Planet C4DM

Michael Small, David M. Walker, Antoinette Tordesillas, and Chi K. Tse
For a given observed time series, it is still a rather difficult problem to provide a useful and compelling description of the underlying dynamics. The approach we take here, and the general philosophy adopted elsewhere, is to reconstruct the (assumed) attractor from the observed time series. From t ... [Chaos 23, 013113 (2013)] published Fri Feb 8, 2013.

Abdeljalil Rachadi, Mohamed Jedra, and Noureddine Zahid
In this paper, we present a new routing algorithm called the self avoiding paths routing algorithm. Its application to traffic flow in scale-free networks shows a great improvement over the so called efficient routing protocol while at the same time maintaining a relatively low average packet travel ... [Chaos 23, 013114 (2013)] published Fri Feb 8, 2013.

I continue to be a massive @MarcFi nerd - happy belated "Popular Music" release day everyone! http://t.co/A0mvi5V7

Recent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform. We investigate the potential for Twitter to act as a gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyze persistent English and German language extreme right communities.

Terrorist network is a paradigms to understand the terrorism. The terrorist involves a lot of people, and among them are involved as perpetrators, but on the contrary it is very difficult to know who they are caused by lack of information. Network structure is used to reveal other things about the terrorist beyond the ability of social sciences.

Urban agglomerations exhibit complex emergent features of which Zipf's law, i.e.\ a power-law size distribution, and fractality may be regarded as the most prominent ones. We propose a simplistic model for the generation of city-like structures which is solely based on the assumption that growth is more likely to take place close to inhabited space. The model involves one parameter which is an exponent determining how strongly the attraction decays with the distance. In addition, the model is run iteratively so that existing clusters can grow (together) and new ones can emerge. The model is capable of reproducing the size distribution and the fractality of the boundary of the largest cluster. While the power-law distribution depends on both, the imposed exponent and the iteration, the fractality seems to be independent of the former and only depends on the latter. Analyzing land-cover data we estimate the parameter-value $\gamma\approx 2.5$ for Paris and it's surroundings.

With the dramatic increase in the number of websites on the internet, tagging has become popular for finding related, personal and important documents. When the potentially increasing internet markets are analyzed, Turkey, in which most of the people use Turkish language on the internet, found to be exponentially increasing. In this paper, a tag-based website recommendation method is presented, where similarity measures are combined with semantic relationships of tags. In order to evaluate the system, an experiment with 25 people from Turkey is undertaken and participants are firstly asked to provide websites and tags in Turkish and then they are asked to evaluate recommended websites.

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

Chuansheng Shen, Hanshuang Chen, Miaolin Ye, and Zhonghuai Hou
Identifying nucleation pathway is important for understanding the kinetics of first-order phase transitions in natural systems. In the present work, we study nucleation pathway of the Ising model in homogeneous and heterogeneous networks using the forward flux sampling method, and find that the nucl ... [Chaos 23, 013112 (2013)] published Thu Feb 7, 2013.

Yongqing Fu, Xingyuan Li, Yanan Li, Wei Yang, and Hailiang Song
Chaotic communication has aroused general interests in recent years, but its communication effect is not ideal with the restriction of chaos synchronization. In this paper a new chaos M-ary digital modulation and demodulation method is proposed. By using region controllable characteristics of spatio ... [Chaos 23, 013111 (2013)] published Thu Feb 7, 2013.

The New Draft National Curriculum for Music | Jonathan Savage http://t.co/hjRD9yMr

The number of systems that collect vast amount of data about users rapidly grow during last few years. Many of these systems contain data not only about people characteristics but also about their relationships with other system users. From this kind of data it is possible to extract a social network that reflects the connections between system's users. Moreover, the analysis of such social network enables to investigate different characteristics of its members and their linkages. One of the types of examining such network is key users extraction. Key users are these who have the biggest impact on other network members as well as have big influence on network evolution. The obtained about these users knowledge enables to investigate and predict changes within the network. So this knowledge is very important for the people or companies who make a profit from the network like telecommunication company. The second important thing is the ability to extract these users as quick as possible, i.e. developed the algorithm that will be time-effective in large social networks where number of nodes and edges equal few millions. In this master thesis the method of key user extraction, which is called social position, was analyzed. Moreover, social position measure was compared with other methods, which are used to assess the centrality of a node. Furthermore, three algorithms used to social position calculation was introduced along with results of comparison between their processing time and others centrality methods.

