title: Object Tracking via Dual Linear Structured SVM and Explicit Feature Map

abstract: Structured support vector machine (SSVM) based methods have demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and expensive optimization limits their deployment in real-world applications. In this paper, we present a simple yet efficient dual linear SSVM (DLSSVM) algorithm to enable fast learning and execution during tracking. By analyzing the dual variables, we propose a primal classifier update formula where the learning step size is computed in closed form. This online learning method significantly improves the robustness of the proposed linear SSVM with lower computational cost. Second, we approximate the intersection kernel for feature representations with an explicit feature map to further improve tracking performance. Finally, we extend the proposed DLSSVM tracker with multi-scale estimation to address the "drift" problem. Experimental results on large benchmark datasets with 50 and 100 video sequences show that the proposed DLSSVM tracking algorithm achieves state-of-the-art performance.


title: Remarks on multi-output Gaussian process regression

abstract: Abstract Multi-output regression problems have extensively arisen in modern engineering community. This article investigates the state-of-the-art multi-output Gaussian processes (MOGPs) that can transfer the knowledge across related outputs in order to improve prediction quality. We classify existing MOGPs into two main categories as (1) symmetric MOGPs that improve the predictions for all the outputs, and (2) asymmetric MOGPs, particularly the multi-fidelity MOGPs, that focus on the improvement of high fidelity output via the useful information transferred from related low fidelity outputs. We review existing symmetric/asymmetric MOGPs and analyze their characteristics, e.g., the covariance functions (separable or non-separable), the modeling process (integrated or decomposed), the information transfer (bidirectional or unidirectional), and the hyperparameter inference (joint or separate). Besides, we assess the performance of ten representative MOGPs thoroughly on eight examples in symmetric/asymmetric scenarios by considering, e.g., different training data (heterotopic or isotopic), different training sizes (small, moderate and large), different output correlations (low or high), and different output sizes (up to four outputs). Based on the qualitative and quantitative analysis, we give some recommendations regarding the usage of MOGPs and highlight potential research directions.


title: Generative Statistical Models with Self-Emergent Grammar of Chord Sequences

abstract: ABSTRACTGenerative statistical models of chord sequences play crucial roles in music processing. To capture syntactic similarities among certain chords (e.g. in C major key, between G and G7 and between F and Dm), we study hidden Markov models and probabilistic context-free grammar models with latent variables describing syntactic categories of chord symbols and their unsupervised learning techniques for inducing the latent grammar from data. Surprisingly, we find that these models often outperform conventional Markov models in predictive power, and the self-emergent categories often correspond to traditional harmonic functions. This implies the need for chord categories in harmony models from the informatics perspective.


title: An FPGA-Based Cloud System for Massive ECG Data Analysis

abstract: In this brief, we propose a stand-alone system-on-a-programmable-chip (SOPC)-based cloud system to accelerate massive electrocardiogram (ECG) data analysis. The proposed system tightly couples network I/O handling hardware to data processing pipelines in a single field-programmable gate array (FPGA), offloading both networking operations and ECG data analysis. In this system, we first propose a massive-sessions optimized TCP/IP hardware stack using a macropipeline architecture to accelerate network packet processing. Second, we propose a streaming architecture to accelerate ECG signal processing, including QRS detection, feature extraction, and classification. We verify our design on XC6VLX550T FPGA using real ECG data. Compared to commercial servers, our system shows up to 38× improvement in performance and 142× improvement in energy efficiency.


title: Inhibition of Autophagy is Involved in the Protective Effects of Ginsenoside Rb1 on Spinal Cord Injury

abstract: Spinal cord injury (SCI) is a devastating neurological disorder. Autophagy is induced and plays a crucial role in SCI. Ginsenoside Rb1 (Rb1), one of the major active components extracted from Panax Ginseng CA Meyer, has exhibited neuroprotective effects in various neurodegenerative diseases. However, it remains unknown whether autophagy is involved in the neuroprotection of Rb1 on SCI. In this study, we examined the regulation of autophagy following Rb1 treatment and its involvement in the Rb1-induced neuroprotection in SCI and in vitro injury model. Firstly, we found that Rb1 treatment decreased the loss of motor neurons and promoted function recovery in the SCI model. Furthermore, we found that Rb1 treatment inhibited autophagy in neurons, and suppressed neuronal apoptosis and autophagic cell death in the SCI model. Finally, in the in vitro injury model, Rb1 treatment increased the viability of PC12 cells and suppressed apoptosis by inhibiting excessive autophagy, whereas stimulation of autophagy by rapamycin abolished the anti-apoptosis effect of Rb1. Taken together, these findings suggest that the inhibition of autophagy is involved in the neuroprotective effects of Rb1 on SCI.


title: Space matters: the impact of formal learning environments on student learning

abstract: The objective of this research is to identify the relationship between formal learning spaces and student learning outcomes. Using a quasi-experimental design, researchers partnered with an instructor who taught identical sections of the same course in two radically different formal learning environments to isolate the impact of the physical environment on student learning. The results of the study reveal that, holding all factors excepting the learning spaces constant, students taking the course in a technologically enhanced environment conducive to active learning techniques outperformed their peers who were taking the same course in a more traditional classroom setting. The evidence suggests strongly that technologically enhanced learning environments, independent of all other factors, have a significant and positive impact on student learning.


Time-series Extreme Event Forecasting with Neural Networks at Uber


title: Kinect v2 for mobile robot navigation: Evaluation and modeling

abstract: With the introduction of the Microsoft Kinect for Windows v2 (Kinect v2), an exciting new sensor is available to robotics and computer vision researchers. Similar to the original Kinect, the sensor is capable of acquiring accurate depth images at high rates. This is useful for robot navigation as dense and robust maps of the environment can be created. Opposed to the original Kinect working with the structured light technology, the Kinect v2 is based on the time-of-flight measurement principle and might also be used outdoors in sunlight. In this paper, we evaluate the application of the Kinect v2 depth sensor for mobile robot navigation. The results of calibrating the intrinsic camera parameters are presented and the minimal range of the depth sensor is examined. We analyze the data quality of the measurements for indoors and outdoors in overcast and direct sunlight situations. To this end, we introduce empirically derived noise models for the Kinect v2 sensor in both axial and lateral directions. The noise models take the measurement distance, the angle of the observed surface, and the sunlight incidence angle into account. These models can be used in post-processing to filter the Kinect v2 depth images for a variety of applications.


title: Analysis of cyber security for industrial control systems

abstract: Industrial control systems (ICS) are specialized information systems that differs significantly form traditional information systems used in the IT world. The main use of ICS is to manage critical infrastructures such as, Oil and Natural Gas facilities, nuclear plants, smart grids, water and waste water … etc. ICS have many unique functional characteristics, including a need for real-time response and extremely high availability, predictability, reliability, as well as distributed intelligence. Which for, many advanced computing, communication and internet technologies were integrated to the ICS to cover more costumers requirements such as mobility, data analytics, extensibility … etc The integration of these technologies makes from the ICS open systems to the external world; this openness exposes the critical infrastructures to several Cyber security critical issues. Nowadays, cyber security emerges to be one of the most critical issues because of the immediate impact and the high cost of cyber-attacks. In this paper, we present a comprehensive analysis of cyber security issues for ICS. Specifically we focus on discussing and reviewing the different types and architectures of an ICS, security requirements, different threats attacks, and existing solutions to secure Industrial control systems. By this survey, we desire to provide a clear understanding of security issues in ICS and clarify the different research issues to solve in the future.


