OpenRaiser/Intern-Atlas huggingface dataset:
The Evaluation of Circuit Selection Methods on Tor
Venue: arXiv cs.CR 2017
Year: 2017

Tor provides anonymity online by routing traffic through encrypted tunnels, called circuits, over paths of anonymizing relays. To enable users to connect to their selected destination servers without waiting for the circuit to be build, the Tor client maintains a few circuits at all times. Nevertheless, Tor is slower to use than directly connecting to the destination server. In this paper, we propose to have the Tor client measure the performance of the pre-built circuits and select the fastest circuits for users to send their traffic over. To this end, we define and evaluate nine metrics for selecting which pre-built circuit to use based on different combinations of circuit length, Round Trip Time (RTT), and congestion. We also explore the effect on performance of the number of pre-built circuits at the time of the selection. Through whole-network experiments in Shadow, we show that using circuit RTT with at least three pre-built circuits allows the Tor client to identify fast circuits and improves median time to first byte (TTFB) by 22% over Tor and 15% over congestion-aware routing, the state-of-the-art in Tor circuit selection. We evaluate the security of the proposed circuit selection mechanism against both a relay-level and a network-level adversary and find no loss of security compared with Tor.
Approximate and Incremental Network Function Placement
Venue: arXiv cs.NI 2017
Year: 2017

The virtualization and softwarization of modern computer networks introduces interesting new opportunities for a more flexible placement of network functions and middleboxes (firewalls, proxies, traffic optimizers, virtual switches, etc.). This paper studies approximation algorithms for the incremental deployment of a minimum number of middleboxes at optimal locations, such that capacity constraints at the middleboxes and length constraints on the communication routes are respected. Our main contribution is a new, purely combinatorial and rigorous proof for the submodularity of the function maximizing the number of communication requests that can be served by a given set of middleboxes. Our proof allows us to devise a deterministic approximation algorithm which uses an augmenting path approach to compute the submodular function. This algorithm does not require any changes to the locations of existing middleboxes or the preemption of previously served communication pairs when additional middleboxes are deployed, previously accepted communication pairs just can be handed over to another middlebox. It is hence particularly attractive for incremental deployments.We prove that the achieved polynomial-time approximation bound is optimal, unless P = NP. This paper also initiates the study of a weighted problem variant, in which entire groups of nodes need to communicate via a middlebox (e.g., a multiplexer or a shared object), possibly at different rates. We present an LP relaxation and randomized rounding algorithm for this problem, leveraging an interesting connection to scheduling.
Grounded Language Learning in a Simulated 3D World
Venue: arXiv cs.CL 2017
Year: 2017

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions. Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions. The agent's comprehension of language extends beyond its prior experience, enabling it to apply familiar language to unfamiliar situations and to interpret entirely novel instructions. Moreover, the speed with which this agent learns new words increases as its semantic knowledge grows. This facility for generalising and bootstrapping semantic knowledge indicates the potential of the present approach for reconciling ambiguous natural language with the complexity of the physical world.
Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting
Venue: arXiv cs.RO 2017
Year: 2017

The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting algorithm for pedestrians, cyclists, and other agents since these predictions will form a necessary step in assessing the risk of any action. This paper presents a novel approach to probabilistic forecasting for pedestrians based on weighted sums of ordinary differential equations that are learned from historical trajectory information within a fixed scene. The resulting algorithm is embarrassingly parallel and is able to work at real-time speeds using a naive Python implementation. The quality of predicted locations of agents generated by the proposed algorithm is validated on a variety of examples and considerably higher than existing state of the art approaches over long time horizons.
Approximate Analytical Solutions of Power Flow Equations Based on Multi-Dimensional Holomorphic Embedding Method
Venue: arXiv cs.SY 2017
Year: 2017

