Query: Self-Adhesive and Capacitive Carbon Nanotube-Based Electrode to Record Electroencephalograph Signals From the Hairy Scalp

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Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces <=SEP=> Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.

ECG measurement on a chair without conductive contact <=SEP=> For the purpose of long-term, everyday electrocardiogram (ECG) monitoring, we present a convenient method of ECG measurement without direct conductive contact with the skin while subjects sat on a chair wearing normal clothes. Measurements were made using electrodes attached to the back of a chair, high-input-impedance amplifiers mounted on the electrodes, and a large ground-plane placed on the chair seat. ECGs were obtained by the presented method for several types of clothing and compared to ECGs obtained from conventional measurement using Ag-AgCl electrodes. Motion artifacts caused by usual desk works were investigated. This study shows the feasibility of the method for long-term, convenient, everyday use

Analysis of emotion recognition using facial expressions, speech and multimodal information <=SEP=> The interaction between human beings and computers will be more natural if computers are able to perceive and respond to human non-verbal communication such as emotions. Although several approaches have been proposed to recognize human emotions based on facial expressions or speech, relatively limited work has been done to fuse these two, and other, modalities to improve the accuracy and robustness of the emotion recognition system. This paper analyzes the strengths and the limitations of systems based only on facial expressions or acoustic information. It also discusses two approaches used to fuse these two modalities: decision level and feature level integration. Using a database recorded from an actress, four emotions were classified: sadness, anger, happiness, and neutral state. By the use of markers on her face, detailed facial motions were captured with motion capture, in conjunction with simultaneous speech recordings. The results reveal that the system based on facial expression gave better performance than the system based on just acoustic information for the emotions considered. Results also show the complementarily of the two modalities and that when these two modalities are fused, the performance and the robustness of the emotion recognition system improve measurably.

ORTHONORMAL BASES OF COMPACTLY SUPPORTED WAVELETS <=SEP=> We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.

Emotion Detection from Speech to Enrich Multimedia Content <=SEP=> The paper describes an experimental study on the detection of emotion from speech. As computer based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters become more important. The study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned into a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.

The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. <=SEP=> The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).

Recent advances on active noise control : open issues and innovative applications <=SEP=> Yoshinobu Kajikawa, Woon­Seng Gan and Sen M. Kuo APSIPA Transactions on Signal and Information Processing / Volume 1 / August 2012 / e3 DOI: 10.1017/ATSIP.2012.4, Published online: Link to this article: http://journals.cambridge.org/abstract_S2048770312000042 How to cite this article: Yoshinobu Kajikawa, Woon­Seng Gan and Sen M. Kuo (2012). Recent advances on active noise control: open issues and innovative applications. APSIPA Transactions on Signal and Information Processing,1, e3 doi:10.1017/ATSIP.2012.4 Request Permissions : Click here

Software Defined Networking Meets Information Centric Networking: A Survey <=SEP=> Information centric networking (ICN) and software-defined networking (SDN) are two emerging networking paradigms that promise to solve different aspects of networking problems. ICN is a clean-slate design for accommodating the ever increasing growth of the Internet traffic by regarding content as the network primitive, adopting in-network caching, and name-based routing, while SDN focuses on agile and flexible network management by decoupling network control logic from data forwarding. ICN and SDN have gained significant research attention separately in the most of the previous work. However, the features of ICN have profound impacts on the design and operation of SDN, such as in-network caching and data-centric security. Conversely, SDN provides a powerful tool for experimenting and deploying ICN architectures and can greatly facilitate ICN functionality and management. In this paper, we point out the necessity of surveying the scattered works on integrating SDN and ICN (SD-ICN) for improving operational networks. Specifically, we analyze the points of SD-ICN strengths and opportunities, and discuss the SDN enablers for deploying ICN architectures. In addition, we review and classify the recent work on improving the network management by SD-ICN and discuss the potential security benefits of SD-ICN. Finally, a number of open issues and future trends are highlighted.

Data-driven 3D Voxel Patterns for object category recognition <=SEP=> Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark [17] and the outdoor-scene dataset [41]. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.

