title: Exploratory Querying of Extended Knowledge Graphs

abstract: Knowledge graphs (KGs) are important assets for search, analytics, and recommendations. However, querying a KG to explore entities and discover facts is difficult and tedious, even for users with skills in SPARQL. First, users are not familiar with the structure and labels of entities, classes and relations. Second, KGs are bound to be incomplete, as they capture only major facts about entities and their relationships and miss out on many of the more subtle aspects. ::: ::: We demonstrate TriniT, a system that facilitates exploratory querying of large KGs, by addressing these issues of "vocabulary" mismatch and KG incompleteness. TriniT supports query relaxation rules that are invoked to allow for relevant answers which are not found otherwise. The incompleteness issue is addressed by extending a KG with additional text-style token triples obtained by running Open IE on Web and text sources. The query language, relaxation methods, and answer ranking are extended appropriately. The demo shows automatic query relaxation and has support for interactively adding user-customized relaxations. In both situations, the demo provides answer explanations and offers additional query suggestions.


title: Application of self-tuning fuzzy PID controller on the AVR system

abstract: Optimal tuning of proportional-integral-derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a tuning fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.


title: Statistical and Computational Limits for Sparse Matrix Detection

abstract: This paper investigates the fundamental limits for detecting a high-dimensional sparse matrix contaminated by white Gaussian noise from both the statistical and computational perspectives. We consider $p\times p$ matrices whose rows and columns are individually $k$-sparse. We provide a tight characterization of the statistical and computational limits for sparse matrix detection, which precisely describe when achieving optimal detection is easy, hard, or impossible, respectively. Although the sparse matrices considered in this paper have no apparent submatrix structure and the corresponding estimation problem has no computational issue at all, the detection problem has a surprising computational barrier when the sparsity level $k$ exceeds the cubic root of the matrix size $p$: attaining the optimal detection boundary is computationally at least as hard as solving the planted clique problem. ::: The same statistical and computational limits also hold in the sparse covariance matrix model, where each variable is correlated with at most $k$ others. A key step in the construction of the statistically optimal test is a structural property for sparse matrices, which can be of independent interest.


title: Design and Characterization of a Low-Cost Self-Oscillating Fluxgate Transducer for Precision Measurement of High-Current

abstract: Precision measurement of dc high current is usually realized by second harmonic fluxgate current transducers, but the complicated modulation and demodulation circuits with high cost have been limiting their applications. This paper presents a low-cost transducer that can substitute the traditional ones for precision measurement of high current. The new transducer, based on the principle of zero-flux, is the combination of an improved self-oscillating fluxgate sensor with a magnetic integrator in a common feedback loop. The transfer function of the zero-flux control strategy of the transducer is established to verify the validity of the qualitative analysis on operating principle. Origins and major influence factors of the modulation ripple, respectively, caused by the useful signal extraction circuit and the transformer effect are studied, and related suppression methods are proposed, which can be considered as one of the major technical modifications for performance improvement. As verification, a prototype is realized, and several key specifications, including the linearity, small-signal bandwidth, modulation ripple, ratio stability under full load, power-on repeatability, magnetic error, and temperature coefficient, are characterized. Measurement results show that the new transducer with the maximum output ripple 0.3 μA can measure dc current up to ±600 A with a relative accuracy 1.3 ppm in the full scale, and it also can measure ac current and has a -3 dB bandwidth greater than 100 kHz.


title: Optimization of the trading rule in foreign exchange using genetic algorithm

abstract: The generation of profitable trading rules for Foreign Exchange (FX) investments is a difficult but popular problem. The use of Machine Learning in this problem allows us to obtain objective results by using information of the past market behavior. In this paper, we propose a Genetic Algorithm (GA) system to automatically generate trading rules based on Technical Indexes. Unlike related researches in the area, our work focuses on calculating the most appropriate trade timing, instead of predicting the trading prices.


title: Chemical composition of rainwater and anthropogenic influences in the Piracicaba River Basin, Southeast Brazil

abstract: Abstract The influences of different kinds of anthropogenic activities on rainwater chemistry in a tropical area were studied during one uninterrupted year at Piracicaba River Basin (Southeast Brazil). A total of 272 rainwater samples collected continuously from August 1997 to July 1998 at four different sites were analyzed for F−, CH3COO−, HCOO−, MSA, Cl−, NO2−, Br−, NO3−, SO42−, C2O42−, PO43−, Na+, NH4+, K+, Mg2+, Ca2+, DOC (dissolved organic carbon), DIC (dissolved inorganic carbon), pH and conductivity. The most abundant ion was H+ and rain acidity was significant at all sampling sites (average pH of 4.4–4.5). The sources of this free acidity differ among sites and appear to be correlated to the different land-uses. The composition of rainwater appeared to be controlled mostly by three sources: soil dust, sugar cane burning and industrial emissions.


title: A simple and fast image cloning algorithm

abstract: Image cloning which could copy the source image and “paste” it into the target image seamlessly is one of the important digital image manipulation techniques. Traditional methods which cope with this problem managed to analyze image information in gradient domains according to solving Poisson equations. As a result, existing methods need high computational cost and memory usage which hinder them from time-critical applications with high-resolution images. In this paper, we propose an algorithm to cope with image cloning problem based on modified mean-value coordinates technique so that we transform the problem into a very simple image interpolation procedure which is easy to implement without diminishing the performances. We also apply image pyramid technique to further accelerate the proposed algorithm and achieve high performances even on a CPU with single thread.


title: An exploration of discussion threads in social news sites: a case study of the Reddit community

abstract: Social news and content aggregation Web sites have become massive repositories of valuable knowledge on a diverse range of topics. Millions of Web-users are able to leverage these platforms to submit, view and discuss nearly anything. The users themselves exclusively curate the content with an intricate system of submissions, voting and discussion. Furthermore, the data on social news Web sites is extremely well organized by its user-base, which opens the door for opportunities to leverage this data for other purposes just like Wikipedia data has been used for many other purposes. In this paper we study a popular social news Web site called Reddit. Our investigation looks at the dynamics of its discussion threads, and asks two main questions: (1) to what extent do discussion threads resemble a topical hierarchy? and (2) Can discussion threads be used to enhance Web search? We show interesting results for these questions on a very large snapshot several sub-communities of the Reddit Web site. Finally, we discuss the implications of these results and suggest ways by which social news Web site's can be used to perform other tasks.


DESCRIPTIVE DATA MINING ON FRAUDULENT ONLINE DATING PROFILES


title: Atrous Faster R-CNN for Small Scale Object Detection

abstract: Deep Convolutional Neural Networks based object detection has made significant progress recent years. However, detecting small scale objects is still a challenging task. This paper addresses the problem and proposes a unified deep neural network building upon the prominent Faster R-CNN framework. This paper has two main contributions. Firstly, an Atrous Region Proposal Network (ARPN) is proposed to explore object contexts at multiple scales by sliding a set of atrous filters with increasing dilation rates over the last convolutional feature map. Secondly, to enrich the representations of small scale image regions, this paper incorporates atrous convolution into Fast R-CNN and proposes a Dense Fast R-CNN (DFRCN), that improves the resolution of the ROI-pooled convolutional feature maps without increasing the number of parameters. In combination of the two, this paper proposes a unified network termed as Atrous Faster R-CNN. On PASCAL object detection challenge dataset, our method achieves superior performance to the state of the arts, especially for small scale objects.


title: 28 GHz channel modeling using 3D ray-tracing in urban environments

abstract: In this paper, we analyze the radio channel characteristics at mmWave frequencies for 5G cellular communications in urban scenarios. 3D-ray tracing simulations in the downtown areas of Ottawa and Chicago are conducted in both the 2 GHz and 28 GHz bands. Each area has two different deployment scenarios, with different transmitter height and different density of buildings. Based on the observations of the ray-tracing experiments, important parameters of the radio channel model, such as path loss exponent, shadowing variance, delay spread and angle spread, are provided, forming the basis of a mmWave channel model. Based on the analysis and the 3GPP 3D-Spatial Channel Model (SCM) framework, we introduce a a preliminary mmWave channel model at 28 GHz.