It is well known that closed-form analytical solutions for AC power flow equations do not exist in general. This paper proposes a multi-dimensional holomorphic embedding method (MDHEM) to obtain an explicit approximate analytical AC power-flow solution by finding a physical germ solution and arbitrarily embedding each power, each load or groups of loads with respective scales. Based on the MDHEM, the complete approximate analytical solutions to the power flow equations in the high-dimensional space become achievable, since the voltage vector of each bus can be explicitly expressed by a convergent multivariate power series of all the loads. Unlike the traditional iterative methods for power flow calculation and inaccurate sensitivity analysis method for voltage control, the algebraic variables of a power system in all operating conditions can be prepared offline and evaluated online by only plugging in the values of any operating conditions into the scales of the non-linear multivariate power series. Case studies implemented on the 4-bus test system and the IEEE 14-bus standard system confirm the effectiveness of the proposed method.
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines
Venue: arXiv cs.AI 2017
Year: 2017

We show how an action-dependent baseline can be used by the policy gradient theorem using function approximation, originally presented with action-independent baselines by (Sutton et al. 2000).
Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents
Venue: arXiv cs.MM 2017
Year: 2017

In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a serious threat. Ability to easily and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters' locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions, their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions, referred as geometric distortion signatures and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of thirteen printers demonstrate that the proposed system achieves state of the art performance and gives much higher accuracy under small training size constraint. For four training and six test pages of three different fonts, the proposed method gives 99\% classification accuracy.
Grilliot's trick in Nonstandard Analysis
Venue: arXiv cs.LO 2017
Year: 2017

The technique known as Grilliot's trick constitutes a template for explicitly defining the Turing jump functional $(\exists^2)$ in terms of a given effectively discontinuous type two functional. In this paper, we discuss the standard extensionality trick: a technique similar to Grilliot's trick in Nonstandard Analysis. This nonstandard trick proceeds by deriving from the existence of certain nonstandard discontinuous functionals, the Transfer principle from Nonstandard analysis limited to $Π_1^0$-formulas; from this (generally ineffective) implication, we obtain an effective implication expressing the Turing jump functional in terms of a discontinuous functional (and no longer involving Nonstandard Analysis). The advantage of our nonstandard approach is that one obtains effective content without paying attention to effective content. We also discuss a new class of functionals which all seem to fall outside the established categories. These functionals directly derive from the Standard Part axiom of Nonstandard Analysis.
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups
Venue: arXiv cs.DB 2017
Year: 2017

Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective. In the big-data world existing methods fail to address the new set of memory and latency constraints. In this paper, we propose ACE (Arrays of (locality-sensitive) Count Estimators) algorithm that can be 60x faster than the ELKI package~\cite{DBLP:conf/ssd/AchtertBKSZ09}, which has the fastest implementation of the unsupervised anomaly detection algorithms. ACE algorithm requires less than $4MB$ memory, to dynamically compress the full data information into a set of count arrays. These tiny $4MB$ arrays of counts are sufficient for unsupervised anomaly detection. At the core of the ACE algorithm, there is a novel statistical estimator which is derived from the sampling view of Locality Sensitive Hashing(LSH). This view is significantly different and efficient than the widely popular view of LSH for near-neighbor search. We show the superiority of ACE algorithm over 11 popular baselines on 3 benchmark datasets, including the KDD-Cup99 data which is the largest available benchmark comprising of more than half a million entries with ground truth anomaly labels.
Computing maximum cliques in $B_2$-EPG graphs
Venue: arXiv cs.DS 2017
Year: 2017

EPG graphs, introduced by Golumbic et al. in 2009, are edge-intersection graphs of paths on an orthogonal grid. The class $B_k$-EPG is the subclass of EPG graphs where the path on the grid associated to each vertex has at most $k$ bends. Epstein et al. showed in 2013 that computing a maximum clique in $B_1$-EPG graphs is polynomial. As remarked in [Heldt et al., 2014], when the number of bends is at least $4$, the class contains $2$-interval graphs for which computing a maximum clique is an NP-hard problem. The complexity status of the Maximum Clique problem remains open for $B_2$ and $B_3$-EPG graphs. In this paper, we show that we can compute a maximum clique in polynomial time in $B_2$-EPG graphs given a representation of the graph. Moreover, we show that a simple counting argument provides a ${2(k+1)}$-approximation for the coloring problem on $B_k$-EPG graphs without knowing the representation of the graph. It generalizes a result of [Epstein et al, 2013] on $B_1$-EPG graphs (where the representation was needed).
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions
Venue: arXiv cs.RO 2017
Year: 2017

