OpenRaiser/Intern-Atlas huggingface dataset:
Generative Adversarial Nets for Multiple Text Corpora
Venue: arXiv cs.CL 2017
Year: 2017

Generative adversarial nets (GANs) have been successfully applied to the artificial generation of image data. In terms of text data, much has been done on the artificial generation of natural language from a single corpus. We consider multiple text corpora as the input data, for which there can be two applications of GANs: (1) the creation of consistent cross-corpus word embeddings given different word embeddings per corpus; (2) the generation of robust bag-of-words document embeddings for each corpora. We demonstrate our GAN models on real-world text data sets from different corpora, and show that embeddings from both models lead to improvements in supervised learning problems.
Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study
Venue: arXiv cs.DC 2017
Year: 2017

Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in academic research and industrial initiatives. Cloud deployments are paramount for institution and organizations of all scales. The availability of a flexible, free open source cloud platform designed with no propriety software and the ability of its integration with legacy systems and third-party applications are fundamental. Open stack is a free and opensource software released under the terms of Apache license with a fragmented and distributed architecture making it highly flexible. This project was initiated and aimed at designing a secured cloud infrastructure called BradStack, which is built on OpenStack in the Computing Laboratory at the University of Bradford. In this report, we present and discuss the steps required in deploying a secured BradStack Multi-node cloud infrastructure and conducting Penetration testing on OpenStack Services to validate the effectiveness of the security controls on the BradStack platform. This report serves as a practical guideline, focusing on security and practical infrastructure related issues. It also serves as a reference for institutions looking at the possibilities of implementing a secured cloud solution.
Large-Scale 3D Scene Classification With Multi-View Volumetric CNN
Venue: arXiv cs.CV 2017
Year: 2017

We introduce a method to classify imagery using a convo- lutional neural network (CNN) on multi-view image pro- jections. The power of our method comes from using pro- jections of multiple images at multiple depth planes near the reconstructed surface. This enables classification of categories whose salient aspect is appearance change un- der different viewpoints, such as water, trees, and other materials with complex reflection/light response proper- ties. Our method does not require boundary labelling in images and works on pixel-level classification with a small (few pixels) context, which simplifies the cre- ation of a training set. We demonstrate this application on large-scale aerial imagery collections, and extend the per-pixel classification to robustly create a consistent 2D classification which can be used to fill the gaps in non- reconstructible water regions. We also apply our method to classify tree regions. In both cases, the training data can quickly be generated using a small number of manually- created polygons on a map. We show that even with a very simple and standard network our CNN outperforms the state-of-the-art image classification, the Inception-V3 model retrained from a large collection of aerial images.
Building Robust Deep Neural Networks for Road Sign Detection
Venue: arXiv cs.LG 2017
Year: 2017

Deep Neural Networks are built to generalize outside of training set in mind by using techniques such as regularization, early stopping and dropout. But considerations to make them more resilient to adversarial examples are rarely taken. As deep neural networks become more prevalent in mission-critical and real-time systems, miscreants start to attack them by intentionally making deep neural networks to misclassify an object of one type to be seen as another type. This can be catastrophic in some scenarios where the classification of a deep neural network can lead to a fatal decision by a machine. In this work, we used GTSRB dataset to craft adversarial samples by Fast Gradient Sign Method and Jacobian Saliency Method, used those crafted adversarial samples to attack another Deep Convolutional Neural Network and built the attacked network to be more resilient against adversarial attacks by making it more robust by Defensive Distillation and Adversarial Training
Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger
Venue: arXiv cs.AI 2017
Year: 2017

Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investigate the robustness and resilience of deep RL to training-time and test-time attacks. Through experimental results, we demonstrate that under noncontiguous training-time attacks, Deep Q-Network (DQN) agents can recover and adapt to the adversarial conditions by reactively adjusting the policy. Our results also show that policies learned under adversarial perturbations are more robust to test-time attacks. Furthermore, we compare the performance of $ε$-greedy and parameter-space noise exploration methods in terms of robustness and resilience against adversarial perturbations.
Smart Fog: Fog Computing Framework for Unsupervised Clustering Analytics in Wearable Internet of Things
Venue: arXiv cs.CY 2017
Year: 2017

