OpenRaiser/Intern-Atlas huggingface dataset:
Time-Invariant LDPC Convolutional Codes
Venue: arXiv cs.IT 2017
Year: 2017

Spatially coupled codes have been shown to universally achieve the capacity for a large class of channels. Many variants of such codes have been introduced to date. We discuss a further such variant that is particularly simple and is determined by a very small number of parameters. More precisely, we consider time-invariant low-density convolutional codes with very large constraint lengths. We show via simulations that, despite their extreme simplicity, such codes still show the threshold saturation behavior known from the spatially coupled codes discussed in the literature. Further, we show how the size of the typical minimum stopping set is related to basic parameters of the code. Due to their simplicity and good performance, these codes might be attractive from an implementation perspective.
Deep Multi-camera People Detection
Venue: arXiv cs.CV 2017
Year: 2017

This paper addresses the problem of multi-view people occupancy map estimation. Existing solutions for this problem either operate per-view, or rely on a background subtraction pre-processing. Both approaches lessen the detection performance as scenes become more crowded. The former does not exploit joint information, whereas the latter deals with ambiguous input due to the foreground blobs becoming more and more interconnected as the number of targets increases. Although deep learning algorithms have proven to excel on remarkably numerous computer vision tasks, such a method has not been applied yet to this problem. In large part this is due to the lack of large-scale multi-camera data-set. The core of our method is an architecture which makes use of monocular pedestrian data-set, available at larger scale then the multi-view ones, applies parallel processing to the multiple video streams, and jointly utilises it. Our end-to-end deep learning method outperforms existing methods by large margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly available a new three-camera HD data-set. Our source code and trained models will be made available under an open-source license.
A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning
Venue: arXiv cs.AI 2017
Year: 2017

In modern machine learning, pattern recognition replaces realtime semantic reasoning. The mapping from input to output is learned with fixed semantics by training outcomes deliberately. This is an expensive and static approach which depends heavily on the availability of a very particular kind of prior raining data to make inferences in a single step. Conventional semantic network approaches, on the other hand, base multi-step reasoning on modal logics and handcrafted ontologies, which are ad hoc, expensive to construct, and fragile to inconsistency. Both approaches may be enhanced by a hybrid approach, which completely separates reasoning from pattern recognition. In this report, a quasi-linguistic approach to knowledge representation is discussed, motivated by spacetime structure. Tokenized patterns from diverse sources are integrated to build a lightly constrained and approximately scale-free network. This is then be parsed with very simple recursive algorithms to generate `brainstorming' sets of reasoned knowledge.
Towards M-Adhesive Categories based on Coalgebras and Comma Categories
Venue: arXiv cs.LO 2017
Year: 2017

In this contribution we investigate several extensions of the powerset that comprise arbitrarily nested subsets, and call them superpower set. This allows the definition of graphs with possibly infinitely nested nodes. additionally we define edges that are incident to edges. Since we use coalgebraic constructions we refer to these graphs as corecursive graphs. The superpower set functors are examined and then used for the definition of $\mathcal{M}$-adhesive categories which are the basic categories for $\mathcal{M}$-adhesive transformation systems. So, we additionally show that coalgebras $\mathbf{Sets}_F$ are $\mathcal{M}$-adhesive categories provided the functor $F:\mathbf{Sets}_F \to \mathbf{Sets}_F$ preserves pullbacks along monomorphisms.
Overview of Cyber Science and Technology Programs at the U.S. Army Research Laboratory
Venue: arXiv cs.CR 2017
Year: 2017

This paper provides an overview of research programs in cyber security performed by the U.S Army Research Laboratory. Although ARL is the U.S. Army's corporate laboratory that focuses on fundamental and early applied research, the fundamental science endeavors are closely integrated with extensive operationally-oriented programs. One example is the Cyber Collaborative Research Alliance (CRA) that brings together ARL scientists with academic researchers from dozens of U.S. universities. ARL cyber scientists are largely driven by challenges unique to the ground operations of the Army; this paper outlines a few of these challenges and the ways in which they are addressed by ARL research efforts. The long-term campaign of cyber research is guided by the vision of the future Army battlefield. In the year 2040, it will be a highly converged virtual-physical space, where cyber operations will be an integral part of the battle.
Joint Routing, Scheduling and Power Control Providing Hard Deadline in Wireless Multihop Networks
Venue: arXiv cs.NI 2017
Year: 2017