title: The Nature of Expertise in Fingerprint Matching: Experts Can Do a Lot with a Little

abstract: Expert decision making often seems impressive, even miraculous. People with genuine expertise in a particular domain can perform quickly and accurately, and with little information. In the series of experiments presented here, we manipulate the amount of "information" available to a group of experts whose job it is to identify the source of crime scene fingerprints. In Experiment 1, we reduced the amount of information available to experts by inverting fingerprint pairs and adding visual noise. There was no evidence for an inversion effect-experts were just as accurate for inverted prints as they were for upright prints-but expert performance with artificially noisy prints was impressive. In Experiment 2, we separated matching and nonmatching print pairs in time. Experts were conservative, but they were still able to discriminate pairs of fingerprints that were separated by five-seconds, even though the task was quite different from their everyday experience. In Experiment 3, we separated the print pairs further in time to test the long-term memory of experts compared to novices. Long-term recognition memory for experts and novices was the same, with both performing around chance. In Experiment 4, we presented pairs of fingerprints quickly to experts and novices in a matching task. Experts were more accurate than novices, particularly for similar nonmatching pairs, and experts were generally more accurate when they had more time. It is clear that experts can match prints accurately when there is reduced visual information, reduced opportunity for direct comparison, and reduced time to engage in deliberate reasoning. These findings suggest that non-analytic processing accounts for a substantial portion of the variance in expert fingerprint matching accuracy. Our conclusion is at odds with general wisdom in fingerprint identification practice and formal training, and at odds with the claims and explanations that are offered in court during expert testimony.


title: A Parallel Algorithm Development Model for the GPU Architecture

abstract: Parallel computing has been in use for decades, and throughout many researchers have sought to define a model for algorithm design for such a platform. Valiant developed a model for parallel computing, which was later extended to later include multi-core processors, but it still may not be best suited for the unique GPU architecture. With the current advances in high performance computing, it is easy to see the role that GPUs can play, and even easier to see the need for a model for GPU algorithm development. Here we propose a parallel GPU model which offers both a general design and a fine-grained approach, intended to accommodate nearly any GPU architecture. We show how our model can result in significant increases in performance when algorithms are designed based on its principles.


title: Motivators of online vulnerability: The impact of social network site use and FOMO

abstract: Continued and frequent use of social network sites (SNS) has been linked to a fear of missing out (FOMO) and online self-promotion in the form of friending and information disclosure. The present paper reports findings from 506 UK based Facebook users (53% male) who responded to an extensive online survey about their SNS behaviours and online vulnerability. Structural equation modelling (SEM) suggests that FOMO mediates the relationship between increased SNS use and decreased self-esteem. Self-promoting SNS behaviours provide more complex mediated associations. Longitudinal support (N=175) is provided for the notion that decreased self-esteem might motivate a potentially detrimental cycle of FOMO-inspired online SNS use. The research considers the implications of social networking on an individual's online vulnerability. Examines the link between social network site use, FOMO and online vulnerability.Combines cross-sectional and longitudinal SEM analysis of online vulnerability.FOMO mediates the relationship between SNS use and online vulnerability.Online friending and self-disclosure are associated with online vulnerability.Decreased self-esteem linked to a potentially detrimental FOMO inspired SNS use.


title: Functionally Oriented Rehabilitation Program for Patients with Fibromyalgia: Preliminary Results

abstract: Objective: To evaluate function and disability in patients with fibromyalgia before and after participation in a functionally oriented, multidisciplinary, 8-wk treatment program. Design: A total of 23 patients who met American College of Rheumatology criteria for the diagnosis of fibromyalgia were enrolled in the study. Outcome measures included: range of motion, 6-min walk test, a modified Fibromyalgia Impact Questionnaire, a modified SF-36 Physical Functioning Scale, and the Fibromyalgia Health Assessment Questionnaire. Pretreatment and posttreatment scores were analyzed using paired t tests. Results: All subjects completed the program, and there were no reported injuries. Three subjects failed to complete the survey instruments at the conclusion of the study. Intention to treat analysis including these subjects was carried out but did not significantly change results. For the remaining subjects (n = 20), a significant improvement was found on the Physical Functioning Scale (P = 0.01). Trends toward improvement on the Fibromyalgia Impact Questionnaire (P = 0.40) and Fibromyalgia Health Assessment Questionnaire (P = 0.14) were seen but did not achieve statistical significance. Range of motion testing revealed significant improvements in lumbar spine extension (P < 0.001), straight-leg raise (P < 0.001), cervical spine flexion (P < 0.01), cervical spine rotation (P < 0.05), and cervical spine side bending (P < 0.05). Distance traveled during the 6-min walk test increased significantly (P < 0.01), whereas perceived exertion as measured by the Borg scale did not change. There were no injuries or other adverse consequences of the program. Conclusions: This study utilized multiple functional outcome measures to demonstrate improved function and decreased disability in patients with fibromyalgia. Our patients reported significantly improved physical function after participation in the 8-wk intensive multidisciplinary treatment program. This progressive, functionally based exercise training program was well tolerated by all participants and outlines an effective exercise prescription for patients with fibromyalgia. Fibromyalgia patients in this study responded favorably to a treatment program that focused on function instead of pain.


title: Amygdala Microcircuits Controlling Learned Fear

abstract: We review recent work on the role of intrinsic amygdala networks in the regulation of classically conditioned defensive behaviors, commonly known as conditioned fear. These new developments highlight how conditioned fear depends on far more complex networks than initially envisioned. Indeed, multiple parallel inhibitory and excitatory circuits are differentially recruited during the expression versus extinction of conditioned fear. Moreover, shifts between expression and extinction circuits involve coordinated interactions with different regions of the medial prefrontal cortex. However, key areas of uncertainty remain, particularly with respect to the connectivity of the different cell types. Filling these gaps in our knowledge is important because much evidence indicates that human anxiety disorders results from an abnormal regulation of the networks supporting fear learning.


title: Improving dictionary learning using the Itakura-Saito divergence

abstract: This paper presents an improved and efficient algorithm for overcomplete, nonnegative dictionary learning for nonnegative sparse representation (NNSR) of signals. We adopt the Itakura-Saito (IS) divergence as the error measure, which is quite different from the conventional dictionary learning methods using the Euclidean (EUC) distance as the error measure. In addition, for enforcing the sparseness of coefficient matrix, we impose l ::: 1 ::: -norm minimization as the sparsity constraint. Numerical experiments on recovery of a dictionary show that the proposed dictionary learning algorithm performs better than other currently available algorithms which use Euclidean distance as the error measure.


title: A noise-estimation algorithm for highly non-stationary environments

abstract: A noise-estimation algorithm is proposed for highly non-stationary noise environments. The noise estimate is updated by averaging the noisy speech power spectrum using time and frequency dependent smoothing factors, which are adjusted based on signal-presence probability in individual frequency bins. Signal presence is determined by computing the ratio of the noisy speech power spectrum to its local minimum, which is updated continuously by averaging past values of the noisy speech power spectra with a look-ahead factor. The local minimum estimation algorithm adapts very quickly to highly non-stationary noise environments. This was confirmed with formal listening tests which indicated that the proposed noise-estimation algorithm when integrated in speech enhancement was preferred over other noise-estimation algorithms.


title: On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex

abstract: In this paper we present a very exciting overlap between emergent nano technology and neuroscience. We are linking one type of memristor nano technology devices to the biological synaptic update rule known as Spike-Time-Dependent-Plasticity found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage driven memristors and focus our discussions on a behavioral macro model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forwards but also backwards. One critical aspect is to use neurons that generate spikes of specific shapes. By changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We show how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three terminal memristive type devices. All files used for the simulations are made available through the journal web site.


Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks


title: Words in Motion: Kinesthetic Language Learning in Virtual Reality

abstract: Embodied theories of language propose that the way we communicate verbally is grounded in our body. Nevertheless, the way a second language is conventionally taught does not capitalize on kinesthetic modalities. The tracking capabilities of room-scale virtual reality systems afford a way to incorporate kinesthetic learning in language education. We present Words in Motion, a virtual reality language learning system that reinforces associations between word-action pairs by recognizing a student's movements and presenting the corresponding name of the performed action in the target language. Results from a user study involving 57 participants suggest that the kinesthetic approach in virtual reality has less immediate learning gain in comparison to a text-only condition and no immediate difference with participants in a non-kinesthetic virtual reality condition. However, virtual kinesthetic learners showed significantly higher retention rates after a week of exposure than all other conditions and higher performance than non-kinesthetic virtual reality learners. Positive correlation between the times a word-action pair was executed and the times a word was remembered by the subjects, supports that virtual reality can impact language learning by leveraging kinesthetic elements.


