title: Microdialysis and mass spectrometric monitoring of dopamine and enkephalins in the globus pallidus reveal reciprocal interactions that regulate movement

abstract: Pallidal dopamine, GABA and the endogenous opioid peptides enkephalins have independently been shown to be important controllers of sensorimotor processes. Using in vivo microdialysis coupled to liquid chromatography-mass spectrometry (LC-MS) and a behavioral assay, we explored the interaction between these three neurotransmitters in the rat globus pallidus. Amphetamine (3 mg/kg i.p.) evoked an increase in dopamine, GABA and methionine/leucine enkephalin. Local perfusion of the dopamine D1 receptor antagonist SCH 23390 (100 μM) fully prevented amphetamine stimulated enkephalin and GABA release in the globus pallidus and greatly suppressed hyperlocomotion. In contrast, the dopamine D2 receptor antagonist raclopride (100 μM) had only minimal effects suggesting a greater role for pallidal D1 over D2 receptors in the regulation of movement. Under basal conditions, opioid receptor blockade by naloxone perfusion (10 μM) in the globus pallidus stimulated GABA and inhibited dopamine release. Amphetamine-stimulated dopamine release and locomotor activation were attenuated by naloxone perfusion with no effect on GABA. These findings demonstrate a functional relationship between pallidal dopamine, GABA and enkephalin systems in the control of locomotor behavior under basal and stimulated conditions. Moreover, these findings demonstrate the usefulness of LC-MS as an analytical tool when coupled to in vivo microdialysis.


title: A systematic review of usability techniques in agile methodologies

abstract: In this paper, we present the results of a systematic review involving the use of usability techniques in software developments where agile methodologies were used. The search strategy identified 307 studies, where only 32 were finally selected for the review. We found that complementary techniques are the most frequent techniques used with agile methodologies. In addition, most studies performed usability evaluations only during the implementation phase, and these evaluations were mostly constructed as elaborations of case studies.


title: Reusing Hardware Performance Counters to Detect and Identify Kernel Control-Flow Modifying Rootkits

abstract: Kernel rootkits are formidable threats to computer systems. They are stealthy and can have unrestricted access to system resources. This paper presents NumChecker, a new virtual machine (VM) monitor based framework to detect and identify control-flow modifying kernel rootkits in a guest VM. NumChecker detects and identifies malicious modifications to a system call in the guest VM by measuring the number of certain hardware events that occur during the system call’s execution. To automatically count these events, NumChecker leverages the hardware performance counters (HPCs), which exist in modern processors. By using HPCs, the checking cost is significantly reduced and the tamper-resistance is enhanced. We implement a prototype of NumChecker on Linux with the kernel-based VM. An HPC-based two-phase kernel rootkit detection and identification technique is presented and evaluated on a number of real-world kernel rootkits. The results demonstrate its practicality and effectiveness.


title: Automated Engine Piston Installation Using Industrial Robots

abstract: This paper presents engine piston installation using industrial robots. Because the current manual and hard automation engine piston installation systems are either labour intensive or inflexible and costly, the three-robot and two-robot piston installation systems are developed and investigated. The robotic piston installation process is simulated. A robotic piston stuffing gripper with ring detection is designed to perform the sophisticated stuffing process. Robot force control is used in the cylinder bore finding. The piston stuffing is implemented using a search-finding method based on the force control. The searching method and parameters are discussed. Related future research and development are proposed.


title: A new depression scale designed to be sensitive to change

abstract: The construction of a depression rating scale designed to be particularly sensitive to treatment effects is described. Ratings of 54 English and 52 Swedish patients on a 65 item comprehensive psychopathology scale were used to identify the 17 most commonly occurring symptoms in primary depressive illness in the combined sample. Ratings on these 17 items for 64 patients participating in studies of four different antidepressant drugs were used to create a depression scale consisting of the 10 items which showed the largest changes with treatment and the highest correlation to overall change. The inner-rater reliability of the new depression scale was high. Scores on the scale correlated significantly with scores on a standard rating scale for depression, the Hamilton Rating Scale (HRS), indicating its validity as a general severity estimate. Its capacity to differentiate between responders and non-responders to antidepressant treatment was better than the HRS, indicating greater sensitivity to change. The practical and ethical implications in terms of smaller sample sizes in clinical trials are discussed.


title: An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning

abstract: The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.


title: Brain reward circuitry beyond the mesolimbic dopamine system: A neurobiological theory

abstract: Abstract Reductionist attempts to dissect complex mechanisms into simpler elements are necessary, but not sufficient for understanding how biological properties like reward emerge out of neuronal activity. Recent studies on intracranial self-administration of neurochemicals (drugs) found that rats learn to self-administer various drugs into the mesolimbic dopamine structures—the posterior ventral tegmental area, medial shell nucleus accumbens and medial olfactory tubercle. In addition, studies found roles of non-dopaminergic mechanisms of the supramammillary, rostromedial tegmental and midbrain raphe nuclei in reward. To explain intracranial self-administration and related effects of various drug manipulations, I outlined a neurobiological theory claiming that there is an intrinsic central process that coordinates various selective functions (including perceptual, visceral, and reinforcement processes) into a global function of approach. Further, this coordinating process for approach arises from interactions between brain structures including those structures mentioned above and their closely linked regions: the medial prefrontal cortex, septal area, ventral pallidum, bed nucleus of stria terminalis, preoptic area, lateral hypothalamic areas, lateral habenula, periaqueductal gray, laterodorsal tegmental nucleus and parabrachical area.


title: Data warehousing and data mining: A case study

abstract: This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS), University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.


title: Fast Anticipatory Reasoning for Computing Anticipatory Systems

abstract: Anticipatory reasoning engine is an indispensable component for computing anticipatory systems. In practical computing anticipatory systems, the efficiency of anticipatory reasoning engine is a key issue to satisfy the real-time requirements from applications. FreeEnCal: a forward reasoning engine for general-purpose purpose is a hopeful candidate for anticipatory reasoning engines. However, the current FreeEnCal is not efficient enough for practical computing anticipatory systems. This paper presents a new implementation of FreeEnCal improved by adopting fast algorithms, and shows its efficiency by comparing the improved FreeEnCal with the old one. The improved FreeEnCal can be used as an anticipatory reasoning engine


title: Theory and Applications of Compressed Sensing

abstract: minimization, random matrices, sparse approximation, sparse recoveryMSC (2000) 94A12, 65F22, 94A20, 68U10, 90C25, 15B52Compressed sensing is a novel research area, which was introduced in 2006, and since thenhas already become a key concept in various areas of applied mathematics, computer science,and electrical engineering. It surprisingly predicts that high-dimensional signals, which allowa sparse representation by a suitable basis or, more generally, a frame, can be recovered fromwhat was previously considered highly incomplete linear measurements by using efﬁcientalgorithms. This article shall serve as an introduction to and a survey about compressedsensing.


title: In-hand scanning with online loop closure

abstract: We present a complete 3D in-hand scanning system that allows users to scan objects by simply turning them freely in front of a real-time 3D range scanner. The 3D object model is reconstructed online as a point cloud by registering and integrating the incoming 3D patches with the online 3D model. The accumulation of registration errors leads to the well-known loop closure problem. We address this issue already during the scanning session by distorting the object as rigidly as possible. Scanning errors are removed by explicitly handling outliers. As a result of our proposed online modeling and error handling procedure, the online model is of sufficiently high quality to serve as the final model. Thus, no additional post-processing is required which might lead to artifacts in the model reconstruction. We demonstrate our approach on several difficult real-world objects and quantitatively evaluate the resulting modeling accuracy.


title: Face Concerns in Interpersonal Conflict A Cross-Cultural Empirical Test of the Face Negotiation Theory

abstract: This study sought to test the underlying assumption of the face-negotiation theory that face is an explanatory mechanism for culture’s influence on conflict behavior. A questionnaire was administered to 768 participants in 4 national cultures (China, Germany, Japan, and the United States) asking them to describe interpersonal conflict. The major findings of this study are as follows: (a) cultural individualism-collectivism had direct and indirect effects on conflict styles, (b) independent self-construal related positively with self-face and interdependent self-construal related positively with other-face, (c) self-face related positively with dominating conflict styles and other-face related positively with avoiding and integrating styles, and (d) face accounted for all of the total variance explained (100% of 19% total explained) in dominating, most of the total variance explained in integrating (70% of 20% total explained), and some of the total variance explained in avoiding (38% of 21% total explaine...


