title: Road Crack Detection Using Visual Features Extracted by Gabor Filters

abstract: Data has been acquired and used to train and test pavement management systems (PMS) and methods. The article discusses how PMS require detailed information on the current state of the roads in order to take appropriate actions to optimize expenditures on maintenance and rehabilitation. The presence of cracks is a crucial aspect to be considered. A solution based on an instrumented vehicle that is equipped with an imaging system, two Inertial Profilers, a Differential Global Positioning System (DGPS), and a webcam is presented. Information about the state of the road is acquired at normal road speed. A method that is based on the use of Gabor filters is used to detect longitudinal and transverse cracks. The methodologies used to create Gabor filter banks and the use of the filtered images as descriptors for subsequent classifiers are discussed in detail in the article. The article also evaluates three different methodologies for setting the threshold of the classifiers. Finally, an AdaBoost algorithm is used for selecting and combining the classifiers, which improves the results provided by a single classifier. The article discusses how suitable results have been obtained in comparison with other reference works.


title: Profile likelihood in systems biology

abstract: Inferring knowledge about biological processes by a mathematical description is a major characteristic of Systems Biology. To understand and predict system's behavior the available experimental information is translated into a mathematical model. Since the availability of experimental data is often limited and measurements contain noise, it is essential to appropriately translate experimental uncertainty to model parameters as well as to model predictions. This is especially important in Systems Biology because typically large and complex models are applied and therefore the limited experimental knowledge might yield weakly specified model components. Likelihood profiles have been recently suggested and applied in the Systems Biology for assessing parameter and prediction uncertainty. In this article, the profile likelihood concept is reviewed and the potential of the approach is demonstrated for a model of the erythropoietin (EPO) receptor.


title: Till Porn Do Us Part? A Longitudinal Examination of Pornography Use and Divorce

abstract: As pornography use becomes more commonplace in the United States, and increasingly so among younger cohorts, a growing literature is considering its potential connection to key social and cultural institutions. The current study examined the relationship between pornography use and one such institution: marriage. We drew on three-wave longitudinal data from 2006 to 2014 General Social Survey panel studies to determine whether married Americans’ pornography use predicted their likelihood of divorce over time and under what social conditions. We employed a doubly robust strategy that combines entropy balancing with logistic regression models. We found that the probability of divorce roughly doubled for married Americans who began pornography use between survey waves (N = 2,120; odds ratio = 2.19), and that this relationship held for both women and men. Conversely, discontinuing pornography use between survey waves was associated with a lower probability of divorce, but only for women. Additional analyses al...


title: A service based framework for integration of ITIL V3 and enterprise architecture

abstract: ITIL V3 could be introduced as a governance framework for IT service management (ITSM) and attempts to align IT investments with business objectives. Enterprise Architecture (EA) is also referred as a general framework for designing business and IT/IS architectures of organizations and it also attempts to align these architectures with business strategies. Our research tends to provide a service based framework for EA in order to meet IT Service Architecture requirements of ITIL V3. Regard to this issue our Integrated Service Architecture Framework (ISAF) is outlined here to be developed through main research. One of our goals is to provide a solution for practitioners in utilization of EA architectures in ITIL V3. Authors also hope this study would contribute in academic domain through bridging computer science and information system disciplines due to service-oriented technology and management aspects of the research.


title: Are workplace health promotion programs effective at improving presenteeism in workers? a systematic review and best evidence synthesis of the literature

abstract: BackgroundPresenteeism is highly prevalent and costly to employers. It is defined as being present at work, but limited in some aspect of job performance by a health problem.Workplace health promotion (WHP) is a common strategy used to enhance on-the-job productivity. The primary objective is to determine if WHP programs are effective in improving presenteeism. The secondary objectives are to identify characteristics of successful programs and potential risk factors for presenteeism.MethodsThe Cochrane Library, Medline, and other electronic databases were searched from 1990 to 2010. Reference lists were examined, key journals were hand-searched and experts were contacted. Included studies were original research that contained data on at least 20 participants (≥ 18 years of age), and examined the impacts of WHP programs implemented at the workplace. The Effective Public Health Practice Project Tool for Quantitative Studies was used to rate studies. 'Strong' and 'moderate' studies were abstracted into evidence tables, and a best evidence synthesis was performed. Interventions were deemed successful if they improved the outcome of interest. Their program components were identified, as were possible risk factors contributing to presenteeism.ResultsAfter 2,032 titles and abstracts were screened, 47 articles were reviewed, and 14 were accepted (4 strong and 10 moderate studies). These studies contained preliminary evidence for a positive effect of some WHP programs. Successful programs offered organizational leadership, health risk screening, individually tailored programs, and a supportive workplace culture. Potential risk factors contributing to presenteeism included being overweight, a poor diet, a lack of exercise, high stress, and poor relations with co-workers and management. Limitations: This review is limited to English publications. A large number of reviewed studies (70%) were inadmissible due to issues of bias, thus limiting the amount of primary evidence. The uncertainties surrounding presenteeism measurement is of significant concern as a source of bias.ConclusionsThe presenteeism literature is young and heterogeneous. There is preliminary evidence that some WHP programs can positively affect presenteeism and that certain risk factors are of importance. Future research would benefit from standard presenteeism metrics and studies conducted across a broad range of workplace settings.


