Analysis of emotion recognition using facial expressions, speech and multimodal information

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

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

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.

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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

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

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.


Evaluating STT-RAM as an energy-efficient main memory alternative

In this paper, we explore the possibility of using STT-RAM technology to completely replace DRAM in main memory. Our goal is to make STT-RAM performance comparable to DRAM while providing substantial power savings. Towards this goal, we first analyze the performance and energy of STT-RAM, and then identify key optimizations that can be employed to improve its characteristics. Specifically, using partial write and row buffer write bypass, we show that STT-RAM main memory performance and energy can be significantly improved. Our experiments indicate that an optimized, equal capacity STT-RAM main memory can provide performance comparable to DRAM main memory, with an average 60% reduction in main memory energy.


Architecting phase change memory as a scalable dram alternative

Memory scaling is in jeopardy as charge storage and sensing mechanisms become less reliable for prevalent memory technologies, such as DRAM. In contrast, phase change memory (PCM) storage relies on scalable current and thermal mechanisms. To exploit PCM's scalability as a DRAM alternative, PCM must be architected to address relatively long latencies, high energy writes, and finite endurance.
 We propose, crafted from a fundamental understanding of PCM technology parameters, area-neutral architectural enhancements that address these limitations and make PCM competitive with DRAM. A baseline PCM system is 1.6x slower and requires 2.2x more energy than a DRAM system. Buffer reorganizations reduce this delay and energy gap to 1.2x and 1.0x, using narrow rows to mitigate write energy and multiple rows to improve locality and write coalescing. Partial writes enhance memory endurance, providing 5.6 years of lifetime. Process scaling will further reduce PCM energy costs and improve endurance.


A novel nonvolatile memory with spin torque transfer magnetization switching: spin-ram

A novel nonvolatile memory utilizing spin torque transfer magnetization switching (STS), abbreviated spin-RAM hereafter, is presented for the first time. The spin-RAM is programmed by magnetization reversal through an interaction of a spin momentum-torque-transferred current and a magnetic moment of memory layers in magnetic tunnel junctions (MTJs), and therefore an external magnetic field is unnecessary as that for a conventional MRAM. This new programming mode has been accomplished owing to our tailored MTJ, which has an oval shape of 100 times 150 nm. The memory cell is based on a 1-transistor and a 1-MTJ (ITU) structure. The 4kbit spin-RAM was fabricated on a 4 level metal, 0.18 mum CMOS process. In this work, writing speed as high as 2 ns, and a write current as low as 200 muA were successfully demonstrated. It has been proved that spin-RAM possesses outstanding characteristics such as high speed, low power and high scalability for the next generation universal memory


Ultra-Thin Phase-Change Bridge Memory Device Using GeSb

An ultra-thin phase-change bridge (PCB) memory cell, implemented with doped GeSb, is shown with < 100muA RESET current. The device concept provides for simplified scaling to small cross-sectional area (60nm2) through ultra-thin (3nm) films; the doped GeSb phase-change material offers the potential for both fast crystallization and good data retention


Phase-change random access memory: A scalable technology

random access memory: A scalable technology S. Raoux G. W. Burr M. J. Breitwisch C. T. Rettner Y.-C. Chen R. M. Shelby M. Salinga D. Krebs S.-H. Chen H.-L. Lung C. H. Lam Nonvolatile RAM using resistance contrast in phase-change materials [or phase-change RAM (PCRAM)] is a promising technology for future storage-class memory. However, such a technology can succeed only if it can scale smaller in size, given the increasingly tiny memory cells that are projected for future technology nodes (i.e., generations). We first discuss the critical aspects that may affect the scaling of PCRAM, including materials properties, power consumption during programming and read operations, thermal cross-talk between memory cells, and failure mechanisms. We then discuss experiments that directly address the scaling properties of the phase-change materials themselves, including studies of phase transitions in both nanoparticles and ultrathin films as a function of particle size and film thickness. This work in materials directly motivated the successful creation of a series of prototype PCRAM devices, which have been fabricated and tested at phase-change material cross-sections with extremely small dimensions as low as 3 nm · 20 nm. These device measurements provide a clear demonstration of the excellent scaling potential offered by this technology, and they are also consistent with the scaling behavior predicted by extensive device simulations. Finally, we discuss issues of device integration and cell design, manufacturability, and reliability.


