OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, an automatic method is proposed to perform image registration in visible and infrared pair of video sequences for multiple targets. In multimodal image analysis like image fusion systems, color and IR sensors are placed close to each other and capture a same scene simultaneously, but the videos are not properly aligned by default because of different fields of view, image capturing information, working principle and other camera specifications. Because the scenes are usually not planar, alignment needs to be performed continuously by extracting relevant common information. In this paper, we approximate the shape of the targets by polygons and use affine transformation for aligning the two video sequences. After background subtraction, keypoints on the contour of the foreground blobs are detected using DCE (Discrete Curve Evolution)technique. These keypoints are then described by the local shape at each point of the obtained polygon. The keypoints are matched based on the convexity of polygon's vertices and Euclidean distance between them. Only good matches for each local shape polygon in a frame, are kept. To achieve a global affine transformation that maximises the overlapping of infrared and visible foreground pixels, the matched keypoints of each local shape polygon are stored temporally in a buffer for a few number of frames. The matrix is evaluated at each frame using the temporal buffer and the best matrix is selected, based on an overlapping ratio criterion. Our experimental results demonstrate that this method can provide highly accurate registered images and that we outperform a previous related method.
Telling Breaking News Stories from Wikipedia with Social Multimedia: A Case Study of the 2014 Winter Olympics
Venue: arXiv cs.SI 2014
Year: 2014

With the ability to watch Wikipedia and Wikidata edits in realtime, the online encyclopedia and the knowledge base have become increasingly used targets of research for the detection of breaking news events. In this paper, we present a case study of the 2014 Winter Olympics, where we tell the story of breaking news events in the context of the Olympics with the help of social multimedia stemming from multiple social network sites. Therefore, we have extended the application Wikipedia Live Monitor-a tool for the detection of breaking news events-with the capability of automatically creating media galleries that illustrate events. Athletes winning an Olympic competition, a new country leading the medal table, or simply the Olympics themselves are all events newsworthy enough for people to concurrently edit Wikipedia and Wikidata-around the world in many languages. The Olympics being an event of common interest, an even bigger majority of people share the event in a multitude of languages on global social network sites, which makes the event an ideal subject of study. With this work, we connect the world of Wikipedia and Wikidata with the world of social network sites, in order to convey the spirit of the 2014 Winter Olympics, to tell the story of victory and defeat, and always following the Olympic motto Citius, Altius, Fortius. The proposed system-generalized for all sort of breaking news stories-has been put in production in form of the Twitter bot @mediagalleries, available and archived at https://twitter.com/mediagalleries.
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
Venue: arXiv cs.NA 2014
Year: 2014

The widespread use of multi-sensor technology and the emergence of big datasets has highlighted the limitations of standard flat-view matrix models and the necessity to move towards more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift towards models that are essentially polynomial and whose uniqueness, unlike the matrix methods, is guaranteed under verymild and natural conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints that match data properties, and to find more general latent components in the data than matrix-based methods. A comprehensive introduction to tensor decompositions is provided from a signal processing perspective, starting from the algebraic foundations, via basic Canonical Polyadic and Tucker models, through to advanced cause-effect and multi-view data analysis schemes. We show that tensor decompositions enable natural generalizations of some commonly used signal processing paradigms, such as canonical correlation and subspace techniques, signal separation, linear regression, feature extraction and classification. We also cover computational aspects, and point out how ideas from compressed sensing and scientific computing may be used for addressing the otherwise unmanageable storage and manipulation problems associated with big datasets. The concepts are supported by illustrative real world case studies illuminating the benefits of the tensor framework, as efficient and promising tools for modern signal processing, data analysis and machine learning applications; these benefits also extend to vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker decomposition, HOSVD, tensor networks, Tensor Train.
Research on Study Mechanical Vibrations with Data Acquisition Systems
Venue: arXiv cs.CE 2014
Year: 2014

The paper presents a new study method of mechanic vibrations with the help of the data acquisition systems. The study of vibrations with the help of data acquisition systems allows the solving of some engineering problems connected to the measurement of some parameters which are difficult to measure having in view the improvement of the technical performances of the industrial equipment or devices
Similarity networks for classification: a case study in the Horse Colic problem
Venue: arXiv cs.LG 2014
Year: 2014

