title: Transfer learning for speech and language processing

abstract: Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.


title: Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images

abstract: Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.


title: The concurrent application of lean production and ERP: Towards an ERP-based lean implementation process

abstract: Lean production and enterprise resource planning systems are often quoted as being the two most important strategies for achieving competitive advantage in today's global manufacturing environments. Though IT has traditionally been viewed as a contributor to waste within lean production, we suggest that modern developments in IT and the onset of hybrid ''push-pull'' production control mechanisms have allowed ERP and lean approaches to converge towards a state where ERP systems can in fact be used to support the deployment of lean practices. This paper analyses typical lean and ERP implementation processes contained within the scientific literature, and by further examining a concurrent implementation process in real-time, we develop and propose a process for ERP-based lean implementations. Our findings suggest that the implementation of a contemporary ERP system can act as a catalyst for the application of lean production practices.


title: Solar Tracker Robot using microcontroller

abstract: Shortage of non-renewable fuel in the future is an upcoming global issue; therefore renewable energy such as solar energy has gradually replaced non-renewable energy. However, the output power provided via the photovoltaic conversion process depends on solar irradiation. The objective of this project is to design and develop an automatic Solar Tracker Robot (STR) which is capable to track maximum light intensity. The efficiency of the solar energy conversion can be optimized by receiving maximum light on the solar panel. The main components of the robot consist of microcontroller namely PIC16F877A, sensors, servo motors and digital compass. This robot is programmed to detect sunlight by using two Light Dependent Resistors (LDR). Servo motor aligns the solar panel to receive maximum light. Digital compass is used to detect the position of the robot. Two modified DC servo motors will move the robot back to the original position once the robot is out of position. The robot is programmed using MPLAB IDE v8.30. Analysis on the power conversion of solar panel is being carried out by using Fluke 1750 power quality recorder.


title: Comparison of Invariant Manifolds for Model Reduction in Chemical Kinetics

abstract: A modern approach to model reduction in chemical kinetics is often based on the notion of slow invariant manifold. The goal of this paper is to give a com- parison of various methods of construction of slow invariant manifolds using a sim- ple Michaelis-Menten catalytic reaction. We explore a recently introduced Method of Invariant Grids (MIG) for iteratively solving the invariance equation. Various initial approximations for the grid are considered such as Quasi Equilibrium Manifold, Spec- tral Quasi Equilibrium Manifold, Intrinsic Low Dimensional Manifold and Symmetric Entropic Intrinsic Low Dimensional Manifold. Slow invariant manifold was also com- puted using the Computational Singular Perturbation (CSP) method. A comparison between MIG and CSP is also reported.


title: It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text

abstract: Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. ::: ::: In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.


title: An enhanced intrusion detection framework for securing network layer of cloud computing

abstract: Virtualization is a key enabler of Cloud computing. Due to the numerous vulnerabilities in current implementations of virtualization, security is the major concern of Cloud computing. In this paper, we propose an enhanced security framework to detect intrusions at the virtual network layer of Cloud. It combines signature and anomaly based techniques to detect possible attacks. It uses different classifiers viz; naive bayes, decision tree, random forest, extra trees and linear discriminant analysis for an efficient and effective detection of intrusions. To detect distributed attacks at each cluster and at whole Cloud, it collects intrusion evidences from each region of Cloud and applies Dempster-Shafer theory (DST) for final decision making. We analyze the proposed security framework in terms of Cloud IDS requirements through offline simulation using different intrusion datasets.


title: Formulation ofBacillusspp. for Biological Control of Plant Diseases

abstract: ABSTRACT Maximizing the potential for successfully developing and deploying a biocontrol product begins with a carefully crafted microbial screening procedure, proceeds with developing mass production protocols that optimize product quantity and quality, and ends with devising a product formulation that preserves shelf-life, aids product delivery, and enhances bioactivity. Microbial selection procedures that require prospective bio-control agents to possess both efficacy and amenability to production in liquid culture increase the likelihood of selecting agents with enhanced commercial development potential. Scale-up of biomass production procedures must optimize product quantity without compromise of product efficacy or amenability to stabilization and formulation. Formulation of Bacillus spp. for use against plant pathogens is an enormous topic in general terms but limited in published specifics regarding formulations used in commercially available products. Types of formulations include dry products such as wettable powders, dusts, and granules, and liquid products including cell suspensions in water, oils, and emulsions. Cells can also be microencapsulated. Considerations critical to designing successful formulations of microbial biomass are many fold and include preserving biomass viability during stabilization, drying, and rehydration; aiding biomass delivery, target coverage, and target adhesion; and enhancing biomass survival and efficacy after delivery to the target. Solutions to these formulation considerations will not necessarily be compatible. Data from several biocontrol systems including the use of B. subtilis OH 131.1 (NRRL B-30212) to reduce Fusarium head blight of wheat are used to illustrate many of these issues. Using our recently described assay for efficiently evaluating biomass production and formulation protocols, we demonstrate the effectiveness, in vitro, of UV protectant compounds lignin (PC 1307) and Blankophor BBH in reducing OH 131.1 morbidity when cells were exposed to UV light from artificial sunlight.


title: A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data

abstract: Highly imbalanced data is common in the real world and it is important but difficult to train an effective classifier. In this paper, Our major point is that the imbalance is the observed phenomenon but not the cause of the problem. The challenge is that useful information is been overshadowed in the large scale of data in both majority and minority classes. We propose a novel two-step strategy, Information Accumulation, which first selects the most discriminative data by the Zooming-in phase, and then leverages unlabeled data by pseudo active learning and self-training in the phase of Learning from Learned Results. Comparative experiments are conducted on large-scale highly imbalanced real customer service data on complaint detection task (where less than 2% of data is positive). The results on eight state-of-the-art classification algorithms show that significant improvements are observed on the performances of all algorithms with Information Accumulation(for example, the F-Measure score of Xgboost is increased by 197% from 0.115 to 0.347), which demonstrates the effectiveness and general applicability of the proposed strategy. This work explores a new idea on dealing with highly imbalanced data that we do not aim to balance the training examples as usual, but focus on finding the most discriminative information from labeled data and the learning results of unlabeled data.


title: Adaptive schemes for incomplete quantum process tomography

abstract: We propose an iterative algorithm for incomplete quantum process tomography with the help of quantum state estimation. The algorithm, which is based on the combined principles of maximum likelihood and maximum entropy, yields a unique estimator for an unknown quantum process when one has less than a complete set of linearly independent measurement data to specify the quantum process uniquely. We apply this iterative algorithm adaptively in various situations and so optimize the amount of resources required to estimate a quantum process with incomplete data.


title: Investigating GSM Control Channels with RTL-SDR and GNU Radio

abstract: This paper focuses on the compliance of publicly available specifications of Global System for Mobile communication (GSM) with the extremely closed GSM industry with the help of RTL-SDR, GNU Radio and Wireshark. GNU Radio is a free and open software which enables the translation of real-world systems to programmable flow graphs. GSM, together with other technologies, is part of the evolution of wireless mobile telecommunications. Hence, verification of GSM working is of crucial importance. During this paper, Control channel information like the bandwidth for specific network providers was reckoned, along with the IMSI number and ARFCN channel in use by mobile device of the respective network. It was also noted that observed cell towers used both frequency hopping and encryption. The channel information thus obtained provides efficient techniques for the detection of spectrum holes providing high spectral resolution capability.


title: Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook

abstract: A crucial task in the analysis of on-line social-networking systems is to identify important people --- those linked by strong social ties --- within an individual's network neighborhood. Here we investigate this question for a particular category of strong ties, those involving spouses or romantic partners. We organize our analysis around a basic question: given all the connections among a person's friends, can you recognize his or her romantic partner from the network structure alone? Using data from a large sample of Facebook users, we find that this task can be accomplished with high accuracy, but doing so requires the development of a new measure of tie strength that we term `dispersion' --- the extent to which two people's mutual friends are not themselves well-connected. The results offer methods for identifying types of structurally significant people in on-line applications, and suggest a potential expansion of existing theories of tie strength.


title: Sparse reconstruction of time-frequency representation using the fractional Fourier transform

abstract: This paper describes a novel method to approximate instantaneous frequency of non-stationary signals through an application of fractional Fourier transform (FRFT). FRFT enables us to build a compact and accurate chirp dictionary for each windowed signal, thus the proposed approach offers improved computational efficiency, and good performance when compared with chirp atom method.