title: A combination of artificial neural network and random walk models for financial time series forecasting

abstract: Properly comprehending and modeling the dynamics of financial data has indispensable practical importance. The prime goal of a financial time series model is to provide reliable future forecasts which are crucial for investment planning, fiscal risk hedging, governmental policy making, etc. These time series often exhibit notoriously haphazard movements which make the task of modeling and forecasting extremely difficult. As per the research evidence, the random walk (RW) is so far the best linear model for forecasting financial data. Artificial neural network (ANN) is another promising alternative with the unique capability of nonlinear self-adaptive modeling. Numerous comparisons of the performances of RW and ANN models have also been carried out in the literature with mixed conclusions. In this paper, we propose a combination methodology which attempts to benefit from the strengths of both RW and ANN models. In our proposed approach, the linear part of a financial dataset is processed through the RW model, and the remaining nonlinear residuals are processed using an ensemble of feedforward ANN (FANN) and Elman ANN (EANN) models. The forecasting ability of the proposed scheme is examined on four real-world financial time series in terms of three popular error statistics. The obtained results clearly demonstrate that our combination method achieves reasonably better forecasting accuracies than each of RW, FANN and EANN models in isolation for all four financial time series.


title: The Exact Rate-Memory Tradeoff for Caching with Uncoded Prefetching

abstract: We consider a basic cache network, in which a single server is connected to multiple users via a shared bottleneck link. The server has a database of files (content). Each user has an isolated memory that can be used to cache content in a prefetching phase. In a following delivery phase, each user requests a file from the database, and the server needs to deliver users’ demands as efficiently as possible by taking into account their cache contents. We focus on an important and commonly used class of prefetching schemes, where the caches are filled with uncoded data. We provide the exact characterization of the rate-memory tradeoff for this problem, by deriving both the minimum average rate (for a uniform file popularity) and the minimum peak rate required on the bottleneck link for a given cache size available at each user. In particular, we propose a novel caching scheme, which strictly improves the state of the art by exploiting commonality among user demands. We then demonstrate the exact optimality of our proposed scheme through a matching converse, by dividing the set of all demands into types, and showing that the placement phase in the proposed caching scheme is universally optimal for all types. Using these techniques, we also fully characterize the rate-memory tradeoff for a decentralized setting, in which users fill out their cache content without any coordination.


title: Network-level polymorphic shellcode detection using emulation

abstract: As state-of-the-art attack detection technology becomes more prevalent, attackers are likely to evolve, employing techniques such as polymorphism and metamorphism to evade detection. Although recent results have been promising, most existing proposals can be defeated using only minor enhancements to the attack vector. We present a heuristic detection method that scans network traffic streams for the presence of polymorphic shellcode. Our approach relies on a NIDS-embedded CPU emulator that executes every potential instruction sequence, aiming to identify the execution behavior of polymorphic shellcodes. Our analysis demonstrates that the proposed approach is more robust to obfuscation techniques like self-modifications compared to previous proposals, but also highlights advanced evasion techniques that need to be more closely examined towards a satisfactory solution to the polymorphic shellcode detection problem.


title: QSARs for PBPK modelling of environmental contaminants

abstract: Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.


title: On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed

abstract: In the field of Data Mining, the estimation of the quality of the learned models is a key step in order to select the most appropriate tool for the problem to be solved. Traditionally, a k-fold validation technique has been carried out so that there is a certain degree of independency among the results for the different partitions. In this way, the highest average performance will be obtained by the most robust approach. However, applying a ''random'' division of the instances over the folds may result in a problem known as dataset shift, which consists in having a different data distribution between the training and test folds. In classification with imbalanced datasets, in which the number of instances of one class is much lower than the other class, this problem is more severe. The misclassification of minority class instances due to an incorrect learning of the real boundaries caused by a not well fitted data distribution, truly affects the measures of performance in this scenario. Regarding this fact, we propose the use of a specific validation technique for the partitioning of the data, known as ''Distribution optimally balanced stratified cross-validation'' to avoid this harmful situation in the presence of imbalance. This methodology makes the decision of placing close-by samples on different folds, so that each partition will end up with enough representatives of every region. We have selected a wide number of imbalanced datasets from KEEL dataset repository for our study, using several learning techniques from different paradigms, thus making the conclusions extracted to be independent of the underlying classifier. The analysis of the results has been carried out by means of the proper statistical study, which shows the goodness of this approach for dealing with imbalanced data.


title: Smartwatch-based activity recognition: A machine learning approach

abstract: Smartwatches and smartphones contain accelerometers and gyroscopes that sense a user's movements, and can help identify the activity a user is performing. Research into smartphone-based activity recognition has exploded over the past few years, but research into smartwatch-based activity recognition is still in its infancy. In this paper we compare smartwatch and smartphone-based activity recognition, and smartwatches are shown to be capable of identifying specialized hand-based activities, such as eating activities, which cannot be effectively recognized using a smartphone (e.g., smartwatches can identify the "drinking" activity with 93.3% accuracy while smartphones achieve an accuracy of only 77.3%). Smartwatch-based activity recognition can form the basis of new biomedical and health applications, including applications that automatically track a user's eating habits.


CS271 Randomness & Computation Fall 2011


1 CHAPTER WIRELESS POSITIONING SYSTEMS: OPERATION, APPLICATION, AND COMPARISON


title: A Study of Preventing Email (Spear) Phishing by Enabling Human Intelligence

abstract: Cyber criminals use phishing emails in high-volume and spear phishing emails in low volume to achieve their malicious objectives. Hereby they inflict financial, reputational, and emotional damages on individuals and organizations. These (spear) phishing attacks get steadily more sophisticated as cyber criminals use social engineering tricks that combine psychological and technical deceptions to make malicious emails as trustworthy as possible. Such sophisticated (spear) phishing emails are hard for email protection systems to detect. Security researchers have studied users' ability to perceive, identify and react upon email (spear) phishing attacks. In this study we have surveyed recent works on understanding how to prevent end-users from falling for email (spear) phishing attacks. Based on the survey we design and propose a novice method that combines interaction methods of reporting, blocking, warning, and embedded education to harness the intelligence of expert and novice users in a corporate environment in detecting email (spear) phishing attacks. We evaluate the design based on a qualitative study, in three experimental steps, by using a mockup prototype, and with 24 participants. We report on the insights gained, indicating that the proposed combination of the interaction methods is promising, and on future research directions.


title: Agile requirements prioritization in large-scale outsourced system projects: An empirical study

abstract: The application of agile practices for requirements prioritization in distributed and outsourced projects is a relatively recent trend. Hence, not all of its facets are well-understood. This exploratory study sets out to uncover the concepts that practitioners in a large software organization use in the prioritization process and the practices that they deem good. We seek to provide a rich analysis and a deep understanding of three cases in an exploratory study that was carried out in a large and mature company, widely recognized for its excellence and its engagement in outsourced software development. We used in-depth interviews for data collection and grounded theory techniques for data analysis. Our exploration efforts yielded the following findings: (i) understanding requirements dependencies is of paramount importance for the successful deployment of agile approaches in large outsourced projects. (ii) Next to business value, the most important prioritization criterion in the setting of outsourced large agile projects is risk. (iii) The software organization has developed a new artefact that seems to be a worthwhile contribution to agile software development in the large: 'delivery stories', which complement user stories with technical implications, effort estimation and associated risk. The delivery stories play a pivotal role in requirements prioritization. (iv) The vendor's domain knowledge is a key asset for setting up successful client-developer collaboration. (v) The use of agile prioritization practices depends on the type of project outsourcing arrangement. Our findings contribute to the empirical software engineering literature by bringing a rich analysis of cases in agile and distributed contexts, from a vendor's perspective. We also discuss the possible implications of the results for research and in practice.


