title: Provenance variation in survival, growth and dry matter partitioning of Parkia biglobosa (Jacq.) R.Br. ex G.Don seedlings in response to water stress

abstract: The effects of drought stress on growth and dry matter partitioning of seven provenances of Parkia biglobosa were assessed in a nursery experiment. Three different water regimes were applied: soil high, medium and low water content (HWC, MWC and LWC) corresponding respectively to 100, 75 and 50 % of field capacity. A split-plot experimental design was applied with the provenance as main plot and the water regime as sub-plot, replicated three times. Each provenance was represented by 30 seedlings in each replication, corresponding to three test periods with ten seedlings each (6, 12 and 18 months after water stress started). There were significant interactions between provenance and water regime for survival rate and the relative growth rates of diameter and height. After 18 months, the dry matter mean was 20.00 ± 0.65 g for the HWC regime, 11.50 ± 2.90 g for the MWC regime while all plants in the LWC regime died. After 6 months, the accumulated water use efficiency (WUE) showed significant differences between water regimes, but not between provenances. Seedlings from the LWC regime showed higher WUE than those from the MWC regime. However, the HWC regime displayed the highest mean value of WUE. Despite differences between provenances, it was not possible to clearly separate them according to the geographical position or climatic parameters, based on the growth performance or both fresh to dry weight and shoot to root dry weight ratios.


title: Sexual behavior and contraceptive risk taking among sexually active adolescent females

abstract: Abstract Drawing on DeLamater's conceptual model of premarital contraceptive activity, this study assessed the relationships between various social, developmental, and behavioral variables and contraceptive risk taking by sexually active adolescent females. The hypotheses were tested on a national random sample of unmarried sexually active adolescent females ( n = 1512) ages 15–20 years from Cycle III of the National Survey of Family Growth. The number of years the subjects had been sexually active was the strongest predictor of their frequency of sexual intercourse, followed by their frequency of attendance at religious services. The inverse relationship between religious attendance and coital frequency was much stronger among whites than blacks. When the influence of these variables on contraceptive risk taking was assessed, coital frequency explained 7.2% of the variation in contraceptive risk taking, with the number of years the adolescent had been dating explaining a small amount of additional variation. Our data support not only the first stage of DeLamater's conceptual model of premarital contraceptive activity, but also aspects of Jessor's more general theory of adolescent risk taking and problem behavior.


title: Don't sweat the small stuff: formal verification of C code without the pain

abstract: We present an approach for automatically generating provably correct abstractions from C source code that are useful for practical implementation verification. The abstractions are easier for a human verification engineer to reason about than the implementation and increase the productivity of interactive code proof. We guarantee soundness by automatically generating proofs that the abstractions are correct. In particular, we show two key abstractions that are critical for verifying systems-level C code: automatically turning potentially overflowing machine-word arithmetic into ideal integers, and transforming low-level C pointer reasoning into separate abstract heaps. Previous work carrying out such transformations has either done so using unverified translations, or required significant proof engineering effort. We implement these abstractions in an existing proof-producing specification transformation framework named AutoCorres, developed in Isabelle/HOL, and demonstrate its effectiveness in a number of case studies. We show scalability on multiple OS microkernels, and we show how our changes to AutoCorres improve productivity for total correctness by porting an existing high-level verification of the Schorr-Waite algorithm to a low-level C implementation with minimal effort.


title: Marginalized Denoising Autoencoders for Domain Adaptation

abstract: Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters--in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB™, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.


title: Programming Situational Mobile Web Applications with Cloud-Mobile Convergence: An Internetware-Oriented Approach

abstract: Mobile Web applications (a.k.a., Web apps) stand for an important trend for next-generation Internet-based software. Currently popular mobile Web apps need to be adapted to various and ever-changing contexts and personalized user requirements. Based on our over-decade research experiences and practice on the Internetware paradigm, this position article describes an Internetware-oriented approach to designing, developing, and deploying situational mobile Web apps, by synthesizing the resources and services of mobile and cloud. Guided by a novel Service-Model-View-Controller (SMVC) software model, a mobile Web app is organized into a well-defined structure that facilitates adaptation including online/offline data access, computation offloading, user interface optimization, hybrid composition, etc. We provide efficient runtime support spanning mobile and cloud to make mobile Web apps more flexibly adaptive. The proof-of-concept evaluation demonstrates that our approach can benefit end-users with optimized user experience of mobile Web apps.


title: Introducing the HOPE (Hypospadias Objective Penile Evaluation)-score: a validation study of an objective scoring system for evaluating cosmetic appearance in hypospadias patients.

abstract: OBJECTIVE ::: To determine the reliability and internal validity of the Hypospadias Objective Penile Evaluation (HOPE)-score, a newly developed scoring system assessing the cosmetic outcome in hypospadias. ::: ::: ::: PATIENTS AND METHODS ::: The HOPE scoring system incorporates all surgically-correctable items: position of meatus, shape of meatus, shape of glans, shape of penile skin and penile axis. Objectivity was established with standardized photographs, anonymously coded patients, independent assessment by a panel, standards for a "normal" penile appearance, reference pictures and assessment of the degree of abnormality. A panel of 13 pediatric urologists completed 2 questionnaires, each consisting of 45 series of photographs, at an interval of at least 1 week. The inter-observer reliability, intra-observer reliability and internal validity were analyzed. ::: ::: ::: RESULTS ::: The correlation coefficients for the HOPE-score were as follows: intra-observer reliability 0.817, inter-observer reliability 0.790, "non-parametric" internal validity 0.849 and "parametric" internal validity 0.842. These values reflect good reproducibility, sufficient agreement among observers and a valid measurement of differences and similarities in cosmetic appearance. ::: ::: ::: CONCLUSIONS ::: The HOPE-score is the first scoring system that fulfills the criteria of a valid measurement tool: objectivity, reliability and validity. These favorable properties support its use as an objective outcome measure of the cosmetic result after hypospadias surgery.


title: Anti-Phishing in Offense and Defense

abstract: Many anti-phishing mechanisms currently focus on helping users verify whether a Web site is genuine. However, usability studies have demonstrated that prevention-based approaches alone fail to effectively suppress phishing attacks and protect Internet users from revealing their credentials to phishing sites. In this paper, instead of preventing human users from "biting the bait", we propose a new approach to protect against phishing attacks with "bogus bites". We develop BogusBiter, a unique client-side anti-phishing tool, which transparently feeds a relatively large number of bogus credentials into a suspected phishing site. BogusBiter conceals a victim's real credential among bogus credentials, and moreover, it enables a legitimate web site to identify stolen credentials in a timely manner. Leveraging the power of client-side automatic phishing detection techniques, BogusBiter is complementary to existing preventive anti-phishing approaches. We implement BogusBiter as an extension to Firefox 2 Web browser, and evaluate its efficacy through real experiments on both phishing and legitimate Web sites.


title: Common Mode Noise Cancellation for Electrically Non-Contact ECG Measurement System on a Chair

abstract: Electrically non-contact ECG measurement system on a chair can be applied to a number of various fields for continuous health monitoring in daily life. However, the body is floated electrically for this system due to the capacitive electrodes and the floated body is very sensitive to the external noises or motion artifacts which affect the measurement system as the common mode noise. In this paper, the driven-seat-ground circuit similar to the driven-right-leg circuit is proposed to reduce the common mode noise. The analysis of this equivalent circuit is performed and the output signal waveforms are compared between with driven-seat-ground and with capacitive ground. As the results, the driven-seat-ground circuit improves significantly the properties of the fully capacitive ECG measurement system as the negative feedback


title: Control of VSC connected to the grid through LCL-filter to achieve balanced currents

abstract: Grid-connected voltage source converters (VSCs) are the heart of many applications with power quality concerns due to their reactive power controllability. However, the major drawback is their sensitivity to grid disturbances. Moreover, when VSCs are used in DG applications, voltage unbalance may be intolerant. The current protection may trip due to current unbalance or due to overcurrent. In this paper, a vector current controller for VSC connected to the grid through LCL-filter is developed with the main focus on producing symmetrical and balanced currents in case of unbalanced voltage dips. Implementing this controller helps the VSC system not to trip during voltage dips.


mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling


title: The Diversity and Anti-Microbial Activity of Endophytic Actinomycetes Isolated from Medicinal Plants in Panxi Plateau, China

abstract: Traditional Chinese medicinal plants are sources of biologically active compounds, providing raw material for pharmaceutical, cosmetic and fragrance industries. The endophytes of medicinal plants participate in biochemical pathways and produce analogous or novel bioactive compounds. Panxi plateau in South-west Sichuan in China with its unique geographical and climatological characteristics is a habitat of a great variety of medicinal plants. In this study, 560 endophytic actinomycetes were isolated from 26 medicinal plant species in Panxi plateau. 60 isolates were selected for 16S rDNA-RFLP analysis and 14 representative strains were chosen for 16S rDNA sequencing. According to the phylogenetic analysis, seven isolates were Streptomyces sp., while the remainder belonged to genera Micromonospora, Oerskovia, Nonomuraea, Promicromonospora and Rhodococcus. Antimicrobial activity analysis combined with the results of amplifying genes coding for polyketide synthetase (PKS-I, PKS-II) and nonribosomal peptide synthetase (NRPS) showed that endophytic actinomycetes isolated from medicinal plants in Panxi plateau had broad-spectrum antimicrobial activity and potential natural product diversity, which further proved that endophytic actinomycetes are valuable reservoirs of novel bioactive compounds.


title: VulPecker: an automated vulnerability detection system based on code similarity analysis

abstract: Software vulnerabilities are the fundamental cause of many attacks. Even with rapid vulnerability patching, the problem is more complicated than it looks. One reason is that instances of the same vulnerability may exist in multiple software copies that are difficult to track in real life (e.g., different versions of libraries and applications). This calls for tools that can automatically search for vulnerable software with respect to a given vulnerability. In this paper, we move a step forward in this direction by presenting Vulnerability Pecker (VulPecker), a system for automatically detecting whether a piece of software source code contains a given vulnerability or not. The key insight underlying VulPecker is to leverage (i) a set of features that we define to characterize patches, and (ii) code-similarity algorithms that have been proposed for various purposes, while noting that no single code-similarity algorithm is effective for all kinds of vulnerabilities. Experiments show that VulPecker detects 40 vulnerabilities that are not published in the National Vulnerability Database (NVD). Among these vulnerabilities, 18 are not known for their existence and have yet to be confirmed by vendors at the time of writing (these vulnerabilities are "anonymized" in the present paper for ethical reasons), and the other 22 vulnerabilities have been "silently" patched by the vendors in the later releases of the vulnerable products.


title: Kernelized Deep Convolutional Neural Network for Describing Complex Images

abstract: With the impressive capability to capture visual content, deep convolutional neural networks (CNN) have demon- strated promising performance in various vision-based ap- plications, such as classification, recognition, and objec- t detection. However, due to the intrinsic structure design of CNN, for images with complex content, it achieves lim- ited capability on invariance to translation, rotation, and re-sizing changes, which is strongly emphasized in the s- cenario of content-based image retrieval. In this paper, to address this problem, we proposed a new kernelized deep convolutional neural network. We first discuss our motiva- tion by an experimental study to demonstrate the sensitivi- ty of the global CNN feature to the basic geometric trans- formations. Then, we propose to represent visual content with approximate invariance to the above geometric trans- formations from a kernelized perspective. We extract CNN features on the detected object-like patches and aggregate these patch-level CNN features to form a vectorial repre- sentation with the Fisher vector model. The effectiveness of our proposed algorithm is demonstrated on image search application with three benchmark datasets.


title: A common role of insula in feelings, empathy and uncertainty

abstract: Although accumulating evidence highlights a crucial role of the insular cortex in feelings, empathy and processing uncertainty in the context of decision making, neuroscientific models of affective learning and decision making have mostly focused on structures such as the amygdala and the striatum. Here, we propose a unifying model in which insula cortex supports different levels of representation of current and predictive states allowing for error-based learning of both feeling states and uncertainty. This information is then integrated in a general subjective feeling state which is modulated by individual preferences such as risk aversion and contextual appraisal. Such mechanisms could facilitate affective learning and regulation of body homeostasis, and could also guide decision making in complex and uncertain environments.


title: iCloudAccess: Cost-Effective Streaming of Video Games From the Cloud With Low Latency

abstract: As a new paradigm, cloud gaming allows users to play high-end video games instantly without downloading or installing the original game software. In this paper, we first conduct a series of well-designed active and passive measurements on a large-scale cloud gaming platform and identify the significant diversity in the queueing delay and response delay among users. We note that the latency problem largely results from user-specified request routing and inelastic server provisioning. To address latency problem of the cloud gaming platform, we further propose an online control algorithm called iCloudAccess to perform intelligent request dispatching and server provisioning. Our main objective is to cut down the provisioning cost of cloud gaming service providers while still ensuring the user quality-of-experience requirements. We formulate the problem as a constrained stochastic optimization problem and apply the Lyapunov optimization theory to derive the online control algorithm with provable upper bounds. We also conduct extensive trace-driven simulations to evaluate the effectiveness of our algorithm, and our results show that our proposed algorithm achieves significant gain over other alternative approaches.


title: Redefining A in RGBA: Towards a Standard for Graphical 3D Printing

abstract: Advances in multimaterial 3D printing have the potential to reproduce various visual appearance attributes of an object in addition to its shape. Since many existing 3D file formats encode color and translucency by RGBA textures mapped to 3D shapes, RGBA information is particularly important for practical applications. In contrast to color (encoded by RGB), which is specified by the object’s reflectance, selected viewing conditions, and a standard observer, translucency (encoded by A) is neither linked to any measurable physical nor perceptual quantity. Thus, reproducing translucency encoded by A is open for interpretation. ::: ::: In this article, we propose a rigorous definition for A suitable for use in graphical 3D printing, which is independent of the 3D printing hardware and software, and which links both optical material properties and perceptual uniformity for human observers. By deriving our definition from the absorption and scattering coefficients of virtual homogenous reference materials with an isotropic phase function, we achieve two important properties. First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is rescaled for printing. Second, determining the value of A for a real (potentially non-homogenous) material, can be achieved by minimizing a distance function between light transport measurements of this material and simulated measurements of the reference materials. Such measurements can be conducted by commercial spectrophotometers used in graphic arts. ::: ::: Finally, we conduct visual experiments employing the method of constant stimuli, and we derive from them an embedding of A into a nearly perceptually uniform scale of translucency for the reference materials.


High Sensitivity Color CMOS Image Sensor with WRGB Color Filter Array and Color Separation Process Using Edge Detection


title: A Test Collection for Research on Depression and Language Use

abstract: Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. However, there is a lack of publicly available data for doing research on the interaction between language and depression. In this paper, we describe our first steps to fill this gap. We outline the methodology we have adopted to build and make publicly available a test collection on depression and language use. The resulting corpus includes a series of textual interactions written by different subjects. The new collection not only encourages research on differences in language between depressed and non-depressed individuals, but also on the evolution of the language use of depressed individuals. Further, we propose a novel early detection task and define a novel effectiveness measure to systematically compare early detection algorithms. This new measure takes into account both the accuracy of the decisions taken by the algorithm and the delay in detecting positive cases. We also present baseline results with novel detection methods that process users’ interactions in different ways.


title: Mesh Parameterization With A Virtual Boundary

abstract: Parameterization of a 3D triangular mesh is a fundamental problem in various applications of meshes. The convex combination approach is widely used for parameterization because of its good properties, such as fast computation and one-to-one embedding. However, the approach has a drawback: most boundary triangles have high distortion in the embedding compared with interior ones. In this paper, we present an extension of the convex combination approach that resolves the drawback by using a virtual boundary. Since the virtual boundary is fixed onto a given convex polygon instead of the real boundary, the real boundary triangles can better reflect the shape of the corresponding 3D triangles. The proposedapproach obtains a parameterization of a 3D mesh with less d istortion than with the original convex combination approach. r 2002 Elsevier Science Ltd. All rights reserved.


