OpenRaiser/Intern-Atlas huggingface dataset:
Selecting wavelengths for least squares range estimation
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of estimating the distance, or range, between two locations by measuring the phase of multiple sinusoidal signals transmitted between the locations. Traditional estimators developed for optical interferometry include the beat wavelength and excess fractions methods. More recently, estimators based on the Chinese remainder theorem (CRT) and least squares have appeared. Recent research suggests the least squares estimator to be most accurate in many cases. The accuracy of all of these range estimators depends upon the wavelengths chosen. This leads to the problem of selecting wavelengths that maximise accuracy. Procedures for selecting wavelengths for the beat wavelength and excess fractions methods have previously been described, but procedures for the CRT and least squares estimators are yet to be developed. In this paper we develop an algorithm to automatically select wavelengths for use with the least square range estimator. The algorithm minimises an optimisation criterion connected with the mean square error. Interesting properties of a particular class of lattices simplify the criterion allowing minimisation by depth first search. Monte-Carlo simulations indicate that wavelengths that minimise the criterion can result is considerably more accurate range estimates than wavelengths selected by ad hoc means.
Immersive Augmented Reality Training for Complex Manufacturing Scenarios
Venue: arXiv cs.HC 2016
Year: 2016

In the complex manufacturing sector a considerable amount of resources are focused on developing new skills and training workers. In that context, increasing the effectiveness of those processes and reducing the investment required is an outstanding issue. In this paper we present an experiment that shows how modern Human Computer Interaction (HCI) metaphors such as collaborative mixed-reality can be used to transmit procedural knowledge and could eventually replace other forms of face-to-face training. We implement a real-time Immersive Augmented Reality (IAR) setup with see-through cameras that allows for collaborative interactions that can simulate conventional forms of training. The obtained results indicate that people who took the IAR training achieved the same performance than people in the conventional face-to-face training condition. These results, their implications for future training and the use of HCI paradigms in this context are discussed in this paper.
RPYS i/o: A web-based tool for the historiography and visualization of citation classics, sleeping beauties, and research fronts
Venue: arXiv cs.DL 2016
Year: 2016

Reference Publication Year Spectroscopy (RPYS) and Multi-RPYS provide algorithmic approaches to reconstructing the intellectual histories of scientific fields. With this brief communication, we describe a technical advancement for developing research historiographies by introducing RPYS i/o, an online tool for performing standard RPYS and Multi-RPYS analyses interactively (at http://comins.leydesdorff.net/). The tool enables users to explore seminal works underlying a research field and to plot the influence of these seminal works over time. This suite of visualizations offers the potential to analyze and visualize the myriad of temporal dynamics of scientific influence, such as citation classics, sleeping beauties, and the dynamics of research fronts. We demonstrate the features of the tool by analyzing--as an example--the references in documents published in the journal Philosophy of Science.
Compressive Spectral Clustering
Venue: arXiv cs.DS 2016
Year: 2016

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes. Each of these three steps becomes computationally intensive for large N and/or k. We propose to speed up the last two steps based on recent results in the emerging field of graph signal processing: graph filtering of random signals, and random sampling of bandlimited graph signals. We prove that our method, with a gain in computation time that can reach several orders of magnitude, is in fact an approximation of spectral clustering, for which we are able to control the error. We test the performance of our method on artificial and real-world network data.
Efficient and Scalable Distributed Autonomous Spatial Aloha Networks via Local Leader Election
Venue: arXiv cs.IT 2016
Year: 2016

This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit-receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of non-overlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R less than or equal to 2 at the LLs, the stability of the entire network can be assured even when each user only has partial network information. For practical implementation, we propose each LL to use R=2 as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput.
Parallel Ordered Sets Using Join
Venue: arXiv cs.DS 2016
Year: 2016