title: Understanding and Evaluating Qualitative Research∗

abstract: Qualitative research aims to address questions concerned with developing an understanding of the meaning and experience dimensions of humans’ lives and social worlds. Central to good qualitative research is whether the research participants’ subjective meanings, actions and social contexts, as understood by them, are illuminated. This paper aims to provide beginning researchers, and those unfamiliar with qualitative research, with an orientation to the principles that inform the evaluation of the design, conduct, findings and interpretation of qualitative research. It orients the reader to two philosophical perspectives, the interpretive and critical research paradigms, which underpin both the qualitative research methodologies most often used in mental health research, and how qualitative research is evaluated. Criteria for evaluating quality are interconnected with standards for ethics in qualitative research. They include principles for good practice in the conduct of qualitative research, and for trus...


title: International comparisons of health care systems: Conceptual and methodological developments over half a century

abstract: This paper builds on the health care systems comparisons undertaken by Ragnar Berfenstam and his colleagues in the 1960’s. It describes the original health systems model guiding that early work and a developed health systems model that we have constructed based on more recent international comparative health systems work. We provide examples to elucidate the orig-inal model and the developed model. Using a selective literature review we note changes and developments in comparative studies over the past 40 or 50 years. We conclude by suggesting the major advances in the field and continuing major challenges to doing international health systems comparisons.


title: Online Job Scheduling in Distributed Machine Learning Clusters

abstract: Nowadays large-scale distributed machine learning systems have been deployed to support various analytics and intelligence services in IT firms. To train a large dataset and derive the prediction/inference model, e.g., a deep neural network, multiple workers are run in parallel to train partitions of the input dataset, and update shared model parameters. In a shared cluster handling multiple training jobs, a fundamental issue is how to efficiently schedule jobs and set the number of concurrent workers to run for each job, such that server resources are maximally utilized and model training can be completed in time. Targeting a distributed machine learning system using the parameter server framework, we design an online algorithm for scheduling the arriving jobs and deciding the adjusted numbers of concurrent workers and parameter servers for each job over its course, to maximize overall utility of all jobs, contingent on their completion times. Our online algorithm design utilizes a primal-dual framework coupled with efficient dual subroutines, achieving good long-term performance guarantees with polynomial time complexity. Practical effectiveness of the online algorithm is evaluated using trace-driven simulation and testbed experiments, which demonstrate its outperformance as compared to commonly adopted scheduling algorithms in today's cloud systems.


title: Numerical simulation of wind effects : a probabilistic perspective

abstract: Abstract Numerical simulations of wind loads and their effects are critical in the design of structures to ensure their safety under winds. The simulations range from generation of time histories of wind velocity, pressure and force fluctuations to structural response and assessment of attendant functionality and safety under service and design loads, respectively. Typically these schemes employ Monte Carlo-based approaches that encompass model-based simulations or information derived from observed data. The scope of simulations spans uni-variate to multi-variate processes; uni-dimensional to multi-dimensional fields; Gaussian to non-Gaussian; stationary to non-stationary; conditional and unconditional cases. In order to accomplish these tasks, methods based on the time, frequency, or time–frequency domains are employed. This paper summarizes a historical perspective, recent developments and future challenges. Also included in the discussion are computational tools employed for data analysis, response analysis and its management, and prediction. Examples are presented to illustrate some of the topics discussed.


title: How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint

abstract: Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.


title: Exploration, novelty, surprise, and free energy minimization

abstract: This paper reviews recent developments under the free energy principle that introduce a normative perspective on classical economic (utilitarian) decision-making based on (active) Bayesian inference. It has been suggested that the free energy principle precludes novelty and complexity, because it assumes that biological systems – like ourselves - try to minimise the long-term average of surprise to maintain their homeostasis. However, recent formulations show that minimising surprise leads naturally to concepts such as exploration and novelty bonuses. In this approach, agents infer a policy that minimises surprise by minimising the difference (or relative entropy) between likely and desired outcomes, which involves both pursuing the goal-state that has the highest expected utility (often termed ‘exploitation’) and visiting a number of different goal-states (‘exploration’). Crucially, the opportunity to visit new states increases the value of the current state. Casting decision-making problems within a variational framework, therefore, predicts that our behaviour is governed by both the entropy and expected utility of future states. This dissolves any dialectic between minimising surprise and exploration or novelty seeking.


title: Recovering True Classifier Performance in Positive-Unlabeled Learning

abstract: A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased empirical estimates of the classifier performance. In this work, we show that the typically used performance measures such as the receiver operating characteristic curve, or the precision-recall curve obtained on such data can be corrected with the knowledge of class priors; i.e., the proportions of the positive and negative examples in the unlabeled data. We extend the results to a noisy setting where some of the examples labeled positive are in fact negative and show that the correction also requires the knowledge of the proportion of noisy examples in the labeled positives. Using state-of-the-art algorithms to estimate the positive class prior and the proportion of noise, we experimentally evaluate two correction approaches and demonstrate their efficacy on real-life data.


title: A big bang-big crunch optimization based approach for interval type-2 fuzzy PID controller design

abstract: In this paper, we will present a big bang-big crunch optimization (BB-BC) based approach for the design of an interval type-2 fuzzy PID controller. The implemented global optimization algorithm has a low computational cost and a high convergence speed. As a consequence, the BB-BC method is a very efficient search algorithm when the number of the optimization parameters is relatively big. The optimized type-2 fuzzy controller is compared with PID and type-1 fuzzy PID controllers which were optimized with either the BB-BC optimization method or conventional design strategies. The paper will also show the effect the extra degrees of freedom provided by the antecedent interval type-2 fuzzy sets on the closed loop system performance. We will present a comparative study performed on the highly nonlinear cascaded tank process to show the superiority of the optimized interval type-2 fuzzy PID controller compared to its optimized PID, type-1 counterparts.


title: Deep Learning Approaches for Predictive Masquerade Detection

abstract: In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly.


title: Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds

abstract: RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC’s ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method.


title: DeepMVS: Learning Multi-view Stereopsis

abstract: We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set of plane-sweep volumes and use the proposed DeepMVS network to predict high-quality disparity maps. The key contributions that enable these results are (1) supervised pretraining on a photorealistic synthetic dataset, (2) an effective method for aggregating information across a set of unordered images, and (3) integrating multi-layer feature activations from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using the ETH3D Benchmark. Our results show that DeepMVS compares favorably against state-of-the-art conventional MVS algorithms and other ConvNet based methods, particularly for near-textureless regions and thin structures.


title: Broadband dual-polarized antenna with high port isolation and polarization purity

abstract: This paper presents a novel high symmetry balun which significantly improves the performance of dipole-based dual-polarized antennas. The new balun structure provides enhanced differential capability leading to high performance in terms of port-to-port isolation and far-field cross polarization. An example antenna using this balun is proposed. The simulated results show 53.5% of fractional bandwidth within the band 1.71–2.96 GHz (VSWR 59 dB. The radiation characteristic shows around 9 dBi of gain and far-field cross polarization <-48 dBi over the entire bandwidth. The detailed balun functioning and full antenna measurements will be presented during the conference. Performance comparison with similar structures will be also provided.


title: A solution to the next best view problem for automated surface acquisition

abstract: A solution to the "next best view" (NBV) problem for automated surface acquisition is presented. The NBV problem is to determine which areas of a scanner's viewing volume need to be scanned to sample all of the visible surfaces of an a priori unknown object and where to position/control the scanner to sample them. A method for determining the unscanned areas of the viewing volume is presented. In addition, a novel representation, positional space, is presented which facilitates a solution to the NBV problem by representing what must be and what can be scanned in a single data structure. The number of costly computations needed to determine if an area of the viewing volume would be occluded from some scanning position is decoupled from the number of positions considered for the NBV, thus reducing the computational cost of choosing one. An automated surface acquisition systems designed to scan all visible surfaces of an a priori unknown object is demonstrated on real objects.