CME The Spectrum of Orofacial Clefting


title: Cutting stock problems and solution procedures

abstract: This paper discusses some of the basic formulation issues and solution procedures for solving one- and two- dimensional cutting stock problems. Linear programming, sequential heuristic and hybrid solution procedures are described. For two-dimensional cutting stock problems with rectangular shapes, we also propose an approach for solving large problems with limits on the number of times an ordered size may appear in a pattern.


title: Ensemble Methods in Machine Learning

abstract: Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.


title: Predicting the evolution of communities in social networks using structural and temporal features

abstract: During the last years, there is increasing interest in analyzing social networks and modeling their dynamics at different scales. This work focuses on predicting the future form of communities, which represent the mesoscale structure of networks, while the communities arise as a result of user interaction. We employ several structural and temporal features to represent communities, along with their past form, that are used to formulate a supervised learning task to predict whether a community will continue as currently is, shrink, grow or completely disappear. To test our methodology, we created a real-life social network dataset consisting of an excerpt of posts from the Mathematics Stack Exchange Q&A site. In the experiments, special care is taken in handling the class imbalance in the dataset and in investigating how the past evolutions of a community affect predictions.


title: Neural Sketch Learning for Conditional Program Generation

abstract: We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount of information about the code that is desired. The generated programs are expected to respect a "realistic" relationship between programs and labels, as exemplified by a corpus of labeled programs available during training. ::: Two challenges in such conditional program generation are that the generated programs must satisfy a rich set of syntactic and semantic constraints, and that source code contains many low-level features that impede learning. We address these problems by training a neural generator not on code but on program sketches, or models of program syntax that abstract out names and operations that do not generalize across programs. During generation, we infer a posterior distribution over sketches, then concretize samples from this distribution into type-safe programs using combinatorial techniques. We implement our ideas in a system for generating API-heavy Java code, and show that it can often predict the entire body of a method given just a few API calls or data types that appear in the method.


Microsoft Word - The semantics and pragmatics of functional coherence in di–


title: Performance measurement for green supply chain management

abstract: Purpose – To introduce and provide an overview of the various issues related to environmental (green) supply chain management performance measurement.Design/methodology/approach – The work relies on experiences, case studies and other literature related to performance measurement in environmental supply chains. It seeks to integrate works in supply chain management, environmental management, and performance management into one framework. A systems framework forms the discussion outline with a focus on controls/pressures, inputs, tools, and outputs as major categories for evaluation and review.Findings – Provides an integrative framework for study, design and evaluation of green supply chain management performance tools. The findings also identify a number of issues that need to still be addressed.Research limitations/implications – We have only one design of the issues in which numerous categorizations could be provided. There is limited research in this area and new and current models/developments can pr...


title: Dynamic Behaviour of an Electronically Commutated (Brushless DC) Motor Drive with Back-emf Sensing

abstract: Conventionally, BLDC motors are commutated in six-step pattern with commutation controlled by position sensors. To reduce cost and complexity of the drive system, sensorless drive is preferred. The existing sensorless control scheme with the conventional back EMF sensing based on motor neutral voltage for BLDC has certain drawbacks, which limit its applications. This paper presents the dynamic behaviour of an analytical and circuit model of a Brushless DC (BLDC) motors with back emf sensing. The circuit model was simulated using LTspice and the results obtained were compared with the experimental results. The value of the motor constant and the back emf measured from the experiment agreed with the simulated model. The starting behaviour of the motor, changing of load torque when current are varied and disturbance of sensing method at peak load shows that the dynamic behaviour results of the experiment obtained from oscilloscope are similar to the simulated value.


Deep Bayesian Learning and Understanding


title: Real-Time Drowsiness Detection Algorithm for Driver State Monitoring Systems

abstract: In this paper, we proposes a novel drowsiness detection algorithm using a camera near the dashboard. The proposed algorithm detects the driver's face in the image and estimates the landmarks in the face region. In order to detect the face, the proposed algorithm uses an AdaBoost classifier based on the Modified Census Transform features. And the proposed algorithm uses regressing Local Binary Features for face landmark detection. Eye states (closed, open) is determined by the value of Eye Aspect Ratio which is easily calculated by the landmarks in eye region. The proposed algorithm provides realtime performance that can be run on the embedded device. We obtained the dataset using video records from the infrared camera which is used the real-field. The proposed algorithm tested in the target board (i.mx6q). The result shows that the proposed algorithm outperformed in the speed and accuracy.


title: A Joint Discriminative Generative Model for Deformable Model Construction and Classification

abstract: Discriminative classification models have been successfully applied for various computer vision tasks such as object and face detection and recognition. However, deformations can change objects coordinate space and perturb robust similarity measurement, which is the essence of all classification algorithms. The common approach to deal with deformations is either to seek for deformation invariant features or to develop models that describe objects deformations. However, the former approach requires a huge amount of data and a good amount of engineering to be properly trained, while the latter require considerable human effort in the form of carefully annotated data. In this paper, we propose a method that jointly learns with minimal human intervention a generative deformable model using only a simple shape model of the object and images automatically downloaded from the Internet, and also extracts features appropriate for classification. The proposed algorithm is applied on various classification problems such as “in-thewild” face recognition, gender classification and eye glasses detection on data retrieved by querying into a web image search engine. We demonstrate that not only it outperforms other automatic methods by large margins, but also performs comparably with supervised methods trained on thousands of manually annotated data.


title: Making Stochastic Neural Networks from Deterministic Ones

abstract: It has been believed that stochastic feedforward neural networks (SFNN) have several advantages beyond deterministic deep neural networks (DNN): they have more expressive power allowing multi-modal mappings and regularize better due to their stochastic nature. However, training SFNN is notoriously harder. In this paper, we aim at developing efficient training methods for large-scale SFNN, in particular using known architectures and pre-trained parameters of DNN. To this end, we propose a new intermediate stochastic model, called Simplified-SFNN, which can be built upon any baseline DNN and approximates certain SFNN by simplifying its upper latent units above stochastic ones. The main novelty of our approach is in establishing the connection between three models, i.e., DNN ::: -> Simplified-SFNN -> SFNN, which naturally leads to an efficient training procedure of the stochastic models utilizing pre-trained parameters of DNN. Using several popular DNNs, we show how they can be effectively transferred to the corresponding stochastic models for both multi-modal and classification tasks on MNIST, TFD, CIFAR-10, CIFAR-100 and SVHN datasets. In particular, our stochastic model built from the wide residual network has 28 layers and 36 million parameters, where the former consistently outperforms the latter for the classification tasks on CIFAR-10 and CIFAR-100 due to its stochastic regularizing effect.


title: Self-regulation of mood: strategies for changing a bad mood, raising energy, and reducing tension.

abstract: Four studies evaluated the success of behaviors and strategies used to self-regulate bad moods, raise energy, and reduce tension. Study 1 (N = 102) used an open-ended questionnaire to identify behavioral categories. Studies 2 and 4 surveyed a representative sample (N = 308) with a fixed-response questionnaire to quantify behaviors, general strategies, and individual differences. Study 2 used psychotherapist (N = 26) judgments of the likely success of the strategies. Therapist and self-rating converged on success of strategies and gender differences. These studies clarify and confirm previous research findings, particularly gender differences in controlling depression. Exercise appears to be the most effective mood-regulating behavior, and the best general strategy to change a bad mood is a combination of relaxation, stress management, cognitive, and exercise techniques


title: Chaotic bee colony algorithms for global numerical optimization

abstract: Artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes new ABC algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical ABC algorithm. Seven new chaotic ABC algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of ABC and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.


title: Overview of BioNLP Shared Task 2011

abstract: The BioNLP Shared Task 2011, an information extraction task held over 6 months up to March 2011, met with community-wide participation, receiving 46 final submissions from 24 teams. Five main tasks and three supporting tasks were arranged, and their results show advances in the state of the art in fine-grained biomedical domain information extraction and demonstrate that extraction methods successfully generalize in various aspects.


title: Dynamic pricing models for electronic business

abstract: Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today's digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing.


title: Learning to Extract Signature and Reply Lines from Email

abstract: We describe methods for automatically identifying signature blocks and reply lines in plain-text email messages. This analysis has many potential applications, such as preprocessing email for text-to-speech systems; anonymization of email corpora; improving automatic content-based mail classifiers; and email threading. Our method is based on applying machine learning methods to a sequential representation of an email message, in which each email is represented as a sequence of lines, and each line is represented as a set of features. We compare several state-of-the-art sequential and non-sequential machine learning algorithms on different feature sets, and present experimental results showing that the presence of a signature block in a message can be detected with accuracy higher than 97%; that signature block lines can be identified with accuracy higher than 99%; and that signature block and reply lines can be simultaneously identified with accuracy of higher than 98%.


title: Clinical and empirical applications of the Rey–Osterrieth Complex Figure Test

abstract: The Rey-Osterrieth Complex Figure Test (ROCF), which was developed by Rey in 1941 and standardized by Osterrieth in 1944, is a widely used neuropsychological test for the evaluation of visuospatial constructional ability and visual memory. Recently, the ROCF has been a useful tool for measuring executive function that is mediated by the prefrontal lobe. The ROCF consists of three test conditions: Copy, Immediate Recall and Delayed Recall. At the first step, subjects are given the ROCF stimulus card, and then asked to draw the same figure. Subsequently, they are instructed to draw what they remembered. Then, after a delay of 30 min, they are required to draw the same figure once again. The anticipated results vary according to the scoring system used, but commonly include scores related to location, accuracy and organization. Each condition of the ROCF takes 10 min to complete and the overall time of completion is about 30 min.


