OpenRaiser/Intern-Atlas huggingface dataset:
Speech Enhancement using Kernel and Normalized Kernel Affine Projection Algorithm
Venue: arXiv cs.MM 2013
Year: 2013

The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Projection Algorithm (KAPA) and Normalized KAPA. The removal of background noise is very important in many applications like speech recognition, telephone conversations, hearing aids, forensic, etc. Kernel adaptive filters shown good performance for removal of noise. If the evaluation of background noise is more slowly than the speech, i.e., noise signal is more stationary than the speech, we can easily estimate the noise during the pauses in speech. Otherwise it is more difficult to estimate the noise which results in degradation of speech. In order to improve the quality and intelligibility of speech, unlike time and frequency domains, we can process the signal in new domain like Reproducing Kernel Hilbert Space (RKHS) for high dimensional to yield more powerful nonlinear extensions. For experiments, we have used the database of noisy speech corpus (NOIZEUS). From the results, we observed the removal noise in RKHS has great performance in signal to noise ratio values in comparison with conventional adaptive filters.
Performance analysis of modified algorithm for finding multilevel association rules
Venue: arXiv cs.DB 2013
Year: 2013

Multilevel association rules explore the concept hierarchy at multiple levels which provides more specific information. Apriori algorithm explores the single level association rules. Many implementations are available of Apriori algorithm. Fast Apriori implementation is modified to develop new algorithm for finding multilevel association rules. In this study the performance of this new algorithm is analyzed in terms of running time in seconds.
Anger is More Influential Than Joy: Sentiment Correlation in Weibo
Venue: arXiv cs.SI 2013
Year: 2013

Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than four years. Connected by online social ties, different users influence each other emotionally. We find the correlation of anger among users is significantly higher than that of joy, which indicates that angry emotion could spread more quickly and broadly in the network. While the correlation of sadness is surprisingly low and highly fluctuated. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends posses more significant sentiment influence to their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks.
Letter to the editor: Against the Resilience of Rejected Manuscripts
Venue: arXiv cs.DL 2013
Year: 2013

In this letter we propose the development of guidelines by the main editors associations as well as protocols within online journal management systems for keeping track of rejected manuscripts that are resubmitted as well as for the interchange of referees reports between journals.
Securing Software as a Service Model of Cloud Computing: Issues and Solutions
Venue: arXiv cs.DC 2013
Year: 2013

Cloud computing, undoubtedly, has become the buzzword in the IT industry today. Looking at the potential impact it has on numerous business applications as well as in our everyday life, it can certainly be said that this disruptive technology is here to stay. Many of the features that make cloud computing attractive, have not just challenged the existing security system, but have also revealed new security issues. This paper provides an insightful analysis of the existing status on cloud computing security issues based on a detailed survey carried by the author. It also makes an attempt to describe the security challenges in Software as a Service (SaaS) model of cloud computing and also endeavors to provide future security research directions.
Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity
Venue: arXiv cs.DB 2013
Year: 2013

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (APST), which is a two step approach to predict the direction of change of stock price indices. First, performs data approximation by using the technique called Multilevel Segment Mean (MSM). In second phase, prediction is performed for the approximated data using Euclidian distance and Nearest-Neighbour technique. The computational cost of data approximation is O(n ni) and computational cost of prediction task is O(m |NN|). Thus, the accuracy and the time required for prediction in the proposed method is comparatively efficient than the existing Label Based Forecasting (LBF) method [1].
On differential passivity of physical systems
Venue: arXiv cs.SY 2013
Year: 2013

Differential passivity is a property that allows to check with a pointwise criterion that a system is incrementally passive, a property that is relevant to study interconnected systems in the context of regulation, synchronization, and estimation. The paper investigates how restrictive is the property, focusing on a class of open gradient systems encountered in the coenergy modeling framework of physical systems, in particular the Brayton-Moser formalism for nonlinear electrical circuits.
Mine Blood Donors Information through Improved K-Means Clustering
Venue: arXiv cs.DB 2013
Year: 2013

The number of accidents and health diseases which are increasing at an alarming rate are resulting in a huge increase in the demand for blood. There is a necessity for the organized analysis of the blood donor database or blood banks repositories. Clustering analysis is one of the data mining applications and K-means clustering algorithm is the fundamental algorithm for modern clustering techniques. K-means clustering algorithm is traditional approach and iterative algorithm. At every iteration, it attempts to find the distance from the centroid of each cluster to each and every data point. This paper gives the improvement to the original k-means algorithm by improving the initial centroids with distribution of data. Results and discussions show that improved K-means algorithm produces accurate clusters in less computation time to find the donors information.
First-Order Provenance Games
Venue: arXiv cs.DB 2013
Year: 2013

