OpenRaiser/Intern-Atlas huggingface dataset:
Concepts and evolution of research in the field of wireless sensor networks
Venue: arXiv cs.NI 2015
Year: 2015

The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous evolution in the scientific and industrial community. The use of this particular type of ad hoc network is becoming increasingly important in many contexts, regardless of geographical position and so, according to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these sensors organized into a network continue to reveal a set of research questions according to particularities target applications. Despite difficulties introduced by sensor resources constraints, research contributions in this field are growing day by day. In this paper, we present a comprehensive review of most recent literature of WSNs and outline open research issues in this field.
Project Risk Management Model Based on PRINCE2 and Scrum Frameworks
Venue: arXiv cs.SE 2015
Year: 2015

There is a lack of formal risk management techniques in agile software development methods Scrum. The need to manage risks in agile project management is also identified by various authors. Authors of this paper conducted a survey to find out the current practices in agile project management. Furthermore authors discuss the new integrated framework of Scrum and PRINCE2 with focus on risk management. Enrichment of Scrum with selected practices from the heavy-weight project management framework PRINCE2 promises better results in delivering software products especially in global development projects.
A Wavelength Broker for Markets of Competing Optical Transport Networks
Venue: arXiv cs.NI 2015
Year: 2015

The current trend in optical networks is to open the entire wholesale market to competition. As a result, we will see, instead of a single big market player, optical transport networks competing with each other to attract customer demand. This paper presents a wavelength broker who acts on behalf of enterprises, web host companies, financial firm etc. to buy certain number of wavelengths from such market. We present the system model, the interaction protocol and provide analysis of the competition. The simulation results of a business scenario are also recorded in the paper
Fundamental Limits of CDF-Based Scheduling: Throughput, Fairness, and Feedback Overhead
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we investigate fundamental performance limits of cumulative distribution function (CDF)-based scheduling (CS) in downlink cellular networks. CS is known as an efficient scheduling method that can assign different time fractions for users or, equivalently, satisfy different channel access ratio (CAR) requirements of users while exploiting multi-user diversity. We first mathematically analyze the throughput characteristics of CS in arbitrary fading statistics and data rate functions. It is shown that the throughput gain of CS increases as the CAR of a user decreases or the number of users in a cell increases. For Nakagami-m fading channels, we obtain the average throughput in closed-form and investigate the effects of the average signal-to-noise ratio, the shape parameter m, and the CAR on the throughput performance. In addition, we propose a threshold-based opportunistic feedback technique in order to reduce feedback overhead while satisfying the CAR requirements of users. We prove that the average feedback overhead of the proposed technique is upper bounded by -ln(p), where p is the probability that no user satisfies the threshold condition in a cell. Finally, we adopt a novel fairness criterion, called qualitative fairness, which considers not only the quantity of the allocated resources to users but also the quality of the resources. It is observed that CS provides a better qualitative fairness than other scheduling algorithms designed for controlling CARs of users.
A Novel Approach for Clone Group Mapping by using Topic Modeling
Venue: arXiv cs.SE 2015
Year: 2015

Clone group mapping has a very important significance in the evolution of code clone. The topic modeling techniques were applied into code clone firstly and a new clone group mapping method was proposed. The method is very effective for not only Type-1 and Type-2 clone but also Type-3 clone .By making full use of the source text and structure information, topic modeling techniques transform the mapping problem of high-dimensional code space into a low-dimensional topic space, the goal of clone group mapping was indirectly reached by mapping clone group topics. Experiments on four open source software show that the recall and precision are up to 0.99, thus the method can effectively and accurately reach the goal of clone group mapping.
Diagnosis of diabetes using classification mining techniques
Venue: arXiv cs.CE 2015
Year: 2015

Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Naïve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.
Coherent-to-Diffuse Power Ratio Estimation for Dereverberation
Venue: arXiv cs.SD 2015
Year: 2015

The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.
Sum Rate Maximization for MU-MISO with Partial CSIT using Joint Multicasting and Broadcasting
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider a MU-MISO system where users have highly accurate Channel State Information (CSI), while the Base Station (BS) has partial CSI consisting of an imperfect channel estimate and statistical knowledge of the CSI error. With the objective of maximizing the Average Sum Rate (ASR) subject to a power constraint, a special transmission scheme is considered where the BS transmits a common symbol in a multicast fashion, in addition to the conventional private symbols. This scheme is termed Joint Multicasting and Broadcasting (JMB). The ASR problem is transformed into an augmented Average Weighted Sum Mean Square Error (AWSMSE) problem which is solved using Alternating Optimization (AO). The enhanced rate performance accompanied with the incorporation of the multicast part is demonstrated through simulations.
eOMP: Finding Sparser Representation by Recursively Orthonormalizing the Remaining Atoms
Venue: arXiv cs.NA 2015
Year: 2015