Deep Dependency Graph Conversion in English


title: A tractable first-order probabilistic logic

abstract: Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. However, these are intractable, losing the original motivation. Here we propose the first non-trivially tractable first-order probabilistic language. It is a subset of Markov logic, and uses probabilistic class and part hierarchies to control complexity.We call it TML (Tractable Markov Logic). We show that TML knowledge bases allow for efficient inference even when the corresponding graphical models have very high treewidth. We also show how probabilistic inheritance, default reasoning, and other inference patterns can be carried out in TML. TML opens up the prospect of efficient large-scale first-order probabilistic inference.


title: ISSCC 2002 / SESSION 15 / GIGABIT COMMUNICATIONS / 15 . 1 15 . 1 OC-192 Transmitter in Standard 0 . 18 μ m CMOS

abstract: A fully integrated SONET OC-192 transmitter IC using a standard CMOS process consists of an input data register, FIFO, CMU, and 16:1 multiplexer to give a 10Gb/s serial output. A higher FEC rate, 10.7Gb/s, is supported. This chip, using a 0.18/spl mu/m process, exceeds SONET requirements, dissipating 450mW.


title: Weakly Supervised Natural Language Processing Framework for Abstractive Multi-Document Summarization: Weakly Supervised Abstractive Multi-Document Summarization

abstract: In this paper, we propose a new weakly supervised abstractive news summarization framework using pattern based approaches. Our system first generates meaningful patterns from sentences. Then, in order to precisely cluster patterns, we propose a novel semisupervised pattern learning algorithm that leverages a hand-crafted list of topic-relevant keywords, which are the only weakly supervised information used by our framework to generate aspect-oriented summarization. After that, our system generates new patterns by fusing existing patterns and selecting top ranked new patterns via the recurrent neural network language model. Finally, we introduce a new pattern based surface realization algorithm to generate abstractive summaries. Automatic and manual evaluations demonstrate the effectiveness and advantages of our new methods. Code is available at: https://github.com/jerryli1981


title: Three-Phase Soft-Switching Inverter With Minimum Components

abstract: In this paper, a novel three-phase soft-switching inverter is presented. The inverter-switch turn on and turn off are performed under zero-voltage switching condition. This inverter has only one auxiliary switch, which is also soft switched. Having one auxiliary switch simplifies the control circuit considerably. The proposed inverter is analyzed, and its operating modes are explained in details. The design considerations of the proposed inverter are presented. The experimental results of the prototype inverter confirm the theoretical analysis.


title: Single-Vertex Origami and Spherical Expansive Motions

abstract: We prove that all single-vertex origami shapes are reachable from the open flat state via simple, non-crossing motions. We also consider conical paper, where the total sum of the cone angles centered at the origami vertex is not 2w. For an angle sum less than 2π, the configuration space of origami shapes compatible with the given metric has two components, and within each component, a shape can always be reconfigured via simple (non-crossing) motions. Such a reconfiguration may not always be possible for an angle sum larger than 2π. The proofs rely on natural extensions to the sphere of planar Euclidean rigidity results regarding the existence and combinatorial characterization of expansive motions. In particular, we extend the concept of a pseudo-triangulation from the Euclidean to the spherical case. As a consequence, we formulate a set of necessary conditions that must be satisfied by three-dimensional generalizations of pointed pseudo-triangulations.


title: Use of LIDAR in landslide investigations: a review

abstract: This paper presents a short history of the appraisal of laser scanner technolo- gies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and suscepti- bility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR- derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.


title: Special purpose computer system for Ising models

abstract: The special purpose processor for Ising spin systems, which is named the m‐TIS, megaspin model of the Tokyo University Ising spin machine, is designed and constructed by the present authors. The m‐TIS can treat several millions of Ising spins per second when it is connected to an appropriate host computer. For example, the speeds in the case of square and cubic models are 1.8 and 1.5 million spins/s, respectively. The maximum ability is 107/s. This machine can treat the Ising spin systems having one translation invariant direction in the lattice and having local Hamiltonians, including less than 13 spins–for example, Ising models with external field, ±J spin glasses, and ±H random field models.


title: Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain

abstract: How human beings integrate information from external sources and internal cognition to produce a coherent experience is still not well understood. During the past decades, anatomical, neurophysiological and neuroimaging research in multimodal integration have stood out in the effort to understand the perceptual binding properties of the brain. Areas in the human lateral occipitotemporal, prefrontal, and posterior parietal cortices have been associated with sensory multimodal processing. Even though this, rather patchy, organization of brain regions gives us a glimpse of the perceptual convergence, the articulation of the flow of information from modality-related to the more parallel cognitive processing systems remains elusive. Using a method called stepwise functional connectivity analysis, the present study analyzes the functional connectome and transitions from primary sensory cortices to higher-order brain systems. We identify the large-scale multimodal integration network and essential connectivity axes for perceptual integration in the human brain.


title: Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method

abstract: Abstract Automatic image segmentation of immunohistologically stained breast tissue sections helps pathologists to discover the cancer disease earlier. The detection of the real number of cancer nuclei in the image is a very tedious and time consuming task. Segmentation of cancer nuclei, especially touching nuclei, presents many difficulties to separate them by traditional segmentation algorithms. This paper presents a new automatic scheme to perform both classification of breast stained nuclei and segmentation of touching nuclei in order to get the total number of cancer nuclei in each class. Firstly, a modified geometric active contour model is used for multiple contour detection of positive and negative nuclear staining in the microscopic image. Secondly, a touching nuclei method based on watershed algorithm and concave vertex graph is proposed to perform accurate quantification of the different stains. Finally, benign nuclei are identified by their morphological features and they are removed automatically from the segmented image for positive cancer nuclei assessment. The proposed classification and segmentation schemes are tested on two datasets of breast cancer cell images containing different level of malignancy. The experimental results show the superiority of the proposed methods when compared with other existing classification and segmentation methods. On the complete image database, the segmentation accuracy in term of cancer nuclei number is over than 97%, reaching an improvement of 3–4% over earlier methods.


title: Automated Functional Testing of Online Search Services

abstract: Search services are the main interface through which people discover information on the Internet. A fundamental challenge in testing search services is the lack of oracles. The sheer volume of data on the Internet prohibits testers from verifying the results. Furthermore, it is difficult to objectively assess the ranking quality because different assessors can have very different opinions on the relevance of a Web page to a query. This paper presents a novel method for automatically testing search services without the need of a human oracle. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality. Copyright © 2010 John Wiley & Sons, Ltd.


title: Listener-aware music recommendation from sensor and social media data

abstract: Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.


