathcal O}={\mathcal I}$ of zero additional knowledge the worst-case performance of this new heuristic can be better than that of the classical greedy algorithm.
Biometric Performance as a Function of Gallery Size
Venue: arXiv cs.CR 2020
Year: 2020

Many developers of biometric systems start with modest samples before general deployment. They are interested in how their systems will work with much larger samples. We evaluated the effect of gallery size on biometric performance. Identification rates describe the performance of biometric identification, whereas ROC-based measures describe the performance of biometric authentication (verification). Therefore, we examined how increases in gallery size affected identification rates (i.e., Rank-1 Identification Rate, or Rank-1 IR) and ROC-based measures such as equal error rate (EER). We studied these phenomena with synthetic data as well as real data from a face recognition study. It is well known that the Rank-1 IR declines with increasing gallery size. We have provided further insight into this decline. We have shown that this relationship is linear in log(Gallery Size). We have also shown that this decline can be counteracted with the inclusion of additional information (features) for larger gallery sizes. We have also described the curves which can be used to predict how much additional information is required to stabilize the Rank-1 IR as a function of gallery size. These equations are also linear in log(gallery size). We have also shown that the entire ROC curve is not systematically affected by gallery size, and so ROC-based scalar performance metrics such as EER are also stable across gallery size.
Augmenting Neural Networks with First-order Logic
Venue: arXiv cs.LG 2020
Year: 2020

Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to perform end-to-end training remains an open question. In this paper, we present a novel framework for introducing declarative knowledge to neural network architectures in order to guide training and prediction. Our framework systematically compiles logical statements into computation graphs that augment a neural network without extra learnable parameters or manual redesign. We evaluate our modeling strategy on three tasks: machine comprehension, natural language inference, and text chunking. Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.
Self-Stabilizing Snapshot Objects for Asynchronous Fail-Prone Network Systems
Venue: arXiv cs.DC 2020
Year: 2020

A snapshot object simulates the behavior of an array of single-writer/multi-reader shared registers that can be read atomically. Delporte-Gallet et al. proposed two fault-tolerant algorithms for snapshot objects in asynchronous crash-prone message-passing systems. Their first algorithm is \emph{non-blocking}; it allows snapshot operations to terminate once all write operations have ceased. It uses $O(n)$ messages of $O(n ν)$ bits, where $n$ is the number of nodes and $ν$ is the number of bits it takes to represent the object. Their second algorithm allows snapshot operations to always terminate independently of write operations. It incurs $O(n^2)$ messages. The fault model of Delporte-Gallet et al. considers node crashes. We aim at the design of even more robust snapshot objects via the lenses of self-stabilization---a very strong notion of fault-tolerance. In addition to Delporte-Gallet et al.'s fault model, our self-stabilizing algorithm can recover after the occurrence of transient faults; these faults represent arbitrary violations of the assumptions according to which the system was designed to operate. We propose self-stabilizing variations of Delporte-Gallet et al.'s non-blocking algorithm and always-terminating algorithm. Our algorithms have similar communication costs to the ones by Delporte-Gallet et al. and $O(1)$ recovery time from transient faults. The main differences are that our proposal considers repeated gossiping of $O(ν)$ bit messages and deals with bounded space. We also consider an input parameter, $δ$, for which we claim an ability to balance the costs of snapshot operations. We validate our correctness proof, evaluate the performance of Delporte-Gallet et al.'s algorithms and our proposed variations and investigate the properties of $δ$ via PlanetLab experiments, where significant latency and communication costs reduction are observed.
Genetic Algorithm Based Resource Minimization in Network Code Based Peer-to-Peer Network
Venue: arXiv cs.NI 2020
Year: 2020

