OpenRaiser/Intern-Atlas huggingface dataset:
A Tighter Real-Time Communication Analysis for Wormhole-Switched Priority-Preemptive NoCs
Venue: arXiv cs.DC 2016
Year: 2016

Simulations and runtime measurements are some of the methods which can be used to evaluate whether a given NoC-based platform can accommodate application workload and fulfil its timing requirements. Yet, these techniques are often time-consuming, and hence can evaluate only a limited set of scenarios. Therefore, these approaches are not suitable for safety-critical and hard real-time systems, where one of the fundamental requirements is to provide strong guarantees that all timing requirements will always be met, even in the worst-case conditions. For such systems the analytic-based real-time analysis is the only viable approach. In this paper the focus is on the real-time communication analysis for wormhole-switched priority-preemptive NoCs. First, we elaborate on the existing analysis and identify one source of pessimism. Then, we propose an extension to the analysis, which efficiently overcomes this limitation, and allows for a less pessimistic analysis. Finally, through a comprehensive experimental evaluation, we compare the newly proposed approach against the existing one, and also observe how the trends change with different traffic parameters.
Multilevel Threshold Secret and Function Sharing based on the Chinese Remainder Theorem
Venue: arXiv cs.CR 2016
Year: 2016

A recent work of Harn and Fuyou presents the first multilevel (disjunctive) threshold secret sharing scheme based on the Chinese Remainder Theorem. In this work, we first show that the proposed method is not secure and also fails to work with a certain natural setting of the threshold values on compartments. We then propose a secure scheme that works for all threshold settings. In this scheme, we employ a refined version of Asmuth-Bloom secret sharing with a special and generic Asmuth-Bloom sequence called the {\it anchor sequence}. Based on this idea, we also propose the first multilevel conjunctive threshold secret sharing scheme based on the Chinese Remainder Theorem. Lastly, we discuss how the proposed schemes can be used for multilevel threshold function sharing by employing it in a threshold RSA cryptosystem as an example.
Finding Synchronization Codes to Boost Compression by Substring Enumeration
Venue: arXiv cs.IT 2016
Year: 2016

Synchronization codes are frequently used in numerical data transmission and storage. Compression by Substring Enumeration (CSE) is a new lossless compression scheme that has turned into a new and unusual application for synchronization codes. CSE is an inherently bit-oriented technique. However, since the usual benchmark files are all byte-oriented, CSE incurred a penalty due to a problem called phase unawareness. Subsequent work showed that inserting a synchronization code inside the data before compressing it improves the compression performance. In this paper, we present two constraint models that compute the shortest synchronization codes, i.e. those that add the fewest synchronization bits to the original data. We find synchronization codes for blocks of up to 64 bits.
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos. Many high-level activities are often composed of multiple temporal parts (e.g., sub-events) with different duration/speed, and our objective is to make the model explicitly learn such temporal structure using multiple attention filters and benefit from them. Our temporal filters are designed to be fully differentiable, allowing end-of-end training of the temporal filters together with the underlying frame-based or segment-based convolutional neural network architectures. This paper presents an approach of learning a set of optimal static temporal attention filters to be shared across different videos, and extends this approach to dynamically adjust attention filters per testing video using recurrent long short-term memory networks (LSTMs). This allows our temporal attention filters to learn latent sub-events specific to each activity. We experimentally confirm that the proposed concept of temporal attention filters benefits the activity recognition, and we visualize the learned latent sub-events.
Cognitive Dynamic Systems: A Technical Review of Cognitive Radar
Venue: arXiv cs.AI 2016
Year: 2016