In this paper, we describe an approach to populate an existing ontology with instance information present in the natural language text provided as input. An ontology is defined as an explicit conceptualization of a shared domain. This approach starts with a list of relevant domain ontologies created by human experts, and techniques for identifying the most appropriate ontology to be extended with information from a given text. Then we demonstrate heuristics to extract information from the unstructured text and for adding it as structured information to the selected ontology. This identification of the relevant ontology is critical, as it is used in identifying relevant information in the text. We extract information in the form of semantic triples from the text, guided by the concepts in the ontology. We then convert the extracted information about the semantic class instances into Resource Description Framework (RDF3) and append it to the existing domain ontology. This enables us to perform more precise semantic queries over the semantic triple store thus created. We have achieved 95% accuracy of information extraction in our implementation.

Classic research on human factors has found that automation never fully eliminates the human operator from the loop. Instead, it shifts the operator’s responsibilities to the machine and changes the operator’s control demands, sometimes with adverse consequences, called the “ironies of automation.” In this paper, we revisit the problem of automation in the era of social media, focusing on privacy concerns. Present-day social media automatically disclose information such as users’ whereabouts, likings, and undertakings. Our review of empirical studies exposes three recurring privacy-related issues in automated disclosure: 1) insensitivity to situational demands, 2) inadequate control of nuance and veracity, and 3) inability to control disclosure with service providers and third parties. We claim that the “all-or-nothing” type of automation has proven problematic and that social network services should design their user controls with all stages of the disclosure process in mind.

"A Cappella - As if by Magic" by Emily Dankworth http://t.co/L2enKyc5

Thanks to @TheCurrent for alerting me to the existence of a new Eels album - woo! http://t.co/KQkTDrzp

We propose a network growth algorithm based on the dynamics of a quantum mechanical system co-evolving together with a graph which is seen as its phase space. The algorithm naturally generalizes Barab\'asi-Albert model of preferential attachment and it has a rich set of tunable parameters -- for example, the initial conditions of the dynamics or the interaction of the system with its environment. We observe that the algorithm can grow networks with two-modal power-law degree distributions and super-hubs.

We characterize the statistical bootstrap for the estimation of information-theoretic quantities from data, with particular reference to its use in the study of large-scale social phenomena. Our methods allow one to preserve, approximately, the underlying axiomatic relationships of information theory---in particular, consistency under arbitrary coarse-graining---that motivate use of these quantities in the first place, while providing reliability comparable to the state of the art for Bayesian estimators. We show how information-theoretic quantities allow for rigorous empirical study of the decision-making capacities of rational agents, and the time-asymmetric flows of information in distributed systems. We provide illustrative examples by reference to ongoing collaborative work on the semantic structure of the British Criminal Court system and the conflict dynamics of the contemporary Afghanistan insurgency.

Discussions of the economics of scholarly communication are usually devoted to Open Access, rising journal prices, publisher profits, and boycotts. That ignores what seems a much more important development in this market. Publishers, through the oft-reviled "Big Deal" packages, are providing much greater and more egalitarian access to the journal literature, an approximation to true Open Access. In the process they are also marginalizing libraries, and obtaining a greater share of the resources going into scholarly communication. This is enabling a continuation of publisher profits as well as of what for decades has been called "unsustainable journal price escalation". It is also inhibiting the spread of Open Access, and potentially leading to an oligopoly of publishers controlling distribution through large-scale licensing.

The "Big Deal" practices are worth studying for several general reasons. The degree to which publishers succeed in diminishing the role of libraries may be an indicator of the degree and speed at which universities transform themselves. More importantly, these "Big Deals" appear to point the way to the future of the whole economy, where progress is characterized by declining privacy, increasing price discrimination, increasing opaqueness in pricing, increasing reliance on low-paid or upaid work of others for profits, and business models that depend on customer inertia.

The transient fluctuation of the prosperity of firms in a network economy is investigated with an abstract stochastic model. The model describes the profit which firms make when they sell materials to a firm which produces a product and the fixed cost expense to the firms to produce those materials and product. The formulae for this model are parallel to those for population dynamics. The swinging changes in the fluctuation in the transient state from the initial growth to the final steady state are the consequence of a topology-dependent time trial competition between the profitable interactions and expense. The firm in a sparse random network economy is more likely to go bankrupt than expected from the value of the limit of the fluctuation in the steady state, and there is a risk of failing to reach by far the less fluctuating steady state.