title: Using Conceptual Class Attributes to Characterize Social Media Users

abstract: We describe a novel approach for automatically predicting the hidden demographic properties of social media users. Building on prior work in common-sense knowledge acquisition from third-person text, we first learn the distinguishing attributes of certain classes of people. For example, we learn that people in the Female class tend to have maiden names and engagement rings. We then show that this knowledge can be used in the analysis of first-person communication; knowledge of distinguishing attributes allows us to both classify users and to bootstrap new training examples. Our novel approach enables substantial improvements on the widelystudied task of user gender prediction, obtaining a 20% relative error reduction over the current state-of-the-art.


title: PoseTrack: Joint Multi-person Pose Estimation and Tracking

abstract: In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos. Existing methods for multi-person pose estimation in images cannot be applied directly to this problem, since it also requires to solve the problem of person association over time in addition to the pose estimation for each person. We therefore propose a novel method that jointly models multi-person pose estimation and tracking in a single formulation. To this end, we represent body joint detections in a video by a spatio-temporal graph and solve an integer linear program to partition the graph into sub-graphs that correspond to plausible body pose trajectories for each person. The proposed approach implicitly handles occlusion and truncation of persons. Since the problem has not been addressed quantitatively in the literature, we introduce a challenging Multi-Person PoseTrack dataset, and also propose a completely unconstrained evaluation protocol that does not make any assumptions about the scale, size, location or the number of persons. Finally, we evaluate the proposed approach and several baseline methods on our new dataset.


title: Osteomyelitis of the Jaws: Definition and Classification

abstract: Osteomyelitis of the jaws is still a fairly common disease in maxillofacial clinics and offices, despite the introduction of antibiotics and the improvement of dental and medical care. The literature on this disease is extensive. Different terminologies and classification systems are used based on a variety of features such as clinical course, pathological–anatomical or radiological features, etiology, and pathogenesis. A mixture of these classification systems has occurred throughout the literature, leading to confusion and thereby hindering comparative studies. An overview of the most commonly used terms and classification systems in osteomyelitis of the jaws is given at the beginning of this chapter. The Zurich classification system, as advocated in this textbook, is primarily based on the clinical course and appearance of the disease as well as on imaging studies. Subclassification is based on etiology and pathogenesis of the disease. Mainly three different types of osteomyelitis are distinguished: acute and secondary chronic osteomyelitis and primary chronic osteomyelitis. Acute and secondary chronic osteomyelitis are basically the same disease separated by the arbitrary time limit of 1 month after onset of the disease. They usually represent a true bacterial infection of the jawbone. Suppuration, fistula formation, and sequestration are characteristic features of this disease entity. Depending on the intensity of the infection and the host bone response, the clinical presentation and course may vary significantly. Acute and secondary chronic osteomyelitis of the jaws is caused mostly by a bacterial focus (odontogenic disease, pulpal and periodontal infection, extraction wounds, foreign bodies, and infected fractures). Primary chronic osteomyelitis of the jaw is a rare, nonsuppurative, chronic inflammation of an unknown cause. Based on differences in age at presentation, clinical appearance and course, as well as radiology and histology, the disease may be subclassified into earlyand adult-onset primary chronic osteomyelitis. Cases with purely mandibular involvement are further distinguished from cases associated with extragnathic dermatoskeletal involvement such as in SAPHO syndrome or chronic recurrent multifocal osteomyelitis (CRMO).


title: A portable powered ankle-foot orthosis for rehabilitation.

abstract: Innovative technological advancements in the field of orthotics, such as portable powered orthotic systems, could create new treatment modalities to improve the functional out come of rehabilitation. In this article, we present a novel portable powered ankle-foot orthosis (PPAFO) to provide untethered assistance during gait. The PPAFO provides both plantar flexor and dorsiflexor torque assistance by way of a bidirectional pneumatic rotary actuator. The system uses a portable pneumatic power source (compressed carbon dioxide bottle) and embedded electronics to control the actuation of the foot. We collected pilot experimental data from one impaired and three nondisabled subjects to demonstrate design functionality. The impaired subject had bilateral impairment of the lower legs due to cauda equina syndrome. We found that data from nondisabled walkers demonstrated the PPAFO's capability to provide correctly timed plantar flexor and dorsiflexor assistance during gait. Reduced activation of the tibialis anterior during stance and swing was also seen during assisted nondisabled walking trials. An increase in the vertical ground reaction force during the second half of stance was present during assisted trials for the impaired subject. Data from nondisabled walkers demonstrated functionality, and data from an impaired walker demonstrated the ability to provide functional plantar flexor assistance.


title: Microstrip Rectangular 4x1 Patch Array Antenna at 2.5GHz for WiMax Application

abstract: This paper presents the design of microstrip rectangular patch antenna with center frequency at 2.5GHz for WiMAX application. The array of four by one (4x1) patch array microstrip rectangular antenna with microstrip line feeding based on quarter wave impedance matching technique was designed and simulated using Computer Simulation Tool (CST) Microwave Environment software. The performance of the designed antenna was than compared with the single patch rectangle antenna in term of return loss, Voltage Standing Wave Ratio (VSWR), bandwidth, directivity, radiation pattern and gain. The array antenna was then fabricated on the substrate type FR-4 with dielectric constant of 4.9 and thickness of 1.6mm respectively. The array antenna was measured in the laboratory using Vector Network Analyzer (VNA) and the results show good agreement with the array antenna simulated performances.


S. YAJING et al.: CUSTOMER VALUE HIERARCHY CUSTOMER VALUE HIERARCHY BASED CUSTOMER DEMAND ANALYSIS IN PERSONALIZED SERVICE RECOMMENDER SYSTEM


title: Convolutional Random Walk Networks for Semantic Image Segmentation

abstract: Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resolution inside the deep layers. This leads to predictions with poor localization around the boundaries. Prior work has attempted to address this issue by post-processing predictions with CRFs or MRFs. But such models often fail to capture semantic relationships between objects, which causes spatially disjoint predictions. To overcome these problems, recent methods integrated CRFs or MRFs into an FCN framework. The downside of these new models is that they have much higher complexity than traditional FCNs, which renders training and testing more challenging. ::: In this work we introduce a simple, yet effective Convolutional Random Walk Network (RWN) that addresses the issues of poor boundary localization and spatially fragmented predictions with very little increase in model complexity. Our proposed RWN jointly optimizes the objectives of pixelwise affinity and semantic segmentation. It combines these two objectives via a novel random walk layer that enforces consistent spatial grouping in the deep layers of the network. Our RWN is implemented using standard convolution and matrix multiplication. This allows an easy integration into existing FCN frameworks and it enables end-to-end training of the whole network via standard back-propagation. Our implementation of RWN requires just $131$ additional parameters compared to the traditional FCNs, and yet it consistently produces an improvement over the FCNs on semantic segmentation and scene labeling.


title: DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning

abstract: Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse acoustic environments (such as, bedrooms, vehicles, or cafes), that users encounter on a daily basis. Towards addressing this challenge, we turn to the field of deep learning; an area of machine learning that has radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar -- the first mobile audio sensing framework built from coupled Deep Neural Networks (DNNs) that simultaneously perform common audio sensing tasks. We train DeepEar with a large-scale dataset including unlabeled data from 168 place visits. The resulting learned model, involving 2.3M parameters, enables DeepEar to significantly increase inference robustness to background noise beyond conventional approaches present in mobile devices. Finally, we show DeepEar is feasible for smartphones by building a cloud-free DSP-based prototype that runs continuously, using only 6% of the smartphone's battery daily.


title: Multiple choice learning: Learning to produce multiple structured outputs

abstract: We address the problem of generating multiple hypotheses for structured prediction tasks that involve interaction with users or successive components in a cascaded architecture. Given a set of multiple hypotheses, such components/users typically have the ability to retrieve the best (or approximately the best) solution in this set. The standard approach for handling such a scenario is to first learn a single-output model and then produce M-Best Maximum a Posteriori (MAP) hypotheses from this model. In contrast, we learn to produce multiple outputs by formulating this task as a multiple-output structured-output prediction problem with a loss-function that effectively captures the setup of the problem. We present a max-margin formulation that minimizes an upper-bound on this loss-function. Experimental results on image segmentation and protein side-chain prediction show that our method outperforms conventional approaches used for this type of scenario and leads to substantial improvements in prediction accuracy.