We start with the history of cognitive radar, where origins of the PAC, Fuster research on cognition and principals of cognition are provided. Fuster describes five cognitive functions: perception, memory, attention, language, and intelligence. We describe the Perception-Action Cyclec as it applies to cognitive radar, and then discuss long-term memory, memory storage, memory retrieval and working memory. A comparison between memory in human cognition and cognitive radar is given as well. Attention is another function described by Fuster, and we have given the comparison of attention in human cognition and cognitive radar. We talk about the four functional blocks from the PAC: Bayesian filter, feedback information, dynamic programming and state-space model for the radar environment. Then, to show that the PAC improves the tracking accuracy of Cognitive Radar over Traditional Active Radar, we have provided simulation results. In the simulation, three nonlinear filters: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter are compared. Based on the results, radars implemented with CKF perform better than the radars implemented with UKF or radars implemented with EKF. Further, radar with EKF has the worst accuracy and has the biggest computation load because of derivation and evaluation of Jacobian matrices. We suggest using the concept of risk management to better control parameters and improve performance in cognitive radar. We believe, spectrum sensing can be seen as a potential interest to be used in cognitive radar and we propose a new approach Probabilistic ICA which will presumably reduce noise based on estimation error in cognitive radar. Parallel computing is a concept based on divide and conquers mechanism, and we suggest using the parallel computing approach in cognitive radar by doing complicated calculations or tasks to reduce processing time.
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies
Venue: arXiv cs.CV 2016
Year: 2016

A recent paper by Gatys et al. describes a method for rendering an image in the style of another image. First, they use convolutional neural network features to build a statistical model for the style of an image. Then they create a new image with the content of one image but the style statistics of another image. Here, we extend this method to render a movie in a given artistic style. The naive solution that independently renders each frame produces poor results because the features of the style move substantially from one frame to the next. The other naive method that initializes the optimization for the next frame using the rendered version of the previous frame also produces poor results because the features of the texture stay fixed relative to the frame of the movie instead of moving with objects in the scene. The main contribution of this paper is to use optical flow to initialize the style transfer optimization so that the texture features move with the objects in the video. Finally, we suggest a method to incorporate optical flow explicitly into the cost function.
A single scale retinex based method for palm vein extraction
Venue: arXiv cs.CV 2016
Year: 2016

Palm vein recognition is a novel biometric identification technology. But how to gain a better vein extraction result from the raw palm image is still a challenging problem, especially when the raw data collection has the problem of asymmetric illumination. This paper proposes a method based on single scale Retinex algorithm to extract palm vein image when strong shadow presents due to asymmetric illumination and uneven geometry of the palm. We test our method on a multispectral palm image. The experimental result shows that the proposed method is robust to the influence of illumination angle and shadow. Compared to the traditional extraction methods, the proposed method can obtain palm vein lines with better visualization performance (the contrast ratio increases by 18.4%, entropy increases by 1.07%, and definition increases by 18.8%).
Structural balance and opinion separation in trust-mistrust social networks
Venue: arXiv cs.SI 2016
Year: 2016

Structural balance theory has been developed in sociology and psychology to explain how interacting agents, e.g., countries, political parties, opinionated individuals, with mixed trust and mistrust relationships evolve into polarized camps. Recent results have shown that structural balance is necessary for polarization in networks with fixed, strongly connected neighbor relationships when the opinion dynamics are described by DeGroot-type averaging rules. We develop this line of research in this paper in two steps. First, we consider fixed, not necessarily strongly connected, neighbor relationships. It is shown that if the network includes a strongly connected subnetwork containing mistrust, which influences the rest of the network, then no opinion clustering is possible when that subnetwork is not structurally balanced; all the opinions become neutralized in the end. In contrast, it is shown that when that subnetwork is indeed structurally balanced, the agents of the subnetwork evolve into two polarized camps and the opinions of all other agents in the network spread between these two polarized opinions. Second, we consider time-varying neighbor relationships. We show that the opinion separation criteria carry over if the conditions for fixed graphs are extended to joint graphs. The results are developed for both discrete-time and continuous-time models.
Ruling Out Static Latent Homophily in Citation Networks
Venue: arXiv cs.DL 2016
Year: 2016

Citation and coauthor networks offer an insight into the dynamics of scientific progress. We can also view them as representations of a causal structure, a logical process captured in a graph. From a causal perspective, we can ask questions such as whether authors form groups primarily due to their prior shared interest, or if their favourite topics are 'contagious' and spread through co-authorship. Such networks have been widely studied by the artificial intelligence community, and recently a connection has been made to nonlocal correlations produced by entangled particles in quantum physics -- the impact of latent hidden variables can be analyzed by the same algebraic geometric methodology that relies on a sequence of semidefinite programming (SDP) relaxations. Following this trail, we treat our sample coauthor network as a causal graph and, using SDP relaxations, rule out latent homophily as a manifestation of prior shared interest leading to the observed patternedness. By introducing algebraic geometry to citation studies, we add a new tool to existing methods for the analysis of content-related social influences.
Stochastic Variance Reduced Riemannian Eigensolver
Venue: arXiv cs.LG 2016
Year: 2016