SamzaSQL: Scalable Fast Data Management with Streaming SQL

As the data-driven economy evolves, enterprises have come to realize a competitive advantage in being able to act on high volume, high velocity streams of data. Technologies such as distributed message queues and streaming processing platforms that can scale to thousands of data stream partitions on commodity hardware are a response. However, the programming API provided by these systems is often low-level, requiring substantial custom code that adds to the programmer learning curve and maintenance overhead. Additionally, these systems often lack SQL querying capabilities that have proven popular on Big Data systems like Hive, Impala or Presto. We define a minimal set of extensions to standard SQL for data stream querying and manipulation. These extensions are prototyped in SamzaSQL, a new tool for streaming SQL that compiles streaming SQL into physical plans that are executed on Samza, an open-source distributed stream processing framework. We compare the performance of streaming SQL queries against native Samza applications and discuss usability improvements. SamzaSQL is a part of the open source Apache Samza project and will be available for general use.


Neuro-fuzzy rule generation: survey in soft computing framework

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.


Dialog Act Modeling for Conversational Speech

We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42`dialog acts' which were hand-labeled in 1155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We developed several models and algorithms to automatically detect dialog acts from transcribed or automatically recognized words and from prosodic properties of the speech signal, and by using a statistical discourse grammar. All of these components were probabilistic in nature and estimated from data, employing a variety of techniques (hidden Markov models, N-gram language models, maximum entropy estimation, decision tree classiiers, and neural networks). In preliminary studies, we achieved a dialog act labeling accuracy of 65% based on recognized words and prosody, and an accuracy of 72% based on word transcripts. Since humans achieve 84% on this task (with chance performance at 35%) we nd these results encouraging.


Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks

In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by learning in humans. Associations are made from embeddings of labeled samples to those of unlabeled ones and back. The optimization schedule encourages correct association cycles that end up at the same class from which the association was started and penalizes wrong associations ending at a different class. The implementation is easy to use and can be added to any existing end-to-end training setup. We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data. In particular, for cases with few labeled data, our training scheme outperforms the current state of the art on SVHN.


Plead or Pitch ? The Role of Language in Kickstarter Project Success

We present an analysis of over 26000 projects from Kickstarter, a popular crowdfunding platform. Specifically, we focus on the language used in project pitches, and how it impacts project success/failure. We train a series of discriminative models for binary success/failure prediction of projects with increasing complexity of linguistic features, and show that successful project pitches are, on average, more emotive, thoughtful and colloquial. In addition, we also present an analysis of pledge rewards in Kickstarter, and how these differ across categories and for successful versus unsuccessful projects.


Mobility Management for Femtocells in LTE-Advanced: Key Aspects and Survey of Handover Decision Algorithms

Support of femtocells is an integral part of the Long Term Evolution - Advanced (LTE-A) system and a key enabler for its wide adoption in a broad scale. Femtocells are short-range, low-power and low-cost cellular stations which are installed by the consumers in an unplanned manner. Even though current literature includes various studies towards understanding the main challenges of interference management in the presence of femtocells, little light has been shed on the open issues of mobility management (MM) in the two-tier macrocell-femtocell network. In this paper, we provide a comprehensive discussion on the key aspects and research challenges of MM support in the presence of femtocells, with the emphasis given on the phases of a) cell identification, b) access control, c) cell search, d) cell selection/reselection, e) handover (HO) decision, and f) HO execution. A detailed overview of the respective MM procedures in the LTE-A system is also provided to better comprehend the solutions and open issues posed in real-life systems. Based on the discussion for the HO decision phase, we subsequently survey and classify existing HO decision algorithms for the two-tier macrocell-femtocell network, depending on the primary HO decision criterion used. For each class, we overview up to three representative algorithms and provide detailed flowcharts to describe their fundamental operation. A comparative summary of the main decision parameters and key features of selected HO decision algorithms concludes this work, providing insights for future algorithmic design and standardization activities.


Directing exploratory search: reinforcement learning from user interactions with keywords

Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.