We propose a new model of provenance, based on a game-theoretic approach to query evaluation. First, we study games G in their own right, and ask how to explain that a position x in G is won, lost, or drawn. The resulting notion of game provenance is closely related to winning strategies, and excludes from provenance all "bad moves", i.e., those which unnecessarily allow the opponent to improve the outcome of a play. In this way, the value of a position is determined by its game provenance. We then define provenance games by viewing the evaluation of a first-order query as a game between two players who argue whether a tuple is in the query answer. For RA+ queries, we show that game provenance is equivalent to the most general semiring of provenance polynomials N[X]. Variants of our game yield other known semirings. However, unlike semiring provenance, game provenance also provides a "built-in" way to handle negation and thus to answer why-not questions: In (provenance) games, the reason why x is not won, is the same as why x is lost or drawn (the latter is possible for games with draws). Since first-order provenance games are draw-free, they yield a new provenance model that combines how- and why-not provenance.
Adaptive Switching Between Single/Concurrent Link Scheme in Single Hop MIMO Networks
Venue: arXiv cs.NI 2013
Year: 2013

Concurrent link communications built on multi-antenna systems have been widely adopted for spatial resource exploitation. MIMA-MAC, a classical MIMO MAC protocol utilizing concurrent link scheme, is able to provide superior link throughput over conventional single link MAC (under certain isolated link topologies). However, when utilizing rich link adaptation functions in MIMO systems, there exists a non-ignorable probability that MIMA-MAC's throughput will be lower than that of single link scheme (such probability is dominated by the statistics of instantaneous link topology and channel response). Inspired by this critical observation, and for adapting to various link topologies, this paper will present a novel MAC design that can adaptively switch between single or concurrent link scheme. With the aim of absolutely outperforming the single link MAC, here our optimization criterion is to guarantee a throughput result that is either better than or at least equal to single link MAC's counterpart. To highlight the design rationale, we first present an idealized implementation having network information perfectly known in a non-causal way. Then for realistic applications, we further develop a practical MAC implementation dealing with realistic system impairments (distributed handshaking and imperfect channel estimation). Simulation results validate that link throughput in our MAC is higher than or equal to single link MAC's counterpart with minimized outage probabilities. And for ergodic link throughput, our proposed MAC can outperform the single link MAC and MIMA-MAC by around 20{%}-30{%}.
Evolutionary Information Diffusion over Social Networks
Venue: arXiv cs.GT 2013
Year: 2013

Social networks have become ubiquitous in our daily life, as such it has attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous information flow, creating the phenomenon of "Big Data". Under such a circumstance, understanding information diffusion over social networks has become an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the information diffusion over two special networks, Erdős-Rényi random network and the Barabási-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world information spreading dataset of Twitter and Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors.
On the Chi square and higher-order Chi distances for approximating f-divergences
Venue: arXiv cs.IT 2013
Year: 2013

We report closed-form formula for calculating the Chi square and higher-order Chi distances between statistical distributions belonging to the same exponential family with affine natural space, and instantiate those formula for the Poisson and isotropic Gaussian families. We then describe an analytic formula for the $f$-divergences based on Taylor expansions and relying on an extended class of Chi-type distances.
Improved Stability Design of Interconnected Distributed Generation Resources
Venue: arXiv cs.SY 2013
Year: 2013

This work provides a design method for achieving a specified level of stability for inverter-based interconnected distributed generation. The stability of parallel connected distributed energy resources determined from a linearized state-space model of the inverter dynamics that includes the admittance matrix of the interconnecting distribution lines. Each inverter uses a localized droop control scheme with the associated voltage and frequency measurements obtained through the application of an enhanced phase locked loop. Previous work on this topic has focused on single inverters connected to an infinite bus without modeling of delays from a phase locked loop implementation. This proposed method overcomes both of these limitations of previous research. A detailed large-signal simulation of a three-bus interconnected power system is analyzed under two different network admittance values. Results confirm the effectiveness of the proposed stability design method.
SINR Diagram with Interference Cancellation
Venue: arXiv cs.CG 2013
Year: 2013

In this paper we study the reception zones of a wireless network in the SINR model with receivers that employ \emph{interference cancellation} (IC), a technique that allows a receiver to decode interfering signals, and \emph{cancel} them from the received signal in order to decode its intended message. We first derive some important topological properties of the diagram describing the reception zones and their connections to \emph{high-order Voronoi diagrams} and other related geometric objects. We then discuss the computational issues that arise when seeking an efficient description of the zones. Our main fundamental result states that although potentially there are exponentially many possible cancellation orderings (and consequently reception cells), in fact there are much fewer nonempty such cells. We prove a (tight) linear bound on the number of cells and provide a polynomial time algorithm to describe the diagram. Moreover, we introduce a novel measure, referred to as the \emph{Compactness Parameter}, which influences the tightness of our bounds. We then utilize the properties established for reception diagrams to devise a logarithmic time algorithm for answering \emph{point-location} queries for networks with IC.
On topological and geometric $(19_4)$ configurations
Venue: arXiv cs.CG 2013
Year: 2013

