title: Sentiment analysis of twitter data

abstract: Social networks are the main resources to gather information about people's opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. In this technical paper, we show the application of sentimental analysis and how to connect to Twitter and run sentimental analysis queries. We run experiments on different queries from politics to humanity and show the interesting results. We realized that the neutral sentiments for tweets are significantly high which clearly shows the limitations of the current works.


title: Abstract Syntax Networks for Code Generation and Semantic Parsing

abstract: Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.


title: Friction from paper and cardboard causing occupational dermatitis in non-atopic individuals

abstract: Keywords: ::: ::: case report; ::: frictional hand dermatitis; ::: non-atopic; ::: occupational; ::: paper and cardboard


title: Integrated Modular Avionics - Past, present, and future

abstract: IMA (Integrated Modular Avionics) approaches have been around for 30 years but vary widely in implementation and the extent of both hardware and software levels of unification. The IMA concept, which replaces numerous separate processors and line replaceable units (LRUs) with fewer, more centralized processing units, has led to significant weight reduction and maintenance savings in both military and commercial airborne platforms. The IMA concept for this definition originated in the United States with the F-22 Joint Integrated Avionics Working Group (JIAWG) 30 years ago and then migrated to business jets and commercial transports in the late 1990s. In the last 10 years, the mainstream IMA definition has incorporated time and space partitioned software environments based on the ARINC 653 standard. During this period, software complexity has exploded. The ARINC 653 extended IMA definition has enabled the development of common software infrastructure to enhance complex systems management and enable greater software reuse. In open literature, civil aviation has cornerstone IMA extended examples in the Airbus 380 and Boeing 787 Dream-liner platforms. This article provides a summary of IMA history, presents these new IMA challenges going forward, and summarizes research focus exploring advanced IMA solutions.


title: Sign Language Alphabet Using Polynomial Classifiers

abstract: Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.


title: When Organizational Identification Elicits Moral Decision-Making: A Matter of the Right Climate

abstract: To advance current knowledge on ethical decision-making in organizations, we integrate two perspectives that have thus far developed independently: the organizational identification perspective and the ethical climate perspective. We illustrate the interaction between these perspectives in two studies (Study 1, N = 144, US sample; and Study 2, N = 356, UK sample), in which we presented participants with moral business dilemmas. Specifically, we found that organizational identification increased moral decision-making only when the organization’s climate was perceived to be ethical. In addition, we disentangle this effect in Study 2 from participants’ moral identity. We argue that the interactive influence of organizational identification and ethical climate, rather than the independent influence of either of these perspectives, is crucial for understanding moral decision-making in organizations.


title: Reliability-Based Optimal Design and Tolerancing for Multibody Systems Using Explicit Design Space Decomposition

abstract: This paper introduces a new approach for the optimal geometric design and tolerancing of multibody systems. The approach optimizes both the nominal system dimensions and the associated tolerances by solving a reliability-based design optimization (RDBO) problem under the assumption of truncated normal distributions of the geometric properties. The solution is obtained by first constructing the explicit boundaries of the failure regions (limit state function) using a support vector machine, combined with adaptive sampling and uniform design of experiments. The use of explicit boundaries enables the treatment of systems with discontinuous or binary behaviors. The explicit boundaries also allow for an efficient calculation of the probability of failure using importance sampling. The probability of failure is subsequently approximated over the whole design space (the nominal system dimensions and the associated tolerances), thus making the solution of the RBDO problem straightforward. The proposed approach is applied to the optimization of a web cutter mechanism.


title: Variables affecting information technology end-user satisfaction

abstract: The level of end-user satisfaction with information technology (IT) has widely been accepted as an indicator of IT success. The present research synthesizes and validates the construct of IT end-user satisfaction using a meta-analysis. It accomplishes this by analysing the empirical results of 45 end-user satisfaction studies published between 1986 and 1998 and by focusing on relationships between end-user satisfaction and nine variables: perceived usefulness, ease of use, user expectations, user experience, user skills, user involvement in system development, organizational support, perceived attitude of top management toward the project and user attitude toward information systems (IS) in widely divergent settings. The present analysis found positive support for the influence of all nine variables on end-user IT satisfaction but to varying degrees. The most significant relationships were found to be user involvement in systems development, perceived usefulness, user experience, organizational support and user attitude toward the IS. This has implications for IS analysis and design as well as user training and the development of training support packages.


title: Improving Login Authorization by Providing Graphical Password (Security)

abstract: Usable security has unique usability challenges because the need for security often means that standard humancomputer-interaction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in selecting better passwords. Users often create memorable passwords that are easy for attackers to guess, but strong system-assigned passwords are difficult for users to remember. So researchers of modern days have gone for alternative methods where in graphical pictures are used as passwords. Graphical passwords essentially use images or representation of images as passwords. Human brain is good in remembering picture than textual character. There are various graphical password schemes or graphical password software in the market. However, very little research has been done to analyze graphical passwords that are still immature. There for, this project work merges persuasive selective click points and password guessing resistant protocol. The major goal of this work is to reduce the guessing attacks as well as encouraging users to select more random, and difficult passwords to guess. Well known security threats like brute force attacks and dictionary attacks can be successfully abolished using this method.


title: Sharing perspectives in distributed decision making

abstract: Complex organizations are characterized by distributed decision making, and require a sharing of perspectives among distributed decision makers if they are to coordinate activity and adapt to changing circumstances. This paper explains the process of perspective taking and its roles in human communication, mutual trust, and organizational learning. SPIDER is a software environment for enriching communication among managers by improving their ability to represent and exchange understandings of the situations they face. Cognitive maps linked to underlying assumptions are used as a basis for sharing their perspectives and enabling coordination of distributed decision making.


title: Continuous collapse models on finite dimensional Hilbert spaces

abstract: We review recent results on the dynamics of continuous collapse models (or equivalently continuous measurement models) on finite dimensional Hilbert spaces. We mainly study the pure collapse dynamics, and the competition between collapse and unitary or Lindbladian dynamics. We discuss the ubiquitous emergence of quantum jumps from a continuous stochastic evolution when collapse dominates, and expand on a finer unexpected feature, the quantum spikes.


title: Millimeter-wave FMCW/monopulse radar front-end for automotive applications

abstract: This paper reports on the development of an FMCW/monopulse radar front-end design capable of acquiring the range and angular information of all obstacles in its field-of-view simultaneously without using a scanning or switching antenna. Design tradeoffs are discussed and radar front-end test results are presented.


title: A Study on Machine Learning Approaches for Outlier Detection in Wireless Sensor Network

abstract: Wireless Sensor Network (WSN) is an important research area nowadays. Wireless Sensor Network is deployed in hostile environment consisting of hundreds to thousands of nodes. They can be deployed for various mission-critical applications, such as health care, military monitoring as well as civilian applications. There are various security issues in these networks. One of such issue is outlier detection. In outlier detection, data obtained by some of the nodes whose behavior is different from the data of other nodes are spotted in the group of data. But identification of such nodes is a little difficult. In this paper, machine learning based methods for outlier detection are discussed among which the Bayesian Network looks advantageous over other methods. Bayesian classification algorithm can be used for calculating the conditional dependency of the available nodes in WSN. This method can also calculate the missing data value.


title: Autonomous Vehicle Speed Control for Safe Navigation of Occluded Pedestrian Crosswalk

abstract: Both humans and the sensors on an autonomous vehicle have limited sensing capabilities. When these limitations coincide with scenarios involving vulnerable road users, it becomes important to account for these limitations in the motion planner. For the scenario of an occluded pedestrian crosswalk, the speed of the approaching vehicle should be a function of the amount of uncertainty on the roadway. In this work, the longitudinal controller is formulated as a partially observable Markov decision process and dynamic programming is used to compute the control policy. The control policy scales the speed profile to be used by a model predictive steering controller.


title: Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention

abstract: Currently, there are increasing interests in text-to-speech (TTS) synthesis to use sequence-to-sequence models with attention. These models are end-to-end meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech with good quality. However, in challenging speaking styles, such as Lombard speech, it is difficult to record sufficiently large speech corpora. Therefore, in this study we propose a transfer learning method to adapt a sequence-to-sequence based TTS system of normal speaking style to Lombard style. Moreover, we experiment with a WaveNet vocoder in synthesis of Lombard speech. We conducted subjective evaluations to assess the performance of the adapted TTS systems. The subjective evaluation results indicated that an adaptation system with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in synthesis of Lombard speech.