The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state of the art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We employed Intel Edison and Raspberry Pi as Fog computer in proposed architecture. We performed validation studies on real-world pathological speech data from in home monitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smartwatches worn by the patients with PD. Results showed that proposed architecture is promising for low-resource clinical machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog.
The Algebraic View of Computation
Venue: arXiv cs.LO 2017
Year: 2017

We argue that computation is an abstract algebraic concept, and a computer is a result of a morphism (a structure preserving map) from a finite universal semigroup.
Comparing Radio Propagation Channels Between 28 and 140 GHz Bands in a Shopping Mall
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we compare the radio propagation channels characteristics between 28 and 140 GHz bands based on the wideband (several GHz) and directional channel sounding in a shopping mall environment. The measurements and data processing are conducted in such a way to meet requirements for a fair comparison of large- and small- scale channel parameters between the two bands. Our results reveal that there is high spatial-temporal correlation between 28 and 140 GHz channels, similar numbers of strong multipath components, and only small variations in the large-scale parameters between the two bands. Furthermore, when including the weak paths there are higher total numbers of clusters and paths in 28 GHz as compared to those in 140 GHz bands. With these similarities, it would be very interesting to investigate the potentials of using 140 GHz band in the future mobile radio communications.
Exploring the Space of Black-box Attacks on Deep Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can "transfer" to attack other learning models. In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model's class probabilities, which do not rely on transferability. We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input. An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs. We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks. We also apply the Gradient Estimation attacks successfully against a real-world Content Moderation classifier hosted by Clarifai. Furthermore, we evaluate black-box attacks against state-of-the-art defenses. We show that the Gradient Estimation attacks are very effective even against these defenses.
Consensus-based Sequence Training for Video Captioning
Venue: arXiv cs.CV 2017
Year: 2017

Captioning models are typically trained using the cross-entropy loss. However, their performance is evaluated on other metrics designed to better correlate with human assessments. Recently, it has been shown that reinforcement learning (RL) can directly optimize these metrics in tasks such as captioning. However, this is computationally costly and requires specifying a baseline reward at each step to make training converge. We propose a fast approach to optimize one's objective of interest through the REINFORCE algorithm. First we show that, by replacing model samples with ground-truth sentences, RL training can be seen as a form of weighted cross-entropy loss, giving a fast, RL-based pre-training algorithm. Second, we propose to use the consensus among ground-truth captions of the same video as the baseline reward. This can be computed very efficiently. We call the complete proposal Consensus-based Sequence Training (CST). Applied to the MSRVTT video captioning benchmark, our proposals train significantly faster than comparable methods and establish a new state-of-the-art on the task, improving the CIDEr score from 47.3 to 54.2.
Finding Two Disjoint Simple Paths on Two Sets of Points is NP-Complete
Venue: arXiv cs.CC 2017
Year: 2017

Finding two disjoint simple paths on two given sets of points is a geometric problem introduced by Jeff Erickson. This problem has various applications in computational geometry, like robot motion planning, generating polygon etc. We will present a reduction from planar Hamiltonian path to this problem, and prove that it is NP-Complete. To the best of our knowledge, no study has considered its complexity up until now. We also present a reduction from planar Hamiltonian path problem to the problem of finding a path on given points in the presence of arbitrary obstacles and prove that it is NP-Complete too. Also, we present a heuristic algorithm with time complexity of O(n4) to solve this problem. The proposed algorithm first calculates the convex hull for each of the entry points and then produces two simple paths on the two entry point sets
Cuckoo++ Hash Tables: High-Performance Hash Tables for Networking Applications
Venue: arXiv cs.NI 2017
Year: 2017