This paper addresses the design and implementation of complex Reinforcement Learning (RL) behaviors where multi-dimensional action spaces are involved, as well as the need to execute the behaviors in real-time using robotic platforms with limited computational resources and training times. For this purpose, we propose the use of decentralized RL, in combination with finite support basis functions as alternatives to Gaussian RBF, in order to alleviate the effects of the curse of dimensionality on the action and state spaces respectively, and to reduce the computation time. As testbed, a RL based controller for the in-walk kick in NAO robots, a challenging and critical problem for soccer robotics, is used. The reported experiments show empirically that our solution saves up to 99.94% of execution time and 98.82% of memory consumption during execution, without diminishing performance compared to classical approaches.
Towards a Recommender System for Undergraduate Research
Venue: arXiv cs.IR 2017
Year: 2017

Several studies indicate that attracting students to research careers requires to engage them from early undergraduate years. Following this paradigm, our Engineering School has developed an undergraduate research program that allows students to enroll in research in exchange for course credits. Moreover, we developed a web portal to inform students about the program and the opportunities, but participation remains lower than expected. In order to promote student engagement, we attempt to build a personalized recommender system of research opportunities to undergraduates. With this goal in mind we investigate two tasks. First, one that identifies students that are more willing to participate on this kind of program. A second task is generating a personalized list of recommendations of research opportunities for each student. To evaluate our approach, we perform a simulated prediction experiment with data from our School, which has more than 4,000 active undergraduate students nowadays. Our results indicate that there is a big potential to create a personalized recommender system for this purpose. Our results can be used as a baseline for colleges seeking strategies to encourage research activities within undergraduate students.
Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer
Venue: arXiv cs.CV 2017
Year: 2017

The main goal of this paper is to analyze the general problem of using Convolutional Neural Networks (CNNs) in robots with limited computational capabilities, and to propose general design guidelines for their use. In addition, two different CNN based NAO robot detectors that are able to run in real-time while playing soccer are proposed. One of the detectors is based on the XNOR-Net and the other on the SqueezeNet. Each detector is able to process a robot object-proposal in ~1ms, with an average number of 1.5 proposals per frame obtained by the upper camera of the NAO. The obtained detection rate is ~97%.
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Venue: arXiv cs.RO 2017
Year: 2017

Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspections are labour intensive and limited by human fallibility,making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type; for example wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colour and depth fusion approaches, including 4 fusion and one non fusion approach; using colour and two types of depth images. Trained and tested on over 600 labelled trip hazards over 4 floors and 2000m$\mathrm{^{2}}$ in an active construction site,this approach was able to differentiate between identical objects in different physical configurations (see Figure 1). Outperforming a colour-only detector, our multi-modal trip detector fuses colour and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset moves us one step closer to assistive or fully automated safety inspection systems on construction sites.
Comicolorization: Semi-Automatic Manga Colorization
Venue: arXiv cs.CV 2017
Year: 2017

We developed "Comicolorization", a semi-automatic colorization system for manga images. Given a monochrome manga and reference images as inputs, our system generates a plausible color version of the manga. This is the first work to address the colorization of an entire manga title (a set of manga pages). Our method colorizes a whole page (not a single panel) semi-automatically, with the same color for the same character across multiple panels. To colorize the target character by the color from the reference image, we extract a color feature from the reference and feed it to the colorization network to help the colorization. Our approach employs adversarial loss to encourage the effect of the color features. Optionally, our tool allows users to revise the colorization result interactively. By feeding the color features to our deep colorization network, we accomplish colorization of the entire manga using the desired colors for each panel.
Object Detection Using Deep CNNs Trained on Synthetic Images
Venue: arXiv cs.CV 2017
Year: 2017

