OpenRaiser/Intern-Atlas huggingface dataset:
Crowdfunding Astronomy Outreach Projects: Lessons Learned from the UNAWE Crowdfunding Campaign
Venue: arXiv cs.CY 2014
Year: 2014

In recent years, crowdfunding has become a popular method of funding new technology or entertainment products, or artistic projects. The idea is that people or projects ask for many small donations from individuals who support the proposed work, rather than a large amount from a single source. Crowdfunding is usually done via an online portal or platform which handles the financial transactions involved. The Universe Awareness (UNAWE) programme decided to undertake a Kickstarter crowdfunding campaign centring on the resource Universe in a Box2. In this article we present the lessons learned and best practices from that campaign.
Considering Fading Effects for Vertical Handover in Heterogenous Wireless Networks
Venue: arXiv cs.NI 2014
Year: 2014

Over the years, vertical handover has attracted the interest of numerous researchers. Despite the attractive benefits of integrating different wireless platforms, mobile users are confronted with the issue of detrimental handover. As a mobile node (MN) moves within a heterogeneous environment, satisfactory quality of service (QoS) is desired by ensuring efficient vertical handover. This demands not only the efficient execution of vertical handover, but also optimized pre-handover decisions, such as: handover necessity estimation (HNE), handover triggering condition estimation (HTCE) and handover target selection (HTS). The existing works on HNE and HTCE optimization considered the coverage region of a point of attachment to be circular, ignoring the fading effect. This paper considers the effect of shadow fading and used extensive geometric and probability analysis in modelling the coverage area of a WLAN cell. Thus, presents a realistic and novel model with an attempt to ensure optimal handover as a mobile node (MN) traverses a heterogeneous wireless environment. Monte-Carlo simulations were carried out to show the behavior of the proposed models. Results were validated by comparing the proposed models with existing works.
An Energy Efficient Risk Notification Message Dissemination Protocol for Vehicular Ad hoc Networks
Venue: arXiv cs.NI 2014
Year: 2014

We propose the design and development of an energy-efficient Risk Notification Message Dissemination Protocol (RNMDP) for vehicular ad hoc networks (VANETs). RNMDP propagates Risk Notification Messages (RNMs) from their location of origin (called the Risk Zone) to vehicles approaching the Risk Zone. RNMDP assumes each node is aware of its current location in the network. The protocol works as follows: A RNM is broadcast in the neighborhood of the Risk Zone. A node receiving the RNM from another node waits for a Rebroadcast-Wait-Time before deciding to rebroadcast the message. The Rebroadcast-Wait-Time for a node is modeled based on the ratio of the distance between the node and the immediate sender of the RNM and the direction of movement of the node. Priority for rebroadcast is given for nodes farthest away from the sender and traveling towards the Risk Zone. Nodes that are traveling in lanes in direction away from the Risk Zone are also considered for rebroadcast, albeit with a larger Rebroadcast-Wait-Time. During the Rebroadcast-Wait-Time, if a node hears the same RNM again rebroadcast in the neighborhood, then the node stops from further broadcasting the message. If a node does not hear the RNM in its neighborhood during the RebroadcastWait-Time, the node broadcasts the message in its neighborhood. A RNM is considered to have been delivered to all the vehicles in the road, if the message reaches the Target Zone. The performance of RNMDP has been compared with that of the commonly used flooding strategy through extensive simulations conducted for highway networks with different number of lanes and lane density. Simulation results indicate that with a slightly larger delay (i.e., no more than 35% of the delay incurred for flooding), RNMDP can achieve the same message delivery ratio attained by flooding, but at a relatively much lower energy loss compared to flooding.
Optimal algorithms for universal random number generation from finite memory sources
Venue: arXiv cs.IT 2014
Year: 2014

We study random number generators (RNGs), both in the fixed to variable-length (FVR) and the variable to fixed-length (VFR) regimes, in a universal setting in which the input is a finite memory source of arbitrary order and unknown parameters, with arbitrary input and output (finite) alphabet sizes. Applying the method of types, we characterize essentially unique optimal universal RNGs that maximize the expected output (respectively, minimize the expected input) length in the FVR (respectively, VFR) case. For the FVR case, the RNG studied is a generalization of Elias's scheme, while in the VFR case the general scheme is new. We precisely characterize, up to an additive constant, the corresponding expected lengths, which include second-order terms similar to those encountered in universal data compression and universal simulation. Furthermore, in the FVR case, we consider also a "twice-universal" setting, in which the Markov order k of the input source is also unknown.
Possible and Necessary Allocations via Sequential Mechanisms
Venue: arXiv cs.AI 2014
Year: 2014