title: Multi-method virtual electromagnetic experiments for developing suitable monitoring designs: A fictitious CO2sequestration scenario in Northern Germany

abstract: We present a numerical study for 3D time-lapse electromagnetic monitoring of a ::: fictitious CO2 sequestration using the geometry of a real geological site and a suite ::: of suitable electromagnetic methods with different source/receiver configurations and ::: different sensitivity patterns. All available geological information is processed and ::: directly implemented into the computational domain, which is discretized by unstructured ::: tetrahedral grids. We thus demonstrate the performance capability of our ::: numerical simulation techniques. ::: The scenario considers a CO2 injection in approximately 1100 m depth. The expected ::: changes in conductivity were inferred from preceding laboratory measurements. ::: A resistive anomaly is caused within the conductive brines of the undisturbed ::: reservoir horizon. The resistive nature of the anomaly is enhanced by the CO2 dissolution ::: regime, which prevails in the high-salinity environment. Due to the physicochemical ::: properties of CO2, the affected portion of the subsurface is laterally widespread ::: but very thin. ::: We combine controlled-source electromagnetics, borehole transient electromagnetics, ::: and the direct-current resistivity method to perform a virtual experiment with ::: the aim of scrutinizing a set of source/receiver configurations with respect to coverage, ::: resolution, and detectability of the anomalous CO2 plume prior to the field ::: survey. Our simulation studies are carried out using the 3D codes developed in our ::: working group. They are all based on linear and higher order Lagrange and N´ed´elec ::: finite-element formulations on unstructured grids, providing the necessary flexibility ::: with respect to the complex real-world geometry. We provide different strategies for ::: addressing the accuracy of numerical simulations in the case of arbitrary structures. ::: The presented computations demonstrate the expected great advantage of positioning ::: transmitters or receivers close to the target. For direct-current geoelectrics, 50% ::: change in electric potential may be detected even at the Earth’s surface. Monitoring ::: with inductive methods is also promising. For a well-positioned surface transmitter, ::: more than 10% difference in the vertical electric field is predicted for a receiver located ::: 200 m above the target. Our borehole transient electromagnetics results demonstrate ::: that traditional transient electromagnetics with a vertical magnetic dipole source is ::: not well suited for monitoring a thin horizontal resistive target. This is due to the ::: mainly horizontal current system, which is induced by a vertical magnetic dipole.


Available bandwidth measurement as simple as running wget


User ’ s Guide for TVAL 3 : TV Minimization by Augmented Lagrangian and Alternating Direction Algorithms


title: The role of social media in tourism marketing in Jordan

abstract: Currently, the world is witnessing a remarkable development in the modern means and electronics techniques, especially the social networking sites which became available in many sectors including the tourism sector. This increases the competition between the tourism companies to provide the best offers to the individuals about the tourists’ sites. Due to the reason that few studies investigated the effect of social media networks in encouraging tourism, this has created the need to conduct this study to reach results about the extent of social media networks in promoting tourism in Jordan. The significance of the study stems from being treating a new topic with a significant effect on the electronic market. The study also addressed the important side of improving the local income resulting from the tourism sector since this study is focusing on vital and essential topics in modern marketing.


title: A Privacy Model for Smart Metering

abstract: Electricity suppliers have started replacing traditional electricity meters with so-called smart meters, which can transmit current power consumption levels to the supplier within short intervals. Though this is advantageous for the electricity suppliers' planning purposes, and also allows the customers a more detailed look at their usage behavior, it means a considerable risk for privacy. The detailed information can be used to judge whether persons are in the household, when they come home, which electric devices they use (e.g. when they watch TV), and so forth. In this work, we introduce the "smart metering privacy model" for measuring the degree of privacy that a smart metering application can provide. Moreover, we present two design solutions both with and without involvement of trusted third parties. We show that the solution with trusted party can provide "perfect privacy" under certain conditions.


title: Stabilization of vehicle rollover by nonlinear model predictive control

abstract: Vehicle rollover is controlled by a nonlinear model predictive control method, in which the C/GMRES (continuation/generalized minimal residual) method is used to solve an optimal control problem in real time. Vehicle rollover can be simply represented by a cart-pole or an inverted pendulum consisting of a pendulum attached to a cart that rolls freely on a flat surface. A necessary condition for tip-up is given that is based on the surface friction coefficient and the location of the vehicle's center of gravity. Simulation results show that the system is stabilized successfully, preventing vehicle rollover.


title: Painting with Polygons: A Procedural Watercolor Engine

abstract: Existing natural media painting simulations have produced high-quality results, but have required powerful compute hardware and have been limited to screen resolutions. Digital artists would like to be able to use watercolor-like painting tools, but at print resolutions and on lower end hardware such as laptops or even slates. We present a procedural algorithm for generating watercolor-like dynamic paint behaviors in a lightweight manner. Our goal is not to exactly duplicate watercolor painting, but to create a range of dynamic behaviors that allow users to achieve a similar style of process and result, while at the same time having a unique character of its own. Our stroke representation is vector based, allowing for rendering at arbitrary resolutions, and our procedural pigment advection algorithm is fast enough to support painting on slate devices. We demonstrate our technique in a commercially available slate application used by professional artists. Finally, we present a detailed analysis of the different vector-rendering technologies available.


title: Standing-up motion support for paraplegic patient with Robot Suit HAL

abstract: This paper proposes a standing-up motion support system for complete paraplegic patients who cannot stand up by himself/herself due to a spinal cord injury. The standing-up motion is the first step for us to move somewhere inside and outside in our daily life. Therefore, the standing-up motion support is indispensable for patients to promote his/her independent life. The proposed support system using an exoskeletal assistive system “Robot Suit HAL” supports the wearer's weight during his/her standing-up motion so that he/she can stand up without any physical efforts. Besides, the support system controls the patient's posture for his/her stability to avoid falling down during the standing-up motion. The system also estimates his/her intention to stand up based on a preliminary motion of his/her upper body. The patient therefore starts the standing-up without any operations, just by bending his/her upper body forward as the preliminary motion. First, the system performance with respect to the weight-bearing and the balance control was confirmed through the experiment, by supporting a mannequin's standing-up. Then, the proposed system including the intention estimation algorithm was provided for a complete paraplegic patient in order to verify the performance of the total system. In consequence, we confirmed that the proposed system safely supported his standing-up according to his intention.


abstract: From the Publisher: ::: Written for researchers and engineers working in the field, Radar Systems Analysis and Design Using MATLAB provides fundamental radar principles, advanced topics, and rigorous but easy-to-follow mathematical derivations.


title: Less-structured time in children's daily lives predicts self-directed executive functioning

abstract: Executive functions in childhood predict important life outcomes. Thus, there is great interest in attempts to improve executive functions early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children’s externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children’s experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children’s daily, annual, and typical schedules. We categorized children’s activities as “structured” or “less-structured” based on categorization schemes from prior studies on child leisure time use. We assessed children’s self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.


title: Reducing temperature dependence of the output energy of a quasi-continuous wave diode-pumped Nd:YAG laser

abstract: It is demonstrated by numerical modeling that spectrally dispersed compound pumping diodes and low-loss pumping chamber reduced the temperature dependence of the output energy of quasi-continuous wave diode-pumped Nd:YAG lasers considerably. Several compound diodes with different spectral profiles were tested for pumping. The laser energy was calculated as a function of diode temperature from −30°C to 60°C. When a compound diode with a flat-top spectrum was used for pumping, the mean laser energy was 83% of the maximum energy of a Nd:YAG laser pumped by a diode with a narrow bandwidth. In addition, a compound diode with three emission lines was tested for pumping. When the wavelength gap between the adjacent emission lines of the pumping diode was in the range of 3–10 nm, the mean energy of the Nd:YAG laser became similar to that of a Nd:YAG laser pumped by a diode with a flat-top spectrum.


title: THE BIG FIVE PERSONALITY TRAITS, GENERAL MENTAL ABILITY, AND CAREER SUCCESS ACROSS THE LIFE SPAN

abstract: The present study investigated the relationship of traits from the 5factor model of personality (often termed the "Big Five") and general mental ability with career success. Career success was argued to be comprised of intrinsic success (job satisfaction) and extrinsic success (income and occupational status) dimensions. Data were obtained from the Intergenerational Studies, a set of 3 studies that followed participants from early childhood to retirement. The most general findings were that conscientiousness positively predicted intrinsic and extrinsic career success, neuroticism negatively predicted extrinsic success, and general mental ability positively predicted extrinsic career success. Personality was related to career success controlling for general mental ability and, though adulthood measures of the Big Five traits were more strongly related to career success than were childhood measures, both contributed unique variance in explaining career success. Considerable evidence has accumulated regarding the antecedents of career success. A recent review of the career success literature (Tharenou, 1997) identified several categories of influences on career success. The most commonly investigated influences were human capital attributes (training, work experience, education) and demographic factors (age, sex, marital status, number of children). Although these classes of influences have provided important insights into the determinants of career success, there is room for further development. Specifically, little research has entertained the idea that career success may have dispositional causes. There have been a few exceptions, such as Howard and Bray's (1988, 1994) study of the career advancement of AT&T managers. However, as Tharenou noted, few studies have taken a more comprehensive, personological approach to career success.


