OpenRaiser/Intern-Atlas huggingface dataset:
Emerging Methods and Tools for Sparking New Global Creative Networks
Venue: arXiv cs.CY 2015
Year: 2015

Emerging methods and tools are changing the ways participants in global creative networks become aware of each other and proceed to interact. Some web-based resources intended to spark new collaborations in creative networks have been plagued by dependence on fragmented or out-of-date information, having shallow recall (e.g. limited to a list of manually curated keywords), offering poor interconnectivity with other systems, and/or obtaining low end-user adoption. Increased availability of information about creative network participants' activities and outputs (e.g. completed sponsored research projects and published results, aggregated into global databases), coupled with advancement in information processing techniques like Natural Language Processing, enables new web-based technologies for discovering subject matter experts, facilities, and networks of current and potential collaborators. Large-scale data resources and NLP allow modern versions of these tools to avoid the problems of having sparse data and also provide for deep recall across many disciplinary vocabularies. These are "passive" technologies, from the perspective of the network participant, because the agent must undertake an action to use the information resources. Emerging "active" methods and tools utilize the same types of information and technologies, but actively intervene in the formation of the creative network by suggesting connections and arranging virtual or physical interactions. Active approaches can achieve very high end-user adoption rates. Both active and passive methods strive to use data-driven approaches to form better-than-chance awareness among networks of potential collaborators. Recent case studies suggest the existence of repeatable strategies for facilitating data-driven matching and better-than-chance interactions designed to spark new global creative networks.
A Game Theoretic Perspective on Self-organizing Optimization for Cognitive Small Cells
Venue: arXiv cs.IT 2015
Year: 2015

In this article, we investigate self-organizing optimization for cognitive small cells (CSCs), which have the ability to sense the environment, learn from historical information, make intelligent decisions, and adjust their operational parameters. By exploring the inherent features, some fundamental challenges for self-organizing optimization in CSCs are presented and discussed. Specifically, the dense and random deployment of CSCs brings about some new challenges in terms of scalability and adaptation; furthermore, the uncertain, dynamic and incomplete information constraints also impose some new challenges in terms of convergence and robustness. For providing better service to the users and improving the resource utilization, four requirements for self-organizing optimization in CSCs are presented and discussed. Following the attractive fact that the decisions in game-theoretic models are exactly coincident with those in self-organizing optimization, i.e., distributed and autonomous, we establish a framework of game-theoretic solutions for self-organizing optimization in CSCs, and propose some featured game models. Specifically, their basic models are presented, some examples are discussed and future research directions are given.
Knowledge-aided Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery
Venue: arXiv cs.IT 2015
Year: 2015

Conventional two-dimensional (2-D) autofocus algorithms blindly estimate the phase error in the sense that they do not exploit any a priori information on the structure of the 2-D phase error. As such, they often suffer from low computational efficiency and lack of data redundancy to accurately estimate the 2-D phase error. In this paper, a knowledge-aided (KA) 2-D autofocus algorithm which is based on exploiting a priori knowledge about the 2-D phase error structure, is presented. First, as a prerequisite of the proposed KA method, the analytical structure of residual 2-D phase error in SAR imagery is investigated in the polar format algorithm (PFA) framework. Then, by incorporating this a priori information, a novel 2-D autofocus approach is proposed. The new method only requires an estimate of azimuth phase error and/or residual range cell migration, while the 2-D phase error can then be computed directly from the estimated azimuth phase error or residual range cell migration. This 2-D autofocus method can also be applied to refocus moving targets in PFA imagery. Experimental results clearly demonstrate the effectiveness and robustness of the proposed method.
Improvement of Control System Performance by Modification of Time Delay
Venue: arXiv cs.SY 2015
Year: 2015

This paper presents a mathematical approach for improving the performance of a control system by modifying the time delay at certain operating conditions. This approach converts a continuous time loop into a discrete time loop. The formula derived is applied successfully to an applicable control system. The results show that the proposed approach efficiently improves the control system performance. The relation between the sampling time and the time delay is obtained. Two different operating conditions are examined to assess the proposed approach in improving the performance of the control system.
Partial Derivative Automaton for Regular Expressions with Shuffle
Venue: arXiv cs.FL 2015
Year: 2015