title: Risks and complications in rhinoplasty

abstract: Rhinoplasty is regarded to be associated with many risks as the expectations of patient and physician are not always corresponding. Besides of postoperative deformities many other risks and complications have to be considered. Reduction-rhinoplasty e.g. can cause breathing disturbances which are reported in 70% of all revision-rhinoplasty-patients. One has to be aware however that scars and loss of mucosal-sensation can also give the feeling of a "blocked nose". The main risks of autogenous transplants are dislocation and resorption, while alloplasts can cause infection and extrusion. In this respect silicone implants can have a complication rate between 5-20%. Less complications are reported with other materials like Gore-Tex. Complications of skin and soft tissues can be atrophy, fibrosis, numbness, cysts originating from displaced mucosa or subcutaneous granulomas caused by ointment material. Postoperative swelling depends mainly on the osteotomy technique. Percutaneous osteotomies cause less trauma, but may result in visible scars. Infections are rare but sometimes life-threatening (toxic-shock-syndrome). The risk is higher, when sinus surgery and rhinoplasty are combined. Osteotomies can also cause injuries of the orbital region. Necrosis of eye-lids by infections and blindness by central artery occlusion are known. There are reports on various other risks like rhinoliquorrhea, brain damage, fistulas between sinus-cavernosus and carotid artery, aneurysms and thrombosis of the cavernous sinus. Discoloration of incisors are possible by damage of vessels and nerves. Rhinoplasty can also become a court-case in dissatisfied patients, a situation that may be called a "typical complication of rhinoplasty". It can be avoided by proper patient selection and consideration of psychological disturbances. Postoperative deformities are considered as main risks of rhinoplasty, causing revision surgery in 5% to 15% of the cases. The analysis of postoperative deformities allowes the identification of specific risks. The most frequent postoperative deformity is the "pollybeak" when a deep naso-frontal angle, cartilaginous hump and reduced tip projection are present preoperatively. The pollybeak is the indication in about 50% of all revision rhinoplasties. Other frequent postoperative deformities are a pendant and wide nasal tip, retractions of the columella base or irregularities of the nasal dorsum. These deformities are very often combined and caused by a loss of septal support. This is why the stability of the caudal septum in septorhinoplasty is the key for a predictable result. Maintaining the position of the tip and the columella is one of the main issues to avoid typical postoperative deformities. The risks for rhinoplasty-complications can be reduced with increasing experience. A prerequisite is continuing education and an earnest distinction between complication and mistake.


title: Transmit Power Control for D2D-Underlaid Cellular Networks Based on Statistical Features

abstract: By allowing two nearby mobile devices to communicate via a direct link between them, device-to-device (D2D) communications can efficiently increase the capacity of D2D-underlaid cellular networks, in which D2D and cellular communications share the same frequency band. However, the coexistence of D2D and cellular communications in the same frequency band will cause interference between the D2D and cellular users, thus degrading their performance. One may control the transmit power based on the real-time channel-state information (CSI) to mitigate this interference, but it is difficult to acquire the real-time CSI between D2D/cellular transmitters and D2D receivers. To overcome this difficulty, we propose novel statistical-feature-based power control (SFPC), which determines the transmit power based on statistical (instead of real-time) CSI. The proposed SFPC combines the power control—that is aware of D2D success likelihood—with opportunistic access control to 1) reduce the interference caused by D2D communications and 2) maximize the area spectral efficiency of D2D communications. Specifically, it aims to minimize the D2D transmit power and optimize the selection of access threshold. Our simulation results show that SFPC can increase both the cellular communication success probability and the energy efficiency of D2D communications, as well as reduce the average D2D transmit power.


title: Performance analysis of multi-user MIMO downlink system in time-varying channels

abstract: The channel state information (CSI) at the base station (BS) in multi-user multiple-input multiple-output (MU-MIMO) downlink system can provide a significant performance enhancement in terms of data transmission rate and interference reduction. However due to the time varying nature of the channel the available CSI at the BS can be outdated and this leads to the performance loss. This paper provides an analysis of the effect of outdated CSI on MU-MIMO downlink system when mobile stations (MSs) experience different speeds and how the channel prediction algorithms can be used to compensate for these delays in the time-varying channel. The simulation results show that even few samples delay can have a profound effect on bit error rate (BER) performance in MU-MIMO downlink system and the channel prediction algorithms can be used to compensate these delays to improvise the performance. For the first time, we propose to use normalized least mean square (NLMS) and recursive least square (RLS) adaptive filters for predicting the channels in MU-MIMO downlink system. Compared to the conventional linear extrapolation channel prediction method, both NLMS and RLS provided better performance, with RLS performing better with an increased computational complexity.


title: Ordered small multiple treemaps for visualizing time-varying hierarchical pesticide residue data

abstract: Small multiples can visually enforce comparisons of changes or differences among objects, revealing potential patterns by providing different views. According to the analyzing requirements in food safety fields and characteristics of pesticide residue detection data, in this paper, we propose a novel visualization approach to explore and analyze the time-varying hierarchical data, which is called ordered small multiple treemaps (OSMT). Inspired by the thought of querying an array by rows or columns, OSMT makes it possible to locate a specific node in the treemap layout by using a unique location 2-tuple and keep a relative stable order of nodes in the layout while we detecting temporal patterns. This algorithm enables the visual representation of the node values varying with time, preserving the hierarchical relationships among nodes in the meanwhile. Based on some interaction techniques (filtering, selecting, highlighting and zooming, etc.), OSMT can help users find some specific changes more easily and thus make corresponding decisions with more efficiency. Besides, we also propose a new metric called TVA (Ability of tracking time-varying data in treemap) with a purpose of evaluating different kinds of treemap layout algorithms from the aspect of the difficulty level for tracking time-varying nodes in the overall layout. Finally, our technique's applicability is demonstrated on the pesticide residues detection results dataset in this study.


title: New radar waveform based on a chirp sequence

abstract: For all radar applications it is required to measure the target range R and radial velocity v r simultaneously and unambiguously with high accuracy and resolution even in multi target situations. In this paper, a new radar waveform is proposed, which is based on a chirp sequence. In the considered case, each chirp signal has a very short duration T chirp . Therefore, the measured beat frequency f B is dominated by the target range R and is less influenced by the radial velocity v r . However, the classical chirp sequence waveform suffers from possible ambiguities in the velocity measurement. It is, therefore, the objective of this paper to modify the classical chirp sequence based on an additional frequency modulation from chirp to chirp to get an unambiguous velocity measurement even in multi target situations.


title: Confer: A Conference Recommendation and Meetup Tool

abstract: One of the primary goals of academic conferences is to promote scientific exchange of advances among people who may otherwise not have the opportunity to hear from one another. We present Confer, a tool designed to help conference attendees find interesting papers and talks, dis-cover and meet people with shared interests, and manage their time using a personalized schedule for the conference. So far, we have deployed Confer to 17 academic confer-ences including several years of CHI and CSCW. Confer is also the primary program tool for CSCW 2016. Log analysis and survey results have shown that the tool helps confer-ence attendees find interesting papers and manage their schedule. Furthermore the recommendation data gener-ated from the tool has been used by conference organizers to help plan conference schedules as well as to organize social gatherings. Finally, the meetup feature seeks to help conference attendees reach out to other attendees with similar interests.


title: Fractured Images of Science, Language, and Power: A Postmodern Optic, or Just Bad Eyesight?

abstract: Almost four centuries ago, Francis Bacon put forth a vision of a kind of knowledge, a veridical reading of the Book of Nature that would provide man with the power to bring nature to her knees, that would establish him in his proper dominion through the development of the techniques to effect mastery over nature. In the intervening centuries, Bacon's rhetoric of dominion served simultaneously to describe and to foster the growth of a social institution that succeeded in transforming that vision into material reality. In the late twentieth century, modern science has come into its maturity, producing technologies next to which Bacon's own vision seems almost puerile. None of the early architects of modern science ever anticipated a day when men would acquire the kind of control over nature that would enable them, should they so choose, to destroy the human species or, if not destroy it, to shape its future according to their fantasies of a personal best. With a few exceptions, it was not until the twentieth century that men dared to dream of the power over life and death that is today enabled by modern physics and promised by modern biology.


