OpenRaiser/Intern-Atlas huggingface dataset:
User Collusion Avoidance Scheme for Privacy-Preserving Decentralized Key-Policy Attribute-Based Encryption -- Full Version
Venue: arXiv cs.CR 2016
Year: 2016

Recent trend towards cloud computing paradigm, smart devices and 4G wireless technologies has enabled seamless data sharing among users. Cloud computing environment is distributed and untrusted, hence data owners have to encrypt their data to enforce data confidentiality. The data confidentiality in a distributed environment can be achieved by using attribute-based encryption technique. Decentralized attribute-based encryption technique is a variant of multiple authority based attribute-based encryption whereby any attribute authority can independently join and leave the system without collaborating with the existing attribute authorities. In this paper, we propose a privacy-preserving decentralized key-policy attribute-based encryption scheme. The scheme preserves the user privacy when users interact with multiple authorities to obtain decryption keys while mitigating the well-known user collusion security vulnerability. We showed that our scheme relies on decisional bilinear Diffie-Hellman standard complexity assumption in contrast to the previous nonstandard complexity assumptions such as $q-$decisional Diffie-Hellman inversion.
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks
Venue: arXiv cs.NE 2016
Year: 2016

We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activation function is simple, differentiable, and parameterized so that it can be trained as the rest of the network is trained. We hypothesize that soft exponential has the potential to improve neural network learning, as it can exactly calculate many natural operations that typical neural networks can only approximate, including addition, multiplication, inner product, distance, polynomials, and sinusoids.
Effect of Data Sharing on Private Cache Design in Chip Multiprocessors
Venue: arXiv cs.AR 2016
Year: 2016

In multithreaded applications with high degree of data sharing, the miss rate of private cache is shown to exhibit a compulsory miss component. It manifests because at least some of the shared data originates from other cores and can only be accessed in a shared cache. The compulsory component does not change with the private cache size, causing its miss rate to diminish slower as the cache size grows. As a result, the peak performance of a Chip Multiprocessor (CMP) for workloads with high degree of data sharing is achieved with a smaller private cache, compared to workloads with no data sharing. The CMP performance can be improved by reassigning some of the constrained area or power resource from private cache to core. Alternatively, the area or power budget of a CMP can be reduced without a performance hit.
C-planarity of Embedded Cyclic c-Graphs
Venue: arXiv cs.CG 2016
Year: 2016

We show that c-planarity is solvable in quadratic time for flat clustered graphs with three clusters if the combinatorial embedding of the underlying graph is fixed. In simpler graph-theoretical terms our result can be viewed as follows. Given a graph $G$ with the vertex set partitioned into three parts embedded on a 2-sphere, our algorithm decides if we can augment $G$ by adding edges without creating an edge-crossing so that in the resulting spherical graph the vertices of each part induce a connected sub-graph. We proceed by a reduction to the problem of testing the existence of a perfect matching in planar bipartite graphs. We formulate our result in a slightly more general setting of cyclic clustered graphs, i.e., the simple graph obtained by contracting each cluster, where we disregard loops and multi-edges, is a cycle.
Optimal Transport Theory for Power-Efficient Deployment of Unmanned Aerial Vehicles
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, the optimal deployment of multiple unmanned aerial vehicles (UAVs) acting as flying base stations is investigated. Considering the downlink scenario, the goal is to minimize the total required transmit power of UAVs while satisfying the users' rate requirements. To this end, the optimal locations of UAVs as well as the cell boundaries of their coverage areas are determined. To find those optimal parameters, the problem is divided into two sub-problems that are solved iteratively. In the first sub-problem, given the cell boundaries corresponding to each UAV, the optimal locations of the UAVs are derived using the facility location framework. In the second sub-problem, the locations of UAVs are assumed to be fixed, and the optimal cell boundaries are obtained using tools from optimal transport theory. The analytical results show that the total required transmit power is significantly reduced by determining the optimal coverage areas for UAVs. These results also show that, moving the UAVs based on users' distribution, and adjusting their altitudes can lead to a minimum power consumption. Finally, it is shown that the proposed deployment approach, can improve the system's power efficiency by a factor of 20 compared to the classical Voronoi cell association technique with fixed UAVs locations.
Fundamental Limits in Multi-image Alignment
Venue: arXiv cs.CV 2016
Year: 2016