title: A 60-GS/s 6-Bit DAC in 0.5-µm InP HBT Technology for Optical Communications Systems

abstract: This paper presents a 60-GS/s 6-bit digital-to-analog converter (DAC) for beyond-100-Gb/s/ch optical communications systems. The DAC was designed and fabricated using our in-house 0.5-im InP HBT technology, which yields a peak ft of 290 GHz, a peak fmax of 320 GHz, and a BVCEO of approximately 4 V. We used a simple R-2R ladder-based current-steering architecture to achieve both high-speed and low-power operation, and a timing alignment technique to suppress glitch noise and improve dynamic linearity. The DAC can provide clear multilevel signals for quadrature amplitude modulation (QAM) transmission at a sampling rate of up to 60 GS/s (60 Gbaud). To our knowledge, our DAC offers the highest sampling rate of any previously reported DAC, and it can be applied to 400-Gb/s/ch-class optical communications systems.


title: Shifting Gears: User Interfaces in the Age of Autonomous Driving

abstract: The field of automotive user interfaces has developed rapidly over the last several years. To date, the field has primarily focused on creating user interfaces that promote safe driving, including when the driver is engaged in a secondary task in addition to operating the vehicle. However, researchers now need to prepare for a major change in the automotive domain: the automated driving revolution. The authors argue for a new research agenda that focuses on four challenges for automotive user interfaces: assuring safety in the age of automation, transforming vehicles into places for productivity and play, taking advantage of new mobility options made possible by automated vehicles, while throughout all this preserving user privacy and data security. This article is part of a special issue on smart vehicle spaces.


title: Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching

abstract: Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by leveraging labeled patterns from a related source domain where there is a large amount of labeled data. However, in many practical domain adaptation learning scenarios, the feature distribution in the source domain is different from that in the target domain. In the extreme, the two distributions could differ completely when the feature representation of the source domain is totally different from that of the target domain. To address the problems of substantial feature distribution divergence across domains and heterogeneous feature representations of different domains, we propose a novel feature space independent semi-supervised kernel matching method for domain adaptation in this work. Our approach learns a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. We evaluate the proposed kernel matching method using both cross domain sentiment classification tasks of Amazon product reviews and cross language text classification tasks of Reuters multilingual newswire stories. Our empirical results demonstrate that the proposed kernel matching method consistently and significantly outperforms comparison methods on both cross domain classification problems with homogeneous feature spaces and cross domain classification problems with heterogeneous feature spaces.


title: User behaviour analysis in context-aware recommender system using hybrid filtering approach

abstract: The incorporation of context in recommendation systems is important for providing good recommendations as it improves the performance. Context awareness is an important concept in many disciplines. There are various methods that have already been proposed for context aware recommendation systems and can be classified into pre-filter, post-filter and contextual generalization. However, we found that there are some problems in using a single approach. So, in this paper we propose a new approach by combining pre-filter and post-filter methods based on the importance of contextual attribute. We believe that the contextual attribute should be dynamically selected for every user, as we found that it varies according to the user behaviour. In our approach, in case of a few rating scenario a simple method is used to provide a solution. The strategy adopted is advantageous over the single approach and is effective in improving the accuracy for the context aware recommender system.


title: Introduction to Tensor Decompositions and their Applications in Machine Learning

abstract: Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions. While tensors first emerged in the psychometrics community in the $20^{\text{th}}$ century, they have since then spread to numerous other disciplines, including machine learning. Tensors and their decompositions are especially beneficial in unsupervised learning settings, but are gaining popularity in other sub-disciplines like temporal and multi-relational data analysis, too. The scope of this paper is to give a broad overview of tensors, their decompositions, and how they are used in machine learning. As part of this, we are going to introduce basic tensor concepts, discuss why tensors can be considered more rigid than matrices with respect to the uniqueness of their decomposition, explain the most important factorization algorithms and their properties, provide concrete examples of tensor decomposition applications in machine learning, conduct a case study on tensor-based estimation of mixture models, talk about the current state of research, and provide references to available software libraries.


title: Trust-Aware Collaborative Filtering for Recommender Systems

abstract: Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.


title: Multi-task learning for classification with dirichlet process priors

abstract: Consider the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution. In such a multi-task learning (MTL) scenario, it is necessary to identify groups of similar tasks that should be learned jointly. Relying on a Dirichlet process (DP) based statistical model to learn the extent of similarity between classification tasks, we develop computationally efficient algorithms for two different forms of the MTL problem. First, we consider a symmetric multi-task learning (SMTL) situation in which classifiers for multiple tasks are learned jointly using a variational Bayesian (VB) algorithm. Second, we consider an asymmetric multi-task learning (AMTL) formulation in which the posterior density function from the SMTL model parameters (from previous tasks) is used as a prior for a new task: this approach has the significant advantage of not requiring storage and use of all previous data from prior tasks. The AMTL formulation is solved with a simple Markov Chain Monte Carlo (MCMC) construction. Experimental results on two real life MTL problems indicate that the proposed algorithms: (a) automatically identify subgroups of related tasks whose training data appear to be drawn from similar distributions; and (b) are more accurate than simpler approaches such as single-task learning, pooling of data across all tasks, and simplified approximations to DP.


title: Improving Security and Efficiency in Attribute-Based Data Sharing

abstract: With the recent adoption and diffusion of the data sharing paradigm in distributed systems such as online social networks or cloud computing, there have been increasing demands and concerns for distributed data security. One of the most challenging issues in data sharing systems is the enforcement of access policies and the support of policies updates. Ciphertext policy attribute-based encryption (CP-ABE) is becoming a promising cryptographic solution to this issue. It enables data owners to define their own access policies over user attributes and enforce the policies on the data to be distributed. However, the advantage comes with a major drawback which is known as a key escrow problem. The key generation center could decrypt any messages addressed to specific users by generating their private keys. This is not suitable for data sharing scenarios where the data owner would like to make their private data only accessible to designated users. In addition, applying CP-ABE in the data sharing system introduces another challenge with regard to the user revocation since the access policies are defined only over the attribute universe. Therefore, in this study, we propose a novel CP-ABE scheme for a data sharing system by exploiting the characteristic of the system architecture. The proposed scheme features the following achievements: 1) the key escrow problem could be solved by escrow-free key issuing protocol, which is constructed using the secure two-party computation between the key generation center and the data-storing center, and 2) fine-grained user revocation per each attribute could be done by proxy encryption which takes advantage of the selective attribute group key distribution on top of the ABE. The performance and security analyses indicate that the proposed scheme is efficient to securely manage the data distributed in the data sharing system.


title: Ghost in the Air(Traffic): On insecurity of ADS-B protocol and practical attacks on ADS-B devices

abstract: In this paper we investigate (in)security aspects of Automatic Dependent Surveillance-Broadcast (ADS-B) protocol. ADS-B is intended to be widely deployed in Air Traffic Management (ATM) Surveillance systems by 2020. One of the goals of ADS-B is to increase safety of air traffic. While the security of ADS-B was previously questioned, in this paper we demonstrate that attacks are both easy and practically feasible, for a moderately sophisticated attacker. Attacks range from passive attacks (eavesdropping) to active attacks (message jamming, replaying of injection). The attacks have been implemented using an Universal Software Radio Peripheral (USRP), a widely available SoftwareDefined Radio (SDR). for which we developed an ADS-B receiver/transmitter chain with GNURadio. We then present and analyze the results of the implemented attacks tested against both USRP-based and commercial-off-the-self (COTS) radio-enthusiast receivers. Subsequently, we discuss the risks associated with the described attacks and their implication on safety of air-traffic, as well as possible solutions on short and long terms. Finally, we argue that ADS-B, which is planned for long-term use, lacks the minimal and necessary security mechanism to ensure necessary security of the air traffic. Keywords-Architecture and Design Air Traffic Control, Air Traffic Management, Automatic Dependent SurveillanceBroadcast, ADS-B, message injection, message replay, wireless security, privacy.