Investigations on speaker adaptation of LSTM RNN models for speech recognition

Recently Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) acoustic models have demonstrated superior performance over deep neural networks (DNN) models in speech recognition and many other tasks. Although a lot of work have been reported on DNN model adaptation, very little has been done on LSTM model adaptation. In this paper we present our extensive studies of speaker adaptation of LSTM-RNN models for speech recognition. We investigated different adaptation methods combined with KL-divergence based regularization, where and which network component to adapt, supervised versus unsupervised adaptation and asymptotic analysis. We made a few distinct and important observations. In a large vocabulary speech recognition task, by adapting only 2.5% of the LSTM model parameters using 50 utterances per speaker, we obtained 12.6% WERR on the dev set and 9.1% WERR on the evaluation set over a strong LSTM baseline model.


A 3D printed low profile magnetic dipole antenna

An electrically small, low profile magnetic dipole antenna is presented. The proposed antenna is composed of multiple small twisted loops connected in parallel. The simulated radiation efficiency is 87 % at the resonant frequency of 222.5 MHz. The corresponding electrical size ka is 0.276 with the height of 0.0016λ. The prototype is built using a selective laser sintering technology and silver paste painting. The measured result is discussed.


Applying connectionist modal logics to distributed knowledge representation problems

Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.


Epigenetics and Signaling Pathways in Glaucoma

Glaucoma is the most common cause of irreversible blindness worldwide. This neurodegenerative disease becomes more prevalent with aging, but predisposing genetic and environmental factors also contribute to increased risk. Emerging evidence now suggests that epigenetics may also be involved, which provides potential new therapeutic targets. These three factors work through several pathways, including TGF-β, MAP kinase, Rho kinase, BDNF, JNK, PI-3/Akt, PTEN, Bcl-2, Caspase, and Calcium-Calpain signaling. Together, these pathways result in the upregulation of proapoptotic gene expression, the downregulation of neuroprotective and prosurvival factors, and the generation of fibrosis at the trabecular meshwork, which may block aqueous humor drainage. Novel therapeutic agents targeting these pathway members have shown preliminary success in animal models and even human trials, demonstrating that they may eventually be used to preserve retinal neurons and vision.


BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.


A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods.

In this initial pilot study, a controlled clinical comparison was made of attention perforance in children with attention deficit-hyperactivity disorder (ADHD) in a virtual reality (VR) classroom. Ten boys diagnosed with ADHD and ten normal control boys participated in the study. Groups did not significantly differ in mean age, grade level, ethnicity, or handedness. No participants reported simulator sickness following VR exposure. Children with ADHD exhibited more omission errors, commission errors, and overall body movement than normal control children in the VR classroom. Children with ADHD were more impacted by distraction in the VR classroom. VR classroom measures were correlated with traditional ADHD assessment tools and the flatscreen CPT. Of note, the small sample size incorporated in each group and higher WISC-III scores of normal controls might have some bearing on the overall interpretation of results. These data suggested that the Virtual Classroom had good potential for controlled performance assessment within an ecologically valid environment and appeared to parse out significant effects due to the presence of distraction stimuli.


Does Twitter language reliably predict heart disease? A commentary on Eichstaedt et al. (2015a)

We comment on Eichstaedt et al.'s (2015a) claim to have shown that language patterns among Twitter users, aggregated at the level of US counties, predicted county-level mortality rates from atherosclerotic heart disease (AHD), with "negative" language being associated with higher rates of death from AHD and "positive" language associated with lower rates. First, we examine some of Eichstaedt et al.'s apparent assumptions about the nature of AHD, as well as some issues related to the secondary analysis of online data and to considering counties as communities. Next, using the data files supplied by Eichstaedt et al., we reproduce their regression- and correlation-based models, substituting mortality from an alternative cause of death-namely, suicide-as the outcome variable, and observe that the purported associations between "negative" and "positive" language and mortality are reversed when suicide is used as the outcome variable. We identify numerous other conceptual and methodological limitations that call into question the robustness and generalizability of Eichstaedt et al.'s claims, even when these are based on the results of their ridge regression/machine learning model. We conclude that there is no good evidence that analyzing Twitter data in bulk in this way can add anything useful to our ability to understand geographical variation in AHD mortality rates.