title: Morals, Materials, and Technoscience The Energy Security Imaginary in the United States

abstract: This article advances recent scholarship on energy security by arguing that the concept is best understood as a sociotechnical imaginary, a collective vision for a “good society” realized through technoscientific-oriented policies. Focusing on the 1952 Resources for Freedom report, the authors trace the genealogy of energy security, elucidating how it establishes a morality of efficiency that orients policy action under the guise of security toward the liberalizing of markets in resource states and a robust program of energy research and development in the United States. This evidence challenges the pervasive historical anchoring of the concept in the 1970s and illustrates the importance of the genealogical approach for the emerging literature on energy and sociotechnical imaginaries. Exploring the genealogy of energy security also unpacks key social, political, and economic undercurrents that disrupt the seeming universality of the language of energy, leading the authors to question whether energy securi...


title: Self-supervised Learning of Motion Capture

abstract: Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection matches measurements in the video (e.g. person segmentation, optical flow, keypoint detections etc.). Optimization models are susceptible to local minima. This has been the bottleneck that forced using clean green-screen like backgrounds at capture time, manual initialization, or switching to multiple cameras as input resource. In this work, we propose a learning based motion capture model for single camera input. Instead of optimizing mesh and skeleton parameters directly, our model optimizes neural network weights that predict 3D shape and skeleton configurations given a monocular RGB video. Our model is trained using a combination of strong supervision from synthetic data, and self-supervision from differentiable rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c) human-background segmentation, in an end-to-end framework. Empirically we show our model combines the best of both worlds of supervised learning and test-time optimization: supervised learning initializes the model parameters in the right regime, ensuring good pose and surface initialization at test time, without manual effort. Self-supervision by back-propagating through differentiable rendering allows (unsupervised) adaptation of the model to the test data, and offers much tighter fit than a pretrained fixed model. We show that the proposed model improves with experience and converges to low-error solutions where previous optimization methods fail.


title: Learning to divide and conquer: applying the L* algorithm to automate assume-guarantee reasoning

abstract: Assume-guarantee reasoning enables a "divide-and-conquer" approach to the verification of large systems that checks system components separately while using assumptions about each component's environment. Developing appropriate assumptions used to be a difficult and manual process. Over the past five years, we have developed a framework for performing assume-guarantee verification of systems in an incremental and fully automated fashion. The framework uses an off-the-shelf learning algorithm to compute the assumptions. The assumptions are initially approximate and become more precise by means of counterexamples obtained by model checking components separately. The framework supports different assume-guarantee rules, both symmetric and asymmetric. Moreover, we have recently introduced alphabet refinement, which extends the assumption learning process to also infer assumption alphabets. This refinement technique starts with assumption alphabets that are a subset of the minimal interface between a component and its environment, and adds actions to it as necessary until a given property is shown to hold or to be violated in the system. We have applied the learning framework to a number of case studies that show that compositional verification by learning assumptions can be significantly more scalable than non-compositional verification.


title: Robotic joint design by agonist and antagonist arrangement with twisting small-diameter round-belts

abstract: This paper develops a novel robotic joint mechanism by means of twisting a small-diameter round-belt, which enables slow movements of joint rotation unlike direct-drive actuator mechanisms. The actuator mechanism proposed in this manuscript is composed of two small-diameter round-belts located near the joint with opposite configurations. The two round-belts are twisted by DC motors placed on a motor stage, which can be activated by a step motor. This novel mechanism realizes movement of the robotic joint around its axis due to contraction forces generated by twisting both round-belts. That is, these round-belts act as agonist and antagonist actuators for the robotic joint, which give a human-like compliance to the rotational motion of the joint. Experimental results using the proposed one-link robot show that a high position resolution of the joint control can be achieved by increases and decreases in the amount of twisting. In addition, we clearly indicate a linear relationship between twisting and the joint angle of the robot. Finally, this paper reveals that the antagonistic twist-actuator system has a secondary role as a gear reducer that is capable of largely decreasing the velocity of joint movement.


title: Image processing based approach to food balance analysis for personal food logging

abstract: Food images have been receiving increased attention in recent dietary control methods. We present the current status of our web-based system that can be used as a dietary management support system by ordinary Internet users. The system analyzes image archives of the user to identify images of meals. Further image analysis determines the nutritional composition of these meals and stores the data to form a Foodlog. The user can view the data in different formats, and also edit the data to correct any mistakes that occurred during image analysis. This paper presents detailed analysis of the performance of the current system and proposes an improvement of analysis by pre-classification and personalization. As a result, the accuracy of food balance estimation is significantly improved.


rector spinae plane ( ESP ) block in the management of post horacotomy pain syndrome : A case series


Current potential health benefits of sulforaphane


title: Cognitive Strategies for the Visual Search of Hierarchical Computer Displays

abstract: This article investigates the cognitive strategies that people use to search computer displays. Several different visual layouts are examined: unlabeled layouts that contain multiple groups of items but no group headings, labeled layouts in which items are grouped and each group has a useful heading, and a target-only layout that contains just one item. A number of plausible strategies were proposed for each layout. Each strategy was programmed into the EPIC cognitive architecture, producing models that simulate the human visual-perceptual, oculomotor, and cognitive processing required for the task. The models generate search time predictions. For unlabeled layouts, the mean layout search times are predicted by a purely random search strategy, and the more detailed positional search times are predicted by a noisy systematic strategy. The labeled layout search times are predicted by a hierarchical strategy in which first the group labels are systematically searched, and then the contents of the target group. The target-only layout search times are predicted by a strategy in which the eyes move directly to the sudden appearance of the target. The models demonstrate that human visual search performance can be explained largely in terms of the cognitive strategy that is used to coordinate the relevant perceptual and motor processes, a clear and useful visual hierarchy triggers a fundamentally different visual search strategy and effectively gives the user greater control over the visual navigation, and cognitive strategies will be an important component of a predictive visual search tool. The models provide insights pertaining to the visual-perceptual and oculomotor processes involved in visual search and contribute to the science base needed for predictive interface analysis.


title: ON SCHÖNHAGE’S ALGORITHM AND SUBQUADRATIC INTEGER GCD COMPUTATION

abstract: We describe a new subquadratic left-to-right GCD algorithm, inspired by Schonhage's algorithm for reduction of binary quadratic forms, and compare it to the first subquadratic GCD algorithm discovered by Knuth and Schonhage, and to the binary recursive GCD algorithm of Stehle and Zimmer-mann. The new GCD algorithm runs slightly faster than earlier algorithms, and it is much simpler to implement. The key idea is to use a stop condition for HGCD that is based not on the size of the remainders, but on the size of the next difference. This subtle change is sufficient to eliminate the back-up steps that are necessary in all previous subquadratic left-to-right GCD algorithms. The subquadratic GCD algorithms all have the same asymptotic running time, O(n(log n)(2) log log n).


title: Culturally Competent Healthcare Systems A Systematic Review

abstract: Culturally competent healthcare systems-those that provide culturally and linguistically appropriate services-have the potential to reduce racial and ethnic health disparities. When clients do not understand what their healthcare providers are telling them, and providers either do not speak the client's language or are insensitive to cultural differences, the quality of health care can be compromised. We reviewed five interventions to improve cultural competence in healthcare systems-programs to recruit and retain staff members who reflect the cultural diversity of the community served, use of interpreter services or bilingual providers for clients with limited English proficiency, cultural competency training for healthcare providers, use of linguistically and culturally appropriate health education materials, and culturally specific healthcare settings. We could not determine the effectiveness of any of these interventions, because there were either too few comparative studies, or studies did not examine the outcome measures evaluated in this review: client satisfaction with care, improvements in health status, and inappropriate racial or ethnic differences in use of health services or in received and recommended treatment.


title: Word segmentation for the Myanmar language

abstract: This study reports the development of a Myanmar word segmentation method using Unicode standard encoding. Word segmentation is an essential step prior to natural language processing in the Myanmar language, because a Myanmar text is a string of characters without explicit word boundary delimiters. The proposed method has two phases: syllable segmentation and syllable merging. A rule-based heuristic approach was adopted for syllable segmentation, and a dictionary-based statistical approach for syllable merging. Evaluation of test results showed that the method is very effective for the Myanmar language.


title: Lightcut interpolation

abstract: Many-light rendering methods replace multi-bounce light transport with direct lighting from many virtual point light sources to allow for simple and efficient computation of global illumination. Lightcuts build a hierarchy over virtual lights, so that surface points can be shaded with a sublinear number of lights while minimizing error. However, the original algorithm needs to run on every shading point of the rendered image. It is well known that the performance of Lightcuts can be improved by exploiting the coherence between individual cuts. We propose a novel approach where we invest into the initial lightcut creation at representative cache records, and then directly interpolate the input lightcuts themselves as well as per-cluster visibility for neighboring shading points. This allows us to improve upon the performance of the original Lightcuts algorithm by a factor of 4-8 compared to an optimized GPU-implementation of Lightcuts, while introducing only a small additional approximation error. The GPU-implementation of our technique enables us to create previews of Lightcuts-based global illumination renderings.