When uncontrollable resources fluctuate, Optimum Power Flow (OPF), routinely used by the electric power industry to re-dispatch hourly controllable generation (coal, gas and hydro plants) over control areas of transmission networks, can result in grid instability, and, potentially, cascading outages. This risk arises because OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by simulations of real grids, would likely resulting in automatic line tripping to protect lines from thermal stress, a risky and undesirable outcome which compromises stability. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our Chance Constrained (CC) OPF corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, our CC-OPF satisfies all the constraints with high probability while simultaneously minimizing the cost of economic re-dispatch. CC-OPF allows efficient implementation, e.g. solving a typical instance over the 2746-bus Polish network in 20 seconds on a standard laptop.

Many systems can be described using graphs, or networks. Detecting communities in these networks can provide information about the underlying structure and functioning of the original systems. Yet this detection is a complex task and a large amount of work was dedicated to it in the past decade. One important feature is that communities can be found at several scales, or levels of resolution, indicating several levels of organisations. Therefore solutions to the community structure may not be unique. Also networks tend to be large and hence require efficient processing. In this work, we present a new algorithm for the fast detection of communities across scales using a local criterion. We exploit the local aspect of the criterion to enable parallel computation and improve the algorithm's efficiency further. The algorithm is tested against large generated multi-scale networks and experiments demonstrate its efficiency and accuracy.

Indirect reciprocity is one of the main mechanisms to explain the emergence and sustainment of altruism in societies. The standard approach to indirect reciprocity are reputation models. These are games in which players base their decisions on their opponent's reputation gained in past interactions with other players (moral assessment). The combination of actions and moral assessment leads to a large diversity of strategies, thus determining the stability of any of them against invasions by all the others is a difficult task. We use a variant of a previously introduced reputation-based model that let us systematically analyze all these invasions and determine which ones are successful. Accordingly we are able to identify the third-order strategies (those which, apart from the action, judge considering both the reputation of the donor and that of the recipient) that are evolutionarily stable. Our results reveal that if a strategy resists the invasion of any other one sharing its same moral assessment, it can resist the invasion of any other strategy. However, if actions are not always witnessed, cheaters (i.e., individuals with a probability of defecting regardless of the opponent's reputation) have a chance to defeat the stable strategies for some choices of the probabilities of cheating and of being witnessed. Remarkably, by analyzing this issue with adaptive dynamics we find that whether a honest population resists the invasion of cheaters is determined by a Hamilton-like rule---with the probability that the cheat is discovered playing the role of the relatedness parameter.

Note: I drafted this a while back but didn't get round to putting it on the blog. Now I have published code and a published paper about the GM-PHD filter, I thought these practical insights might be useful:

I've been tweaking the GM-PHD filter which I blogged about recently. (Gaussian mixture PHD is a GM implementation of the Probability Hypothesis Density filter, for tracking multiple objects in a set of noisy observations.)

I think there are some subtleties to it which are not immediately obvious from the research articles.

Also, I've published my open source GM-PHD Python code so if anyone finds it useful (or has patches to contribute) I'd be happy. There's also a short research paper about using the GM-PHD filter for multi-pitch tracking.

In that original blog post I said the results were noisier than I was hoping. I think there are a couple of reasons for this:

• The filter benefits from a high-entropy representation and a good model of the target's movement. I started off with a simple 1D collection of particles with fixed velocity, and in my modelling I didn't tell the GM-PHD about the velocity - I just said there was position with some process noise and observation noise. Well, if I update this so the model knows about velocity too, and I specify the correct linear model (i.e. position is updated by adding the velocity term on to it) the results improve a little. I was hoping that I coud be a bit more generic than that. It may also be that my 1D example is too low-complexity, and a 2D example would give it more to focus on. Whatever happened to "keep it simple"?!