Belief space planning assuming maximum likelihood observations <=SEP=> We cast the partially observable control problem as a fully observable underactuated stochastic control problem in belief space and apply standard planning and control techniques. One of the difficulties of belief space planning is modeling the stochastic dynamics resulting from unknown future observations . The core of our proposal is to define deterministic beliefsystem dynamics based on an assumption that the maximum likelihood observation (calculated just prior to the observation) is always obtained. The stochastic effects of future observation s are modeled as Gaussian noise. Given this model of the dynamics, two planning and control methods are applied. In the first, linear quadratic regulation (LQR) is applied to generate policies in the belief space. This approach is shown to be optimal for linearGaussian systems. In the second, a planner is used to find locally optimal plans in the belief space. We propose a replanning approach that is shown to converge to the belief space goal in a finite number of replanning steps. These approaches are characterized in the context of a simple nonlinear manipulation problem where a planar robot simultaneously locates and grasps an object.

Very High Resolution Spaceborne SAR Tomography in Urban Environment <=SEP=> Synthetic aperture radar tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. It uses stacks of several acquisitions from slightly different viewing angles (the elevation aperture) to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. The new class of meter-resolution spaceborne SAR systems (TerraSAR-X and COSMO-Skymed) offers a tremendous improvement in tomographic reconstruction of urban areas and man-made infrastructure. The high resolution fits well to the inherent scale of buildings (floor height, distance of windows, etc.). This paper demonstrates the tomographic potential of these SARs and the achievable quality on the basis of TerraSAR-X spotlight data of urban environment. A new Wiener-type regularization to the singular-value decomposition method-equivalent to a maximum a posteriori estimator-for TomoSAR is introduced and is extended to the differential case (4-D, i.e., space-time). Different model selection schemes for the estimation of the number of scatterers in a resolution cell are compared and proven to be applicable in practice. Two parametric estimation algorithms of the scatterers' elevation and their velocities are evaluated. First 3-D and 4-D reconstructions of an entire building complex (including its radar reflectivity) with very high level of detail from spaceborne SAR data by pixelwise TomoSAR are presented.

Softmax Discriminant Classifier <=SEP=> A simple but effective classifier, which is called soft max discriminant classifier or SDC for short, is presented. Based on the soft max discriminant function, SDC assigns the label information to a new testing sample by nonlinear transformation of the distance between the testing sample and training samples. Experimental results on some well-known data sets demonstrate the feasibility and effectiveness of the proposed algorithm.

An improved power quality induction heater using zeta converter <=SEP=> This paper presents an induction heater (IH) with power factor correction (PFC) for domestic induction heating applications. A half bridge voltage fed series resonant inverter (VFSRI) based IH is fed from the front end PFC corrected zeta converter. The PFC-zeta converter operating in continuous inductor conduction mode (CICM) is used to regulate the DC link voltage. The switch current stress of PFC-zeta converter is controlled by operating zeta converter in CICM mode using a well-established internal current loop and outer voltage loop approach. The PFC-zeta converter based voltage fed IH converts intermediate DC bus voltage to high frequency (30 kHz) voltage suitable for domestic induction heating. A 2 kW PFC-zeta converter based IH is designed and its performance is simulated in MATLAB with power quality indices within an IEC 61000-3-2 standard.

Semi-Supervised Nonnegative Matrix Factorization <=SEP=> Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, providing a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are labeled, the performance of clustering or classification is improved if the information on class labels is incorporated into NMF. To this end, we present semi-supervised NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label matrix into NMF. We develop multiplicative updates for SSNMF to minimize a sum of weighted residuals, each of which involves the nonnegative 2-factor decomposition of the data matrix or the label matrix, sharing a common factor matrix. Experiments on document datasets and EEG datasets in BCI competition confirm that our method improves clustering as well as classification performance, compared to the standard NMF, stressing that semi-supervised NMF yields semi-supervised feature extraction.