title: Summary on Inertia Weight in Particle Swarm Optimization Algorithm

abstract: Particle swarm optimization(thereafter as PSO) algorithm is a kind of new group intelligent algorithm based on foraging of birds while inertial weight is one of important parameters in PSO algorithm of which selection for the value is directly affecting development and exploration capabilities during the period of optimizing of the particle.On the basis of introducing basic principles of PSO algorithm,this paper analyzes impact of inertial weight on astringency and discusses study progress of inertial weight of present documents.In addition,it makes comments on research achievements and shortages of various value strategies for inertial weight.


title: Growth of ZnO hemispheres on silicon by a hydrothermal method

abstract: Abstract This paper reports a hydrothermal method to control the morphology of hemisphere ZnO nanostructures (NSs) fabricated on a Si substrate by using surfactant additive molecules. The shape of ZnO NSs was controlled by the concentration of the trisodium citrate (TSC) added during the main growth step. With increasing the TSC concentration from 0 to 1.3 and 2.6 mM, the shape of ZnO NSs changed from a bundle of nanorods to a flower-like-shape composed of merged large hexagonal crystals, and finally to ZnO hemispheres. X-ray diffraction and photoluminescence spectroscopy revealed that the ZnO NSs had a wurtzite crystalline structure. The shape evolution mechanism of ZnO NSs could be explained using the surfactant-mediated growth model.


title: Experience with SCRAM, a SCenario Requirements Analysis Method

abstract: A method of scenario based requirements engineering is described that uses a combination of early prototypes, scenario scripts and design rationale to elicit and validate user requirements. Experience in using the method on an EU project, Multimedia Broker, is reported. Quantitative data on requirements sessions is analysed to assess user participation and quality of requirements captured. The method worked well but there were problems in the use of design rationale and control of turn taking in RE sessions. Lessons learned in using the method are summarised and future improvements for the method are discussed.


title: Multistability in the lactose utilization network of Escherichia coli

abstract: Multistability, the capacity to achieve multiple internal states in response to a single set of external inputs, is the defining characteristic of a switch. Biological switches are essential for the determination of cell fate in multicellular organisms, the regulation of cell-cycle oscillations during mitosis and the maintenance of epigenetic traits in microbes. The multistability of several natural and synthetic systems has been attributed to positive feedback loops in their regulatory networks. However, feedback alone does not guarantee multistability. The phase diagram of a multistable system, a concise description of internal states as key parameters are varied, reveals the conditions required to produce a functional switch. Here we present the phase diagram of the bistable lactose utilization network of Escherichia coli. We use this phase diagram, coupled with a mathematical model of the network, to quantitatively investigate processes such as sugar uptake and transcriptional regulation in vivo. We then show how the hysteretic response of the wild-type system can be converted to an ultrasensitive graded response. The phase diagram thus serves as a sensitive probe of molecular interactions and as a powerful tool for rational network design.


title: Analysing and improving the diagnosis of ischaemic heart disease with machine learning

abstract: Abstract Ischaemic heart disease is one of the world’s most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy, and finally coronary angiography (which is considered to be the reference method). Machine learning methods may enable objective interpretation of all available results for the same patient and in this way may increase the diagnostic accuracy of each step. We conducted many experiments with various learning algorithms and achieved the performance level comparable to that of clinicians. We also extended the algorithms to deal with non-uniform misclassification costs in order to perform ROC analysis and control the trade-off between sensitivity and specificity. The ROC analysis shows significant improvements of sensitivity and specificity compared to the performance of the clinicians. We further compare the predictive power of standard tests with that of machine learning techniques and show that it can be significantly improved in this way.


title: The principle and implementation of monopulse antenna angle error signal simulator

abstract: A dynamic simulation of the angle error signal is required for testing the performance of the monopulse antenna angle tracking system. This paper analyses the simplified model of the monopulse antenna beam pattern and the principle of monopulse antenna angle tracking system. The corresponding relation between antenna deflection angle and amplitude of the sum-difference signal in the antenna beam pattern is described. Also, this paper introduces a method of simulating the angle error signal based on “antenna deflection angle — attenuation” lookup table and digital attenuators.


title: Characterizing the Design Space of Rendered Robot Faces

abstract: Faces are critical in establishing the agency of social robots; however, building expressive mechanical faces is costly and difficult. Instead, many robots built in recent years have faces that are rendered onto a screen. This gives great flexibility in what a robot's face can be and opens up a new design space with which to establish a robot's character and perceived properties. Despite the prevalence of robots with rendered faces, there are no systematic explorations of this design space. Our work aims to fill that gap. We conducted a survey and identified 157 robots with rendered faces and coded them in terms of 76 properties. We present statistics, common patterns, and observations about this data set of faces. Next, we conducted two surveys to understand people's perceptions of rendered robot faces and identify the impact of different face features. Survey results indicate preferences for varying levels of realism and detail in robot faces based on context, and indicate how the presence or absence of specific features affects perception of the face and the types of jobs the face would be appropriate for.


title: The fear of being laughed at: Individual and group differences in Gelotophobia

abstract: Single case studies led to the discovery and phenomenological description of Gelotophobia anti its definition as the pathological fear of appearing to social partners as a ridiculous object (Titze 1995, 1996, 1997). The aim of the present study is to empirically examine the core assumptions about the fear of being laughed at in a sample comprising a total of 863 clinical and non-clinical participants. Discriminant function analysis yielded that gelotophobes can be separated from other shame-based neurotics, non-shame-based neurotics, anti controls. Separation was best for statements specifically describing the gelotophobic symptomatology and less potent for more general questions describing socially avoidant behaviors. Factor analysis demonstrates that while Gelotophobia is composed of a set of correlated elements in homogenous samples, overall the concept is best conceptualized as unidimensional. Predicted and actual group membership converged well in a cross-classification (approximately 69% of correctly classified cases). Overall, it can be concluded that the fear of being laughed at varies tremendously among adults and might hold a key to understanding certain forms of humorlessness.


title: Parallel data mining on graphics processors

abstract: We introduce GPUMiner, a novel parallel data mining system that utilizes new-generation graphics processing units (GPUs). Our system relies on the massively multi-threaded SIMD (Single Instruction, Multiple-Data) architecture provided by GPUs. As specialpurpose co-processors, these processors are highly optimized for graphics rendering and rely on the CPU for data input/output as well as complex program control. Therefore, we design GPUMiner to consist of the following three components: (1) a CPU-based storage and buffer manager to handle I/O and data transfer between the CPU and the GPU, (2) a GPU-CPU co-processing parallel mining module, and (3) a GPU-based mining visualization module. We design the GPU-CPU co-processing scheme in mining depending on the complexity and inherent parallelism of individual mining algorithms. We provide the visualization module to facilitate users to observe and interact with the mining process online. We have implemented the k-means clustering and the Apriori frequent pattern mining algorithms in GPUMiner. Our preliminary results have shown significant speedups over state-of-the-art CPU implementations on a PC with a G80 GPU and a quad-core CPU. We will demonstrate the mining process through our visualization module. Code and documentation of GPUMiner are available at http://code.google.com/p/gpuminer/.


title: FADES: Fine-Grained Edge Offloading with Unikernels

abstract: FADES is an edge offloading architecture that empowers us to run compact, single purpose tasks at the edge of the network to support a variety of IoT and cloud services. The design principle behind FADES is to efficiently exploit the resources of constrained edge devices through fine-grained computation offloading. FADES takes advantage of MirageOS unikernels to isolate and embed application logic in concise Xen-bootable images. We have implemented FADES and evaluated the system performance under various hardware and network conditions. Our results show that FADES can effectively strike a balance between running complex applications in the cloud and simple operations at the edge. As a solid step to enable fine-grained edge offloading, our experiments also reveal the limitation of existing IoT hardware and virtualization platforms, which shed light on future research to bring unikernel into IoT domain.


title: The Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults

abstract: The present study reports on the construction of a dimensional measure of gender identity (gender dysphoria) for adolescents and adults. The 27-item gender identity/gender dysphoria questionnaire for adolescents and adults (GIDYQ-AA) was administered to 389 university students (heterosexual and nonheterosexual) and 73 clinic-referred patients with gender identity disorder. Principal axis factor analysis indicated that a one-factor solution, accounting for 61.3% of the total variance, best fits the data. Factor loadings were all ≥ .30 (median, .82; range, .34–.96). A mean total score (Cronbach's alpha, .97) was computed, which showed strong evidence for discriminant validity in that the gender identity patients had significantly more gender dysphoria than both the heterosexual and nonheterosexual university students. Using a cut-point of 3.00, we found the sensitivity was 90.4% for the gender identity patients and specificity was 99.7% for the controls. The utility of the GIDYQ-AA is discussed.


title: Transfer learning for collaborative filtering via a rating-matrix generative model

abstract: Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.