A simple mechanism for allocating indivisible resources is sequential allocation in which agents take turns to pick items. We focus on possible and necessary allocation problems, checking whether allocations of a given form occur in some or all mechanisms for several commonly used classes of sequential allocation mechanisms. In particular, we consider whether a given agent receives a given item, a set of items, or a subset of items for five natural classes of sequential allocation mechanisms: balanced, recursively balanced, balanced alternating, strictly alternating and all policies. We identify characterizations of allocations produced balanced, recursively balanced, balanced alternating policies and strictly alternating policies respectively, which extend the well-known characterization by Brams and King [2005] for policies without restrictions. In addition, we examine the computational complexity of possible and necessary allocation problems for these classes.
Energy-Efficient Resource Allocation for Device-to-Device Underlay Communication
Venue: arXiv cs.NI 2014
Year: 2014

Device-to-device (D2D) communication underlaying cellular networks is expected to bring significant benefits for utilizing resources, improving user throughput and extending battery life of user equipments. However, the allocation of radio and power resources to D2D communication needs elaborate coordination, as D2D communication can cause interference to cellular communication. In this paper, we study joint channel and power allocation to improve the energy efficiency of user equipments. To solve the problem efficiently, we introduce an iterative combinatorial auction algorithm, where the D2D users are considered as bidders that compete for channel resources, and the cellular network is treated as the auctioneer. We also analyze important properties of D2D underlay communication, and present numerical simulations to verify the proposed algorithm.
Paradigm shifts. Part II. Reverse Transcriptase. Analysis of reference stability and word frequencies
Venue: arXiv cs.DL 2014
Year: 2014

The reverse transcription paradigm shift in RNA tumor virus research marked by the discovery of the reverse transcriptase in 1970 was traced using co-citation and title word frequency analysis. It is shown that this event is associated with a break in citation patterns and the occurrence of previously unknown technical terms.
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness
Venue: arXiv cs.CC 2014
Year: 2014

Polynomial approximations to boolean functions have led to many positive results in computer science. In particular, polynomial approximations to the sign function underly algorithms for agnostically learning halfspaces, as well as pseudorandom generators for halfspaces. In this work, we investigate the limits of these techniques by proving inapproximability results for the sign function. Firstly, the polynomial regression algorithm of Kalai et al. (SIAM J. Comput. 2008) shows that halfspaces can be learned with respect to log-concave distributions on $\mathbb{R}^n$ in the challenging agnostic learning model. The power of this algorithm relies on the fact that under log-concave distributions, halfspaces can be approximated arbitrarily well by low-degree polynomials. We ask whether this technique can be extended beyond log-concave distributions, and establish a negative result. We show that polynomials of any degree cannot approximate the sign function to within arbitrarily low error for a large class of non-log-concave distributions on the real line, including those with densities proportional to $\exp(-|x|^{0.99})$. Secondly, we investigate the derandomization of Chernoff-type concentration inequalities. Chernoff-type tail bounds on sums of independent random variables have pervasive applications in theoretical computer science. Schmidt et al. (SIAM J. Discrete Math. 1995) showed that these inequalities can be established for sums of random variables with only $O(\log(1/δ))$-wise independence, for a tail probability of $δ$. We show that their results are tight up to constant factors. These results rely on techniques from weighted approximation theory, which studies how well functions on the real line can be approximated by polynomials under various distributions. We believe that these techniques will have further applications in other areas of computer science.
Applicability of DUKPT Key Management Scheme to Cloud Wallet and other Mobile Payments
Venue: arXiv cs.CR 2014
Year: 2014

After discussing the concept of DUKPT based symmetric encryption key management (e.g., for 3DES) and definition of cloud or remote wallet, the paper analyses applicability of DUKPT to different use cases like mobile banking, NFC payment using EMV contactless card and mobile based EMV card emulation, web browser based transaction and cloud or remote wallet. Cloud wallet is an emerging payment method and is gaining momentum very fast. Anticipating that the wallet product managers and security specialists may face these questions from different stakeholders, the authors have addressed applicability of DUKPT to cloud wallet use case quite elaborately. As per knowledge of the authors, this topic has been analysed and discussed for the first time.
A systematic literature review of cloud computing in eHealth
Venue: arXiv cs.CY 2014
Year: 2014

