OpenRaiser/Intern-Atlas huggingface dataset:
Strongvelope Multi-Party Encrypted Messaging Protocol design document
Venue: arXiv cs.CR 2016
Year: 2016

In this document we describe the design of a multi-party messaging encryption protocol "Strongvelope". We hope that it will prove useful to people interested in understanding the inner workings of this protocol as well as cryptography and security experts to review the underlying concepts and assumptions. In this design paper we are outlining the perspective of chat message protection through the Strongvelope module. This is different from the product (the Mega chat) and the transport means which it will be used with. Aspects of the chat product and transport are only referred to where appropriate, but are not subject to discussion in this document.
mpENC Multi-Party Encrypted Messaging Protocol design document
Venue: arXiv cs.CR 2016
Year: 2016

This document is a technical overview and discussion of our work, a protocol for secure group messaging. By secure we mean for the actual users i.e. end-to-end security, as opposed to "secure" for irrelevant third parties. Our work provides everything needed to run a messaging session between real users on top of a real transport protocol. That is, we specify not just a key exchange, but when and how to run these relative to transport-layer events; how to achieve liveness properties such as reliability and consistency, that are time-sensitive and lie outside of the send-receive logic that cryptography-only protocols often restrict themselves to; and offer suggestions for displaying accurate (i.e. secure) but not overwhelming information in user interfaces. We aim towards a general-purpose unified protocol. In other words, we'd prefer to avoid creating a completely new protocol merely to support automation, or asynchronity, or a different transport protocol. This would add complexity to the overall ecosystem of communications protocols. It is simply unnecessary if the original protocol is designed well, as we have tried to do. That aim is not complete -- our full protocol system, as currently implemented, is suitable only for use with certain instant messaging protocols. However, we have tried to separate out conceptually-independent concerns, and solve these individually using minimal assumptions even if other components make extra assumptions. This means that many components of our full system can be reused in future protocol extensions, and we know exactly which components must be replaced in order to lift the existing constraints on our full system.
Deep Reinforcement Learning With Macro-Actions
Venue: arXiv cs.LG 2016
Year: 2016

Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforcement learning approaches. We concentrate on macro-actions, and evaluate these on different Atari 2600 games, where we show that they yield significant improvements in learning speed. Additionally, we show that they can even achieve better scores than DQN. We offer analysis and explanation for both convergence and final results, revealing a problem deep RL approaches have with sparse reward signals.
A Study of MatchPyramid Models on Ad-hoc Retrieval
Venue: arXiv cs.IR 2016
Year: 2016

Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.
Constitutional Precedent of Amicus Briefs
Venue: arXiv cs.CL 2016
Year: 2016

We investigate shared language between U.S. Supreme Court majority opinions and interest groups' corresponding amicus briefs. Specifically, we evaluate whether language that originated in an amicus brief acquired legal precedent status by being cited in the Court's opinion. Using plagiarism detection software, automated querying of a large legal database, and manual analysis, we establish seven instances where interest group amici were able to formulate constitutional case law, setting binding legal precedent. We discuss several such instances for their implications in the Supreme Court's creation of case law.
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informative-ness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because i) it re-uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that our DeepProposals outperform most of the previously proposed object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.
A Convex Relaxation Approach to Higher-Order Statistical Approaches to Signal Recovery
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we investigate an efficient numerical approach for solving higher order statistical methods for blind and semi-blind signal recovery from non-ideal channels. We develop numerical algorithms based on convex optimization relaxation for minimization of higher order statistical cost functions. The new formulation through convex relaxation overcomes the local convergence problem of existing gradient descent based algorithms and applies to several well-known cost functions for effective blind signal recovery including blind equalization and blind source separation in both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. We also propose a fourth order pilot based cost function that benefits from this approach. The simulation results demonstrate that our approach is suitable for short-length packet data transmission using only a few pilot symbols.
The ND-IRIS-0405 Iris Image Dataset
Venue: arXiv cs.CV 2016
Year: 2016