DETERMINANTS OF DIVORCE AMONG WOMEN IN MALAYSIA


title: Online modeling for realtime facial animation

abstract: We present a new algorithm for realtime face tracking on commodity RGB-D sensing devices. Our method requires no user-specific training or calibration, or any other form of manual assistance, thus enabling a range of new applications in performance-based facial animation and virtual interaction at the consumer level. The key novelty of our approach is an optimization algorithm that jointly solves for a detailed 3D expression model of the user and the corresponding dynamic tracking parameters. Realtime performance and robust computations are facilitated by a novel subspace parameterization of the dynamic facial expression space. We provide a detailed evaluation that shows that our approach significantly simplifies the performance capture workflow, while achieving accurate facial tracking for realtime applications.


title: Timing and Frequency Synchronization for OFDM Downlink Transmissions Using Zadoff-Chu Sequences

abstract: Orthogonal frequency division multiplexing (OFDM) technique has been widely adopted in wireless systems, but it is known for being sensitive to synchronization errors. In this paper, we present timing and frequency synchronization algorithms for OFDM downlink transmissions using Zadoff-Chu sequences. We show that the current timing synchronization method employing Zadoff-Chu sequences is sensitive to carrier frequency offsets. To reduce this sensitivity, we develop conditions for the selection of appropriate Zadoff-Chu sequences. We then design a training sequence and propose joint signal detection, timing, and carrier frequency offset estimation algorithms for OFDM downlink transmissions. We show that the proposed schemes simplify the overall synchronization architecture and improve the performance compared to the existing schemes in the literature.


title: A novel fingerprint matching and classification schema based on nested convex polygons

abstract: In this paper, we present a new approach to fingerprint matching and classification using an onion layer algorithm of computational geometry. In order to extract valid minutiae we apply some image processing steps on input fingerprint. Using an Onion layer algorithm we construct nested convex polygons of minutiae, then based on polygons property, we perform matching and classification of fingerprints; we use the most interior polygon in order to calculate the rigid transformations parameters and perform local matching, consequently, global matching applied. This method rejects non matching fingerprint in local matching and avoid time consuming global matching steps. ::: ::: We develop new classification scheme of fingerprints based on this approach. Unlike classic classification of fingerprints, this novel approach distributed fingerprints in classes equally, and none of image processing techniques are required for this classification. This normal distribution of fingerprints in different classes has great effect on identification time.


title: Applicability of 3D printing techniques for compact Ku-band medium/high-gain antennas

abstract: This paper investigates the capabilities of additive manufacturing techniques for building RF antennas in the Ku-band frequency range. Two different compact medium to high-gain antennas, a ring-focus dual-reflector antenna and a spline-profiled smooth horn antenna, were designed, built and tested for this purpose. The antennas were manufactured using the latest 3D printing techniques, i.e. based on stereolithography and an additional copper coating. RF simulation results compared with measurements are provided in this paper for a specific telecommunication antenna application. Furthermore, additional test results will be presented at the conference itself.


title: Map alignment based on PLICP algorithm for multi-robot SLAM

abstract: Inter-robot observation (Rendezvous) strategy is usually adopted to align local maps in multi-robot SLAM system while the initial relative positions are unknown. However, the accuracy of the estimated alignment is often affected by various uncertainty sources. This paper presents a new approach based on the Point-to-Line Iterative Closest Point (PLICP) algorithm to improve the accuracy of the map alignment: First, local maps are generated by means of the FastSLAM algorithm, then the initial alignment parameters are calculated by inter-robot observation, and finally the PLICP algorithm is used to update these parameters. Experimental results illustrate the accuracy improvement of the proposed approach.


title: Hierarchical Control for Multiple DC-Microgrids Clusters

abstract: This paper presents a distributed hierarchical control framework to ensure reliable operation of dc microgrid (MG) clusters. In this hierarchy, primary control is used to regulate the common bus voltage inside each MG locally. An adaptive droop method is proposed for this level, which determines droop coefficients according to the state-of-charge (SOC) of batteries automatically. A small-signal model is developed to investigate effects of the system parameters, constant power loads, as well as line impedance between the MGs on stability of these systems. In the secondary level, a distributed consensus-based voltage regulator is introduced to eliminate the average voltage deviation over the MGs. This distributed averaging method allows the power flow control between the MGs to be achieved at the same time, as it can be accomplished only at the cost of having voltage deviation inside the system. Another distributed policy is employed then to regulate the power flow among the MGs according to their local SOCs. The proposed distributed controllers on each MG communicate with only the neighbor MGs through a communication infrastructure. Finally, the developed small-signal model is expanded for MG clusters with all the proposed control loops. The effectiveness of the proposed hierarchical scheme is verified through detailed hardware-in-the-loop simulations.


title: Building energy for sustainable development in Malaysia: A review

abstract: Malaysia is located in Southeast Asia near the equator within the typical tropical climatic zone. The efficient use of energy is vital due to the dependency on fossil resources that are being exhausted, which ultimately cause CO2 emissions. Economic development and population growth are deemed to affect the growing energy demand in the country. Therefore, sustainability, energy security, and climate change are crucial challenges for the power sector in Malaysia. The aforementioned issues can be tackled with energy efficient measures in the building sector. Buildings in Malaysia consume 14.3% of the overall energy and 53% of only electrical energy is being consumed in residential and commercial sectors. Therefore, energy efficiency in buildings is crucial in order to reduce the energy use and improve the local environmental sustainability. This paper discusses a review on the building energy scenario, the policy perspectives, building energy efficiency programs along with landmark buildings and their characteristics. Besides, the potential of renewable energy resources in buildings and various prospective issues and challenges faced by the country have also been discussed. The significant review content thus benefits researchers, scientists and practitioners for a better understanding on energy efficiency and the sustainable measures that have been so far taken. The review also puts forward some actions to promote building energy efficiency and conservation.


title: Boosting structured prediction for imitation learning

abstract: The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with a loss-scaled margin) than any other policy for a given planning domain. We provide a novel approach, MMPBOOST, based on the functional gradient descent view of boosting (Mason et al., 1999; Friedman, 1999a) that extends MMP by "boosting" in new features. This approach uses simple binary classification or regression to improve performance of MMP imitation learning, and naturally extends to the class of structured maximum margin prediction problems. (Taskar et al., 2005) Our technique is applied to navigation and planning problems for outdoor mobile robots and robotic legged locomotion.


title: The product market and the market for "ideas": commercialization strategies for technology entrepreneurs

abstract: This paper presents a synthetic framework identifying the central drivers of start-up commercialization strategy and the implications of these drivers for industrial dynamics. We link strategy to the commercialization environment - the microeconomic and strategic conditions facing a firm that is translating an "idea" into a value proposition for customers. The framework addresses why technology entrepreneurs in some environments undermine established firms, while others cooperate with incumbents and reinforce existing market power. Our analysis suggests that competitive interaction between start-up innovators and established firms depends on the presence or absence of a "market for ideas." By focusing on the operating requirements, efficiency, and institutions associated with markets for ideas, this framework holds several implications for the management of high-technology entrepreneurial firms.


title: Car detection at night using latent filters

abstract: Deformable part models (DPM) already proved great performance for objects detection and many extensions have already published in literature. DPMs generally have high performance, though they dramatically fail to detect objects in challenging environments such as night time. This paper proposes a method based on the idea of latent parts to optimize the structure of objects in deformable part models. Even in challenging environment some parts of the objects are still visible and latent parts can optimize the structure of DPM's object model to catch significant features. We have evaluated proposed method to detect cars at night time in urban area using IR camera. It is a challenging problem due to low visibility, light distortion and illumination/glare in urban area. Experimental results prove the effectiveness of the model to detect close and medium range cars in urban scenes at night time.


title: Mandibular asymmetry and the fourth dimension.