Greedy algorithms for minimizing L0-norm of sparse decomposition have profound application impact on many signal processing problems. In the sparse coding setup, given the observations $\mathrm{y}$ and the redundant dictionary $\mathbfΦ$, one would seek the most sparse coefficient (signal) $\mathrm{x}$ with a constraint on approximation fidelity. In this work, we propose a greedy algorithm based on the classic orthogonal matching pursuit (OMP) with improved sparsity on $\mathrm{x}$ and better recovery rate, which we name as eOMP. The key ingredient of the eOMP is recursively performing one-step orthonormalization on the remaining atoms, and evaluating correlations between residual and orthonormalized atoms. We show a proof that the proposed eOMP guarantees to maximize the residual reduction at each iteration. Through extensive simulations, we show the proposed algorithm has better exact recovery rate on i.i.d. Gaussian ensembles with Gaussian signals, and more importantly yields smaller L0-norm under the same approximation fidelity compared to the original OMP, for both synthetic and practical scenarios. The complexity analysis and real running time result also show a manageable complexity increase over the original OMP. We claim that the proposed algorithm has better practical perspective for finding more sparse representations than existing greedy algorithms.
Semi-supervised Data Representation via Affinity Graph Learning
Venue: arXiv cs.LG 2015
Year: 2015

We consider the general problem of utilizing both labeled and unlabeled data to improve data representation performance. A new semi-supervised learning framework is proposed by combing manifold regularization and data representation methods such as Non negative matrix factorization and sparse coding. We adopt unsupervised data representation methods as the learning machines because they do not depend on the labeled data, which can improve machine's generation ability as much as possible. The proposed framework forms the Laplacian regularizer through learning the affinity graph. We incorporate the new Laplacian regularizer into the unsupervised data representation to smooth the low dimensional representation of data and make use of label information. Experimental results on several real benchmark datasets indicate that our semi-supervised learning framework achieves encouraging results compared with state-of-art methods.
Skeleton Matching based approach for Text Localization in Scene Images
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a skeleton matching based approach which aids in text localization in scene images. The input image is preprocessed and segmented into blocks using connected component analysis. We obtain the skeleton of the segmented block using morphology based approach. The skeletonized images are compared with the trained templates in the database to categorize into text and non-text blocks. Further, the newly designed geometrical rules and morphological operations are employed on the detected text blocks for scene text localization. The experimental results obtained on publicly available standard datasets illustrate that the proposed method can detect and localize the texts of various sizes, fonts and colors.
Extractions: Computable and Visible Analogues of Localizations for Polynomial Ideals
Venue: arXiv cs.SC 2015
Year: 2015

When studying local properties of a polynomial ideal, one usually needs a theoretic technique called localization. For most cases, in spite of its importance, the computation in a localized ring cannot be algorithmically preformed. On the other hand, the standard basis method is very effective for the computation in a special kind of localized rings, but for a general semigroup order the geometry of the localization of a positive-dimensional ideal is difficult to interpret. In this paper, we introduce a new ideal operation called extraction. For an ideal $I$ in a polynomial ring $K[x_1,\ldots,x_n]$ over a field $K$, we use another ideal $J$ to control the primary components of $I$ and the result $β(I,J)$ is called the extraction of $I$ by $J$. It is still a polynomial ideal and has a concrete geometric meaning in $\bar{K}^n$, i.e., we keep the branches of $\textbf{V}(I) \subset \bar{K}^n$ that intersect with $\textbf{V}(J) \subset \bar{K}^n$ and delete others, where $\bar{K}$ is the algebraic closure of $K$. This is what we mean by visible. On the other hand, we can use the standard basis method to compute a localized ideal corresponding to $β(I,J)$ without a complete primary decomposition, and can do further computation in the localized ring such as determining the membership problem of $β(I,J)$. Moreover, we prove that extractions are as powerful as localizations in the sense that for any multiplicatively closed subset $S$ of $K[x_1,\ldots,x_n]$ and any polynomial ideal $I$, there always exists a polynomial ideal $J$ such that $β(I,J)=(S^{-1}I)^c$.
Near-optimal adjacency labeling scheme for power-law graphs
Venue: arXiv cs.DC 2015
Year: 2015

An adjacency labeling scheme is a method that assigns labels to the vertices of a graph such that adjacency between vertices can be inferred directly from the assigned label, without using a centralized data structure. We devise adjacency labeling schemes for the family of power-law graphs. This family that has been used to model many types of networks, e.g. the Internet AS-level graph. Furthermore, we prove an almost matching lower bound for this family. We also provide an asymptotically near- optimal labeling scheme for sparse graphs. Finally, we validate the efficiency of our labeling scheme by an experimental evaluation using both synthetic data and real-world networks of up to hundreds of thousands of vertices.
Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks
Venue: arXiv cs.IT 2015
Year: 2015

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem.
Long-short Term Motion Feature for Action Classification and Retrieval
Venue: arXiv cs.CV 2015
Year: 2015