title: A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks

abstract: The Advanced Sensor Networks SARChI Chair program, co-hosted ::: by University of Pretoria (UP) and Council for Scientific and Industrial Research (CSIR), through the National ::: Research Foundation (NRF) of South Africa.


title: Accurate small object detection via density map aided saliency estimation

abstract: Small object detection (SOD) in crowded scenes is a challenging task since objects are densely distributed and partially overlapped. In this paper, we propose a novel SOD method by fully exploring the information provided by the image and its estimated density map. Our proposed SOD method consists of two main stages. Initial object locations are firstly computed based on object spatial distribution information obtained from the estimated density maps. Inspired by the human visual attention mechanism, a saliency map which offers object boundaries is then employed to accurately estimate the bounding boxes with the support of the estimated initial object locations. Experimental results on three public small object datasets and a self-built snipe dataset demonstrate the effectiveness of our proposed SOD method, especially under small training set condition. It is encouraged to see that our SOD method only requires the dotted annotation training datasets and is able to estimate the bounding boxes fitting the shape of the objects accurately.


title: Evidence from Biosynthetically Incorporated Strontium and FTIR Difference Spectroscopy that the C-Terminus of the D1 Polypeptide of Photosystem II Does Not Ligate Calcium†

abstract: Recent FTIR studies have provided evidence that the C-terminal α-COO- group of the D1 polypeptide at D1-Ala344 is a unidentate ligand of a Mn ion in photosystem II [Chu, H.-A., Hiller, W., and Debus, R. J. (2004) Biochemistry 43, 3152−3166; Kimura, Y., Mizusawa, N., Yamanari, T., Ishii, A., and Ono, T.-A. (2005) J. Biol. Chem. 280, 2078−2083]. However, the FTIR data could not exclude Ca ligation. Furthermore, the recent ∼3.5 A X-ray crystallographic structural model positions the α-COO- group of D1-Ala344 near a Ca ion [Ferreira, K. N., Iverson, T. M., Maghlaoui, K., Barber, J., and Iwata, S. (2004) Science 303, 1831−1838]. Therefore, to conclusively establish whether the α-COO- group of D1-Ala344 ligates Mn or Ca, the symmetric carboxylate stretching mode of the α-COO- group of D1-Ala344 was identified in the S2-minus-S1 FTIR difference spectrum of PSII particles having Sr substituted for Ca. Cells of the cyanobacterium Synechocystis sp. PCC 6803 were propagated in media having Sr substituted for Ca and ...


title: Postal address block location using a convolutional locator network

abstract: This paper describes the use of a convolutional neural network to perform address block location on machine-printed mail pieces. Locating the address block is a difficult object recognition problem because there is often a large amount of extraneous printing on a mail piece and because address blocks vary dramatically in size and shape. ::: ::: We used a convolutional locator network with four outputs, each trained to find a different corner of the address block. A simple set of rules was used to generate ABL candidates from the network output. The system performs very well: when allowed five guesses, the network will tightly bound the address delivery information in 98.2% of the cases.


08 67 0 v 2 [ cs . C V ] 2 6 M ar 2 01 8 Object Detection for Comics using Manga 109 Annotations


title: A comparison of food portion size estimation using geometric models and depth images

abstract: Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper we compare two techniques of estimating food portion size from images of food. The techniques are based on 3D geometric models and depth images. An expectation-maximization based technique is developed to detect the reference plane in depth images, which is essential for portion size estimation using depth images. Our experimental results indicate that volume estimation based on geometric models is more accurate for objects with well-defined 3D shapes compared to estimation using depth images.


title: Second Generation Biofuels: High-Efficiency Microalgae for Biodiesel Production

abstract: The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.


title: Shape parameter analysis using cloud spectra and gamma functions in the numerical modeling RAMS during LBA Project at Amazonian region, Brazil

abstract: The microphysical variable shape parameters have been investigated considering a numerical simulation based on RAMS (Regional Atmospheric Modeling System), with focus on southwest Amazon Basin during the transition from dry to wet seasons within the scope of LBA (The Large Scale Biosphere Atmosphere Experiment in Amazonia). All data were obtained during the dry-to-wet campaign from September to October 2003. Three precipitation events were chosen during the campaign, classified as polluted, intermediary and clean events. The dataset, for the description of the observed convective systems, includes airborne observations with a microphysics airplane. The variables were cloud droplet contents and spectra, in several flights; radiosonde launches; a 10 cm Doppler radar; visible and IR satellite maps and rain gauge network. The preliminary results have shown that over polluted areas, shape parameters with higher values, from 5 to 7 are better fitted to cloud spectra observations, than the usual shape parameter 2 used in RAMS microphysics default table. For its turn, shape parameter 2 is related to the cleaner areas, i.e., without biomass burning. On the other hand, the numerical simulations, reproducing as close as possible the microphysical variable spatial distributions in the observed dataset, have shown that the average total rainfall precipitation at the surface generally decreases as the CCN concentration and shape parameter increases. CCN vertical and temporal profiles support that result, emphasizing that the higher CCN concentrations are related to biomass burning, where higher shape parameters are better fitted. Additionally, it is possible to conclude that different shape parameters and CCN concentrations may influence directly the maximum and average amount of precipitation and cloud water, implying that different regions should have each the proper specification characteristics. Shape parameters and CCN concentrations are quite useful tools in order to evaluate the spatial and temporal cloud patterns as well as, the rainfall amount with regional implications.


title: Social signal classification using deep blstm recurrent neural networks

abstract: Non-verbal speech cues play an important role in human communication such as expressing emotional states or maintaining the conversational flow. In this paper we investigate the effect of applying deep bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks to the Interspeech 2013 Computational Paralinguistics Social Signals Sub-Challenge dataset requiring frame-wise, speaker-independent detection and classification of laughter and filler vocalizations in speech. BLSTM networks tend to prevail over conventional neural network architectures whenever the recognition or regression task relies on an intelligent exploitation of temporal context information. We introduce deep BLSTM models by stacking several BLSTMs and by combining non-recurrent deep neural networks with BLSTMs. We demonstrate that this new approach achieves significant improvements over previous attempts and we increase the current state-of-the-art unweighted average area-under-the-curve (UAAUC) value of 92.4% to 94.0%. This is the best result on this task reported in the literature so far.


title: Sorting And Indexing With Partitioned B-Trees

abstract: Partitioning within a B-tree, based on an artificial leading key column and combined with online reorganization, can be exploited during external merge sort for accurate deep read-ahead and dynamic resource allocation, during index creation for a reduced delay until the first query can search the new index, during data loading for streaming integration of new data into a fully indexed database, and for miscellaneous other operations. Despite improving multiple fundamental database operations using a single basic mechanism, the proposal offers these benefits without requiring data structures or algorithms not yet supported in modern relational database management systems. While some of the ideas discussed here have been touched upon elsewhere, the focus here is on re-thinking the relationship between sorting and B-trees more thoroughly, on exploiting this relationship to simplify and unify data structures and algorithms, and on gathering comprehensive lists of issues and benefits.