Teaching and Learning in the Pleistocene: A Biocultural Account of Human Pedagogy and Its Implications for AIED


title: Two orchids, one scent? Floral volatiles of Catasetum cernuum and Gongora bufonia suggest convergent evolution to a unique pollination niche

abstract: Floral volatile organic compounds (VOCs) are important signals in plant-pollinator communication and thus subjected to olfactory-mediated selection. Occasionally, phylogenetically unrelated plant species will converge towards similar patterns of floral scent signalling to harness pollinators, representing a common adaptive pollination niche. Catasetum cernuum and Gongora bufonia, epiphytic orchids that are native to the Atlantic Forest of southeastern South America, are exclusively pollinated by male Eufriesea violacea orchid-bees; we hypothesised that the two species have converged towards similar floral scent compositions to effectively attract these particular fragrance-seeking floral visitors. The similarity of the chemical composition of floral scent samples obtained through headspace collection was tested using multiple approaches of multivariate analyses (Multiple Response Permutation Procedure—MRPP). According to MRPP, floral scents of C. cernuum and G. bufonia are significantly different. Additionally, the floral scents of C. cernuum and G. bufonia present (E)-β-ocimene and (E)-epoxyocimene as their major compounds, respectively. (E)-β-ocimene is a common constituent of floral scents from euglossine-pollinated orchids, while (E)-epoxyocimene is quite rare in orchids’ floral scents and it is found in only a few other species which occur outside the distribution range of C. cernuum, G. bufonia and E. violacea. However, 12 out of the 34 identified VOCs, including (E)-β-ocimene and (E)-epoxyocimene, are shared between both species. Therefore, we assume that both (E)-β-ocimene and (E)-epoxyocimene, either alone or mixed with other floral VOCs, play a major role in the specific association between these two orchids and their exclusive euglossine pollinator.


title: CSI-Net: Unified Human Body Characterization and Action Recognition

abstract: Channel State Information (CSI) of WiFi signals becomes increasingly attractive in human sensing applications due to the pervasiveness of WiFi, robustness to illumination and view points, and little privacy concern comparing to cameras. In majority of existing works, CSI sequences are analyzed by traditional signal processing approaches. These approaches rely on strictly imposed assumption on propagation paths, reflection and attenuation of signal interacting with human bodies and indoor background. This makes existing approaches very difficult to model the delicate body characteristics and activities in the real applications. To address these issues, we build CSI-Net, a unified Deep Neural Network (DNN), that fully utilizes the strength of deep feature representation and the power of existing DNN architectures for CSI-based human sensing problems. Using CSI-Net, we jointly solved two body characterization problems: biometrics estimation (including body fat, muscle, water and bone rates) and human identification. We also demonstrated the application of CSI-Net on two distinctive action recognition tasks: the hand sign recognition (fine-scaled action of the hand) and falling detection (coarse-scaled motion of the body). Besides the technical contribution of CSI-Net, we present major discoveries and insights on how the multi-frequency CSI signals are encoded and processed in DNNs, which, to the best of our knowledge, is the first attempt that bridges the WiFi sensing and deep learning in human sensing problems.


title: Attention to Refine through Multi-Scales for Semantic Segmentation

abstract: This paper proposes a novel attention model for semantic segmentation, which aggregates multi-scale and context features to refine prediction. Specifically, the skeleton convolutional neural network framework takes in multiple different scales inputs, by which means the CNN can get representations in different scales. The proposed attention model will handle the features from different scale streams respectively and integrate them. Then location attention branch of the model learns to softly weight the multi-scale features at each pixel location. Moreover, we add an recalibrating branch, parallel to where location attention comes out, to recalibrate the score map per class. We achieve quite competitive results on PASCAL VOC 2012 and ADE20K datasets, which surpass baseline and related works.


title: A multi-criteria decision making approach for recommending a product using sentiment analysis

abstract: Nowadays, online platform has become a modern means of shopping among people. The reviews of products by customers have been proliferating on the online platform for a while. Since a large number of reviews are available, invariably customers read reviews before buying the product. Majority of the reviews are lengthy and repetitive, some of them even have nothing to do with the product itself. Going through the reviews before making a decision has become a tedious task. Further, the product selection is a complex decision making problem where several criteria are involved in the decision making process. Researchers have used methods like machine learning and sentiment classification to analyze the review of customers to summarize them. However, review summarization does not suggest the best/worst product. This study aims to recommend the best product based on the opinions expressed in the customers' reviews. We analyze the reviews of customers from various online platforms and use effective multi criteria decision making approach to evaluate and recommend the best suitable product. Real-time dataset from Flipkart and Amazon are used to evaluate our system's performance. Different case studies have shown that our proposed method produces a promising result which can help the user in the decision making process.


title: Computer-generated pen-and-ink illustration

abstract: This paper describes the principles of traditional pen-and-ink illustration, and shows how a great number of them can be implemented as part of an automated rendering system. It introduces “stroke textures,” which can be used for achieving both texture and tone with line drawing. Stroke textures also allow resolution-dependent rendering, in which the choice of strokes used in an illustration is appropriately tied to the resolution of the target medium. We demonstrate these techniques using complex architectural models, including Frank Lloyd Wright's “Robie House.”