title: Action recognition by saliency-based dense sampling

abstract: Action recognition, aiming to automatically classify actions from a series of observations, has attracted more attention in the computer vision community. The state-of-the-art action recognition methods utilize dense sampled trajectories to build feature representations. However, their performances are limited due to action region clutters and camera motions in real world applications. No matter how the scenario changes in different backgrounds, the salient cues of actions are highly dependent on their appearances and motions. Based on this discovery, in this paper we propose a novel saliency-based dense sampling strategy named improved dense trajectories (iDT) on salient region-based contrast boundary (iDT-RCB). Without any external human detector, a robust mask is generated to overcome the limitations of global contrast based saliency in action sequences. Warped optical flow is exploited to adjust the interest points sampling to remove subtle motions. We show that an appropriate pruning of feature points can achieve a good balance between saliency and density of the sampled points. Experiments conducted on three benchmark datasets have demonstrated the effectiveness of the proposed method. More specifically, the fusion of deep-learned features and our hand-crafted features can even improve the recognition performance over baseline dense sampling methods. In particular, the fusion scheme achieves the state-of-the-art accuracy at 73.8% and 94.8% on Hollywood2 and UCF50, respectively.


title: Supervised classification of combined copy number and gene expression data

abstract: Summary In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.


title: Geo-spatial aerial video processing for scene understanding and object tracking

abstract: This paper presents an approach to extracting and using semantic layers from low altitude aerial videos for scene understanding and object tracking. The input video is captured by low flying aerial platforms and typically consists of strong parallax from non-ground-plane structures. A key aspect of our approach is the use of geo-registration of video frames to reference image databases (such as those available from Terraserver and Google satellite imagery) to establish a geo-spatial coordinate system for pixels in the video. Geo-registration enables Euclidean 3D reconstruction with absolute scale unlike traditional monocular structure from motion where continuous scale estimation over long periods of time is an issue. Geo-registration also enables correlation of video data to other stored information sources such as GIS (geo-spatial information system) databases. In addition to the geo-registration and 3D reconstruction aspects, the key contributions of this paper include: (1) exploiting appearance and 3D shape constraints derived from geo-registered videos for labeling of structures such as buildings, foliage, and roads for scene understanding, and (2) elimination of moving object detection and tracking errors using 3D parallax constraints and semantic labels derived from geo-registered videos. Experimental results on extended time aerial video data demonstrates the qualitative and quantitative aspects of our work.


Big Data: Astronomical or Genomical?


title: The social cognitive model of job satisfaction among teachers: Testing and validation

abstract: Abstract The study empirically tests an integrative model of work satisfaction ( Lent and Brown, 2006 , Duffy and Lent, 2009 , Lent et al., 2008 , Lent et al., 2011 ) in a sample of 5,022 teachers in Abu Dhabi in the United Arab Emirates. The study provided more support for the Lent and Brown (2006) model. Results revealed that this model was a strong fit for the data and accounted for 82% of the variance in work satisfaction. Of the five predictor classes, work conditions, goal progress, and positive affect were each found to explain unique predictive variance. This suggests that teachers who are most satisfied with their jobs see their work environment as supportive, experience positive goal progress, and report high levels of trait positive affect. Self-efficacy was related indirectly to work satisfaction (via work conditions and via goal progress). Goal support was also related indirectly to work satisfaction (via work conditions, and via self efficacy, but through goal progress. Implications of the findings for future research and efforts to promote teachers’ job satisfaction in Abu Dhabi are discussed.


title: Extracellular Polymeric Matrix Production and Relaxation under Fluid Shear and Mechanical Pressure inStaphylococcus aureusBiofilms

abstract: ABSTRACT The viscoelasticity of a biofilm9s EPS (extracellular polymeric substance) matrix conveys protection against mechanical challenges, but adaptive responses of biofilm inhabitants to produce EPS are not well known. Here, we compare the responses of a biofilm of an EPS-producing (ATCC 12600) and a non-EPS producing (5298) Staphylococcus aureus strain to fluid shear and mechanical challenge. Confocal laser scanning microscopy confirmed absence of calcofluor-white-stainable EPS in biofilms of S. aureus 5298. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with tribometry indicated that polysaccharide production per bacterium in the initial adhering layer was higher during growth at high shear than at low shear and that this increased EPS production extended to entire biofilms, as indicated by tribometrically measured coefficients of friction (CoF). CoF of biofilms grown under high fluid shear were higher than those when grown under low shear, likely due to wash-off polysaccharides. Measurement of a biofilm9s CoF implies application of mechanical pressure that yielded an immediate increase in the polysaccharide band area of S. aureus ATCC 12600 biofilms due to their compression. Compression decreased after relief of pressure to the level observed prior to mechanical pressure. For biofilms grown under high shear, this coincided with a higher percent whiteness in optical coherence tomography-images indicative of water outflow, returning back into the biofilm during stress relaxation. Biofilms grown under low shear, however, were stimulated during tribometry to produce EPS, also after relief of stress. Knowledge of factors that govern EPS production and water flow in biofilms will allow better control of biofilms under mechanical challenge and better understanding of the barrier properties of biofilms against antimicrobial penetration. IMPORTANCE Adaptive responses of biofilm inhabitants in nature to environmental challenges such as fluid shear and mechanical pressure often involve EPS production with the aim of protecting biofilm inhabitants. EPS can assist biofilm bacteria in remaining attached or can impede antimicrobial penetration. The TriboChemist is a recently introduced instrument, allowing the study of initially adhering bacteria to a germanium crystal using ATR-FTIR spectroscopy, while simultaneously allowing measurement of the coefficient of friction of a biofilm, which serves as an indicator of the EPS content of a biofilm. EPS production can be stimulated by both fluid shear during growth and mechanical pressure, while increased EPS production can continue after pressure relaxation of the biofilm. Since EPS is pivotal in the protection of biofilm inhabitants against mechanical and chemical challenges, knowledge of the factors that make biofilm inhabitants decide to produce EPS, as provided in this study, is important for the development of biofilm control measures.


title: Learning Operations on a Stack with Neural Turing Machines

abstract: Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed recently to address the difficulty of storing information over long time periods. In this paper, we experiment with the capacity of Neural Turing Machines (NTMs) to deal with these long-term dependencies on well-balanced strings of parentheses. We show that not only does the NTM emulate a stack with its heads and learn an algorithm to recognize such words, but it is also capable of strongly generalizing to much longer sequences.


title: $k$-Nearest Neighbor Augmented Neural Networks for Text Classification

abstract: In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from $k$-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize $k$-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.


title: Anatomical study of myofascial continuity in the anterior region of the upper limb.

abstract: Fifteen unembalmed cadavers were dissected in order to study the "anatomical continuity" between the various muscles involved in the movement of flexion of the upper limb. This study demonstrated the existence of specific myofascial expansions, with a nearly constant pattern, which originate from the flexor muscles and extend to the overlying fascia. The clavicular part of the pectoralis major sends a myofascial expansion, with a mean length of 3.6cm, to the anterior region of the brachial fascia, and the costal part sends one to the medial region of the brachial fascia (mean length: 6.8cm). The biceps brachii presents two expansions: the lacertus fibrosus, oriented medially, with a mean height of 4.7cm and a base of 1.9cm, and a second, less evident, longitudinal expansion (mean length: 4.5cm, mean width: 0.7cm). Lastly, the palmaris longus sends an expansion to the fascia overlying the thenar muscles (mean length: 1.6cm, mean width: 0.5cm). During flexion, as these muscles contract, the anterior portion of the brachial and antebrachial fascia is subject to tension. As the fascia is rich in proprioceptive nerve endings, it is hypothesized that this tension activates a specific pattern of receptors, contributing to perception of motor direction. If the muscular fascia is in a non-physiological state, these mechanisms are altered, and the proprioceptors in the fascia may be incorrectly activated, thus giving rise to many types of extra-articular pain.


title: Analysis of the Modulation Strategy for the Minimization of the Leakage Current in the PV Grid-Connected Cascaded Multilevel Inverter

abstract: This paper presents a pulse width modulation (PWM) technique for the minimization of the leakage current in the grid-connected/stand-alone transformerless photovoltaic (PV)-cascaded multilevel inverter (CMLI). The proposed PWM technique is integrated with the MPPT algorithm and is applied to the five-level CMLI. Furthermore, using the proposed PWM technique the high-frequency voltage transitions in the terminal and common mode voltages are minimized. Thus, the proposed PWM technique minimizes the leakage current of the PV array and electromagnetic interference filter requirement in the system without addition of any extra switches. Furthermore, this paper also presents the analysis for the terminal voltage across the PV array and the common mode voltage of the inverter based on the switching function. Using the given analysis, the effect of the PWM technique can be analyzed, as it directly links the switching function with the common mode voltage and leakage current. Also, the proposed PWM technique requires reduced number of carrier waves compared to the conventional sinusoidal pulse width modulation technique for the given CMLI. Complete details of the working principle and analysis with the support of simulation and experimental results of the proposed PWM technique are presented in this paper.