title: Antennas: state of the art

abstract: Aim of this contribution is to illustrate the state of the art of smart antenna research from several perspectives. The bow is drawn from transmitter issues via channel measurements and modeling, receiver signal processing, network aspects, technological challenges towards first smart antenna applications and current status of standardization. Moreover, some future prospects of different disciplines in smart antenna research are given.


title: Bitcoin as money

abstract: The spectacular rise late last year in the price of Bitcoin, the dominant virtual currency, has attracted much public attention as well as scholarly interest. This policy brief discusses how some features of Bitcoin, as designed and executed to date, have hampered its ability to perform the functions required of a fiat money––as a medium of exchange, unit of account, and store of value. Furthermore, we document how various forms of intermediaries have emerged and evolved within the Bitcoin network, particularly noting the convergence toward concentrated processing, both on and off the blockchain. We argue that much of this process would have been predicted by established theories of financial intermediation, and we consider the theories’ implication for the future evolution of intermediaries serving users of Bitcoin or alternative virtual currencies. We then compare Bitcoin with other innovations to facilitate payment services, from competing alternative digital currencies to electronic payment protocols. We conclude with a broad consideration of the major factors that will likely shape the future development of Bitcoin versus other alternative payment systems. We predict that Bitcoin’s lasting legacy will be the innovations it has spurred to payment technology, although the payment system will remain dominated by large processors because of economies of scale.


title: Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction

abstract: Limited-angle computed tomography (CT) is often used in clinical applications such as C-arm CT for interventional imaging. However, CT images from limited angles suffers from heavy artifacts due to incomplete projection data. Existing iterative methods require extensive calculations but can not deliver satisfactory results. Based on the observation that the artifacts from limited angles have some directional property and are globally distributed, we propose a novel multi-scale wavelet domain residual learning architecture, which compensates for the artifacts. Experiments have shown that the proposed method effectively eliminates artifacts, thereby preserving edge and global structures of the image.


title: Pair of Replacement Algorithms MFMR and AF-LRU on L1 and L2 Cache for Proxy Server

abstract: Access to the internet and WWW is growing extensively, which results in heavy network traffic. To reduce the network traffic proxy server is used. Proxy server reduces the load of server. If the cache replacement algorithm of proxy server's cache is efficient then proxy server will be helpful to reduce the network traffic in more efficient manner. In this paper we are considering proxy server cache to be Level 1 (L1) cache and storage cache of proxy server to be Level 2 (L2) cache. For collecting the real trace proxy server is used in an organization. Log of proxy server gives the information of various URLs accessed by various clients with time. For performing experiments various URLs were given a numeric identity. This paper analyzes the behavior of various replacement algorithms. The replacement algorithms taken into consideration are Least Recently Used (LRU), Least Frequently Used (LFU), First In First Out (FIFO). This paper proposes a new Replacement Algorithm named MFMR (Most Frequent with Maximum Reusability) for L1 which works about 16% better than existing algorithms considered in this paper. This paper also proposes a new replacement policy for L2 named AF_LRU (Average Frequency, Least Recently Used). Simulation results shows that pair of MFMR and AF-LRU is approximately 28% better than other existing pairs of replacement algorithms considered. Key Word- Level 1 Cache (L1); Level 2 Cache (L2); web access pattern; Replacement Algorithm; Proxy server; client.


title: A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks

abstract: Deep networks are state-of-the-art models used for understanding the content of images, videos, audio and raw input data. Current computing systems are not able to run deep network models in real-time with low power consumption. In this paper we present nn-X: a scalable, low-power coprocessor for enabling real-time execution of deep neural networks. nn-X is implemented on programmable logic devices and comprises an array of configurable processing elements called collections. These collections perform the most common operations in deep networks: convolution, subsampling and non-linear functions. The nn-X system includes 4 high-speed direct memory access interfaces to DDR3 memory and two ARM Cortex-A9 processors. Each port is capable of a sustained throughput of 950 MB/s in full duplex. nn-X is able to achieve a peak performance of 227 G-ops/s, a measured performance in deep learning applications of up to 200 G-ops/s while consuming less than 4 watts of power. This translates to a performance per power improvement of 10 to 100 times that of conventional mobile and desktop processors.


title: A Short History of Cleaning Robots

abstract: The definition of the desired functions and the design of an ultimate versatile personal robot is an ongoing debate. Meanwhile, however, precursors of this yet to evolve species are well on their way to become commercial products. Cleaning robots for public environments as well as for private households seem to be able to provide the breakthrough which the designers of non-industrial robot systems have long awaited. ::: ::: This survey describes a selection of 30 different cleaning robots, with the first developments reaching back more than 15 years. With a few exceptions we have focused on floor cleaning, in particular indoor floor cleaning. We describe a variety of scrubbing and vacuuming robots which were developed for this task. The described systems range from heavy, large, and expensive industrial cleaning vehicles to small-size, light-weight, low-cost household devices. The survey does not include, however, systems for cleaning facades of buildings, or windows, or production tools. ::: ::: Although not all of the 30 cleaning robots abovementioned have yet reached the state of commercial products, their number alone certainly reflects the expectations regarding the economic value associated with the automation of cleaning tasks. In Europe only the estimates for the market for cleaning services range up to the order of USd 100 billion per year. It is therefore not surprising that the cleaning industry and the manufacturers of cleaning devices are rather enthusiastic with respect to the automation of cleaning tasks using (semi-)autonomous mobile robot systems.


title: Automated Text Summarization Base on Lexicales Chain and graph Using of WordNet and Wikipedia Knowledge Base

abstract: The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. The lexical cohesion structure of the text can be exploited to determine the importance of a sentence/phrase. Lexical chains are useful tools to analyze the lexical cohesion structure in a text .In this paper we consider the effect of the use of lexical cohesion features in Summarization, And presenting a algorithm base on the knowledge base. Ours algorithm at first find the correct sense of any word, Then constructs the lexical chains, remove Lexical chains that less score than other, detects topics roughly from lexical chains, segments the text with respect to the topics and selects the most important sentences. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.


title: An integrative study of information systems security effectiveness

abstract: As organizations become increasingly dependent on information systems (IS) for strategic advantage and operations, the issue of IS security also becomes increasingly important. In the interconnected electronic business environment of today, security concerns are paramount. Management must invest in IS security to prevent abuses that can lead to competitive disadvantage. Using the literature on security practices and organizational factors, this study develops an integrative model of IS security effectiveness and empirically tests the model. The data were collected through a survey of IS managers from various sectors of the economy. Small and medium-sized enterprises were found to engage in fewer deterrent efforts compared to larger organizations. Organizations with stronger top management support were found to engage in more preventive efforts than organizations with weaker support from higher management. Financial organizations were found to undertake more deterrent efforts and have stiffer deterrent severity than organizations in other sectors. Moreover, greater deterrent efforts and preventive measures were found to lead to enhanced IS security effectiveness. Implications of these findings for further research and practice are discussed.


title: Joint POS Tagging and Transition-based Constituent Parsing in Chinese with Non-local Features

abstract: We propose three improvements to address the drawbacks of state-of-the-art transition-based constituent parsers. First, to resolve the error propagation problem of the traditional pipeline approach, we incorporate POS tagging into the syntactic parsing process. Second, to alleviate the negative influence of size differences among competing action sequences, we align parser states during beam-search decoding. Third, to enhance the power of parsing models, we enlarge the feature set with non-local features and semisupervised word cluster features. Experimental results show that these modifications improve parsing performance significantly. Evaluated on the Chinese TreeBank (CTB), our final performance reaches 86.3% (F1) when trained on CTB 5.1, and 87.1% when trained on CTB 6.0, and these results outperform all state-of-the-art parsers.


title: Downsampling for sparse subspace clustering

abstract: Sparse subspace clustering (SSC) is a technique to partition unlabeled samples according to the subspaces they locate in. With the rapid increase of data amount, efficiently downsampling a big dataset, while at the same time keeping the structure of subspaces, becomes an important topic for SSC. In order to reduce the computational cost while preserving clustering accuracy, a new approach of SSC with downsampling (SSCD) is proposed in this paper. In SSCD, the numbers of samples located in respective subspaces are estimated utilizing the l 1 norm of the sparse representation. Then a downsampling strategy is designed to decimate samples with the probabilities that are in reverse ratio to the amounts of samples in respective subspaces. As a consequence, the samples in different subspaces are expected to be balanced after the downsampling operation. Theoretical analysis proves the correctness of the proposed strategy. Numerical simulations also verify the efficiency of SSCD.