The performance of multi-image alignment, bringing different images into one coordinate system, is critical in many applications with varied signal-to-noise ratio (SNR) conditions. A great amount of effort is being invested into developing methods to solve this problem. Several important questions thus arise, including: Which are the fundamental limits in multi-image alignment performance? Does having access to more images improve the alignment? Theoretical bounds provide a fundamental benchmark to compare methods and can help establish whether improvements can be made. In this work, we tackle the problem of finding the performance limits in image registration when multiple shifted and noisy observations are available. We derive and analyze the Cramér-Rao and Ziv-Zakai lower bounds under different statistical models for the underlying image. The accuracy of the derived bounds is experimentally assessed through a comparison to the maximum likelihood estimator. We show the existence of different behavior zones depending on the difficulty level of the problem, given by the SNR conditions of the input images. We find that increasing the number of images is only useful below a certain SNR threshold, above which the pairwise MLE estimation proves to be optimal. The analysis we present here brings further insight into the fundamental limitations of the multi-image alignment problem.
Correction of Data and Syndrome Errors by Stabilizer Codes
Venue: arXiv cs.IT 2016
Year: 2016

Performing active quantum error correction to protect fragile quantum states highly depends on the correctness of error information--error syndromes. To obtain reliable error syndromes using imperfect physical circuits, we propose the idea of quantum data-syndrome (DS) codes that are capable of correcting both data qubits and syndrome bits errors. We study fundamental properties of quantum DS codes and provide several CSS-type code constructions of quantum DS codes.
Long-term Planning by Short-term Prediction
Venue: arXiv cs.LG 2016
Year: 2016

We consider planning problems, that often arise in autonomous driving applications, in which an agent should decide on immediate actions so as to optimize a long term objective. For example, when a car tries to merge in a roundabout it should decide on an immediate acceleration/braking command, while the long term effect of the command is the success/failure of the merge. Such problems are characterized by continuous state and action spaces, and by interaction with multiple agents, whose behavior can be adversarial. We argue that dual versions of the MDP framework (that depend on the value function and the $Q$ function) are problematic for autonomous driving applications due to the non Markovian of the natural state space representation, and due to the continuous state and action spaces. We propose to tackle the planning task by decomposing the problem into two phases: First, we apply supervised learning for predicting the near future based on the present. We require that the predictor will be differentiable with respect to the representation of the present. Second, we model a full trajectory of the agent using a recurrent neural network, where unexplained factors are modeled as (additive) input nodes. This allows us to solve the long-term planning problem using supervised learning techniques and direct optimization over the recurrent neural network. Our approach enables us to learn robust policies by incorporating adversarial elements to the environment.
On the Construction of High Dimensional Simple Games
Venue: arXiv cs.GT 2016
Year: 2016

Voting is a commonly applied method for the aggregation of the preferences of multiple agents into a joint decision. If preferences are binary, i.e., "yes" and "no", every voting system can be described by a (monotone) Boolean function $χ\colon\{0,1\}^n\rightarrow \{0,1\}$. However, its naive encoding needs $2^n$ bits. The subclass of threshold functions, which is sufficient for homogeneous agents, allows a more succinct representation using $n$ weights and one threshold. For heterogeneous agents, one can represent $χ$ as an intersection of $k$ threshold functions. Taylor and Zwicker have constructed a sequence of examples requiring $k\ge 2^{\frac{n}{2}-1}$ and provided a construction guaranteeing $k\le {n\choose {\lfloor n/2\rfloor}}\in 2^{n-o(n)}$. The magnitude of the worst-case situation was thought to be determined by Elkind et al.~in 2008, but the analysis unfortunately turned out to be wrong. Here we uncover a relation to coding theory that allows the determination of the minimum number $k$ for a subclass of voting systems. As an application, we give a construction for $k\ge 2^{n-o(n)}$, i.e., there is no gain from a representation complexity point of view.
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
Venue: arXiv cs.CV 2016
Year: 2016