4.7 Statistical Modeling of Photographic Images


title: Automated Short Answer Scoring using Weighted Cosine Coefficient

abstract: Scoring short answer question (consisting of at most 100 words) requires a special treatment, particularly on the term weighting process. With such limitation, it is therefore infeasible to have term weighting with term frequency model because the frequency of term appearance is very rare. This paper proposes a new method, named Weighted Cosine Coefficient. The experiment result shows that Weighted Cosine Coefficient method can improve the correlation value on Automated Short Answer Scoring (ASAS) system compared to that of Jaccard Coefficient, Dice Coefficient, Cosine Coefficient, Weighted Jaccard Coefficient, and Weighted Dice Coefficient. The average improvement of the correlation value on the tested data set is 0.56.


title: Transforming the Dynamic Response of Robotic Structures and Systems Through Laminar Jamming

abstract: Researchers have developed variable-impedance mechanisms to control the dynamic response of robotic systems and improve their adaptivity, robustness, and efficiency. However, these mechanisms have limitations in size, cost, and convenience, particularly for variable damping. We demonstrate that laminar jamming structures can transform the dynamic response of robotic structures and systems while overcoming these limitations. In laminar jamming, an external pressure gradient is applied to a laminate of compliant material, changing its stiffness and damping. In this latter, we combine analysis, simulation, and characterization to formulate a lumped-parameter model that captures the nonlinear mechanical behavior of jamming structures and can be used to rapidly simulate their dynamic response. We illustrate that by adjusting the vacuum pressure, the fundamental features of the dynamic response (i.e., frequency, amplitude, decay rate, and steady-state value) can be tuned on command. Finally, we demonstrate that jamming structures can be integrated into soft structures and traditional rigid robots to considerably alter their response to impacts. With the models and demonstrations provided here, researchers may move further toward building versatile and transformative robots.


title: Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters

abstract: In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as dirt, illumination highlights or blur.


title: The BUGS Book: A Practical Introduction to Bayesian Analysis

abstract: Introduction: Probability and Parameters Probability Probability distributions Calculating properties of probability distributions Monte Carlo integration Monte Carlo Simulations Using BUGS Introduction to BUGS DoodleBUGS Using BUGS to simulate from distributions Transformations of random variables Complex calculations using Monte Carlo Multivariate Monte Carlo analysis Predictions with unknown parameters Introduction to Bayesian Inference Bayesian learning Posterior predictive distributions Conjugate Bayesian inference Inference about a discrete parameter Combinations of conjugate analyses Bayesian and classical methods Introduction to Markov Chain Monte Carlo Methods Bayesian computation Initial values Convergence Efficiency and accuracy Beyond MCMC Prior Distributions Different purposes of priors Vague, 'objective' and 'reference' priors Representation of informative priors Mixture of prior distributions Sensitivity analysis Regression Models Linear regression with normal errors Linear regression with non-normal errors Nonlinear regression with normal errors Multivariate responses Generalised linear regression models Inference on functions of parameters Further reading Categorical Data 2 x 2 tables Multinomial models Ordinal regression Further reading Model Checking and Comparison Introduction Deviance Residuals Predictive checks and Bayesian p-values Model assessment by embedding in larger models Model comparison using deviances Bayes factors Model uncertainty Discussion on model comparison Prior-data conflict Issues in Modelling Missing data Prediction Measurement error Cutting feedback New distributions Censored, truncated and grouped observations Constrained parameters Bootstrapping Ranking Hierarchical Models Exchangeability Priors Hierarchical regression models Hierarchical models for variances Redundant parameterisations More general formulations Checking of hierarchical models Comparison of hierarchical models Further resources Specialised Models Time-to-event data Time series models Spatial models Evidence synthesis Differential equation and pharmacokinetic models Finite mixture and latent class models Piecewise parametric models Bayesian nonparametric models Different Implementations of BUGS Introduction BUGS engines and interfaces Expert systems and MCMC methods Classic BUGS WinBUGS OpenBUGS JAGS A Appendix: BUGS Language Syntax Introduction Distributions Deterministic functions Repetition Multivariate quantities Indexing Data transformations Commenting B Appendix: Functions in BUGS Standard functions Trigonometric functions Matrix algebra Distribution utilities and model checking Functionals and differential equations Miscellaneous C Appendix: Distributions in BUGS Continuous univariate, unrestricted range Continuous univariate, restricted to be positive Continuous univariate, restricted to a finite interval Continuous multivariate distributions Discrete univariate distributions Discrete multivariate distributions Bibliography Index


title: Flow Based Security for IoT Devices Using an SDN Gateway

abstract: With near exponential growth predicted in the number of Internet of Things (IoT) based devices within networked systems there is need of a means of providing their flexible and secure integration. Software Defined Networking (SDN) is a concept that allows for the centralised control and configuration of network devices, and also provides opportunities for the dynamic control of network traffic. This paper proposes the use of an SDN gateway as a distributed means of monitoring the traffic originating from and directed to IoT based devices. This gateway can then both detect anomalous behaviour and perform an appropriate response (blocking, forwarding, or applying Quality of Service). Initial results demonstrate that, while the addition of the attack detection functionality has an impact on the number of flow installations possible per second, it can successfully detect and block TCP and ICMP flood based attacks.


title: A Survey of Definitions and Models of Exploratory Search

abstract: Exploratory search has an unclear and open-ended definition. The complexity of the task and the difficulty of defining this activity are reflected in the limits of existing evaluation methods for exploratory search systems. In order to improve them, we intend to design an evaluation method based on a user-centered model of exploratory search. In this work, we identified and defined the characteristics of exploratory search and used them as an information seeking model evaluation grid. We tested this analytic grid on two information seeking models: Ellis' and Marchionini's models. The results show that Marchonini's model does not match our evaluation method's requirements whereas on the other hand Ellis' model could be adapted to better suit exploratory search.


title: Augmented climbing: interacting with projected graphics on a climbing wall

abstract: This paper describes our efforts in developing a novel augmented climbing wall. Our system combines projected graphics on an artificial climbing wall and body tracking using computer vision technology. The system is intended for accelerating motor skill learning or to make monotonous parts of the training fun by adding relevant goals and encouraging social collaboration. We describe six initial prototypes and the feedback obtained from testing them with intermediate and experienced climbers.


title: Assessing loan risks: a data mining case study

abstract: The USDA's (US Department of Agriculture) Rural Housing Service administers a loan program that lends or guarantees mortgage loans to people living in rural areas. To administer these nearly 600000 loans, the department maintains extensive information about each one in its data warehouse. As with most lending programs, some USDA loans perform better than others. Basic data mining techniques helped the USDA's Rural Housing Service better understand and classify problem loans.