title: Bandwidth Improvement Methods of Transmitarray Antennas

abstract: Despite several advantages of planar transmitarray antennas compared to conventional lens antennas, they have a narrow bandwidth. The goal of this paper is to improve the bandwidth of transmitarray antennas through the control of the transmission phase range and the optimization of the phase distribution on the transmitarray aperture. To validate the proposed approaches, two quad-layer transmitarrays using double square loop elements have been designed, fabricated, and tested at Ku-band. The transmission phase distribution is optimized for both antennas, while they differ only in the transmission phase ranges. It is shown that the transmitarray antennas designed using the proposed techniques achieve 1-dB gain bandwidth of 9.8% and 11.7%, respectively. The measured gains at 13.5 GHz are 30.22 and 29.95 dB, respectively, leading to aperture efficiencies of 50% and 47%, respectively.


title: Using the Human Response to Illness Model to assess altered level of consciousness in patients with subdural hematomas

abstract: Head injuries are the leading cause of trauma mortality and account for nearly half of all deaths related to trauma injuries. Patients who present with subdural hematomas are at risk for initial damage to the brain, as well as for subsequent brain damage related to re-bleed, ischemia or cerebral edema. These injuries can be acute or chronic in nature, and may be manifested in the patient as an altered level of consciousness. Skilled nursing assessment of altered level of consciousness leads to early nursing and medical intervention, which, in turn, can improve patient outcomes. In this paper, a critical review of the literature will focus on altered level of consciousness in patients presenting with a sub dural hematoma. The Human Response to Illness Model will be utilized as a framework for this review. Accordingly, the physio logical, pathophysiological, behavioural, and experiential per spectives of altered level of consciousness will be examined. Thus, a comprehensive understanding of this human response and rationale for evidence-based interventions will be established.


Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts


Extreme harmonic functions and boundary value problems


title: 3D Backbone Network for 3D Object Detection

abstract: The task of detecting 3D objects in point cloud has a pivotal role in many real-world applications. However, 3D object detection performance is behind that of 2D object detection due to the lack of powerful 3D feature extraction methods. In order to address this issue, we propose to build a 3D backbone network to learn rich 3D feature maps by using sparse 3D CNN operations for 3D object detection in point cloud. The 3D backbone network can inherently learn 3D features from almost raw data without compressing point cloud into multiple 2D images and generate rich feature maps for object detection. The sparse 3D CNN takes full advantages of the sparsity in the 3D point cloud to accelerate computation and save memory, which makes the 3D backbone network achievable. Empirical experiments are conducted on the KITTI benchmark and results show that the proposed method can achieve state-of-the-art performance for 3D object detection.


title: Learning Features of Music from Scratch

abstract: This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. ::: The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.


title: Formalising Hypothesis Virtues in Knowledge Graphs: A General Theoretical Framework and its Validation in Literature-Based Discovery Experiments

abstract: We introduce an approach to discovery informatics that uses so called knowledge graphs as the essential representation structure. Knowledge graph is an umbrella term that subsumes various approaches to tractable representation of large volumes of loosely structured knowledge in a graph form. It has been used primarily in the Web and Linked Open Data contexts, but is applicable to any other area dealing with knowledge representation. In the perspective of our approach motivated by the challenges of discovery informatics, knowledge graphs correspond to hypotheses. We present a framework for formalising so called hypothesis virtues within knowledge graphs. The framework is based on a classic work in philosophy of science, and naturally progresses from mostly informative foundational notions to actionable specifications of measures corresponding to particular virtues. These measures can consequently be used to determine refined sub-sets of knowledge graphs that have large relative potential for making discoveries. We validate the proposed framework by experiments in literature-based discovery. The experiments have demonstrated the utility of our work and its superiority w.r.t. related approaches.


title: Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming

abstract: A new algorithm is proposed for efficient stereo and novel view synthesis. Given the video streams acquired by two synchronized cameras the proposed algorithm synthesises images from a virtual camera in arbitrary position near the physical cameras. The new technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel view generation. The two main contributions of this paper are: (i) a new four state matching graph for dense stereo dynamic programming, that supports accurate occlusion labelling; (ii) a compact geometric derivation for novel view synthesis by direct projection of the minimum cost surface. Furthermore, the paper presents an algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly in the three-dimensional matching cost space. ::: ::: The proposed algorithm has been designed to work with input images with large disparity range, a common practical situation. The enhanced occlusion handling capabilities of the new dynamic programming algorithm are evaluated against those of the most powerful state-of-the-art dynamic programming and graph-cut techniques. Four-state DP is also evaluated against the disparity-based Middlebury error metrics and its performance found to be amongst the best of the efficient algorithms. A number of examples demonstrate the robustness of four-state DP to artefacts in stereo video streams. This includes demonstrations of cyclopean view synthesis in extended conversational sequences, synthesis from a freely translating virtual camera and, finally, basic 3D scene editing.


title: A coherent framework for fingerprint analysis: are fingerprints Holograms?

abstract: We propose a coherent mathematical model for human fingerprint images. Fingerprint structure is represented simply as a hologram - namely a phase modulated fringe pattern. The holographic form unifies analysis, classification, matching, compression, and synthesis of fingerprints in a self-consistent formalism. Hologram phase is at the heart of the method; a phase that uniquely decomposes into two parts via the Helmholtz decomposition theorem. Phase also circumvents the infinite frequency singularities that always occur at minutiae. Reliable analysis is possible using a recently discovered two-dimensional demodulator. The parsimony of this model is demonstrated by the reconstruction of a fingerprint image with an extreme compression factor of 239.


title: Adapting to User Interest Drift for POI Recommendation

abstract: Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical preferences for POI recommendation, they ignore the phenomenon of user interest drift across geographical regions, i.e., users tend to have different interests when they travel in different regions, which discounts the recommendation quality of existing methods, especially for out-of-town users. In this paper, we propose a latent class probabilistic generative model Spatial-Temporal LDA (ST-LDA) to learn region-dependent personal interests according to the contents of their checked-in POIs at each region. As the users’ check-in records left in the out-of-town regions are extremely sparse, ST-LDA incorporates the crowd’s preferences by considering the public’s visiting behaviors at the target region. To further alleviate the issue of data sparsity, a social-spatial collective inference framework is built on ST-LDA to enhance the inference of region-dependent personal interests by effectively exploiting the social and spatial correlation information. Besides, based on ST-LDA, we design an effective attribute pruning (AP) algorithm to overcome the curse of dimensionality and support fast online recommendation for large-scale POI data. Extensive experiments have been conducted to evaluate the performance of our ST-LDA model on two real-world and large-scale datasets. The experimental results demonstrate the superiority of ST-LDA and AP, compared with the state-of-the-art competing methods, by making more effective and efficient mobile recommendations.


title: Behavioral oscillation in priming: competing perceptual predictions conveyed in alternating theta-band rhythms

abstract: The brain constantly creates perceptual predictions about forthcoming stimuli to guide perception efficiently. Abundant studies have demonstrated that perceptual predictions modulate sensory activities depending on whether the actual inputs are consistent with one particular prediction. In real-life contexts, however, multiple and even conflicting predictions might concurrently exist to be tested, requiring a multiprediction coordination process. It remains largely unknown how multiple hypotheses are conveyed and harmonized to guide moment-by-moment perception. Based on recent findings revealing that multiple locations are sampled alternatively in various phase of attentional rhythms, we hypothesize that this oscillation-based temporal organization mechanism may also underlie the multiprediction coordination process. To address the issue, we used well established priming paradigms in combination with a time-resolved behavioral approach to investigate the fine temporal dynamics of the multiprediction harmonization course in human subjects. We first replicate classical priming effects in slowly developing trends of priming time courses. Second, after removing the typical priming patterns, we reveal a new theta-band (∼4 Hz) oscillatory component in the priming behavioral data regardless of whether the prime was masked. Third, we show that these theta-band priming oscillations triggered by congruent and incongruent primes are in an out-of-phase relationship. These findings suggest that perceptual predictions return to low-sensory areas not continuously but recurrently in a theta-band rhythm (every 200–300 ms) and that multiple predictions are dynamically coordinated in time by being conveyed in different phases of the theta-band oscillations to achieve dissociated but temporally organized neural representations.


title: Partial Key Grouping: Load-Balanced Partitioning of Distributed Streams

abstract: We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce PARTIAL KEY GROUPING (PKG), a new stream partitioning sch ...


title: A Distributed Infrastructure for Democratic Cloud Federations

abstract: Cloud federation is a novel concept that has been drawing attention from research and industry. However, there is a lack of solid proposal that can be widely adopted in practice to guarantee adequate governance of federations, especially in the Public Sector contexts due to legal requirements. In this paper, we propose an innovative governance approach that ensures distributed and democratic control in cloud federations. Starting from FaaS, a recent cloud federation proposal, we propose a blockchain infrastructure for the federation registry that implements the proposed governance approach.