title: Multi-Scale Patch-Based Image Restoration

abstract: Many image restoration algorithms in recent years are based on patch processing. The core idea is to decompose the target image into fully overlapping patches, restore each of them separately, and then merge the results by a plain averaging. This concept has been demonstrated to be highly effective, leading often times to the state-of-the-art results in denoising, inpainting, deblurring, segmentation, and other applications. While the above is indeed effective, this approach has one major flaw: the prior is imposed on intermediate (patch) results, rather than on the final outcome, and this is typically manifested by visual artifacts. The expected patch log likelihood (EPLL) method by Zoran and Weiss was conceived for addressing this very problem. Their algorithm imposes the prior on the patches of the final image , which in turn leads to an iterative restoration of diminishing effect. In this paper, we propose to further extend and improve the EPLL by considering a multi-scale prior. Our algorithm imposes the very same prior on different scale patches extracted from the target image. While all the treated patches are of the same size, their footprint in the destination image varies due to subsampling. Our scheme comes to alleviate another shortcoming existing in patch-based restoration algorithms—the fact that a local (patch-based) prior is serving as a model for a global stochastic phenomenon. We motivate the use of the multi-scale EPLL by restricting ourselves to the simple Gaussian case, comparing the aforementioned algorithms and showing a clear advantage to the proposed method. We then demonstrate our algorithm in the context of image denoising, deblurring, and super-resolution, showing an improvement in performance both visually and quantitatively.


title: Parking monitor system based on magnetic field senso

abstract: To easily find an unoccupied parking space in a large car park is a problem for many drivers. Thus it is useful to have technical solutions which can provide information on parking space occupancy. A new monitor system is described in the following. It is based on passive magnetic field sensors. It provides occupancy information for car park users and helps them to place the car in a most efficient way


title: Beam Codebook Based Beamforming Protocol for Multi-Gbps Millimeter-Wave WPAN Systems

abstract: In this paper, we propose a feasible beamforming (BF) scheme realized in media access control (MAC) layer following the guidelines of the IEEE 802.15.3c criteria for millimeter-wave 60GHz wireless personal area networks (60GHz WPANs). The proposed BF targets to minimize the BF set-up time and mitigates the high path loss of 60GHz WPAN systems. It is based on designed multi-resolution codebooks, which generate three kinds of patterns of different half power beam widths (HPBWs): quasi-omni pattern, sector and beam. These three kinds of patterns are employed in the three stages of the BF protocol, namely device-to-device (DEV-to-DEV) linking, sector-level searching and beam-level searching. All the three stages can be completed within one superframe, which minimizes the potential interference to other systems during BF set-up period. In this paper, we show some example codebooks and provide the details of BF procedure. Simulation results show that the setup time of the proposed BF protocol is as small as 2% when compared to the exhaustive searching protocol. The proposed BF is a complete design, it re-uses commands specified in IEEE 802.15.3c, completely compliant to the standard; It has thus been adopted by IEEE 802.15.3c as an optional functionality to realize Giga-bit-per-second (Gbps) communication in WPAN Systems.


title: A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees

abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.


title: Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers

abstract: We report improved AMR parsing results by adding a new action to a transitionbased AMR parser to infer abstract concepts and by incorporating richer features produced by auxiliary analyzers such as a semantic role labeler and a coreference resolver. We report final AMR parsing results that show an improvement of 7% absolute in F1 score over the best previously reported result. Our parser is available at: https://github.com/ Juicechuan/AMRParsing


title: The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun

abstract: This paper argues for the recognition of important experiential aspects of consumption. Specifically, a general framework is constructed to represent typical consumer behavior variables. Based on this paradigm, the prevailing information processing model is contrasted with an experiential view that focuses on the symbolic, hedonic, and esthetic nature of consumption. This view regards the consumption experience as a phenomenon directed toward the pursuit of fantasies, feelings, and fun.


title: Trust evaluation between users of social networks using the quality of service requirements and call log histories

abstract: Purpose ::: ::: ::: ::: ::: As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.


title: Multiple Instance Learning for Malware Classification

abstract: This work addresses classification of unknown binaries executed in sandbox by modeling their interaction with system resources (files, mutexes, registry keys and communication with servers over the network) and error messages provided by the operating system, using vocabulary-based method from the multiple instance learning paradigm. It introduces similarities suitable for individual resource types that combined with an approximative clustering method efficiently group the system resources and define features directly from data. This approach effectively removes randomization often employed by malware authors and projects samples into low-dimensional feature space suitable for common classifiers. An extensive comparison to the state of the art on a large corpus of binaries demonstrates that the proposed solution achieves superior results using only a fraction of training samples. Moreover, it makes use of a source of information different than most of the prior art, which increases the diversity of tools detecting the malware, hence making detection evasion more difficult.


title: Recent Developments of Liposomes as Nanocarriers for Theranostic Applications

abstract: Liposomes are nanocarriers comprised of lipid bilayers encapsulating an aqueous core. The ability of liposomes to encapsulate a wide variety of diagnostic and therapeutic agents has led to significant interest in utilizing liposomes as nanocarriers for theranostic applications. In this review, we highlight recent progress in developing liposomes as nanocarriers for a) diagnostic applications to detect proteins, DNA, and small molecule targets using fluorescence, magnetic resonance, ultrasound, and nuclear imaging; b) therapeutic applications based on small molecule-based therapy, gene therapy and immunotherapy; and c) theranostic applications for simultaneous detection and treatment of heavy metal toxicity and cancers. In addition, we summarize recent studies towards understanding of interactions between liposomes and biological components. Finally, perspectives on future directions in advancing the field for clinical translations are also discussed.


title: Latent Fingerprint Matching

abstract: Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.


title: Pragmatic approach to structured data querying via natural language interface

abstract: As the use of technology increases and data analysis becomes integral in many businesses, the ability to quickly access and interpret data has become more important than ever. Information retrieval technologies are being utilized by organizations and companies to manage their information systems and processes. Despite information retrieval of a large amount of data being efficient organized in relational databases, a user still needs to master the DB language/schema to completely formulate the queries. This puts a burden on organizations and companies to hire employees that are proficient in DB languages/schemas to formulate queries. To reduce some of the burden on already overstretched data teams, many organizations are looking for tools that allow non-developers to query their databases. Unfortunately, writing a valid SQL query that answers the question a user is trying to ask isn't always easy. Even seemingly simple questions, like "Which start-up companies received more than $200M in funding?" can actually be very hard to answer, let alone convert into a SQL query. How do you define start-up companies? By size, location, duration of time they have been incorporated? This may be fine if a user is working with a database they're already familiar with, but what if users are not familiar with the database. What is needed is a centralized system that can effectively translate natural language queries into specific database queries for different customer database types. There is a number of factors that can dramatically affect the system architecture and the set of algorithms used to translate NL queries into a structured query representation.


title: Fractal feature analysis and classification in medical imaging.

abstract: Following B.B. Mandelbrot's fractal theory (1982), it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion is discussed. Two applications are found: (1) classification; (2) edge enhancement and detection. For the purpose of classification, a normalized fractional Brownian motion feature vector is defined from this estimation concept. It represented the normalized average absolute intensity difference of pixel pairs on a surface of different scales. The feature vector uses relatively few data items to represent the statistical characteristics of the medial image surface and is invariant to linear intensity transformation. For edge enhancement and detection application, a transformed image is obtained by calculating the fractal dimension of each pixel over the whole medical image. The fractal dimension value of each pixel is obtained by calculating the fractal dimension of 7x7 pixel block centered on this pixel.