The role of debriefing in simulation-based learning. <=SEP=> The aim of this paper is to critically review what is felt to be important about the role of debriefing in the field of simulation-based learning, how it has come about and developed over time, and the different styles or approaches that are used and how effective the process is. A recent systematic review of high fidelity simulation literature identified feedback (including debriefing) as the most important feature of simulation-based medical education.1 Despite this, there are surprisingly few papers in the peer-reviewed literature to illustrate how to debrief, how to teach or learn to debrief, what methods of debriefing exist and how effective they are at achieving learning objectives and goals. This review is by no means a systematic review of all the literature available on debriefing, and contains information from both peer and nonpeer reviewed sources such as meeting abstracts and presentations from within the medical field and other disciplines versed in the practice of debriefing such as military, psychology, and business. It also contains many examples of what expert facilitators have learned over years of practice in the area. We feel this would be of interest to novices in the field as an introduction to debriefing, and to experts to illustrate the gaps that currently exist, which might be addressed in further research within the medical simulation community and in collaborative ventures between other disciplines experienced in the art of debriefing.

The use of the interactive whiteboard for creative teaching and learning in literacy and mathematics: a case study <=SEP=> This paper considers the ways in which the interactive whiteboard may support and enhance pedagogic practice through whole-class teaching within literacy and numeracy. Data collected from observations of whole-class lessons, alongside individual interviews and focus group discussions with class teachers and Initial Teacher Education students, has provided opportunities to consider the potential of such technology to facilitate a more creative approach to whole-class teaching. The data suggests that, in the first instance, the special features of information and communications technology such as interactivity, ‘provisionality,’ speed, capacity and range enhance the delivery and pace of the session. This research seems to indicate that it is the skill and the professional knowledge of the teacher who mediates the interaction, and facilitates the development of pupils’ creative responses at the interface of technology, which is critical to the enhancement of the whole-class teaching and learning processes. Introduction The globalising phenomenon of information and communication technologies (ICT) is a distinct characteristic of modern times. The speed and immediacy of ICT, coupled with opportunities for increased information flow through multiple routes of communication, suggest that we are living in a time of unprecedented change, with ICT affecting the way we live and function as individuals and as a society (Castells, 2004). Within the context of education there are some technologies that appear to have attracted more interest than others; however, the degree to which they have been British Journal of Educational Technology Vol 39 No 1 2008 84–96 doi:10.1111/j.1467-8535.2007.00703.x © 2007 The Authors. Journal compilation © 2007 British Educational Communications and Technology Agency. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. successfully integrated into the classroom environment has been varied. In recent years, there has been a growing level of interest in the electronic or interactive whiteboard (IWB), well documented by the educational press. Such technology is generally comprised of a triangulation between data projector, computer and an electronic screen. This allows an individual to interact with software at the front of a class rather than from the computer. Effectively, the computer screen is projected onto the electronic whiteboard and presented to the class with the teacher, or perhaps student, selecting, activating and interacting with the programs. At a time in England when the government has promoted whole-class interactive teaching, particularly within Literacy and Numeracy, access to IWB technology through targeted government funding is also increasing, and the IWB is steadily becoming a feature of most numeracy and literacy lessons. In January 2004, Charles Clarke, the Secretary of State for Education in England, announced that, in addition to the £25 million previously made available to schools in September 2003; a further £25 million would be released for the purchase of IWBs. This technology is therefore likely to become a key resource in most schools. Introduction of new technologies such as this within the classroom context raises questions regarding the ways in which pedagogic practice may be supported and enhanced; this being the focus of this study, specifically, the links between three areas; whole-class direct teaching, creativity and the integration of technology. As IWBs are becoming more familiar within the educational market, additional challenges, which imply new priorities, have arisen from recent demands for ‘a much stronger emphasis on creative and cultural education and a new balance in teaching and in the curriculum.’ (The National Advisory Committee on Creative and Cultural Education [NACCCE], 1999) The teacher educator is therefore faced with a complex set of demands that require resolution in terms of pedagogic practice. The aim of this study is to investigate how IWBs can provide opportunities for creativity in teaching and learning, particularly within whole-class lessons, by drawing upon observations of, and discussions with, classroom practitioners and ITE students. In so doing, the study will contribute to debates within educational fields (Bleakley, 2004; Craft, 2005; NACCCE, 1999) regarding the notion of creativity in primary teaching and learning. Current developments in creativity, interactivity and whole-class direct teaching The current focus on direct whole-class teaching, particularly in mathematics, developed in response to concerns in England about the level of children’s performance in English and mathematics compared to those in other countries. In particular, in the Third International Mathematics and Science Study of 1999 (Mullis et al, 2000) England was placed low in basic number skills although as Muijs and Reynolds (2001) note, faring better in geometry and problem solving. Research conducted by Professor David Reynolds, chair of the newly established Numeracy Task Force, singled out a substantial amount of direct whole-class teaching as a key feature of mathematics sessions in the top-attaining countries (Reynolds & Farrell, 1996), these findings The use of the interactive whiteboard in literacy and maths: a case study 85 © 2007 The Authors. Journal compilation © 2007 British Educational Communications and Technology Agency. echoing those of previous studies, (Galton & Croll, 1980; Rosenshine, 1979), and more recently, Bierhoff (1996), Bierhoff and Prais (1995) and Burghes (1995). In response to these concerns about attainment and the related research findings, the National Literacy Strategy (NLS; DfEE, 1998) and National Numeracy Strategy (NNS) were introduced into most state-funded primary schools; the NLS in 1998 and the NNS in 1999, both providing a clear framework for the content of the curriculum and recommended teaching approaches. In both Literacy and Numeracy, sessions were expected to consist of a substantial amount of direct, whole-class teaching. A wholeclass approach, it was claimed, enables the teacher to interact more with each pupil; adapt activities quickly in response to pupils’ responses; use errors and misconceptions as a teaching point for the whole class and keep pupils on task for longer periods of time (Muijs & Reynolds, 2001). However, Muijs and Reynolds also noted that this approach was not necessarily the best to use in all circumstances. Brophy and Good (1986) are cited as finding this method more suited for teaching rules, procedures and basic skills, especially to younger pupils. Less structured and teacher-directed approaches, Muijs and Reynolds (2001) suggest, would be more appropriate when the aims of the lesson are more complex or open-ended (eg, developing students’ thinking skills) With the IWB featuring widely in whole-class teaching, there is a concern that its full interactive potential may not be explored through this structured, teacher-directed approach as the teaching and modelling of rules, procedures and basic skills is likely to take precedence over more complex and cognitively demanding activities. Where whole-class interactive teaching in mathematics lessons is concerned, the Numeracy Task Force stressed that ‘it is the quality of the whole class teaching which determines its success’ (Department for Education and Employment [DfEE], 1999) rather than the whole-class approach per se. They note that whole-class teaching should not be viewed as a purely transmission-based approach, with children given instruction regarding knowledge and understanding in a product-driven mode of teaching. Rather, it should be seen as an interactive, process-oriented approach to learning with the quality of the interaction within the classroom being of prime importance, maximised effectively and efficiently through good whole-class teaching. The NNS goes on to state that ‘interaction is a two-way process in which pupils are expected to play an active part’ and that ‘high quality interactive teaching is oral, interactive and lively’, which is fundamental to a social constructivist view of learning (Vygotsky, 1978). This may be a laudable claim, however; Muijs and Reynolds (2001) caution that with direct whole-class teaching, pupils may find it easy to adopt a more passive role, becoming too dependent on the teacher and failing to develop independent learning skills. Nevertheless, it may be within a more social, interactive and lively learning environment that the IWB could be seen to make a valuable contribution. At present, there is a limited amount of research available that focuses specifically upon the IWB and associated pedagogy, however, Smith, Hardman and Higgins (2006) have undertaken a substantial study involving observations made in primary schools of 184 lessons in literacy and numeracy conducted over a 2-year period. The study suggests that, although the use of IWBs engages the pupils and sessions are generally faster in 86 British Journal of Educational Technology Vol 39 No 1 2008 © 2007 The Authors. Journal compilation © 2007 British Educational Communications and Technology Agency. their pace of delivery, the underlying pedagogy of whole-class teaching appears to remain unaffected, with teacher-led recitation and emphasis upon recall dominating proceedings. Previous research has highlighted the motivational impact of IWBs upon pupils, with the large screen, the multimedia capability and the element of fun enhancing the presentational aspects of a lesson (Glover & Miller, 2001; Levy, 2002). Essentially, there appears to be the potential for enhancements in whole-class teaching and learning through the use of IWBs if pedagogic practice were to adapt and change through creative and innovative use of the particular features of this new technology. Creativity Creativity has been described as ‘the word of the moment’ (Bruce, 2004, p. vi

Multi Objective Segmentation for Vehicle License Plate Detection with Immune-based Classifier: A General Framework <=SEP=> Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.

Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network <=SEP=> Recently, Generative Adversarial Network (GAN) has been found wide applications in style transfer, image-to-image translation and image super-resolution. In this paper, a colordepth conditional GAN is proposed to concurrently resolve the problems of depth super-resolution and color super-resolution in 3D videos. Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene. Secondly, three loss functions, including data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network in order to keep generated images close to the real ones, in addition to the adversarial loss. Experimental results demonstrate that the proposed approach produces highquality color image and depth image from low-quality image pair, and it is superior to several other leading methods. Besides, the applications of the proposed method in other tasks are image smoothing and edge detection at the same time.

Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence <=SEP=> The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools.

Classification and Regression using SAS <=SEP=> K-Nearest Neighbor (KNN) classification and regression are two widely used analytic methods in predictive modeling and data mining fields. They provide a way to model highly nonlinear decision boundaries, and to fulfill many other analytical tasks such as missing value imputation, local smoothing, etc. In this paper, we discuss ways in SAS R © to conduct KNN classification and KNN Regression. Specifically, PROC DISCRIM is used to build multi-class KNN classification and PROC KRIGE2D is used for KNN regression tasks. Technical details such as tuning parameter selection, etc are discussed. We also discuss tips and tricks in using these two procedures for KNN classification and regression. Examples are presented to demonstrate full process flow in applying KNN classification and regression in real world business projects.

Cardiac pulse detection in BCG signals implemented on a regular classroom chair integrated to an emotional and learning model for personalization of learning resources <=SEP=> Emotions are related with learning processes, and physiological signals can be used to detect them. In this work, it is presented a model for the dynamic personalization of a learning environment. In the model, a specific combination of emotions and cognition processes are connected and integrated with the concept of ‘flow’, since it may provide a way to improve learning abilities. Physiological signals can be used to relate temporal emotions and subject's learning processes; the cardiac pulse is a reliable signal that carries useful information about the subject's emotional condition, which is detected using a classroom chair adapted with non invasive Electro-Mechanical Film (EMFi) sensors and an acquisition system that generates a ballistocardiogram (BCG), which is analyzed by a developed algorithm to obtain cardiac pulse statistics. A study including different data obtained from the chair's sensors was carried out and interpreted in terms of emotions, which together with a cognitive model is used for the personalization of content in a learning scenario. Such model establishes a relation between the learner's knowledge, the content of a course or a learning activity and the emotional condition of each student.

A quantitative analysis of the speedup factors of FPGAs over processors <=SEP=> The speedup over a microprocessor that can be achieved by implementing some programs on an FPGA has been extensively reported. This paper presents an analysis, both quantitative and qualitative, at the architecture level of the components of this speedup. Obviously, the spatial parallelism that can be exploited on the FPGA is a big component. By itself, however, it does not account for the whole speedup.In this paper we experimentally analyze the remaining components of the speedup. We compare the performance of image processing application programs executing in hardware on a Xilinx Virtex E2000 FPGA to that on three general-purpose processor platforms: MIPS, Pentium III and VLIW. The question we set out to answer is what is the inherent advantage of a hardware implementation over a von Neumann platform. On the one hand, the clock frequency of general-purpose processors is about 20 times that of typical FPGA implementations. On the other hand, the iteration level parallelism on the FPGA is one to two orders of magnitude that on the CPUs. In addition to these two factors, we identify the efficiency advantage of FPGAs as an important factor and show that it ranges from 6 to 47 on our test benchmarks. We also identify some of the components of this factor: the streaming of data from memory, the overlap of control and data flow and the elimination of some instruction on the FPGA. The results provide a deeper understanding of the tradeoff between system complexity and performance when designing Configurable SoC as well as designing software for CSoC. They also help understand the one to two orders of magnitude in speedup of FPGAs over CPU after accounting for clock frequencies.

