OpenRaiser/Intern-Atlas huggingface dataset:
Privacy-preserving Network Functionality Outsourcing
Venue: arXiv cs.CR 2015
Year: 2015

Since the advent of software defined networks ({SDN}), there have been many attempts to outsource the complex and costly local network functionality, i.e. the middlebox, to the cloud in the same way as outsourcing computation and storage. The privacy issues, however, may thwart the enterprises' willingness to adopt this innovation since the underlying configurations of these middleboxes may leak crucial and confidential information which can be utilized by attackers. To address this new problem, we use firewall as an sample functionality and propose the first privacy preserving outsourcing framework and schemes in SDN. The basic technique that we exploit is a ground-breaking tool in cryptography, the \textit{cryptographic multilinear map}. In contrast to the infeasibility in efficiency if a naive approach is adopted, we devise practical schemes that can outsource the middlebox as a blackbox after \textit{obfuscating} it such that the cloud provider can efficiently perform the same functionality without knowing its underlying private configurations. Both theoretical analysis and experiments on real-world firewall rules demonstrate that our schemes are secure, accurate, and practical.
Structured Occlusion Coding for Robust Face Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the occlusions are predictable and available for sampling. We propose the structured occlusion coding (SOC) to address occlusion problems. The structured coding here lies in two folds. On one hand, we employ a structured dictionary for recognition. On the other hand, we propose to use the structured sparsity in this formulation. Specifically, SOC simultaneously separates the occlusion and classifies the image. In this way, the problem of recognizing an occluded image is turned into seeking a structured sparse solution on occlusion-appended dictionary. In order to construct a well-performing occlusion dictionary, we propose an occlusion mask estimating technique via locality constrained dictionary (LCD), showing striking improvement in occlusion sample. On a category-specific occlusion dictionary, we replace norm sparsity with the structured sparsity which is shown more robust, further enhancing the robustness of our approach. Moreover, SOC achieves significant improvement in handling large occlusion in real world. Extensive experiments are conducted on public data sets to validate the superiority of the proposed algorithm.
A constructive proof of the phase-type characterization theorem
Venue: arXiv cs.SY 2015
Year: 2015

The paper presents a new proof of O'Cinneide's characterization theorem. It is much simpler than the original one and constructive in the sense that we not only show the existence of a phase type representation, but present a procedure which creates a phase type representation. We prove that the procedure succeeds when the conditions of the characterization theorem hold.
VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting
Venue: arXiv cs.SI 2015
Year: 2015

Information spread in social media depends on a number of factors, including how the site displays information, how users navigate it to find items of interest, users' tastes, and the `virality' of information, i.e., its propensity to be adopted, or retweeted, upon exposure. Probabilistic models can learn users' tastes from the history of their item adoptions and recommend new items to users. However, current models ignore cognitive biases that are known to affect behavior. Specifically, people pay more attention to items at the top of a list than those in lower positions. As a consequence, items near the top of a user's social media stream have higher visibility, and are more likely to be seen and adopted, than those appearing below. Another bias is due to the item's fitness: some items have a high propensity to spread upon exposure regardless of the interests of adopting users. We propose a probabilistic model that incorporates human cognitive biases and personal relevance in the generative model of information spread. We use the model to predict how messages containing URLs spread on Twitter. Our work shows that models of user behavior that account for cognitive factors can better describe and predict user behavior in social media.
E-BLOW: E-Beam Lithography Overlapping aware Stencil Planning for MCC System
Venue: arXiv cs.OH 2015
Year: 2015

Electron beam lithography (EBL) is a promising maskless solution for the technology beyond 14nm logic node. To overcome its throughput limitation, industry has proposed character projection (CP) technique, where some complex shapes (characters) can be printed in one shot. Recently the traditional EBL system is extended into multi-column cell (MCC) system to further improve the throughput. In MCC system, several independent CPs are used to further speed-up the writing process. Because of the area constraint of stencil, MCC system needs to be packed/planned carefully to take advantage of the characters. In this paper, we prove that the overlapping aware stencil planning (OSP) problem is NP-hard. To solve OSP problem in MCC system, we present a tool, E-BLOW, with several novel speedup techniques, such as successive relaxation, dynamic programming, and KD-Tree based clustering. Experimental results show that, compared with previous works, E-BLOW demonstrates better performance for both conventional EBL system and MCC system.
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks
Venue: arXiv cs.LG 2015
Year: 2015