title: Sorption Characteristics of CO 2 on Rocks and Minerals in Storing CO 2 Processes

abstract: As CO2 is injected into pore spaces of water-filled reservoir rocks, it displaces much of the pore fluids. In short terms (several to tens of years), the greater part of the injected CO2 is predicted to stay as free CO2, i.e. in a CO2 rich dense phase that may contain some water. This paper investigates the sorption characteristics for rocks (quartzose arenite, greywacke, shale, granite and serpentine) and minerals (quartz and albite) in the CO2 rich dense phase. The measurements were conducted at 50˚C and 100˚C, and pressures up to 20 MPa. Our results demonstrated that significant quantities of CO2 were sorbed with all the samples. Particularly, at 50˚C and 100˚C, quartzose arenite showed largest sorption capacity among the other samples in higher pressures (>10 MPa). Furthermore, comparison with model prediction based on the pore filling model, which assumed that CO2 acts as filling pore spaces of the rocks and minerals, suggested the importance of the sorption mechanism in the CO2 geological storage in addition to the pore-filling mechanism. The present results should be pointed out that the sorption characteristics may have significant and meaningful effect on the assessment of CO2 storage capacity in geological media.


title: The short-form McGill pain questionnaire

abstract: A short form of the McGill Pain Questionnaire (SF-MPQ) has been developed. The main component of the SF-MPQ consists of 15 descriptors (11 sensory; 4 affective) which are rated on an intensity scale as 0 = none, 1 = mild, 2 = moderate or 3 = severe. Three pain scores are derived from the sum of the intensity rank values of the words chosen for sensory, affective and total descriptors. The SF-MPQ also includes the Present Pain Intensity (PPI) index of the standard MPQ and a visual analogue scale (VAS). The SF-MPQ scores obtained from patients in post-surgical and obstetrical wards and physiotherapy and dental departments were compared to the scores obtained with the standard MPQ. The correlations were consistently high and significant. The SF-MPQ was also shown to be sufficiently sensitive to demonstrate differences due to treatment at statistical levels comparable to those obtained with the standard form. The SF-MPQ shows promise as a useful tool in situations in which the standard MPQ takes too long to administer, yet qualitative information is desired and the PPI and VAS are inadequate.


title: VCO gain calibration technique for GSM/EDGE polar modulated transmitter

abstract: This paper describes a VCO gain calibration technique for the two-point modulation scheme using Delta-Sigma frequency synthesizer. The proposed technique enables PLL-based phase modulator to have wide bandwidth with good signal quality. A fully integrated GSM/EDGE polar modulated transmitter, implemented in a 0.13 mum CMOS process, is presented to show the feasibility of this calibration technique. After the calibration, it shows a margin of 8 dB to the spectrum mask at 400 kHz offset with EVM of 2%.


title: Learning a Deep Convolutional Network for Light-Field Image Super-Resolution

abstract: Commercial Light-Field cameras provide spatial and angular information, but its limited resolution becomes an important problem in practical use. In this paper, we present a novel method for Light-Field image super-resolution (SR) via a deep convolutional neural network. Rather than the conventional optimization framework, we adopt a datadriven learning method to simultaneously up-sample the angular resolution as well as the spatial resolution of a Light-Field image. We first augment the spatial resolution of each sub-aperture image to enhance details by a spatial SR network. Then, novel views between the sub-aperture images are generated by an angular super-resolution network. These networks are trained independently but finally finetuned via end-to-end training. The proposed method shows the state-of-the-art performance on HCI synthetic dataset, and is further evaluated by challenging real-world applications including refocusing and depth map estimation.


title: Relationship between Customer Satisfaction and Mobile Banking Adoption in Pakistan

abstract: Mobile banking has marked itself as an emerging technology adopted by banks around the globe. The purpose of this research is to identify the key factors of mobile technology adoption which influence customer satisfaction in Pakistan. Questionnaires are used to conduct data collection and then analyzed using statistical techniques: regression analysis, correlation and factor analysis. The findings show that customer's concerns about security, authenticity and reliability of the technology are of significance. The results imply that firms should focus upon IT application, innovative services, security, customer trust and risk as these are the key indicators of technology adoption.


title: Latent dirichlet allocation in web spam filtering

abstract: Latent Dirichlet allocation (LDA) (Blei, Ng, Jordan 2003) is a fully generative statistical language model on the content and topics of a corpus of documents. In this paper we apply a modification of LDA, the novel multi-corpus LDA technique for web spam classification. We create a bag-of-words document for every Web site and run LDA both on the corpus of sites labeled as spam and as non-spam. In this way collections of spam and non-spam topics are created in the training phase. In the test phase we take the union of these collections, and an unseen site is deemed spam if its total spam topic probability is above a threshold. As far as we know, this is the first web retrieval application of LDA. We test this method on the UK2007-WEBSPAM corpus, and reach a relative improvement of 11% in F-measure by a logistic regression based combination with strong link and content baseline classifiers.


title: Effectiveness of reverse body bias for leakage control in scaled dual Vt CMOS ICs

abstract: Examines the effectiveness of opportunistic use of reverse body bias (RBB) to reduce leakage power during active operation, burn-in, and standby in 0.18 /spl mu/m single-V/sub t/ and 0.13 /spl mu/m dual-V/sub t/ logic process technologies. Investigates its dependencies on channel length, target V/sub t/, temperature and technology generation. Shows that RBB becomes less effective for leakage reduction at shorter channel lengths and lower V/sub t/ at both high and room temperatures, especially when target intrinsic leakage currents are high. RBB effectiveness also diminishes with technology scaling primarily because of worsening short-channel effects (SCE), particularly when target V/sub t/ values are low. A model is given that relates different transistor leakage components to full-chip leakage current, and is validated through test-chip measurements across a range of RBB values.


title: Crowd Build: A Methodology for Enterprise Software Development Using Crowdsourcing

abstract: We present and evaluate a software development methodology that addresses key challenges for the application of Crowd sourcing to an enterprise application development. Our methodology presents a mechanism to systematically break the overall business application into small tasks such that the tasks can be completed independently and in parallel by the crowd. Our methodology supports automated testing and automatic integration. We evaluate our methodology by developing a web application through Crowd sourcing. The methodology was tested through two Crowd sourcing models: one through contests and the other through hiring freelancers. We present various metrics of the Crowd sourcing experiment and compare against the estimate for the traditional software development methodology.


title: Towards a Quality-centric Big Data Architecture for Federated Sensor Services

abstract: As the Internet of Things (IoT) paradigm gains popularity, the next few years will likely witness 'servitization' of domain sensing functionalities. We envision a cloud-based eco-system in which high quality data from large numbers of independently-managed sensors is shared or even traded in real-time. Such an eco-system will necessarily have multiple stakeholders such as sensor data providers, domain applications that utilize sensor data (data consumers), and cloud infrastructure providers who may collaborate as well as compete. While there has been considerable research on wireless sensor networks, the challenges involved in building cloud-based platforms for hosting sensor services are largely unexplored. In this paper, we present our vision for data quality (DQ)-centric big data infrastructure for federated sensor service clouds. We first motivate our work by providing real-world examples. We outline the key features that federated sensor service clouds need to possess. This paper proposes a big data architecture in which DQ is pervasive throughout the platform. Our architecture includes a markup language called SDQ-ML for describing sensor services as well as for domain applications to express their sensor feed requirements. The paper explores the advantages and limitations of current big data technologies in building various components of the platform. We also outline our initial ideas towards addressing the limitations.