Computational Complexity of Linear Large Margin Classification With Ramp Loss <=SEP=> Minimizing the binary classification error with a linear model leads to an NP-hard problem. In practice, surrogate loss functions are used, in particular loss functions leading to large margin classification such as the hinge loss and the ramp loss. The intuitive large margin concept is theoretically supported by generalization bounds linking the expected classification error to the empirical margin error and the complexity of the considered hypotheses class. This article addresses the fundamental question about the computational complexity of determining whether there is a hypotheses class with a hypothesis such that the upper bound on the generalization error is below a certain value. Results of this type are important for model comparison and selection. This paper takes a first step and proves that minimizing a basic margin-bound is NP-hard when considering linear hypotheses and the ρmargin loss function, which generalizes the ramp loss. This result directly implies the hardness of ramp loss minimization.

Effectiveness of Chinese massage therapy (Tui Na) for chronic low back pain: study protocol for a randomized controlled trial <=SEP=> BACKGROUND
Low back pain is a common, disabling musculoskeletal disorder in both developing and developed countries. Although often recommended, the potential efficacy of massage therapy in general, and Chinese massage (tuina) in particular, for relief of chronic low back pain (CLBP) has not been fully established due to inadequate sample sizes, low methodological quality, and subclinical dosing regimens of trials to date. Thus, the purpose of this randomized controlled trial (RCT) is to evaluate the comparative effectiveness of tuina massage therapy versus conventional analgesics for CLBP.


METHODS/DESIGN
The present study is a single center, two-arm, open-label RCT. A total of 150 eligible CLBP patients will be randomly assigned to either a tuina treatment group or a conventional drug control group in a 1:1 ratio. Patients in the tuina group receive a 20 minutes, 4-step treatment protocol which includes both structural and relaxation massage, administered in 20 sessions over a period of 4 weeks. Patients in the conventional drug control group are instructed to take a specific daily dose of ibuprofen. The primary outcome measure is the change from baseline back pain and function, measured by Roland-Morris Disability Questionnaire, at two months. Secondary outcome measures include the visual analogue scale, Japanese orthopedic association score (JOAS), and McGill pain questionnaire.


DISCUSSION
The design and methodological rigor of this trial will allow for collection of valuable data to evaluate the efficacy of a specific tuina protocol for treating CLBP. This trial will therefore contribute to providing a solid foundation for clinical treatment of CLBP, as well as future research in massage therapy.


TRIAL REGISTRATION
This trial was registered with ClinicalTrials.gov of the National Institute of Health on 22 October 2013 (http://NCT01973010).

Autonomic Communications in Software-Driven Networks <=SEP=> Autonomic communications aim to provide the quality-of-service in networks using self-management mechanisms. It inherits many characteristics from autonomic computing, in particular, when communication systems are running as specialized applications in software-defined networking (SDN) and network function virtualization (NFV)-enabled cloud environments. This paper surveys autonomic computing and communications in the context of software-driven networks, i.e., networks based on SDN/NFV concepts. Autonomic communications create new challenges in terms of security, operations, and business support. We discuss several goals, research challenges, and development issues on self-management mechanisms and architectures in software-driven networks. This paper covers multiple perspectives of autonomic communications in software-driven networks, such as automatic testing, integration, and deployment of network functions. We also focus on self-management and optimization, which make use of machine learning techniques.