This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting neuron model is capable of dealing directly with variables of potentially different nature (continuous, fuzzy, ordinal, categorical). There is also provision for missing values. The network is trained using a two-stage procedure very similar to that used to train a radial basis function (RBF) neural network. The network is compared to two types of RBF networks in a non-trivial dataset: the Horse Colic problem, taken as a case study and analyzed in detail.
A Maximal Concurrency and Low Latency Distributed Scheduling Protocol for Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

Existing work that schedules concurrent transmissions collision-free suffers from low channel utilization. We propose the Optimal Node Activation Multiple Access (ONAMA) protocol to achieve maximal channel spatial reuse through a distributed maximal independent set (DMIS) algorithm. To overcome DMIS's excessive delay in finding a maximal independent set, we devise a novel technique called pipelined precomputation that decouples DMIS from data transmission. We implement ONAMA on resource-constrained TelosB motes using TinyOS. Extensive measurements on two testbeds independently attest to ONAMA's superb performance compared to existing work: improving concurrency, throughput, and delay by a factor of 3.7, 3.0, and 5.3, respectively, while still maintaining reliability.
Communication Communities in MOOCs
Venue: arXiv cs.CY 2014
Year: 2014

Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate. We introduce a new content-analysed MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to extract communities of learners based on the nature of their online forum posts. We see that BNMF yields a superior probabilistic generative model for online discussions when compared to other models, and that the communities it learns are differentiated by their composite students' demographic and course performance indicators. These findings suggest that computationally efficient probabilistic generative modelling of MOOCs can reveal important insights for educational researchers and practitioners and help to develop more intelligent and responsive online learning environments.
MFCC based Enlargement of the Training Set for Emotion Recognition in Speech
Venue: arXiv cs.CV 2014
Year: 2014

Emotional state recognition through speech is being a very interesting research topic nowadays. Using subliminal information of speech, denominated as prosody, it is possible to recognize the emotional state of the person. One of the main problems in the design of automatic emotion recognition systems is the small number of available patterns. This fact makes the learning process more difficult, due to the generalization problems that arise under these conditions. In this work we propose a solution to this problem consisting in enlarging the training set through the creation the new virtual patterns. In the case of emotional speech, most of the emotional information is included in speed and pitch variations. So, a change in the average pitch that does not modify neither the speed nor the pitch variations does not affect the expressed emotion. Thus, we use this prior information in order to create new patterns applying a gender dependent pitch shift modification in the feature extraction process of the classification system. For this purpose, we propose a frequency scaling modification of the Mel Frequency Cepstral Coefficients, used to classify the emotion. For this purpose, we propose a gender dependent frequency scaling modification. This proposed process allows us to synthetically increase the number of available patterns in the training set, thus increasing the generalization capability of the system and reducing the test error. Results carried out with two different classifiers with different degree of generalization capability demonstrate the suitability of the proposal.
Sensory fusion in Physarum polycephalum and implementing multi-sensory functional computation
Venue: arXiv cs.ET 2014
Year: 2014

Surface electrical potential and observational growth recordings were made of a protoplasmic tube of the slime mould Physarum polycephalum in response to a multitude of stimuli with regards to sensory fusion or multisensory integration. Each stimulus was tested alone and in combination in order to evaluate for the first time the effect that multiple stimuli have on the frequency of streaming oscillation. White light caused a decrease in frequency whilst increasing the temperature and applying a food source in the form of oat flakes both increased the frequency. Simultaneously stimulating P. polycephalum with light and oat flake produced no net change in frequency, while combined light and heat stimuli showed an increase in frequency smaller than that observed for heat alone. When the two positive stimuli, oat flakes and heat, were combined, there was a net increase in frequency similar to the cumulative increases caused by the individual stimuli. Boolean logic gates were derived from the measured frequency change.
Distributed System Identification with ADMM
Venue: arXiv cs.SY 2014
Year: 2014