title: Dense Descriptors for Optical Flow Estimation: A Comparative Study

abstract: Estimating the displacements of intensity patterns between sequential frames is a very well-studied problem, which is usually referred to as optical flow estimation. The first assumption among many of the methods in the field is the brightness constancy during movements of pixels between frames. This assumption is proven to be not true in general, and therefore, the use of photometric invariant constraints has been studied in the past. One other solution can be sought by use of structural descriptors rather than pixels for estimating the optical flow. Unlike sparse feature detection/description techniques and since the problem of optical flow estimation tries to find a dense flow field, a dense structural representation of individual pixels and their neighbors is computed and then used for matching and optical flow estimation. Here, a comparative study is carried out by extending the framework of SIFT-flow to include more dense descriptors, and comprehensive comparisons are given. Overall, the work can be considered as a baseline for stimulating more interest in the use of dense descriptors for optical flow estimation.


title: Research trends on branding in consumer psychology

abstract: Brands have received much research attention from consumer psychologists, as brands serve as the embodiment of a firm within the lives of consumers. This work has relied upon a variety of well-established psychological theories (e.g., Big Five human personality dimensions, associative network theory, attachment theory, self-concept) to explore the strong influence that brands can have on consumers. Recent research has focused particular attention on how consumers respond to brand name linguistics, luxury brands, brand extensions, and on the application of psychological theories to brands. Variability in these trends suggests the need for a research agenda to guide brand research by consumer psychologists.


title: Effects of Probiotic and Prebiotic on Average Daily Gain, Fecal Shedding of Escherichia Coli, and Immune System Status in Newborn Female Calves

abstract: Thirty two Holstein female calves (initial body weight = 40±3.0 kg) were used to investigate the effects of probiotic and prebiotic on average daily gain (ADG), fecal E. coli count, white blood cell count, plasma IgG1 level and cell-mediated immune response to injection of phytohemagglutinin in suckling female calves. Calves were assigned randomly to one of the four treatments, including whole milk without additives (control), whole milk containing probiotic, whole milk containing prebiotic and whole milk containing probiotic and prebiotic (synbiotic). Average daily gain was greater in calves fed probiotic, prebiotic and synbiotic at weeks 6, 7 and 8 (p<0.05). E. coli count was significantly lower in calves fed probiotic, prebiotic and synbiotic on d 56 (p<0.05). There was no significant difference between treatments in blood samples and cell-mediated response. This study showed that addition of probiotic, prebiotic and combination of these additives to milk enhanced ADG and reduced fecal E. coli count in preruminant calves.


title: The adolescent brain and age-related behavioral manifestations

abstract: To successfully negotiate the developmental transition between youth and adulthood, adolescents must maneuver this often stressful period while acquiring skills necessary for independence. Certain behavioral features, including age-related increases in social behavior and risk-taking/novelty-seeking, are common among adolescents of diverse mammalian species and may aid in this process. Reduced positive incentive values from stimuli may lead adolescents to pursue new appetitive reinforcers through drug use and other risk-taking behaviors, with their relative insensitivity to drugs supporting comparatively greater per occasion use. Pubertal increases in gonadal hormones are a hallmark of adolescence, although there is little evidence for a simple association of these hormones with behavioral change during adolescence. Prominent developmental transformations are seen in prefrontal cortex and limbic brain regions of adolescents across a variety of species, alterations that include an apparent shift in the balance between mesocortical and mesolimbic dopamine systems. Developmental changes in these stressor-sensitive regions, which are critical for attributing incentive salience to drugs and other stimuli, likely contribute to the unique characteristics of adolescence.


title: An Ontology in Project Management Knowledge Domain T.Sheeba, Muscat College,

abstract: Learning is a process in which electronic medium is used to access the defined set of applications and processes. In e- Learning environment, automatic classification of learning materials is essential due to lack of common vocabulary of the knowledge area in some context. Ontology has the potential to play an important role in representing an area of knowledge. This paper proposes ontology to automatic classification of learning materials to the Project Management knowledge area domain. This ontology aims to facilitate the search for learning materials within the given domain. The Project Management Body of Knowledge (PMBOK) was used to define the hierarchical structures of knowledge as it is intended to cover broad area of Project Management. The Rational Unified Process (RUP) was used to add axioms to define the relationships between the main concepts. Two specific examples were designed to show the automatic classification of learning materials. Experiments were performed using the OWL reasoner Pellet and editor Protege 4.2 alpha version. The results of our performance evaluation show that the ontology is able to classify and locate learning materials from the Project Management area, according to the desired area, role, artifact and activity. KeywordsPMBOK; RUP; Project Management; Protege 4.2 alpha


title: Using time-dependent neural networks for EEG classification

abstract: This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs, which use FIR filters instead of static weights to allow temporal processing inside the classifier. A theoretical comparison of the two architectures is presented. The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.


title: Turtles, termites, and traffic jams: explorations in massively parallel microworlds

abstract: Part 1 Foundations: introduction the era of decentralization. Part 2 Constructions: constructionism LEGO/logo StarLogo objects and parallelism. Part 3 Explorations: simulations and stimulations slime mould artificial ants traffic jams termites turtles and frogs turtle ecology new turtle geometry forest fire recursive trees. Part 4 Reflections: the centralized mindset beyond the centralized mindset. Part 5 Projections: growing up. Appendices: student participants StarLogo overview.