title: A Comparative Analysis of Community Detection Algorithms on Artificial Networks

abstract: Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time.


title: Multi-Task Learning for Argumentation Mining in Low-Resource Settings

abstract: We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.


title: Why We Need Quantum Physics for Cognitive Neuroscience

abstract: For the past 20 years and more, arguments about the role of quantum mechanics in consciousness and mind theory have been mounting. On the one side are traditional neuroscientists who believe that the way to understanding the brain is through looking at the nerve cells. On the other side are various physicists who suggest that the laws of quantum mechanics may have an influence on the dynamics of consciousness and the mind. At the same time however, consciousness and the mind cannot be separated from matter. They originate in the microscopic world of the human brain. There can be no definite separation between mind and matter; there is no ‘mind’ without ‘matter’, and no ‘matter’ without ‘mind’. In terms of cognitive neuroscience, we know a great deal about the working of nerve cells. For example, we understand quite well about the formation of action potential, ion exchange, energy use, axonal transport, the vesicle cycle, and formation, oscillation and breakdown in nerve transmission. But we still do not understand how experience is formed in our material brain (color, sound, smell, taste, pain, imagination, decision, dreams, love, or orgasm) and how consciousness arises in an unconscious material organ. The insufficiency of these answers no doubt arises from the insufficiency of the methods used by cognitive science.


title: Overview of Fiber Optic Sensor Technologies for Strain/Temperature Sensing Applications in Composite Materials

abstract: This paper provides an overview of the different types of fiber optic sensors (FOS) that can be used with composite materials and also their compatibility with and suitability for embedding inside a composite material. An overview of the different types of FOS used for strain/temperature sensing in composite materials is presented. Recent trends, and future challenges for FOS technology for condition monitoring in smart composite materials are also discussed. This comprehensive review provides essential information for the smart materials industry in selecting of appropriate types of FOS in accordance with end-user requirements.


title: A predictive model for the passenger demand on a taxi network

abstract: In the last decade, the real-time vehicle location systems attracted everyone attention for the new kind of rich spatio-temporal information. The fast processing of this large amount of information is a growing and explosive challenge. Taxi companies are already exploring such information in efficient taxi dispatching and time-saving route finding. In this paper, we propose a novel methodology to produce online short term predictions on the passenger demand spatial distribution over 63 taxi stands in the city of Porto, Portugal. We did so using time series forecasting techniques to the processed events constantly communicated for 441 taxi vehicles. Our tests - using 4 months of real data - demonstrated that this model is a true major contribution to the driver mobility intelligence: 76% of the 86411 demanded taxi services were accurately forecasted in a 30 minutes time horizon.


title: The economic costs of pain in the United States.

abstract: In 2008, according to the Medical Expenditure Panel Survey (MEPS), about 100 million adults in the United States were affected by chronic pain, including joint pain or arthritis. Pain is costly to the nation because it requires medical treatment and complicates treatment for other ail- ments. Also, pain lowers worker productivity. Using the 2008 MEPS, we estimated 1) the portion of total U.S. health care costs attributable to pain; and 2) the annual costs of pain associated with lower worker productivity. We found that the total costs ranged from $560 to $635 billion in 2010 dollars. The additional health care costs due to pain ranged from $261 to $300 billion. This represents an increase in annual per person health care costs ranging from $261 to $300 compared to a base of about $4,250 for persons without pain. The value of lost productivity due to pain ranged from $299 to $335 billion. We found that the annual cost of pain was greater than the annual costs of heart disease ($309 billion), cancer ($243 billion), and diabetes ($188 billion). Our estimates are conservative be- cause they do not include costs associated with pain for nursing home residents, children, military personnel, and persons who are incarcerated. Perspective: This study estimates that the national cost of pain ranges from $560 to $635 billion, larger than the cost of the nation's priority health conditions. Because of its economic toll on society, the nation should invest in research, education, and training to advocate the successful treatment, management, and prevention of pain. a 2012 by the American Pain Society


title: Nrityakosha: Preserving the intangible heritage of Indian classical dance

abstract: Preservation of intangible cultural heritage, such as music and dance, requires encoding of background knowledge together with digitized records of the performances. We present an ontology-based approach for designing a cultural heritage repository for that purpose. Since dance and music are recorded in multimedia format, we use Multimedia Web Ontology Language (MOWL) to encode the domain knowledge. We propose an architectural framework that includes a method to construct the ontology with a labeled set of training data and use of the ontology to automatically annotate new instances of digital heritage artifacts. The annotations enable creation of a semantic navigation environment in a cultural heritage repository. We have demonstrated the efficacy of our approach by constructing an ontology for the cultural heritage domain of Indian classical dance, and have developed a browsing application for semantic access to the heritage collection of Indian dance videos.


title: Mixed sums of primes and other terms

abstract: In this paper, we study mixed sums of primes and linear recurrences. We show that if m ≡ 2 (mod 4) and m + 1 is a prime, then \(({m}^{{2}^{n}-1 } - 1)/(m - 1)\not ={m}^{n} + {p}^{a}\) for any n = 3, 4, … and prime power p a . We also prove that if a > 1 is an integer, u 0 = 0, u 1 = 1, and \({u}_{i+1} = a{u}_{i} + {u}_{i-1}\) for i = 1, 2, 3, …, then all the sums \({u}_{m} + a{u}_{n}\ (m,n = 1,2,3,\ldots )\) are distinct. One of our conjectures states that any integer n > 4 can be written as the sum of an odd prime and two positive Fibonacci numbers.


CCFRS – Community based Collaborative Filtering Recommender System


title: Container-based microservice architecture for cloud applications

abstract: Cloud Environment allows enterprises to scale their application on demand. Microservice design is a new paradigm for cloud application development which is gaining popularity due to its granular approach and loosely coupled services unlike monolithic design with single code base. Applications developed using microservice design results in better scaling and gives extended flexibility to the developers with minimum cost. In this paper, first, different challenges in deployment and continuous integration of microservices are analyzed. To overcome these challenges, later, an automated system is proposed and designed which helps in deployment and continuous integration of microservices. Containers are recently heavily used in deploying the applications as they are easy to manage and lightweight when compared to traditional Virtual Machines (VMs). We have deployed the proposed microservices architecture on the docker containers and tested using a social networking application as case study. Finally, the results are presented and the performance of monolithic and microservice approach is compared using various parameters such as response time, throughput, deployment time etc. Results show that application developed using microservice approach and deployed using the proposed design reduce the time and effort for deployment and continuous integration of the application. Results also shows that microservice based application outperform monolithic design because of its low response time and high throughput.


title: MRI brain classification using support vector machine

abstract: The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed.


title: DOI 10.3758/s13420-012-0082-6 Evaluating the TD model of classical conditioning

abstract: The temporal-difference (TD) algorithm from reinforcement learning provides a simple method for incrementally learning predictions of upcoming events. Applied to classical conditioning, TD models suppose that animals learn a real-time prediction of the unconditioned stimulus (US) on the basis of all available conditioned stimuli (CSs). In the TD model, similar to other error-correction models, learning is driven by prediction errors—the difference between the change in US prediction and the actual US. With the TD model, however, learning occurs continuously from moment to moment and is not artificially constrained to occur in trials. Accordingly, a key feature of any TD model is the assumption about the representation of a CS on a moment-to-moment basis. Here, we evaluate the performance of the TD model with a heretofore unexplored range of classical conditioning tasks. To do so, we consider three stimulus representations that vary in their degree of temporal generalization and evaluate how the representation influences the performance of the TD model on these conditioning tasks.


title: Multi-stage attack detection and signature generation with ICS honeypots

abstract: New attack surfaces are emerging with the rise of Industrial Control System (ICS) devices exposed on the Internet. ICS devices must be protected in a holistic and efficient manner; especially when these are supporting critical infrastructure. Taking this issue into account, cyber-security research is recently being focused on providing early detection and warning mechanisms for ICSs. In this paper we present a novel honeypot capable of detecting multi-stage attacks targeting ICS networks. Upon detecting a multi-stage attack, our honeypot can generate signatures so that misuse Intrusion Detection Systems (IDSs) can subsequently thwart attacks of the same type. Our experimental results indicate that our honeypot and the signatures it generates provide good detection accuracy and that the Bro IDS can successfully use the signatures to prevent future attacks.