Hash tables are an essential data-structure for numerous networking applications (e.g., connection tracking, firewalls, network address translators). Among these, cuckoo hash tables provide excellent performance by allowing lookups to be processed with very few memory accesses (2 to 3 per lookup). Yet, for large tables, cuckoo hash tables remain memory bound and each memory access impacts performance. In this paper, we propose algorithmic improvements to cuckoo hash tables allowing to eliminate some unnecessary memory accesses; these changes are conducted without altering the properties of the original cuckoo hash table so that all existing theoretical analysis remain applicable. On a single core, our hash table achieves 37M lookups per second for positive lookups (i.e., when the key looked up is present in the table), and 60M lookups per second for negative lookups, a 50% improvement over the implementation included into the DPDK. On a 18-core, with mostly positive lookups, our implementation achieves 496M lookups per second, a 45% improvement over DPDK.
Analysis of Concurrent Lock-Free Hash Tries with Constant-Time Operations
Venue: arXiv cs.DS 2017
Year: 2017

Ctrie is a scalable concurrent non-blocking dictionary data structure, with good cache locality, and non-blocking linearizable iterators. However, operations on most existing concurrent hash tries run in O(log n) time. In this technical report, we extend the standard concurrent hash-tries with an auxiliary data structure called a cache. The cache is essentially an array that stores pointers to a specific level of the hash trie. We analyze the performance implications of adding a cache, and prove that the running time of the basic operations becomes O(1).
Combining Representation Learning with Logic for Language Processing
Venue: arXiv cs.NE 2017
Year: 2017

The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.
CHOKeD: A Fair Active Queue Management System
Venue: arXiv cs.NI 2017
Year: 2017

Fairness is the significant factor to sustain best effort delivery of network services. Now-a-days, real-time multimedia applications have evolved largely over the Internet. Most of multimedia services are unresponsive during network congestion. Unresponsive traffic streams steal most network bandwidth and starve out some other flows which are responsive during network congestion. In the presence of these unresponsive traffic flows, protection of responsive flows has become a major issue. Many Active Queue Management (AQM) based solutions have been recommended to protect responsive traffic flows from unresponsive ones and to ensure fairness among all traffic flows. The thesis proposes a novel AQM scheme CHOKeD, to deliver fairness among all flows of a congested link. It is a completely stateless approach. CHOKeD is based on dynamic drawing factor to penalize unresponsive traffic. It successfully protects responsive flows in the presence of unresponsive flows. CHOKeD features such as fairness, high throughput of responsive traffic and stateless design, are encouraging factors for its deployment over the edge as well as the network core routers. Extensive simulations have been carried out to evaluate its performance under real-time network scenarios
Beyond-Planarity: Density Results for Bipartite Graphs
Venue: arXiv cs.DM 2017
Year: 2017

Beyond-planarity focuses on the study of geometric and topological graphs that are in some sense nearly-planar. Here, planarity is relaxed by allowing edge crossings, but only with respect to some local forbidden crossing configurations. Early research dates back to the 1960s (e.g., Avital and Hanani 1966) for extremal problems on geometric graphs, but is also related to graph drawing problems where visual clutter by edge crossings should be minimized (e.g., Huang et al. 2008) that could negatively affect the readability of the drawing. Different types of forbidden crossing configurations give rise to different families of nearly-planar graphs. Most of the literature focuses on Turán-type problems, which ask for the maximum number of edges a nearly-planar graph can have. Here, we study this problem for bipartite topological graphs, considering several types of nearly-planar graphs, i.e., 1-planar, 2-planar, fan-planar, and RAC graphs. We prove bounds on the number of edges that are tight up to small additive constants; some of them are surprising and not along the lines of the known results for non-bipartite graphs. Our findings lead to an improvement of the leading constant of the well-known Crossing Lemma for bipartite graphs, as well as to a number of interesting research questions on topological graphs.
A ReRAM Physically Unclonable Function (ReRAM PUF)-based Approach to Enhance Authentication Security in Software Defined Wireless Networks
Venue: arXiv cs.CR 2017
Year: 2017