In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning.
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons
Venue: arXiv cs.NE 2015
Year: 2015

Echo state networks (ESN), a type of reservoir computing (RC) architecture, are efficient and accurate artificial neural systems for time series processing and learning. An ESN consists of a core of recurrent neural networks, called a reservoir, with a small number of tunable parameters to generate a high-dimensional representation of an input, and a readout layer which is easily trained using regression to produce a desired output from the reservoir states. Certain computational tasks involve real-time calculation of high-order time correlations, which requires nonlinear transformation either in the reservoir or the readout layer. Traditional ESN employs a reservoir with sigmoid or tanh function neurons. In contrast, some types of biological neurons obey response curves that can be described as a product unit rather than a sum and threshold. Inspired by this class of neurons, we introduce a RC architecture with a reservoir of product nodes for time series computation. We find that the product RC shows many properties of standard ESN such as short-term memory and nonlinear capacity. On standard benchmarks for chaotic prediction tasks, the product RC maintains the performance of a standard nonlinear ESN while being more amenable to mathematical analysis. Our study provides evidence that such networks are powerful in highly nonlinear tasks owing to high-order statistics generated by the recurrent product node reservoir.
A New Cell Association Scheme In Heterogeneous Networks
Venue: arXiv cs.NI 2015
Year: 2015

Cell association scheme determines which base station (BS) and mobile user (MU) should be associated with and also plays a significant role in determining the average data rate a MU can achieve in heterogeneous networks. However, the explosion of digital devices and the scarcity of spectra collectively force us to carefully re-design cell association scheme which was kind of taken for granted before. To address this, we develop a new cell association scheme in heterogeneous networks based on joint consideration of the signal-to-interference-plus-noise ratio (SINR) which a MU experiences and the traffic load of candidate BSs1. MUs and BSs in each tier are modeled as several independent Poisson point processes (PPPs) and all channels experience independently and identically distributed ( i.i.d.) Rayleigh fading. Data rate ratio and traffic load ratio distributions are derived to obtain the tier association probability and the average ergodic MU data rate. Through numerical results, We find that our proposed cell association scheme outperforms cell range expansion (CRE) association scheme. Moreover, results indicate that allocating small sized and high-density BSs will improve spectral efficiency if using our proposed cell association scheme in heterogeneous networks.
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection
Venue: arXiv cs.CV 2015
Year: 2015

This paper proposes a reconfigurable model to recognize and detect multiclass (or multiview) objects with large variation in appearance. Compared with well acknowledged hierarchical models, we study two advanced capabilities in hierarchy for object modeling: (i) "switch" variables(i.e. or-nodes) for specifying alternative compositions, and (ii) making local classifiers (i.e. leaf-nodes) shared among different classes. These capabilities enable us to account well for structural variabilities while preserving the model compact. Our model, in the form of an And-Or Graph, comprises four layers: a batch of leaf-nodes with collaborative edges in bottom for localizing object parts; the or-nodes over bottom to activate their children leaf-nodes; the and-nodes to classify objects as a whole; one root-node on the top for switching multiclass classification, which is also an or-node. For model training, we present an EM-type algorithm, namely dynamical structural optimization (DSO), to iteratively determine the structural configuration, (e.g., leaf-node generation associated with their parent or-nodes and shared across other classes), along with optimizing multi-layer parameters. The proposed method is valid on challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves state-of-the-arts performance.
Grafting Hypersequents onto Nested Sequents
Venue: arXiv cs.LO 2015
Year: 2015

We introduce a new Gentzen-style framework of grafted hypersequents that combines the formalism of nested sequents with that of hypersequents. To illustrate the potential of the framework, we present novel calculi for the modal logics $\mathsf{K5}$ and $\mathsf{KD5}$, as well as for extensions of the modal logics $\mathsf{K}$ and $\mathsf{KD}$ with the axiom for shift reflexivity. The latter of these extensions is also known as $\mathsf{SDL}^+$ in the context of deontic logic. All our calculi enjoy syntactic cut elimination and can be used in backwards proof search procedures of optimal complexity. The tableaufication of the calculi for $\mathsf{K5}$ and $\mathsf{KD5}$ yields simplified prefixed tableau calculi for these logic reminiscent of the simplified tableau system for $\mathsf{S5}$, which might be of independent interest.
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints
Venue: arXiv cs.CV 2015
Year: 2015

Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. However, in practice, this requirement is usually impaired by the lack of labelled training samples. Fortunately, previous research has shown that the requirement for a redundant dictionary can be less rigorous if simultaneous sparse approximation is employed, which can be carried out by enforcing various structured sparsity constraints on the sparse codes of the neighboring pixels. In addition, numerous works have shown that applying a variety of dictionary learning methods for the sparse representation model can also improve the classification performance. In this paper, we highlight the task-driven dictionary learning algorithm, which is a general framework for the supervised dictionary learning method. We propose to enforce structured sparsity priors on the task-driven dictionary learning method in order to improve the performance of the hyperspectral classification. Our approach is able to benefit from both the advantages of the simultaneous sparse representation and those of the supervised dictionary learning. We enforce two different structured sparsity priors, the joint and Laplacian sparsity, on the task-driven dictionary learning method and provide the details of the corresponding optimization algorithms. Experiments on numerous popular hyperspectral images demonstrate that the classification performance of our approach is superior to sparse representation classifier with structured priors or the task-driven dictionary learning method.
Extended Unary Coding
Venue: arXiv cs.IT 2015
Year: 2015

Extended variants of the recently introduced spread unary coding are described. These schemes, in which the length of the code word is fixed, allow representation of approximately n^2 numbers for n bits, rather than the n numbers of the standard unary coding. In the first of two proposed schemes the spread increases, whereas in the second scheme the spread remains constant.
A Type System for proving Depth Boundedness in the pi-calculus
Venue: arXiv cs.LO 2015
Year: 2015

The depth-bounded fragment of the pi-calculus is an expressive class of systems enjoying decidability of some important verification problems. Unfortunately membership of the fragment is undecidable. We propose a novel type system, parameterised over a finite forest, that formalises name usage by pi-terms in a manner that respects the forest. Type checking is decidable and type inference is computable; furthermore typable pi-terms are guaranteed to be depth bounded. The second contribution of the paper is a proof of equivalence between the semantics of typable terms and nested data class memory automata, a class of automata over data words. We believe this connection can help to establish new links between the rich theory of infinite-alphabet automata and nominal calculi.
Classification of Hyperspectral Imagery on Embedded Grassmannians
Venue: arXiv cs.CV 2015
Year: 2015

We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points on the Grassmannian have representations as orthonormal matrices and as such do not reside in Euclidean space in the usual sense. There are a variety of metrics which allow us to determine a distance matrices that can be used to realize the Grassmannian as an embedding in Euclidean space. We illustrate that we can achieve an approximately isometric embedding of the Grassmann manifold using the chordal metric while this is not the case with geodesic distances. However, non-isometric embeddings generated by using a pseudometric on the Grassmannian lead to the best classification results. We observe that as the dimension of the Grassmannian grows, the accuracy of the classification grows to 100% on two illustrative examples. We also observe a decrease in classification rates if the dimension of the points on the Grassmannian is too large for the dimension of the Euclidean space. We use sparse support vector machines to perform additional model reduction. The resulting classifier selects a subset of dimensions of the embedding without loss in classification performance.
Practical and Legal Challenges of Cloud Investigations
Venue: arXiv cs.CY 2015
Year: 2015

An area presenting new opportunities for both legitimate business, as well as criminal organizations, is Cloud computing. This work gives a strong background in current digital forensic science, as well as a basic understanding of the goal of Law Enforcement when conducting digital forensic investigations. These concepts are then applied to digital forensic investigation of cloud environments in both theory and practice, and supplemented with current literature on the subject. Finally, legal challenges with digital forensic investigations in cloud environments are discussed.
Effect of Energy Harvesting on Stable Throughput in Cooperative Relay Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, the impact of energy constraints on a two-hop network with a source, a relay and a destination under random medium access is studied. A collision channel with erasures is considered, and the source and the relay nodes have energy harvesting capabilities and an unlimited battery to store the harvested energy. Additionally, the source and the relay node have external traffic arrivals and the relay forwards a fraction of the source node's traffic to the destination; the cooperation is performed at the network level. An inner and an outer bound of the stability region for a given transmission probability vector are obtained. Then, the closure of the inner and the outer bound is obtained separately and they turn out to be identical. This work is not only a step in connecting information theory and networking, by studying the maximum stable throughput region metric but also it taps the relatively unexplored and important domain of energy harvesting and assesses the effect of that on this important measure.
Modeling Curved Carbon Fiber Composite (CFC) Structures in the Transmission-Line Modeling (TLM) Method
Venue: arXiv cs.CE 2015
Year: 2015