title: Links Between Creativity and Mental Disorder

abstract: A link between mental disorder and decreased ability is commonly assumed, but evidence to the contrary also exists. In reviewing any association between creativity and mental disorder, our aim is not only to update the literature but also to include an epidemiological and theoretical discussion of the topic. For literature retrieval, we used Medline, PsycINFO, and manual literature searches. Studies are numerous: most are empirical, many having methodological difficulties and variations in definitions and concepts. There is little consensus. However, some trends are apparent. We found 13 major case series (over 100 cases), case-control studies, or population-based studies, with valid, reliable measures of mental disorders. The results of all but one of these studies supported the association, at least when concerning particular groups of mental disorders; the findings were somewhat unclear in two studies. Most of the remainder that are not included in our more detailed examination also show a fragile association between creativity and mental disorder, but the link is not apparent for all groups of mental disorders or for all forms of creativity. In conclusion, evidence exists to support some form of association between creativity and mental disorder, but the direction of any causal link remains obscure.


title: Parallel fixed point digital differential analyzer

abstract: Two main serial algorithms to scan convert straight lines have been proposed: Bresenham and Digital Differential Analyzer. The Bresenham algorithm has became a standard because of integer arithmetic. Many theoretical solutions have been proposed to parallelize Bresenham algorithm but its implementation is difficult. So most parallelizations take advantage of repeated patterns, massive parallel computers and so on. Sequential Digital Differential Analyzer shows better peformance than Bresenham if fixed point arithmetic is used. This algorithm can be pipe lined and parallelized. It is easily hardware implemented and scalable. Hardware cost is linear with speed up. Utilization is nearly 100% and hardware waste is low.


title: Structured Content Preservation for Unsupervised Text Style Transfer

abstract: Text style transfer aims to modify the style of a sentence while keeping its content unchanged. Recent style transfer systems often fail to faithfully preserve the content after changing the style. This paper proposes a structured content preserving model that leverages linguistic information in the structured fine-grained supervisions to better preserve the style-independent content during style transfer. In particular, we achieve the goal by devising rich model objectives based on both the sentence's lexical information and a language model that conditions on content. The resulting model therefore is encouraged to retain the semantic meaning of the target sentences. We perform extensive experiments that compare our model to other existing approaches in the tasks of sentiment and political slant transfer. Our model achieves significant improvement in terms of both content preservation and style transfer in automatic and human evaluation.


title: Neural Nearest Neighbors Networks

abstract: Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration. Existing approaches, however, rely on k-nearest neighbor (KNN) matching in a fixed feature space. The main hurdle in optimizing this feature space w.r.t. application performance is the non-differentiability of the KNN selection rule. To overcome this, we propose a continuous deterministic relaxation of the KNN selection that maintains differentiability w.r.t. pairwise distances, but retains the original KNN as the limit of a temperature parameter approaching zero. To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures. We show its effectiveness for the set reasoning task of correspondence classification and for image restoration tasks of image denoising and single image super-resolution, where we outperform strong convolutional neural network (CNN) baselines and recent non-local models that rely on KNN selection in hand-chosen features spaces.


title: USING COMICS AND GRAPHIC NOVELS IN SCHOOL AND LIBRARIES TO PROMOTE LITERACIES

abstract: The majorities of comics and graphic novels readers are children and teenagers. Living in the society dominated by printed and electronic media, there is a huge demand for a variety of information sources ranging from films, television shows, magazines and the Internet. Exposures from these sources also affect the learning process in school and classroom. Apart from films, the dynamic style and presentation with infused visual storytelling in comics and graphic novel are making these medium much more interesting than textbooks. Using comics and graphic novels in schools and libraries will help to encourage the undeveloped and special needs students to get into reading and promote literacies. Although comics are always being associated with humor, heavier themes and genre (such as humanity, fiction, fantasy, religion and history) contents can also be delivered in much simpler and interesting way with this medium. Involvement and continuous support from the comic industry (Comic artist and cartoonist, publisher), academic institution (schools, libraries, researchers, universities) together with the government is crucial in realizing these efforts. This paper will look forward into the potential collaborative roles, functions and awareness of using comics and graphic novels as teaching and learning aid in the classroom and library.


title: Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism

abstract: Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and glutamine addiction as features of a growth state that provides resistance to metabolic stress through excess redox and energy production. Furthermore, overflow metabolism observed may indicate that mitochondrial catabolic capacity is a key constraint setting an upper limit on the rate of cofactor production possible. These results provide a greater context within which the metabolic alterations in cancer can be understood.


title: Intelligent Multiattribute Decision Support Model for Medical Triage

abstract: The paper describes a model for intelligent multiattribute decision support for medical triage. Triage is a preliminary clinical assessment of a patient aimed at categorising the treatment category according to priority level or urgency. The model uses a combination of rule-based reasoning and multiattribute decisionmaking to assist a nurse in selecting the best treatment category for a patient. The model uses a combination of rule-based reasoning and multiattribute decisionmaking to assist a nurse in selecting the best treatment category for a patient in emergency care. The proposed model can potentially address the issues of accuracy, consistency and timeliness in medical triage decisions.


title: The efficacy of a treatment program focusing on specific stabilizing exercises for pelvic girdle pain after pregnancy: a two-year follow-up of a randomized clinical trial.

abstract: STUDY DESIGN ::: A randomized clinical trial. ::: ::: ::: OBJECTIVES ::: To examine the effects of a treatment program focusing on specific stabilizing exercises after a 2-year follow-up period. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: An individualized treatment approach with specific stabilizing exercises is shown to be effective for women with pelvic girdle pain 1 year after delivery. No previous study has examined the long-term effects of treatment for women with postpartum pelvic girdle pain. ::: ::: ::: METHODS ::: Eighty-one women with pelvic girdle pain postpartum were assigned randomly to 2 treatment groups for 20 weeks. Patient self-reported questionnaires measuring pain, disability, and health-related quality of life were collected after 20 weeks of treatment and 1 and 2 years postpartum. ::: ::: ::: RESULTS ::: All 81 women returned the questionnaires for the 2-year follow-up. Sixteen were excluded from the analysis, mainly due to new pregnancies. The significant differences between the groups in functional status, pain, and physical health (SF-36) were maintained 2 years after delivery. Minimal disability was found in 85% of the specific stabilizing exercise group as compared to 47% in the control group. The control group showed significant improvement in functional status with median change score of 6.0 (Q1-Q3 of -12-0). Minimal evening pain was reported by 68% in the specific stabilizing exercise group versus 23% in the control group. However, the group differences disappeared for all measures when controlling for score level 1 year after delivery by regression analysis. ::: ::: ::: CONCLUSION ::: The significant differences between the groups persisted with continued low levels of pain and disability in the specific stabilizing exercise group 2 years after delivery. Significant reduction in disability was found within the control group. Those with the highest level of disability and greatest potential for improvements recovered most, regardless of intervention group.


title: KINETICS OF C2F4 DISSOCIATION IN NITROGEN SHOCKS

abstract: Abstract : A kinetic study of the tetrafluoroethylene difluorocarbene radical reaction was conducted in excess nitrogen behind incident shock waves over the temperature range from 1200 to 1600 K at total gas concentration around 0. 0000125 mole/cc. The rate of formation of CF2 was observed spectrophotometrically. Comparisons between the tetrafluoroethylene difluorocarbene thermal equilibrium constants in N2 and Ar shocks indicate that N2 is vibrationally unrelaxed during chemical relaxation. The temperature determined from the chemical equilibrium constant and the time to reach 0.98 chemical equilibrium after shock compression are used to calculate the N2 relaxation time. The results show that N2 vibrational relaxation in the 1:00 tetrafluoroethylene-nitrogen mixture is about 10 to 50 times faster than in pure nitrogen.


title: Structured prediction with reinforcement learning

abstract: We formalize the problem of Structured Prediction as a Reinforcement Learning task. We first define a Structured Prediction Markov Decision Process (SP-MDP), an instantiation of Markov Decision Processes for Structured Prediction and show that learning an optimal policy for this SP-MDP is equivalent to minimizing the empirical loss. This link between the supervised learning formulation of structured prediction and reinforcement learning (RL) allows us to use approximate RL methods for learning the policy. The proposed model makes weak assumptions both on the nature of the Structured Prediction problem and on the supervision process. It does not make any assumption on the decomposition of loss functions, on data encoding, or on the availability of optimal policies for training. It then allows us to cope with a large range of structured prediction problems. Besides, it scales well and can be used for solving both complex and large-scale real-world problems. We describe two series of experiments. The first one provides an analysis of RL on classical sequence prediction benchmarks and compares our approach with state-of-the-art SP algorithms. The second one introduces a tree transformation problem where most previous models fail. This is a complex instance of the general labeled tree mapping problem. We show that RL exploration is effective and leads to successful results on this challenging task. This is a clear confirmation that RL could be used for large size and complex structured prediction problems.