Block scheduling is difficult to implement in P2P network since there is no central coordinator. This problem can be solved by employing network coding technique which allows intermediate nodes to perform the coding operation instead of conventional store and forward the received data. There is a general assumption in this area of research so far that a target download rate is always attainable at every peer as long as coding operation is performed at all the nodes in the network. An interesting study is made that a maximum download rate can be attained by performing the coding operation at relatively small portion of the network. The problem of finding the minimal set of node to perform the coding operation and links to carry the coded data is called as a network code minimization problem (NCMP). It is proved to be NP hard problem. It can be solved using genetic algorithm (GA) because GA can be used to solve the diverse NP hard problem. A new NCMP model is proposed which considers both minimize the resources needed to perform coding operation and dynamic change in network topology due to disconnection. Based on this new NCMP model, an effective and novel GA is proposed by implementing problem specific GA operators into the evolutionary process. There is an attempt to implement the different compositions and several options of GA elements which worked well in many other problems and pick the one that works best for this resource minimization problem. Our simulation results prove that the proposed system outperforms the random selection and coding at all possible node mechanisms in terms of both download time and system throughput.
Computing Theta Functions with Julia
Venue: arXiv cs.MS 2020
Year: 2020

We present a new package Theta.jl for computing with the Riemann theta function. It is implemented in Julia and offers accurate numerical evaluation of theta functions with characteristics and their derivatives of arbitrary order. Our package is optimized for multiple evaluations of theta functions for the same Riemann matrix, in small dimensions. As an application, we report on experimental approaches to the Schottky problem in genus five.
A Comprehensive Formal Security Analysis and Revision of the Two-phase Key Exchange Primitive of TPM 2.0
Venue: arXiv cs.CR 2020
Year: 2020

The Trusted Platform Module (TPM) version 2.0 provides a two-phase key exchange primitive which can be used to implement three widely-standardized authenticated key exchange protocols: the Full Unified Model, the Full MQV, and the SM2 key exchange protocols. However, vulnerabilities have been found in all of these protocols. Fortunately, it seems that the protections offered by TPM chips can mitigate these vulnerabilities. In this paper, we present a security model which captures TPM's protections on keys and protocols' computation environments and in which multiple protocols can be analyzed in a unified way. Based on the unified security model, we give the first formal security analysis of the key exchange primitive of TPM 2.0, and the analysis results show that, with the help of hardware protections of TPM chips, the key exchange primitive indeed satisfies the well-defined security property of our security model, but unfortunately under some impractical limiting conditions, which would prevent the application of the key exchange primitive in real-world networks. To make TPM 2.0 applicable to real-world networks, we present a revision of the key exchange primitive of TPM 2.0, which can be secure without the limiting conditions. We give a rigorous analysis of our revision, and the results show that our revision achieves not only the basic security property of modern AKE security models but also some further security properties.
A concise guide to existing and emerging vehicle routing problem variants
Venue: arXiv cs.DM 2020
Year: 2020

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
Venue: arXiv cs.CV 2020
Year: 2020

Supervised deep networks have achieved promisingperformance on image denoising, by learning image priors andnoise statistics on plenty pairs of noisy and clean images. Unsupervised denoising networks are trained with only noisy images. However, for an unseen corrupted image, both supervised andunsupervised networks ignore either its particular image prior, the noise statistics, or both. That is, the networks learned from external images inherently suffer from a domain gap problem: the image priors and noise statistics are very different between the training and test images. This problem becomes more clear when dealing with the signal dependent realistic noise. To circumvent this problem, in this work, we propose a novel "Noisy-As-Clean" (NAC) strategy of training self-supervised denoising networks. Specifically, the corrupted test image is directly taken as the "clean" target, while the inputs are synthetic images consisted of this corrupted image and a second and similar corruption. A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. Experiments on synthetic and realistic noise removal demonstrate that, the DnCNN and ResNet networks trained with our self-supervised NAC strategy achieve comparable or better performance than the original ones and previous supervised/unsupervised/self-supervised networks. The code is publicly available at https://github.com/csjunxu/Noisy-As-Clean.
Attention Guided Graph Convolutional Networks for Relation Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.