An $(n_k)$ configuration is a set of $n$ points and $n$ lines such that each point lies on $k$ lines while each line contains $k$ points. The configuration is geometric, topological, or combinatorial depending on whether lines are considered to be straight lines, pseudolines, or just combinatorial lines. The existence and enumeration of $(n_k)$ configurations for a given $k$ has been subject to active research. A current front of research concerns geometric $(n_4)$ configurations: it is now known that geometric $(n_4)$ configurations exist for all $n \ge 18$, apart from sporadic exceptional cases. In this paper, we settle by computational techniques the first open case of $(19_4)$ configurations: we obtain all topological $(19_4)$ configurations among which none are geometrically realizable.
Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
Venue: arXiv cs.NE 2013
Year: 2013

In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are employed as the activation function and there is a normalized operation in the hidden layer, the modification of the scale factor and translation factor in the wavelet functions are ignored to avoid the fluctuations of the error curves. When there are 30 neurons in the hidden layer, to achieve the same square sum error (SSE) $ε_{min}=10^{-3}$, EWNN needs 31 iteration steps, while the basic Elman neural network (BENN) model needs 86 steps. The Volterra-Laguerre model has 605 parameters to be estimated but still can't achieve the same magnitude accuracy of EWNN. Simulation results show that the proposed approach of EWNN model has fewer parameters and higher accuracy than the Volterra-Laguerre model and its convergence rate is much faster than the BENN model.
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm
Venue: arXiv cs.AI 2013
Year: 2013

Although fuzzy techniques promise fast meanwhile accurate modeling and control abilities for complicated systems, different difficulties have been re-vealed in real situation implementations. Usually there is no escape of it-erative optimization based on crisp domain algorithms. Recently memristor structures appeared promising to implement neural network structures and fuzzy algorithms. In this paper a novel adaptive real-time fuzzy modeling algorithm is proposed which uses active learning method concept to mimic recent understandings of right brain processing techniques. The developed method is based on processing fuzzy numbers to provide the ability of being sensitive to each training data point to expand the knowledge tree leading to plasticity while used defuzzification technique guaranties enough stability. An outstanding characteristic of the proposed algorithm is its consistency to memristor crossbar hardware processing concepts. An analog implemen-tation of the proposed algorithm on memristor crossbars structure is also introduced in this paper. The effectiveness of the proposed algorithm in modeling and pattern recognition tasks is verified by means of computer simulations
Analytical and experimental stability investigation of a hardware-in-the-loop satellite docking simulator
Venue: arXiv cs.OH 2013
Year: 2013

The European Proximity Operation Simulator (EPOS) of the DLR-German Aerospace Center is a robotics-based simulator that aims at validating and verifying a satellite docking phase. The generic concept features a robotics tracking system working in closed loop with a force/torque feedback signal. Inherent delays in the tracking system combined with typical high stiffness at contact challenge the stability of the closed-loop system. The proposed concept of operations is hybrid: the feedback signal is a superposition of a measured value and of a virtual value that can be tuned in order to guarantee a desired behavior. This paper is concerned with an analytical study of the system's closed-loop stability, and with an experimental validation of the hybrid concept of operations in one dimension (1D). The robotics simulator is modeled as a second-order loop-delay system and closed-form expressions for the critical delay and associated frequency are derived as a function of the satellites' mass and the contact dynamics stiffness and damping parameters. A numerical illustration sheds light on the impact of the parameters on the stability regions. A first-order Pade approximation provides additional means of stability investigation. Experiments were performed and tests results are described for varying values of the mass and the damping coefficients. The empirical determination of instability is based on the coefficient of restitution and on the observed energy. There is a very good agreement between the critical damping values predicted by the analysis and observed during the tests...
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
Venue: arXiv cs.NI 2013
Year: 2013

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.
Protecting Public OSN Posts from Unintended Access
Venue: arXiv cs.SI 2013
Year: 2013

The design of secure and usable access schemes to personal data represent a major challenge of online social networks (OSNs). State of the art requires prior interaction to grant access. Sharing with users who are not subscribed or previously have not been accepted as contacts in any case is only possible via public posts, which can easily be abused by automatic harvesting for user profiling, targeted spear-phishing, or spamming. Moreover, users are restricted to the access rules defined by the provider, which may be overly restrictive, cumbersome to define, or insufficiently fine-grained. We suggest a complementary approach that can be easily deployed in addition to existing access control schemes, does not require any interaction, and includes even public, unsubscribed users. It exploits the fact that different social circles of a user share different experiences and hence encrypts arbitrary posts. Hence arbitrary posts are encrypted, such that only users with sufficient knowledge about the owner can decrypt. Assembling only well-established cryptographic primitives, we prove that the security of our scheme is determined by the entropy of the required knowledge. We consequently analyze the efficiency of an informed dictionary attack and assess the entropy to be on par with common passwords. A fully functional implementation is used for performance evaluations, and available for download on the Web.