title: Derivation and verification of synergy coordinates for the DLR hand arm system

abstract: Dexterous robotic hands are still not used in production lines, mainly because of the complexity to operate such hands. Most often a robotic hand is used to grasp and hold the object of interest while the object motion is performed by the arm. From studies on human grasping it is known that most of the grasping task can be reduced to two independent degree of freedom which drive the joints of the hand. Based on this idea, we analyzed a robotic grasp database to find suitable robotic “synergy coordinates”. 77% of these grasps can be represented by two coordinates that were originally defined by the 19 joint variables of the DLR Hand Arm System. In this work we also discuss the importance of the grasp approach configurations, which have significant influence on the results. The synergy coordinates were used to extend the joint level impedance controller and to provide a complexity reduced interface to an user or a planning algorithm, which leads to simplified operation of the hand and to reduced teach-in-times. From a different perspective this concept allows to imitate the behavior of a synergistic, respectively underactuated, hand by means of programming. The controller was evaluated successfully on the DLR Hand Arm System by realizing several different grasps covering a simplified grasping taxonomy. The DLR Hand Arm System has additional degrees of freedom to adjust the mechanical joint stiffness, which was used to modify the grasp force at a given pinch grasp configuration.


title: LPV subspace identification of the edgewise vibrational dynamics of a wind turbine rotor

abstract: In this paper we apply a state-of-the-art algorithm for subspace identification of linear parameter-varying (LPV) systems to identify the coupled dynamics of the drive-train and the edgewise bending motion of the rotor blades of three-bladed wind turbines. These dynamics are varying with the rotor speed. The identification algorithm uses a factorization which makes it possible to form predictors based on past inputs, outputs, and the known rotor speed. The predictors contain the LPV equivalent of the Markov parameters. Using the predictors, ideas from Predictor Based Subspace IDentification (PBSID) were developed to estimate the state sequence from which the LPV system matrices can be constructed. The algorithm was applied not only to synthetic data generated by a computer simulation of a reference wind turbine, but also to data measured from the CART3 research wind turbine at the National Wind Technology Center of the National Renewable Energy Laboratory (NREL). This paper demonstrates that the linear time-varying behavior of the aeroelastic dynamics of the wind turbine rotor can be captured in an LPV model identified with measured input-output data.


title: Fast Embedded Software Hashing

abstract: We present new software speed records for several popular hash functions on low-end 8-bit AVR microcontrollers. Target algorithms include widely deployed hash functions like SHA-1 and SHA-256 as well as the SHA-3 (second round) candidates Blake-32 and Skein-256. A significant aspect of our implementations is that they reduce the overall resource requirements, improving not only execution time but also RAM footprint and sometimes ROM/Flash memory footprint at the same time, providing the best memory/performance trade-offs reported so far. We believe that our results will shed new light on the ongoing SHA-3 competition, and be helpful for the final stage of the competition.


title: Policy-based self-adaptive architectures: a feasibility study in the robotics domain

abstract: Robotics is a challenging domain which sometimes exhibits a clear need for self-adaptive capabilities, as such functionality offers the potential for robots to account for their unstable and unpredictable deployment domains. This paper focuses on a feasibility study in applying a policy- and architecture-based approach to the development of self-adaptive robotic systems. We describe two case studies in which we construct self-adaptive Robocode and Mindstorms robots, report on our development experiences, and discuss the challenges we encountered. The paper establishes that it is feasible to apply our approach to the robotics domain, contributes a discussion of the architectural issues we encountered, and further evaluates our general-purpose approach.


title: Ultra-Wideband Crossover Using Microstrip-to-Coplanar Waveguide Transitions

abstract: The design of an ultra-wideband crossover for use in printed microwave circuits is presented. It employs a pair of broadside-coupled microstrip-to-coplanar waveguide (CPW) transitions, and a pair of uniplanar microstrip-to-CPW transitions. A lumped-element equivalent circuit is used to explain the operation of the proposed crossover. Its performance is evaluated via full-wave electromagnetic simulations and measurements. The designed device is constructed on a single substrate, and thus, it is fully compatible with microstrip-based microwave circuits. The crossover is shown to operate across the frequency band from 3.1 to 11 GHz with more than 15 dB of isolation, less than 1 dB of insertion loss, and less than 0.1 ns of deviation in the group delay.


Discovering New Biological Resources: Chance or Reason?


title: Clustering of Local Optima in Combinatorial Fitness Landscapes

abstract: Using the recently proposed model of combinatorial landscapes: local optima networks, we study the distribution of local optima in two classes of instances of the quadratic assignment problem. Our results indicate that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the optima networks possess a clear modular structure, while the networks belonging to the class of random uniform instances are less well partitionable into clusters. We briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.


title: Passive adaptive grasp multi-fingered humanoid robot hand with high under-actuated function

abstract: This paper proposed a design idea of a novel under-actuated finger mechanism, and designed the finger mechanism. The finger has no actuator in itself, is only driven by the other finger joints and object grasped. The finger is similar to a human finger and can be easily arranged in series to realize a finger with super under-actuation and high integration. It can be mounted in humanoid robot hand to make the hand obtain more DOFs with less actuators, and good grasping function of shape adaptation, decrease the requirement of control system. This paper analyzed the relationship between the grasping force of the finger and its design parameters, proposed the design principle of structure optimization of the finger. Based on the finger, a multi-fingered humanoid robot hand: TH-2 Hand has been designed. TH-2 Hand has many excellent features: high personification, super under-actuation and be very compact, easy to real-time control, small volume, light in weight, strong grasping function, etc.


title: Towards Real-Time Unsupervised Monocular Depth Estimation on CPU

abstract: Unsupervised depth estimation from a single image is a very attractive technique with several implications in robotic, autonomous navigation, augmented reality and so on. This topic represents a very challenging task and the advent of deep learning enabled to tackle this problem with excellent results. However, these architectures are extremely deep and complex. Thus, real-time performance can be achieved only by leveraging power-hungry GPUs that do not allow to infer depth maps in application fields characterized by low-power constraints. To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image. Similarly to state-of-the-art, we train our network in an unsupervised manner casting depth estimation as an image reconstruction problem. Extensive experimental results on the KITTI dataset show that compared to the top performing approach our network has similar accuracy but a much lower complexity (about 6% of parameters) enabling to infer a depth map for a KITTI image in about 1.7 s on the Raspberry Pi 3 and at more than 8 Hz on a standard CPU. Moreover, by trading accuracy for efficiency, our network allows to infer maps at about 2 Hz and 40 Hz respectively, still being more accurate than most state-of-the-art slower methods. To the best of our knowledge, it is the first method enabling such performance on CPUs paving the way for effective deployment of unsupervised monocular depth estimation even on embedded systems.


title: CVSS Attack Graphs

abstract: Attack models and attack graphs are efficient tools to describe and analyse attack scenarios aimed at computer networks. More precisely, attack graphs give all possible scenarios for an attacker to reach a certain goal, exploiting vulnerabilities of the targeted network. Nevertheless they give no information about the damages induced by these attacks, nor about the probability of exploitation of these scenarios. In this paper, we propose to combine attack graphs and CVSS framework, in order to add damage and exploitability probability information. Then, we define a notion of risk for each attack scenario, which is based on quantitative information added to attack graphs.


Accelerating Multi-agent Reinforcement Learning with Dynamic Co-learning


title: Generating Textual Adversarial Examples for Deep Learning Models: A Survey

abstract: With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs were vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples for image DNNs, research efforts on attacking DNNs for textual applications emerges in recent years. However, existing perturbation methods for images cannotbe directly applied to texts as text data is discrete. In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs. We collect, select, summarize, discuss and analyze these works in a comprehensive way andcover all the related information to make the article self-contained. Finally, drawing on the reviewed literature, we provide further discussions and suggestions on this topic.


title: Syntactic parsing of chat language in contact center conversation corpus

abstract: Chat language is often referred to as Computer-mediated communication (CMC). Most of the previous studies on chat language has been dedicated to collecting " chat room " data as it is the kind of data which is the most accessible on the WEB. This kind of data falls under the informal register whereas we are interested in this paper in understanding the mechanisms of a more formal kind of CMC: dialog chat in contact centers. The particularities of this type of dialogs and the type of language used by customers and agents is the focus of this paper towards understanding this new kind of CMC data. The challenges for processing chat data comes from the fact that Natural Language Processing tools such as syntactic parsers and part of speech taggers are typically trained on mismatched conditions, we describe in this study the impact of such a mismatch for a syntactic parsing task.