title: A Novel Broadband Feed Network for Dual-Fed Circularly Polarized Microstrip Antennas

abstract: In this paper, a broadband feed network comprising a Wilkinson power divider and a broadband 90° parallel double Schiffman phase shifter is presented with the aim of enhancing the wideband circular polarization performance of dual-fed type microstrip antennas. The proposed device presents good performance in terms of return loss (S11


title: Germanium/HfO/sub 2//TiN gate stacks for advanced nodes: influence of surface preparation on MOS capacitor characteristics

abstract: For the first time, we report electrical and physical characterization of metal-oxide-semiconductor (MOS) capacitors fabricated on 2.5/spl mu/m epitaxial germanium layers grown on (100) silicon. These capacitors were made using HfO/sub 2/ as the dielectric and TiN as the metal gate electrode. We have studied the influence of the Ge surface preparation on the MOS electrical characteristics. It is demonstrated that a surface anneal step in a NH/sub 3/ ambient before the HfO/sub 2/ deposition results in significant improvements in both the equivalent oxide thickness (EOT) and the gate leakage current. We show that it is possible to achieve Ge/GeON/HfO/sub 2//TiN gate stacks with an EOT of 0.7 nm and a leakage current of 0.84 A/cm/sup 2/ at -2 V gate bias. The better transport properties of Ge and these performances show the interest of Ge and GeOI for the ITRS advanced nodes.


title: Do You Know Your Brain? A Survey on Public Neuroscience Literacy at the Closing of the Decade of the Brain

abstract: What does the public know about the developments offered by brain research? What factors influence public neuroscience literacy? What issues need to be emphasized to the public? To address these questions, a survey was conducted using a questionnaire with 95 assertions, answered by indicating yes, no, or I don’t know. The opinions of 35 senior neuroscientists and 2158 members of the public of Rio de Janeiro were heard on issues such as the mind-brain relationship, the senses, learning, and memory. The incidence of “correct” answers among the public improved the most with schooling, followed by reading of popular science magazines and of newspapers. An analysis of the responses to each assertion revealed which themes are well- or poorly known to the public. The results attest for the importance of popular scientific communication and indicate issues on which communication efforts should be concentrated in order to increase public awareness about the brain.


title: Miro: Middleware for Autonomous Mobile Robots

abstract: Abstract Implementing software for autonomous mobile robots is a non-trivial task, because such robots incorporate several sensor systems and actuators that must be controlled simultaneously by a heterogeneous ensemble of networked computers and microcontrollers. Additionally, the use of modern software engineering technologies like object-oriented and distributed programming and client/server architectures is essential in order to maintain program code effectively. In this paper, we present Miro, a new CORBA-based robot programming framework which allows a rapid development of reliable and safe software on heterogeneous computer networks and supports the mixed use of several programming languages.


title: Physician and nursing perceptions concerning interprofessional communication and collaboration.

abstract: The aim of the study was to investigate the physician and nursing perceptions regarding communication and collaboration as well as the factors that may influence these activities. A self-administered questionnaire survey was sent to a random sample of 93 physicians and 197 nurses based in two large public hospitals in Athens, Greece. Descriptive statistics, t-test and chi square test were performed with the SPSS 19.0 statistical package. Years of experience, the size of the clinic, the university degree and the postgraduate studies were found to be significant factors according to nurses' view (p < 0.05). For the physicians, age, sex, years of experience and the size of clinic affected the communication and collaboration with the nursing staff significantly (p < 0.05). In summary, these findings suggest that nurses and physicians do not share the same views concerning the effectiveness of their communication and nurses' role in the decision-making process of the patients' care. The most important barrier for the establishment of good relations between these professions, according to the physicians, was that they did not recognize the nurses' professional role. The study also indicated that the absence of interprofessional collaboration may result in a higher possibility of errors and omissions in patients' care. Therefore, in everyday practice, both nurses and physicians should acknowledge the importance of their effective communication and they should develop and implement interprofessional teamwork interventions to improve collaboration. Moreover, nurses have to constantly consolidate their role in the decision process and patients' care, especially in countries with limited interprofessional collaboration culture. In addition, factors that improve physicians' attitudes toward collaboration and effective communication should be further explored.


title: Combined transient and dynamic analysis of HVDC and FACTS systems

abstract: A new approach to HVDC and FACTS transient/dynamic simulation based on an interactive execution of an AC transient stability program (TSP) and the Electromagnetic Transients Program (EMTP) is described. Through the integration of the detailed transient model of FACTS with the transient stability program, authentic simulation is achieved without simplifications. Both HVDC and thyristor controlled series capacitor (TCSC) systems are used to validate the approach, under different coupling situations between both TSP and EMTP.


title: Improved cuckoo search for reliability optimization problems

abstract: An efficient approach to solve engineering optimization problems is the cuckoo search algorithm. It is a recently developed meta-heuristic optimization algorithm. Normally, the parameters of the cuckoo search are kept constant. This may result in decreasing the efficiency of the algorithm. To cope with this issue, the cuckoo search parameters should be tuned properly. In this paper, an improved cuckoo search algorithm, enhancing the accuracy and convergence rate of the cuckoo search algorithm, is presented. Then, the performance of the proposed algorithm is tested on some complex engineering optimization problems. They are four well-known reliability optimization problems, a large-scale reliability optimization problem as well as a complex system, which is a 15-unit system reliability optimization problem. Finally, the results are compared with those given by several well-known methods. Simulation results demonstrate the effectiveness of the proposed algorithm.


title: A 77-GHz FMCW MIMO radar based on loosely coupled stations

abstract: In this work the realization of a frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar for the 77-GHz frequency band is presented. The proposed system consists of two separate radar units only loosely synchronized by the ramp trigger signal. To realize the separation of each transmitter's signal at reception, as it is required for the MIMO principle, a delayed ramp start of the slave station is implemented leading to different output frequencies for each transmitter. Since the delay caused by the ramp trigger is estimated in each measurement, the required accuracy of the ramp start timing is only defined by the maximum achievable sampling frequency of the used analog-to-digital converter. Due to the derived signal processing algorithms a coherent measurement with the two stations can be realized without requiring an RF link between them.


title: Block Truncation Coding-based Audio Compression Technique

abstract: There have been an increasing utilization and consumption of audio files in various applications across different desktop and mobile devices. Thus, audio data compression has been beneficial in reducing the bandwidth and disk space requirements of such data. Block Truncation Coding (BTC), a well-known data compression technique for digital images, was used as the underlying algorithm in encoding audio data. A new encoding technique for audio data was proposed with the following components: (1) quadtree for audio block segmentation, (2) AMBTC for computation of representative values, and (3) Huffman Coding for lossless data representation. The performance of the proposed encoding technique was benchmarked and measured using Peak Signal-to-Noise Ratio (PSNR) and compression rate.


title: Integrated ultrasonic imaging systems based on CMUT arrays: recent progress

abstract: The paper describes the development of an ultrasonic imaging system based on a two-dimensional capacitive micromachined ultrasonic transducer (CMUT) array. The transducer array and front-end electronics are designed to fit in a 5-mm endoscopic channel. A custom-designed integrated circuit, which comprises the front-end electronics, is connected with the transducer elements via through-wafer interconnects and flip-chip bonding. FPGA-based signal-processing hardware provides real-time three-dimensional imaging. The imaging system is being developed to demonstrate a means of integrating the front-end electronics with the transducer array and to provide a clinically useful technology. Integration of the electronics can improve signal-to-noise ratio, reduce the number of cables connecting the imaging probe to a separate processing unit, and provide a means of connecting electronics to large two-dimensional transducer arrays. The paper describes the imaging system architecture and the progress we have made on implementing each of its components: a 16/spl times/16 CMUT array; custom-designed integrated circuits; a flip-chip bonding technique; signal-processing hardware.


title: Job Burnout

abstract: Burnout is a prolonged response to chronic emotional and interpersonal stressors on the job, and is defined by the three dimensions of exhaustion, cynicism, and inefficacy. The past 25 years of research has established the complexity of the construct, and places the individual stress experience within a larger organizational context of people's relation to their work. Recently, the work on burnout has expanded internationally and has led to new conceptual models. The focus on engagement, the positive antithesis of burnout, promises to yield new perspectives on interventions to alleviate burnout. The social focus of burnout, the solid research basis concerning the syndrome, and its specific ties to the work domain make a distinct and valuable contribution to people's health and well-being.


title: Generalizing motion edits with Gaussian processes

abstract: One way that artists create compelling character animations is by manipulating details of a character's motion. This process is expensive and repetitive. We show that we can make such motion editing more efficient by generalizing the edits an animator makes on short sequences of motion to other sequences. Our method predicts frames for the motion using Gaussian process models of kinematics and dynamics. These estimates are combined with probabilistic inference. Our method can be used to propagate edits from examples to an entire sequence for an existing character, and it can also be used to map a motion from a control character to a very different target character. The technique shows good generalization. For example, we show that an estimator, learned from a few seconds of edited example animation using our methods, generalizes well enough to edit minutes of character animation in a high-quality fashion. Learning is interactive: An animator who wants to improve the output can provide small, correcting examples and the system will produce improved estimates of motion. We make this interactive learning process efficient and natural with a fast, full-body IK system with novel features. Finally, we present data from interviews with professional character animators that indicate that generalizing and propagating animator edits can save artists significant time and work.