The ordered set is one of the most important data type in both theoretical algorithm design and analysis and practical programming. In this paper we study the set operations on two ordered sets, including Union, Intersect and Difference, based on four types of balanced Binary Search Trees (BST) including AVL trees, red-black trees, weight balanced trees and treaps. We introduced only one subroutine Join that needs to be implemented differently for each balanced BST, and on top of which we can implement generic, simple and efficient parallel functions for ordered sets. We first prove the work-efficiency of these Join-based set functions using a generic proof working for all the four types of balanced BSTs. We also implemented and tested our algorithm on all the four balancing schemes. Interestingly the implementations on all four data structures and three set functions perform similarly in time and speedup (more than 45x on 64 cores). We also compare the performance of our implementation to other existing libraries and algorithms.
Sequence Classification with Neural Conditional Random Fields
Venue: arXiv cs.LG 2016
Year: 2016

The proliferation of sensor devices monitoring human activity generates voluminous amount of temporal sequences needing to be interpreted and categorized. Moreover, complex behavior detection requires the personalization of multi-sensor fusion algorithms. Conditional random fields (CRFs) are commonly used in structured prediction tasks such as part-of-speech tagging in natural language processing. Conditional probabilities guide the choice of each tag/label in the sequence conflating the structured prediction task with the sequence classification task where different models provide different categorization of the same sequence. The claim of this paper is that CRF models also provide discriminative models to distinguish between types of sequence regardless of the accuracy of the labels obtained if we calibrate the class membership estimate of the sequence. We introduce and compare different neural network based linear-chain CRFs and we present experiments on two complex sequence classification and structured prediction tasks to support this claim.
Mining Software Quality from Software Reviews: Research Trends and Open Issues
Venue: arXiv cs.CL 2016
Year: 2016

Software review text fragments have considerably valuable information about users experience. It includes a huge set of properties including the software quality. Opinion mining or sentiment analysis is concerned with analyzing textual user judgments. The application of sentiment analysis on software reviews can find a quantitative value that represents software quality. Although many software quality methods are proposed they are considered difficult to customize and many of them are limited. This article investigates the application of opinion mining as an approach to extract software quality properties. We found that the major issues of software reviews mining using sentiment analysis are due to software lifecycle and the diverse users and teams.
Smoothing Brascamp-Lieb Inequalities and Strong Converses for Common Randomness Generation
Venue: arXiv cs.IT 2016
Year: 2016

We study the infimum of the best constant in a functional inequality, the Brascamp-Lieb-like inequality, over auxiliary measures within a neighborhood of a product distribution. In the finite alphabet and the Gaussian cases, such an infimum converges to the best constant in a mutual information inequality. Implications for strong converse properties of two common randomness (CR) generation problems are discussed. In particular, we prove the strong converse property of the rate region for the omniscient helper CR generation problem in the discrete and the Gaussian cases. The latter case is perhaps the first instance of a strong converse for a continuous source when the rate region involves auxiliary random variables.
Reducing training requirements through evolutionary based dimension reduction and subject transfer
Venue: arXiv cs.NE 2016
Year: 2016

Training Brain Computer Interface (BCI) systems to understand the intention of a subject through Electroencephalogram (EEG) data currently requires multiple training sessions with a subject in order to develop the necessary expertise to distinguish signals for different tasks. Conventionally the task of training the subject is done by introducing a training and calibration stage during which some feedback is presented to the subject. This training session can take several hours which is not appropriate for on-line EEG-based BCI systems. An alternative approach is to use previous recording sessions of the same person or some other subjects that performed the same tasks (subject transfer) for training the classifiers. The main aim of this study is to generate a methodology that allows the use of data from other subjects while reducing the dimensions of the data. The study investigates several possibilities for reducing the necessary training and calibration period in subjects and the classifiers and addresses the impact of i) evolutionary subject transfer and ii) adapting previously trained methods (retraining) using other subjects data. Our results suggest reduction to 40% of target subject data is sufficient for training the classifier. Our results also indicate the superiority of the approaches that incorporated evolutionary subject transfer and highlights the feasibility of adapting a system trained on other subjects.
Importance Sampling for Minibatches
Venue: arXiv cs.LG 2016
Year: 2016