This paper presents identification of both network connected systems as well as distributed systems governed by PDEs in the framework of distributed optimization via the Alternating Direction Method of Multipliers. This approach opens first the possibility to identify distributed models in a global manner using all available data sequences and second the possibility for a distributed implementation. The latter will make the application to large scale complex systems possible. In addition to outlining a new large scale identification method, illustrations are shown for identifying both network connected systems and discretized PDEs.
Zero forcing sets and controllability of dynamical systems defined on graphs
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, controllability of systems defined on graphs is discussed. We consider the problem of controllability of the network for a family of matrices carrying the structure of an underlying directed graph. A one-to-one correspondence between the set of leaders rendering the network controllable and zero forcing sets is established. To illustrate the proposed results, special cases including path, cycle, and complete graphs are discussed. Moreover, as shown for graphs with a tree structure, the proposed results of the present paper together with the existing results on the zero forcing sets lead to a minimal leader selection scheme in particular cases.
Evolutionary Algorithm for Drug Discovery Interim Design Report
Venue: arXiv cs.NE 2014
Year: 2014

A software program which aims to provide an exploration capability over the Search Space of potential drug molecules. The program explores the search space by generating random molecules, determining their fitness and then breeding a new generation from the fittest individuals. The search space, in theory any combination of any elements in any order, is constrained by the use of a subset of elements and a list of fragments, molecular parts that are known to be useful in drug development. The resultant molecules from each generation are stored in a searchable database, so that the user can browse through previous generations looking for interesting molecules.
A Compressive Sensing Based Approach to Sparse Wideband Array Design
Venue: arXiv cs.IT 2014
Year: 2014

Sparse wideband sensor array design for sensor location optimisation is highly nonlinear and it is traditionally solved by genetic algorithms, simulated annealing or other similar optimization methods. However, this is an extremely time-consuming process and more efficient solutions are needed. In this work, this problem is studied from the viewpoint of compressive sensing and a formulation based on a modified $l_1$ norm is derived. As there are multiple coefficients associated with each sensor, the key is to make sure that these coefficients are simultaneously minimized in order to discard the corresponding sensor locations. Design examples are provided to verify the effectiveness of the proposed methods.
Bracing Heterogeneous Distributed Systems via Built-in Frameworks
Venue: arXiv cs.DC 2014
Year: 2014

This paper introduces a novel architecture of distributed systems--called framed distributed system, or FDS--that braces a given system via a built-in virtual framework that controls the flow of messages between system components and between them and their environment, while being oblivious of the code of the communicating components. This control is carried out in a decentralized, and thus scalable, manner. The FDS architecture is expected to have a significant impact on the dependability and security of distributed systems, and on the whole life cycle of such systems. Although this architecture has been designed specifically for SOA-like heterogeneous and open systems--whose components may be written in different languages, may run on different platforms, and may be designed, constructed, and even maintained under different administrative domains--it should be useful for distributed systems in general.
Universal and Distinct Properties of Communication Dynamics: How to Generate Realistic Inter-event Times
Venue: arXiv cs.SI 2014
Year: 2014

With the advancement of information systems, means of communications are becoming cheaper, faster and more available. Today, millions of people carrying smart-phones or tablets are able to communicate at practically any time and anywhere they want. Among others, they can access their e-mails, comment on weblogs, watch and post comments on videos, make phone calls or text messages almost ubiquitously. Given this scenario, in this paper we tackle a fundamental aspect of this new era of communication: how the time intervals between communication events behave for different technologies and means of communications? Are there universal patterns for the inter-event time distribution (IED)? In which ways inter-event times behave differently among particular technologies? To answer these questions, we analyze eight different datasets from real and modern communication data and we found four well defined patterns that are seen in all the eight datasets. Moreover, we propose the use of the Self-Feeding Process (SFP) to generate inter-event times between communications. The SFP is extremely parsimonious point process that requires at most two parameters and is able to generate inter-event times with all the universal properties we observed in the data. We show the potential application of SFP by proposing a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications (e.g. phone calls, e-mails, comments on blogs) and an algorithm to detect anomalies.
Distributed Approximation Algorithms for Weighted Shortest Paths
Venue: arXiv cs.DS 2014
Year: 2014