title: Bipolar voltage level shifter

abstract: Most of the advanced CCD sensors require bipolar clocks with large voltage swings. Hence there is a need of voltage level shifter to translate TTL/CMOS compatible levels to bipolar voltage swings (up to 20V). The CMOS drivers have advantages of low power and high input impedance but its main challenge is to attain negative voltage swing. Hence different topologies have been studied to implement the bipolar CMOS voltage level shifter design to guarantee a wide bipolar voltage conversion range with limited static power and total energy consumption. This circuit is implemented in a high voltage CMOS process. Design simulations have been carried out for TTL/CMOS input signals and output is verified for operational frequencies up to 20MHz and voltage swings up to 20V for all process-voltage-temperature variations. Designed level shifter provides output with rise /fall time less than 2ns. This paper discusses the designed circuit and detailed simulation results.


title: Lifelogging: You're Wearing a Camera?

abstract: The appearance of a new generation of highly portable, maybe even fashionable image-based lifelogging device has the potential to disrupt our current understanding of what is “socially acceptable” in the same way that the Kodak Camera did over a century ago. Back then, it prompted society to reflect on an individual's “right to privacy.” Soon, our lifelogging devices will require us to revisit such rights and maybe find a compromise between a right to privacy and a “right to remember.”


title: Modified Booth modulo 2/sup n/-1 multipliers

abstract: — ::: is one of the most commonly used moduli in Residue Number Systems. In this paper, we propose a new method for designing modified Booth modulo multipliers, which, for even values of ::: , require one less partial product than the already known. CMOS implementations reveal that the proposed multipliers compared to earlier solutions offer savings up to 28.7 percent and up to 29.3 percent in the implementation area and execution delay, respectively.


title: Group Sparse Beamforming for Green Cloud-RAN

abstract: A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near- optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted $\ell_1/\ell_2$-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.


title: Gait cycle spectrogram analysis using a torso-attached inertial sensor

abstract: Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We describe a new way to detect walking, and measure gait cadence, by using time-frequency signal processing together with spectrogram analysis of signals from a chest-worn inertial measurement unit (IMU). A pilot study of 11 participants suggests that this method is able to distinguish between walk and non-walk activities with up to 88.70% sensitivity and 97.70% specificity. Limitations of the method include instability associated with manual fine-tuning of local and global threshold levels.


title: Design of soft robotic actuators using fluid-filled fiber-reinforced elastomeric enclosures in parallel combinations

abstract: Complex controlled motions, soft human interaction, and minimal moving mass all drive the need for soft robots using fluid filled fiber reinforced elastomer enclosures (FREEs). While a narrow class of FREEs known as McKibben's actuators have been extensively studied, there is a wide unexplored class with complex sets of motion patterns. Combining these actuators in parallel can yield versatile motion patterns resulting in a large overall workspace. Mobility of individual actuators is enforced by inextensibility of fibers and incompressibility of fluids, which in turn drives the net attainable motions. In this paper, we map the mobility of individual FREE actuators to all possible resultant motions that a combination of three sets of actuators arranged in a triangular configuration would undergo. This understanding has resulted in a preliminary design tool that determines individual FREE topologies for a required set of motion patterns. Five case studies that result from this methodology are prototyped and tested for comparison of the predicted and obtained motion directions.


title: A heterogeneous sensor web node meta-model for the management of a flood monitoring system

abstract: A geospatial sensor web can be seen as a complex observation system consisting of distributed nodes with different functions and web-based access interfaces. Because of its heterogeneous characteristic, the precise discovery and efficient allocation of available nodes have become key problems in the field of sensor web resource integrated management. Considering the management requirements of a flood monitoring system, we propose a sensor web heterogeneous node meta-model and discuss the development of five basic metadata components, the design of a nine-tuple node information description structure. A prototype system GeosensorNodeManager is implemented to provide the functions of modeling, querying, and visualizing node resources. For the application scenario of flood monitoring, the flood forecasting task is selected as an example to test the versatility of the proposed node meta-model and the applicability of the formal expression based on the nine-tuple metadata framework. Results show that the proposed meta-model can be used in the modeling of three different types of sensor web nodes with the extension mechanism. By querying and visualizing the sensor web node instances in the Yangtze River basin, the node meta-model provides an effective way for the resource management of a flood monitoring sensor web system. Compared with the typical flood monitoring platform ''Sentinel Asia,'' the proposed meta-model can offer a succinct description of the system and lay the component model foundation for architecture design.