Summarization Using Neural Networks


title: A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Forecasting Resource Consumption in an IIS Web Server

abstract: Software aging has been observed in a long running software application. A technique named rejuvenation is proposed to counteract this problem. The key to the aging and rejuvenation problem is how to analyze/forecast the resource consumption of software system. In this paper, we propose a methodology of hybrid ARIMA and artificial neural networks to forecast resource consumption in an IIS web server which is a running commercial server and subjected to software aging. The proposed hybrid method consists of two steps. In the first step, an ARIMA model is used to analyze the linear component of the data. In the second step, an artificial neural network model is developed to model the residuals from ARIMA model. The results show that the proposed hybrid model can be a good trade-off to forecast resource consumption.


Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation


PRCash: Centrally-Issued Digital Currency with Privacy and Regulation


title: An RF scanning receiver based on photonics for electronic warfare applications

abstract: We present the design and simulative results of an innovative RF receiver based on photonic techniques that quickly scans the RF band of interest in warfare applications, avoiding the spectrum channelization and thus reducing the receiver SWaP. The scheme is based on a stable mode-locked laser and a specifically designed tunable optical filter, and it simultaneously samples, filters, and down-converts the detected spectrum. The analysis confirms the scheme capability for detecting signals beyond 18GHz, with instantaneous bandwidth >1GHz and dynamic range >40dB. The scheme implementation with integrated photonics technologies will ensure fast scanning (filter repositioning in <100ns), high environmental stability, and reduced SWaP, meeting the increasingly stringent requirements of electronic warfare applications.


title: A Low Rank Study of Two Spatial Unknown Groups as a Function of Separation Distance

abstract: This paper presents the results of a frequency domain MOM numerical study of a two-body problem where the rank, rank fraction (compressibility), SVD singular values and norm properties of the interaction matrix are examined as a function of body separation from 0.1 to $100\ \boldsymbol{\lambda}$ . Three practical real world two-body configurations were examined: two coplanar $4 \times 4\ \boldsymbol{\lambda}$ plates, two spheres, $\mathbf{ka}=6.28$ each, and two parallel $4 \times 4\ \boldsymbol{\lambda}$ plates. Of particular interest is the numerical rank of the interaction and the rank fraction for a normalized error tolerance of $\boldsymbol{\varepsilon}=10^{-5}$ and at what separation distance can the $\mathbf{Z}_{12}$ interaction matrix be expressed as an $\mathbf{R}_{\mathbf{k}}$ low rank outer product UV form. The results show that even at $0.1 \boldsymbol{\lambda}, \mathbf{Z}_{12}$ can be well approximated in $\mathbf{R}_{\mathbf{k}}$ form for the co-planar and sphere cases. In the parallel $4\ \ \boldsymbol{\lambda}$ plate configuration, an $\mathbf{R}_{\mathbf{k}}$ representation can be utilized for separations greater than about $0.4\ \boldsymbol{\lambda}$ . These results are far less restrictive than the admissibility criteria suggested in the hierarchical methodology.


Objectives and Contents of Basic Medical Sciences in Nursing Education


title: Adaptive and Intelligent Web-based Educational Systems

abstract: Currently, Web-based educational systems form one of the fastest growing areas in educational technology research and development. Benefits of Web-based education are independence of teaching and learning with respect to time and space. Courseware installed and maintained in one place may be used by a huge number of users all over the world. A challenging research goal is the development of adaptive and intelligent Web-based educational systems (W-AIES) that offer some amount of adaptivity and intelligence.


title: Umbilical venous catheter position: evaluation by ultrasound

abstract: OBJECTIVE ::: Umbilical venous catheter (UVC) insertion is frequently performed in critically ill neonates, with catheter position evaluated by an abdominal radiograph. The position of the catheter can be difficult to assess based on supine film alone. We aimed to determine whether ultrasound enables precise evaluation of the catheter tip position. ::: ::: ::: METHODS ::: We prospectively evaluated UVC position in 75 neonates by ultrasound and supine abdominal radiograph. Distance from the catheter tip to the diaphragm was measured for both modalities. Anatomical location of the catheter was recorded for both studies. The Student's t-test was used to compare the means of the differences in catheter position on radiograph and ultrasound. ::: ::: ::: RESULTS ::: In 46 patients there was no difference in the distance between the catheter tip and the diaphragm on ultrasound and radiograph. In 29 babies there was a difference of 1-to-7 mm. Ultrasound identified malposition of the catheter in three cases in which the position of the catheter on the plain film was deemed acceptable. ::: ::: ::: CONCLUSIONS ::: Our study suggests that ultrasound is reliable and may replace plain radiograph for the identification of the UVC position, preventing complications from malposition, and reducing exposure to ionizing radiation.


title: GPU-accelerated string matching for database applications

abstract: Implementations of relational operators on GPU processors have resulted in order of magnitude speedups compared to their multicore CPU counterparts. Here we focus on the efficient implementation of string matching operators common in SQL queries. Due to different architectural features the optimal algorithm for CPUs might be suboptimal for GPUs. GPUs achieve high memory bandwidth by running thousands of threads, so it is not feasible to keep the working set of all threads in the cache in a naive implementation. In GPUs the unit of execution is a group of threads and in the presence of loops and branches, threads in a group have to follow the same execution path; if some threads diverge, then different paths are serialized. We study the cache memory efficiency of single- and multi-pattern string matching algorithms for conventional and pivoted string layouts in the GPU memory. We evaluate the memory efficiency in terms of memory access pattern and achieved memory bandwidth for different parallelization methods. To reduce thread divergence, we split string matching into multiple steps. We evaluate the different matching algorithms in terms of average- and worst-case performance and compare them against state-of-the-art CPU and GPU libraries. Our experimental evaluation shows that thread and memory efficiency affect performance significantly and that our proposed methods outperform previous CPU and GPU algorithms in terms of raw performance and power efficiency. The Knuth---Morris---Pratt algorithm is a good choice for GPUs because its regular memory access pattern makes it amenable to several GPU optimizations.


title: Rule-based System for Automatic Grammar Correction Using Syntactic N-grams for English Language Learning (L2)

abstract: We describe the system developed for the CoNLL-2013 shared task—automatic English L2 grammar error correction. The system is based on the rule-based approach. It uses very few additional resources: a morphological analyzer and a list of 250 common uncountable nouns, along with the training data provided by the organizers. The system uses the syntactic information available in the training data: this information is represented as syntactic n-grams, i.e. n-grams extracted by following the paths in dependency trees. The system is simple and was developed in a short period of time (1 month). Since it does not employ any additional resources or any sophisticated machine learning methods, it does not achieve high scores (specifically, it has low recall) but could be considered as a baseline system for the task. On the other hand, it shows what can be obtained using a simple rule-based approach and presents a few situations where the rule-based approach can perform better than ML approach.


title: Detection and Characterization of Anomalies in Multivariate Time Series

abstract: Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other domains. This paper presents a robust algorithm for detecting anomalies in noisy multivariate time series data by employing a kernel matrix alignment method to capture the dependence relationships among variables in the time series. Anomalies are found by performing a random walk traversal on the graph induced by the aligned kernel matrix. We show that the algorithm is flexible enough to handle different types of time series anomalies including subsequence-based and local anomalies. Our framework can also be used to characterize the anomalies found in a target time series in terms of the anomalies present in other time series. We have performed extensive experiments to empirically demonstrate the effectiveness of our algorithm. A case study is also presented to illustrate the ability of the algorithm to detect ecosystem disturbances in Earth science data.