Cascading Bandits for Large-Scale Recommendation Problems

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items from a large set of L candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items L. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.


SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent

TheSGD-QN algorithm is a stochastic gradient descent algorithm that m akes careful use of secondorder information and splits the parameter update into inde pendently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first-order stochastic gradient escent but requires less iterations to achieve the same accuracy. This algorith m won the “Wild Track” of the first PASCAL Large Scale Learning Challenge (Sonnenburg et al., 2008 ).


Autonomous human-robot proxemics: socially aware navigation based on interaction potential

To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control proxemics—the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and modelswere implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human–robot proxemics with respect to “interaction potential”—predicted automated speech and gesture recognition rates as the robot enters into and engages in faceto-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI. Electronic supplementary material The online version of this article (doi:10.1007/s10514-016-9572-2) contains supplementary material, which is available to authorized users. B Ross Mead rossmead@usc.edu 1 3710 McClintock Avenue, RTH 423, Los Angeles, CA 90089-0781, USA 2 3710 McClintock Avenue, RTH 407, Los Angeles, CA 90089-0781, USA


Advanced Maintenance Simulation by Means of Hand-Based Haptic Interfaces

Aerospace industry has been involved in virtual simulation for design and testing since the birth of virtual reality. Today this industry is showing a growing interest in the development of haptic-based maintenance training applications, which represent the most advanced way to simulate maintenance and repair tasks within a virtual environment by means of a visual-haptic approach. The goal is to allow the trainee to experiment the service procedures not only as a workflow reproduced at a visual level but also in terms of the kinaesthetic feedback involved with the manipulation of tools and components. This study, conducted in collaboration with aerospace industry specialists, is aimed to the development of an immersive virtual capable of immerging the trainees into a virtual environment where mechanics and technicians can perform maintenance simulation or training tasks by directly manipulating 3D virtual models of aircraft parts while perceiving force feedback through the haptic interface. The proposed system is based on ViRstperson, a virtual reality engine under development at the Italian Center for Aerospace Research (CIRA) to support engineering and technical activities such as design-time maintenance procedure validation, and maintenance training. This engine has been extended to support haptic-based interaction, enabling a more complete level of interaction, also in terms of impedance control, and thus fostering the development of haptic knowledge in the user. The user’s “sense of touch” within the immersive virtual environment is simulated through an Immersion CyberForce® hand-based force-feedback device. Preliminary testing of the proposed system seems encouraging.


Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.


E-TOURISM: AN INNOVATIVE APPROACH FOR THE SMALL AND MEDIUM-SIZED TOURISM ENTERPRISES (SMTES) IN KOREA

This paper deals with e-tourism, innovation and growth. The Internet is revolutionising the distribution of tourism information and sales. The Korean small and medium-sized tourism enterprises (SMTEs) with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This paper examines problems and solutions related to electronic commerce in the tourism industry and suggests recommendations for successful e-commerce strategies in tourism to be applied by the industry and the government in Korea. Introduction The definitions of tourism innovation (e.g. product, service and technological innovations) remains unclear, with the exception maybe of the Internet. New technologies can produce an essential contribution to tourism development. For tourism businesses, the Internet offers the potential to make information and booking facilities available to large numbers of tourists at relatively low costs. It also provides a tool for communication between tourism suppliers, intermediaries, as well as end-consumers. OECD (2000) revealed that the advent of Internet-based electronic commerce offers considerable opportunities for firms to expand their customer base, enter new product markets and rationalise their business. WTO (2001) also indicated that electronic business offers SMEs the opportunity to undertake their business in new and more cost-effective ways. According to WTO, the Internet is revolutionising the distribution of tourism information and sales. An increasing proportion of Internet users are buying on–line and tourism will gain a larger and larger share of the online commerce market. Obviously, the Internet is having a major impact as a source of information for tourism. However, the SMTEs are facing more stringent impediments to the adoption of new information technology, in particular, e-business. Part of the problem relates to the scale and affordability of information technology, as well as the facility of implementation within rapidly growing and changing organisations. In addition, new solutions configured for large, stable, and internationally-oriented firms do not fit well for small, dynamic, and locally-based tourism firms. Despite these challenges, SMTEs with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This implies equal access to telecom infrastructure, as well as to marketing management and education. According to a UN report (2001), “it is not the cost of being there, on the on-line market place, which must be reckoned with, but the cost of not being there.” It is certain that embracing digital communication and information technology is no longer an option, but a necessity. Thus, one of the most important characteristics of


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

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.