Coordinated Control of the Energy Router-Based Smart Home Energy Management System <=SEP=> Home area energy networks will be an essential part of the future Energy Internet in terms of energy saving, demand-side management and stability improvement of the distribution network, while an energy router will be the perfect choice to serve as an intelligent and multi-functional energy interface between the home area energy network and power grid. This paper elaborates on the design, analysis and implementation of coordinated control of the low-voltage energy router-based smart home energy management system (HEMS). The main contribution of this paper is to develop a novel solution to make the energy router technically feasible and practical for the HEMS to make full use of the renewable energy sources (RESs), while maintaining “operational friendly and beneficial” to the power grid. The behaviors of the energy router-based HEMS in correlation with the power grid are investigated, then the coordinated control scheme composed of a reference voltage and current compensation strategy and a fuzzy logic control-based power management strategy is developed. The system model is built on the MATLAB/Simulink platform, simulation results have demonstrated that the presented control scheme is a strong performer in making full use of the RES generations for the HEMS while maintaining the operational stability of the whole system, as well as in collaboration with the power grid to suppress the impact of RES output fluctuations and load consumption variations.

The brain's conversation with itself: neural substrates of dialogic inner speech. <=SEP=> Inner speech has been implicated in important aspects of normal and atypical cognition, including the development of auditory hallucinations. Studies to date have focused on covert speech elicited by simple word or sentence repetition, while ignoring richer and arguably more psychologically significant varieties of inner speech. This study compared neural activation for inner speech involving conversations ('dialogic inner speech') with single-speaker scenarios ('monologic inner speech'). Inner speech-related activation differences were then compared with activations relating to Theory-of-Mind (ToM) reasoning and visual perspective-taking in a conjunction design. Generation of dialogic (compared with monologic) scenarios was associated with a widespread bilateral network including left and right superior temporal gyri, precuneus, posterior cingulate and left inferior and medial frontal gyri. Activation associated with dialogic scenarios and ToM reasoning overlapped in areas of right posterior temporal cortex previously linked to mental state representation. Implications for understanding verbal cognition in typical and atypical populations are discussed.

Judgments of genuine, suppressed, and faked facial expressions of pain. <=SEP=> The process of discriminating among genuine, suppressed, and faked expressions of pain was examined. Untrained judges estimated the severity of pain being experienced when viewing videotaped facial expressions of chronic pain patients undergoing a painful diagnostic test or dissimulating reactions. Verbal feedback as to whether pain was experienced also was provided, so as to be either consistent or inconsistent with the facial expression. Judges were able to distinguish genuine pain faces from baseline expressions but, relative to genuine pain faces, attributed more pain to faked faces and less pain to suppressed ones. Advance warning of deception did not improve discrimination but led to a more conservative or nonempathic judging style. Verbal feedback increased or decreased judgments, as appropriate, but facial information consistently was assigned greater weight. An augmenting model of the judgment process that attaches considerable importance to the context in which information is provided was supported.

SRFeat: Single Image Super-Resolution with Feature Discrimination <=SEP=> Generative adversarial networks (GANs) have recently been adopted to single image super-resolution (SISR) and showed impressive results with realistically synthesized high-frequency textures. However, the results of such GAN-based approaches tend to include less meaningful high-frequency noise that is irrelevant to the input image. In this paper, we propose a novel GAN-based SISR method that overcomes the limitation and produces more realistic results by attaching an additional discriminator that works in the feature domain. Our additional discriminator encourages the generator to produce structural high-frequency features rather than noisy artifacts as it distinguishes synthetic and real images in terms of features. We also design a new generator that utilizes long-range skip connections so that information between distant layers can be transferred more effectively. Experiments show that our method achieves the state-of-the-art performance in terms of both PSNR and perceptual quality compared to recent GAN-based methods.

Time Series Featurization via Topological Data Analysis: an Application to Cryptocurrency Trend Forecasting <=SEP=> We propose a novel methodology for feature extraction from time series data based on topological data analysis. The proposed procedure applies a dimensionality reduction technique via principal component analysis to the point cloud of the Takens’ embedding from the observed time series and then evaluates the persistence landscape and silhouettes based on the corresponding Rips complex. We define a new notion of Rips distance function that is especially suited for persistence homologies built on Rips complexes and prove stability theorems for it. We use these results to demonstrate in turn some stability properties of the topological features extracted using our procedure with respect to additive noise and sampling. We further apply our method to the problem of trend forecasting for cryptocurrency prices, where we manage to achieve significantly lower error rates than more standard, non TDA-based methodologies in complex pattern classification tasks. We expect our method to provide a new insight on feature engineering for granular, noisy time series data.