The need for large annotated image datasets for training Convolutional Neural Networks (CNNs) has been a significant impediment for their adoption in computer vision applications. We show that with transfer learning an effective object detector can be trained almost entirely on synthetically rendered datasets. We apply this strategy for detecting pack- aged food products clustered in refrigerator scenes. Our CNN trained only with 4000 synthetic images achieves mean average precision (mAP) of 24 on a test set with 55 distinct products as objects of interest and 17 distractor objects. A further increase of 12% in the mAP is obtained by adding only 400 real images to these 4000 synthetic images in the training set. A high degree of photorealism in the synthetic images was not essential in achieving this performance. We analyze factors like training data set size and 3D model dictionary size for their influence on detection performance. Additionally, training strategies like fine-tuning with selected layers and early stopping which affect transfer learning from synthetic scenes to real scenes are explored. Training CNNs with synthetic datasets is a novel application of high-performance computing and a promising approach for object detection applications in domains where there is a dearth of large annotated image data.
Turing Completeness of Finite, Epistemic Programs
Venue: arXiv cs.FL 2017
Year: 2017

In this note, we show the class of finite, epistemic programs to be Turing complete. Epistemic programs is a widely used update mechanism used in epistemic logic, where it such are a special type of action models: One which does not contain postconditions.
Probabilistic Amplitude Shaping with Hard Decision Decoding and Staircase Codes
Venue: arXiv cs.IT 2017
Year: 2017

We consider probabilistic amplitude shaping (PAS) as a means of increasing the spectral efficiency of fiber-optic communication systems. In contrast to previous works in the literature, we consider probabilistic shaping with hard decision decoding (HDD). In particular, we apply the PAS recently introduced by Böcherer \emph{et al.} to a coded modulation (CM) scheme with bit-wise HDD that uses a staircase code as the forward error correction code. We show that the CM scheme with PAS and staircase codes yields significant gains in spectral efficiency with respect to the baseline scheme using a staircase code and a standard constellation with uniformly distributed signal points. Using a single staircase code, the proposed scheme achieves performance within $0.57$--$1.44$ dB of the corresponding achievable information rate for a wide range of spectral efficiencies.
Geo-Encryption Protocol For Mobile Networks
Venue: arXiv cs.CR 2017
Year: 2017

We propose a geo-encryption protocol that allow mobile nodes to communicate securely by restricting the decryption of a message to a particular location and time period. Our protocol will handle the exchange of movement parameters, so that a sender is able to geo-encrypt messages to a moving decryption zone that contains a mobile node's estimated location. We also present methods for estimating the node's movement parameters to allow for geo-encryption. Finally, we evaluate our model by measuring the induced overhead to the network and its performance in terms of decryption ratio.
Measurement and Statistical Analysis of End User Satisfaction with Mobile Network Coverage in Afghanistan
Venue: arXiv cs.NI 2017
Year: 2017

Network coverage is one of the fundamental requirements of any business of a service provider. Mobile operators are expected to deploy base stations in an effective way in order to cover most of the residential areas of a particular country. Improved network coverage leads to increase total revenue, provide end users with enhanced Quality of Services QoS anytime anywhere, and play vital role in the development of telecom sector. In this paper, we measure and statistically analyze network coverage of mobile operators in Afghanistan. Our study is based on primary data collected on random basis from 1,515 mobile phone users of cellular operators. The relationship between No Network Coverage in some residential areas and Satisfaction of Mobile Phone Users is also investigated. We furthermore propose realistic, feasible and cost-efficient solutions to mobile operators and policy makers in order to expand network coverage to non-covered residential areas as well as enhance the performance of networks in existing covered areas of the country.
Significance of Side Information in the Graph Matching Problem
Venue: arXiv cs.SI 2017
Year: 2017

Percolation based graph matching algorithms rely on the availability of seed vertex pairs as side information to efficiently match users across networks. Although such algorithms work well in practice, there are other types of side information available which are potentially useful to an attacker. In this paper, we consider the problem of matching two correlated graphs when an attacker has access to side information, either in the form of community labels or an imperfect initial matching. In the former case, we propose a naive graph matching algorithm by introducing the community degree vectors which harness the information from community labels in an efficient manner. Furthermore, we analyze a variant of the basic percolation algorithm proposed in literature for graphs with community structure. In the latter case, we propose a novel percolation algorithm with two thresholds which uses an imperfect matching as input to match correlated graphs. We evaluate the proposed algorithms on synthetic as well as real world datasets using various experiments. The experimental results demonstrate the importance of communities as side information especially when the number of seeds is small and the networks are weakly correlated.