PIXELSNAIL: AN IMPROVED AUTOREGRESSIVE GEN-


Information Security Maturity Model for Nist Cyber Security Framework


title: Personalized recommendation via cross-domain triadic factorization

abstract: Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models.


title: Image Classification for Ultrasound Fetal Images with Increased Nuchal Translucency during First Trimester Using SVM Classifier

abstract: Increased Nuchal Translucency is an indicator of increased risk for Down syndrome, which is identified by measuring Nuchal Translucency from ultrasound fetal images during 11 to 13 +6 weeks of gestation. Increased NT is associated with chromosomal abnormalities. In this study an efficient classification system based on Discrete Wavelet Transform (DWT) is proposed to detect the normal and abnormal images with NT. Feature extraction is an essential pre-processing step for pattern recognition and machine learning problems. In order to classify the ultrasound image accurately, the texture features must be extracted effectively. In the proposed system, wavelet band signature, energy is used as features to classify the ultrasound image for the detection of Down syndrome using Support Vector Machine (SVM) classifier. The experimental results of pre diagnosed database with Discrete wavelet Transform and SVM classifier give best results for classification of Down Syndrome images with Normal NT and abnormal NT.


Climate change and human health: risks and responses


title: A near nonlinear optimal controller for the nonlinear inverted pendulum

abstract: In this paper, a new analytical method to find an optimal high gain controller for the nonlinear inverted pendulum is introduced. This controller is derived based on the closed form solution of the Hamilton-Jacobi-Bellman (HJB) equation associated with the cheap control problem. This methodology employs an algebraic equation with parametric coefficients for the systems with scalar internal dynamics and a differential equation for those systems with the internal dynamics of order higher than one. It is shown that for the far initial conditions the proposed controller significantly outperforms the LQR controller while for the near initial conditions; the regulation error is less than that of the closed-loop system with the high gain LQR controller.


title: Oral cavity and oropharyngeal squamous cell carcinoma-an update

abstract: Answer questions and earn CME/CNE ::: ::: Oral cavity squamous cell carcinoma (OC-SCC) is the most common malignancy of the head and neck (excluding nonmelanoma skin cancer). Recent trends have shown a dramatic rise in the incidence of oropharyngeal squamous cell carcinoma (OP-SCC), with a marked increase in lesions related to human papillomavirus infection. This update presents the latest evidence regarding OC-SCC and OP-SCC. In particular, the authors compare and contrast tumors at these two sites with respect to epidemiology, etiopathogenesis, clinicopathologic presentation, clinical assessment, imaging, management, and prognosis. It is important for clinicians to be aware of differences between OC-SCC and OP-SCC so that appropriate patient education and multidisciplinary care can be provided to optimize outcomes. CA Cancer J Clin 2015;65:401–421. © 2015 American Cancer Society.


title: HAEP: Hospital Assignment for Emergency Patients in a Big City

abstract: In the largely populated city of a developing country, the ambulance service usually sends an emergent patient to the available hospital with shortest pre-consultation delay. The problem is, a life-critical patient may encounter the lack of treatment resource, such as sickbed, in desired hospitals, and the delay to a next appropriate hospital would cause his death, because non-critical patients already occupied the resources. In the worst case, the service encountering a catastrophe may hold hundreds of people on their way to the hospital and require sickbeds be reserved in advance. In this paper, we propose a resource allocation to balance delay in sending patients to hospitals. We extend the scheme to consider sickbed reservation along the time scale by estimating from the past records in history. As a result, the occupancy is balanced in order to reduce the risk of life-critical patients being delayed. Then we develop an in-hospital waiting queue to keep serious patients waiting locally, when it costs more to reach another available hospital. Simulation results show the substantial improvement of our approach in average delay and number of failure-of-assignment.


title: A unified approach to mapping and clustering of bibliometric networks

abstract: In the analysis of bibliometric networks, researchers often use mapping and clustering techniques in a combined fashion. Typically, however, mapping and clustering techniques that are used together rely on very different ideas and assumptions. We propose a unified approach to mapping and clustering of bibliometric networks. We show that the VOS mapping technique and a weighted and parameterized variant of modularity-based clustering can both be derived from the same underlying principle. We illustrate our proposed approach by producing a combined mapping and clustering of the most frequently cited publications that appeared in the field of information science in the period 1999-2008.


title: Performance Analysis and Application of Mobile Blockchain

abstract: Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. However, the blockchain technology has not been adopted and implemented widely in m-commerce because its mining processes usually require to be performed on standard computing units, e.g., computers. Therefore, in this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.


title: Simple passive lossless snubber for high-power multilevel inverters

abstract: A passive lossless snubber circuit for multilevel inverters is proposed in this paper. The topology is simple and requires no extra control circuit. In order to reduce the high-voltage stress on power switches with this snubber circuit, an improved snubber circuit is presented by adding separate low-power direct current voltage sources into the original one. The operating principles and design considerations are described in detail in this paper. A prototype of a three-phase three-level diode-clamped inverter with the improved passive lossless snubber is built and tested. The simulation and experimental results indicate that not only can it realize the soft switching operation of the three-level inverter with low-voltage stress but also the topology and the control are simple.


title: Women in Banking: Continuity and Change Since the Second World War

abstract: Employment relations in the major clearing banks have been the subject of sociological attention since the 1950s, when they presented the apparent epitome of the bureaucratic career. However, the banks have increasingly become major employers of female labour. Women in banks have not historically had the same career opportunities as men, for a variety of reasons ranging from deliberate male exclusion practices to the broken and often short-term nature of many women's work histories. This contrast between the experiences of men and women in the same occupation is used to question the `conventional view' of occupational class analysis, where the (male) occupational structure is treated as if it were the `class structure'. The argument is developed using a number of sources, including the occupational history of a female bank employee with over thirty years work experience, as well as the findings of recent equal opportunities investigations.


title: Mathematics of Deep Learning

abstract: Recently there has been a dramatic increase in the performance of recognition systems due to the introduction of deep architectures for representation learning and classification. However, the mathematical reasons for this success remain elusive. This tutorial will review recent work that aims to provide a mathematical justification for several properties of deep networks, such as global optimality, geometric stability, and invariance of the learned representations.


title: A real-time lane marking localization, tracking and communication system

abstract: In this paper, we present an in-vehicle computing system capable of localizing lane markings and communicating them to drivers. To the best of our knowledge, this is the first system that combines the Maximally Stable Extremal Region (MSER) technique with the Hough transform to detect and recognize lane markings (i.e., lines and pictograms). Our system begins by localizing the region of interest using the MSER technique. A three-stage refinement computing algorithm is then introduced to enhance the results of MSER and to filter out undesirable information such as trees and vehicles. To achieve the requirements of real-time systems, the Progressive Probabilistic Hough Transform (PPHT) is used in the detection stage to detect line markings. Next, the recognition of the color and the form of line markings is performed; this it is based on the results of the application of the MSER to left and right line markings. The recognition of High-Occupancy Vehicle pictograms is performed using a new algorithm, based on the results of MSER regions. In the tracking stage, Kalman filter is used to track both ends of each detected line marking. Several experiments are conducted to show the efficiency of our system.


title: Modeling Alzheimer’s disease progression using deep recurrent neural networks

abstract: Multi-step prediction, predicting values multiple steps ahead, is a difficult problem because of the accumulation of error after successive predictions. Recurrent neural networks (RNN) have been applied successfully to multi-step prediction problems such as language modeling where training data are abundant and regular. However, applying RNN to problems in healthcare is still challenging as RNN requires large, feature complete training data in which intervals between time points are fixed which are not reflective of healthcare data. To overcome these problems, we propose strategies to train RNN using missing data which involve using the RNN to fill in the incomplete or missing time points online. The strategies enables the efficient use of training data as well as the adoption of RNN in problems where the variables in a multivariate time series are recorded at irregular intervals or at different frequencies, where there are a lot of missing data. We train RNN using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to model the monthly progression of Alzheimer’s disease for seven years into the future which is a multi-step prediction problem. We show that RNN outperforms baseline models.


title: Click Carving: Segmenting Objects in Video with Point Clicks

abstract: We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest. Whereas conventional interactive pipelines take the user's initialization as a starting point, we show the value in the system taking the lead even in initialization. In particular, for a given video frame, the system precomputes a ranked list of thousands of possible segmentation hypotheses (also referred to as object region proposals) using image and motion cues. Then, the user looks at the top ranked proposals, and clicks on the object boundary to carve away erroneous ones. This process iterates (typically 2-3 times), and each time the system revises the top ranked proposal set, until the user is satisfied with a resulting segmentation mask. Finally, the mask is propagated across the video to produce a spatio-temporal object tube. On three challenging datasets, we provide extensive comparisons with both existing work and simpler alternative methods. In all, the proposed Click Carving approach strikes an excellent balance of accuracy and human effort. It outperforms all similarly fast methods, and is competitive or better than those requiring 2 to 12 times the effort.


title: Gland Instance Segmentation by Deep Multichannel Neural Networks

abstract: In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images. Our proposed system, deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirement by altering channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent methods of instance segmentation, we observe state-of-the-art results based on a number of evaluation metrics.