abstract: This paper represents more than 30 years of discussion and collaboration with Drs Joseph Murray and John Mulliken in an attempt to understand growth patterns over time (ie, fourth dimension) in patients with hemifacial microsomia (HFM). This is essential for the development of rational treatment protocols for children and adults with jaw asymmetry. Traditionally, HFM was thought of as a unilateral deformity, but it was recognized that 20% to 30% of patients had bilateral abnormalities. However, early descriptions of skeletal correction addressed almost exclusively lengthening of the short (affected) side of the face. Based on longitudinal clinical observations of unoperated HFM patients, we hypothesized that abnormal mandibular growth is the earliest skeletal manifestation and that restricted growth of the mandible plays a pivotal role in progressive distortion of both the ipsilateral and contralateral facial skeleton. This hypothesis explains the progressive nature of the asymmetry in patients with HFM and provides the rationale for surgical lengthening of the mandible in children to prevent end-stage deformity. During the past 30 years, we have learned that this phenomenon of progressive distortion of the adjacent and contralateral facial skeleton occurs with other asymmetric mandibular undergrowth (tumor resection, radiation therapy, or posttraumatic defects) and overgrowth (mandibular condylar hyperplasia) conditions. In this paper, I describe the progression of deformity with time in patients with mandibular asymmetry as a result of undergrowth and overgrowth. Understanding these concepts is critical for the development of rational treatment protocols for adults with end-stage asymmetry and for children to minimize secondary deformity.


title: Lumitrack: low cost, high precision, high speed tracking with projected m-sequences

abstract: We present Lumitrack, a novel motion tracking technology that uses projected structured patterns and linear optical sensors. Each sensor unit is capable of recovering 2D location within the projection area, while multiple sensors can be combined for up to six degree of freedom (DOF) tracking. Our structured light approach is based on special patterns, called m-sequences, in which any consecutive sub-sequence of m bits is unique. Lumitrack can utilize both digital and static projectors, as well as scalable embedded sensing configurations. The resulting system enables high-speed, high precision, and low-cost motion tracking for a wide range of interactive applications. We detail the hardware, operation, and performance characteristics of our approach, as well as a series of example applications that highlight its immediate feasibility and utility.


title: Touch Is Everywhere: Floor Surfaces as Ambient Haptic Interfaces

abstract: Floor surfaces are notable for the diverse roles that they play in our negotiation of everyday environments. Haptic communication via floor surfaces could enhance or enable many computer-supported activities that involve movement on foot. In this paper, we discuss potential applications of such interfaces in everyday environments and present a haptically augmented floor component through which several interaction methods are being evaluated. We describe two approaches to the design of structured vibrotactile signals for this device. The first is centered on a musical phrase metaphor, as employed in prior work on tactile display. The second is based upon the synthesis of rhythmic patterns of virtual physical impact transients. We report on an experiment in which participants were able to identify communication units that were constructed from these signals and displayed via a floor interface at well above chance levels. The results support the feasibility of tactile information display via such interfaces and provide further indications as to how to effectively design vibrotactile signals for them.


title: Document Image Retrieval: An Overview

abstract: economic feasibility of creating a large database of document image has left a tremendous need for robust ways to access the information. Printed documents are scanned for archiving or in an attempt to move towards a paperless office and are stored as images. In this paper, we provide a survey of methods developed by researchers to access document images. The survey includes papers covering the current state of art on the research in document image retrieval based on images such as signature, logo, machine-print, different fonts etc.


title: Instant Outdoor Localization and SLAM Initialization from 2.5D Maps

abstract: We present a method for large-scale geo-localization and global tracking of mobile devices in urban outdoor environments. In contrast to existing methods, we instantaneously initialize and globally register a SLAM map by localizing the first keyframe with respect to widely available untextured 2.5D maps. Given a single image frame and a coarse sensor pose prior, our localization method estimates the absolute camera orientation from straight line segments and the translation by aligning the city map model with a semantic segmentation of the image. We use the resulting 6DOF pose, together with information inferred from the city map model, to reliably initialize and extend a 3D SLAM map in a global coordinate system, applying a model-supported SLAM mapping approach. We show the robustness and accuracy of our localization approach on a challenging dataset, and demonstrate unconstrained global SLAM mapping and tracking of arbitrary camera motion on several sequences.


title: Vibro-acoustic behaviour of an induction machine with stator inter-turn short-circuit

abstract: This paper focuses on the calculation of noise and vibrations due to electromagnetic forces in rotating electrical machines in faulty conditions. The effects of a stator inter turn short circuit is analysed through an analytical model. Theoretical results give the radial forces and explain the reasons behind the noise and vibrations. They also show how inter-turn-short-circuit is responsible for additional low-mode forces, generating noise and vibrations increase. Experimental measurements are carried out to validate the theoretical results.


title: Estimating True and False Positive Rates in Higher Dimensional Problems and Its Data Mining Applications

abstract: If we can estimate the accuracy of our observations then we can estimate the true and false positive rates over a series of samples in high dimensional data mining problems. To date such issues have been largely neglected and previously no algorithm has been provided to facilitate the computations involved. In high dimensional data mining tasks, increasing sparsity leads to decreasing true positive rates. Estimating this effect allows the estimation of the true size of membership of a class or cluster allowing us to identify the top candidates for these false negatives, while tracking the likelihood of false positives. These estimates of true and false positive rates can also help researchers avoid unnecessary costs by collecting only the number of samples that are really needed. We propose an algorithm for these computations designated the statistical error rate algorithm (SERA) and give an example of its use.


title: Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing

abstract: Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures. Microsc. Res. Tech., 2016. © 2016 Wiley Periodicals, Inc.


Irregular Repeat Accumulate Codes


title: Online Phase-Noise Estimation in FMCW Radar Transceivers Using an Artificial On-Chip Target

abstract: The presence of phase noise (PN) severely deteriorates the sensitivity and range of frequency-modulated continuous wave radar systems. Thus, characterization and continuous monitoring of the PN is indispensable, and its estimation is often employed directly on chip. Although many contributions investigate methods for PN estimation, almost all of them consider a continuous-wave (CW) input signal. Differently, in this paper, we aim to estimate the PN from a linear frequency modulated continuous wave (FMCW) signal. For that, we propose two methods utilizing a so-called artificial on-chip target and further digital signal processing. These methods are evaluated in real-time and are the first-known solutions to determine PN estimates during the operation of an FMCW radar transceiver. We prove our methods with both simulation and measurement results from a hardware prototype. Further, we present techniques to efficiently realize the concepts in digital hardware. Finally, we compare and trade off the proposed methods against computational complexity and performance. The novel techniques may be applied to arbitrary input frequencies and bandwidths, and do not require a reference-clock input.


title: An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition

abstract: We propose an integrated end-to-end automatic speech recognition (ASR) paradigm by joint learning of the front-end speech signal processing and back-end acoustic modeling. We believe that “only good signal processing can lead to top ASR performance” in challenging acoustic environments. This notion leads to a unified deep neural network (DNN) framework for distant speech processing that can achieve both high-quality enhanced speech and high-accuracy ASR simultaneously. Our goal is accomplished by two techniques, namely: (i) a reverberation-time-aware DNN based speech dereverberation architecture that can handle a wide range of reverberation times to enhance speech quality of reverberant and noisy speech, followed by (ii) DNN-based multicondition training that takes both clean-condition and multicondition speech into consideration, leveraging upon an exploitation of the data acquired and processed with multichannel microphone arrays, to improve ASR performance. The final end-to-end system is established by a joint optimization of the speech enhancement and recognition DNNs. The recent REverberant Voice Enhancement and Recognition Benchmark (REVERB) Challenge task is used as a test bed for evaluating our proposed framework. We first report on superior objective measures in enhanced speech to those listed in the 2014 REVERB Challenge Workshop on the simulated data test set. Moreover, we obtain the best single-system word error rate (WER) of 13.28% on the 1-channel REVERB simulated data with the proposed DNN-based pre-processing algorithm and clean-condition training. Leveraging upon joint training with more discriminative ASR features and improved neural network based language models, a low single-system WER of 4.46% is attained. Next, a new multi-channel-condition joint learning and testing scheme delivers a state-of-the-art WER of 3.76% on the 8-channel simulated data with a single ASR system. Finally, we also report on a preliminary yet promising experimentation with the REVERB real test data.


title: What factors influence failure to engage in a supervised self‐help programme for bulimia nervosa and binge eating disorder?

abstract: The study investigated factors influencing failure to engage among a group of bulimic and binge eating disordered clients referred to a supervised self-help programme. A total of 125 patients referred to a supervised self-help programme for people with bulimia and binge eating disorder were grouped into those who were seen in treatment (n = 85), and those who failed to engage i.e. did not attend their initial appointment (n = 40). Diagnosis, age, gender and waiting times of the two groups were compared. Diagnosis, age and gender were not significantly different between the two groups. The only significant factor measured was the length of time that they had waited between being referred to the service and their appointment date. Clients who waited less than 4 weeks were 2.4 times more likely to attend their appointment. For every week that lapsed between referral and appointment date, the odds of attending were reduced by 15% (p = 0.002). The results are discussed in light of the importance of maximizing engagement in treatment. Copyright © 2004 John Wiley & Sons, Ltd and Eating Disorders Association.


title: Creating a Living Document: The National Association of School Nurses Strategic Plan 1998–2001 Mid-Course Evaluation

abstract: This is the third in a series of three articles devoted to the development, implementation, and evaluation of the National Association of School Nurses 1998-2001 Strategic Plan and how it relates to the practice of school nursing. Information regarding the extent to which each of the seven goals in the Strategic Plan has been met, and the future efforts in developing the 2001-2004 Strategic Plan will be reviewed. Areas of focus of the new NASN leaders and the priorities of the NASN management and Board of Directors will shape the future goals of the organization. The new definition of the practice of school nursing, the NASN Mission Statement, and the current Strategic Plan goals will drive the development of the next Strategic Plan. NASN leaders have a firm grasp on the future direction of the organization and are available and responsive to assist individual members and state affiliates in managing change and empowering school nurses to meet the challenges of the future.


