OpenRaiser/Intern-Atlas huggingface dataset:
Outage Analysis and Optimization for Time Switching-based Two-Way Relaying with Energy Harvesting Relay Node
Venue: arXiv cs.IT 2014
Year: 2014

Energy harvesting (EH) and network coding (NC) have emerged as two promising technologies for future wireless networks. In this paper, we combine them together in a single system and then present a time switching-based network coding relaying (TSNCR) protocol for the two-way relay system, where an energy constrained relay harvests energy from the transmitted radio frequency (RF) signals from two sources, and then helps the two-way relay information exchange between the two sources with the consumption of the harvested energy. To evaluate the system performance, we derive an explicit expression of the outage probability for the proposed TSNCR protocol. In order to explore the system performance limit, we formulate an optimization problem to minimize the system outage probability. Since the problem is non-convex and cannot be directly solved, we design a genetic algorithm (GA)-based optimization algorithm for it. Numerical results validate our theoretical analysis and show that in such an EH two-way relay system, if NC is applied, the system outage probability can be greatly decreased. Moreover, it is shown that the relay position greatly affects the system performance of TSNCR, where relatively worse outage performance is achieved when the relay is placed in the middle of the two sources. This is the first time to observe such a phenomena in EH two-way relay systems.
A self-organizing geometric algorithm for autonomous data partitioning
Venue: arXiv cs.DS 2014
Year: 2014

A model of a geometric algorithm is introduced and methodology of its operation is presented for the dynamic partitioning of data spaces.
Designing pricing schemes based on progressive tariff and consumer grouping in migration to a future smart grid
Venue: arXiv cs.CE 2014
Year: 2014

We study the design of pricing schemes for a group of consumers with smart meters (e.g., in a Greenfield area) who are connected through a gateway to a traditional electricity greed with a progressive tariff. Because the progressive tariff cannot take into account the time aspect of electricity demands, we apply it to consumers in both an individual and a group basis over a shorter time period, which can flatten the overall demand over time and thereby reduce peak load. This scenario for the coexistence of traditional and smart girds and the pricing schemes under this scenario can enable smooth migration to a future smart grid.
Through Global Monitoring to School of the Future: Smartphone as a Laboratory in Pocket of Each Student
Venue: arXiv cs.CY 2014
Year: 2014

The idea to unite smartphones used as personal environmental sensors and health indicators into a scalable network for data collection and processing by the internet-cloud is proposed. Access to the sensors, which are available in every smartphone, will provide the appropriate software. Such a monitoring at the global level would reveal the impact of the electromagnetic radiation, environmental pollution and weather factors on human health. Participation of students in these measurements increases their educational and social activities.
Case Studies: Business and Technical Perspectives in Migration of Legacy Systems to Service Oriented Architecture
Venue: arXiv cs.CY 2014
Year: 2014

In adoption process of Service Oriented Architecture (SOA), the legacy systems of a company can not be neglected. The reason is the legacy systems have been deployed in the past and have been running critical business processes within an enterprise in its current IT architecture. However not all migration process of legacy systems to SOA has been successfull. Highlighting the right factors to reach legacy systems migration success in a specific company is the key value. The main adopted research method in this study has been interviewed for different companies with different enterprises including bank, furniture, engineering and airline companies in Europe. Through separate interviews, critical success factors of migrating legacy systems into SOA have been collected and identified in each case company. Finally collected results are analyzed and presented as the recognized factors affecting successful migration of legacy assets into SOA from business and technical perspectives.
Knowledge Propagation in Contextualized Knowledge Repositories: an Experimental Evaluation
Venue: arXiv cs.AI 2014
Year: 2014

As the interest in the representation of context dependent knowledge in the Semantic Web has been recognized, a number of logic based solutions have been proposed in this regard. In our recent works, in response to this need, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework. CKR is not only a theoretical framework, but it has been effectively implemented over state-of-the-art tools for the management of Semantic Web data: inference inside and across contexts has been realized in the form of forward SPARQL-based rules over different RDF named graphs. In this paper we present the first evaluation results for such CKR implementation. In particular, in first experiment we study its scalability with respect to different reasoning regimes. In a second experiment we analyze the effects of knowledge propagation on the computation of inferences.
Different Types of Conflicting Knowledge in AmI Environments
Venue: arXiv cs.AI 2014
Year: 2014