Reconciling Event-Based Knowledge Through RDF2VEC


title: Data encryption standard visual representation

abstract: This paper describes a method for visual representation of Data Encryption Standard (DES) algorithm. The described method was implemented in the COALA (CryptOgraphic Algorithms visuaL simulAtion) system, which was developed at the School of Electrical Engineering, University of Belgrade. The COALA system is designed to help students who study Computer security course to better understand one of the core topics, i.e. cryptographic algorithms. Some major problems considering the visual representation of the DES algorithm are elaborated in the paper. The manner in which these problems were overcame in the COALA system and the specifics about the implementation of the DES algorithm are explained. At the end a short discussion on the effects of the COALA system in education and directions for future work are given.


title: Smartphones and BLE Services: Empirical Insights

abstract: Driven by the rapid market growth of sensors and beacons that offer Bluetooth Low Energy (BLE) based connectivity, this paper empirically investigates the performance characteristics of the BLE interface on multiple Android smartphones, and the consequent impact on a proposed BLE-based service: continuous indoor location. We first use extensive measurement studies with multiple Android devices to establish that the BLE interface on current smartphones is not as "low-energy" as nominally expected, and establish that continuous use of such a BLE interface is not feasible unless we choose a moderately large scan interval and a low duty cycle. We then explore the implications of such constraints, on the parameters of a smart phone's BLE stack, on the accuracy of a BLE-based indoor localization techniques. We show that while RF-based indoor location can be highly accurate (80% of estimates have errors less than or equal to 4 meters) for stationary users only if the density of beacons is high, the combination of (large scan interval, low duty cycle) causes the location error to degrade significantly for moving users. These results provide practical insights into the use cases and limitations for future BLE-based mobile services.


title: A comparison of two pipeline organizations

abstract: We examine two pipeline structures which are employed in commercial microprocessors. The first is the load-use interlock (LUI) pipeline, which employs an interlock to ensure correct operation during load-use hazards. The second is the address-generation interlock (AGI) pipeline. It eliminates the load-use hazard, but has an address-generation hazard which requires an address-generation interlock for correct operation. We compare the performance of these two pipelines on existing binaries and on applications which have been recompiled with a local code scheduler that understands the difference in the pipeline structures. When branch prediction is more than 80% accurate and the data cache access time is greater than two cycles, the AGI pipeline performs significantly better than the LUI pipeline on existing binaries. Recompiling the benchmarks with a new local code scheduler provides little additional performance improvement.


title: Lineage tracing for general data warehouse transformations

abstract: Data warehousing systems integrate information from operational data sources into a central repository to enable analysis and mining of the integrated information. During the integration process, source data typically undergoes a series of transformations, which may vary from simple algebraic operations or aggregations to complex “data cleansing” procedures. In a warehousing environment, the data lineage problem is that of tracing warehouse data items back to the original source items from which they were derived. We formally define the lineage tracing problem in the presence of general data warehouse transformations, and we present algorithms for lineage tracing in this environment. Our tracing procedures take advantage of known structure or properties of transformations when present, but also work in the absence of such information. Our results can be used as the basis for a lineage tracing tool in a general warehousing setting, and also can guide the design of data warehouses that enable efficient lineage tracing.


title: IoT based monitoring and control system for home automation

abstract: The project proposes an efficient implementation for IoT (Internet of Things) used for monitoring and controlling the home appliances via World Wide Web. Home automation system uses the portable devices as a user interface. They can communicate with home automation network through an Internet gateway, by means of low power communication protocols like Zigbee, Wi-Fi etc. This project aims at controlling home appliances via Smartphone using Wi-Fi as communication protocol and raspberry pi as server system. The user here will move directly with the system through a web-based interface over the web, whereas home appliances like lights, fan and door lock are remotely controlled through easy website. An extra feature that enhances the facet of protection from fireplace accidents is its capability of sleuthing the smoke in order that within the event of any fireplace, associates an alerting message and an image is sent to Smartphone. The server will be interfaced with relay hardware circuits that control the appliances running at home. The communication with server allows the user to select the appropriate device. The communication with server permits the user to pick out the acceptable device. The server communicates with the corresponding relays. If the web affiliation is down or the server isn't up, the embedded system board still will manage and operate the appliances domestically. By this we provide a climbable and price effective Home Automation system.


title: Conformal SIW H-Plane Horn Antenna on a Conducting Cylinder

abstract: This letter presents a conformal wide-band substrate integrated waveguide (SIW) H-plane horn antenna mounted on a large conducting cylinder. The effect of a curved low-profile SIW H-plane horn is examined when it is mounted on a conducting cylinder of varying radius. In order to compensate the deterioration of the voltage standing wave ratio (VSWR) of the H-plane horn antenna, two air slots along the sides of the flare part of the antenna are cut. With the utilization of the slots, the matching between the feed line and the horn antenna is improved and the working frequency bandwidth is broadened simultaneously. Measured VSWR of the fabricated antenna is below 2.5 from 6.1 GHz to 19 GHz. The antenna also exhibits stable radiation beam over the same frequency range. It is observed that measured results agree well with simulated ones.


title: A 1.6Gb/s CMOS LVDS transmitter with a programmable pre-emphasis system

abstract: A 12 parallel low voltage differential signaling (LVDS) transmitter fabricated in 0.13 μm CMOS is presented. Each LVDS channel can operate over 1.6 Gb/s and includes a programmable pre-emphasis circuit designed to reduce the data-dependent jitter (DDJ) caused by different lengths of PCB traces. Experimental results of the fabricated LVDS confirm the correct operation of the programmable pre-equalization circuit. The power consumption and area per channel is less than 20 mW and 0.084 mm2, respectively.


title: The robustness of localization algorithms to signal strength attacks: a comparative study

abstract: In this paper, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We propose several performance metrics that quantify the estimator's precision and error, including Holder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of several point-based and area-based algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. We observed both strong experimental and theoretic evidence that all the algorithms have similar average responses to signal strength attacks.