title: Broadband microstrip-fed modified quasi-Yagi antenna

abstract: A broadband design of the microstrip-fed modified quasi-Yagi antenna is presented. The two arms of the driving dipole are connected separately to two microstrip sections tapered from the feeding microstrip line and its truncated ground plane. The end points of the two tapered sections can be suitably adjusted to obtain a 10-dB return loss bandwidth more than 50%. Measured radiation patterns are end-fire and the in-band peak gains range from 3.9 to 7.2 dBi. Details of the antenna design and the experimental results are presented and discussed.


title: Strong authentication for RFID systems using the AES algorithm

abstract: Radio frequency identification (RFID) is an emerging technology which brings enormous productivity benefits in applications where objects have to be identified automatically. This paper presents issues concerning security and privacy of RFID systems which are heavily discussed in public. In contrast to the RFID community, which claims that cryptographic components are too costly for RFID tags, we describe a solution using strong symmetric authentication which is suitable for today’s requirements regarding low power consumption and low die-size. We introduce an authentication protocol which serves as a proof of concept for authenticating an RFID tag to a reader device using the Advanced Encryption Standard (AES) as cryptographic primitive. The main part of this work is a novel approach of an AES hardware implementation which encrypts a 128-bit block of data within 1000 clock cycles and has a power consumption below 9 μA on a 0.35 μm CMOS process.


Development of a front-end application using AngularJS: 1UP Media company case


title: Biologically inspired multi-layered synthetic skin for tactile feedback in prosthetic limbs

abstract: The human body offers a template for many state-of-the-art prosthetic devices and sensors. In this work, we present a novel, sensorized synthetic skin that mimics the natural multi-layered nature of mechanoreceptors found in healthy glabrous skin to provide tactile information. The multi-layered sensor is made up of flexible piezoresistive textiles that act as force sensitive resistors (FSRs) to convey tactile information, which are embedded within a silicone rubber to resemble the compliant nature of human skin. The top layer of the synthetic skin is capable of detecting small loads less than 5 N whereas the bottom sensing layer responds reliably to loads over 7 N. Finite element analysis (FEA) of a simplified human fingertip and the synthetic skin was performed. Results suggest similarities in behavior during loading. A natural tactile event is simulated by loading the synthetic skin on a prosthetic limb. Results show the sensors' ability to detect applied loads as well as the ability to simulate neural spiking activity based on the derivative and temporal differences of the sensor response. During the tactile loading, the top sensing layer responded 0.24 s faster than the bottom sensing layer. A synthetic biologically-inspired skin such as this will be useful for enhancing the functionality of prosthetic limbs through tactile feedback.


title: SideSwipe: detecting in-air gestures around mobile devices using actual GSM signal

abstract: Current smartphone inputs are limited to physical buttons, touchscreens, cameras or built-in sensors. These approaches either require a dedicated surface or line-of-sight for interaction. We introduce SideSwipe, a novel system that enables in-air gestures both above and around a mobile device. Our system leverages the actual GSM signal to detect hand gestures around the device. We developed an algorithm to convert the discrete and bursty GSM pulses to a continuous wave that can be used for gesture recognition. Specifically, when a user waves their hand near the phone, the hand movement disturbs the signal propagation between the phone's transmitter and added receiving antennas. Our system captures this variation and uses it for gesture recognition. To evaluate our system, we conduct a study with 10 participants and present robust gesture recognition with an average accuracy of 87.2% across 14 hand gestures.


title: Portable Camera-Based Assistive Text and Product Label Reading From Hand-Held Objects for Blind Persons

abstract: We propose a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we first propose an efficient and effective motion-based method to define a region of interest (ROI) in the video by asking the user to shake the object. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binarized and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm is quantitatively evaluated on ICDAR-2003 and ICDAR-2011 Robust Reading Datasets. Experimental results demonstrate that our algorithm achieves the state of the arts. The proof-of-concept prototype is also evaluated on a dataset collected using ten blind persons to evaluate the effectiveness of the system's hardware. We explore user interface issues and assess robustness of the algorithm in extracting and reading text from different objects with complex backgrounds.


title: On probabilistic inference by weighted model counting

abstract: A recent and effective approach to probabilistic inference calls for reducing the problem to one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the approach calls for encoding the probabilistic model, typically a Bayesian network, as a propositional knowledge base in conjunctive normal form (CNF) with weights associated to each model according to the network parameters. Given this CNF, computing the probability of some evidence becomes a matter of summing the weights of all CNF models consistent with the evidence. A number of variations on this approach have appeared in the literature recently, that vary across three orthogonal dimensions. The first dimension concerns the specific encoding used to convert a Bayesian network into a CNF. The second dimensions relates to whether weighted model counting is performed using a search algorithm on the CNF, or by compiling the CNF into a structure that renders WMC a polytime operation in the size of the compiled structure. The third dimension deals with the specific properties of network parameters (local structure) which are captured in the CNF encoding. In this paper, we discuss recent work in this area across the above three dimensions, and demonstrate empirically its practical importance in significantly expanding the reach of exact probabilistic inference. We restrict our discussion to exact inference and model counting, even though other proposals have been extended for approximate inference and approximate model counting.


3D Shape Descriptor Based on 3D Fourier Transform


title: Empirical assessment of machine learning-based malware detectors for Android

abstract: To address the issue of malware detection through large sets of applications, researchers have recently started to investigate the capabilities of machine-learning techniques for proposing effective approaches. So far, several promising results were recorded in the literature, many approaches being assessed with what we call in the lab validation scenarios. This paper revisits the purpose of malware detection to discuss whether such in the lab validation scenarios provide reliable indications on the performance of malware detectors in real-world settings, aka in the wild. To this end, we have devised several Machine Learning classifiers that rely on a set of features built from applications' CFGs. We use a sizeable dataset of over 50 000 Android applications collected from sources where state-of-the art approaches have selected their data. We show that, in the lab, our approach outperforms existing machine learning-based approaches. However, this high performance does not translate in high performance in the wild. The performance gap we observed--F-measures dropping from over 0.9 in the lab to below 0.1 in the wild--raises one important question: How do state-of-the-art approaches perform in the wild?


title: oneM2M Architecture Based User Centric IoT Application Development

abstract: This paper presents a user centric and cross-domain IoT application development framework for smart devices. The framework includes automatic discovery of M2M devices, provisioning of sensors and IoT domains, semantic reasoning on sensor data and actuation based on the suggestions. The framework integrates a lightweight version of the Machine-to-Machine Measurement framework and has been adapted for smart devices enabling them to reason on sensor data. This allows us to build cross-domain IoT applications for end users. The issues of interoperability, heterogeneity of domains and multimodality of M2M devices are handled by the semantic web technologies. The framework is integrated into one M2M architecture and its prototype implementation is also presented. The performance of the mobile application is evaluated in terms of CPU load and power consumption. Finally overall contributions and future directions are summarized.


title: SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

abstract: We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.


title: Tregex and Tsurgeon: tools for querying and manipulating tree data structures

abstract: Abstract With syntactically annotated corpora becoming increasingly available for a variety of languages and grammatical frameworks, tree query tools have proven invaluable to linguists and computer scientists for both data exploration and corpusbased research. We provide a combined engine for tree query (Tregex) and manipulation (Tsurgeon) that can operate on arbitrary tree data structures with no need for preprocessing. Tregex remedies several expressive and implementational limitations of existing query tools, while Tsurgeon is to our knowledge the most expressive tree manipulation utility available.


title: Metrics for assessing blockchain-based healthcare decentralized apps

abstract: Blockchain is a decentralized, trustless protocol that combines transparency, immutability, and consensus properties to enable secure, pseudo-anonymous transactions. Smart contracts are built atop a blockchain to support on-chain storage and enable Decentralized Apps (DApps) to interact with the blockchain programatically. Programmable blockchains have generated interest in the healthcare domain as a potential solution to resolve key challenges, such as gapped communications, inefficient clinical report delivery, and fragmented health records. This paper provides evaluation metrics to assess blockchain-based DApps in terms of their feasibility, intended capability, and compliance in the healthcare domain.