OpenRaiser/Intern-Atlas huggingface dataset:
Poisson Hole Process: Theory and Applications to Wireless Networks
Venue: arXiv cs.IT 2016
Year: 2016

Interference field in wireless networks is often modeled by a homogeneous Poisson Point Process (PPP). While it is realistic in modeling the inherent node irregularity and provides meaningful first-order results, it falls short in modeling the effect of interference management techniques, which typically introduce some form of spatial interaction among active transmitters. In some applications, such as cognitive radio and device-to-device networks, this interaction may result in the formation of holes in an otherwise homogeneous interference field. The resulting interference field can be accurately modeled as a Poisson Hole Process (PHP). Despite the importance of PHP in many applications, the exact characterization of interference experienced by a typical node in a PHP is not known. In this paper, we derive several tight upper and lower bounds on the Laplace transform of this interference. Numerical comparisons reveal that the new bounds outperform all known bounds and approximations, and are remarkably tight in all operational regimes of interest. The key in deriving these tight and yet simple bounds is to capture the local neighborhood around the typical point accurately while simplifying the far field to attain tractability. Ideas for tightening these bounds further by incorporating the effect of overlaps in the holes are also discussed. These results immediately lead to an accurate characterization of the coverage probability of the typical node in a PHP under Rayleigh fading.
A pragmatic approach to multi-class classification
Venue: arXiv cs.LG 2016
Year: 2016

We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.
Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study
Venue: arXiv cs.CV 2016
Year: 2016

We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches. Here we answer to questions that are specific to our application including how to utilize DNN for vehicle detection, what features are useful for vehicle classification, and how to extend a model trained on a limited size dataset, to the cases of extreme lighting condition. Answering these questions we propose our approach that outperforms state-of-the-art methods, and achieves promising results on image with extreme lighting conditions.
Optimal Power Allocation for Artificial Noise under Imperfect CSI against Spatially Random Eavesdroppers
Venue: arXiv cs.IT 2016
Year: 2016

In this correspondence, we study the secure multiantenna transmission with artificial noise (AN) under imperfect channel state information in the presence of spatially randomly distributed eavesdroppers. We derive the optimal solutions of the power allocation between the information signal and the AN for minimizing the secrecy outage probability (SOP) under a target secrecy rate and for maximizing the secrecy rate under a SOP constraint, respectively. Moreover, we provide an interesting insight that channel estimation error affects the optimal power allocation strategy in opposite ways for the above two objectives. When the estimation error increases, more power should be allocated to the information signal if we aim to decrease the rate-constrained SOP, whereas more power should be allocated to the AN if we aim to increase the SOP-constrained secrecy rate.
Angrier Birds: Bayesian reinforcement learning
Venue: arXiv cs.AI 2016
Year: 2016

We train a reinforcement learner to play a simplified version of the game Angry Birds. The learner is provided with a game state in a manner similar to the output that could be produced by computer vision algorithms. We improve on the efficiency of regular ε-greedy Q-Learning with linear function approximation through more systematic exploration in Randomized Least Squares Value Iteration (RLSVI), an algorithm that samples its policy from a posterior distribution on optimal policies. With larger state-action spaces, efficient exploration becomes increasingly important, as evidenced by the faster learning in RLSVI.
A New Image Steganographic Technique using Pattern based Bits Shuffling and Magic LSB for Grayscale Images
Venue: arXiv cs.MM 2016
Year: 2016

Image Steganography is a growing research area of information security where secret information is embedded in innocent-looking public communication. This paper proposes a novel crystographic technique for grayscale images in spatial domain. The secret data is encrypted and shuffled using pattern based bits shuffling algorithm (PBSA) and a secret key. The encrypted data is then embedded in the cover image using magic least significant bit (M-LSB) method. Experimentally, the proposed method is evaluated by qualitative and quantitative analysis which validates the effectiveness of the proposed method in contrast to several state-of-the-art methods.
Comparison of cinepak, intel, microsoft video and indeo codec for video compression
Venue: arXiv cs.MM 2016
Year: 2016

The file size and picture quality are factors to be considered for streaming, storage and transmitting videos over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video compression. The peak signal to noise ratio is used to compare the quality of such video compressed using AVI codecs. The most widely used objective measurement by developers of video processing systems is Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and depends on the mean squared error (MSE) between an original and an impaired image or video, relative to (2n-1)2. Previous research done regarding assessing of video quality has been mainly by the use of subjective methods, and there is still no standard method for objective assessments. Although it has been considered that compression might not be significant in future as storage and transmission capabilities improve, but at low bandwidths compression makes communication possible.
Physical Layer Security in Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

The heterogeneous cellular network (HCN) is a promising approach to the deployment of 5G cellular networks. This paper comprehensively studies physical layer security in a multi-tier HCN where base stations (BSs), authorized users and eavesdroppers are all randomly located. We first propose an access threshold based secrecy mobile association policy that associates each user with the BS providing the maximum \emph{truncated average received signal power} beyond a threshold. Under the proposed policy, we investigate the connection probability and secrecy probability of a randomly located user, and provide tractable expressions for the two metrics. Asymptotic analysis reveals that setting a larger access threshold increases the connection probability while decreases the secrecy probability. We further evaluate the network-wide secrecy throughput and the minimum secrecy throughput per user with both connection and secrecy probability constraints. We show that introducing a properly chosen access threshold significantly enhances the secrecy throughput performance of a HCN.
Green 5G Heterogeneous Networks through Dynamic Small-Cell Operation
Venue: arXiv cs.IT 2016
Year: 2016