We consider optimal/efficient power allocation policies in a single/multihop wireless network in the presence of hard end-to-end deadline delay constraints on the transmitted packets. Such constraints can be useful for real time voice and video. Power is consumed in only transmission of the data. We consider the case when the power used in transmission is a convex function of the data transmitted. We develop a computationally efficient online algorithm, which minimizes the average power for the single hop. We model this problem as dynamic program (DP) and obtain the optimal solution. Next, we generalize it to the multiuser, multihop scenario when there are multiple real time streams with different hard deadline constraints.
An Equivalence Between Secure Network and Index Coding
Venue: arXiv cs.IT 2017
Year: 2017

We extend the equivalence between network coding and index coding by Effros, El Rouayheb, and Langberg to the secure communication setting in the presence of an eavesdropper. Specifically, we show that the most general versions of secure network-coding setup by Chan and Grant and the secure index-coding setup by Dau, Skachek, and Chee, which also include the randomised encoding setting, are equivalent.
Learning to Use Learners' Advice
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we study a variant of the framework of online learning using expert advice with limited/bandit feedback. We consider each expert as a learning entity, seeking to more accurately reflecting certain real-world applications. In our setting, the feedback at any time $t$ is limited in a sense that it is only available to the expert $i^t$ that has been selected by the central algorithm (forecaster), \emph{i.e.}, only the expert $i^t$ receives feedback from the environment and gets to learn at time $t$. We consider a generic black-box approach whereby the forecaster does not control or know the learning dynamics of the experts apart from knowing the following no-regret learning property: the average regret of any expert $j$ vanishes at a rate of at least $O(t_j^{\regretRate-1})$ with $t_j$ learning steps where $\regretRate \in [0, 1]$ is a parameter. In the spirit of competing against the best action in hindsight in multi-armed bandits problem, our goal here is to be competitive w.r.t. the cumulative losses the algorithm could receive by following the policy of always selecting one expert. We prove the following hardness result: without any coordination between the forecaster and the experts, it is impossible to design a forecaster achieving no-regret guarantees. In order to circumvent this hardness result, we consider a practical assumption allowing the forecaster to "guide" the learning process of the experts by filtering/blocking some of the feedbacks observed by them from the environment, \emph{i.e.}, not allowing the selected expert $i^t$ to learn at time $t$ for some time steps. Then, we design a novel no-regret learning algorithm \algo for this problem setting by carefully guiding the feedbacks observed by experts. We prove that \algo achieves the worst-case expected cumulative regret of $O(\Time^\frac{1}{2 - \regretRate})$ after $\Time$ time steps.
Chord Angle Deviation using Tangent (CADT), an Efficient and Robust Contour-based Corner Detector
Venue: arXiv cs.CV 2017
Year: 2017

Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to calculate the value of curvature in this paper. By demonstrating experimental results, our proposed technique outperforms CTAA and other detectors mentioned in this paper. The results exhibit that our proposed method is simple yet efficient at finding out corners more accurately and reliably.
Visualization and Analysis of Large-Scale, Tree-Based, Adaptive Mesh Refinement Simulations with Arbitrary Rectilinear Geometry
Venue: arXiv cs.GR 2017
Year: 2017

We present here the first systematic treatment of the problems posed by the visualization and analysis of large-scale, parallel adaptive mesh refinement (AMR) simulations on an Eulerian grid. When compared to those obtained by constructing an intermediate unstructured mesh with fully described connectivity, our primary results indicate a gain of at least 80\% in terms of memory footprint, with a better rendering while retaining similar execution speed. In this article, we describe the key concepts that allow us to obtain these results, together with the methodology that facilitates the design, implementation, and optimization of algorithms operating directly on such refined meshes. This native support for AMR meshes has been contributed to the open source Visualization Toolkit (VTK). This work pertains to a broader long-term vision, with the dual goal to both improve interactivity when exploring such data sets in 2 and 3 dimensions, and optimize resource utilization.
Modeling the Throughput of the Linux-Based Agile-SD Transmission Control Protocol
Venue: arXiv cs.NI 2017
Year: 2017

Agile-SD is one of the latest versions of loss-based congestion control algorithm (CCA), which has been proposed to improve the total performance of transmission control protocol (TCP) over high-speed and short-distance networks. It has introduced a new mechanism, called agility factor mechanism, which shortens the epoch time to reduce the sensitivity to packet losses and in turn to increase the average throughput. Agile-SD has only been tested via simulation; however, it has not been mathematically proven or evaluated. The contribution of this paper is twofold. First, a new mathematical model for the throughput of NewReno and Agile-SD is proposed. This model is designed using the well-known Markov chains to validate the correctness of Agile-SD and to show the impact of buffer size, multiplicative decrease factor, and maximum limit of agility factor (λmax ) on the total performance. Second, an automated algorithm configuration and parameter tuning (AACPT) technique is employed to optimize and automate the configuration of λmax . Furthermore, the numerical results for both NewReno and Agile-SD are compared with the simulation results, in which the validity of the proposed model is confirmed. Moreover, the output of the AACPT is exploited to formulate a new equation, which calculates the optimal λmax from a given \b{eta} in order to conserve the standard interface of the TCP. This equation increases the scalability of Agile-SD and improves its total performance.
Models of Type Theory with Strict Equality
Venue: arXiv cs.LO 2017
Year: 2017