A METHODOLOGY FOR REDESIGNING AN ORGANIZATIONAL STRUCTURE BASED ON BUSINESS PROCESS MODELS USING SNA TECHNIQUES


title: Double Modulation Technique for a ZVS Self-Oscillating Half-Bridge Inverter

abstract: This paper proposes a simple power control scheme in order to convert the conventional nonadjustable self-oscillating half-bridge series-resonant inverter into an adjustable one. The proposed topology, based on a self-oscillating structure, can automatically ensure the turning on and the turning off of the switches without any external control. A simple modification in one of the gate drivers side circuit allows one to adjust the output power without compromising the simplicity, reliability, and low cost, which characterize the self-oscillating half-bridge inverter. Moreover, this topology achieves a stable and efficient zero-voltage switching over the entire operating range with a constant switching frequency; as a consequence, the inverter overall efficiency is significantly improved. Experimental results are proposed to validate the analysis and to demonstrate the performance of the proposed control scheme.


title: Integrated SDN/NFV management and orchestration architecture for dynamic deployment of virtual SDN control instances for virtual tenant networks [invited]

abstract: Software-defined networking (SDN) and network function virtualization (NFV) have emerged as the most promising candidates for improving network function and protocol programmability and dynamic adjustment of network resources. On the one hand, SDN is responsible for providing an abstraction of network resources through well-defined application programming interfaces. This abstraction enables SDN to perform network virtualization, that is, to slice the physical infrastructure and create multiple coexisting application-specific virtual tenant networks (VTNs) with specific quality-of-service and service-levelagreement requirements, independent of the underlying optical transport technology and network protocols. On the other hand, the notion of NFV relates to deploying network functions that are typically deployed in specialized and dedicated hardware, as software instances [called virtual network functions (VNFs)] running on commodity servers (e.g., in data centers) through software virtualization techniques. Despite all the attention that has been given to virtualizing IP functions (e.g., firewall; authentication, authorization, and accounting) or Long-Term Evolution control functions (e.g., mobility management entity, serving gateway, and packet data network gateway), some transport control functions can also be virtualized and moved to the cloud as a VNF. In this work we propose virtualizing the tenant SDN control functions of a VTN and moving them into the cloud. The control of a VTN is a key requirement associated with network virtualization, since it allows the dynamic programming (i.e., direct control and configuration) of the virtual resources allocated to the VTN. We experimentally assess and evaluate the first SDN/NFV orchestration architecture in a multipartner testbed to dynamically deploy independent SDN controller instances for each instantiated VTN and to provide the required connectivity within minutes.


title: Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes

abstract: In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features-point and line segments-have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.


title: Meta-Analysis of Combination Therapy of Chinese Herbs Plus Interferon and Ribavirin in Patients with Chronic Hepatitis C

abstract: BACKGROUND We aimed to evaluate the combination therapy of Chinese herbs plus interferon and ribavirin in treatment of patients with chronic hepatitis C (CHC). MATERIAL AND METHODS Related databases were searched to identify randomized controlled trials (RCTs) that evaluated biochemical response, virological response, histological response, and/or adverse reactions to combination therapy of interferon and ribavirin with and without Chinese herbs. The RR (relative risk) with a 95% confidence interval (CI) was calculated. Sensitivity analysis was conducted by omitting one study at a time. Publication bias among the eligible studies was evaluated by Egger's test. RESULTS A total of 17 RCTs matched the selection criteria. Overall, combination therapies of Chinese herbs plus interferon and ribavirin achieved significantly higher ALT (alanine transaminase) and ETVR (the end-of-treatment viral response), and significantly lower levels of HA (hyaluronic acid), LN (laminin), PC III (procollagen iii peptide), IV-C (type IV collagen), decreased LC (decreasing leukocyte count), ATF (abnormal thyroid function), psychosis, and anemia in CHC patients compared with those treated without Chinese herbs. Sensitivity analysis showed no changes and no potential publication bias was found. CONCLUSIONS The current evidence suggests that combination therapy of Chinese herb plus interferon and ribavirin yields better outcome and fewer adverse events in CHC patients than that of interferon plus ribavirin therapy.


title: Image formation through walls using a distributed radar sensor array

abstract: Through the wall surveillance is a difficult but important problem for both law enforcement and military personnel. Getting information on both the internal features of a structure and the location of people inside improves the operational effectiveness in search and rescue, hostage, and barricade situations. However, the electromagnetic properties of walls constrain the choices available as sensor candidates. We have demonstrated that a high range resolution radar operating between 450 MHz and 2 GHz can be used with a fixed linear array of antennas to produce images and detect motion through both interior and exterior walls. While the experimental results are good, it has been shown that the linear array causes signal processing artifacts that appear as ghosts in the resultant images. By moving toward a sensor concept where the antennas in the array are randomly spaced, the effect of ghost images can be reduced and operational and performance benefits gained.


title: A Light-Weight Permutation Based Method for Data Privacy in Mobile Cloud Computing

abstract: Cloud computing paradigm provides virtual IT infrastructures with a set of resources that are shared with multi-tenant users. Data Privacy is one of the major challenges when users outsource their data to a cloud computing system. Privacy can be violated by the cloud vendor, vendor's authorized users, other cloud users, unauthorized users, or external malicious entities. Encryption is one of the solutions to protect and maintain privacy of cloud-stored data. However, encryption methods are complex and expensive for mobile devices. In this paper, we propose a new light-weight method for mobile clients to store data on one or multiple clouds by using pseudo-random permutation based on chaos systems. The proposed method can be used in the client mobile devices to store data in the cloud(s) without using cloud computing resources for encryption to maintain user's privacy. We consider JPEG image format as a case study to present and evaluate the proposed method. Our experimental results show that the proposed method achieve superior performance compared to over encryption methods, such as AES and encryption on JPEG encoders while protecting the mobile user data privacy. We review major security attack scenarios against the proposed method that shows the level of security.


title: DIRD is an illumination robust descriptor

abstract: Many robotics applications nowadays use cameras for various task such as place recognition, localization, mapping etc. These methods heavily depend on image descriptors. A plethora of descriptors have recently been introduced but hardly any address the problem of illumination robustness. Herein we introduce an illumination robust image descriptor which we dub DIRD (Dird is an Illumination Robust Descriptor). First a set of Haar features are computed and individual pixel responses are normalized to L2 unit length. Thereafter features are pooled over a predefined neighborhood region. The concatenation of several such features form the basis DIRD vector. These features are then quantized to maximize entropy allowing (among others) a binary version of DIRD consisting of only ones and zeros for very fast matching. We evaluate DIRD on three test sets and compare its performance with (extended) USURF, BRIEF and a baseline gray level descriptor. All proposed DIRD variants substantially outperform these methods by times more than doubling the performance of USURF and BRIEF.


title: Experimental realization of quantum algorithm for solving linear systems of equations

abstract: National Key Basic Research Program of China [2013CB921800, 2014CB848700]; National Natural Science Foundation of China [11227901, 91021005, 11375167, 11004181, 11161160553]; Strategic Priority Research Program (B) of the CAS [XDB01030400]; NSF Center for Quantum Information and Computation for Chemistry [CHE-1037992]


title: Multi-dimensional Bin Packing Problems with Guillotine Constraints

abstract: The problem addressed in this paper is the decision problem of determining if a set of multi-dimensional rectangular boxes can be orthogonally packed into a rectangular bin while satisfying the requirement that the packing should be guillotine cuttable. That is, there should exist a series of face parallel straight cuts that can recursively cut the bin into pieces so that each piece contains a box and no box has been intersected by a cut. The unrestricted problem is known to be NP-hard. In this paper we present a generalization of a constructive algorithm for the multi-dimensional bin packing problem, with and without the guillotine constraint, based on constraint programming.


Abstract A Sarsa based Autonomous Stock Trading Agent


title: An Integration of CoTraining and Affinity Propagation for PU Text Classification

abstract: Under the framework of PU(Positive data and Unlabeled data), this paper originally proposes a three-setp algorithm. First, CoTraining is employed for filtering out the likely positive data from the unlabeled dataset U. Second, affinity propagation (AP) approach attempts to pick out the strong positive from likely positive set which is produced in first step. Those data picked out can be supplied to positive dataset P. Finally, a linear One-Class SVM will learn from both the purified U as negative and the expanded P as positive. Because of the algorithm's characteristic of automatic expanding positive dataset, the proposed algorithm especially performs well in situations where given positive dataset P is insufficient. A comprehensive experiment had proved that our algorithm is preferable to the existing ones.


title: Parametric shape-from-shading by radial basis functions

abstract: We present a new method of shape from shading by using radial basis functions to parameterize the object depth. The radial basis functions are deformed by adjusting their centers, widths, and weights such that the intensity errors are minimized. The initial centers and widths are arranged hierarchically to speed up convergence and to stabilize the solution. Although the smoothness constraint is used, it can be eventually dropped out without causing instabilities in the solution. An important feature of our parametric shape-from-shading method is that it offers a unified framework for integration of multiple sensory information. We show that knowledge about surface depth and/or surface normals anywhere in the image can be easily incorporated into the shape from shading process. It is further demonstrated that even qualitative knowledge can be used in shape from shading to improve 3D reconstruction. Experimental comparisons of our method with several existing ones are made by using both synthetic and real images. Results show that our solution is more accurate than the others.