title: A Comparative Analysis of Classification Algorithms for Students College Enrollment Approval Using Data Mining

abstract: In big data universities, there may be several problems related to college admission and enrollment due to the increasing volume of students' data applying for higher education. So that there is a need to apply efficient data mining algorithms for better decision making for students' data classification. In this paper, nine classification algorithms are comparatively tested to find the optimum algorithm for students' dataset classification. The KAUODUS+ data base for the preparatory year students are used as an approval dataset for the experimental purposes. The Weka-knowledge analysis tool which is open source data mining workbench software is used for simulation of practical measurements. The classification technique that has the potential to significantly improve the performance is suggested for use in colleges' admission and enrollment applications. Impact of students GPA and their grades qualified materials with respect to their colleges' admission desire are visually analyzed. Results show that C4.5, PART and Random Forest algorithms give the highest performance and accuracy with lowest errors while IBK-E and IBK-M algorithms give high errors and low accuracy.


title: Cylindrical panoramic image stitching method based on multi-cameras

abstract: Panoramic stitching technology is the focus of current panoramic technology, and cylindrical panoramas are commonly used because of their ease of capture and storage. Moreover it is a simple method for constructing panoramic video. This paper presents a cylindrical panoramic generation method based on multi-cameras. First, we use a backward-division model, which can rapidly solve the distortion of fish eye lens. Second, in order to maintain consistency of stitching, use the cylindrical projection. Third, we apply SIFT feature detection method to image mosaic process. To ensure the accuracy of matching, we use the RANSAC algorithm to purify the detected feature points. In addition, we use an image fusion method based on Lapalace pyramid. The original images captured by fisheye multi-cameras device are processed according to the procedure described in this paper, and then the cylindrical panoramic image is obtained.


title: Predicting HDD failures from compound SMART attributes

abstract: Hard Disk Drives (HDD s) sometimes fail with no apparent reason; some SMART (Self-Monitoring, Analysis and Reporting Technology) attributes present strong correlations with drive failures[3], yet a drive may also fail without (supposedly) any previous indication. Most of the host systems today utilize alert-methods which are reactive by nature-a drive is indicated to fail when some SMART attribute exceeds its vendor defined threshold for valid operation[4]. This approach does not take the cross correlation between different attributes into account and the fact that thresholds vary across different vendors. Unlike more conventional studies that focused on reliability statistics such as the annualized failure rate (AFR) at the population level[5], [6], we use machine learning (ML) algorithms that attempt to predict the failure of individual drives. Former ML approaches applied to the drive prediction failure domain include methods for dealing with sequential data, such as sliding windows and hidden Markov models [1], or anomaly detection algorithms, adhering to the often-low proportion of failed drives in the population [4] and [2]. We present a mechanism that performs a sophisticated samples aggregation inside a distributed database, allowing for the efficient extraction of compound features representing the behavior of the drive during a continuous time window prior its failure. We use here an open source dataset from BACKBLAZE, comprising extensive SMART information collected daily from a large drive population at its data center Thus we can use not only the last cumulative SMART counts but also include new features extracted over different time windows of drives last operational days. These capture the dynamics of the drives attributes, for example their growth rate which is highly indicative of the drive s failure probability [7]. Our results suggest that the use of compound features reduce the amount of false positives, which is primary performance measure for algorithms in the drive reliability domain[3], by as much as 60%. In a consecutive evaluation scenario a final decision is made based on a collection of the most recent data samples. This way we are able to capture more soon-to-fail drives at low cost to the false-positive rate. On average, a drive is predicted to fail long enough in advance (30 days) to allow for the modification of business strategy in fields such as drive replacement logistics. This approach, when implemented in a production environment may have a direct effect on business savings related to drive logistics.


title: An argument for increasing TCP's initial congestion window

abstract: TCP flows start with an initial congestion window of at most four segments or approximately 4KB of data. Because most Web transactions are short-lived, the initial congestion window is a critical TCP parameter in determining how quickly flows can finish. While the global network access speeds increased dramatically on average in the past decade, the standard value of TCP's initial congestion window has remained unchanged. In this paper, we propose to increase TCP's initial congestion window to at least ten segments (about 15KB). Through large-scale Internet experiments, we quantify the latency benefits and costs of using a larger window, as functions of network bandwidth, round-trip time (RTT), bandwidth-delay product (BDP), and nature of applications. We show that the average latency of HTTP responses improved by approximately 10% with the largest benefits being demonstrated in high RTT and BDP networks. The latency of low bandwidth networks also improved by a significant amount in our experiments. The average retransmission rate increased by a modest 0.5%, with most of the increase coming from applications that effectively circumvent TCP's slow start algorithm by using multiple concurrent connections. Based on the results from our experiments, we believe the initial congestion window should be at least ten segments and the same be investigated for standardization by the IETF.


title: Predictive coding: a fresh view of inhibition in the retina

abstract: Interneurons exhibiting centre--surround antagonism within their receptive fields are commonly found in peripheral visual pathways. We propose that this organization enables the visual system to encode spatial detail in a manner that minimizes the deleterious effects of intrinsic noise, by exploiting the spatial correlation that exists within natural scenes. The antagonistic surround takes a weighted mean of the signals in neighbouring receptors to generate a statistical prediction of the signal at the centre. The predicted value is subtracted from the actual centre signal, thus minimizing the range of outputs transmitted by the centre. In this way the entire dynamic range of the interneuron can be devoted to encoding a small range of intensities, thus rendering fine detail detectable against intrinsic noise injected at later stages in processing. This predictive encoding scheme also reduces spatial redundancy, thereby enabling the array of interneurons to transmit a larger number of distinguishable images, taking into account the expected structure of the visual world. The profile of the required inhibitory field is derived from statistical estimation theory. This profile depends strongly upon the signal: noise ratio and weakly upon the extent of lateral spatial correlation. The receptive fields that are quantitatively predicted by the theory resemble those of X-type retinal ganglion cells and show that the inhibitory surround should become weaker and more diffuse at low intensities. The latter property is unequivocally demonstrated in the first-order interneurons of the fly's compound eye. The theory is extended to the time domain to account for the phasic responses of fly interneurons. These comparisons suggest that, in the early stages of processing, the visual system is concerned primarily with coding the visual image to protect against subsequent intrinsic noise, rather than with reconstructing the scene or extracting specific features from it. The treatment emphasizes that a neuron's dynamic range should be matched to both its receptive field and the statistical properties of the visual pattern expected within this field. Finally, the analysis is synthetic because it is an extension of the background suppression hypothesis (Barlow & Levick 1976), satisfies the redundancy reduction hypothesis (Barlow 1961 a, b) and is equivalent to deblurring under certain conditions (Ratliff 1965).


title: Scattered data modeling

abstract: A variety of methods for modeling scattered data are discussed, with an emphasis on two types of data: volumetric and spherical. To demonstrate the performance of the various methods, results from an empirical study using trivariate scattered data are presented. The author's objective is to provide information to aid in selecting a type of method or to form the basis for customizing a method for a particular application. >


title: Design algorithm of grid-side LCL-filter for three-phase voltage source PWM rectifier

abstract: A design algorithm for grid-side LCL-filter of three-phase voltage source PWM rectifier is presented, which allows to use reduced values of inductance, improve system dynamic performance and reduce cost compared to traditional L-filter. These advantages are even more attractive in medium and high power applications. In this paper, the design criterion and calculation procedures are introduced in detail. A design example is reported, and the obtained LCL-filter has been realized and tested by simulation and experiments. Experimental results show that the obtained LCL-filter can provide sufficient attenuation of current harmonics and meanwhile ensure a high grid-side power factor. The goodness of this design method is demonstrated.


title: ESP8266 based implementation of wireless sensor network with Linux based web-server

abstract: A Wi-Fi wireless platform with embedded Linux web server and its integration into a network of sensor nodes for building automation and industrial automation is implemented here. In this system focus is on developing an ESP8266 based Low cost Wi-Fi based wireless sensor network, the IEEE 802.11n protocol is used for system. In most of the existing wireless sensor network are designed based on ZigBee and RF. The pecking order of the system is such that the lowest level is that of the sensors, the in-between level is the controllers, and the highest level is a supervisory node. The supervisor can be react as an active or passive. The system is shown to permit all achievable controller failure scenarios. The supervisor can handle the entire control load of all controllers, should the need arise. An integrated system platform which can provide Linux web server, database, and PHP run-time environment was built by using ARM Linux development board with Apache+PHP+SQLite3. Various Internet accesses were offered by using Wi-Fi wireless networks communication technology. Raspberry Pi use as a main server in the system and which connects the sensor nodes via Wi-Fi in the wireless sensor network and collects sensors data from different sensors, and supply multi-clients services including data display through an Embedded Linux based Web-Server.