This thesis introduces the idea of two-level type theory, an extension of Martin-Löf type theory that adds a notion of strict equality as an internal primitive. A type theory with a strict equality alongside the more conventional form of equality, the latter being of fundamental importance for the recent innovation of homotopy type theory (HoTT), was first proposed by Voevodsky, and is usually referred to as HTS. Here, we generalise and expand this idea, by developing a semantic framework that gives a systematic account of type formers for two-level systems, and proving a conservativity result relating back to a conventional type theory like HoTT. Finally, we show how a two-level theory can be used to provide partial solutions to open problems in HoTT. In particular, we use it to construct semi-simplicial types, and lay out the foundations of an internal theory of $(\infty, 1)$-categories.
Femto-Caching with Soft Cache Hits: Improving Performance through Recommendation and Delivery of Related Content
Venue: arXiv cs.NI 2017
Year: 2017

Pushing popular content to cheap "helper" nodes (e.g., small cells) during off-peak hours has recently been proposed to cope with the increase in mobile data traffic. User requests can be served locally from these helper nodes, if the requested content is available in at least one of the nearby helpers. Nevertheless, the collective storage of a few nearby helper nodes does not usually suffice to achieve a high enough hit rate in practice. We propose to depart from the assumption of hard cache hits, common in existing works, and consider "soft" cache hits, where if the original content is not available, some related contents that are locally cached can be recommended instead. Given that Internet content consumption is entertainment-oriented, we argue that there exist scenarios where a user might accept an alternative content (e.g., better download rate for alternative content, low rate plans, etc.), thus avoiding to access expensive/congested links. We formulate the problem of optimal edge caching with soft cache hits in a relatively generic setup, propose efficient algorithms, and analyze the expected gains. We then show using synthetic and real datasets of related video contents that promising caching gains could be achieved in practice.
Towards a Theory of Complexity of Regular Languages
Venue: arXiv cs.FL 2017
Year: 2017

We survey recent results concerning the complexity of regular languages represented by their minimal deterministic finite automata. In addition to the quotient complexity of the language -- which is the number of its (left) quotients, and is the same as its state complexity -- we also consider the size of its syntactic semigroup and the quotient complexity of its atoms -- basic components of every regular language. We then turn to the study of the quotient/state complexity of common operations on regular languages: reversal, (Kleene) star, product (concatenation) and boolean operations. We examine relations among these complexity measures. We discuss several subclasses of regular languages defined by convexity. In many, but not all, cases there exist "most complex" languages, languages satisfying all these complexity measures.
Peak Transmission Rate Resilient Crosslayer Broadcast for Body Area Networks
Venue: arXiv cs.NI 2017
Year: 2017

Wireless Body Area Networks (WBAN) open an interdisciplinary area within Wireless Sensor Networks (WSN) research, with a tremendous impact in healthcare area where sensors are used to monitor, collect and transmit biological parameters of the human body. We propose the rst network-MAC cross-layer broadcast protocol in WBAN. Our protocol, evaluated in the OMNET++ simulator enriched with realistic human body mobility models and channel models issued from the recent research on biomedical and health informatics, outperforms existing at broadcast strategies in terms of percentage of covered nodes, energy consumption and correct reception of causally-ordered packets (i.e. packets are received in the same order as they were sent by the sink). Furthermore, we investigate the resilience of both existing at broadcast strategies and our new protocol face to various transmission rates and human body mobility. Existing at broadcast strategies without exception start to have a drastic drop of performances for transmission rates above 11Kb/s while our cross-layer protocol performances maintains its good performances for transmission rates up to 190Kb/s.
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors
Venue: arXiv cs.CV 2017
Year: 2017

Keypoint detection is one of the most important pre-processing steps in tasks such as face modeling, recognition and verification. In this paper, we present an iterative method for Keypoint Estimation and Pose prediction of unconstrained faces by Learning Efficient H-CNN Regressors (KEPLER) for addressing the face alignment problem. Recent state of the art methods have shown improvements in face keypoint detection by employing Convolution Neural Networks (CNNs). Although a simple feed forward neural network can learn the mapping between input and output spaces, it cannot learn the inherent structural dependencies. We present a novel architecture called H-CNN (Heatmap-CNN) which captures structured global and local features and thus favors accurate keypoint detecion. HCNN is jointly trained on the visibility, fiducials and 3D-pose of the face. As the iterations proceed, the error decreases making the gradients small and thus requiring efficient training of DCNNs to mitigate this. KEPLER performs global corrections in pose and fiducials for the first four iterations followed by local corrections in the subsequent stage. As a by-product, KEPLER also provides 3D pose (pitch, yaw and roll) of the face accurately. In this paper, we show that without using any 3D information, KEPLER outperforms state of the art methods for alignment on challenging datasets such as AFW and AFLW.
RIPML: A Restricted Isometry Property based Approach to Multilabel Learning
Venue: arXiv cs.IR 2017
Year: 2017

