OpenRaiser/Intern-Atlas huggingface dataset:
Towards Loop-Free Forwarding of Anonymous Internet Datagrams that Enforce Provenance
Venue: arXiv cs.NI 2016
Year: 2016

The way in which addressing and forwarding are implemented in the Internet constitutes one of its biggest privacy and security challenges. The fact that source addresses in Internet datagrams cannot be trusted makes the IP Internet inherently vulnerable to DoS and DDoS attacks. The Internet forwarding plane is open to attacks to the privacy of datagram sources, because source addresses in Internet datagrams have global scope. The fact an Internet datagrams are forwarded based solely on the destination addresses stated in datagram headers and the next hops stored in the forwarding information bases (FIB) of relaying routers allows Internet datagrams to traverse loops, which wastes resources and leaves the Internet open to further attacks. We introduce PEAR (Provenance Enforcement through Addressing and Routing), a new approach for addressing and forwarding of Internet datagrams that enables anonymous forwarding of Internet datagrams, eliminates many of the existing DDoS attacks on the IP Internet, and prevents Internet datagrams from looping, even in the presence of routing-table loops.
Dual Deep Network for Visual Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work. To highlight geometric contours of the target, we integrate the hierarchical feature maps with an edge detector as the coarse prior maps to further embed local details around the target. To leverage the robustness of our dual network, we train it with random patches measuring the similarities between the network activation and target appearance, which serves as a regularization to enforce the dual network to focus on target object. The proposed dual network is updated online in a unique manner based on the observation that the target being tracked in consecutive frames should share more similar feature representations than those in the surrounding background. It is also found that for a target object, the prior maps can help further enhance performance by passing message into the output maps of the dual network. Therefore, an independent component analysis with reference algorithm (ICA-R) is employed to extract target context using prior maps as guidance. Online tracking is conducted by maximizing the posterior estimate on the final maps with stochastic and periodic update. Quantitative and qualitative evaluations on two large-scale benchmark data sets show that the proposed algorithm performs favourably against the state-of-the-arts.
Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework
Venue: arXiv cs.IT 2016
Year: 2016

Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This paper focuses on distributed interference management for downlink small cell networks. We address the optimization of transmit strategies from both the game theoretical and the network utility maximization (NUM) perspectives and show that they can be unified in a generalized Nash equilibrium problem (GNEP) framework. Specifically, the small cell network design is first formulated as a GNEP, where the SBSs and MBS compete for the spectral resources by maximizing their own rates while satisfying global quality of service (QoS) constraints. We analyze the GNEP via variational inequality theory and propose distributed algorithms, which only require the broadcasting of some pricing information, to achieve a generalized Nash equilibrium (GNE). Then, we also consider a nonconvex NUM problem that aims to maximize the sum rate of all BSs subject to global QoS constraints. We establish the connection between the NUM problem and a penalized GNEP and show that its stationary solution can be obtained via a fixed point iteration of the GNE. We propose GNEP-based distributed algorithms that achieve a stationary solution of the NUM problem at the expense of additional signaling overhead and complexity. The convergence of the proposed algorithms is proved and guaranteed for properly chosen algorithm parameters. The proposed GNEP framework can scale from a QoS constrained game to a NUM design for small cell networks by trading off signaling overhead and complexity.
Talk it up or play it down? (Un)expected correlations between (de-)emphasis and recurrence of discussion points in consequential U.S. economic policy meetings
Venue: arXiv cs.SI 2016
Year: 2016

In meetings where important decisions get made, what items receive more attention may influence the outcome. We examine how different types of rhetorical (de-)emphasis -- including hedges, superlatives, and contrastive conjunctions -- correlate with what gets revisited later, controlling for item frequency and speaker. Our data consists of transcripts of recurring meetings of the Federal Reserve's Open Market Committee (FOMC), where important aspects of U.S. monetary policy are decided on. Surprisingly, we find that words appearing in the context of hedging, which is usually considered a way to express uncertainty, are more likely to be repeated in subsequent meetings, while strong emphasis indicated by superlatives has a slightly negative effect on word recurrence in subsequent meetings. We also observe interesting patterns in how these effects vary depending on social factors such as status and gender of the speaker. For instance, the positive effects of hedging are more pronounced for female speakers than for male speakers.
Exploring Structure for Long-Term Tracking of Multiple Objects in Sports Videos
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a novel approach for exploiting structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion. We use a model-free approach that relies only on annotations given in the first frame of the video to track all the objects online, i.e. without knowledge from future frames. We initialize a probabilistic Attributed Relational Graph (ARG) from the first frame, which is incrementally updated along the video. Instead of using the structural information only to evaluate the scene, the proposed approach considers it to generate new tracking hypotheses. In this way, our method is capable of generating relevant object candidates that are used to improve or recover the track of lost objects. The proposed method is evaluated on several videos of table tennis, volleyball, and on the ACASVA dataset. The results show that our approach is very robust, flexible and able to outperform other state-of-the-art methods in sports videos that present structural patterns.
Grammar rules for the isiZulu complex verb
Venue: arXiv cs.CL 2016
Year: 2016