title: Influence of social and technical factors for evaluating contribution in GitHub

abstract: Open source software is commonly portrayed as a meritocracy, where decisions are based solely on their technical merit. However, literature on open source suggests a complex social structure underlying the meritocracy. Social work environments such as GitHub make the relationships between users and between users and work artifacts transparent. This transparency enables developers to better use information such as technical value and social connections when making work decisions. We present a study on open source software contribution in GitHub that focuses on the task of evaluating pull requests, which are one of the primary methods for contributing code in GitHub. We analyzed the association of various technical and social measures with the likelihood of contribution acceptance. We found that project managers made use of information signaling both good technical contribution practices for a pull request and the strength of the social connection between the submitter and project manager when evaluating pull requests. Pull requests with many comments were much less likely to be accepted, moderated by the submitter's prior interaction in the project. Well-established projects were more conservative in accepting pull requests. These findings provide evidence that developers use both technical and social information when evaluating potential contributions to open source software projects.


title: Analyzing Posture and Affect in Task-Oriented Tutoring

abstract: Intelligent tutoring systems research aims to produce systems that meet or exceed the effectiveness of one-on-one expert human tutoring. Theory and empirical study suggest that affective states of the learner must be addressed to achieve this goal. While many affective measures can be utilized, posture offers the advantages of non-intrusiveness and ease of interpretation. This paper presents an accurate posture estimation algorithm applied to a computer-mediated tutoring corpus of depth recordings. Analyses of posture and session-level student reports of engagement and cognitive load identified significant patterns. The results indicate that disengagement and frustration may coincide with closer postural positions and more movement, while focused attention and less frustration occur with more distant, stable postural positions. It is hoped that this work will lead to intelligent tutoring systems that recognize a greater breadth of affective expression through channels of posture and gesture.


Key Lengths Contribution to The Handbook of Information Security


title: Arabesque: a system for distributed graph mining

abstract: Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs and finding patterns that match some "interestingness" criteria desired by the user. These algorithms are very important for areas such as social networks, semantic web, and bioinformatics. In this paper, we present Arabesque, the first distributed data processing platform for implementing graph mining algorithms. Arabesque automates the process of exploring a very large number of subgraphs. It defines a high-level filter-process computational model that simplifies the development of scalable graph mining algorithms: Arabesque explores subgraphs and passes them to the application, which must simply compute outputs and decide whether the subgraph should be further extended. We use Arabesque's API to produce distributed solutions to three fundamental graph mining problems: frequent subgraph mining, counting motifs, and finding cliques. Our implementations require a handful of lines of code, scale to trillions of subgraphs, and represent in some cases the first available distributed solutions.


title: Time and Frequency Resource Allocation using Graph Theory in OFDMA Wireless Mesh Networks

abstract: A wireless network with a mesh topology works reliably and offers redundancy. In modern broadband wireless mesh networks that use MIMO and OFDMA techniques, the problems of time, frequency, and space resource allocations are different from a cellular system and more complicated due to system architecture and distributed control and management. This paper focuses on the resource allocation problem of the OFDMA system and we define the term of separability order. For simple topologies like the square grid configuration, the allocations are simple and an optimal solution can be shown, but for an arbitrary architecture we need advanced tools and we use Graph Theory tools to present two different algorithmic solutions, to allow frequency reuse.


title: The finite length analysis of spatially-coupled codes for 1-D magnetic recording channels

abstract: Spatially-coupled (SC) codes have recently attracted significant attention due to their capability to achieve capacity approaching performance. Significant recent research has been devoted to the asymptotic study of SC codes. Although the asymptotic analysis is relevant, it cannot be directly applied in the finite-length setting due to cycle-free and averaging assumptions. In this paper, we consider the problem of finite length analysis of SC codes for magnetic-recording (MR) applications. First, we propose an efficient approach to enumerate certain combinatorial objects inside the Tanner graph representation of SC codes, objects that are found to lead to decoding errors in the high reliability regime. This enumeration approach exploits the structure of SC codes to dramatically decrease the complexity of the enumeration. Next, we consider the class of array-based SC codes with column weight 3 as a representative example, and identify the problematic combinatorial objects that cause performance degradation in the error floor area. We show that SC codes designed to have a minimal number of these problematic objects have a notable performance improvement in the error floor area.


title: Three-Dimensional Printing in Orthopedic Surgery

abstract: Three-dimensional (3D) printing is emerging as a clinically promising technology for rapid prototyping of surgically implantable products. With this commercially available technology, computed tomography or magnetic resonance images can be used to create graspable objects from 3D reconstructed images. Models can enhance patients' understanding of their pathology and surgeon preoperative planning. Customized implants and casts can be made to match an individual's anatomy. This review outlines 3D printing, its current applications in orthopedics, and promising future directions.


title: A two-term penalty function for inverse problems with sparsity constrains

abstract: Inverse problems with sparsity constrains, such Basis Pursuit denoising (BPDN) and Convolutional BPDN (CBPDN), usually use the l 1 -norm as the penalty function; however such choice leads to a solution that is biased towards zero. Recently, several works have proposed and assessed the properties of other non-standard penalty functions (most of them non-convex), which avoid the above mentioned drawback and at the same time are intended to induce sparsity more strongly than the l 1 -norm. In this paper we propose a two-term penalty function consisting of a synthesis between the ?i-norm and the penalty function associated with the Non-Negative Garrote (NNG) thresholding rule. Although the proposed two-term penalty function is non-convex, the total cost function for the BPDN / CBPDN problems is still convex. The performance of the proposed two-term penalty function is compared with other reported choices for practical denoising, deconvolution and convolutional sparse coding (CSC) problems within the BPDN / CBPDN frameworks. Our experimental results show that the proposed two-term penalty function is particularly effective (better reconstruction with sparser solutions) for the CSC problem while attaining competitive performance for the denoising and deconvolution problems.


title: An Extended Reinforcement Learning Framework to Model Cognitive Development With Enactive Pattern Representation

abstract: In order to make machines more intelligent, it is inevitable to understand human-like cognitive development, in which adaptive, autonomous and progressive evolution of cognitive decision-making in interacting with the environment plays a key role. Inspired by enactive artificial intelligence and evolutionary sampling learning, a new cognitive development learning model termed evolutionary enactive learning (EEL) is proposed in this paper. The proposed model is constructed by extending the reinforcement learning framework and introducing the utility-selection theory to guide the coevolution of pattern representation and decision-making policies. Theoretical analysis on the model’s validity of EEL is given. To further demonstrate the effectiveness of the proposed method, two simulated cognitive decision-making tasks are designed, in which pattern representation and decision-making must be jointly developed to achieve good cognitive performance. Our experimental results clearly demonstrate that the resulting learning process is rational and effective. Finally, we indicate that the proposed EEL could be readily further extended by introducing existing machine learning techniques to solve more practical applications.


title: Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

abstract: Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the Extreme Risk Index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, our study seems to be the first application of extreme value techniques in portfolio management on a large scale. The primary aim of our investigation is the potential of ERI in practice. The performance of this strategy is benchmarked against the minimum variance portfolio and the equally weighted portfolio. These fundamental strategies are important benchmarks for large-scale applications. Our comparison includes annualized portfolio returns, maximal drawdowns, transaction costs, portfolio concentration, and asset diversity in the portfolio. In addition to that we study the impact of an alternative tail index estimator. Our results show that the ERI strategy significantly outperforms both the minimum-variance portfolio and the equally weighted portfolio on assets with heavy tails.


title: Theory and Network Applications of Dynamic Bloom Filters

abstract: A bloom filter is a simple, space-efficient, randomized data structure for concisely representing a static data set, in order to support approximate membership queries. It has great potential for distributed applications where systems need to share information about what resources they have. The space efficiency is achieved at the cost of a small probability of false positive in membership queries. However, for many applications the space savings and short locating time consistently outweigh this drawback. In this paper, we introduce dynamic bloom filters (DBF) to support concise representation and approximate membership queries of dynamic sets, and study the false positive probability and union algebra operations. We prove that DBF can control the false positive probability at a low level by adjusting the number of standard bloom filters used according to the actual size of current dynamic set. The space complexity is also acceptable if the actual size of dynamic set does not deviate too much from the predefined threshold. Furthermore, we present multidimension dynamic bloom filters (MDDBF) to support concise representation and approximate membership queries of dynamic sets in multiple attribute dimensions, and study the false positive probability and union algebra operations through mathematic analysis and experimentation. We also explore the optimization approach and three network applications of bloom filters, namely bloom joins, informed search, and global index implementation. Our simulation shows that informed search based on bloom filters can obtain higher recall and success rate of query than the blind search protocol.