Cloud computing in eHealth is an emerging area for only few years. There needs to identify the state of the art and pinpoint challenges and possible directions for researchers and applications developers. Based on this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1) cloud-based eHealth framework design (n=13); (2) applications of cloud computing (n=17); and (3) security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the review were about designs and concept-proof. Only very few studies have evaluated their research in the real world, which may indicate that the application of cloud computing in eHealth is still very immature. However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and security protection mechanisms will be a main research area for developing citizen centred home-based healthcare applications.
Solving bandwidth-guaranteed routing problem using routing data
Venue: arXiv cs.NI 2014
Year: 2014

This paper introduces a traffic engineering routing algorithm that aims to accept as many routing demands as possible on the condition that a certain amount of bandwidth resource is reserved for each accepted demand. The novel idea is to select routes based on not only network states but also information derived from routing data such as probabilities of the ingress egress pairs and usage frequencies of the links. Experiments with respect to acceptance ratio and computation time have been conducted against various test sets. Results indicate that the proposed algorithm has better performance than the existing popular algorithms including Minimum Interference Routing Algorithm (MIRA) and Random Race based Algorithm for Traffic Engineering (RRATE).
On the complexity of the vector connectivity problem
Venue: arXiv cs.DM 2014
Year: 2014

We study a relaxation of the Vector Domination problem called Vector Connectivity (VecCon). Given a graph $G$ with a requirement $r(v)$ for each vertex $v$, VecCon asks for a minimum cardinality set $S$ of vertices such that every vertex $v\in V\setminus S$ is connected to $S$ via $r(v)$ disjoint paths. In the paper introducing the problem, Boros et al. [Networks, 2014] gave polynomial-time solutions for VecCon in trees, cographs, and split graphs, and showed that the problem can be approximated in polynomial time on $n$-vertex graphs to within a factor of $\log n+2$, leaving open the question of whether the problem is NP-hard on general graphs. We show that VecCon is APX-hard in general graphs, and NP-hard in planar bipartite graphs and in planar line graphs. We also generalize the polynomial result for trees by solving the problem for block graphs.
Minkowski sum of HV-polytopes in Rn
Venue: arXiv cs.CG 2014
Year: 2014

Minkowski sums cover a wide range of applications in many different fields like algebra, morphing, robotics, mechanical CAD/CAM systems ... This paper deals with sums of polytopes in a n dimensional space provided that both H-representation and V-representation are available i.e. the polytopes are described by both their half-spaces and vertices. The first method uses the polytope normal fans and relies on the ability to intersect dual polyhedral cones. Then we introduce another way of considering Minkowski sums of polytopes based on the primal polyhedral cones attached to each vertex.
Estimating Food Consumption and Poverty Indices with Mobile Phone Data
Venue: arXiv cs.CY 2014
Year: 2014

Recent studies have shown the value of mobile phone data to tackle problems related to economic development and humanitarian action. In this research, we assess the suitability of indicators derived from mobile phone data as a proxy for food security indicators. We compare the measures extracted from call detail records and airtime credit purchases to the results of a nationwide household survey conducted at the same time. Results show high correlations (> .8) between mobile phone data derived indicators and several relevant food security variables such as expenditure on food or vegetable consumption. This correspondence suggests that, in the future, proxies derived from mobile phone data could be used to provide valuable up-to-date operational information on food security throughout low and middle income countries.
Plan or not: Remote Human-robot Teaming with Incomplete Task Information
Venue: arXiv cs.AI 2014
Year: 2014

Human-robot interaction can be divided into two categories based on the physical distance between the human and robot: remote and proximal. In proximal interaction, the human and robot often engage in close coordination; in remote interaction, the human and robot are less coupled due to communication constraints. As a result, providing automation for the robot in remote interaction becomes more important. Thus far, human factor studies on automation in remote human-robot interaction have been restricted to various forms of supervision, in which the robot is essentially being used as a smart mobile manipulation platform with sensing capabilities. In this paper, we investigate the incorporation of general planning capability into the robot to facilitate peer-to-peer human-robot teaming, in which the human and robot are viewed as teammates that are physically separated. The human and robot share the same global goal and collaborate to achieve it. Note that humans may feel uncomfortable at such robot autonomy, which can potentially reduce teaming performance. One important difference between peer-to-peer teaming and supervised teaming is that an autonomous robot in peer-to-peer teaming can achieve the goal alone when the task information is completely specified. However, incompleteness often exists, which implies information asymmetry. While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance. We aim to investigate the various trade-offs, e.g., mental workload and situation awareness, between these two types of remote human-robot teaming.
Survey of Strong Authentication Approaches for Mobile Proximity and Remote Wallet Applications - Challenges and Evolution
Venue: arXiv cs.CR 2014
Year: 2014