We generalize the partial derivative automaton to regular expressions with shuffle and study its size in the worst and in the average case. The number of states of the partial derivative automata is in the worst case at most 2^m, where m is the number of letters in the expression, while asymptotically and on average it is no more than (4/3)^m.
Success factors for Crowdfunding founders and funders
Venue: arXiv cs.CY 2015
Year: 2015

Crowdfunding has been used as one of the effective ways for entrepreneurs to raise funding especially in creative industries. Individuals as well as organizations are paying more attentions to the emergence of new crowdfunding platforms. In the Netherlands, the government is also trying to help artists access financial resources through crowdfunding platforms. This research aims at discovering the success factors for crowdfunding projects through crowdfunding platforms from both founders and funders perspective. We designed our own website for founders and funders to observe crowdfunding behaviors. Our research will contribute to crowdfunding success factors related to issues of trust and decision making and provide practical recommendations for practitioners and researchers.
A Novel Image Steganographic Approach for Hiding Text in Color Images using HSI Color Model
Venue: arXiv cs.MM 2015
Year: 2015

Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods
Learning Mixtures of Gaussians in High Dimensions
Venue: arXiv cs.LG 2015
Year: 2015

Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices of these Gaussians. This learning problem arises in many areas ranging from the natural sciences to the social sciences, and has also found many machine learning applications. Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case. In this work, we show that provided we are in high enough dimensions, the class of Gaussian mixtures is learnable in its most general form under a smoothed analysis framework, where the parameters are randomly perturbed from an adversarial starting point. In particular, given samples from a mixture of Gaussians with randomly perturbed parameters, when n > Ω(k^2), we give an algorithm that learns the parameters with polynomial running time and using polynomial number of samples. The central algorithmic ideas consist of new ways to decompose the moment tensor of the Gaussian mixture by exploiting its structural properties. The symmetries of this tensor are derived from the combinatorial structure of higher order moments of Gaussian distributions (sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop new tools for bounding smallest singular values of structured random matrices, which could be useful in other smoothed analysis settings.
Complexity aspects of the triangle path convexity
Venue: arXiv cs.DM 2015
Year: 2015

A path $P = v_1, ..., v_t$ is a {\em triangle path} (respectively, {\em monophonic path}) of $G$ if no edges exist joining vertices $v_i$ and $v_j$ of $P$ such that $|j - i| > 2$; (respectively, $|j - i| > 1$). A set of vertices $S$ is {\em convex} in the triangle path convexity (respectively, monophonic convexity) of $G$ if the vertices of every triangle path (respectively, monophonic path) joining two vertices of $S$ are in $S$. The cardinality of a maximum proper convex set of $G$ is the {\em convexity number of $G$} and the cardinality of a minimum set of vertices whose convex hull is $V(G)$ is the {\em hull number of $G$}. Our main results are polynomial time algorithms for determining the convexity number and the hull number of a graph in the triangle path convexity.
Behavioral Aspects of Social Network Analysis
Venue: arXiv cs.SI 2015
Year: 2015

Contrary to the structural aspect of conventional social network analysis, a new method in behavioral analysis is proposed. We define behavioral measures including self-loops and multiple links and illustrate the behavioral analysis with the networks of Wikipedia editing. Behavioral social network analysis provides an explanation of human behavior that may be further extended to the explanation of culture through social phenomena.
A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing
Venue: arXiv cs.DL 2015
Year: 2015

Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with "old" data. It is therefore not surprising that data sharing is advocated by funding agencies, journals, and researchers alike. We surveyed 2661 individual academic researchers across all disciplines on their dealings with data, their publication practices, and motives for sharing or withholding research data. The results for 1564 valid responses show that researchers across disciplines recognise the benefit of secondary research data for their own work and for scientific progress as a whole-still they only practice it in moderation. An explanation for this evidence could be an academic system that is not driven by monetary incentives, nor the desire for scientific progress, but by individual reputation-expressed in (high ranked journal) publications. We label this system a Reputation Economy. This special economy explains our findings that show that researchers have a nuanced idea how to provide adequate formal recognition for making data available to others-namely data citations. We conclude that data sharing will only be widely adopted among research professionals if sharing pays in form of reputation. Thus, policy measures that intend to foster research collaboration need to understand academia as a reputation economy. Successful measures must value intermediate products, such as research data, more highly than it is the case now.
A neuromorphic hardware framework based on population coding
Venue: arXiv cs.NE 2015
Year: 2015