Minibatching is a very well studied and highly popular technique in supervised learning, used by practitioners due to its ability to accelerate training through better utilization of parallel processing power and reduction of stochastic variance. Another popular technique is importance sampling -- a strategy for preferential sampling of more important examples also capable of accelerating the training process. However, despite considerable effort by the community in these areas, and due to the inherent technical difficulty of the problem, there is no existing work combining the power of importance sampling with the strength of minibatching. In this paper we propose the first {\em importance sampling for minibatches} and give simple and rigorous complexity analysis of its performance. We illustrate on synthetic problems that for training data of certain properties, our sampling can lead to several orders of magnitude improvement in training time. We then test the new sampling on several popular datasets, and show that the improvement can reach an order of magnitude.
NED: An Inter-Graph Node Metric Based On Edit Distance
Venue: arXiv cs.DB 2016
Year: 2016

Node similarity is a fundamental problem in graph analytics. However, node similarity between nodes in different graphs (inter-graph nodes) has not received a lot of attention yet. The inter-graph node similarity is important in learning a new graph based on the knowledge of an existing graph (transfer learning on graphs) and has applications in biological, communication, and social networks. In this paper, we propose a novel distance function for measuring inter-graph node similarity with edit distance, called NED. In NED, two nodes are compared according to their local neighborhood structures which are represented as unordered k-adjacent trees, without relying on labels or other assumptions. Since the computation problem of tree edit distance on unordered trees is NP-Complete, we propose a modified tree edit distance, called TED*, for comparing neighborhood trees. TED* is a metric distance, as the original tree edit distance, but more importantly, TED* is polynomially computable. As a metric distance, NED admits efficient indexing, provides interpretable results, and shows to perform better than existing approaches on a number of data analysis tasks, including graph de-anonymization. Finally, the efficiency and effectiveness of NED are empirically demonstrated using real-world graphs.
The benefit of a 1-bit jump-start, and the necessity of stochastic encoding, in jamming channels
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of communicating a message $m$ in the presence of a malicious jamming adversary (Calvin), who can erase an arbitrary set of up to $pn$ bits, out of $n$ transmitted bits $(x_1,\ldots,x_n)$. The capacity of such a channel when Calvin is exactly causal, i.e. Calvin's decision of whether or not to erase bit $x_i$ depends on his observations $(x_1,\ldots,x_i)$ was recently characterized to be $1-2p$. In this work we show two (perhaps) surprising phenomena. Firstly, we demonstrate via a novel code construction that if Calvin is delayed by even a single bit, i.e. Calvin's decision of whether or not to erase bit $x_i$ depends only on $(x_1,\ldots,x_{i-1})$ (and is independent of the "current bit" $x_i$) then the capacity increases to $1-p$ when the encoder is allowed to be stochastic. Secondly, we show via a novel jamming strategy for Calvin that, in the single-bit-delay setting, if the encoding is deterministic (i.e. the transmitted codeword is a deterministic function of the message $m$) then no rate asymptotically larger than $1-2p$ is possible with vanishing probability of error, hence stochastic encoding (using private randomness at the encoder) is essential to achieve the capacity of $1-p$ against a one-bit-delayed Calvin.
Approximate Capacity of Index Coding for Some Classes of Graphs
Venue: arXiv cs.IT 2016
Year: 2016

For a class of graphs for which the Ramsey number $R(i,j)$ is upper bounded by $ci^aj^b$, for some constants $a,b,$ and $c$, it is shown that the clique covering scheme approximates the broadcast rate of every $n$-node index coding problem in the class within a multiplicative factor of $c^{\frac{1}{a+b+1}} n^{\frac{a+b}{a+b+1}}$ for every $n$. Using this theorem and some graph theoretic arguments, it is demonstrated that the broadcast rate of planar graphs, line graphs and fuzzy circular interval graphs is approximated by the clique covering scheme within a factor of $n^{\frac{2}{3}}$.
Energy and Spectral Efficiency Gains From Multi-User MIMO-based Small Cell Reassignments
Venue: arXiv cs.NI 2016
Year: 2016