A robust adaptive mixing control for improved forward flight of a tilt-rotor UAV

This work presents the modeling and control of a tilt-rotor UAV, with tail controlled surfaces, for path tracking with improved forward flight. The dynamic model is obtained using the Euler-Lagrange formulation considering the aerodynamic forces and torques exerted on the horizontal and vertical stabilizers, and fuselage. For control design purposes, the equations of motion are linearized around different operation points to cover a large range of forward velocity. Based on these linearized dynamic models, a mixed H2/H∞ robust controller is designed for each operation point. Therefore, an adaptive mixing scheme is used to perform an on-line smooth gain-scheduling between them. Simulation results show the control strategy efficiency when the UAV is designated to have a forward acceleration and perform a circular trajectory subject to a wind disturbance.


Fatigue in soccer: a brief review.

This review describes when fatigue may develop during soccer games and the potential physiological mechanisms that cause fatigue in soccer. According to time-motion analyses and performance measures during match-play, fatigue or reduced performance seems to occur at three different stages in the game: (1) after short-term intense periods in both halves; (2) in the initial phase of the second half; and (3) towards the end of the game. Temporary fatigue after periods of intense exercise in the game does not appear to be linked directly to muscle glycogen concentration, lactate accumulation, acidity or the breakdown of creatine phosphate. Instead, it may be related to disturbances in muscle ion homeostasis and an impaired excitation of the sarcolemma. Soccer players' ability to perform maximally is inhibited in the initial phase of the second half, which may be due to lower muscle temperatures compared with the end of the first half. Thus, when players perform low-intensity activities in the interval between the two halves, both muscle temperature and performance are preserved. Several studies have shown that fatigue sets in towards the end of a game, which may be caused by low glycogen concentrations in a considerable number of individual muscle fibres. In a hot and humid environment, dehydration and a reduced cerebral function may also contribute to the deterioration in performance. In conclusion, fatigue or impaired performance in soccer occurs during various phases in a game, and different physiological mechanisms appear to operate in different periods of a game.


Combining Gradient and Albedo Data for Rotation Invariant Classification of 3D Surface Texture

We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant, However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and an also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient 1D polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.


A Software-Based Sonar Ranging Sensor for Smart Phones

We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone's microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.


Recent Advances in Augmented Reality

The field of Augmented Reality (AR) has existed for just over one decade, but the growth and progress in the past few years has been remarkable. In 1997, the first author published a survey [3] (based on a 1995 SIGGRAPH course lecture) that defined the field, described many problems, and summarized the developments up to that point. Since then, the field has grown rapidly. In the late 1990s, several conferences specializing in this area were started, including the International Workshop and Symposium on Augmented Reality [29], the International Symposium on Mixed Reality [30], and the Designing Augmented Reality Environments workshop. Some wellfunded interdisciplinary consortia were formed that focused on AR, notably the Mixed Reality Systems Laboratory [50] in Japan and Project ARVIKA [61] in Germany. A freely-available software toolkit (the ARToolkit) for rapidly building AR applications is now available [2]. Because of this wealth of new developments, an updated survey is needed to guide and encourage further research in this exciting area.


Exploring the Benefits of Augmented Reality Documentation for Maintenance and Repair

We explore the development of an experimental augmented reality application that provides benefits to professional mechanics performing maintenance and repair tasks in a field setting. We developed a prototype that supports military mechanics conducting routine maintenance tasks inside an armored vehicle turret, and evaluated it with a user study. Our prototype uses a tracked headworn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, localization, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: the same headworn display providing untracked text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed that mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.