title: THE EFFECT OF POWERPOINT PRESENTATIONS ON STUDENT LEARNING AND ATTITUDES

abstract: ABSTRACT In recent years, the uses of PowerPoint (a form of multimedia) presentations in classroom instruction have significantly increased globally without examination of their effects on student learning and attitudes. In this study, we test whether using PowerPoint in an accounting course enhances student short-term memory, long-term memory, and attitudes toward class presentation and the instructor. We conducted an experiment, which includes a treatment-control design, in a classroom setting throughout a semester. In one section of an accounting principles II (Managerial Accounting) course, PowerPoint was used as the delivery system, while the second section was taught using a traditional delivery system. The results show that PowerPoint presentation may improve student attitudes toward the instructor and class presentation. The results do not provide conclusive evidence that PowerPoint presentations improve short-term or long-term memory. The latter results are consistent with other media comparison studies that show the medium alone does not influence learning. Key words: PowerPoint, learning, attitudes, short-term memory, long-term memory, representational style, dual-coding theory Data availability: Contact the authors INTRODUCTION This study investigates whether PowerPoint presentations (a form of multimedia) improve student learning and attitudes compared with traditional classroom presentations. While the use of PowerPoint and multimedia in the classroom has significantly increased globally in recent years (Connor and Wong, 2004; Bartsch and Cobern, 2003), few studies have systematically investigated its impact on student learning and attitudes. Rebele et al. (1998) note that little research exists regarding integration of technology in the accounting curriculum, and suggest that accounting researchers should examine whether technology improves learning. Further, Rebele et al. (1998) recommend "accounting researchers should explore how educational technology can contribute to the continuing evolution and improvement of accounting education" (p. 207). One study that has examined the relationship between multimedia and student learning and attitudes was conducted by Butler and Mautz (1996). In a laboratory experiment conducted during a 30-minute time period, they found that multimedia did not affect student recall in all situations. Butler and Mautz did find an interaction between the effects of the multimedia presentation and the student's preferred class representation style (i.e., whether the student was considered a "verbal" or "imaginal" learner). The present study extends Butler and Mautz (1996) in two ways. First, it examines the effect of using PowerPoint presentations throughout a semester on both short-term and long-term memory. While both Butler and Mautz (1996) and this study examine the effect of multimedia and/or PowerPoint presentations on students' learning, the former study only focuses on short-term memory. Second, this study investigates the generalizability of Butler and Mautz's (1996) findings by conducting the research in a classroom setting. While generalizability comes at the cost of experimental control, researchers are invariably interested in whether laboratory results will extend outside the controlled laboratory environment. The current study finds that students who received instruction via PowerPoint did not (on average) perform better on quizzes or exams. However, the results of our study show that the effect of PowerPoint on short-term memory might depend on other factors such as the topic under discussion and the students' preferred representation style. For example, for more difficult and challenging chapters, students with higher use of imagery performed better on quizzes in the PowerPoint section than did students in the traditional section. This could be of interest to educators since it suggests that for more difficult and challenging chapters, the use of PowerPoint could be beneficial. …


title: Derivation of programs which traverse their input data only once

abstract: Publisher Summary The process of deriving programs from specifications can sometimes be viewed as a simplification process. However, not all program derivations are simplifications. Some other derivations described in the literature are (1) the synchronizations of computations like die ones based on the tupling strategy or (2) the definitions of more general computations, like the ones based on the generalization strategy. Lambda abstraction is a transformation strategy based on the idea of forced folding, which is a method for obtaining efficient recursive programs and inventing suitable auxiliary functions when transforming programs. The application of the lambda abstraction consists in the introduction of a new function that is obtained by breaking a given expression into a lambda formula applied to suitable arguments. The expression to be broken is often the one that does not allow a folding step because of a sub-expression mismatch. This chapter analyzes the transformation process from locally recursive programs to nonlocal recursive ones and presents the comparison of abstraction strategy with the techniques of lazy evaluation and local recursion. It discusses synergism between lambda abstraction and Tupling.


Technology Acceptance Model in the Domains of LIS and Education: A Review of Selected Literature


title: State of flat-panel display technology and future trends

abstract: The rivalry between the cathode-ray tube and flat-panel displays (FPDs) has intensified as performance of some FPDs now exceeds that of that entrenched leader in many cases. Besides the well-known active-matrix-addressed liquid-crystal display, plasma, organic light-emitting diodes, and liquid-crystal-on-silicon displays are now finding new applications as the manufacturing, process engineering, materials, and cost structures become standardized and suitable for large markets.


Self-Presentation and Deception Looks and Lies : The Role of Physical Attractiveness in Online Dating


title: Deep reinforcement learning for frontal view person shooting using drones

abstract: Unmanned Aerial Vehicles (UAVs), also known as drones, are increasingly used for a wide variety of novel tasks, including drone-based cinematography. However, flying drones in such setting requires the coordination of several people, increasing the cost of using drones for aerial cinematography and limiting the shooting flexibility by putting a significant cognitive load on the director and drone/camera operators. To overcome some of these limitation, this paper proposes a deep reinforcement learning (RL) method for performing autonomous frontal view shooting. To this end, a realistic simulation environment is developed, which ensures that the learned agent can be directly deployed on a drone. Then, a deep RL algorithm, tailored to the needs of the specific application, is derived building upon the well known deep Q-learning approach. The effectiveness of the proposed technique is experimentally demonstrated using several quantitative and qualitative experiments.


title: Compressed representations of sequences and full-text indexes

abstract: Given a sequence S = s1s2…sn of integers smaller than r = O(polylog(n)), we show how S can be represented using nH0(S) + o(n) bits, so that we can know any sq, as well as answer rank and select queries on S, in constant time. H0(S) is the zero-order empirical entropy of S and nH0(S) provides an information-theoretic lower bound to the bit storage of any sequence S via a fixed encoding of its symbols. This extends previous results on binary sequences, and improves previous results on general sequences where those queries are answered in O(log r) time. For larger r, we can still represent S in nH0(S) + o(n log r) bits and answer queries in O(log r/log log n) time. ::: Another contribution of this article is to show how to combine our compressed representation of integer sequences with a compression boosting technique to design compressed full-text indexes that scale well with the size of the input alphabet Σ. Specifically, we design a variant of the FM-index that indexes a string T[1, n] within nHk(T) + o(n) bits of storage, where Hk(T) is the kth-order empirical entropy of T. This space bound holds simultaneously for all k ≤ α log|Σ| n, constant 0 < α < 1, and |Σ| = O(polylog(n)). This index counts the occurrences of an arbitrary pattern P[1, p] as a substring of T in O(p) time; it locates each pattern occurrence in O(log1+ϵ n) time for any constant 0 < ϵ < 1; and reports a text substring of length ℓ in O(ℓ + log1+ϵ n) time. ::: Compared to all previous works, our index is the first that removes the alphabet-size dependance from all query times, in particular, counting time is linear in the pattern length. Still, our index uses essentially the same space of the kth-order entropy of the text T, which is the best space obtained in previous work. We can also handle larger alphabets of size |Σ| = O(nβ), for any 0 < β < 1, by paying o(n log|Σ|) extra space and multiplying all query times by O(log |Σ|/log log n).


Boosting Knowledge-Building with Cognitive Dialog Games.


title: Anomaly detection: A survey

abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.


title: "Best of Both Worlds": Opportunities for Technology in Cross-Cultural Parenting

abstract: Families are becoming more culturally heterogeneous due to a rise in intermarriage, geographic mobility, and access to a greater diversity of cultural perspectives online. Investigating the challenges of cross-cultural parenting can help us support this growing demographic, as well as better understand how families integrate and negotiate advice from diverse online and offline sources in making parenting decisions. We interviewed parents from 18 families to understand the practices they adopt to meet the challenges of cross-cultural parenting. We investigated how these families respond to conflicts while integrating diverse cultural views, as well as how they utilize the wealth of parenting resources available online in navigating these tasks. We identify five themes focused on how these families find and evaluate advice, connect with social support, resolve intra-family tensions, incorporate multicultural practices, and seek out diverse views. Based on our findings, we contribute three implications for design and translations of these implications to concrete technology ideas that aim to help families better integrate multiple cultures into everyday life.


title: Semantic Similarity in Biomedical Ontologies

abstract: In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. ::: ::: We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. ::: ::: Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research.


title: On the Synthesis of Discrete Controllers for Timed Systems

abstract: This paper presents algorithms for the automatic synthesis of real-time controllers by finding a winning strategy for certain games defined by the timed-automata of Alur and Dill. In such games, the outcome depends on the players' actions as well as on their timing. We believe that these results will pave the way for the application of program synthesis techniques to the construction of real-time embedded systems from their specifications.


An AI Approach to Automatic Natural Music Transcription


Extractive Based Automatic Text Summarization