We study the stochastic Riemannian gradient algorithm for matrix eigen-decomposition. The state-of-the-art stochastic Riemannian algorithm requires the learning rate to decay to zero and thus suffers from slow convergence and sub-optimal solutions. In this paper, we address this issue by deploying the variance reduction (VR) technique of stochastic gradient descent (SGD). The technique was originally developed to solve convex problems in the Euclidean space. We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem. We are the first to propose and analyze the generalization of SVRG to Riemannian manifolds. Specifically, we propose the general variance reduction form, SVRRG, in the framework of the stochastic Riemannian gradient optimization. It's then specialized to the problem with eigensolvers and induces the SVRRG-EIGS algorithm. We provide a novel and elegant theoretical analysis on this algorithm. The theory shows that a fixed learning rate can be used in the Riemannian setting with an exponential global convergence rate guaranteed. The theoretical results make a significant improvement over existing studies, with the effectiveness empirically verified.
K-method of cognitive mapping analysis
Venue: arXiv cs.SI 2016
Year: 2016

Introduced a new calculation method (K-method) for cognitive maps. K - method consists of two consecutive steps. In the first stage, allocated subgraph composed of all paths from one selected node (concept) to another node (concept) from the cognitive map (directed weighted graph) . In the second stage, after the transition to an undirected graph (symmetrization adjacency matrix) the influence of one node to another calculated with Kirchhoff method. In the proposed method, there is no problem inherent in the impulse method. In addition to "pair" influence of one node to another, the average characteristics are introduced, allowing to calculate the impact of the selected node to all other nodes and the influence of all on the one selected. For impulse method similar to the average characteristics in the case where the pulse method "works" are introduced and compared with the K-method.
Secure Repairable Fountain Codes
Venue: arXiv cs.IT 2016
Year: 2016

In this letter, we provide the construction of repairable fountain codes (RFCs) for distributed storage systems that are information-theoretically secure against an eavesdropper that has access to the data stored in a subset of the storage nodes and the data downloaded to repair an additional subset of storage nodes. The security is achieved by adding random symbols to the message, which is then encoded by the concatenation of a Gabidulin code and an RFC. We compare the achievable code rates of the proposed codes with those of secure minimum storage regenerating codes and secure locally repairable codes.
Overview of ISM bands and Software-defined Radio Experimentation
Venue: arXiv cs.NI 2016
Year: 2016

Wireless systems using low-power wireless communication protocol are rapidly gain popularity in the license-free industrial scientific, and medical (ISM) frequency bands. One such emerging trend in ISM frequency bands is home automation. Historically, all the home devices were once unconnected, today are now being connected either by a wired or wireless connection. The low-power wireless communication protocols enable integration of all the digital home devices into a single system and enhance end user product experience. The rapid prototyping of these end user product using programmable radio system such as software-defined radio (SDR) has been the game-changer in the field of home automation. In this article, we present an overview of popular ISM bands in different regions of the world. Furthermore, we analyze the signals from the wireless electric switch using a software-defined radio in 433 MHz ISM band.
User Performance in Small Cells Networks with Inter-Cell Mobility
Venue: arXiv cs.NI 2016
Year: 2016