Traditional macro-cell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macro-cells. Given the massive deployment of small-cells in a macro-cell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macro-cell. Compared to the macro-cell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning off the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are non-uniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1\% performance loss in our simulations) in polynomial time.
On Some Properties of Calibrated Trifocal Tensors
Venue: arXiv cs.CV 2016
Year: 2016

In two-view geometry, the essential matrix describes the relative position and orientation of two calibrated images. In three views, a similar role is assigned to the calibrated trifocal tensor. It is a particular case of the (uncalibrated) trifocal tensor and thus it inherits all its properties but, due to the smaller degrees of freedom, satisfies a number of additional algebraic constraints. Some of them are described in this paper. More specifically, we define a new notion --- the trifocal essential matrix. On the one hand, it is a generalization of the ordinary (bifocal) essential matrix, and, on the other hand, it is closely related to the calibrated trifocal tensor. We prove the two necessary and sufficient conditions that characterize the set of trifocal essential matrices. Based on these characterizations, we propose three necessary conditions on a calibrated trifocal tensor. They have a form of 15 quartic and 99 quintic polynomial equations. We show that in the practically significant real case the 15 quartic constraints are also sufficient.
An O(m log n) Algorithm for Stuttering Equivalence and Branching Bisimulation
Venue: arXiv cs.LO 2016
Year: 2016

We provide a new algorithm to determine stuttering equivalence with time complexity $O(m \log n)$, where $n$ is the number of states and $m$ is the number of transitions of a Kripke structure. This algorithm can also be used to determine branching bisimulation in $O(m(\log |\mathit{Act}|+ \log n))$ time where $\mathit{Act}$ is the set of actions in a labelled transition system. Theoretically, our algorithm substantially improves upon existing algorithms which all have time complexity $O(m n)$ at best. Moreover, it has better or equal space complexity. Practical results confirm these findings showing that our algorithm can outperform existing algorithms with orders of magnitude, especially when the sizes of the Kripke structures are large. The importance of our algorithm stretches far beyond stuttering equivalence and branching bisimulation. The known $O(m n)$ algorithms were already far more efficient (both in space and time) than most other algorithms to determine behavioural equivalences (including weak bisimulation) and therefore it was often used as an essential preprocessing step. This new algorithm makes this use of stuttering equivalence and branching bisimulation even more attractive.
Decidability, Introduction Rules and Automata
Venue: arXiv cs.LO 2016
Year: 2016

We present a method to prove the decidability of provability in several well-known inference systems. This method generalizes both cut-elimination and the construction of an automaton recognizing the provable propositions.
On the Executability of Interactive Computation
Venue: arXiv cs.LO 2016
Year: 2016

The model of interactive Turing machines (ITMs) has been proposed to characterise which stream translations are interactively computable; the model of reactive Turing machines (RTMs) has been proposed to characterise which behaviours are reactively executable. In this article we provide a comparison of the two models. We show, on the one hand, that the behaviour exhibited by ITMs is reactively executable, and, on the other hand, that the stream translations naturally associated with RTMs are interactively computable. We conclude from these results that the theory of reactive executability subsumes the theory of interactive computability. Inspired by the existing model of ITMs with advice, which provides a model of evolving computation, we also consider RTMs with advice and we establish that a facility of advice considerably upgrades the behavioural expressiveness of RTMs: every countable transition system can be simulated by some RTM with advice up to a fine notion of behavioural equivalence.
Distributed Synthesis in Continuous Time
Venue: arXiv cs.DC 2016
Year: 2016

We introduce a formalism modelling communication of distributed agents strictly in continuous-time. Within this framework, we study the problem of synthesising local strategies for individual agents such that a specified set of goal states is reached, or reached with at least a given probability. The flow of time is modelled explicitly based on continuous-time randomness, with two natural implications: First, the non-determinism stemming from interleaving disappears. Second, when we restrict to a subclass of non-urgent models, the quantitative value problem for two players can be solved in EXPTIME. Indeed, the explicit continuous time enables players to communicate their states by delaying synchronisation (which is unrestricted for non-urgent models). In general, the problems are undecidable already for two players in the quantitative case and three players in the qualitative case. The qualitative undecidability is shown by a reduction to decentralized POMDPs for which we provide the strongest (and rather surprising) undecidability result so far.
Framework for cloud computing adoption: A road map for Smes to cloud migration
Venue: arXiv cs.CY 2016
Year: 2016