The Fastest Fourier Transform in the West. MIT-LCS-TR-728


title: BlindBox: Deep Packet Inspection over Encrypted Traffic

abstract: Many network middleboxes perform deep packet inspection (DPI), a set of useful tasks which examine packet payloads. These tasks include intrusion detection (IDS), exfiltration detection, and parental filtering. However, a long-standing issue is that once packets are sent over HTTPS, middleboxes can no longer accomplish their tasks because the payloads are encrypted. Hence, one is faced with the choice of only one of two desirable properties: the functionality of middleboxes and the privacy of encryption. We propose BlindBox, the first system that simultaneously provides {\em both} of these properties. The approach of BlindBox is to perform the deep-packet inspection {\em directly on the encrypted traffic. BlindBox realizes this approach through a new protocol and new encryption schemes. We demonstrate that BlindBox enables applications such as IDS, exfiltration detection and parental filtering, and supports real rulesets from both open-source and industrial DPI systems. We implemented BlindBox and showed that it is practical for settings with long-lived HTTPS connections. Moreover, its core encryption scheme is 3-6 orders of magnitude faster than existing relevant cryptographic schemes.


title: Implementation of collaborative e-supply-chain initiatives: an initial challenging and final success case from grocery retailing

abstract: We discuss the challenges of implementing an Internet-based platform for creating collaborative supply chains using a case study in the retail sector. The case presents how an Internet-based collaboration platform was implemented to address the strategic issue of increasing shelf availability and customer service in grocery retailing, an issue that has emerged as one of the major confrontations for the whole sector over the past years. The case presented shows the challenges of executing such strategic collaborative supply-chain initiatives which, although arguably beneficial, can be hindered by IT adoption failures. A longitudinal view of the case is presented, from an initial pilot back in 2001 to the final success in 2005. We discuss the particular challenges of the execution of Internet-enabled collaborative supply-chain initiatives as well as possible managerial actions through a simple framework we develop based on the lessons from the case.


title: A decision support system for post-disaster interim housing

abstract: The Northridge earthquake of 1994 displaced almost 10,000 families and destroyed major transportation infrastructure within Southern California, and Hurricane Katrina created the largest national housing crisis since the Dust Bowl of 1930, destroying over 300,000 homes and leaving over one million people seeking shelter. Numerous smaller disasters each year such as tornados, costal or inland flooding, and less severe earthquakes also destroy homes and displace families, although on a much smaller scale. Arranging housing for disaster victims ranks as a top priority after the immediate needs for food and medical care are met. This task becomes more challenging as families are displaced for a longer period of time due to increases in costs, government involvement, and expectations of the victims. In early 2009, FEMA released the first-ever National Disaster Housing Strategy which calls for improved planning and outlines the key principles and policies guiding disaster sheltering, interim housing, and restoration of permanent housing. While all three housing problems are very difficult, the provision of adequate temporary or interim housing is perhaps the most challenging. A few researchers have addressed the issue of optimal allocation of temporary housing, but have focused primarily on the first part of the problem which focuses on the selection of adequate capacity from among available interim housing alternatives. The second part of the problem, which consists of recommending housing alternatives to individual families from the pool of temporary housing units selected in phase one such that educational, healthcare, and socio-economic needs are met, has not yet been addressed to the best of our knowledge. We propose a decision support system for assigning families to housing units which addresses these needs. We develop a benchmark integer programming model for developing a balanced housing plan, and then use the model to evaluate three heuristics which could be practically applied with our system. We use a prototypical example to illustrate the model and evaluate the heuristics, and to demonstrate their appropriateness for the development of realistic real-time housing recommendations. We model the recommendation of housing alternatives to disaster victims.Our goal is to minimize distance to socio-economic support structure.Heuristics are developed to allow for real-time support.The best heuristic uses a weighted preference between distance and housing size.We use this heuristic as the foundation of a decision support system.


title: A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals

abstract: Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files


title: A Review of Skew Detection Techniques for Document

abstract: Skew detection and correction of documents is a problematic step in document image analysis. Many methods have been proposed by researchers for estimating the angle at which a document image is rotated (document skew) in binary image documents. Therefore, this paper aims to evaluate the most frequently skew detection techniques cited in the literature which are (i) Projection Profile Analysis (PP), (ii) Hough Transform (HT) and (iii) Nearest Neighbour (NN). This study points out the weaknesses and the strengths of each method and compares the performance of these methods in term of speed and accuracy. The evaluation result shows that in term of speed, the NN technique achieves the fastest time. However, NN performs poorly for the accuracy estimation. PP gives the best angle estimation even though it takes the longest time to execute. Hence, this finding can be used as the basis evaluation review for image analysis researchers in improving the existing technique of skew detection and recommend algorithm with a better accuracy in a shorter time.


One Reading Comprehension : A Conceptual Framework from Word Meaning to Text Meaning


title: On detecting acceptable air contamination in classrooms using low cost sensors

abstract: In present scenario of the world, environmental pollution is one of the leading challenges. Most often the educational institutes and organizations in developing countries suffer from polluted environment due to overcrowded rooms, improper planning and poor infrastructure. Students/faculties in a classroom could suffer from health issues due to prolonged exposure to such environment. On an average a student/faculty is exposed to such environment for eight hours per day. A student/faculty could undergo physical as well as cognitive hazards. This paper tends to detect the duration for which a classroom environment can be considered healthy for a given number of students. We built an Air Quality Monitoring Unit using low cost gas sensors which could compare the air contamination level of the environment with specified standards to detect when the environment tends to get uncomfortable for students/faculties. This in turn could result in reduced absentees and improved performance of students/faculties. Some useful results came to our observation such as, in a class of 30 students the concentration level of CO2 increases about 28.14% as compared to empty classroom whereas in a class of 40 students in the same classroom it increases about 55.33% in a duration of 2 hours.


title: Lipoleiomyoma Causing Adnexal Torsion: A Rare Mass Initially Diagnosed on Bedside Screening Sonogram

abstract: Abdominal pain in women of reproductive age is a frequently encountered chief complaint in the emergency department. Adnexal torsion is a time-sensitive diagnosis that is essential for emergency physicians to consider in the initial differential diagnosis. Bedside sonography can be used to assist the clinician in the detection of adnexal masses placing the patient at risk for torsion. Lipoleiomyomas are uncommon, benign neoplasms consisting of variable portions of mature lipocytes, smooth muscle, or fibrous tissue. Very few cases have been reported, with no cases previously reported by emergency physicians using bedside ultrasound. A case is presented of a middle-aged woman who presented to the emergency department with acute lower abdominal pain. At the time of the initial history and physical examination, bedside screening sonogram was performed, which allowed rapid diagnosis of a large, heterogeneous, relatively avascular adnexal mass that was later identified as a lipoleiomyoma, a rare adnexal neoplas...


title: Traffic sign recognition using HOG-SVM and grid search

abstract: Considering the lower accuracy of existing traffic sign recognition methods, a new traffic sign recognition method using histogram of oriented gradient - support vector machine (HOG-SVM) and grid search (GS) is proposed. First, the histogram of oriented gradient (HOG) is used to extract the characteristics of traffic sign. Then the grid search technique is applied to optimize the parameters of support vector machine (SVM). Finally, the traffic sign is recognized by using the trained SVM classifier. Experimental results indicate that the proposed method could achieve high accuracy for traffic sign recognition.