In the biological nervous system, large neuronal populations work collaboratively to encode sensory stimuli. These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded. Based on these principles, we have designed a neuromorphic system called a Trainable Analogue Block (TAB), which encodes given input stimuli using a large population of neurons with a heterogeneous tuning curve profile. Heterogeneity of tuning curves is achieved using random device mismatches in VLSI (Very Large Scale Integration) process and by adding a systematic offset to each hidden neuron. Here, we present measurement results of a single test cell fabricated in a 65nm technology to verify the TAB framework. We have mimicked a large population of neurons by re-using measurement results from the test cell by varying offset. We thus demonstrate the learning capability of the system for various regression tasks. The TAB system may pave the way to improve the design of analogue circuits for commercial applications, by rendering circuits insensitive to random mismatch that arises due to the manufacturing process.
Well-quasi-ordering does not imply bounded clique-width
Venue: arXiv cs.DM 2015
Year: 2015

We present a hereditary class of graphs of unbounded clique-width which is well-quasi-ordered by the induced subgraph relation. This result provides a negative answer to the question asked by Daligault, Rao and Thomassé in ("Well-quasi-order of relabel functions", Order, 27(3):301--315, 2010).
Evaluation of spatial audio reproduction schemes for application in hearing aid research
Venue: arXiv cs.SD 2015
Year: 2015

Loudspeaker-based spatial audio reproduction schemes are increasingly used for evaluating hearing aids in complex acoustic conditions. To further establish the feasibility of this approach, this study investigated the interaction between spatial resolution of different reproduction methods and technical and perceptual hearing aid performance measures using computer simulations. Three spatial audio reproduction methods -- discrete speakers, vector base amplitude panning and higher order ambisonics -- were compared in regular circular loudspeaker arrays with 4 to 72 channels. The influence of reproduction method and array size on performance measures of representative multi-microphone hearing aid algorithm classes with spatially distributed microphones and a representative single channel noise-reduction algorithm was analyzed. Algorithm classes differed in their way of analyzing and exploiting spatial properties of the sound field, requiring different accuracy of sound field reproduction. Performance measures included beam pattern analysis, signal-to-noise ratio analysis, perceptual localization prediction, and quality modeling. The results show performance differences and interaction effects between reproduction method and algorithm class that may be used for guidance when selecting the appropriate method and number of speakers for specific tasks in hearing aid research.
Deep Transfer Network: Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2015
Year: 2015

Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given features (conditional distribution). In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process. This is achieved by two types of layers in DTN: the shared feature extraction layers which learn a shared feature subspace in which the marginal distributions of the source and the target samples are drawn close, and the discrimination layers which match conditional distributions by classifier transduction. We also show that DTN has a computation complexity linear to the number of training samples, making it suitable to large-scale problems. By combining the best paradigms in both worlds (deep neural networks in recognition, and matching marginal and conditional distributions in domain adaptation), we demonstrate by extensive experiments that DTN improves significantly over former methods in both execution time and classification accuracy.
More Analysis of Double Hashing for Balanced Allocations
Venue: arXiv cs.DS 2015
Year: 2015

With double hashing, for a key $x$, one generates two hash values $f(x)$ and $g(x)$, and then uses combinations $(f(x) +i g(x)) \bmod n$ for $i=0,1,2,...$ to generate multiple hash values in the range $[0,n-1]$ from the initial two. For balanced allocations, keys are hashed into a hash table where each bucket can hold multiple keys, and each key is placed in the least loaded of $d$ choices. It has been shown previously that asymptotically the performance of double hashing and fully random hashing is the same in the balanced allocation paradigm using fluid limit methods. Here we extend a coupling argument used by Lueker and Molodowitch to show that double hashing and ideal uniform hashing are asymptotically equivalent in the setting of open address hash tables to the balanced allocation setting, providing further insight into this phenomenon. We also discuss the potential for and bottlenecks limiting the use this approach for other multiple choice hashing schemes.
A review of mean-shift algorithms for clustering
Venue: arXiv cs.LG 2015
Year: 2015

