Speech processing, the field of analysing input speech signals and methods of processing them has emerged in the recent days. Additionally, the development of a speech processing system involves several components in the design phase with probabilistic approximations for enhanced audio sampling and de-noising. In this work, we focus into use of Gaussian random variables while modelling and filtering noise that gets added after being passed through an additive noise channel in a communication system, and the applications of Hidden Markov models. Moreover, we apply deep learning methods for emotion classification via a robust and accurate ensemble learning scheme that is applied to a joint deep network which incorporates audiovisual inputs and generates the emotion prediction effectively reaching satisfactory accuracy.

At the dawn of the Internet era graph analytics play an important role in high- and low-level network policymaking across a wide array of fields so diverse as transportation network design, supply chain engineering and logistics, social media analysis, and computer communication networks, to name just a few. This can be attributed not only to the size of the original graph but also to the nature of the problem parameters. For instance, algorithmic solutions depend heavily on the approximation criterion selection. Moreover, iterative or heuristic solutions are often sought as it is a high dimensional problem given the high number of vertices and edges involved as well as their complex interaction. Replacing under constraints a directed graph with an undirected one having the same vertex set is often sought in applications such as data visualization, community structure discovery, and connection-based vertex centrality metrics. Polar decomposition is a key matrix factorization which represents a matrix as a product of a symmetric positive (semi)definite factor and an orthogonal one. The former can be an undirected approximation of the original adjacency matrix. The proposed graph approximation has been tested with three Twitter graphs with encouraging results with respect to density, Fiedler number, and certain vertex centrality metrics based on matrix power series. The dataset was hosted in an online MongoDB instance.

Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered.

Percentage of population living in cities is expected to reach 60% by 2030, accounting for 60% - 80% of world annual energy needs and making the impact of energy efficient solutions in cities quite significant for environmental protection and fighting climate change. The building sector uses about 40% of European energy and emits approximately 1/3 of greenhouse gas emissions. Black-box measurement based modeling methods allow the estimation of consumption in buildings relying on smart metering devices installed. Vast amount of data generated poses new challenges with reference to their handling and timely processing. The paper presents an approach related to building energy load profiling utilising profile compression and clustering. It discusses the application of different clustering algorithms through their experimental evaluation.

Range queries are important in many diverse applications. In its simplest one-dimensional form, a range query is expressed by an interval [a, b] on the real line, whereas the answer consists of all elements e ∈ [a, b]. In this work, we focus on efficient range query processing techniques in the Apache Spark engine, which is the state-of-the-art solution for big data management and analytics. We aim at developing a Spark-based indexing scheme that supports range queries in such large-scale decentralized environments and scale well w.r.t. the number of nodes and the data items stored. Towards this goal, there have been solutions in the last few years, which however turn out to be inadequate at the envisaged scale, since the classic linear or even the logarithmic complexity (for point queries) is still too expensive, whereas range query processing is even more demanding. In this paper, we go one step further and present a solution with sub-logarithmic complexity. In particular, we present SPIS (SPark-based Interpolation Search), a tree structure that outperforms the existing Spark built-in lookup techniques. We carry out an experimental evaluation by using synthetic data sets. Our experimental results demonstrate the efficiency and scalability of the proposed approach.

Non-linear system identification is a challenging problem with a plethora of engineering applications including digital telecommunications, adaptive control of biological systems, assessing integrity of mechanical constructs, and geological surveys. Various approaches have been proposed in the scientific literature, including Volterra and multivariate Taylor series, fuzzy neural networks, state space models, and wavelets. This conference paper proposes a succinct model of a non-linear system with memory based on a third order tensor whose coefficients are trained in an LMS-like way. Moreover, two variants deriving from sign LMS and batch LMS algorithms respectively are also implemented in TensorFlow. The results of applying the three training algorithms to this system are compared in terms of the mean square error in validation phase, the convergence rate of the coefficients, and the convergence rate of the Euclidean norm of the local gradients of the system model.

The focus of this work is the on-demand resource provisioning in cloud computing, which is commonly referredto as cloud elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without quantifying or guaranteeing the quality of their operation. We present an approach towards the development of more formalized and dependable elasticity policies. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes(MDP) using probabilistic model checking. Second, various concrete elasticity models and elasticity policies are studied. We evaluate the decision policies using traces from a realNoSQL database cluster under constantly evolving externalload. We reason about the behaviour of different modelling and elasticity policy options and we show that our proposal can improve upon the state-of-the-art in significantly decreasing under-provisioning while avoiding over-provisioning.