The Computer Vision Research Lab at the University of Notre Dame began collecting iris images in the spring semester of 2004. The initial data collections used an LG 2200 iris imaging system for image acquisition. Image datasets acquired in 2004-2005 at Notre Dame with this LG 2200 have been used in the ICE 2005 and ICE 2006 iris biometric evaluations. The ICE 2005 iris image dataset has been distributed to over 100 research groups around the world. The purpose of this document is to describe the content of the ND-IRIS-0405 iris image dataset. This dataset is a superset of the iris image datasets used in ICE 2005 and ICE 2006. The ND 2004-2005 iris image dataset contains 64,980 images corresponding to 356 unique subjects, and 712 unique irises. The age range of the subjects is 18 to 75 years old. 158 of the subjects are female, and 198 are male. 250 of the subjects are Caucasian, 82 are Asian, and 24 are other ethnicities.
Designing a Human-Machine Hybrid Computing System for Unstructured Data Analytics
Venue: arXiv cs.HC 2016
Year: 2016

Current machine algorithms for analysis of unstructured data typically show low accuracies due to the need for human-like intelligence. Conversely, though humans are much better than machine algorithms on analyzing unstructured data, they are unpredictable, slower and can be erroneous or even malicious as computing agents. Therefore, a hybrid platform that can intelligently orchestrate machine and human computing resources would potentially be capable of providing significantly better benefits compared to either type of computing agent in isolation. In this paper, we propose a new hybrid human-machine computing platform with integrated service level objectives (SLO) management for complex tasks that can be decomposed into a dependency graph where nodes represent subtasks. Initial experimental results are highly encouraging. To the best of our knowledge, ours is the first work that attempts to design such a hybrid human-machine computing platform with support for addressing the three SLO parameters of accuracy, budget and completion time.
Signaling equilibria for dynamic LQG games with asymmetric information
Venue: arXiv cs.GT 2016
Year: 2016

We consider a finite horizon dynamic game with two players who observe their types privately and take actions, which are publicly observed. Players' types evolve as independent, controlled linear Gaussian processes and players incur quadratic instantaneous costs. This forms a dynamic linear quadratic Gaussian (LQG) game with asymmetric information. We show that under certain conditions, players' strategies that are linear in their private types, together with Gaussian beliefs form a perfect Bayesian equilibrium (PBE) of the game. Furthermore, it is shown that this is a signaling equilibrium due to the fact that future beliefs on players' types are affected by the equilibrium strategies. We provide a backward-forward algorithm to find the PBE. Each step of the backward algorithm reduces to solving an algebraic matrix equation for every possible realization of the state estimate covariance matrix. The forward algorithm consists of Kalman filter recursions, where state estimate covariance matrices depend on equilibrium strategies.
Adaptive Quorum-Based Channel-Hopping Distributed Coordination Scheme for Cognitive Radio Networks
Venue: arXiv cs.NI 2016
Year: 2016

One of the most important challenges in deploying cognitive radio networks (CRNs) is to find a common control channel (CCC) to all secondary users (SUs) that enables efficient CR communications. This challenge is attributed to the dynamic time-varying change of network topology, location and spectrum availability conditions. Rendezvous, which is the process of establishing control communications, is an essential requirement to enable efficient communication between any two pair of CR nodes. The most popular CR rendezvous protocols are based on quorum systems (QSs). Quorum systems are systematic approaches, which have several attractive properties that can be utilized to establish communication without the need of a CCC and so overcome the rendezvous (RDV) problem. In this thesis, we propose new channel-hopping-based distributed rendezvous algorithm based on grid-based-quorum techniques. The proposed algorithm increases the probability of RDV within a single cycle by allowing CR nodes to meet more often according to intersection property of quorum systems. Our proposed algorithm is called Adaptive_Quorum-Based Channel-Hopping Distributed Coordination Scheme for Cognitive Radio Networks. The main idea of our algorithm is to dynamically adjusting the selected QS by CR users according to the varying traffic loads in the CRN. The proposed algorithm decreases the average time to rendezvous (TTR) and increase the probability of RDV. We evaluate the performance of our algorithm through Matlab simulations. The performance of proposed algorithm is compared with two different design scheme. The results show that our algorithm can reduce TTR, increase the RDV, and decrease the energy consumption per successful RDV.
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices
Venue: arXiv cs.DS 2016
Year: 2016