title: Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy

abstract: INTRODUCTION ::: Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. ::: ::: ::: METHOD ::: We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. ::: ::: ::: RESULTS ::: We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408). ::: ::: ::: DISCUSSION ::: We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.


title: Design and Analysis of LTCC-Integrated Planar Microstrip-to-Waveguide Transition at 300 GHz

abstract: A 300-GHz planar microstrip-to-waveguide transition in low-temperature co-fired ceramic (LTCC) is numerically and experimentally analyzed. A via fence and air holes are used to form a three-dimensional transition structure and a vertical hollow waveguide inside the multilayer LTCC substrate. The transition consists of a coplanar-waveguide-fed slot radiator backed by a short-wall of the waveguide and an open-circuited microstrip resonator. On the basis of the dual operation mechanism, the design guideline for the complex 3D structure is described using full wave analysis. The overall design is experimentally verified by a back-to-back transition which exhibits insertion loss of 4 dB at 300 GHz and 36-GHz bandwidth with better than 10-dB return loss. To evaluate the loss of a single transition, we carried out a loss component analysis by evaluating different lengths of microstrip line and hollow waveguide. The estimated loss for a single transition is 1 dB at 300 GHz. The planar transition without a metal back-short significantly reduces the size of terahertz packages and eliminates the need for additional components for hermetic sealing. The compact transition is easy to integrate in a low-cost LTCC package with an MMIC chip.


title: Automatic categorization of figures in scientific documents

abstract: Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose an architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for realworld use. Our tools will be integrated into a scientific document digital library.


Optimisation of facility layout design problem with safety and environmental factors by stochastic DEA and simulation approach


title: The Effectiveness of Intrinsic and Extrinsic Motivations: A Study of Malaysian Amway Company's Direct Sales Forces

abstract: This research utilizes the survey questionnaire data collected in May/June 2010 from 200 Amway Company’s direct sales forces in Klang Valley areas (Malaysia) to analyze the effectiveness of intrinsic and extrinsic motivation in influencing job satisfaction. The research will be analyzed using the well-established correlation analysis, regression analysis, independent sample t-test, and one-way ANOVA. There are four major findings. First, there is a relationship between intrinsic and extrinsic motivations with job satisfaction. According to the correlation value, intrinsic motivation compared to extrinsic motivation tends to contribute more in job satisfaction. Second, there are significant and positive relationship between intrinsic and extrinsic motivations and job satisfaction. Both of the intrinsic and extrinsic motivation is identified as the predictor for job satisfaction. In other words, they are both significantly contribute in better job satisfaction. Third, there is no difference between gender and intrinsic and extrinsic motivations. Hence, gender is not a factor that affects both of the intrinsic and extrinsic motivations. Lastly, the result indicates that there is a difference between age and intrinsic and extrinsic motivations. Therefore, age is the factor that influences on both intrinsic and extrinsic motivations. Last but not least, the results have demonstrated the effectiveness of intrinsic and extrinsic motivations in influencing job satisfaction among the Amway Company’s direct sales forces, as well as establishing appropriate intrinsic motivations may promote higher job satisfaction.


title: Development, implementation, and evaluation of a community- and hospital-based respiratory syncytial virus prophylaxis program.

abstract: PURPOSE ::: To implement and deliver a respiratory syncytial virus prophylaxis (RSVP) program in response to the Canadian Pediatric Society recommendations. ::: ::: ::: METHODS ::: A novel program was designed to provide inpatient RSVP for at-risk infants cared for in 1 tertiary care newborn intensive care unit (NICU). This inpatient program was part of a coordinated approach to RSVP, designed and implemented by 3 hospitals. An RSVP program logic model was created and used by a multidisciplinary team to evaluate the in-house program and identify areas of program activity requiring improvement. ::: ::: ::: RESULTS ::: Following the 2000 to 2001 RSV season, a compliance and outcomes audit was performed in the tertiary center; 193 infants were enrolled in the RSVP program and 162 infants had received RSVP in the NICU [Mean = 1.64 doses]. Telephone follow-up with the parents of discharged infants identified that 159 infants (98%) had successfully completed their full course of RSVP. Using the RSVP program logic model, 5 areas for program improvement were identified including infant recruitment, patient transfer/discharge processes, product procurement, preparation/distribution/administration of doses, and healthcare team communication. ::: ::: ::: CONCLUSIONS ::: Interdisciplinary collaboration is an important factor in the success of the RSVP program and has supported a consistent model of care for the delivery of RSVP. The program logic model provided a useful structure to systematically review the RSVP program in this organization.


title: Two Factor Authentication for Cloud Computing

abstract: The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. However due to the loss of direct control over the systems/applications, users are concerned about the risks of cloud services if it is truly secured. In the literature, there are cases where attackers masquerade as cloud users, illegally access to their accounts, by stealing the static login password or breaking the poor authentication gate. In this paper, we propose a two-factor authentication framework to enforce cloud services’ authentication process, which are Public Key Infrastructure (PKI) authentication and mobile out-of-band (OOB) authentication. We discuss the framework’s security analysis in later session and conclude that it is robust to phishing and replay attacks, prohibiting fraud users from accessing to the cloud services.


title: Traffic prediction in a bike-sharing system

abstract: Bike-sharing systems are widely deployed in many major cities, providing a convenient transportation mode for citizens' commutes. As the rents/returns of bikes at different stations in different periods are unbalanced, the bikes in a system need to be rebalanced frequently. Real-time monitoring cannot tackle this problem well as it takes too much time to reallocate the bikes after an imbalance has occurred. In this paper, we propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed in advance. We first propose a bipartite clustering algorithm to cluster bike stations into groups, formulating a two-level hierarchy of stations. The total number of bikes that will be rent in a city is predicted by a Gradient Boosting Regression Tree (GBRT). Then a multi-similarity-based inference model is proposed to predict the rent proportion across clusters and the inter-cluster transition, based on which the number of bikes rent from/ returned to each cluster can be easily inferred. We evaluate our model on two bike-sharing systems in New York City (NYC) and Washington D.C. (D.C.) respectively, confirming our model's advantage beyond baseline approaches (0.03 reduction of error rate), especially for anomalous periods (0.18/0.23 reduction of error rate).


title: An empirical study of the important factors for knowledge‐management adoption in the SME sector

abstract: Purpose – To investigate the critical success factors (CSFs) for adopting knowledge management (KM) in small and medium‐sized enterprises (SMEs) – an area that has, to date, received very little attention in the literature.Design/methodology/approach – A survey instrument comprising 11 factors and 66 elements was developed. Through a postal survey, data were sought from SMEs in the UK. A parallel one was also administered to a group of academics, consultants and practitioners in the KM field in order to provide a more holistic view of the CSFs.Findings – The survey instrument was shown to be both reliable and valid. Pertinent statistical analyses were then performed. By integrating the results from both groups of respondents, a prioritised list of CSFs, in order of importance for implementing KM, was generated.Research limitations/implications – The number of responses received was rather small since KM is a new and emerging discipline, and not many SMEs have formally implemented it.Practical implications...


title: Amplitude envelope kinematics of speech signal: parameter extraction and applications

abstract: In this paper, we model the amplitude envelope of the broadband speech signal as a kinematic system and calculate its basic parameters, including displacement, velocity and acceleration. Such system captures the smoothed amplitude fluctuation pattern over time, illustrating how energy is distributed across the signal. Although the pulmonic air pressure is the primary energy source of speech, the amplitude modulation pattern is largely determined by articulatory behaviours, especially mandible and lip movements. Therefore, there should be a correspondence between signal envelope kinematics and articulator kinematics. Previous research showed that a tremendous amount of speaker idiosyncrasies in articulation existed. Such idiosyncrasies should therefore be reflected in the envelope kinematics as well. From the signal envelope kinematics, it may be possible to infer individual articulatory behaviours. This is particularly useful for forensic phoneticians who usually have no access to articulatory data, and clinical speech pathologists who usually find it difficult to make articulatory measurement in clinical consultations. Also in this paper, we illustrate a correspondence between the amplitude envelope kinematics and the lower lip kinematics (X-ray pellet history data) of one speaker reading one sentence. For future research, more speakers are needed to record both speech and articulatory signals to build a statistical model between the kinematics data of both domains.