Suppose we have a virus or one competing idea/product that propagates over a multiple profile (e.g., social) network. Can we predict what proportion of the network will actually get "infected" (e.g., spread the idea or buy the competing product), when the nodes of the network appear to have different sensitivity based on their profile? For example, if there are two profiles A and B in a network and the nodes of profile A and profile B are susceptible to a highly spreading virus with probabilities βA and βB respectively, what percentage of both profiles will actually get infected from the virus at the end? To reverse the question, what are the necessary conditions so that a predefined percentage of the network is infected? We assume that nodes of different profiles can infect one another and we prove that under realistic conditions, apart from the weak profile (great sensitivity), the stronger profile (low sensitivity) will get infected as well. First, we focus on cliques with the goal to provide exact theoretical results as well as to get some intuition as to how a virus affects such a multiple profile network. Then, we move to the theoretical analysis of arbitrary networks. We provide bounds on certain properties of the network based on the probabilities of infection of each node in it when it reaches the steady state. Finally, we provide extensive experimental results that verify our theoretical results and at the same time provide more insight on the problem.

This work presents TIRAMOLA, a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to user-defined policies, it then proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster. TIRAMOLA's modular architecture and standard API support allows interaction with most current IaaS platforms and increased customization. An extensive experimental evaluation on an HBase cluster confirms our assertions: The system resizes clusters in real-time and adapts its performance through different optimization strategies, different permissible actions, different input and training loads. Besides the automation of the process, it exhibits a learning feature which allows it to make very close to optimal decisions even with input loads 130% larger or alternating 10 times faster compared to the accumulated information.

Positioning capabilities offered in modern mobile devices enable usage of location-based services. Privacy and security is of great importance for related applications. We present a framework that allows conducting research on anonymity techniques in a real-life environment using smartphones. The proposed solution also includes logging mechanisms that facilitate positioning research dataset development in open format. To present the capabilities of the solution, we deliver the concept of K-anonymity to protect mobile users that issue queries to location-based services. Experimental evaluation of the solution includes development of real-life logging dataset using smartphones by volunteers. Different flavours of anonymity algorithms are easy to be included and tested. The solution has received encouraging feedback and successfully assists the researchers of location based services to experiment, validate and develop their techniques in real life environment.

NoSQL databases focus on analytical processing of large scale datasets, offering increased scalability over commodity hardware. One of their strongest features is elasticity, which allows for fairly portioned premiums and high-quality performance. Yet, the process of adaptive expansion and contraction of resources usually involves a lot of manual effort, often requiring the definition of the conditions for scaling up or down to be provided by the users. To date, there exists no open-source system for automatic resizing of NoSQL clusters. In this demonstration, we present TIRAMOLA, a modular, cloud-enabled framework for monitoring and adaptively resizing NoSQL clusters. Our system incorporates a decision-making module which allows for optimal cluster resize actions in order to maximize any quantifiable reward function provided together with life-long adaptation to workload or infrastructural changes. The audience will be able to initiate HBase clusters of various sizes and apply varying workloads through multiple YCSB clients. The attendees will be able to watch, in real-time, the system perform automatic VM additions and removals as well as how cluster performance metrics change relative to the optimization parameters of their choice.

We present I/O-efficient fully persistent B-Trees that support range searches at any version in O(logB n + t/B) I/Os and updates at any version in O(logB n + log2 B) amortized I/Os, using space O(m/B) disk blocks. By n we denote the number of elements in the accessed version, by m the total number of updates, by t the size of the query's output, and by B the disk block size. The result improves the previous fully persistent B-Trees of Lanka and Mays by a factor of O(logB m) for the range query complexity and O(logB n) for the update complexity. To achieve the result, we first present a new B-Tree implementation that supports searches and updates in O(logB n) I/Os, using O(n/B) blocks of space. Moreover, every update makes in the worst case a constant number of modifications to the data structure. We make these B-Trees fully persistent using an I/O-efficient method for full persistence that is inspired by the node-splitting method of Driscoll et al. The method we present is interesting in its own right and can be applied to any external memory pointer based data structure with maximum in-degree din bounded by a constant and out-degree bounded by O(B), where every node occupies a constant number of blocks on disk. The I/O-overhead per modification to the ephemeral structure is O(din log2 B) amortized I/Os, and the space overhead is O(din/B) amortized blocks. Access to a field of an ephemeral block is supported in O(log2 din) worst case I/Os.