• The filter really benefits from knowing where targets are likely to come from. In the original paper, the simulation examples are of objects coming from a fixed small number of "air bases" and so they can be tracked as soon as they "take off". If I'm looking to model audio, then I don't know what frequency things will start from, there's no strong model for that. So, I can give it a general "things can come from anywhere" prior, but that leads to the burn-in problem that I mentioned in my first blog post - targets will not accumulate much evidence for themselves, until many frames have elapsed. (It also adds algorithmic complexity, see below.)

• Cold-start problem: the model doesn't include anything about pre-existing targets that might already be in the space, before the first frame (i.e. when the thing is "turned on"). It's possible to account for this slightly hackily by using a boosted "birth" distribution when processing the first frame, but this can't answer the question of how many objects to expect in the first frame - so you'd have to add a user parameter. It would be nice to come up with a neat closed-form way to decide what the steady-state expectation should be. (You can't just burn it in by running the thing with no observations for a while before you start - "no observations" is expressed as "empty set", which the model takes to mean definitely nothing there rather than ignorance. Ignorance would be expressed as an equal distribution over all possible observation sets, which is not something you can just drop in to the existing machinery.)

One mild flaw I spotted is in the pruning algorithm. It's needed because without it the number of Gaussians would diverge exponentially, so to keep it manageable you want to reduce this to some maximum limit at each step. However, the pruning algorithm given in the paper is a bit arbitrary, and in particular it fails to maintain the total sum of weights. It chops off low-weight components, and doesn't assign their lost weight to any of the survivors. This is important because the sum of weights for a GMPHD filter is essentially the estimated number of tracked objects. If you have a strong clean signal then it'll get over this flaw, but if not, you'll be leaking away density from your model at every step. So in my own code I renormalise the total mass after simplification - a simple change, hopefully a good one.

And a note about runtime: the size of the birth GMM strongly affects the running speed of the model. If you read through the description of how it works, this might not be obvious because the "pruning" is supposed to keep the number of components within a fixed limit so you might think it allows it to scale fine. However, the if birth GMM has many components, then they all must be cross-fertilised with each observation point at every step, and then pruned afterwards, so even if they don't persist they are still in action for the CPU-heavy part of the process. (The complexity has a kind of dependence on number-of-observations * number-of-birth-Gaussians.) If like me you have a model where you don't know where tracks will be born from, then you need many components to represent a flat distribution. (In my tests, using a single very wide Gaussian led to unpleasant bias towards the Gaussian's centre, no matter how wide I spread it.)

In citation networks, the activity of papers usually decreases with age and dormant papers may be discovered and become fashionable again. To model this phenomenon, a competition mechanism is suggested which incorporates two factors: vigorousness and dormancy. Based on this idea, a citation network model is proposed, in which a node has two discrete stage: vigorous and dormant. Vigorous nodes can be deactivated and dormant nodes may be activated and become vigorous. The evolution of the network couples addition of new nodes and state transitions of old ones. Both analytical calculation and numerical simulation show that the degree distribution of nodes in generated networks displays a good right-skewed behaviour. Particularly, scale-free networks are obtained as the deactivated vertex is target selected and exponential networks are realized for the random-selected case. Moreover, the measurement of four real-world citation networks achieves a good agreement with the stochastic model.

The notion of complex-valued information entropy measure is presented. It applies in particular to directed networks (digraphs). The corresponding statistical physics notions are outlined. The studied network, serving as a case study, in view of illustrating the discussion, concerns citations by agents belonging to two distinct communities which have markedly different opinions: the Neocreationist and Intelligent Design Proponents, on one hand, and the Darwinian Evolution Defenders, on the other hand. The whole, intra- and inter-community adjacency matrices, resulting from quotations of published work by the community agents, are elaborated and eigenvalues calculated. Since eigenvalues can be complex numbers, the information entropy may become also complex-valued. It is calculated for the illustrating case. The role of the imaginary part finiteness is discussed in particular and given some physical sense interpretation through local interaction range consideration. It is concluded that such generalizations are not only interesting and necessary for discussing directed networks, but also may give new insight into conceptual ideas about directed or other networks. Notes on extending the above to Tsallis entropy measure are found in an Appendix.

Cooperation among unrelated individuals is frequently observed in social groups when their members join efforts and resources to obtain a shared benefit which is unachievable by singles. However, understanding why cooperation arises despite the natural tendency of individuals towards selfish behaviors is still an open problem and represents one of the most fascinating challenges in volutionary dynamics.