Comparison Of Learning Algorithms For Handwritten Digit Recognition


title: On the need to study the impact of model driven engineering on software processes

abstract: There is an increasing use of model-driven engineering (MDE) in the industry. Despite the existence of research proposals for MDE-specific processes, the question arises whether and how the processes that are already used within a company can be reused, when MDE is introduced. In this position paper we report on a systematic literature review on the question how standard processes, such as SCRUM or the V-Model XT, can be combined with MDE. We come up with the observation that - although it is in some cases possible to reuse standard processes - the combination with MDE can also result in heavyweight changes to a process. Our goal is to draw attention to two arising research needs: the need to collect systematic knowledge about the influence of MDE on software processes and the need to provide guidance for the tailoring of processes based on the set of used MDE techniques.


title: Voodoo Machine Learning for Clinical Predictions

abstract: The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map that data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is important to reliably quantify their prediction accuracy. Cross-validation is the standard approach for evaluating the accuracy of such algorithms; however, several cross-validations methods exist and only some of them are statistically meaningful. Here we compared two popular cross-validation methods: record-wise and subject-wise. Using both a publicly available dataset and a simulation, we found that record-wise cross-validation often massively overestimates the prediction accuracy of the algorithms. We also found that this erroneous method is used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as erroneous results can mislead both clinicians and data scientists.


title: The spacing effect depends on an encoding deficit, retrieval, and time in working memory: evidence from once-presented words.

abstract: The spacing effect in list learning occurs because identical massed items suffer encoding deficits and because spaced items benefit from retrieval and increased time in working memory. Requiring the retrieval of identical items produced a spacing effect for recall and recognition, both for intentional and incidental learning. Not requiring retrieval produced spacing only for intentional learning because intentional learning encourages retrieval. Once-presented words provided baselines for these effects. Next, massed and spaced word pairs were judged for matches on their first three letters, forcing retrieval. The words were not identical, so there was no encoding deficit. Retrieval could and did cause spacing only for the first word of each pair; time in working memory, only for the second.


title: A Theory of Crowdfunding: A Mechanism Design Approach with Demand Uncertainty and Moral Hazard

abstract: Crowdfunding provides innovation in that it enables entrepreneurs to contract with consumers before investment. Under aggregate demand uncertainty, this improves screening for valuable projects. Entrepreneurial moral hazard threatens this benefit. Studying the subsequent trade-off between screening and moral hazard, the paper characterizes optimal mechanisms. Popular all-or-nothing reward-crowdfunding schemes reflect their salient features. Efficiency is sustainable only if returns exceed investment costs by a margin reflecting the degree of moral hazard. Constrained efficient mechanisms exhibit underinvestment. As a screening tool for valuable projects, crowdfunding promotes social welfare. Crowdfunding complements rather than substitutes traditional entrepreneurial financing.


title: Performance and design of SIC receiver for downlink NOMA with open-loop SU-MIMO

abstract: This paper investigates the performance and design of successive interference cancellation (SIC) receiver for downlink non-orthogonal multiple access (NOMA) combined with 2-by-2 open-loop single-user (SU)-MIMO. Compared to NOMA with 1-by-2 SIMO (Single Input Multiple Output), for NOMA with 2-by-2 SU-MIMO we have to deal with both inter-user and interstream interference at the receiver of each user. Therefore, we introduce different weight generation schemes for both before SIC and after SIC according to the transmission rank combination between the user equipment (UEs), while taking into account inter-user interference. In addition, we compare the performance of different SIC receivers; specifically, codeword level SIC (CWIC), symbol level SIC (SLIC), and ideal SIC. Assuming LTE (Long Term Evolution) Transmission Mode 3 (TM3), link-level simulations are conducted under different allocated transmit powers, rank combinations, and modulation and coding schemes (MCS). The link-level simulation results showed that the CWIC receiver achieves higher performance compared to SLIC and achieves almost the same performance compared to ideal SIC when the power ratio of cell-center UE is below 0.35, and the MCS of cell-center UE and cell-edge UE are 16QAM (R = 0.49) and QPSK (R = 0.49), respectively.


title: Manifold Mixup: Learning Better Representations by Interpolating Hidden States

abstract: Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. Ideally, a model would assign lower confidence to points unlike those from the training distribution. We propose a regularizer which addresses this issue by training with interpolated hidden states and encouraging the classifier to be less confident at these points. Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes. This has a major advantage in that it avoids the underfitting which can result from interpolating in the input space. We prove that the exact condition for this problem of underfitting to be avoided by Manifold Mixup is that the dimensionality of the hidden states exceeds the number of classes, which is often the case in practice. Additionally, this concentration can be seen as making the features in earlier layers more discriminative. We show that despite requiring no significant additional computation, Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.


title: QoE and power efficiency tradeoff for fog computing networks with fog node cooperation

abstract: This paper studies the workload offloading problem for fog computing networks in which a set of fog nodes can offload part or all the workload originally targeted to the cloud data centers to further improve the quality-of-experience (QoE) of users. We investigate two performance metrics for fog computing networks: users' QoE and fog nodes' power efficiency. We observe a fundamental tradeoff between these two metrics for fog computing networks. We then consider cooperative fog computing networks in which multiple fog nodes can help each other to jointly offload workload from cloud data centers. We propose a novel cooperation strategy referred to as offload forwarding, in which each fog node, instead of always relying on cloud data centers to process its unprocessed workload, can also forward part or all of its unprocessed workload to its neighboring fog nodes to further improve the QoE of its users. A distributed optimization algorithm based on distributed alternating direction method of multipliers (ADMM) via variable splitting is proposed to achieve the optimal workload allocation solution that maximizes users' QoE under the given power efficiency. We consider a fog computing platform that is supported by a wireless infrastructure as a case study to verify the performance of our proposed framework. Numerical results show that our proposed approach significantly improves the performance of fog computing networks.