A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering based on kernel density estimates and mean-shift algorithms. We discuss the blurring and non-blurring versions of mean-shift; theoretical results about mean-shift algorithms and Gaussian mixtures; relations with scale-space theory, spectral clustering and other algorithms; extensions to tracking, to manifold and graph data, and to manifold denoising; K-modes and Laplacian K-modes algorithms; acceleration strategies for large datasets; and applications to image segmentation, manifold denoising and multivalued regression.
A Distributed Algorithm for Solving a Linear Algebraic Equation
Venue: arXiv cs.SY 2015
Year: 2015

A distributed algorithm is described for solving a linear algebraic equation of the form $Ax=b$ assuming the equation has at least one solution. The equation is simultaneously solved by $m$ agents assuming each agent knows only a subset of the rows of the partitioned matrix $(A,b)$, the current estimates of the equation's solution generated by its neighbors, and nothing more. Each agent recursively updates its estimate by utilizing the current estimates generated by each of its neighbors. Neighbor relations are characterized by a time-dependent directed graph $\mathbb{N}(t)$ whose vertices correspond to agents and whose arcs depict neighbor relations. It is shown that for any matrix $A$ for which the equation has a solution and any sequence of "repeatedly jointly strongly connected graphs" $\mathbb{N}(t)$, $t=1,2,\ldots$, the algorithm causes all agents' estimates to converge exponentially fast to the same solution to $Ax=b$. It is also shown that the neighbor graph sequence must actually be repeatedly jointly strongly connected if exponential convergence is to be assured. A worst case convergence rate bound is derived for the case when $Ax=b$ has a unique solution. It is demonstrated that with minor modification, the algorithm can track the solution to $Ax = b$, even if $A$ and $b$ are changing with time, provided the rates of change of $A$ and $b$ are sufficiently small. It is also shown that in the absence of communication delays, exponential convergence to a solution occurs even if the times at which each agent updates its estimates are not synchronized with the update times of its neighbors. A modification of the algorithm is outlined which enables it to obtain a least squares solution to $Ax=b$ in a distributed manner, even if $Ax=b$ does not have a solution.
Development of an Android Application for an Electronic Medical Record System in an Outpatient Environment for Healthcare in Fiji
Venue: arXiv cs.CY 2015
Year: 2015

The outpatients department in a developing country is typically understaffed and inadequately equipped to handle a large numbers of patients filing through on an average day. The use of electronic medical record (EMR) systems can resolve some of the longstanding medical inefficiencies common in developing countries. This paper presents the design and implementation of a proposed outpatient management system that enables efficient management of a patient's medical details. We present a system to create appointments with medical practitioners by integrating a proposed Android-based mobile application with a selected open source EMR system. The application allows both the patient and the medical practitioners to manage appointments and make use of the electronic messaging facility to send reminders when the appointed time is approaching in real-time. A mobile application prototype is developed and the road map for implementation is also discussed.
Undirected Rigid Formations are Problematic
Venue: arXiv cs.SY 2015
Year: 2015

By an undirected rigid formation of mobile autonomous agents is meant a formation based on graph rigidity in which each pair of "neighboring" agents is responsible for maintaining a prescribed target distance between them. In a recent paper a systematic method was proposed for devising gradient control laws for asymptotically stabilizing a large class of rigid, undirected formations in two dimensional space assuming all agents are described by kinematic point models. The aim of this paper is to explain what happens to such formations if neighboring agents have slightly different understandings of what the desired distance between them is supposed to be or equivalently if neighboring agents have differing estimates of what the actual distance between them is. In either case, what one would expect would be a gradual distortion of the formation from its target shape as discrepancies in desired or sensed distances increase. While this is observed for the gradient laws in question, something else quite unexpected happens at the same time. It is shown that for any rigidity-based, undirected formation of this type which is comprised of three or more agents, that if some neighboring agents have slightly different understandings of what the desired distances between them are suppose to be, then almost for certain, the trajectory of the resulting distorted but rigid formation will converge exponentially fast to a closed circular orbit in two-dimensional space which is traversed periodically at a constant angular speed.