Very recently, the structural characterization of the networks upon which social interactions take place has shed some light on the mechanisms by which cooperative behaviours emerge and eventually overcome the individual temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly-knit communities lead to a significant increase of cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here we investigate the role of social ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating and intermitting over time, and can be represented by time-varying networks, that is graphs where connections between nodes appear, disappear, or are rewired over time. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a profound dramatic impact on the evolution of cooperation: the observed dynamics of pairwise interactions tend to favor selfish behaviors.

The number of followers is acknowledged as the presumably most basic popularity measure of Twitter users. However, because it is subjected to manipulations and therefore may be deceptive, some alternative methods for ranking Twitter users that take into account users' activities such as the tweet and retweet rate have been proposed. In the present work, we take a purely network approach to this fundamental question. First of all, we show that there are two types of users possessing a large number of followers. The first type of user follows a small number of others. The second type of user follows almost as equally many others as the number of its followers. Such a distinction is prominent for Japanese, Russian, and Korean users among the seven language groups that we examined. Then, we compare local (i.e., egocentric) followership networks around the two types of users with many followers. We show that the latter type, which is presumably uninfluential users despite its large number of followers, is characterized by high link reciprocity, large clustering coefficient, a large fraction of the second type of users among the followers, and a small PageRank. We conclude that the number of others that a user follows is as equally important as the number of followers when estimating the importance of a user in the Twitter blogosphere.

Network epidemiology's most important assumption is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets.

Benchmarking the performance of community detection methods on empirical social network data has been identified as critical for improving these methods. In particular, while most current research focuses on detecting communities in data that has been digitally extracted from large social media and telecommunications services, most evaluation of this research is based on small, hand-curated datasets. We argue that these two types of networks differ so significantly that by evaluating algorithms solely on the former, we know little about how well they perform on the latter. To address this problem, we consider the difficulties that arise in constructing benchmarks based on digitally extracted network data, and propose a task-based strategy which we feel addresses these difficulties. To demonstrate that our scheme is effective, we use it to carry out a substantial benchmark based on Facebook data. The benchmark reveals that some of the most popular algorithms fail to detect fine-grained community structure.

I report the results of the test, where the takers had to tell the prose of Charles Dickens from that of Edward Bulwer-Lytton, who is considered by many to be the worst writer in history of letters. The average score is about 50%, which is on the level of random guessing. This suggests that the quality of Dickens' prose is the same as of that of Bulwer-Lytton.

Verhulst logistic curve either grows OR decays, depending on the {\it growth rate} parameter value. A similar situation is found in the Gompertz law about human mortality. However, growth can neither be infinite nor reach a finite steady state at an infinite asymptotic time. Moreover before some decay, some growth must have occurred. For example, some levelling-off could occur at finite time, followed either by some growth again or then by some decay. Numerous examples show that the Verhulst and Gompertz modelisations are too reductive (or restrictive) descriptions of their original purpose. It is aimed, in the present note, to encompass into ONE simple differential equation the growth AND decay features of, e.g., population sizes, or numbers, but also of many other measured characteristics found in social and physical science systems. Previous generalisations of Verhulst or Gompertz functions are recalled. It is shown that drastic growth or decay jumps or turnovers can be readily described through drastic changes in values of the growth or decay rate. However smoother descriptions can be found if the growth or decay rate is modified in order to take into account some time or size dependence. Similar arguments can be carried through, but not so easily, for the so called carrying capacity, indeed leading to more elaborate algebraic work.

Quantum networks provide access to exchange of quantum information. The primary task of quantum networks is to distribute entanglement between remote nodes. Although quantum repeater protocol enables long distance entanglement distribution, it has been restricted to one-dimensional linear network. Here we develop a general framework that allows application of quantum repeater protocol to arbitrary quantum repeater networks with fractal structure. Entanglement distribution across such networks is mapped to renormalization. Furthermore, we demonstrate that logarithmical times of recursive such renormalization transformations can trigger fractal to small-world transition, where a scalable quantum small-world network is achieved. Our result provides new insight into quantum repeater theory towards realistic construction of large-scale quantum networks.