The isiZulu verb is known for its morphological complexity, which is a subject for on-going linguistics research, as well as for prospects of computational use, such as controlled natural language interfaces, machine translation, and spellcheckers. To this end, we seek to answer the question as to what the precise grammar rules for the isiZulu complex verb are (and, by extension, the Bantu verb morphology). To this end, we iteratively specify the grammar as a Context Free Grammar, and evaluate it computationally. The grammar presented in this paper covers the subject and object concords, negation, present tense, aspect, mood, and the causative, applicative, stative, and the reciprocal verbal extensions, politeness, the wh-question modifiers, and aspect doubling, ensuring their correct order as they appear in verbs. The grammar conforms to specification.
Efficient Target Activity Detection based on Recurrent Neural Networks
Venue: arXiv cs.SD 2016
Year: 2016

This paper addresses the problem of Target Activity Detection (TAD) for binaural listening devices. TAD denotes the problem of robustly detecting the activity of a target speaker in a harsh acoustic environment, which comprises interfering speakers and noise (cocktail party scenario). In previous work, it has been shown that employing a Feed-forward Neural Network (FNN) for detecting the target speaker activity is a promising approach to combine the advantage of different TAD features (used as network inputs). In this contribution, we exploit a larger context window for TAD and compare the performance of FNNs and Recurrent Neural Networks (RNNs) with an explicit focus on small network topologies as desirable for embedded acoustic signal processing systems. More specifically, the investigations include a comparison between three different types of RNNs, namely plain RNNs, Long Short-Term Memories, and Gated Recurrent Units. The results indicate that all versions of RNNs outperform FNNs for the task of TAD.
Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model
Venue: arXiv cs.LG 2016
Year: 2016

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest.
Feedback Does Not Increase the Capacity of Compound Channels with Additive Noise
Venue: arXiv cs.IT 2016
Year: 2016

A discrete compound channel with memory is considered, where no stationarity, ergodicity or information stability is required, and where the uncertainty set can be arbitrary. When the discrete noise is additive but otherwise arbitrary and there is no cost constraint on the input, it is shown that the causal feedback does not increase the capacity. This extends the earlier result obtained for general single-state channels with full transmitter (Tx) channel state information (CSI) to the compound setting. It is further shown that, for this compound setting and under a mild technical condition on the additive noise, the addition of the full Tx CSI does not increase the capacity either, so that the worst-case and compound channel capacities are the same. This can also be expressed as a saddle-point in the information-theoretic game between the transmitter (who selects the input distribution) and the nature (who selects the channel state), even though the objective function (the inf-information rate) is not convex/concave in the right way. Cases where the Tx CSI does increase the capacity are identified. Conditions under which the strong converse holds for this channel are studied. The ergodic behaviour of the worst-case noise in otherwise information-unstable channel is shown to be both sufficient and necessary for the strong converse to hold, including feedback and no feedback cases.
Unsupervised Place Discovery for Visual Place Classification
Venue: arXiv cs.CV 2016
Year: 2016

In this study, we explore the use of deep convolutional neural networks (DCNNs) in visual place classification for robotic mapping and localization. An open question is how to partition the robot's workspace into places to maximize the performance (e.g., accuracy, precision, recall) of potential DCNN classifiers. This is a chicken and egg problem: If we had a well-trained DCNN classifier, it is rather easy to partition the robot's workspace into places, but the training of a DCNN classifier requires a set of pre-defined place classes. In this study, we address this problem and present several strategies for unsupervised discovery of place classes ("time cue," "location cue," "time-appearance cue," and "location-appearance cue"). We also evaluate the efficacy of the proposed methods using the publicly available University of Michigan North Campus Long-Term (NCLT) Dataset.
Nondeterministic unitary OBDDs
Venue: arXiv cs.CC 2016
Year: 2016