The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that only a few labels are active in any given datapoint as compared to the total number of labels. However, only a small number of existing work take direct advantage of this inherent extreme sparsity in the label space. By the virtue of Restricted Isometry Property (RIP), satisfied by many random ensembles, we propose a novel procedure for multilabel learning known as RIPML. During the training phase, in RIPML, labels are projected onto a random low-dimensional subspace followed by solving a least-square problem in this subspace. Inference is done by a k-nearest neighbor (kNN) based approach. We demonstrate the effectiveness of RIPML by conducting extensive simulations and comparing results with the state-of-the-art linear dimensionality reduction based approaches.
Completing a joint PMF from projections: a low-rank coupled tensor factorization approach
Venue: arXiv cs.LG 2017
Year: 2017

There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches have found considerable success in the area of recommender systems, under machine learning. From a statistical estimation point of view, the gold standard is to have access to the joint probability distribution of all pertinent random variables, from which any desired optimal estimator can be readily derived. In practice high-dimensional joint distributions are very hard to estimate, and only estimates of low-dimensional projections may be available. We show that it is possible to identify higher-order joint PMFs from lower-order marginalized PMFs using coupled low-rank tensor factorization. Our approach features guaranteed identifiability when the full joint PMF is of low-enough rank, and effective approximation otherwise. We provide an algorithmic approach to compute the sought factors, and illustrate the merits of our approach using rating prediction as an example.
Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions
Venue: arXiv cs.NI 2017
Year: 2017

We consider the problem of efficient packet dissemination in wireless networks with point-to-multi-point wireless broadcast channels. We propose a dynamic policy, which achieves the broadcast capacity of the network. This policy is obtained by first transforming the original multi-hop network into a precedence-relaxed virtual single-hop network and then finding an optimal broadcast policy for the relaxed network. The resulting policy is shown to be throughput-optimal for the original wireless network using a sample-path argument. We also prove the NP-completeness of the finite-horizon broadcast problem, which is in contrast with the polynomial time solvability of the problem with point-to-point channels. Illustrative simulation results demonstrate the efficacy of the proposed broadcast policy in achieving the full broadcast capacity with low delay.
Hybrid Indexes to Expedite Spatial-Visual Search
Venue: arXiv cs.DB 2017
Year: 2017

Due to the growth of geo-tagged images, recent web and mobile applications provide search capabilities for images that are similar to a given query image and simultaneously within a given geographical area. In this paper, we focus on designing index structures to expedite these spatial-visual searches. We start by baseline indexes that are straightforward extensions of the current popular spatial (R*-tree) and visual (LSH) index structures. Subsequently, we propose hybrid index structures that evaluate both spatial and visual features in tandem. The unique challenge of this type of query is that there are inaccuracies in both spatial and visual features. Therefore, different traversals of the index structures may produce different images as output, some of which more relevant to the query than the others. We compare our hybrid structures with a set of baseline indexes in both performance and result accuracy using three real world datasets from Flickr, Google Street View, and GeoUGV.
On Ladder Logic Bombs in Industrial Control Systems
Venue: arXiv cs.CR 2017
Year: 2017

In industrial control systems, devices such as Programmable Logic Controllers (PLCs) are commonly used to directly interact with sensors and actuators, and perform local automatic control. PLCs run software on two different layers: a) firmware (i.e. the OS) and b) control logic (processing sensor readings to determine control actions). In this work, we discuss ladder logic bombs, i.e. malware written in ladder logic (or one of the other IEC 61131-3-compatible languages). Such malware would be inserted by an attacker into existing control logic on a PLC, and either persistently change the behavior, or wait for specific trigger signals to activate malicious behaviour. For example, the LLB could replace legitimate sensor readings with manipulated values. We see the concept of LLBs as a generalization of attacks such as the Stuxnet attack. We introduce LLBs on an abstract level, and then demonstrate several designs based on real PLC devices in our lab. In particular, we also focus on stealthy LLBs, i.e. LLBs that are hard to detect by human operators manually validating the program running in PLCs. In addition to introducing vulnerabilities on the logic layer, we also discuss countermeasures and we propose two detection techniques.