This paper presents the use of a novel Peer to Peer (P2P) infrastructure in order to provide rapid broad digital content exchange, digital rights protection and efficient transaction management through watermarking technologies. Copyright owners use digital watermarking techniques so as to encrypt copyright information to the content. This information is represented by multiple watermarking keys used, amongst other reasons, for proof of ownership, unique identification and transaction management. Especially for transaction management the watermarking keys used are constantly changing following the content's route from user to user. The challenges for this research are a) the use of P2P technologies for efficient Digital Rights Management (DRM), b) to apply a robust watermarking algorithm to digital content which successfully embeds and detects multiple keys for each use case and c) to successfully maintain consistent the P2P system when watermarking keys are changing during content's transaction. This paper concludes that DRM and P2P can be quite complementary and not always contradictory.

Modeling friendship is a challenging task in social networking given the opportunistic behavior of human relationships that is hard to model. In this paper a simple two-state markov chain model is introduced attempting to give further insight on friendship and particularly how relations evolve as time passes, given that the corresponding graph is a time evolving one with interesting properties. Based on this model four distinct behavioral categories are identified and studied. As it is analytically shown, and subsequently confirmed by simulations, any network of nodes having the same friendship characteristics (e.g., a network consisted exclusively of nodes of one of the behavioral categories characterized in this work) eventually results to a network with properties similar to that of random graphs. Since modern society is characterized by power-law distributions, it is shown by simulations that there exists a certain mix of the previously mentioned categories such that the resulting graph has similar to power-law distribution.

This work studies the problem of 2-dimensional searching for the 3-sided range query of the form [a, b] x (-∞, c] in both main and external memory, by considering a variety of input distributions. A dynamic linear main memory solution is proposed, which answers 3-sided queries in O(log n + t) worst case time and scales with O (log log n) expected with high probability update time, under continuous μ-random distributions of the x and y coordinates, where n is the current number of stored points and t is the size of the query output. Our expected update bound constitutes a considerable improvement over the O(log n) update time bound achieved by the classic Priority Search Tree of McCreight [23], as well as over the Fusion Priority Search Tree of Willard [30], which requires O(log n/log log n) time for all operations. Moreover, we externalize this solution, gaining O(logB n + t/B) worst case and O(logBlogn) amortized expected with high probability I/Os for query and update operations respectively, where B is the disk block size. Then, combining the Modified Priority Search Tree [27] with the Priority Search Tree [23], we achieve a query time of O(log log n + t) expected with high probability and an update time of O(log log n) expected with high probability, under the assumption that the x-coordinates are continuously drawn from a smooth distribution and the y-coordinates are continuously drawn from a more restricted class of distributions. The total space is linear. Finally, we externalize this solution, obtaining a dynamic data structure that answers 3-sided queries in O(logB log n + t/B) I/Os expected with high probability, and it can be updated in O(logB log n) I/Os amortized expected with high probability and consumes O(n/B) space, under the same assumptions.

The IEEE 802.16e standard provides a high degree of flexibility for setting up and operating mobile WiMAX networks in metropolitan environments. While the standard supports differentiated Quality of Service (QoS), crucial functions such as traffic scheduling and slot allocation are left as open issues to the research community. This paper focuses on these two features, that consist an NP-Hard problem, and provides an integrated solution. The key component of the proposal is the “Colored OFDMA Frame Registry Tree Scheduler” (CO-FRTS) that achieves QoS-aware resource allocation by using an advanced tree structure to prepare the creation of time frames, and reduce processing requirements. An extended simulation model reveals the improved performance of the proposed solution, compared to other competent proposals.

We focus on range query processing on large-scale, typically distributed infrastructures. In this work we present the ART (Autonomous Range Tree) structure, which outperforms the most popular decentralized structures, including Chord (and some of its successors), BATON (and its successor) and Skip-Graphs. ART supports the join/leave and range query operations in O(log log N) and O(log2b logN +|A|) expected w.h.p number of hops respectively, where the base b is a double-exponentially power of two, N is the total number of peers and |A| the answer size.