The exponentially increasing number of ubiquitous wireless devices connected to the Internet in Internet of Things (IoT) networks highlights the need for a new paradigm of data flow management in such large-scale networks under software defined wireless networking (SDWN). The limited power and computation capability available at IoT devices as well as the centralized management and decision-making approach in SDWN introduce a whole new set of security threats to the networks. In particular, the authentication mechanism between the controllers and the forwarding devices in SDWNs is a key challenge from both secrecy and integrity aspects. Conventional authentication protocols based on public key infrastructure (PKI) are no longer sufficient for these networks considering the large-scale and heterogeneity nature of the networks as well as their deployment cost, and security vulnerabilities due to key distribution and storage. We propose a novel security protocol based on physical unclonable functions (PUFs) known as hardware security primitives to enhance the authentication security in SDWNs. In this approach, digital PUFs are developed using the inherent randomness of the nanomaterials of Resistive Random Access Memory (ReRAM) that are embedded in most IoT devices to enable a secure authentication and access control in these networks. These PUFs are developed based on a novel approach of multi-states, in which the natural drifts due to the physical variations in the environment are predicted to reduce the potential errors in challenge-response pairs of PUFs being tested in different situations. We also proposed a PUF-based PKI protocol to secure the controller in SDWNs. The performance of the developed ReRAM-based PUFs are evaluated in the experimental results.
Minimizing Polarization and Disagreement in Social Networks
Venue: arXiv cs.SI 2017
Year: 2017

The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: given $n$ agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes {\em polarization} and {\em disagreement} simultaneously? This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the other's viewpoint of the world, and decrease overall levels of polarization? Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a network with $O(n/ε^2)$ edges that is a $(1+ε)$ approximation to the optimum. For a fixed graph, we additionally show how to optimize our objective function over the agents' innate opinions in polynomial time. We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both polarization and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a $\sim 60\,000$-fold reduction in polarization and disagreement.
A PSPACE Construction of a Hitting Set for the Closure of Small Algebraic Circuits
Venue: arXiv cs.CC 2017
Year: 2017

In this paper we study the complexity of constructing a hitting set for the closure of VP, the class of polynomials that can be infinitesimally approximated by polynomials that are computed by polynomial sized algebraic circuits, over the real or complex numbers. Specifically, we show that there is a PSPACE algorithm that given n,s,r in unary outputs a set of n-tuples over the rationals of size poly(n,s,r), with poly(n,s,r) bit complexity, that hits all n-variate polynomials of degree-r that are the limit of size-s algebraic circuits. Previously it was known that a random set of this size is a hitting set, but a construction that is certified to work was only known in EXPSPACE (or EXPH assuming the generalized Riemann hypothesis). As a corollary we get that a host of other algebraic problems such as Noether Normalization Lemma, can also be solved in PSPACE deterministically, where earlier only randomized algorithms and EXPSPACE algorithms (or EXPH assuming the generalized Riemann hypothesis) were known. The proof relies on the new notion of a robust hitting set which is a set of inputs such that any nonzero polynomial that can be computed by a polynomial size algebraic circuit, evaluates to a not too small value on at least one element of the set. Proving the existence of such a robust hitting set is the main technical difficulty in the proof. Our proof uses anti-concentration results for polynomials, basic tools from algebraic geometry and the existential theory of the reals.
Parallel Active Subspace Decomposition for Scalable and Efficient Tensor Robust Principal Component Analysis
Venue: arXiv cs.NA 2017
Year: 2017

Tensor robust principal component analysis (TRPCA) has received a substantial amount of attention in various fields. Most existing methods, normally relying on tensor nuclear norm minimization, need to pay an expensive computational cost due to multiple singular value decompositions (SVDs) at each iteration. To overcome the drawback, we propose a scalable and efficient method, named Parallel Active Subspace Decomposition (PASD), which divides the unfolding along each mode of the tensor into a columnwise orthonormal matrix (active subspace) and another small-size matrix in parallel. Such a transformation leads to a nonconvex optimization problem in which the scale of nulcear norm minimization is generally much smaller than that in the original problem. Furthermore, we introduce an alternating direction method of multipliers (ADMM) method to solve the reformulated problem and provide rigorous analyses for its convergence and suboptimality. Experimental results on synthetic and real-world data show that our algorithm is more accurate than the state-of-the-art approaches, and is orders of magnitude faster.