title: Signal processing on simplicial complexes

abstract: Theoretical development and applications of graph signal processing (GSP) have attracted much attention. In classical GSP, the underlying structures are restricted in terms of dimensionality. A graph is a combinatorial object that models binary relations, and it does not directly model complex $n$-ary relations. One possible high dimensional generalization of graphs are simplicial complexes. They are a step between the constrained case of graphs and the general case of hypergraphs. In this paper, we develop a signal processing framework on simplicial complexes, such that we recover the traditional GSP theory when restricted to signals on graphs. We demonstrate how to perform signal processing with the framework using numerical examples.


title: Spectral Relaxation for K means Clustering

abstract: The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix of the data vectors. Furthermore, we show that a relaxed version of the trace maximization problem possesses global optimal solutions which can be obtained by computing a partial eigendecomposition of the Gram matrix, and the cluster assignment for each data vectors can be found by computing a pivoted QR decomposition of the eigenvector matrix. As a by-product we also derive a lower bound for the minimum of the sum-of-squares cost function.


title: AMCloud: Toward a Secure Autonomic Mobile Ad Hoc Cloud Computing System

abstract: Cloud computing is a revolutionary paradigm to deliver computing resources, ranging from data storage/processing to software, as a service over the network, with the benefits of efficient resource utilization and improved manageability. The current popular cloud computing models encompass a cluster of expensive and dedicated machines to provide cloud computing services, incurring significant investment in capital outlay and ongoing costs. A more cost effective solution would be to exploit the capabilities of an ad hoc cloud which consists of a cloud of distributed and dynamically untapped local resources. The ad hoc cloud can be further classified into static and mobile clouds: an ad hoc static cloud harnesses the underutilized computing resources of general purpose machines, whereas an ad hoc mobile cloud harnesses the idle computing resources of mobile devices. However, the dynamic and distributed characteristics of ad hoc cloud introduce challenges in system management. In this article, we propose a generic em autonomic mobile cloud (AMCloud) management framework for automatic and efficient service/resource management of ad hoc cloud in both static and mobile modes. We then discuss in detail the possible security and privacy issues in ad hoc cloud computing. A general security architecture is developed to facilitate the study of prevention and defense approaches toward a secure autonomic cloud system. This article is expected to be useful for exploring future research activities to achieve an autonomic and secure ad hoc cloud computing system.


title: Design of flight control system for quad tilt-wing UAV

abstract: Aircraft control system is designed to provide adequate stability during transition flight of a quad-tilt wing (QTW) UAV. The dynamic inversion method, which is a linearization method without an approximation algorithm, is applied to a control problem of the UAV, because of strong nonlinearity of its dynamical behavior. The validity of the proposed control system is verified through numerical simulation and experiment.


title: A comparative analysis of Cross Site Scripting (XSS) detecting and defensive techniques

abstract: Now the web applications are highly useful and powerful for usage in most fields such as finance, e-commerce, healthcare and more, so it must be well secured. The web applications may contain vulnerabilities, which are exploited by attackers to steal the user's credential. The Cross Site Scripting (XSS) attack is a critical vulnerability that affects on the web applications security. XSS attack is an injection of malicious script code into the web application by the attacker in the client-side within user's browser or in the server-side within the database, this malicious script is written in JavaScript code and injected within untrusted input data on the web application. This study discusses the XSS attack, its taxonomy, and its incidence. In addition, the paper presents the XSS mechanisms used to detect and prevent the XSS attacks.


title: Insole plantar pressure systems in the gait analysis of post-stroke rehabilitation

abstract: This article presents a review of the application of insole plantar pressure sensor system in recognition and analysis of the hemiplegic gait in stroke patients. Based on the review, tailor made 3D insoles for plantar pressure measurement were designed and fabricated. The function is to compare with that of conventional flat insoles. Tailor made 3D contour of the insole can improve the contact between insole and foot and enable sampling plantar pressure at a high reproducibility.


title: Design Decision Support for Steel Frame Buildings through an Earthquake-Induced Loss Assessment

abstract: In recent years, there is an increasing need to quantify earthquake-induced losses throughout the expected life of a building in order to evaluate alternative design options such that we can minimize repairs in the aftermath of an earthquake. This paper discusses an analytical study that quantifies the expected economic losses in a portfolio of archetype steel frame buildings designed with perimeter special moment frames or special concentrically braced frames in urban California in accordance with current seismic provisions in the U.S. The expected economic losses associated with repair are computed based on an established loss estimation framework within the context of performance-based earthquake engineering. It is shown that repair costs in the aftermath of earthquakes vary significantly depending on the employed lateral load-resisting system, seismic design considerations as well as the analytical model representation of the archetype frame building itself.