In general, the signal-to-noise ratio as well as the signal-to-reverberation ratio of speech received by a microphone decrease when the distance between the talker and microphone increases. Dereverberation and noise reduction algorithm are essential for many applications such as videoconferencing, hearing aids, and automatic speech recognition to improve the quality and intelligibility of the received desired speech that is corrupted by reverberation and noise. In the last decade, researchers have aimed at estimating the reverberant desired speech signal as received by one of the microphones. Although this approach has let to practical noise reduction algorithms, the spatial diversity of the received desired signal is not exploited to dereverberate the speech signal. In this paper, a two-stage beamforming approach is presented for dereverberation and noise reduction. In the first stage, a signal-independent beamformer is used to generate a reference signal which contains a dereverberated version of the desired speech signal as received at the microphones and residual noise. In the second stage, the filtered microphone signals and the noisy reference signal are used to obtain an estimate of the dereverberated desired speech signal. In this stage, different signal-dependent beamformers can be used depending on the desired operating point in terms of noise reduction and speech distortion. The presented performance evaluation demonstrates the effectiveness of the proposed two-stage approach.

Just found this lovely website about Google Map Maker.

This paper discusses underdetermined blind source separation (BSS) using a compressed sensing (CS) approach, which contains two stages. In the first stage we exploit a modified K-means method to estimate the unknown mixing matrix. The second stage is to separate the sources from the mixed signals using the estimated mixing matrix from the first stage. In the second stage a two-layer sparsity model is used. The two-layer sparsity model assumes that the low frequency components of speech signals are sparse on K-SVD dictionary and the high frequency components are sparse on discrete cosine transformation (DCT) dictionary. This model, taking advantage of two dictionaries, can produce effective separation performance even if the sources are not sparse in time-frequency (TF) domain.

This paper proposes a new variable regularized QR decomposition (QRD)-based recursive least M-estimate (VR-QRRLM) adaptive filter and studies its convergence performance and acoustic applications. Firstly, variable $L_{2}$ regularization is introduced to an efficient QRD-based implementation of the conventional RLM algorithm to reduce its variance and improve the numerical stability. Difference equations describing the convergence behavior of this algorithm in Gaussian inputs and additive contaminated Gaussian noises are derived, from which new expressions for the steady-state excess mean square error (EMSE) are obtained. They suggest that regularization can help to reduce the variance, especially when the input covariance matrix is ill-conditioned due to lacking of excitation, with slightly increased bias. Moreover, the advantage of the M-estimation algorithm over its least squares counterpart is analytically quantified. For white Gaussian inputs, a new formula for selecting the regularization parameter is derived from the MSE analysis, which leads to the proposed VR-QRRLM algorithm. Its application to acoustic path identification and active noise control (ANC) problems is then studied where a new filtered-x (FX) VR-QRRLM ANC algorithm is derived. Moreover, the performance of this new ANC algorithm under impulsive noises and regularization can be characterized by the proposed theoretical analysis. Simulation results show that the VR-QRRLM-based algorithms considerably outperform the traditional algorithms when the input signal level is low or in the presence of impulsive noises and the theoretical predictions are in good agreement with simulation results.

In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cramér-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.

This work aims at reducing the memory demand of the data structures that are usually pre-computed and stored for fast computation of the i-vectors, a compact representation of spoken utterances that is used by most state-of-the-art speaker recognition systems. We propose two new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices, and with the recently proposed fast eigen-decomposition technique. The first approach computes an i-vector in a Variational Bayes (VB) framework by iterating the estimation of one sub-block of i-vector elements at a time, keeping fixed all the others, and can obtain i-vectors as accurate as the ones obtained by the standard technique but requiring only 25% of its memory. The second technique is based on the Conjugate Gradient solution of a linear system, which is accurate and uses even less memory, but is slower than the VB approach. We analyze and compare the time and memory resources required by all these solutions, which are suited to different applications, and we show that it is possible to get accurate results greatly reducing memory demand compared with the standard solution at almost the same speed.

We present an algorithm for sound analysis and re-synthesis with local automatic adaptation of time-frequency resolution. The reconstruction formula we propose is highly efficient, and gives a good approximation of the original signal from analyses with different time-varying resolutions within complementary frequency bands: this is a typical case where perfect reconstruction cannot in general be achieved with fast algorithms, which provides an error to be minimized. We provide a theoretical upper bound for the reconstruction error of our method, and an example of automatic adaptive analysis and re-synthesis of a music sound.