title: EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

abstract: EEG based brain computer interface has emerged as a hot spot in the study of neuroscience, machine learning and rehabilitation in the recent years. A BCI provides a platform for direct communication between a human brain and a computer without the normal neurophysiology pathways. The electrical signals in the brain, because of their fast response to cognitive processes are most suitable as non-motor controlled mediation between the human and a computer. It can serve as a communication and control channel for different applications. Though the primary goal is to restore communication in severely paralyzed population, the BCI for speech communication fetches recognition in a variety of non-medical fields, the silent speech communication, cognitive biometrics and synthetic telepathy to name a few. A survey of diverse applications and principles of the BCI technology used for speech communication is discussed in this chapter. An ample evidence of speech communication used by “locked-in” patients is specified. Through the aid of assistive computer technology, they were able to pen their memoir. The current state-of-the-art techniques and control signals used for speech communication is described in brief. Possible future research directions are discussed. A comparison of indirect and direct methods of BCI speech production is shown. The direct method involves capturing the brain signals of the intended speech or speech imagery, processes the signals to predict the speech and synthesizes the speech production in real-time. There is enough evidence that the direct speech prediction from the neurological signals is a promising technology with fruitful results and challenging issues.


title: Layout Validation Using Graph Grammar and Generation of Yard Specific Safety Properties for Railway Interlocking Verification

abstract: In railway electronic interlocking system, the automatic signalling equipment is programmed with the configuration data derived manually from the yard layout. This step is prone to human errors and any error can be a severe threat to signalling safety. The yard-layout data and the configured system both need to be verified to satisfy the desired safety requirements. The configured signalling system is needed to be verified against those relevant safety properties before deploying. Accordingly, the contributions in this paper include, (a) validation of input in terms of yard-layout data against some spatial properties (b) generation of yard-specific properties from the validated layout data for interlocking system verification. The work described in this paper is applied for signalling rules followed by Indian Railways. To capture the actual environment, a set of complex properties including properties involving timers is considered. Higher level safety properties such as no-collision and no-derailment are also considered for enumeration.


title: True Random Number Generators

abstract: Random numbers are needed in many areas: cryptography, Monte Carlo computation and simulation, industrial testing and labeling, hazard games, gambling, etc. Our assumption has been that random numbers cannot be computed; because digital computers operate deterministically, they cannot produce random numbers. Instead, random numbers are best obtained using physical (true) random number generators (TRNG), which operate by measuring a well-controlled and specially prepared physical process. Randomness of a TRNG can be precisely, scientifically characterized and measured. Especially valuable are the information-theoretic provable random number generators (RNGs), which, at the state of the art, seem to be possible only by exploiting randomness inherent to certain quantum systems. On the other hand, current industry standards dictate the use of RNGs based on free-running oscillators (FRO) whose randomness is derived from electronic noise present in logic circuits and which cannot be strictly proven as uniformly random, but offer easier technological realization. The FRO approach is currently used in 3rd- and 4th-generation FPGA and ASIC hardware, unsuitable for realization of quantum RNGs. In this chapter we compare weak and strong aspects of the two approaches. Finally, we discuss several examples where use of a true RNG is critical and show how it can significantly improve security of cryptographic systems, and discuss industrial and research challenges that prevent widespread use of TRNGs.


title: A provable time and space efficient implementation of NESL

abstract: In this paper we prove time and space bounds for the implementation of the programming language NESL on various parallel machine models. NESL is a sugared typed l-calculus with a set of array primitives and an explicit parallel map over arrays. Our results extend previous work on provable implementation bounds for functional languages by considering space and by including arrays. For modeling the cost of NESL we augment a standard call-by-value operational semantics to return two cost measures: a DAG representing the sequential dependence in the computation, and a measure of the space taken by a sequential implementation. We show that a NESL program with w work (nodes in the DAG), d depth (levels in the DAG), and s sequential space can be implemented on a p processor butterfly network, hypercube, or CRCW PRAM using O(w/p + d log p) time and O(s + dp log p) reachable space.1 For programs with sufficient parallelism these bounds are optimal in that they give linear speedup and use space within a constant factor of the sequential space.


title: Multi-Product Utility Maximization for Economic Recommendation

abstract: Basic economic relations such as substitutability and complementarity between products are crucial for recommendation tasks, since the utility of one product may depend on whether or not other products are purchased. For example, the utility of a camera lens could be high if the user possesses the right camera (complementarity), while the utility of another camera could be low because the user has already purchased one (substitutability). We propose \emph{multi-product utility maximization} (MPUM) as a general approach to recommendation driven by economic principles. MPUM integrates the economic theory of consumer choice with personalized recommendation, and focuses on the utility of \textit{sets} of products for individual users. MPUM considers what the users already have when recommending additional products. We evaluate MPUM against several popular recommendation algorithms on two real-world E-commerce datasets. Results confirm the underlying economic intuition, and show that MPUM significantly outperforms the comparison algorithms under top-K evaluation metrics.


title: Automated Usability Testing Framework

abstract: Handheld device applications with poor usability can reduce the productivity of users and incur costs for businesses, thus usability testing should play a vital role in application development. Conventional usability testing methodologies, such as formal user testing, can be expensive, time consuming and labour intensive; less resource-demanding alternatives can yield unreliable results. Automating aspects of usability testing would improve its efficiency and make it more practical to perform throughout development. ::: ::: An automated usability testing tool should capture as input the properties of an application's graphical user interface, the sequence of user actions as they use the application to achieve particular tasks, their behaviour and comments, as well as a description of these tasks. The tool should evaluate both the static and dynamic properties of the interface, examine navigational burden and suggest modifications or templates that would improve usability. Results should be quick and easy to interpret, and be understandable by personnel other than specialised testers. ::: ::: Several existing tools that are typical of the tools available today meet some but not all of these requirements. In this paper we describe the design of the HUIA testing framework, in which we have to meet as many of these requirements as possible.


title: A review on soccer player tracking techniques based on extracted features

abstract: Playing games specially soccer is one of the most likable activities which performed by a large mass base in most countries. All the broadcasted videos of these games are taken as an extensive challenging by researchers in computer vision field. The topics which get the big chance of importance in industry are the tracking of both player and field. This can be used to describe the match for people who are visually impaired. In this review, we summarize some of the recent researcher's work related to different player tracking techniques according to specific features extracted from the scene to build a robust object (player and/or ball) tracking, such as motion, color, edges, etc., which considered as the most commonly used in object tracking. Also, we compare some of these techniques according to two groups of publicly available data sets in order to study the effect of selecting these features on the tracking accuracy.


Sentiment Detection using Convolutional Neural Networks with Multi-Task Training and Distant Supervision


title: Joint Learning Templates and Slots for Event Schema Induction

abstract: Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type. In this paper, we propose a joint entity-driven model to learn templates and slots simultaneously based on the constraints of templates and slots in the same sentence. In addition, the entities' semantic information is also considered for the inner connectivity of the entities. We borrow the normalized cut criteria in image segmentation to divide the entities into more accurate template clusters and slot clusters. The experiment shows that our model gains a relatively higher result than previous work.


title: Quality of Experience of VoIP Service: A Survey of Assessment Approaches and Open Issues

abstract: This survey gives a comprehensive review of recent advances related to the topic of VoIP QoE (Quality of user' Experience). It starts by providing some insight into the QoE arena and outlines the principal building blocks of a VoIP application. The sources of impairments over data IP networks are identified and distinguished from signal-oriented sources of quality degradation observed over telecom networks. An overview of existing subjective and objective methodologies for the assessment of the QoE of voice conversations is then presented outlining how subjective and objective speech quality methodologies have evolved to consider time-varying QoS transport networks. A description of practical procedures for measuring VoIP QoE and illustrative results is then given. Utilization methodology of several speech quality assessment frameworks is summarized. A survey of emerging single-ended parametric-model speech quality assessment algorithms dedicated to VoIP service is then given. In particular, after presenting a primitive single-ended parametric-model algorithm especially conceived for the evaluation of VoIP conversations, new artificial assessors of VoIP service are detailed. In particular, we describe speech quality assessment algorithms that consider, among others, packet loss burstiness, unequal importance of speech wave, and transient loss of connectivity. The following section concentrates on the integration of VoIP service over mobile data networks. The impact of quality-affecting phenomena, such as handovers and CODEC changeover are enumerated and some primary subjective results are summarized. The survey concludes with a review of open issues relating to automatic assessment of VoIP.