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes.
Appearance Based Robot and Human Activity Recognition System
Venue: arXiv cs.RO 2016
Year: 2016

In this work, we present an appearance based human activity recognition system. It uses background modeling to segment the foreground object and extracts useful discriminative features for representing activities performed by humans and robots. Subspace based method like principal component analysis is used to extract low dimensional features from large voluminous activity images. These low dimensional features are then used to classify an activity. An apparatus is designed using a webcam, which watches a robot replicating a human fall under indoor environment. In this apparatus, a robot performs various activities (like walking, bending, moving arms) replicating humans, which also includes a sudden fall. Experimental results on robot performing various activities and standard human activity recognition databases show the efficacy of our proposed method.
A Green Enterprise Computing Architecture for Developing Countries
Venue: arXiv cs.NI 2016
Year: 2016

Developing countries often have access to limited energy resources, which frequently results in power cuts and failures. During these power cuts, enterprises rely on backup sources for power such as uninterruptible power supplies (UPS) and electric generators. This paper proposes AnywareDC, an architecture that builds on the recent work on Anyware to reduce energy utilization in the presence of such intermittent power supplies. Anyware reduces energy usage by providing enterprise users laptops instead of desktops, while maintaining performance using a central compute cluster. Our basic insight is that in the presence of power cuts, only the routers and the cluster needs to be provided power: the laptops can continue to run on their own batteries. This reduces both energy usage and UPS load allowing it to supply power for longer, thus also saving generator fuel costs. Simulations show that this architecture reduces energy usage by up to 80% compared to one not using Anyware, and by up to 20% compared to Anyware.
Fuzzy Object-Oriented Dynamic Networks. II
Venue: arXiv cs.AI 2016
Year: 2016

This article generalizes object-oriented dynamic networks to the fuzzy case, which allows one to represent knowledge on objects and classes of objects that are fuzzy by nature and also to model their changes in time. Within the framework of the approach described, a mechanism is proposed that makes it possible to acquire new knowledge on the basis of basic knowledge and considerably differs from well-known methods used in existing models of knowledge representation. The approach is illustrated by an example of construction of a concrete fuzzy object-oriented dynamic network.
Improved Query Topic Models via Pseudo-Relevant Pólya Document Models
Venue: arXiv cs.IR 2016
Year: 2016

Query-expansion via pseudo-relevance feedback is a popular method of overcoming the problem of vocabulary mismatch and of increasing average retrieval effectiveness. In this paper, we develop a new method that estimates a query topic model from a set of pseudo-relevant documents using a new language modelling framework. We assume that documents are generated via a mixture of multivariate Polya distributions, and we show that by identifying the topical terms in each document, we can appropriately select terms that are likely to belong to the query topic model. The results of experiments on several TREC collections show that the new approach compares favourably to current state-of-the-art expansion methods.
NeRD: a Neural Response Divergence Approach to Visual Salience Detection
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a novel approach to visual salience detection via Neural Response Divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low level cues that can be used to compute image region distinctiveness. Based on this concept , an efficient visual salience detection framework is proposed using deep convolutional StochasticNets. Experimental results using CSSD and MSRA10k natural image datasets show that the proposed NeRD approach can achieve improved performance when compared to state-of-the-art image saliency approaches, while the attaining low computational complexity necessary for near-real-time computer vision applications.
The Parameterized Complexity of the Minimum Shared Edges Problem
Venue: arXiv cs.CC 2016
Year: 2016