A distributed network is modeled by a graph having $n$ nodes (processors) and diameter $D$. We study the time complexity of approximating {\em weighted} (undirected) shortest paths on distributed networks with a $O(\log n)$ {\em bandwidth restriction} on edges (the standard synchronous \congest model). The question whether approximation algorithms help speed up the shortest paths (more precisely distance computation) was raised since at least 2004 by Elkin (SIGACT News 2004). The unweighted case of this problem is well-understood while its weighted counterpart is fundamental problem in the area of distributed approximation algorithms and remains widely open. We present new algorithms for computing both single-source shortest paths (\sssp) and all-pairs shortest paths (\apsp) in the weighted case. Our main result is an algorithm for \sssp. Previous results are the classic $O(n)$-time Bellman-Ford algorithm and an $\tilde O(n^{1/2+1/2k}+D)$-time $(8k\lceil \log (k+1) \rceil -1)$-approximation algorithm, for any integer $k\geq 1$, which follows from the result of Lenzen and Patt-Shamir (STOC 2013). (Note that Lenzen and Patt-Shamir in fact solve a harder problem, and we use $\tilde O(\cdot)$ to hide the $O(\poly\log n)$ term.) We present an $\tilde O(n^{1/2}D^{1/4}+D)$-time $(1+o(1))$-approximation algorithm for \sssp. This algorithm is {\em sublinear-time} as long as $D$ is sublinear, thus yielding a sublinear-time algorithm with almost optimal solution. When $D$ is small, our running time matches the lower bound of $\tilde Ω(n^{1/2}+D)$ by Das Sarma et al. (SICOMP 2012), which holds even when $D=Θ(\log n)$, up to a $\poly\log n$ factor.
NUROA: A Numerical Roadmap Algorithm
Venue: arXiv cs.RO 2014
Year: 2014

Motion planning has been studied for nearly four decades now. Complete, combinatorial motion planning approaches are theoretically well-rooted with completeness guarantees but they are hard to implement. Sampling-based and heuristic methods are easy to implement and quite simple to customize but they lack completeness guarantees. Can the best of both worlds be ever achieved, particularly for mission critical applications such as robotic surgery, space explorations, and handling hazardous material? In this paper, we answer affirmatively to that question. We present a new methodology, NUROA, to numerically approximate the Canny's roadmap, which is a network of one-dimensional algebraic curves. Our algorithm encloses the roadmap with a chain of tiny boxes each of which contains a piece of the roadmap and whose connectivity captures the roadmap connectivity. It starts by enclosing the entire space with a box. In each iteration, remaining boxes are shrunk on all sides and then split into smaller sized boxes. Those boxes that are empty are detected in the shrink phase and removed. The algorithm terminates when all remaining boxes are smaller than a resolution that can be either given as input or automatically computed using root separation lower bounds. Shrink operation is cast as a polynomial optimization with semialgebraic constraints, which is in turn transformed into a (series of) semidefinite programs (SDP) using the Lasserre's approach. NUROA's success is due to fast SDP solvers. NUROA correctly captured the connectivity of multiple curves/skeletons whereas competitors such as IBEX and Realpaver failed in some cases. Since boxes are independent from one another, NUROA can be parallelized particularly on GPUs. NUROA is available as an open source package at http://nuroa.sourceforge.net/.
Design Architecture-Based on Web Server and Application Cluster in Cloud Environment
Venue: arXiv cs.DC 2014
Year: 2014

Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The major idea is to design a web server in the map phase using the jetty web server which will give a fast and efficient way of searching data in MapReduce paradigm. For real time processing on Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in web server with multi-level index keys. The web server uses to handle traffic throughput. By web clustering technology we can improve the application performance. To keep the work down, the load balancer should automatically be able to distribute load to the newly added nodes in the server.
Exploring Indonesian Web Based Career Center Discrepancy of Web Popularity and Type of Services
Venue: arXiv cs.CY 2014
Year: 2014

Utilization of the Internet in higher education focus on the learning process or the provision of academic information for students. The subject of this research is in the form of web-based management alumnus Career Center with specific sub domain. Colleges that already have a Career Center only 34 of the 264 colleges as sample. Type the service the most are information jobs, while others are still rarely available as a forum of alumni and career consultation. Ownership Career Center contributed to the popularity of college website. Providing services such as communication and consultation career impact on the popularity of the Career Center website.
A New Complete Class Complexity Metric
Venue: arXiv cs.SE 2014
Year: 2014

Software complexity metrics is essential for minimizing the cost of software maintenance. Package level and System level complexity cannot be measured without class level complexity. This research addresses the class complexity metrics. This paper studies the existing class complexity metrics and proposes a new class complexity metric CCC (Complete class complexity metric). The CCC metric is then analytically evaluated by Weyuker's property.