ENHANCING THE TOURISM EXPERIENCE THROUGH MOBILE AUGMENTED REALITY: CHALLENGES AND PROSPECTS

The paper discusses the use of Augmented Reality (AR) applications for the needs of tourism. It describes the technology’s evolution from pilot applications into commercial mobile applications. We address the technical aspects of mobile AR applications development, emphasizing on the technologies that render the delivery of augmented reality content possible and experientially superior. We examine the state of the art, providing an analysis concerning the development and the objectives of each application. Acknowledging the various technological limitations hindering AR’s substantial end-user adoption, the paper proposes a model for developing AR mobile applications for the field of tourism, aiming to release AR’s full potential within the field.


A head-mounted three dimensional display

The fundamental idea behind the three-dimensional display is to present the user with a perspective image which changes as he moves. The retinal image of the real objects which we see is, after all, only two-dimensional. Thus if we can place suitable two-dimensional images on the observer's retinas, we can create the illusion that he is seeing a three-dimensional object. Although stereo presentation is important to the three-dimensional illusion, it is less important than the change that takes place in the image when the observer moves his head. The image presented by the three-dimensional display must change in exactly the way that the image of a real object would change for similar motions of the user's head. Psychologists have long known that moving perspective images appear strikingly three-dimensional even without stereo presentation; the three-dimensional display described in this paper depends heavily on this "kinetic depth effect."


Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR

Although Augmented Reality technology was first developed over forty years ago, there has been little survey work giving an overview of recent research in the field. This paper reviews the ten-year development of the work presented at the ISMAR conference and its predecessors with a particular focus on tracking, interaction and display research. It provides a roadmap for future augmented reality research which will be of great value to this relatively young field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area.


Dynamic program slicing methods

A dynamic program slice is that part of a program that ‘‘affects’’ the computation of a variable of interest during program execution on a specific program input. Dynamic program slicing refers to a collection of program slicing methods that are based on program execution and may significantly reduce the size of a program slice because run-time information, collected during program execution, is used to compute program slices. Dynamic program slicing was originally proposed only for program debugging, but its application has been extended to program comprehension, software testing, and software maintenance. Different types of dynamic program slices, together with algorithms to compute them, have been proposed in the literature. In this paper we present a classification of existing dynamic slicing methods and discuss the algorithms to compute dynamic slices. In the second part of the paper, we compare the existing methods of dynamic slice computation.  1998 Elsevier Science B.V. All rights reserved.


For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users’ preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoderdecoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.


Entity resolution using inferred relationships and behavior

We present a method for entity resolution that infers relationships between observed identities and uses those relationships to aid in mapping identities to underlying entities. We also introduce the idea of using graphlets for entity resolution. Graphlets are collections of small graphs that can be used to characterize the “role” of a node in a graph. The idea is that graphlets can provide a richer set of features to characterize identities. We validate our method on standard author datasets, and we further evaluate our method using data collected from Twitter. We find that inferred relationships and graphlets are useful for entity resolution.


A Hidden Semi-Markov Model-Based Speech Synthesis System

Recently, a statistical speech synthesis system based on the hidden Markov model (HMM) has been proposed. In this system, spectrum, excitation, and duration of human speech are modeled simultaneously by context-dependent HMMs and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative model framework and solves it using the maximum likelihood (ML) criterion. However, there is an inconsistency: although state duration models are explicitly used in the synthesis part of the system, they have not been incorporated in its training part. This inconsistency may degrade the naturalness of synthesized speech. In the present paper, a statistical speech synthesis system based on a hidden semi-Markov model (HSMM), which can be viewed as an HMM with explicit state duration models, is developed and evaluated. The use of HSMMs allows us to incorporate the state duration models explicitly not only in the synthesis part but also in the training part of the system and resolves the inconsistency in the HMM-based speech synthesis system. Subjective listening test results show that the use of HSMMs improves the reported naturalness of synthesized speech. key words: hidden Markov model, hidden semi-Markov model, HMMbased speech synthesis


Estimating mobile application energy consumption using program analysis

Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior.


Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

This keynote paper: presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.


Cogeneration of mechanical, electrical, and software designs for printable robots from structural specifications

Designing and fabricating new robotic systems is typically limited to experts, requiring engineering background, expensive tools, and considerable time. In contrast, to facilitate everyday users developing custom robots for personal use, this work presents a new system to easily create printable foldable robots from high-level structural specifications. A user merely needs to select electromechanical components from a library of basic building blocks and pre-designed mechanisms, then connect them to define custom robot assemblies. The system then generates complete mechanical drawings suitable for fabrication, instructions for the assembly of electronics, and software to control and drive the final robot. Several robots designed in this manner demonstrate the ability and versatility of this process.