We study the NP-complete Minimum Shared Edges (MSE) problem. Given an undirected graph, a source and a sink vertex, and two integers p and k, the question is whether there are p paths in the graph connecting the source with the sink and sharing at most k edges. Herein, an edge is shared if it appears in at least two paths. We show that MSE is W[1]-hard when parameterized by the treewidth of the input graph and the number k of shared edges combined. We show that MSE is fixed-parameter tractable with respect to p, but does not admit a polynomial-size kernel (unless NP is contained in coNP/poly). In the proof of the fixed-parameter tractability of MSE parameterized by p, we employ the treewidth reduction technique due to Marx, O'Sullivan, and Razgon [ACM TALG 2013].
Visual Tracking via Reliable Memories
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks. First, we design a Discrete Fourier Transform (DFT) based tracker which is able to exploit a large number of tracked samples while still ensures real-time performance. Second, we propose a clustering method with temporal constraints to explore and memorize consistent patterns from previous frames, named as reliable memories. By virtue of this method, our tracker can utilize uncontaminated information to alleviate drifting issues. Experimental results show that our tracker performs favorably against other state of-the-art methods on benchmark datasets. Furthermore, it is significantly competent in handling drifts and able to robustly track challenging long videos over 4000 frames, while most of others lose track at early frames.
Analysis of Network Coding in a Slotted ALOHA-based Two-Way Relay Network
Venue: arXiv cs.NI 2016
Year: 2016

This paper deals with a two-way relay network (TWRN) based on a slotted ALOHA protocol which utilizes network coding to exchange the packets. We proposed an analytical approach to study the behavior of such networks and the effects of network coding on the throughput, power, and queueing delay of the relay node. In addition, when end nodes are not saturated, our approach enables us to achieve the stability region of the network in different situations. Finally, we carry out some simulation to confirm the validity of the proposed analytical approach.
MyAdChoices: Bringing Transparency and Control to Online Advertising
Venue: arXiv cs.CY 2016
Year: 2016

The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and anti-tracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web ---in which users get content free in return for allowing advertisers to show them ads--- is at serious peril. In this paper, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.
Swivel: Improving Embeddings by Noticing What's Missing
Venue: arXiv cs.CL 2016
Year: 2016

We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods.
Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation
Venue: arXiv cs.NA 2016
Year: 2016

There has been a large increase in the amount of work on hierarchical low-rank approximation methods, where the interest is shared by multiple communities that previously did not intersect. This objective of this article is two-fold; to provide a thorough review of the recent advancements in this field from both analytical and algebraic perspectives, and to present a comparative benchmark of two highly optimized implementations of contrasting methods for some simple yet representative test cases. We categorize the recent advances in this field from the perspective of compute-memory tradeoff, which has not been considered in much detail in this area. Benchmark tests reveal that there is a large difference in the memory consumption and performance between the different methods.
On the Limits of Coexisting Coverage and Capacity in Multi-RAT Heterogeneous Networks
Venue: arXiv cs.IT 2016
Year: 2016

This paper devises a general modeling and analyzing framework for a heterogeneous wireless network (HetNet) in which several wireless subnetworks coexist and use multiple radio access technologies (multi-RATs). The coexisting coverage and network capacity in such a multi-RAT HetNet are hardly investigated in prior works. To characterize the coexisting interactions in a multi-RAT HetNet, in this paper we consider a HetNet consisting of K-tier APs and two different RATs, RAT-L and RAT-U, are adopted in the HetNet. RAT-L is adopted by the access points (APs) in the first K-1 tiers and APs in the Kth tier only use RAT-U. Both noncrossing-RAT and crossing-RAT user association scenarios are considered. In each scenario, the void probability and channel access probability of the APs in each tier are first found and then the tight lower bounds and their lowest limits on the proposed coexisting coverage and network capacity are derived. We show that multi-RAT networks in general can achieve higher link coverage and capacity by using opportunistic CSMA/CA that avoids/alleviates severe interfering between all coexisting APs. Also, crossing-RAT user association is shown to achieve much higher coexisting coverage and network capacity than noncrossing-RAT user association. Finally, numerical simulations for the LTE-U and WiFi networks coexisting in the HetNet validate our findings.