We characterize different types of conflicts that may occur in complex distributed multi-agent scenarios, such as in Ambient Intelligence (AmI) environments, and we argue that these conflicts should be resolved in a suitable order and with the appropriate strategies for each individual conflict type. We call for further research with the goal of turning conflict resolution in AmI environments and similar multi-agent domains into a more coordinated and agreed upon process.
Context-Aware Analytics in MOM Applications
Venue: arXiv cs.AI 2014
Year: 2014

Manufacturing Operations Management (MOM) systems are complex in the sense that they integrate data from heterogeneous systems inside the automation pyramid. The need for context-aware analytics arises from the dynamics of these systems that influence data generation and hamper comparability of analytics, especially predictive models (e.g. predictive maintenance), where concept drift affects application of these models in the future. Recently, an increasing amount of research has been directed towards data integration using semantic context models. Manual construction of such context models is an elaborate and error-prone task. Therefore, we pose the challenge to apply combinations of knowledge extraction techniques in the domain of analytics in MOM, which comprises the scope of data integration within Product Life-cycle Management (PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES). We describe motivations, technological challenges and show benefits of context-aware analytics, which leverage from and regard the interconnectedness of semantic context data. Our example scenario shows the need for distribution and effective change tracking of context information.
The Computational Theory of Intelligence: Information Entropy
Venue: arXiv cs.AI 2014
Year: 2014

This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computational intelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications.
Multidimensional epidemic thresholds in diffusion processes over interdependent networks
Venue: arXiv cs.SI 2014
Year: 2014

Several systems can be modeled as sets of interdependent networks where each network contains distinct nodes. Diffusion processes like the spreading of a disease or the propagation of information constitute fundamental phenomena occurring over such coupled networks. In this paper we propose a new concept of multidimensional epidemic threshold characterizing diffusion processes over interdependent networks, allowing different diffusion rates on the different networks and arbitrary degree distributions. We analytically derive and numerically illustrate the conditions for multilayer epidemics, i.e., the appearance of a giant connected component spanning all the networks. Furthermore, we study the evolution of infection density and diffusion dynamics with extensive simulation experiments on synthetic and real networks.
Squash: A Scalable Quantum Mapper Considering Ancilla Sharing
Venue: arXiv cs.ET 2014
Year: 2014

Quantum algorithms for solving problems of interesting size often result in circuits with a very large number of qubits and quantum gates. Fortunately, these algorithms also tend to contain a small number of repetitively-used quantum kernels. Identifying the quantum logic blocks that implement such quantum kernels is critical to the complexity management for realizing the corresponding quantum circuit. Moreover, quantum computation requires some type of quantum error correction coding to combat decoherence, which in turn results in a large number of ancilla qubits in the circuit. Sharing the ancilla qubits among quantum operations (even though this sharing can increase the overall circuit latency) is important in order to curb the resource demand of the quantum algorithm. This paper presents a multi-core reconfigurable quantum processor architecture, called Requp, which supports a layered approach to mapping a quantum algorithm and ancilla sharing. More precisely, a scalable quantum mapper, called Squash, is introduced, which divides a given quantum circuit into a number of quantum kernels- each kernel comprises k parts such that each part will run on exactly one of k available cores. Experimental results demonstrate that Squash can handle large-scale quantum algorithms while providing an effective mechanism for sharing ancilla qubits.
Asymptotic stability of stochastic LTV systems with applications to distributed dynamic fusion
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, we investigate asymptotic stability of linear time-varying systems with (sub-) stochastic system matrices. Motivated by distributed dynamic fusion over networks of mobile agents, we impose some mild regularity conditions on the elements of time-varying system matrices. We provide sufficient conditions under which the asymptotic stability of the LTV system can be guaranteed. By introducing the notion of slices, as non-overlapping partitions of the sequence of systems matrices, we obtain stability conditions in terms of the slice lengths and some network parameters. In addition, we apply the LTV stability results to the distributed leader-follower algorithm, and show the corresponding convergence and steady-state. An illustrative example is also included to validate the effectiveness of our approach.
A Decision Matrix and Monitoring based Framework for Infrastructure Performance Enhancement in A Cloud based Environment
Venue: arXiv cs.DC 2014
Year: 2014