We investigate the width complexity of nondeterministic unitary OBDDs (NUOBDDs). Firstly, we present a generic lower bound on their widths based on the size of strong 1-fooling sets. Then, we present classically cheap functions that are expensive for NUOBDDs and vice versa by improving the previous gap. We also present a function for which neither classical nor unitary nondeterminism does help. Moreover, based on our results, we present a width hierarchy for NUOBDDs. Lastly, we provide the bounds on the widths of NUOBDDs for the basic Boolean operations negation, union, and intersection.
A deep learning approach for predicting the quality of online health expert question-answering services
Venue: arXiv cs.IR 2016
Year: 2016

Currently, a growing number of health consumers are asking health-related questions online, at any time and from anywhere, which effectively lowers the cost of health care. The most common approach is using online health expert question-answering (HQA) services, as health consumers are more willing to trust answers from professional physicians. However, these answers can be of varying quality depending on circumstance. In addition, as the available HQA services grow, how to predict the answer quality of HQA services via machine learning becomes increasingly important and challenging. In an HQA service, answers are normally short texts, which are severely affected by the data sparsity problem. Furthermore, HQA services lack community features such as best answer and user votes. Therefore, the wisdom of the crowd is not available to rate answer quality. To address these problems, in this paper, the prediction of HQA answer quality is defined as a classification task. First, based on the characteristics of HQA services and feedback from medical experts, a standard for HQA service answer quality evaluation is defined. Next, based on the characteristics of HQA services, several novel non-textual features are proposed, including surface linguistic features and social features. Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers. Our results prove that the proposed framework overcomes the problem of overly sparse textual features in short text answers and effectively identifies high-quality answers.
Sparsity Measure of a Network Graph: Gini Index
Venue: arXiv cs.DM 2016
Year: 2016

This article examines the application of a popular measure of sparsity, Gini Index, on network graphs. A wide variety of network graphs happen to be sparse. But the index with which sparsity is commonly measured in network graphs is edge density, reflecting the proportion of the sum of the degrees of all nodes in the graph compared to the total possible degrees in the corresponding fully connected graph. Thus edge density is a simple ratio and carries limitations, primarily in terms of the amount of information it takes into account in its definition. In this paper, we have provided a formulation for defining sparsity of a network graph by generalizing the concept of Gini Index and call it sparsity index. A majority of the six properties (viz., Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates and Babies) with which sparsity measures are commonly compared are seen to be satisfied by the proposed index. A comparison between edge density and the sparsity index has been drawn with appropriate examples to highlight the efficacy of the proposed index. It has also been shown theoretically that the two measures follow similar trend for a changing graph, i.e., as the edge density of a graph increases its sparsity index decreases. Additionally, the paper draws a relationship, analytically, between the sparsity index and the exponent term of a power law distribution, a distribution which is known to approximate the degree distribution of a wide variety of network graphs. Finally, the article highlights how the proposed index together with Gini index can reveal important properties of a network graph.
Study of Raspberry Pi 2 Quad-core Cortex A7 CPU Cluster as a Mini Supercomputer
Venue: arXiv cs.DC 2016
Year: 2016

High performance computing (HPC) devices is no longer exclusive for academic, R&D, or military purposes. The use of HPC device such as supercomputer now growing rapidly as some new area arise such as big data, and computer simulation. It makes the use of supercomputer more inclusive. Todays supercomputer has a huge computing power, but requires an enormous amount of energy to operate. In contrast a single board computer (SBC) such as Raspberry Pi has minimum computing power, but require a small amount of energy to operate, and as a bonus it is small and cheap. This paper covers the result of utilizing many Raspberry Pi 2 SBCs, a quad-core Cortex A7 900 MHz, as a cluster to compensate its computing power. The high performance linpack (HPL) is used to benchmark the computing power, and a power meter with resolution 10mV / 10mA is used to measure the power consumption. The experiment shows that the increase of number of cores in every SBC member in a cluster is not giving significant increase in computing power. This experiment give a recommendation that 4 nodes is a maximum number of nodes for SBC cluster based on the characteristic of computing performance and power consumption.
Splitting $B_2$-VPG graphs into outer-string and co-comparability graphs
Venue: arXiv cs.CG 2016
Year: 2016

In this paper, we show that any $B_2$-VPG graph (i.e., an intersection graph of orthogonal curves with at most 2 bends) can be decomposed into $O(\log n)$ outerstring graphs or $O(\log^3 n)$ permutation graphs. This leads to better approximation algorithms for hereditary graph problems, such as independent set, clique and clique cover, on $B_2$-VPG graphs.
Evaluation of a RGB-LED-based Emotion Display for Affective Agents
Venue: arXiv cs.HC 2016
Year: 2016