title: On the number of response regions of deep feed forward networks with piece-wise linear activations

abstract: This paper explores the complexity of deep feedforward networks with linear pre-synaptic couplings and rectified linear activations. This is a contribution to the growing body of work contrasting the representational power of deep and shallow network architectures. In particular, we offer a framework for comparing deep and shallow models that belong to the family of piecewise linear functions based on computational geometry. We look at a deep rectifier multi-layer perceptron (MLP) with linear outputs units and compare it with a single layer version of the model. In the asymptotic regime, when the number of inputs stays constant, if the shallow model has $kn$ hidden units and $n_0$ inputs, then the number of linear regions is $O(k^{n_0}n^{n_0})$. For a $k$ layer model with $n$ hidden units on each layer it is $\Omega(\left\lfloor {n}/{n_0}\right\rfloor^{k-1}n^{n_0})$. The number $\left\lfloor{n}/{n_0}\right\rfloor^{k-1}$ grows faster than $k^{n_0}$ when $n$ tends to infinity or when $k$ tends to infinity and $n \geq 2n_0$. Additionally, even when $k$ is small, if we restrict $n$ to be $2n_0$, we can show that a deep model has considerably more linear regions that a shallow one. We consider this as a first step towards understanding the complexity of these models and specifically towards providing suitable mathematical tools for future analysis.


title: Improving Click Fraud Detection by Real Time Data Fusion

abstract: Click fraud is a type of Internet crime that occurs in pay per click online advertising when a person, automated script, or computer program imitates a legitimate user of a Web browser clicking on an ad, for the purpose of generating a charge per click without having actual interest in the target of the ad's link. Most of the available commercial solutions are just click fraud reporting systems, not real-time click fraud detection and prevention systems. A new solution is proposed in this paper that will analyze the detailed user click activities based on data collected form different sources. More information about each click enables better evaluation of the quality of click traffic. We utilize the multi source data fusion to merge client side and server side activities. Proposed solution is integrated in our CCFDP V1.0 system for a real-time detection and prevention of click fraud. We have tested the system with real world data from an actual ad campaign where the results show that additional real-time information about clicks improve the quality of click fraud analysis.


title: An agile strategy for implementing CMMI project management practices in software organizations

abstract: The transition from traditional methods to agile project management methods and the changes needed to obtain their real benefits are difficult to achieve. Applying agile methodologies based on maturity models such as Capability Maturity Model Integration (CMMI) has been the focus of much debate in academic circles and in the software industry. Given the high and widespread rate of failure in adopting agility, and also arising from many of the reasons given to project management, this paper proposes a strategy for implementing agile project management in companies which seek to comply with CMMI by making use of the best practices of Agile Project Management and of the main agile methods and frameworks in a gradual and disciplined manner thereby contributing to the increased success rate of software development projects.


abstract: Abstract Ziegler-Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R. Estimating the prevalence of limb loss in the United States: 2005 to 2050. Objective To estimate the current prevalence of limb loss in the United States and project the future prevalence to the year 2050. Design Estimates were constructed using age-, sex-, and race-specific incidence rates for amputation combined with age-, sex-, and race-specific assumptions about mortality. Incidence rates were derived from the 1988 to 1999 Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project, corrected for the likelihood of reamputation among those undergoing amputation for vascular disease. Incidence rates were assumed to remain constant over time and applied to historic mortality and population data along with the best available estimates of relative risk, future mortality, and future population projections. To investigate the sensitivity of our projections to increasing or decreasing incidence, we developed alternative sets of estimates of limb loss related to dysvascular conditions based on assumptions of a 10% or 25% increase or decrease in incidence of amputations for these conditions. Setting Community, nonfederal, short-term hospitals in the United States. Participants Persons who were discharged from a hospital with a procedure code for upper-limb or lower-limb amputation or diagnosis code of traumatic amputation. Interventions Not applicable. Main Outcome Measures Prevalence of limb loss by age, sex, race, etiology, and level in 2005 and projections to the year 2050. Results In the year 2005, 1.6 million persons were living with the loss of a limb. Of these subjects, 42% were nonwhite and 38% had an amputation secondary to dysvascular disease with a comorbid diagnosis of diabetes mellitus. It is projected that the number of people living with the loss of a limb will more than double by the year 2050 to 3.6 million. If incidence rates secondary to dysvascular disease can be reduced by 10%, this number would be lowered by 225,000. Conclusions One in 190 Americans is currently living with the loss of a limb. Unchecked, this number may double by the year 2050.


title: A Universal Part-of-Speech Tagset

abstract: To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-ofspeech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common partsof-speech for 22 different languages. We highlight the use of this resource via two experiments, including one that reports competitive accuracies for unsupervised grammar induction without gold standard part-of-speech tags.


title: Modeling Documents with a Deep Boltzmann Machine

abstract: We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.


title: Can the Elephants Handle the NoSQL Onslaught?

abstract: In this new era of "big data", traditional DBMSs are under attack from two sides. At one end of the spectrum, the use of document store NoSQL systems (e.g. MongoDB) threatens to move modern Web 2.0 applications away from traditional RDBMSs. At the other end of the spectrum, big data DSS analytics that used to be the domain of parallel RDBMSs is now under attack by another class of NoSQL data analytics systems, such as Hive on Hadoop. So, are the traditional RDBMSs, aka "big elephants", doomed as they are challenged from both ends of this "big data" spectrum? In this paper, we compare one representative NoSQL system from each end of this spectrum with SQL Server, and analyze the performance and scalability aspects of each of these approaches (NoSQL vs. SQL) on two workloads (decision support analysis and interactive data-serving) that represent the two ends of the application spectrum. We present insights from this evaluation and speculate on potential trends for the future.


title: Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

abstract: In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.


title: Digital Service Innovation from Open Data: Exploring the Value Proposition of an Open Data Marketplace

abstract: Open data marketplaces have emerged as a mode of addressing open data adoption barriers. However, knowledge of how such marketplaces affect digital service innovation in open data ecosystems is limited. This paper explores their value proposition for open data users based on an exploratory case study. Five prominent perceived values are identified: lower task complexity, higher access to knowledge, increased possibilities to influence, lower risk and higher visibility. The impact on open data adoption barriers is analyzed and the consequences for ecosystem sustainability is discussed. The paper concludes that open data marketplaces can lower the threshold of using open data by providing better access to open data and associated support services, and by increasing knowledge transfer within the ecosystem.


title: NoC synthesis flow for customized domain specific multiprocessor systems-on-chip

abstract: The growing complexity of customizable single-chip multiprocessors is requiring communication resources that can only be provided by a highly-scalable communication infrastructure. This trend is exemplified by the growing number of network-on-chip (NoC) architectures that have been proposed recently for system-on-chip (SoC) integration. Developing NoC-based systems tailored to a particular application domain is crucial for achieving high-performance, energy-efficient customized solutions. The effectiveness of this approach largely depends on the availability of an ad hoc design methodology that, starting from a high-level application specification, derives an optimized NoC configuration with respect to different design objectives and instantiates the selected application specific on-chip micronetwork. Automatic execution of these design steps is highly desirable to increase SoC design productivity. This work illustrates a complete synthesis flow, called Netchip, for customized NoC architectures, that partitions the development work into major steps (topology mapping, selection, and generation) and provides proper tools for their automatic execution (SUNMAP, xpipescompiler). The entire flow leverages the flexibility of a fully reusable and scalable network components library called xpipes, consisting of highly-parameterizable network building blocks (network interface, switches, switch-to-switch links) that are design-time tunable and composable to achieve arbitrary topologies and customized domain-specific NoC architectures. Several experimental case studies are presented In the work, showing the powerful design space exploration capabilities of the proposed methodology and tools.


title: Sensorfusion Using Spatio-Temporal Aligned Video and Lidar for Improved Vehicle Detection

abstract: This contribution introduces a novel approach to cross-calibrate automotive vision and ranging sensors. The resulting sensor alignment allows the incorporation of multiple sensor data into a detection and tracking framework. Exemplarily, we show how a realtime vehicle detection system, intended for emergency breaking or ACC applications, benefits from the low level fusion of multibeam lidar and vision sensor measurements in discrimination performance and computational complexity.