Wallet may be described as container application used for configuring, accessing and analysing data from underlying payment application(s). There are two dominant types of digital wallet applications, proximity wallet and remote wallet. In the payment industry, one often hears about authentication approach for proximity or remote wallets or the underlying payment applications separately, but there is no such approach, as per our knowledge, for combined wallet, the holder application. While Secure Element (SE) controlled by the mobile network operator (i.e., SIM card) may ensure strong authentication, it introduces strong dependencies among business partners in payments and hence is not getting fraction. Embedded SE in the form of trusted execution environment [3, 4, 5] or trusted computing [24] may address this issue in future. But such devices tend to be a bit expensive and are not abundant in the market. Meanwhile, for many years, context based authentication involving device fingerprinting and other contextual information for conditional multi-factor authentication, would prevail and would remain as the most dominant and strong authentication mechanism for mobile devices from various vendors in different capability and price ranges. EMVCo payment token standard published in 2014 tries to address security of wallet based payment in a general way. The authors believe that it is quite likely that EMVCo payment token implementations would evolve in course of time in such a way that token service providers would start insisting on device fingerprinting as strong means of authentication before issuing one-time-use payment token. This paper talks about challenges of existing authentication mechanisms used in payment and wallet applications, and their evolution.
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Venue: arXiv cs.LG 2014
Year: 2014

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.
Anisotropy-based optimal filtering in linear discrete time invariant systems
Venue: arXiv cs.SY 2014
Year: 2014

This paper is concerned with a problem of robust filtering for a finite-dimensional linear discrete time invariant system with two output signals, one of which is directly observed while the other has to be estimated. The system is assumed to be driven by a random disturbance produced from the Gaussian white noise sequence by an unknown shaping filter. The worst-case performance of an estimator is quantified by the maximum ratio of the root-mean-square (RMS) value of the estimation error to that of the disturbance over stationary Gaussian disturbances whose mean anisotropy is bounded from above by a given parameter $a \ge 0$. The mean anisotropy is a combined entropy theoretic measure of temporal colouredness and spatial "nonroundness" of a signal. We construct an $a$-anisotropic estimator which minimizes the worst-case error-to-noise RMS ratio. The estimator retains the general structure of the Kalman filter, though with modified state-space matrices. Computing the latter is reduced to solving a set of two coupled algebraic Riccati equations and an equation involving the determinant of a matrix. In two limiting cases, where $a = 0$ or $a \to +\infty$, the $a$-anisotropic estimator leads to the standard steady-state Kalman filter or the $H_{\infty}$-optimal estimator, respectively.
Computoser - rule-based, probability-driven algorithmic music composition
Venue: arXiv cs.AI 2014
Year: 2014

This paper presents the Computoser hybrid probability/rule based algorithm for music composition (http://computoser.com) and provides a reference implementation. It addresses the issues of unpleasantness and lack of variation exhibited by many existing approaches by combining the two methods (basing the parameters of the rules on data obtained from preliminary analysis). A sample of 500+ musical pieces was analyzed to derive probabilities for musical characteristics and events (e.g. scale, tempo, intervals). The algorithm was constructed to produce musical pieces using the derived probabilities combined with a large set of composition rules, which were obtained and structured after studying established composition practices. Generated pieces were published on the Computoser website where evaluation was performed by listeners. The feedback was positive (58.4% approval), asserting the merits of the undertaken approach. The paper compares this hybrid approach to other approaches to algorithmic composition and presents a survey of the pleasantness of the resulting music.
NetSci High: Bringing Network Science Research to High Schools
Venue: arXiv cs.SI 2014
Year: 2014

We present NetSci High, our NSF-funded educational outreach program that connects high school students who are underrepresented in STEM (Science Technology Engineering and Mathematics), and their teachers, with regional university research labs and provides them with the opportunity to work with researchers and graduate students on team-based, year-long network science research projects, culminating in a formal presentation at a network science conference. This short paper reports the content and materials that we have developed to date, including lesson plans and tools for introducing high school students and teachers to network science; empirical evaluation data on the effect of participation on students' motivation and interest in pursuing STEM careers; the application of professional development materials for teachers that are intended to encourage them to use network science concepts in their lesson plans and curriculum; promoting district-level interest and engagement; best practices gained from our experiences; and the future goals for this project and its subsequent outgrowth.