Cloud environment is very different from traditional computing environment and therefore tracking the performance of cloud leverages additional requirements. The movement of data in cloud is very fast. Hence, it requires that resources and infrastructure available at disposal must be equally competent. Infrastructure level performance in cloud involves the performance of servers, network and storage which act as the heart and soul for driving the entire cloud business. Thus a constant improvement and enhancement of infrastructure level performance is an important task that needs to be taken into account. This paper proposes a framework for infrastructure performance enhancement in a cloud based environment. The framework is broadly divided into four steps: a) Infrastructure level monitoring of usage pattern and behaviour of the cloud end users, b) Reporting of the monitoring activities to the cloud service provider c) Cloud service provider assigns priority according to our decision matrix based max-min algorithm (DMMM) d) Providing services to cloud users leading to infrastructure performance enhancement. Our framework is based on decision matrix and monitoring in cloud using our proposed decision matrix based max-min algorithm, which draws its inspiration from the original min-min algorithm. This algorithm makes use of decision matrix to make decisions regarding distribution of resources among the cloud users.
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method
Venue: arXiv cs.CL 2014
Year: 2014

In the recent decade, with the enormous growth of digital content in internet and databases, sentiment analysis has received more and more attention between information retrieval and natural language processing researchers. Sentiment analysis aims to use automated tools to detect subjective information from reviews. One of the main challenges in sentiment analysis is feature selection. Feature selection is widely used as the first stage of analysis and classification tasks to reduce the dimension of problem, and improve speed by the elimination of irrelevant and redundant features. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on lemmatization and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a manually gathered collection of cellphone reviews, where the results show the effectiveness of the proposed approaches.
Extraction of Web Usage Profiles using Simulated Annealing Based Biclustering Approach
Venue: arXiv cs.IR 2014
Year: 2014

In this paper, the Simulated Annealing (SA) based biclustering approach is proposed in which SA is used as an optimization tool for biclustering of web usage data to identify the optimal user profile from the given web usage data. Extracted biclusters are consists of correlated users whose usage behaviors are similar across the subset of web pages of a web site where as these users are uncorrelated for remaining pages of a web site. These results are very useful in web personalization so that it communicates better with its users and for making customized prediction. Also useful for providing customized web service too. Experiment was conducted on the real web usage dataset called CTI dataset. Results show that proposed SA based biclustering approach can extract highly correlated user groups from the preprocessed web usage data.
Performance Comparison of Minimum Hop vs. Minimum Edge Based Multicast Routing under Different Mobility Models for Mobile Ad hoc Networks
Venue: arXiv cs.NI 2014
Year: 2014

The high-level contribution of this paper is to establish benchmarks for the minimum hop count per source-receiver path and the minimum number of edges per tree for multicast routing in mobile ad hoc networks (MANETs) under different mobility models. In this pursuit, we explore the tradeoffs between these two routing strategies with respect to hop count, number of edges and lifetime per multicast tree with respect to the Random Waypoint, City Section and Manhattan mobility models. We employ the Breadth First Search algorithm and the Minimum Steiner Tree heuristic for determining a sequence of minimum hop and minimum edge trees respectively. While both the minimum hop and minimum edge trees exist for a relatively longer time under the Manhattan mobility model; the number of edges per tree and the hop count per source-receiver path are relatively low under the Random Waypoint model. For all the three mobility models, the minimum edge trees have a longer lifetime compared to the minimum hop trees and the difference in lifetime increases with increase in network density and/or the multicast group size. Multicast trees determined under the City Section model incur fewer edges and lower hop count compared to the Manhattan mobility model.
A Sketching Algorithm for Spectral Graph Sparsification
Venue: arXiv cs.DS 2014
Year: 2014