Small and Medium size Enterprises (SME) are considered as a backbone of many developing and developed economies of the world; they are the driving force to any major economy across the globe. Through Cloud Computing firms outsource their entire information technology (IT) process while concentrating more on their core business. It allows businesses to cut down heavy cost incurred over IT infrastructure without losing focus on customer needs. However, Cloud industry to an extent has struggled to grow among SMEs due to the reluctance and concerns expressed by them. Throughout the course of this study several interviews were conducted and the literature was reviewed to understand how cloud providers offer services and what challenges SMEs are facing. The study identified issues like cloud knowledge, interoperability, security and contractual concerns to be hindering SMEs adoption of cloud services. From the interviews common practices followed by cloud vendors and what concerns SMEs have were identified as a basis for a cloud framework which will bridge gaps between cloud vendors and SMEs. A stepwise framework for cloud adoption is formulated which identifies and provides recommendation to four most predominant challenges which are hurting cloud industry and taking SMEs away from cloud computing, as well as guide SMEs aiding in successful cloud adoption. Moreover, this framework streamlines the cloud adoption process for SMEs by removing ambiguity in regards to fundamentals associated with their organisation and cloud adoption process.
Decidability of Verification of Safety Properties of Spatial Families of Linear Hybrid Automata
Venue: arXiv cs.LO 2016
Year: 2016

We consider systems composed of an unbounded number of uniformly designed linear hybrid automata, whose dynamic behavior is determined by their relation to neighboring systems. We present a class of such systems and a class of safety properties whose verification can be reduced to the verification of (small) families of neighbouring systems of bounded size, and identify situations in which such verification problems are decidable, resp. fixed parameter tractable. We illustrate the approach with an example from coordinated vehicle guidance, and describe an implementation which allows us to perform such verification tasks automatically.
Ensemble Methods of Classification for Power Systems Security Assessment
Venue: arXiv cs.AI 2016
Year: 2016

One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable to build a reliable decision rules for feature space classification in the presence of many possible states of the system. In this paper, novel techniques based on decision trees are used for evaluation of the reliability of the regime of electric power systems. We proposed hybrid approach based on random forests models and boosting models. Such techniques can be applied to predict the interaction of increasing renewable power, storage devices and swiching of smart loads from intelligent domestic appliances, heaters and air-conditioning units and electric vehicles with grid for enhanced decision making. The ensemble classification methods were tested on the modified 118-bus IEEE power system showing that proposed technique can be employed to examine whether the power system is secured under steady-state operating conditions.
Acyclicity in Edge-Colored Graphs
Venue: arXiv cs.DM 2016
Year: 2016

A walk $W$ in edge-colored graphs is called properly colored (PC) if every pair of consecutive edges in $W$ is of different color. We introduce and study five types of PC acyclicity in edge-colored graphs such that graphs of PC acyclicity of type $i$ is a proper superset of graphs of acyclicity of type $i+1$, $i=1,2,3,4.$ The first three types are equivalent to the absence of PC cycles, PC trails, and PC walks, respectively. While graphs of types 1, 2 and 3 can be recognized in polynomial time, the problem of recognizing graphs of type 4 is, somewhat surprisingly, NP-hard even for 2-edge-colored graphs (i.e., when only two colors are used). The same problem with respect to type 5 is polynomial-time solvable for all edge-colored graphs. Using the five types, we investigate the border between intractability and tractability for the problems of finding the maximum number of internally vertex disjoint PC paths between two vertices and the minimum number of vertices to meet all PC paths between two vertices.
Facial age estimation using BSIF and LBP
Venue: arXiv cs.CV 2016
Year: 2016

Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP)histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database.
Toward a Robust Diversity-Based Model to Detect Changes of Context
Venue: arXiv cs.IR 2016
Year: 2016

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by each item relatively to a short sequence of consultations, corresponding to the recent user history. Our model has a complexity in constant time, and is generic since it can apply to any type of items within an online service (e.g. profiles, products, music tracks) and any application domain (e-commerce, social network, music streaming), as long as we have partial item descriptions. The observation of the diversity level over time allows us to detect implicit changes. In the long term, we plan to characterize the context, i.e. to find common features among a contiguous sub-sequence of items between two changes of context determined by our model. This will allow us to make context-aware and privacy-preserving recommendations, to explain them to users. As this is an ongoing research, the first step consists here in studying the robustness of our model while detecting changes of context. In order to do so, we use a music corpus of 100 users and more than 210,000 consultations (number of songs played in the global history). We validate the relevancy of our detections by finding connections between changes of context and events, such as ends of session. Of course, these events are a subset of the possible changes of context, since there might be several contexts within a session. We altered the quality of our corpus in several manners, so as to test the performances of our model when confronted with sparsity and different types of items. The results show that our model is robust and constitutes a promising approach.
Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
Venue: arXiv cs.AI 2016
Year: 2016

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).
Reduction Rules for Colored Workflow Nets
Venue: arXiv cs.LO 2016
Year: 2016

We study Colored Workflow nets, a model based on Workflow nets enriched with data. Based on earlier work by Esparza and Desel[arXiv:1307.2145,arXiv:1403.4958] on the negotiation model of concurrency, we present reduction rules for our model. Contrary to previous work, our rules preserve not only soundness, but also the data flow semantics. For free choice nets, the rules reduce all sound nets (and only them) to a net with one single transition and the same data flow semantics. We give an explicit algorithm that requires only a polynomial number of rule applications.