title: A framework for query refinement with user feedback

abstract: SQL queries in the existing relational data model implement the binary satisfaction of tuples. That is, a data tuple is filtered out from the result set if it does not satisfy the constraints expressed in the predicates of the user submitted query. Posing appropriate queries for ordinary users is very difficult in the first place if they lack knowledge of the underlying dataset. Therefore, imprecise queries are commonplace for many users. In connection with this, this paper presents a framework for capturing user intent through feedback for refining the initial imprecise queries that can fulfill the users' information needs. The feedback in our framework consists of both unexpected tuples currently present in the query output and expected tuples that are missing from the query output. We show that our framework does not require users to provide the complete set of feedback tuples because only a subset of this feedback can suffice. We provide the point domination theory to complement the other members of feedback. We also provide algorithms to handle both soft and hard requirements for the refinement of initial imprecise queries. Experimental results suggest that our approach is promising compared to the decision tree based query refinement approach.


title: Compatible Embedding for 2D Shape Animation

abstract: We present new algorithms for the compatible embedding of 2D shapes. Such embeddings offer a convenient way to interpolate shapes having complex, detailed features. Compared to existing techniques, our approach requires less user input, and is faster, more robust, and simpler to implement, making it ideal for interactive use in practical applications. Our new approach consists of three parts. First, our boundary matching algorithm locates salient features using the perceptually motivated principles of scale-space and uses these as automatic correspondences to guide an elastic curve matching algorithm. Second, we simplify boundaries while maintaining their parametric correspondence and the embedding of the original shapes. Finally, we extend the mapping to shapes' interiors via a new compatible triangulation algorithm. The combination of our algorithms allows us to demonstrate 2D shape interpolation with instant feedback. The proposed algorithms exhibit a combination of simplicity, speed, and accuracy that has not been achieved in previous work.


title: BIOSMILE: A semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features

abstract: BackgroundBioinformatics tools for automatic processing of biomedical literature are invaluable for both the design and interpretation of large-scale experiments. Many information extraction (IE) systems that incorporate natural language processing (NLP) techniques have thus been developed for use in the biomedical field. A key IE task in this field is the extraction of biomedical relations, such as protein-protein and gene-disease interactions. However, most biomedical relation extraction systems usually ignore adverbial and prepositional phrases and words identifying location, manner, timing, and condition, which are essential for describing biomedical relations. Semantic role labeling (SRL) is a natural language processing technique that identifies the semantic roles of these words or phrases in sentences and expresses them as predicate-argument structures. We construct a biomedical SRL system called BIOSMILE that uses a maximum entropy (ME) machine-learning model to extract biomedical relations. BIOSMILE is trained on BioProp, our semi-automatic, annotated biomedical proposition bank. Currently, we are focusing on 30 biomedical verbs that are frequently used or considered important for describing molecular events.ResultsTo evaluate the performance of BIOSMILE, we conducted two experiments to (1) compare the performance of SRL systems trained on newswire and biomedical corpora; and (2) examine the effects of using biomedical-specific features. The experimental results show that using BioProp improves the F-score of the SRL system by 21.45% over an SRL system that uses a newswire corpus. It is noteworthy that adding automatically generated template features improves the overall F-score by a further 0.52%. Specifically, ArgM-LOC, ArgM-MNR, and Arg2 achieve statistically significant performance improvements of 3.33%, 2.27%, and 1.44%, respectively.ConclusionWe demonstrate the necessity of using a biomedical proposition bank for training SRL systems in the biomedical domain. Besides the different characteristics of biomedical and newswire sentences, factors such as cross-domain framesets and verb usage variations also influence the performance of SRL systems. For argument classification, we find that NE (named entity) features indicating if the target node matches with NEs are not effective, since NEs may match with a node of the parsing tree that does not have semantic role labels in the training set. We therefore incorporate templates composed of specific words, NE types, and POS tags into the SRL system. As a result, the classification accuracy for adjunct arguments, which is especially important for biomedical SRL, is improved significantly.


title: Returners and explorers dichotomy in human mobility

abstract: The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.


title: IT Governance Structures, Processes and Relational Mechanisms: Achieving IT/Business Alignment in a Major Belgian Financial Group

abstract: IT governance is one of these concepts that suddenly emerged and became an important issue in the information technology area. Some organisations have started with the implementation of IT governance in order to achieve the fusion between business and IT. This paper describes how an organisation can implement IT governance, using a mixture of processes, structures and relational mechanisms, and analyses the IT governance implementation at KBC, a major Belgian financial group.


title: Document Filtering for Long-tail Entities

abstract: Filtering relevant documents with respect to entities is an essential task in the context of knowledge base construction and maintenance. It entails processing a time-ordered stream of documents that might be relevant to an entity in order to select only those that contain vital information. State-of-the-art approaches to document filtering for popular entities are entity-dependent: they rely on and are also trained on the specifics of differentiating features for each specific entity. Moreover, these approaches tend to use so-called extrinsic information such as Wikipedia page views and related entities which is typically only available only for popular head entities. Entity-dependent approaches based on such signals are therefore ill-suited as filtering methods for long-tail entities. In this paper we propose a document filtering method for long-tail entities that is entity-independent and thus also generalizes to unseen or rarely seen entities. It is based on intrinsic features, i.e., features that are derived from the documents in which the entities are mentioned. We propose a set of features that capture informativeness, entity-saliency, and timeliness. In particular, we introduce features based on entity aspect similarities, relation patterns, and temporal expressions and combine these with standard features for document filtering. Experiments following the TREC KBA 2014 setup on a publicly available dataset show that our model is able to improve the filtering performance for long-tail entities over several baselines. Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document. The overall performance across all entities---i.e., not just long-tail entities---improves upon the state-of-the-art without depending on any entity-specific training data.