In this paper we deal with security and historical privacy in Location Based Service (LBS) applications where users submit accurate location samples to an LBS provider. Specifically we propose a distributed scheme that establishes access control while protecting the privacy of a user in both sporadic and continuous LBS queries. Our solution employs a hybrid network architecture where LBS users: (a) are able to communicate with an LBS provider through a network (e.g., cellular) operator, and (b) they are also able to create wireless ad-hoc networks with other peers in order to obtain privacy against an adversary that performs traffic analysis. Our threat model considers the network operator, the LBS provider and other peers, as potential privacy adversaries. For historical privacy we adopt the generic approach of using multiple pseudonyms that are changed frequently. In order to establish untraceability against traffic analysis attacks, a message is not sent directly to the cellular operator, but it is distributed among mobile neighbors who act like mixes and re-encrypt a message before sending it to the LBS provider via the cellular operator. As an extension, we also discuss how to aggregate independent data from different mobile peers before sending them to the LBS provider. This approach may be suitable in applications where aggregate location data are useful (e.g., traffic monitoring and control).

In this paper we introduce a novel distributed simulation environment with GUI for P2P simulations (D-P2P-Sim). The key aim is to provide the appropriate integrated set of tools in a single software solution to evaluate the performance of various protocols. The basic architecture of the distributed P2P simulator is based on a multi-threading, asynchronous, message passing and distributed environment with graphical user interface to facilitate ease of use by both researchers and programmers.

In this paper we discuss the integration of QoS-awa research and personalization algorithms in order to discover effectual Web Services for the case of mobile web users. We present a number of novel ranking algorithms specially designed for mobile web architectures. To validate and evaluate the proposed algorithms we developed a fully working prototype for mobile-PDA devices. The prototype enables PDA users to access electronic shops and to retrieve information about their products, while going shopping. Comparative experimental results have shown encouraging results that prove m-scroutz to be effective.

After the debunking of some myths about why P2P overlays are not feasible in sensornets, many such solutions have been proposed. None of the existing P2P overlays for sensornets provide "Energy-Level Application and Services". On this purpose and based on the efficient P2P method presented in [16], we design a novel P2P overlay for Energy Level discovery in a sensornet, the so-called ELDT (Energy Level Distributed Tree). Sensor nodes are mapped to peers based on their energy level. As the energy levels change, the sensor nodes would have to move from one peer to another and this oparation is the most crucial for the efficient scalability of the proposed system. Similarly, as the energy level of a sensor node becomes extremelly low, that node may want to forward it's task to another node with the desired energy level. The adaptation of the P2P index presented in [16] quarantees the best-known query performance of the above operation. We experimentally verify this performance via an appropriate simulator we have designed for this purpose.

We present a time-efficient approach to index objects moving on the plane to efficiently answer range queries about their future positions. Each object is moving with non small velocity u, meaning that the velocity value distribution is skewed (Zipf) towards umin in some range [umin, umax], where umin is a positive lower threshold. Our algorithm enhances a previously described solution [18] by accommodating the ISB-tree access method as presented in [6]. Experimental evaluation shows the improved performance, scalability and efficiency of the new algorithm.

A multirepeat in a string is a substring (factor) that appears a predefined number of times. A multirepeat is maximal if it cannot be extended either to the right or to the left and produce a multirepeat. In this paper, we present algorithms for two different versions of the problem of finding maximal multirepeats in a set of strings. In the case of arbitrary gaps, we propose an algorithm with O ( σN2n + α ) time complexity. When the gap is bounded in a small range c , we propose an algorithm with O (( c2 + σ2 ) mN2n log( Nn ) + α ) time complexity. Here, N is the number of strings, n the mean length of each string, m the multiplicity of the multirepeat and α the number of reported occurrences. Our results extend previous work by considering sets of strings as well as by generalizing pairs to multirepeats.

Describes a system offering a solution to the problem of ambulance management and emergency incident handling in the prefecture of Attica in Greece. It is based on a geographic information system (GIS) coupled with GPS (Global Positioning System) and GSM (Global System for Mobile Communication) technologies. The system's operation is expected to minimize the ambulances' response time. Consequently, there will be a drastic improvement in the way emergency incidents are being handled. This fact will thus significantly affect the quality of health services offered to citizens.