With massive high-dimensional data now commonplace in research and industry, there is a strong and growing demand for more scalable computational techniques for data analysis and knowledge discovery. Key to turning these data into knowledge is the ability to learn statistical models with high interpretability. Current methods for learning statistical models either produce models that are not interpretable or have prohibitive computational costs when applied to massive data. In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data. We propose a novel grammar-compressed representation of data matrices that supports fast row and column access while the data matrix is in a compressed form. The original data matrix is grammar-compressed and then the linear model in PLS is learned on the compressed data matrix, which results in a significant reduction in working space, greatly improving scalability. We experimentally test cPLS on its ability to learn linear models for classification, regression and feature extraction with various massive high-dimensional data, and show that cPLS performs superiorly in terms of prediction accuracy, computational efficiency, and interpretability.
Zero-Shot Hashing via Transferring Supervised Knowledge
Venue: arXiv cs.CV 2016
Year: 2016

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.
Performance Analysis of Target Parameters Estimation Using Multiple Widely Separated Antenna Arrays
Venue: arXiv cs.IT 2016
Year: 2016

Target parameter estimation performance is investigated for a radar employing a set of widely separated transmitting and receiving antenna arrays. Cases with multiple extended targets are considered under two signal model assumptions: stochastic and deterministic. The general expressions for the corresponding Cramer-Rao lower bound (CRLB) and the asymptotic properties of the maximum-likelihood (ML) estimator are derived for a radar with $M_t$ arrays of $L_t$ transmitting elements and $M_r$ arrays of $L_r$ receiving elements for both types of signal models. It is shown that for an infinitely large product $M_tM_r$, and a finite $L_r$, the ML estimator is consistent and efficient under the stochastic model, while the deterministic model requires $M_tM_r$ to be finite and $L_r$ to be infinitely large in order to guarantee consistency and efficiency. Monte Carlo simulations further investigate the estimation performance of the proposed radar configuration in practical scenarios with finite $M_tM_r$ and $L_r$, and a fixed total number of available receiving antenna elements, $M_r L_r$. The numerical results demonstrate that grouping receiving elements into properly sized arrays reduces the mean squared error (MSE) and decreases the threshold SNR. In the numerical examples considered, the preferred configurations employ $M_t M_r > 1$. In fact, when $M_t M_r$ becomes too small, due to the loss of the geometric gain, the estimation performance becomes strongly dependent on the particular scenario and can degrade significantly, while the CRLB may become a poor prediction of the MSE even for high SNR. This suggests it may be advantageous to employ approaches where neither $M_tM_r$ nor $L_r$ are too small.
Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy
Venue: arXiv cs.DB 2016
Year: 2016

Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data. However, doing so raises privacy concerns, and risks accidental privacy breaches with serious consequences. Local differential privacy (LDP) techniques address this problem by only collecting randomized answers from each user, with guarantees of plausible deniability; meanwhile, the aggregator can still build accurate models and predictors by analyzing large amounts of such randomized data. So far, existing LDP solutions either have severely restricted functionality, or focus mainly on theoretical aspects such as asymptotical bounds rather than practical usability and performance. Motivated by this, we propose Harmony, a practical, accurate and efficient system for collecting and analyzing data from smart device users, while satisfying LDP. Harmony applies to multi-dimensional data containing both numerical and categorical attributes, and supports both basic statistics (e.g., mean and frequency estimates), and complex machine learning tasks (e.g., linear regression, logistic regression and SVM classification). Experiments using real data confirm Harmony's effectiveness.
Adaptable Symbol Table Management by Meta Modeling and Generation of Symbol Table Infrastructures
Venue: arXiv cs.SE 2016
Year: 2016