A new embedded model for curved thin panels is developed in the Transmission Line Modeling (TLM) method. In this model, curved panels are first linearized and then embedded between adjacent 2D TLM nodes allowing for arbitrary positioning between adjacent node centers. The embedded model eliminates the necessity for fine discretization thus reducing the run time and memory requirements for the calculation. The accuracy and convergence of the model are verified by comparing the resonant frequencies of an elliptical cylinder formed using carbon fiber composite (CFC) materials with those of the equivalent metal cylinder. Furthermore, the model is used to analyze the shielding performance of CFC airfoil NACA2415.
Running Identical Threads in C-Slow Retiming based Designs for Functional Failure Detection
Venue: arXiv cs.AR 2015
Year: 2015

This paper shows the usage of C-Slow Retiming (CSR) in safety critical and low power applications. CSR generates C copies of a design by reusing the given logic resources in a time sliced fashion. When all C design copies are stimulated with the same input values, then all C design copies should behave the same way and will therefore create a redundant system. The paper shows that this special method of using CSR offers great benefits when used in safety critical and low power applications. Additional optimization techniques towards reducing register count are shown and an on-the-fly recovery mechanism is discussed.
A Secure Electronic Prescription System Using Steganography with Encryption Key Implementation
Venue: arXiv cs.CR 2015
Year: 2015

Over the years health care has seen major improvement due to the introduction information and communication technology with electronic medical prescription being one the areas benefiting from it. Within the overall context of protection of health care information, privacy of prescription data needs special treatment. This paper presents an e-prescription system that addresses some challenges pertaining to the prescription privacy protection in the process of drug prescription. The developed system uses spread spectrum image steganography algorithm with Advanced Encryption Standard (AES) key implementation to provide a secure means of delivering medical prescription to the parties involved. The architecture for encoding and decoding was implemented with an electronic health record. The software development tools used were PHP and MySQL database management system for front end and backend data management respectively. The designed system demonstration shows that the synergistic combination of steganography and cryptography technologies in medical prescription is capable of providing a secure transmission to properly provide security for patients medical prescription.
Numerical Solution of Fuzzy Stochastic Differential Equation
Venue: arXiv cs.NA 2015
Year: 2015

In this paper an alternative approach to solve uncertain Stochastic Differential Equation (SDE) is proposed. This uncertainty occurs due to the involved parameters in system and these are considered as Triangular Fuzzy Numbers (TFN). Here the proposed fuzzy arithmetic in [2] is used as a tool to handle Fuzzy Stochastic Differential Equation (FSDE). In particular, a system of Ito stochastic differential equations is analysed with fuzzy parameters. Further exact and Euler Maruyama approximation methods with fuzzy values are demonstrated and solved some standard SDE.
Approximately Counting H-Colourings is #BIS-Hard
Venue: arXiv cs.CC 2015
Year: 2015

We consider the problem of counting H-colourings from an input graph G to a target graph H. We show that if H is any fixed graph without trivial components, then the problem is as hard as the well-known problem #BIS, which is the problem of (approximately) counting independent sets in a bipartite graph. #BIS is a complete problem in an important complexity class for approximate counting, and is believed not to have an FPRAS. If this is so, then our result shows that for every graph H without trivial components, the H-colouring counting problem has no FPRAS. This problem was studied a decade ago by Goldberg, Kelk and Paterson. They were able to show that approximately sampling H-colourings is #BIS-hard, but it was not known how to get the result for approximate counting. Our solution builds on non-constructive ideas using the work of Lovasz.
Cyclic LRC Codes and their Subfield Subcodes
Venue: arXiv cs.IT 2015
Year: 2015

We consider linear cyclic codes with the locality property, or locally recoverable codes (LRC codes). A family of LRC codes that generalizes the classical construction of Reed-Solomon codes was constructed in a recent paper by I. Tamo and A. Barg (IEEE Transactions on Information Theory, no. 8, 2014; arXiv:1311.3284). In this paper we focus on the optimal cyclic codes that arise from the general construction. We give a characterization of these codes in terms of their zeros, and observe that there are many equivalent ways of constructing optimal cyclic LRC codes over a given field. We also study subfield subcodes of cyclic LRC codes (BCH-like LRC codes) and establish several results about their locality and minimum distance.