title: Geosocial media data as predictors in a GWR application to forecast crime hotspots

abstract: In this paper we forecast hotspots of street crime in Portland, Oregon. Our approach uses geosocial media posts, which define the predictors in geographically weighted regression (GWR) models. We use two predictors that are both derived from Twitter data. The first one is the population at risk of being victim of street crime. The second one is the crime related tweets. These two predictors were used in GWR to create models that depict future street crime hotspots. The predicted hotspots enclosed more than 23% of the future street crimes in 1% of the study area and also outperformed the prediction efficiency of a baseline approach. Future work will focus on optimizing the prediction parameters and testing the applicability of this approach to other mobile crime types.


title: An Error-Based Controlled Single-Inductor 10-Output DC-DC Buck Converter With High Efficiency Under Light Load Using Adaptive Pulse Modulation

abstract: An error-based control method is proposed for a single-inductor multiple-output (SIMO) converter that maintains high efficiency under various load conditions. A peak efficiency of 88.7% was achieved with one output $({\rm I}_{{\rm O}1})$ of 5 mA, while the other outputs are at 432 mA. A hybrid topology composed of a switching converter and a linear regulator is also presented for fast load transient response. The proposed SIMO buck converter was fabricated with a 1P4M 0.35 $\mu$ m BCD process and has 10 independently regulated outputs. The measured load transient waveform shows cross regulation of 0.1 mV/mA and a load transient of 0.17 mV/mA. This was achieved under load transient conditions for one output $({\rm I}_{{\rm O}9})$ in between 1 mA and 100 mA with the other outputs at 422 mA.


title: Google Cardboard Dates Augmented Reality : Issues, Challenges and Future Opportunities

abstract: The Google's frugal Cardboard solution for immersive Virtual Reality experiences has come a long way in the VR market. The Google Cardboard VR applications will support us in the fields such as education, virtual tourism, entertainment, gaming, design etc. Recently, Qualcomm's Vuforia SDK has introduced support for developing mixed reality applications for Google Cardboard which can combine Virtual and Augmented Reality to develop exciting and immersive experiences. In this work, we present a comprehensive review of Google Cardboard for AR and also highlight its technical and subjective limitations by conducting a feasibility study through the inspection of a Desktop computer use-case. Additionally, we recommend the future avenues for the Google Cardboard in AR. This work also serves as a guide for Android/iOS developers as there are no published scholarly articles or well documented studies exclusively on Google Cardboard with both user and developer's experience captured at one place.


title: Practical shadow mapping

abstract: In this paper, we present several methods that can greatly improve image quality when using the shadow mapping algorithm. Shadow artifacts introduced by shadow mapping are mainly due to low resolution shadow maps and/or the limited numerical precision used when performing the shadow test. These problems especially arise when the light source's viewing frustum, from which the shadow map is generated, is not adjusted to the actual camera view. We show how a tight-fitting frustum can be computed such that the shadow mapping algorithm concentrates on the visible parts of the scene and takes advantage of nearly the full available precision. Furthermore, we recommend uniformly spaced depth values in contrast to perspectively spaced depths in order to equally sample the scene seen from the light source.


Web Personalization Using Web Mining: Concept and Research Issue


title: Human-robot interaction in rescue robotics

abstract: Rescue robotics has been suggested by a recent DARPA/NSF study as an application domain for the research in human-robot integration (HRI). This paper provides a short tutorial on how robots are currently used in urban search and rescue (USAR) and discusses the HRI issues encountered over the past eight years. A domain theory of the search activity is formulated. The domain theory consists of two parts: 1) a workflow model identifying the major tasks, actions, and roles in robot-assisted search (e.g., a workflow model) and 2) a general information flow model of how data from the robot is fused by various team members into information and knowledge. The information flow model also captures the types of situation awareness needed by each agent in the rescue robot system. The article presents a synopsis of the major HRI issues in reducing the number of humans it takes to control a robot, maintaining performance with geographically distributed teams with intermittent communications, and encouraging acceptance within the existing social structure.


title: Optimized Projections for Compressed Sensing

abstract: Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond.


title: Assertion-based QA with Question-Aware Open Information Extraction

abstract: We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments. An assertion conveys more evidences than a short answer span in reading comprehension, and it is more concise than a tedious passage in passage-based QA. These advantages make ABQA more suitable for human-computer interaction scenarios such as voice-controlled speakers. Further progress towards improving ABQA requires richer supervised dataset and powerful models of text understanding. To remedy this, we introduce a new dataset called WebAssertions, which includes hand-annotated QA labels for 358,427 assertions in 55,960 web passages. To address ABQA, we develop both generative and extractive approaches. The backbone of our generative approach is sequence to sequence learning. In order to capture the structure of the output assertion, we introduce a hierarchical decoder that first generates the structure of the assertion and then generates the words of each field. The extractive approach is based on learning to rank. Features at different levels of granularity are designed to measure the semantic relevance between a question and an assertion. Experimental results show that our approaches have the ability to infer question-aware assertions from a passage. We further evaluate our approaches by incorporating the ABQA results as additional features in passage-based QA. Results on two datasets show that ABQA features significantly improve the accuracy on passage-based~QA.


title: A martingale framework for concept change detection in time-varying data streams

abstract: In a data streaming setting, data points are observed one by one. The concepts to be learned from the data points may change infinitely often as the data is streaming. In this paper, we extend the idea of testing exchangeability online (Vovk et al., 2003) to a martingale framework to detect concept changes in time-varying data streams. Two martingale tests are developed to detect concept changes using: (i) martingale values, a direct consequence of the Doob's Maximal Inequality, and (ii) the martingale difference, justified using the Hoeffding-Azuma Inequality. Under some assumptions, the second test theoretically has a lower probability than the first test of rejecting the null hypothesis, "no concept change in the data stream", when it is in fact correct. Experiments show that both martingale tests are effective in detecting concept changes in time-varying data streams simulated using two synthetic data sets and three benchmark data sets.


title: Depth saliency based on anisotropic center-surround difference

abstract: Most previous works on saliency detection are dedicated to 2D images. Recently it has been shown that 3D visual information supplies a powerful cue for saliency analysis. In this paper, we propose a novel saliency method that works on depth images based on anisotropic center-surround difference. Instead of depending on absolute depth, we measure the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration. Besides, two common priors based on depth and location are used for refinement. The proposed method works within a complexity of O(N) and the evaluation on a dataset of over 1000 stereo images shows that our method outperforms state-of-the-art.


title: Periorbital facial rejuvenation; applied anatomy and pre-operative assessment

abstract: Abstract Purpose Since different subspecialties are currently performing a variety of upper facial rejuvenation procedures, and the level of knowledge on the ocular and periocular anatomy and physiology is different, this review aims to highlight the most important preoperative examinations and tests with special attention to the eye and periocular adnexal structures for general ophthalmologist and specialties other than oculo-facial surgeons in order to inform them about the fine and important points that should be considered before surgery to have both cosmetic and functional improvement. Methods English literature review was performed using PubMed with the different keywords of “periorbital rejuvenation”, “blepharoptosis”, “eyebrow ptosis”, “blepharoplasty”, “eyelid examination”, “facial assessment”, and “lifting”. Initial screening was performed by the senior author to include the most pertinent articles. The full text of the selected articles was reviewed, and some articles were added based upon the references of the initial articles. Included articles were then reviewed with special attention to the preoperative assessment of the periorbital facial rejuvenation procedures. Results There were 254 articles in the initial screening from which 84 articles were found to be mostly related to the topic of this review. The number finally increased to 112 articles after adding the pertinent references of the initial articles. Conclusion Static and dynamic aging changes of the periorbital area should be assessed as an eyelid-eyebrow unit paying more attention to the anthropometric landmarks. Assessing the facial asymmetry, performing comprehensive and detailed ocular examination, and asking about patients' expectation are three key elements in this regard. Furthermore, taking standard facial pictures, obtaining special consent form, and finally getting feedback are also indispensable tools toward a better outcome.


title: A comparative evaluation of procedural level generators in the Mario AI framework

abstract: Evaluation is an open problem in procedural content generation research. The field is now in a state where there is a glut of content generators, each serving different purposes and using a variety of techniques. It is difficult to understand, quantitatively or qualitatively, what makes one generator different from another in terms of its output. To remedy this, we have conducted a large-scale comparative evaluation of level generators for the Mario AI Benchmark, a research-friendly clone of the classic platform game Super Mario Bros. In all, we compare the output of seven different level generators from the literature, based on different algorithmic methods, plus the levels from the original Super Mario Bros game. To compare them, we have defined six expressivity metrics, of which two are novel contributions in this paper. These metrics are shown to provide interestingly different characterizations of the level generators. The results presented in this paper, and the accompanying source code, is meant to become a benchmark against which to test new level generators and expressivity metrics.