Many textual software languages share common concepts such as defining and referencing elements, hierarchical structures constraining the visibility of names, and allowing for identical names for different element kinds. Symbol tables are useful to handle those reference and visibility concepts. However, developing a symbol table can be a tedious task that leads to an additional effort for the language engineer. This paper presents a symbol table meta model usable to define languagespecific symbol tables. Furthermore, we integrate this symbol table meta model with a meta model of a grammar-based language definition. This enables the language engineer to switch between the model structure and the symbol table as needed. Finally, based on a grammarannotation mechanism, our approach is able to generate a symbol table infrastructure that can be used as is or serve as a basis for custom symbol tables.
Robust Active Perception via Data-association aware Belief Space planning
Venue: arXiv cs.RO 2016
Year: 2016

We develop a belief space planning (BSP) approach that advances the state of the art by incorporating reasoning about data association (DA) within planning, while considering additional sources of uncertainty. Existing BSP approaches typically assume data association is given and perfect, an assumption that can be harder to justify while operating, in the presence of localization uncertainty, in ambiguous and perceptually aliased environments. In contrast, our data association aware belief space planning (DA-BSP) approach explicitly reasons about DA within belief evolution, and as such can better accommodate these challenging real world scenarios. In particular, we show that due to perceptual aliasing, the posterior belief becomes a mixture of probability distribution functions, and design cost functions that measure the expected level of ambiguity and posterior uncertainty. Using these and standard costs (e.g.~control penalty, distance to goal) within the objective function, yields a general framework that reliably represents action impact, and in particular, capable of active disambiguation. Our approach is thus applicable to robust active perception and autonomous navigation in perceptually aliased environments. We demonstrate key aspects in basic and realistic simulations.
Distributed Beam Scheduling for Multi-RAT Coexistence in mm-Wave 5G Networks
Venue: arXiv cs.IT 2016
Year: 2016

Millimetre-wave communication (licensed or unlicensed) is envisaged to be an important part of the fifth generation (5G) multi-RAT ecosystem. In this paper, we consider the spectrum bands shared by 5G cellular base stations and some existing networks, such as WiGig. Sharing the same band among such multiple radio access technologies (RATs) is very challenging due to the lack of centralized coordination and demands novel and efficient interference mitigation and coexistence mechanisms to reduce the mutual interference. To address this important challenge, we propose in this paper a novel multi-RAT coexistence mechanism where neighbouring 5G and WiGig base stations, each serving their own associated UEs, schedule their beam configurations in a distributed manner such that their own utility function, e.g. spectral efficiency, is maximized. We formulate the problem as a combinatorial optimization problem and show via simulations that our proposed distributed algorithms yield a comparable spectral efficiency for the entire networks as that using an exhaustive search, which requires global coordination among coexisting RATs and also has a much higher algorithmic complexity.
HARQ and AMC: Friends or Foes?
Venue: arXiv cs.IT 2016
Year: 2016

To ensure reliable communication in randomly varying and error-prone channels, wireless systems use adaptive modulation and coding (AMC) as well as hybrid ARQ (HARQ). In order to elucidate their compatibility and interaction, we compare the throughput provided by AMC, HARQ, and their combination (AMC-HARQ) under two operational conditions: in slow- and fast block-fading channels. Considering both, incremental redundancy HARQ (HARQ-IR) and repetition redundancy HARQ (HARQ-RR) we optimize the rate-decision regions for AMC/HARQ and compare them in terms of attainable throughput. Under a fairly general model of the channel variation and the decoding functions, we conclude that i) adding HARQ on top of AMC may be counterproductive in the high average signal- to-noise ratio regime for fast fading channels, and ii) HARQ is useful for slow fading channels, but it provides moderate throughput gains. We provide explanations for these results which allow us to propose paths to improve AMC-HARQ systems.