title: TrustShadow: Secure Execution of Unmodified Applications with ARM TrustZone

abstract: The rapid evolution of Internet-of-Things (IoT) technologies has led to an emerging need to make them smarter. A variety of applications now run simultaneously on an ARM-based processor. For example, devices on the edge of the Internet are provided with higher horsepower to be entrusted with storing, processing and analyzing data collected from IoT devices. This significantly improves efficiency and reduces the amount of data that needs to be transported to the cloud for data processing, analysis and storage. However, commodity OSes are prone to compromise. Once they are exploited, attackers can access the data on these devices. Since the data stored and processed on the devices can be sensitive, left untackled, this is particularly disconcerting. In this paper, we propose a new system, TrustShadow that shields legacy applications from untrusted OSes. TrustShadow takes advantage of ARM TrustZone technology and partitions resources into the secure and normal worlds. In the secure world, TrustShadow constructs a trusted execution environment for security-critical applications. This trusted environment is maintained by a lightweight runtime system that coordinates the communication between applications and the ordinary OS running in the normal world. The runtime system does not provide system services itself. Rather, it forwards requests for system services to the ordinary OS, and verifies the correctness of the responses. To demonstrate the efficiency of this design, we prototyped TrustShadow on a real chip board with ARM TrustZone support, and evaluated its performance using both microbenchmarks and real-world applications. We showed TrustShadow introduces only negligible overhead to real-world applications.


title: Biotechnological production of cellulose by acetic acid bacteria: current state and perspectives

abstract: Bacterial cellulose is an attractive biopolymer for a number of applications including food, biomedical, cosmetics, and engineering fields. In addition to renewability and biodegradability, its unique structure and properties such as chemical purity, nanoscale fibrous 3D network, high water-holding capacity, high degree of polymerization, high crystallinity index, light transparency, biocompatibility, and mechanical features offer several advantages when it is used as native polymer or in composite materials. Structure and properties play a functional role in both the biofilm life cycle and biotechnological applications. Among all the cellulose-producing bacteria, acetic acid bacteria of the Komagataeibacter xylinus species play the most important role because they are considered the highest producers. Bacterial cellulose from acetic acid bacteria is widely investigated as native and modified biopolymer in functionalized materials, as well as in terms of differences arising from the static or submerged production system. In this paper, the huge amount of knowledge on basic and applied aspects of bacterial cellulose is reviewed to the aim to provide a comprehensive viewpoint on the intriguing interplay between the biological machinery of synthesis, the native structure, and the factors determining its nanostructure and applications. Since in acetic acid bacteria biofilm and cellulose production are two main phenotypes with industrial impact, new insights into biofilm production are provided.


Uc_Sense: An Ontology for Scientifically-based Unambiguous Characterization of Sensory Experiences


title: Distilling free-form natural laws from experimental data

abstract: For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the “alphabet” used to describe those systems.


title: The Role of Violent Media Preference in Cumulative Developmental Risk for Violence and General Aggression

abstract: The impact of exposure to violence in the media on the long-term development and short-term expression of aggressive behavior has been well documented. However, gaps in this literature remain, and in particular the role of violent media exposure in shaping violent and other serious antisocial behavior has not been investigated. Further, studies of violent media effects typically have not sampled from populations with confirmed histories of violent and/or nonviolent antisocial behavior. In this study, we analyzed data on 820 youth, including 390 juvenile delinquents and 430 high school students, to examine the relation of violent media use to involvement in violence and general aggression. Using criterion scores developed through cross-informant modeling of data from self, parent/guardian, and teacher/staff reports, we observed that childhood and adolescent violent media preferences contributed significantly to the prediction of violence and general aggression from cumulative risk totals. Findings represent a new and important direction for research on the role of violent media use in the broader matrix of risk factors for youth violence.


title: The intellectual core and impact of the knowledge management academic discipline

abstract: Purpose – The purpose of this paper is two‐fold: to explore the intellectual core of the knowledge management (KM) academic discipline in order to test whether it exhibits signs of a reference discipline; and to analyze the theoretical and practical impact of the discipline.Design/methodology/approach – The most influential articles published in the Journal of Knowledge Management were selected and their cited and citing works were scientometrically analysed.Findings – The KM discipline: builds its knowledge primarily upon research reports published in the English language; successfully disseminates its knowledge in both English and non‐English publications; does not exhibit a problematic self‐citation behavior; uses books and practitioner journals in the development of KM theory; converts experiential knowledge into academic knowledge; is not yet a reference discipline, but is progressing well towards becoming one; exerts a somewhat limited direct impact on practice; and is not a scientific fad.Practical...


title: Learning Styles' Recognition in E-Learning Environments with Feed-Forward Neural Networks.

abstract: People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for determining a student learning style; and then adapting their behaviour according to the students' styles. However, the use of questionnaires is shown to be not only a time-consuming investment but also an unreliable method for acquiring learning style characterisations. In this paper, we present an approach to recognize automatically the learning styles of individual students according to the actions that he or she has performed in an e-learning environment. This recognition technique is based upon feed-forward neural networks.


title: A Statistical Language Modeling Approach to Online Deception Detection

abstract: Online deception is disrupting our daily life, organizational process, and even national security. Existing approaches to online deception detection follow a traditional paradigm by using a set of cues as antecedents for deception detection, which may be hindered by ineffective cue identification. Motivated by the strength of statistical language models (SLMs) in capturing the dependency of words in text without explicit feature extraction, we developed SLMs to detect online deception. We also addressed the data sparsity problem in building SLMs in general and in deception detection in specific using smoothing and vocabulary pruning techniques. The developed SLMs were evaluated empirically with diverse datasets. The results showed that the proposed SLM approach to deception detection outperformed a state-of-the-art text categorization method as well as traditional feature-based methods.


title: Considerations for optimization of microRNA PCR assays for molecular diagnosis

abstract: The remarkable stability of microRNAs in biofluids underlies their potential as biomarkers, but their small size presents challenges for detection by RT-qPCR. The heterogeneity of microRNAs, with each one comprising a series of variants or 'isomiRs', adds additional complexity. Presented here are the key considerations for use of RT-qPCR to measure microRNAs and their isomiRs, with a focus on plasma. Modified nucleotides can be incorporated into primer sequences to enhance affinity and provide increased specificity and sensitivity for RT-qPCR assays. Approaches based upon polyA tailing and use of a common oligo(dT)-based reverse transcription oligonucleotide will detect most isomiRs. Conversely, stem-loop RT oligonucleotides and sequence specific probes can enable detection of specific isomiRs of interest. Next generation sequencing of all the products of a microRNA RT-PCR reaction is a promising new approach for both microRNA quantification and characterization.


title: Hierarchical classification for dealing with the Class imbalance problem

abstract: The aim of classification in machine learning is to utilize knowledge gained from applying learning algorithms on a given data so as determine what class an unlabelled data having same pattern belongs to. However, algorithms do not learn properly when a massive difference in size between data classes exist. This classification problem exists in many real world application domains and has been a popular area of focus by machine learning and data mining researchers. The class imbalance problem is further made complex with the presence of associative data difficult factors. The duo have proven to greatly deteriorate classification performance. This paper introduces a two-phased data level approach for binary classes which entails the temporary re-labelling of classes. The proposed approach takes advantage of the local neighbourhood of the minority instances to identify and treat difficult examples belonging to both classes. Its outcome was satisfactory when compared against various data-level methods using datasets extracted from KEEL and UCI datasets repository.


title: Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

abstract: Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.


title: Hyperlocal Home Location Identification of Twitter Profiles

abstract: Knowledge of user's location provides valuable information that can be used to build region-specific models (e.g. language used in a particular region and map-based visualisations of social media posts). Determining a user's home location presents a challenge. Current approaches make use of geo-located tweets or textual cues but are often only able to predict location to a coarse level of granularity (e.g. city level), while many applications require finer-grained (hyperlocal) predictions. A novel approach for hyperlocal home location identification, based on clustering of geo-located tweets, is presented. A gold-standard data set for home location identification is developed by making use of indicative phrases in geo-located tweets. We find that the cluster-based approaches outperform current techniques for hyperlocal location prediction.