title: Design of PID controllers for 4-DOF planar and spatial manipulators

abstract: Robotic manipulators are the most important components in the present manufacturing industry for handling materials and manipulation. It is important to note that these manipulators are used in the complex planar and spatial work spaces to perform the task. Further, it is also very difficult to manipulate and dynamically control the manipulators when the degrees of freedom (DOF) of the manipulator are increased. In the present manuscript, the dynamic analysis and design of PID controller for two different manipulator configurations, namely planar and spatial manipulators have been attempted. Moreover, both the manipulators considered in this study are provided with 4-DOF and dynamic analysis has been carried out using Lagrange-Euler (L-E) formulation. The main difference between the planar and spatial manipulator is that the former works in the two dimensional space and later works in a three dimensional space. In addition to the dynamic analysis, PID controllers have also been designed for the said configurations to follow a path between the given boundary conditions. Further, the developed controllers are tested in simulations, and found working satisfactorily.


title: Test Automation: Not Just for Test Execution

abstract: To work more efficiently and effectively, test engineers must be aware of various automated-testing strategies and tools that assist test activities other than test execution. However, automation doesn't come for free, so it must be carefully implemented.


title: Construction of Cubic Structures with Quadrotor Teams

abstract: We propose and investigate a system in which teams of quadrotor helicopters assemble 2.5-D structures from simple structural nodes and members equipped with magnets. The structures, called Special Cubic Structures (SCS), are a class of 2.5-D truss-like structures free of overhangs and holes. Grippers attached to the bottom of each quadrotor enable them to pick up, transport, and assemble the structural elements. The design of the nodes and members imposes constraints on assembly which are incorporated into the design of the algorithms used for assembly. We show that any SCS can be built using only the feasible assembly modes for individual structural elements and present simulation and experimental results with a team of quadrotors performing automated assembly. The paper includes a theoretical analysis of the SCS construction algorithm, the rationale for the design of the structural nodes, members and quadrotor gripper, a description of the quadrotor control methods for part pickup, transport and assembly, and an empirical analysis of system performance.


title: A Simulink model for an aircraft landing system using energy functions

abstract: A Simulink model for an aircraft landing system using Lyapunov function is discussed. This paper also illustrates the concepts of the Nonlinear Energy Based Control Method (NEM) and modifying the aircraft energy to automatically land an aircraft. The aircraft dynamics are expressed in the form of the Euler- Lagrange (EL) equations of motion, which are derived from the energy functions. The structure of a controller consists of an inner loop, energy loop and a tracking loop which controls the aircraft model dynamics. Using Matlab, Simulink and S functions, perform simulation for the control structure. The system considered in this paper is a Research Civil Aircraft Model (RCAM) developed by the Group for Aeronautical Research and Technology in Europe. (GARTEUR).


title: Clustering Big Spatiotemporal-Interval Data

abstract: We propose a model for clustering data with spatiotemporal intervals. This model is used to effectively evaluate clusters of spatiotemporal interval data. A new energy function is used to measure similarity and balance between clusters in spatial and temporal dimensions. We employ as a case study a large collection of parking data from a real CBD area. The proposed model is applied to existing traditional algorithms to address spatiotemporal interval data clustering problem. Results from traditional clustering algorithms are compared and analysed using the proposed energy function.


title: Elbow Imaging in Sport: Sports Imaging Series

abstract: Nonenhanced MR imaging is the routine reference standard for evaluation of soft-tissue abnormalities around the elbow in athletes.


title: Habits, Rituals, and the Evaluative Brain

abstract: Scientists in many different fields have been attracted to the study of habits because of the power habits have over behavior and because they invoke a dichotomy between the conscious, voluntary control over behavior, considered the essence of higher-order deliberative behavioral control, and lower-order behavioral control that is scarcely available to consciousness. A broad spectrum of behavioral routines and rituals can become habitual and stereotyped through learning. Others have a strong innate basis. Repetitive behaviors can also appear as cardinal symptoms in a broad range of neurological and neuropsychiatric illness and in addictive states. This review suggests that many of these behaviors could emerge as a result of experience-dependent plasticity in basal ganglia–based circuits that can influence not only overt behaviors but also cognitive activity. Culturally based rituals may reflect privileged interactions between the basal ganglia and cortically based circuits that influence social, emotional...


Image Multi-Classification using PHOW Features


title: Review: A survey on intelligent routing protocols in wireless sensor networks

abstract: This paper surveys intelligent routing protocols which contribute to the optimization of network lifetime in wireless sensor networks (WSNs). Different from other surveys on routing protocols for WSNs, this paper first puts forward new ideas on the definition of network lifetime. Then, with a view to prolonging network lifetime, it discusses the routing protocols based on such intelligent algorithms as reinforcement learning (RL), ant colony optimization (ACO), fuzzy logic (FL), genetic algorithm (GA), and neural networks (NNs). Intelligent algorithms provide adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. Inspired by such an idea, some intelligent routing protocols have recently been designed for WSNs. Under each category, it discusses the representative routing algorithms and further analyzes the performance of network lifetime defined in three aspects. This paper intends to give assistance in the optimization of network lifetime in WSNs, together with offering a guide for the collaboration between WSNs and computational intelligence (CI).


title: Exploration of approximate multipliers design space using carry propagation free compressors

abstract: Many emerging application domains, such as machine learning, can tolerate limited amounts of arithmetic inaccuracy. When designing custom compute accelerators for these domains, hardware designers can explore tradeoffs that sacrifice accuracy in order to reduce area, delay, and/or power consumption. This paper explores the design space of approximate multipliers using a family of approximate compressors as building blocks for the partial product reduction tree. We present a tool that allows the user to specify an allowable level of error tolerance, and returns the minimum area, delay, or power approximate multiplier that provides that level of accuracy. Our experimental results indicate that our proposed compressors generate more accurate and more efficient approximate multipliers than existing state-of-the-art techniques.


title: Data Management and Data Quality in PERCH, a Large International Case-Control Study of Severe Childhood Pneumonia

abstract: The Pneumonia Etiology Research for Child Health (PERCH) study is the largest multicountry etiology study of pediatric pneumonia undertaken in the past 3 decades. The study enrolled 4232 hospitalized cases and 5325 controls over 2 years across 9 research sites in 7 countries in Africa and Asia. The volume and complexity of data collection in PERCH presented considerable logistical and technical challenges. The project chose an internet-based data entry system to allow real-time access to the data, enabling the project to monitor and clean incoming data and perform preliminary analyses throughout the study. To ensure high-quality data, the project developed comprehensive quality indicator, data query, and monitoring reports. Among the approximately 9000 cases and controls, analyzable laboratory results were available for ≥96% of core specimens collected. Selected approaches to data management in PERCH may be extended to the planning and organization of international studies of similar scope and complexity.


title: CBOS: Continuos bag of sentences for learning sentence embeddings

abstract: There has been recent work learning distributed sentence representations, which utilise neighbouring sentences as context for learning the embedding of a sentence. The setting is reminiscent of training word embeddings, yet no work has reported a baseline using the same training objective as learning word vectors. We fill this gap by empirically investigating the use of a Continuous Bag-of-Word (CBOW) objective, predicting the current sentence using its context sentences. We name this method a Continuous Bag-of-Sentences (CBOS) method. Results on standard benchmark show that CBOS is a highly competitive baseline for training sentence embeddings, outperforming most existing methods for text similarity measurement.


title: 110+ GHz Transimpedance Amplifier in InP-HBT Technology for 100 Gbit Ethernet

abstract: A 50 dB-Ω transimpedance amplifier (TIA) with a bandwidth greater than 110 GHz has been demonstrated using InP HBT technology. The wide-band amplifier with a cutoff frequency > 110 GHz was designed and fabricated in HRL Laboratories' G4 0.25 μm InP HBT IC technology. The amplifier operates at -4.0 V with a current draw of 76 mA for a power consumption of 304 mW. The basic Cherry-Hooper gain cell is used with a single-ended input and a differential output buffer stage.


title: The Randomized Dependence Coefficient

abstract: We introduce the Randomized Dependence Coefficient (RDC), a measure of non-linear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-R\'enyi Maximum Correlation Coefficient. RDC is defined in terms of correlation of random non-linear copula projections; it is invariant with respect to marginal distribution transformations, has low computational cost and is easy to implement: just five lines of R code, included at the end of the paper.