Characterization of silicone rubber based soft pneumatic actuators

Conventional pneumatic actuators have been a popular choice due to their decent force/torque output. Nowadays, new generation of pneumatic actuator made out of highly compliant elastomers, which we call soft pneumatic actuators (SPA), are drawing increasing attention due to their ease of fabrication, high customizability and innately softness. However, there is no effective method presented to characterize and understand these actuators, such as to measure the force and torque output, range of motion and the speed of actuation. In this work, we present two types of SPAs: bending and rotary actuators. In addition, we have developed two measurement setups to characterize actuators of different geometries. The measured force/torque outputs of different actuators are presented and analyzed. Step responses to certain pressure input are presented and discussed. A simple model is presented to provide physical insight to the observed behavior of the soft actuators. This work provides the basis for designing customized SPAs with application-specific requirements.


Numerical Recipes 3rd Edition: The Art of Scientific Computing

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A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets

Traditional rough set theory is mainly used to extract rules from and reduce attributes in databases in which attributes are characterized by partitions, while the covering rough set theory, a generalization of traditional rough set theory, does the same yet characterizes attributes by covers. In this paper, we propose a way to reduce the attributes of covering decision systems, which are databases characterized by covers. First, we define consistent and inconsistent covering decision systems and their attribute reductions. Then, we state the sufficient and the necessary conditions for reduction. Finally, we use a discernibility matrix to design algorithms that compute all the reducts of consistent and inconsistent covering decision systems. Numerical tests on four public data sets show that the proposed attribute reductions of covering decision systems accomplish better classification performance than those of traditional rough sets. 2007 Elsevier Inc. All rights reserved.


Recognizing tactic patterns in broadcast basketball video using player trajectory

The explosive growth of the sports fandom inspires much research on manifold sports video analyses and applications. The audience, sports fans, and even professionals require more than traditional highlight extraction or semantic summarization. Computer-assisted sports tactic analysis is inevitably in urging demand. Recognizing tactic patterns in broadcast basketball video is a challenging task due to its complicated scenes, varied camera motion, frequently occlusions between players, etc. In basketball games, the action screen means that an offensive player perform a blocking move via standing beside or behind a defender for freeing a teammate to shoot, to receive a pass, or to drive in for scoring. In this paper, we propose a screen-strategy recognition system capable of detecting and classifying screen patterns in basketball video. The proposed system automatically detects the court lines for camera calibration, tracks players, and discriminates the offensive/defensive team. Player trajectories are calibrated to the realworld court model for screen pattern recognition. Our experiments on broadcast basketball videos show promising results. Furthermore, the extracted player trajectories and the recognized screen patterns visualized on a court model indeed assist the coach/players or the fans in comprehending the tactics executed in basketball games informatively and efficiently. 2012 Elsevier Inc. All rights reserved.


Integration of it governance and security risk management: A systematic literature review

GRC is an umbrella acronym covering the three disciplines of governance, risk management and compliance. In this context, IT GRC is the subset of GRC dealing with IT aspects of GRC. The main challenge of GRC is to have an approach as integrated as possible of the three domains. The objective of our paper is to study one facet of IT GRC: the links and integration between IT governance and risk management that we consider today as the least integrated. To do so, the method followed in this paper is a systematic literature review, in order to identify the existing research works in this field. The resulting contribution of the paper is a set of recommendations established for practitioners and for researchers on how better deal with the integration between IT governance and risk management.


Emotional intelligence: the most potent factor in the success equation.

Star performers can be differentiated from average ones by emotional intelligence. For jobs of all kinds, emotional intelligence is twice as important as a person's intelligence quotient and technical skills combined. Excellent performance by top-level managers adds directly to a company's "hard" results, such as increased profitability, lower costs, and improved customer retention. Those with high emotional intelligence enhance "softer" results by contributing to increased morale and motivation, greater cooperation, and lower turnover. The author discusses the five components of emotional intelligence, its role in facilitating organizational change, and ways to increase an organization's emotional intelligence.