title: From QoS to QoE: A Tutorial on Video Quality Assessment

abstract: Quality of experience (QoE) is the perceptual quality of service (QoS) from the users' perspective. For video service, the relationship between QoE and QoS (such as coding parameters and network statistics) is complicated because users' perceptual video quality is subjective and diversified in different environments. Traditionally, QoE is obtained from subjective test, where human viewers evaluate the quality of tested videos under a laboratory environment. To avoid high cost and offline nature of such tests, objective quality models are developed to predict QoE based on objective QoS parameters, but it is still an indirect way to estimate QoE. With the rising popularity of video streaming over the Internet, data-driven QoE analysis models have newly emerged due to availability of large-scale data. In this paper, we give a comprehensive survey of the evolution of video quality assessment methods, analyzing their characteristics, advantages, and drawbacks. We also introduce QoE-based video applications and, finally, identify the future research directions of QoE.


title: THE INFLUENCE OF PERSONALITY TRAITS ON INFORMATION SEEKING BEHAVIOUR OF STUDENTS

abstract: The present study was undertaken with the objective to explore the influence of the five personality dimensions on the information seeking behaviour of the students in higher educational institutions. Information seeking behaviour is defined as the sum total of all those activities that are usually undertaken by the students of higher education to collect, utilize and process any kind of information needed for their studies. Data has been collected from 600 university students of the three broad disciplines of studies from the Universities of Eastern part of India (West Bengal). The tools used for the study were General Information schedule (GIS), Information Seeking Behaviour Inventory (ISBI) and NEO-FFI Personality Inventory. Product moment correlation has been worked out between the scores in ISBI and those in NEO-FFI Personality Inventory. The findings indicated that the five personality traits are significantly correlated to all the dimensions of information seeking behaviour of the university students.


title: Self-presentation and verbal deception: do self-presenters lie more

abstract: This study examined the effects of self-presentation goals on the amount and type of verbal deception used by participants in same-gender and mixed-gender dyads. Participants were asked to engage in a conversation that was secretly videotaped. Self-presentational goal was manipulated, where one member of the dyad (the self-presenter) was told to either appear (a) likable, (b) competent, or (c) was told to simply get to know his or her partner (control condition). After the conversation, self-presenters were asked to review a video recording of the interaction and identify the instances in which they had deceived the other person. Overall, participants told more lies when they had a goal to appear likable or competent compared to participants in the control condition, and the content of the lies varied according to self-presentation goal. In addition, lies told by men and women differed in content, although not in quantity.


title: Verification and validation meet planning and scheduling

abstract: A planning and scheduling (P&S) system takes as input a domain model and a goal, and produces a plan of actions to be executed, which will achieve the goal. A P&S system typically also offers plan execution and monitoring engines. Due to the non-deterministic nature of planning problems, it is a challenge to construct correct and reliable P&S systems, including, for example, declarative domain models. Verification and validation (V&V) techniques have been applied to address these issues. Furthermore, V&V systems have been applied to actually perform planning, and conversely, P&S systems have been applied to perform V&V of more traditional software. This article overviews some of the literature on the fruitful interaction between V&V and P&S.


title: Separate Source–Channel Coding for Transmitting Correlated Gaussian Sources Over Degraded Broadcast Channels

abstract: The problem of transmitting a pair of correlated Gaussian sources over degraded broadcast channels using optimal separate source and channel codes is studied. Upper bounds are derived for the rate penalty (in terms of channel uses per source symbol) and the power loss endured by separate coding compared to joint coding. Although source-channel separation is suboptimal in general, it is demonstrated that the performance of separate coding comes close to that of optimal joint coding, especially for low distortion pairs. In fact, in some cases, separate coding performs better than the best known joint schemes so far. It is also shown analytically that separate coding is optimal when either of the sources is to be reconstructed in a near-lossless fashion.


title: Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation

abstract: Domain adaptation addresses the problem where data instances of a source domain have different distributions from that of a target domain, which occurs frequently in many real life scenarios. This work focuses on unsupervised domain adaptation, where labeled data are only available in the source domain. We propose to interpolate subspaces through dictionary learning to link the source and target domains. These subspaces are able to capture the intrinsic domain shift and form a shared feature representation for cross domain recognition. Further, we introduce a quantitative measure to characterize the shift between two domains, which enables us to select the optimal domain to adapt to the given multiple source domains. We present experiments on face recognition across pose, illumination and blur variations, cross dataset object recognition, and report improved performance over the state of the art.


title: Overview of Control and Grid Synchronization for Distributed Power Generation Systems

abstract: Renewable energy sources like wind, sun, and hydro are seen as a reliable alternative to the traditional energy sources such as oil, natural gas, or coal. Distributed power generation systems (DPGSs) based on renewable energy sources experience a large development worldwide, with Germany, Denmark, Japan, and USA as leaders in the development in this field. Due to the increasing number of DPGSs connected to the utility network, new and stricter standards in respect to power quality, safe running, and islanding protection are issued. As a consequence, the control of distributed generation systems should be improved to meet the requirements for grid interconnection. This paper gives an overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines. In addition, control structures of the grid-side converter are presented, and the possibility of compensation for low-order harmonics is also discussed. Moreover, control strategies when running on grid faults are treated. This paper ends up with an overview of synchronization methods and a discussion about their importance in the control


title: Single Image Dehazing via Multi-scale Convolutional Neural Networks

abstract: The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multi-scale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.


title: Information retrieval and artificial intelligence

abstract: Abstract This paper addresses the relations between information retrieval (IR) and AI. It examines document retrieval, summarising its essential features and illustrating the state of its art by presenting one probabilistic model in detail, with some test results showing its value. The paper then analyses this model and related successful approaches, concentrating on and justifying their use of weak, redundant representation and reasoning. It goes on to other information management tasks and considers how the concepts and methods developed for retrieval may be applied to these, concluding by arguing that such ways of dealing with information may also have wider relevance to AI.


title: Through the HoloLens™ looking glass: augmented reality for extremity reconstruction surgery using 3D vascular models with perforating vessels

abstract: Precision and planning are key to reconstructive surgery. Augmented reality (AR) can bring the information within preoperative computed tomography angiography (CTA) imaging to life, allowing the surgeon to 'see through' the patient's skin and appreciate the underlying anatomy without making a single incision. This work has demonstrated that AR can assist the accurate identification, dissection and execution of vascular pedunculated flaps during reconstructive surgery. Separate volumes of osseous, vascular, skin, soft tissue structures and relevant vascular perforators were delineated from preoperative CTA scans to generate three-dimensional images using two complementary segmentation software packages. These were converted to polygonal models and rendered by means of a custom application within the HoloLens™ stereo head-mounted display. Intraoperatively, the models were registered manually to their respective subjects by the operating surgeon using a combination of tracked hand gestures and voice commands; AR was used to aid navigation and accurate dissection. Identification of the subsurface location of vascular perforators through AR overlay was compared to the positions obtained by audible Doppler ultrasound. Through a preliminary HoloLens-assisted case series, the operating surgeon was able to demonstrate precise and efficient localisation of perforating vessels.


title: Efficient and flexible chaotic communication waveform family

abstract: Harris has developed, implemented, and characterized a highly efficient and flexible digitally generated family of chaotic waveforms for high information density, anti-jam, LPI/LPD/LPE, and “self-encrypted” application spaces. This paper introduces an efficient mechanism to generate a chaotic signal digitally, while tailoring waveform characteristics for maximum performance in various application spaces, overcoming known limitations in DSSS communications. The paper discusses the desirable characteristics of the various chaotic waveform variants as well as the advantages these operational digital chaotic communication systems provide over traditional but yet to be practically realized analog based systems. The architecture of a hardware prototype implementation is presented along with key measured performance parameters. Harris bases our family of chaotic waveforms on an efficient number theoretic digital chaotic implementation that enables extreme epoch lengths without apparent chaotic attractors in a small and power efficient hardware or software footprint. The chaotic characteristics can be customized for the target application space. For example, the chaos can be shaped into a quadrature Gaussian distribution for maximum information density and maximum LPI/LPD/LPE effectiveness, it can have a constant amplitude envelope and uniformly distributed random phase for AJ applications, it can be simply divided into code division multiplexing or orthogonal waveform division for multiple access communications, or it can be easily adapted to hybrid distributions with time-varying characteristics to provide environment aware ‘cognitive’ links. Because of the absence of apparent attractors and code epoch times measured in googol-years (10100 years), the waveforms are extremely secure. We describe chaotic spread-spectrum waveforms in this paper, although chaotic techniques can be extended to other systems. As such, chaotic state synchronization becomes carrier frequency, phase, and timing synchronization of a digital communication system that are well understood and straight forward to implement. Adapting basic techniques used in direct sequence spread spectrum communications to the chaotic waveform enables a robust synchronization between independent chaotic circuits and their resulting waveforms. The paper briefly introduces the desirable features of a chaotic communication system and contrasts the advantages of digital and analog chaos based communication systems. We present the digital chaotic sequence generator architecture, followed by a prototype coherent chaotic communication system implementation, including characteristic measurements. Finally, we correlate the specific advantages of the different chaotic waveforms to a range of potential applications.