We propose a method for representing motion information for video classification and retrieval. We improve upon local descriptor based methods that have been among the most popular and successful models for representing videos. The desired local descriptors need to satisfy two requirements: 1) to be representative, 2) to be discriminative. Therefore, they need to occur frequently enough in the videos and to be be able to tell the difference among different types of motions. To generate such local descriptors, the video blocks they are based on must contain just the right amount of motion information. However, current state-of-the-art local descriptor methods use video blocks with a single fixed size, which is insufficient for covering actions with varying speeds. In this paper, we introduce a long-short term motion feature that generates descriptors from video blocks with multiple lengths, thus covering motions with large speed variance. Experimental results show that, albeit simple, our model achieves state-of-the-arts results on several benchmark datasets.
Using and Designing Platforms for In Vivo Education Experiments
Venue: arXiv cs.HC 2015
Year: 2015

In contrast to typical laboratory experiments, the everyday use of online educational resources by large populations and the prevalence of software infrastructure for A/B testing leads us to consider how platforms can embed in vivo experiments that do not merely support research, but ensure practical improvements to their educational components. Examples are presented of randomized experimental comparisons conducted by subsets of the authors in three widely used online educational platforms Khan Academy, edX, and ASSISTments. We suggest design principles for platform technology to support randomized experiments that lead to practical improvements enabling Iterative Improvement and Collaborative Work and explain the benefit of their implementation by WPI co-authors in the ASSISTments platform.
Cardiac MR Image Segmentation Techniques: an overview
Venue: arXiv cs.CV 2015
Year: 2015

Broadly speaking, the objective in cardiac image segmentation is to delineate the outer and inner walls of the heart to segment out either the entire or parts of the organ boundaries. This paper will focus on MR images as they are the most widely used in cardiac segmentation -- as a result of the accurate morphological information and better soft tissue contrast they provide. This cardiac segmentation information is very useful as it eases physical measurements that provides useful metrics for cardiac diagnosis such as infracted volumes, ventricular volumes, ejection fraction, myocardial mass, cardiac movement, and the like. But, this task is difficult due to the intensity and texture similarities amongst the different cardiac and background structures on top of some noisy artifacts present in MR images. Thus far, various researchers have proposed different techniques to solve some of the pressing issues. This seminar paper presents an overview of representative medical image segmentation techniques. The paper also highlights preferred approaches for segmentation of the four cardiac chambers: the left ventricle (LV), right ventricle (RV), left atrium (LA) and right atrium (RA), on short axis image planes.
Spatial Stimuli Gradient Sketch Model
Venue: arXiv cs.CV 2015
Year: 2015

The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance.
Application-Aware Consistency: An Application to Social Network
Venue: arXiv cs.DC 2015
Year: 2015

This work weakens well-known consistency models using graphs that capture applications' characteristics. The weakened models not only respect application semantic, but also yield a performance benefit. We introduce a notion of dependency graphs, which specify relations between events that are important with respect to application semantic, and then weaken traditional consistency models (e.g., causal consistency) using these graphs. Particularly, we consider two types of graphs: intra-process and inter-process dependency graphs, where intra-process dependency graphs specify how events in a single process are related, and inter-process dependency graphs specify how events across multiple processes are related. Then, based on these two types of graphs, we define new consistency model, namely {\em application-aware} consistency. We also discuss why such application-aware consistency can be useful in social network applications. This is a work in progress, and we present early ideas regarding application-aware consistency here.
PolyMorph: Increasing P300 Spelling Efficiency by Selection Matrix Polymorphism and Sentence-Based Predictions
Venue: arXiv cs.HC 2015
Year: 2015

P300 is an electric signal emitted by brain about 300 milliseconds after a rare, but relevant-for-the-user event. One of the applications of this signal is sentence spelling that enables subjects who lost the control of their motor pathways to communicate by selecting characters in a matrix containing all the alphabet symbols. Although this technology has made considerable progress in the last years, it still suffers from both low communication rate and high error rate. This article presents a P300 speller, named PolyMorph, that introduces two major novelties in the field: the selection matrix polymorphism, that reduces the size of the selection matrix itself by removing useless symbols, and sentence-based predictions, that exploit all the spelt characters of a sentence to determine the probability of a word. In order to measure the effectiveness of the presented speller, we describe two sets of tests: the first one in vivo and the second one in silico. The results of these experiments suggest that the use of PolyMorph in place of the naive character-by-character speller both increases the number of spelt characters per time unit and reduces the error rate.
Towards Building Deep Networks with Bayesian Factor Graphs
Venue: arXiv cs.CV 2015
Year: 2015

We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice. The Latent Variable Model (LVM) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image, a feature map, or more generally a collection of spatially distributed discrete variables. The multi-layer architecture implements a hierarchical data representation that, via belief propagation, can be used for learning and inference. Typical uses are pattern completion, correction and classification. The FGrn paradigm provides great flexibility and modularity and appears as a promising candidate for building deep networks: the system can be easily extended by introducing new and different (in cardinality and in type) variables. Prior knowledge, or supervised information, can be introduced at different scales. The FGrn paradigm provides a handy way for building all kinds of architectures by interconnecting only three types of units: Single Input Single Output (SISO) blocks, Sources and Replicators. The network is designed like a circuit diagram and the belief messages flow bidirectionally in the whole system. The learning algorithms operate only locally within each block. The framework is demonstrated in this paper in a three-layer structure applied to images extracted from a standard data set.