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Front Cover

Call for Papers

Table of Contents

Layering at the protocol stack and beyond has contributed hugely to the Internet's success, letting functionality be introduced basically overnight. However, it's increasingly hampering the reliability of the network it created.

Experts predict that we're at the dawn of a mobile-dominated Internet in which geolocation and mapping platforms will drive considerable financial growth and spending. For these forecasts to come true, however, the maps that are the base of these mobile location services must be accurate. Several intriguing questions surround the overall development of the mapping ecosystem.

Information and communications technology (ICT) is expected to play a major role in reducing worldwide energy requirements by optimizing energy generation, transportation, and consumption. Recent research, however, also reveals staggering facts about how ICT is becoming a major component of the energy consumption budget. Some projections indicate that next-generation Internet applications will require electricity in amounts that can't be generated or transported to major metropolitan areas. This special issue provides a snapshot of ongoing efforts toward a global solution to these urgent challenges that will lead, in the end, to a new energy-efficient and sustainable Internet.

As Internet traffic grows, current core network technologies will raise issues in terms of energy consumption. Here, the authors propose three possible optical network architectures for the next-generation Internet core, including an all-optical hybrid optical switching (HOS) network, an optical/electronic HOS network, and an all-electronic switching network. They evaluate and compare all three networks with regard to performance and power consumption via an event-driven simulator.

This article proposes a time-division multiplexing passive optical network (TDM-PON) architecture in which optical network units (ONUs) enter active mode only when the optical line terminal has traffic to deliver. The authors use a hybrid ONU (H-ONU) equipped with a low-cost, low-energy IEEE 802.15.4 module. This module notifies H-ONUs when they should move from sleep to active mode to receive downstream traffic. The authors show that the proposed solution leads to a significant energy saving while satisfying PON delay requirements.

Renewable energy could be the key for sustainable next-generation cellular networks. The authors' approach would let mobile operators feed base stations in a heterogeneous network using renewable energy sources. The authors compare their method to a classical grid-powered solution. They evaluate costs and CO₂ emissions savings for different scenarios to demonstrate that properly powering a heterogeneous network with renewable energy can be a sustainable and economically convenient solution.

Today's information and communications technology (ICT) services emit an increasing amount of greenhouse gases. Carbon footprint models can enable research into ICT energy efficiency and carbon reduction. The GreenStar Network (GSN) testbed is a prototype wide-area network of data centers powered by renewable energy sources. Through their work developing the GSN, the authors have researched fundamental aspects of green ICT such as virtual infrastructure, unified management of compute, network, power, and climate resources, smart power control, and a carbon assessment protocol.

Can the Google App Engine cloud service be used, free of charge, to execute parameter study problems? That question drove this research, which is founded on the App Engine's newly developed Task Queue API. The authors created a simple and extensible framework implementing the master-worker model to enable usage of the App Engine application servers as computational nodes. This article presents and discusses the results of the feasibility study, as well as compares the solution with EC2, Amazon's free cloud offering.

Cybercrime's tentacles reach deeply into the Internet. A complete, underground criminal economy has developed that lets malicious actors steal money through the Web. The authors detail this enterprise, showing how information, expertise, and money flow through it. Understanding the underground economy's structure is critical for fighting it.

The emergence of yet more cloud offerings from a multitude of service providers calls for a meta cloud to smoothen the edges of the jagged cloud landscape. This meta cloud could solve the vendor lock-in problems that current public and hybrid cloud users face.

The fast and unpredictable evolution of social data poses challenges for capturing user activities and complex associations. Evolving social graph clustering promises to uncover the dynamics of latent user and content patterns. This Web extra overviews evolving data clustering approaches.

With the increasing importance of big data, many new systems have been developed to "solve" the big data challenge. At the same time, famous database researchers argue that there is nothing new about these systems and that they're actually a step backward. This article sheds some light on this discussion.

Deceptive email that leads unwary users to disclose sensitive information on fake websites is the most common form of malware seen by today's users. The technology behind these attacks uses the Internet's weak notion of "place" and the increasing use of websites for financial transactions. Users can protect themselves through precautionary measures, and experts learn to accurately identify malicious email.

In this introductory column, the author explores some issues regarding the definition of "digital citizenship," focusing on the associated rights and responsibilities, the value to be gained from citizenship, and the problems caused by conflict.