Technology has become an essential part in every aspect of our lives. However the key to a successful implementation of a technology depends on the acceptance by the general public. In order to increase the acceptance various approaches can be applied. In this paper, we will examine the human-robot emotional interaction by investigating the capabilities of a developed low-resolution RGB-LED display in the context of artificial emotions. We are focusing on four of the most representative human emotions which include happiness, anger, sadness and fear. We will work with colors and dynamic light patterns which are supposed to evoke various associations. In an experiment, the use these patterns as expressions of emotions are validated. The results of the conducted study show that some of the considered basic emotions can be recognized by human observers.
Improving the Quality of Random Number Generators by Applying a Simple Ratio Transformation
Venue: arXiv cs.OH 2016
Year: 2016

It is well-known that the quality of random number generators can often be improved by combining several generators, e.g. by summing or subtracting their results. In this paper we investigate the ratio of two random number generators as an alternative approach: the smaller of two input random numbers is divided by the larger, resulting in a rational number from $[0,1]$. We investigate theoretical properties of this approach and show that it yields a good approximation to the ideal uniform distribution. To evaluate the empirical properties we use the well-known test suite \textsc{TestU01}. We apply the ratio transformation to moderately bad generators, i.e. those that failed up to 40\% of the tests from the test battery \textsc{Crush} of \textsc{TestU01}. We show that more than half of them turn into very good generators that pass all tests of \textsc{Crush} and \textsc{BigCrush} from \textsc{TestU01} when the ratio transformation is applied. In particular, generators based on linear operations seem to benefit from the ratio, as this breaks up some of the unwanted regularities in the input sequences. Thus the additional effort to produce a second random number and to calculate the ratio allows to increase the quality of available random number generators.
A Proximity Measure using Blink Model
Venue: arXiv cs.SI 2016
Year: 2016

This paper proposes a new graph proximity measure. This measure is a derivative of network reliability. By analyzing its properties and comparing it against other proximity measures through graph examples, we demonstrate that it is more consistent with human intuition than competitors. A new deterministic algorithm is developed to approximate this measure with practical complexity. Empirical evaluation by two link prediction benchmarks, one in coauthorship networks and one in Wikipedia, shows promising results. For example, a single parameterization of this measure achieves accuracies that are 14-35% above the best accuracy for each graph of all predictors reported in the 2007 Liben-Nowell and Kleinberg survey.
Delay Optimal Joint Scheduling-and-Power-Control for Cognitive Radio Uplinks
Venue: arXiv cs.IT 2016
Year: 2016

An uplink cognitive radio system with a single primary user (PU) and multiple secondary users (SUs) is considered. The SUs have an individual average delay constraint and an aggregate average interference constraint to the PU. If the interference channels between the SUs and the PU are statistically different due to the different physical locations of the SUs, the SUs will experience different delay performances. This is because SUs located closer to the PU transmit with lower power levels. A dynamic scheduling-and-power-allocation policy that uses the dynamic programming, is proposed. The proposed policy can provide the required average delay guarantees to all SUs irrespective of their location as well as protect the PU from harmful interference. The policy is shown to be asymptotically delay optimal in the light traffic regime. Motivated, by the high complexity of the dynamic programming algorithm in the optimal policy we exploit the structure of the problem's solution to present an alternative suboptimal policy. Through simulations we show that in both light and heavy traffic regimes, the delay performance of the suboptimal policy is within $0.3\%$ from the optimal policy and both outperforming existing methods.
A hardness result and new algorithm for the longest common palindromic subsequence problem
Venue: arXiv cs.DS 2016
Year: 2016

The 2-LCPS problem, first introduced by Chowdhury et al. [Fundam. Inform., 129(4):329-340, 2014], asks one to compute (the length of) a longest palindromic common subsequence between two given strings $A$ and $B$. We show that the 2-LCPS problem is at least as hard as the well-studied longest common subsequence problem for four strings (the 4-LCS problem). Then, we present a new algorithm which solves the 2-LCPS problem in $O(σM^2 + n)$ time, where $n$ denotes the length of $A$ and $B$, $M$ denotes the number of matching positions between $A$ and $B$, and $σ$ denotes the number of distinct characters occurring in both $A$ and $B$. Our new algorithm is faster than Chowdhury et al.'s sparse algorithm when $σ= o(\log^2n \log\log n)$.