title: An image authentication method by applying Hamming code on rearranged bits

abstract: In 2007, Chan and Chang proposed an image authentication method using the Hamming code technique. The parity check bits were produced from pixels by using the Hamming code technique, and the produced bits were embedded in other pixels. When recovering, the method had to predict the value of the most-significant bit of each tampered pixel first. Then, the tampered pixel was able to be recovered by referring to the predicted bit and its parity check bits. However, using the most-significant bit is unsuitable because of the risk of making an incorrect prediction. In this paper, the parity check bits are produced from pixels whose bits have been rearranged. This way, the value of the most-significant bit of each tampered pixel can be determined according to its parity check bits. The recovery procedure is also modified to accommodate the rearranging procedure. The experimental results show that the proposed method has a better ability to recover the tampered areas, compared with Chan and Chang's method. Moreover, the quality of the authenticated images of the proposed method is also higher than that of Chan and Chang's method.


title: Evolvement of Information Security Research on Employees' Behavior: A Systematic Review and Future Direction

abstract: Information Security (IS) is one of the biggest concerns for many organizations. This concern has led many to focus a huge effort into studying different IS areas. One of these critical areas is the human aspect, where investigation of employees' behaviors has emerged as an important topic. In this paper, we conduct a systematic review of all empirical studies published on this topic. The review will highlight the theoretical and methodological development and the dissemination of related empirical studies in academic journals throughout the years. At the end of the review, future research considerations are discussed and shared.


Analyzing the effectiveness and applicability of co-training


title: PointFlowNet: Learning Representations for Rigid Motion Estimation From Point Clouds

abstract: Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.


title: Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead

abstract: This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields.


title: Early Recognition of 3D Human Actions

abstract: Action recognition is an important research problem of human motion analysis (HMA). In recent years, 3D observation-based action recognition has been receiving increasing interest in the multimedia and computer vision communities, due to the recent advent of cost-effective sensors, such as depth camera Kinect. This work takes this one step further, focusing on early recognition of ongoing 3D human actions, which is beneficial for a large variety of time-critical applications, e.g., gesture-based human machine interaction, somatosensory games, and so forth. Our goal is to infer the class label information of 3D human actions with partial observation of temporally incomplete action executions. By considering 3D action data as multivariate time series (m.t.s.) synchronized to a shared common clock (frames), we propose a stochastic process called dynamic marked point process (DMP) to model the 3D action as temporal dynamic patterns, where both timing and strength information are captured. To achieve even more early and better accuracy of recognition, we also explore the temporal dependency patterns between feature dimensions. A probabilistic suffix tree is constructed to represent sequential patterns among features in terms of the variable-order Markov model (VMM). Our approach and several baselines are evaluated on five 3D human action datasets. Extensive results show that our approach achieves superior performance for early recognition of 3D human actions.


title: Statistical data mining for efficient quality control in manufacturing

abstract: Extensive use of machines, flexible/re-configurable manufacturing and transition towards the fully automated factories call for intelligent use of information recorded during the manufacturing process. Modern manufacturing processes produce Terabytes of information during different stages of the process e.g sensor measurements, machine readings etc, and the major contributor of these big data sets are different quality control processes. In this article we will present methodology to extract valuable insight from manufacturing data. The proposed methodology is based on comparison of probabilities and extension of likelihood principles in statistics as a performance function for Genetic Algorithm.


title: Strategic Communication with Lying Costs

abstract: talk and verifiable disclosure games, unifying the orthogonal predictions they make under large conflicts of interest.


title: MPMA: mixture probabilistic matrix approximation for collaborative filtering

abstract: Matrix approximation (MA) is one of the most popular techniques for collaborative filtering (CF). Most existing MA methods train user/item latent factors based on a user-item rating matrix and then use the global latent factors to model all users/items. However, globally optimized latent factors may not reflect the unique interests shared among only subsets of users/items, without which unique interests of users may not be accurately modelled. As a result, existing MA methods, which cannot capture the uniqueness of different user/item, cannot provide optimal recommendation. ::: ::: In this paper, a mixture probabilistic matrix approximation (MPMA) method is proposed, which unifies globally optimized user/item feature vectors (on the entire rating matrix) and locally optimized user/item feature vectors (on subsets of user/item ratings) to improve recommendation accuracy. More specifically, in MPMA, a method is developed to find both globally and locally optimized user/item feature vectors. Then, a Gaussian mixture model is adopted to combine global predictions and local predictions to produce accurate rating predictions. Experimental study using MovieLens and Netflix datasets demonstrates that MPMA outperforms five state-of-the-art MA based CF methods in recommendation accuracy with good scalability.


title: Fabrication of zero-speed sensor using weakly coupled NiFe/CoFe multilayer films

abstract: A magnetic sensor consisting of NiFe/Al/sub 2/O/sub 3//CoFe thin film was fabricated. Magnetization reversal of the NiFe soft layer was asymmetric in switching to parallel and antiparallel alignments to magnetization of CoFe because of magnetostatic coupling between the NiFe and CoFe layers. When the magnetization of the NiFe was paralleled to that of CoFe, a steep switching accompanied a large Barkhausen jump-induced pulse voltage in a pickup coil as sensor output. Amplitude of the pulse was not dependent on frequency of an external magnetic field. The pulse voltage output was obtained from the sensor element consisting of the optimized film structure excited by the magnetic field at almost zero frequency. These features are also achieved by twisted FeCoV and other wire materials. However, an integrated on-chip sensor with electronic circuits can be fabricated with film-based materials. The film-based sensor elements fabricated in this study can be utilized for speed sensor, rotation sensor, flow meter, and other applications.


EFFECTIVENESS OF YOUTUBE ADVERTISING 2 The members of the Committee approve the thesis of


title: Context-Aware QoE Modelling, Measurement, and Prediction in Mobile Computing Systems

abstract: Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement, and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users’ QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.


title: Grid information services for distributed resource sharing

abstract: Grid technologies enable large-scale sharing of resources within formal or informal consortia of individuals and/or institutions: what are sometimes called virtual organizations. In these settings, the discovery, characterization, and monitoring of resources, services, and computations are challenging problems due to the considerable diversity; large numbers, dynamic behavior, and geographical distribution of the entities in which a user might be interested. Consequently, information services are a vital part of any Grid software infrastructure, providing fundamental mechanisms for discovery and monitoring, and hence for planning and adapting application behavior. We present an information services architecture that addresses performance, security, scalability, and robustness requirements. Our architecture defines simple low-level enquiry and registration protocols that make it easy to incorporate individual entities into various information structures, such as aggregate directories that support a variety of different query languages and discovery strategies. These protocols can also be combined with other Grid protocols to construct additional higher-level services and capabilities such as brokering, monitoring, fault detection, and troubleshooting. Our architecture has been implemented as MDS-2, which forms part of the Globus Grid toolkit and has been widely deployed and applied.


How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis.


title: Proactive network fault detection

abstract: The increasing role of communication networks in today's society results in a demand for higher levels of network availability and reliability. At the same time, fault management is becoming more difficult due to the dynamic nature and heterogeneity of networks. We propose an intelligent monitoring system using adaptive statistical techniques. The system continually learns the normal behavior of the network and detects deviations from the norm. Within the monitoring system, the measurements are segmented, and features extracted from the segments are used to describe the normal behavior of the measurement variables. This information is combined in the structure of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. Experimental results on real network data demonstrate that the proposed system can detect abnormal behavior before a fault actually occurs.