We study the problem of compressing a weighted graph $G$ on $n$ vertices, building a "sketch" $H$ of $G$, so that given any vector $x \in \mathbb{R}^n$, the value $x^T L_G x$ can be approximated up to a multiplicative $1+ε$ factor from only $H$ and $x$, where $L_G$ denotes the Laplacian of $G$. One solution to this problem is to build a spectral sparsifier $H$ of $G$, which, using the result of Batson, Spielman, and Srivastava, consists of $O(n ε^{-2})$ reweighted edges of $G$ and has the property that simultaneously for all $x \in \mathbb{R}^n$, $x^T L_H x = (1 \pm ε) x^T L_G x$. The $O(n ε^{-2})$ bound is optimal for spectral sparsifiers. We show that if one is interested in only preserving the value of $x^T L_G x$ for a {\it fixed} $x \in \mathbb{R}^n$ (specified at query time) with high probability, then there is a sketch $H$ using only $\tilde{O}(n ε^{-1.6})$ bits of space. This is the first data structure achieving a sub-quadratic dependence on $ε$. Our work builds upon recent work of Andoni, Krauthgamer, and Woodruff who showed that $\tilde{O}(n ε^{-1})$ bits of space is possible for preserving a fixed {\it cut query} (i.e., $x\in \{0,1\}^n$) with high probability; here we show that even for a general query vector $x \in \mathbb{R}^n$, a sub-quadratic dependence on $ε$ is possible. Our result for Laplacians is in sharp contrast to sketches for general $n \times n$ positive semidefinite matrices $A$ with $O(\log n)$ bit entries, for which even to preserve the value of $x^T A x$ for a fixed $x \in \mathbb{R}^n$ (specified at query time) up to a $1+ε$ factor with constant probability, we show an $Ω(n ε^{-2})$ lower bound.
Wireless Information and Energy Transfer for Two-Hop Non-Regenerative MIMO-OFDM Relay Networks
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the simultaneous wireless information and energy transfer for the non-regenerative multipleinput multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relaying system. By considering two practical receiver architectures, we present two protocols, time switchingbased relaying (TSR) and power splitting-based relaying (PSR). To explore the system performance limit, we formulate two optimization problems to maximize the end-to-end achievable information rate with the full channel state information (CSI) assumption. Since both problems are non-convex and have no known solution method, we firstly derive some explicit results by theoretical analysis and then design effective algorithms for them. Numerical results show that the performances of both protocols are greatly affected by the relay position. Specifically, PSR and TSR show very different behaviors to the variation of relay position. The achievable information rate of PSR monotonically decreases when the relay moves from the source towards the destination, but for TSR, the performance is relatively worse when the relay is placed in the middle of the source and the destination. This is the first time to observe such a phenomenon. In addition, it is also shown that PSR always outperforms TSR in such a MIMO-OFDM relaying system. Moreover, the effect of the number of antennas and the number of subcarriers are also discussed.
On the Smallest Eigenvalue of General correlated Gaussian Matrices
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the behaviour of the spectrum of generally correlated Gaussian random matrices whose columns are zero-mean independent vectors but have different correlations, under the specific regime where the number of their columns and that of their rows grow at infinity with the same pace. This work is, in particular, motivated by applications from statistical signal processing and wireless communications, where this kind of matrices naturally arise. Following the approach proposed in [1], we prove that under some specific conditions, the smallest singular value of generally correlated Gaussian matrices is almost surely away from zero.
Spectral classification using convolutional neural networks
Venue: arXiv cs.CV 2014
Year: 2014

There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from one-dimension spectra only. Author developed several scripts and C programs for datasets preparation, preprocessing and postprocessing of the data. EBLearn library (developed by Pierre Sermanet and Yann LeCun) was used to create ConvNets. Application on dataset of more than 60000 spectra yielded success rate of nearly 95%. This thesis conclusively proved great potential of convolutional neural networks and deep learning methods in astrophysics.
A Structured Hardware Software Architecture for Peptide Based Diagnosis - Sub-string Matching Problem with Limited Tolerance (ICIAfS14)
Venue: arXiv cs.CE 2014
Year: 2014

The problem of inferring proteins from complex peptide samples in shotgun proteomic workflow sets extreme demands on computational resources. This is exacerbated by the fact that, in general, a given protein cannot be defined by a fixed sequence of amino acids due to the existence of splice variants and isoforms of that protein. Therefore, the problem of protein inference could be considered as one of identifying sequences of amino acids with some limited tolerance. Two problems arise from this: a) due to these variations, the applicability of exact string matching methodologies could be questioned and b) the difficulty of defining a reference sequence for a particular set of proteins that are functionally indistinguishable, but with some variation in features. This paper presents a model-based inference approach that is developed and validated to solve the inference problem. Our approach starts from an examination of the known set of splice variants and isoforms of a target protein to identify the Greatest Common Stable Substring (GCSS) of amino acids and the Substrings Subjects to Limited Variation (SSLV) and their respective locations on the GCSS. Then we define and solve the Sub-string Matching Problem with Limited Tolerance (SMPLT). This approach is validated on identified peptides in a labelled and clustered data set from UNIPROT. Identification of Baylisascaris Procyonis infection was used as an application instance that achieved up to 70 times speedup compared to a software only system. This workflow can be generalised to any inexact multiple pattern matching application by replacing the patterns in a clustered and distributed environment which permits a distance between member strings to account for permitted deviations such as substitutions, insertions and deletions.