We analyze the impact of intra-cell mobility on user performance in dense networks such as that enabled by LTE-A and 5G. To this end, we consider a homogeneous network of small cells and first show how to reduce the evaluation of user performance to the case of a single representative cell. We then propose simple analytical models that capture mobility through the distribution of the residual sojourn time of mobile users in the cell. An approximate model, based on Quasi-Stationary (QS) assumptions, is developed in order to speed up computation in the Markovian framework. We use these models to derive the average throughput of both mobile and static users, along with the probability of handover for mobile users. Numerical evaluation and simulation results are provided to assess the accuracy of the proposed models. We show, in particular, that both classes of users benefit from a throughput gain induced by the "opportunistic" displacement of mobile users among cells.
On Modeling Coverage and Rate of Random Cellular Networks under Generic Channel Fading
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we provide an analytic framework for computing the expected downlink coverage probability, and the associated data rate of cellular networks, where base stations are distributed in a random manner. The provided expressions are in computable integral forms that accommodate generic channel fading conditions. We develop these expressions by modelling the cellular interference using stochastic geometry analysis, then we employ them for comparing the coverage resulting from various channel fading conditions namely Rayleigh and Rician fading, in addition to the fading-less channel. Furthermore, we expand the work to accommodate the effects of random frequency reuse on the cellular coverage and rate. Monte-Carlo simulations are conducted to validate the theoretical analysis, where the results show a very close match.
Dense Volume-to-Volume Vascular Boundary Detection
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data. Our fine-to-fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label training and prediction (2) precise voxel-level prediction to capture fine scale structures prevalent in medical data and (3) directed multi-scale, multi-level feature learning. We evaluate our approach on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. In the process, we also introduce HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED). We show that our deep learning approach out-performs, the current state-of-the-art in 3D vascular boundary detection (structured forests 3D), by a large margin, as well as HED applied to slices, and HED-3D while successfully localizing fine structures. With our approach, boundary detection takes about one minute on a typical 512x512x512 volume.
Energy-Efficient Algorithms
Venue: arXiv cs.DS 2016
Year: 2016

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms. We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity. These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.
Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance
Venue: arXiv cs.CV 2016
Year: 2016

We propose a low cost and effective way to combine a free simulation software and free CAD models for modeling human-object interaction in order to improve human & object segmentation. It is intended for research scenarios related to safe human-robot collaboration (SHRC) and interaction (SHRI) in the industrial domain. The task of human and object modeling has been used for detecting activity, and for inferring and predicting actions, different from those works, we do human and object modeling in order to learn interactions in RGB-D data for improving segmentation. For this purpose, we define a novel density function to model a three dimensional (3D) scene in a virtual environment (VREP). This density function takes into account various possible configurations of human-object and object-object relationships and interactions governed by their affordances. Using this function, we synthesize a large, realistic and highly varied synthetic RGB-D dataset that we use for training. We train a random forest classifier, and the pixelwise predictions obtained is integrated as a unary term in a pairwise conditional random fields (CRF). Our evaluation shows that modeling these interactions improves segmentation performance by ~7\% in mean average precision and recall over state-of-the-art methods that ignore these interactions in real-world data. Our approach is computationally efficient, robust and can run real-time on consumer hardware.
Uplink Spectral Efficiency Analysis of Decoupled Access in Multiuser MIMO Communications
Venue: arXiv cs.IT 2016
Year: 2016

In a heterogeneous network consisting of macro base stations (MBSs) and small base stations (SBSs), the traditional cell association policy, i.e., coupled access (CA), is far from optimal, due to the significant difference between the coverage and transmit powers of MBSs and SBSs. Hence, users may choose to associate with different types of BSs in downlink (DL) and uplink (UL), i.e., decoupled access (DA), to enhance spectral efficiency. In this paper, DA in multiuser MIMO communications is investigated in terms of UL spectral efficiency. Firstly, we obtain the UL association probabilities. In contrast to the CA scenario, association probabilities for DA scenario only depend on the densities of BSs. Hence, DA allows UL and DL to be totally independent. Secondly, we derive lower bounds on the spectral efficiency. The lower bounds show that, different from CA, the UL spectral efficiency for DA scenario is irrelative with the transmit powers of BSs, which implies DA allows users to associate with any BSs that can achieve the highest UL spectral efficiency. Finally, the spectral efficiencies for DA and CA scenarios are compared via simulation results, where it can be concluded that the spectral efficiency in multiuser MIMO systems is improved by DA.
Induced Minor Free Graphs: Isomorphism and Clique-width
Venue: arXiv cs.DM 2016
Year: 2016

Given two graphs $G$ and $H$, we say that $G$ contains $H$ as an induced minor if a graph isomorphic to $H$ can be obtained from $G$ by a sequence of vertex deletions and edge contractions. We study the complexity of Graph Isomorphism on graphs that exclude a fixed graph as an induced minor. More precisely, we determine for every graph $H$ that Graph Isomorphism is polynomial-time solvable on $H$-induced-minor-free graphs or that it is GI-complete. Additionally, we classify those graphs $H$ for which $H$-induced-minor-free graphs have bounded clique-width. These two results complement similar dichotomies for graphs that exclude a fixed graph as an induced subgraph, minor, or subgraph.