In this work we investigate the reassignment of User Equipments (UEs) between adjacent small cells to concurrently enable spatial multiplexing gains through Multi-User MIMO (MU-MIMO) and reductions in energy consumption though switching emptied small cells to a sleep state. We consider a case where UEs can be reassigned between adjacent small cells provided that the targeted neighbouring cell contains a UE with which the reassigned UE can perform MU-MIMO without experiencing excessive multi-user interference, and whilst achieving a minimum expected gain in spectral efficiency over the previous original cell transmissions as a result. We formulate the selection decision of which UEs to reassign as a set covering problem with the objective of maximising the number of small cell base stations to switch to a sleep state. Our results show that, for both indoor and outdoor LTE small cell scenarios, the proposed MU-MIMO-based reassignments achieve significant reductions in the required number of active small cell base stations, whilst simultaneously achieving increases in spectral efficiency.
Privacy Preserving Architectures for Collaborative Intrusion Detection
Venue: arXiv cs.CR 2016
Year: 2016

Collaboration among multiple organizations is imperative for contemporary intrusion detection. As modern threats become well sophisticated it is difficult for organizations to defend with threat context local to their networks alone. Availability of global \emph{threat intelligence} is must for organizations to defend against modern advanced persistent threats (APTs). In order to benefit from such global context of attacks, privacy concerns continue to be of major hindrance. In this position paper we identify real world privacy problems as precise use cases, relevant cryptographic technologies and discuss privacy preserving architectures for collaborative intrusion detection.
A Large Dataset of Object Scans
Venue: arXiv cs.CV 2016
Year: 2016

We have created a dataset of more than ten thousand 3D scans of real objects. To create the dataset, we recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators scanned objects of their choosing, outside the laboratory and without direct supervision by computer vision professionals. The result is a large and diverse collection of object scans: from shoes, mugs, and toys to grand pianos, construction vehicles, and large outdoor sculptures. We worked with an attorney to ensure that data acquisition did not violate privacy constraints. The acquired data was irrevocably placed in the public domain and is available freely at http://redwood-data.org/3dscan .
Analysis of Location Management Schemes for MANET using Synthetic Mobility Models
Venue: arXiv cs.NI 2016
Year: 2016

In the performance evaluation of a protocol for an ad hoc network, the protocol should be tested under realistic conditions including, but not limited to, a sensible transmission range, limited buffer space for the storage of messages, representative data traffic models, and realistic movements of the mobile users and several mobility models that represent mobile nodes whose movements are dependent on each other (i.e., group mobility models ).The goal of this paper is to simulate the movements of mobile nodes within a network and present a number of mobility models in order to demonstrate its effect on Location management scheme for mobile ad hoc network or personal communication services networks. Specifically, to illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated. Location management is a fundamental problem in personal communication services network. The current standard of location management is HLR/VLR scheme. It has been observed that the performance of any location management scheme depends on space requirements, bandwidth requirements and time requirements. To avoid certain drawbacks in HLR/VLR scheme, many approaches including hierarchical approaches have been suggested. Working set idea is chosen to analyze its performance for location management in PCS networks. Due to inadequacy of standard network simulators to provide the output in the format desired, a new location management simulator can be built. Two variants of working set idea viz. Working set scheme for HLR/VLR approach and working set scheme for hierarchical approach can be used and then compare the performance of HLR/VLR scheme and working set scheme using the results obtained by the simulator with respect to already available mobile activity traces.
Bounds on the Price of Anarchy for a More General Class of Directed Graphs in Opinion Formation Games
Venue: arXiv cs.GT 2016
Year: 2016

In opinion formation games with directed graphs, a bounded price of anarchy is only known for weighted Eulerian graphs. Thus, we bound the price of anarchy for a more general class of directed graphs with conditions intuitively meaning that each node does not influence the others more than she is influenced, where the bounds depend on such difference (in a ratio). We also show that there exists an example just slightly violating the conditions with an unbounded price of anarchy.
The happiness paradox: your friends are happier than you
Venue: arXiv cs.SI 2016
Year: 2016

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of $39,110$ Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users cluster assortatively by level of happiness. Our results indicate that the topology of online social networks and the distribution of happiness in some populations can cause widespread psycho-social effects that affect the well-being of billions of individuals.