title: A new active clamping zero-voltage switching PWM current-fed half-bridge converter

abstract: A new active clamping zero-voltage switching (ZVS) pulse-width modulation (PWM) current-fed half-bridge converter (CFHB) is proposed in this paper. Its active clamping snubber (ACS) can not only absorb the voltage surge across the turned-off switch, but also achieve the ZVS of all power switches. Moreover, it can be applied to all current-fed power conversion topologies and its operation as well as structure is very simple. Since auxiliary switches in the snubber circuit are switched in a complementary way to main switches, an additional PWM IC is not necessary. In addition, it does not need any clamp winding and auxiliary circuit besides additional two power switches and one capacitor while the conventional current-fed half bridge converter has to be equipped with two clamp windings, two ZVS circuits, and two snubbers. Therefore, it can ensure the higher operating frequency, smaller-sized reactive components, lower cost of production, easier implementation, and higher efficiency. The operational principle, theoretical analysis, and design considerations are presented. To confirm the operation, validity, and features of the proposed circuit, experimental results from a 200-W, 24-200Vdc prototype are presented.


title: Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity

abstract: A general framework for solving image inverse problems with piecewise linear estimations is introduced in this paper. The approach is based on Gaussian mixture models, which are estimated via a maximum a posteriori expectation-maximization algorithm. A dual mathematical interpretation of the proposed framework with a structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques. We demonstrate that, in a number of image inverse problems, including interpolation, zooming, and deblurring of narrow kernels, the same simple and computationally efficient algorithm yields results in the same ballpark as that of the state of the art.


title: Real-time clothoid approximation by Rational Bezier curves

abstract: This paper presents a novel technique for implementing Clothoidal real-time paths for mobile robots. As first step, rational Bezier curves are obtained as approximation of the Fresnel integrals. By rescaling, rotating and translating the previously computed RBC, an on-line Clothoidal path is obtained. In this process, coefficients, weights and control points are kept invariant. This on-line approach guarantees that an RBC has the same behavior as the original Clothoid using a low curve order. The resulting Clothoidal path allows any two arbitrary poses to be joined in a plane. RBCs working as Clothoids are also used to search for the shortest bounded-curvature path with a significant computational cost reduction. In addition to this, the proposed technique is tested on a real mobile robot for trajectory generation and kinematic control. To the authors' knowledge, the present approach is the first technique which allows real-time Clothoidal path computation.


title: The Effect of Vibrotactile Stimulation on the Emotional Response to Horror Films

abstract: Over the last decade consumers have witnessed a dramatic increase in the use of low fidelity, discrete vibrotactile feedback to enhance or replace audio stimuli in entertainment systems. However, use of high-resolution continuous vibrotactile displays remains quite uncommon. The Emoti-Chair is a high-resolution continuous vibrotactile display that may be driven by any type of audio signal, and is purported to convey the emotional properties of sound through organized vibrotactile stimulation. In the current study, we examined the ability of the Emoti-Chair to convey the emotional content of the soundtrack in a horror film. Increases in skin conductance levels were observed when vibrotactile stimuli were added to audio/visual film content. These results provide evidence that users can augment the communication of emotion in film through use of a high-resolution continuous vibrotactile display.


title: Q-learning with experience replay in a dynamic environment

abstract: Most research in reinforcement learning has focused on stationary environments. In this paper, we propose several adaptations of Q-learning for a dynamic environment, for both single and multiple agents. The environment consists of a grid of random rewards, where every reward is removed after a visit. We focus on experience replay, a technique that receives a lot of attention nowadays, and combine this method with Q-learning. We compare two variations of experience replay, where experiences are reused based on time or based on the obtained reward. For multi-agent reinforcement learning we compare two variations of policy representation. In the first variation the agents share a Q-function, while in the second variation both agents have a separate Q-function. Furthermore, in both variations we test the effect of reward sharing between the agents. This leads to four different multi-agent reinforcement learning algorithms, from which sharing a Q-function and sharing the rewards is the most cooperative method. The results show that in the single-agent environment both experience replay algorithms significantly outperform standard Q-learning and a greedy benchmark agent. In the multi-agent environment the highest maximum reward sum in a trial is achieved by using one Q-function and reward sharing. The highest mean reward sum is obtained with separate Q-functions and separate rewards.


A survey about ASR for children.


title: Android botnets for multi-targeted attacks

abstract: Today, mobile Botnets are well known in the IT security field. Whenever we talk about Botnets on mobile phones, we mostly deal with denial of service attacks (Kifayat and Wilson http://www.cms.livjm.ac.uk/pgnet2012/Proceedings/Papers/1569607737.pdf, 2012). This is due to the fact that we refer to classical Botnets on computers. But mobile phones are “mobiles” by definition. Indeed, they offer a lot of information not present on personal computers. They have a lot of sensors which are interesting for attackers. Most of the time, we used to think that targeted attacks have a single target. But with mobile phones, targeting a group of people does make sense. Coupled with data collected by the Sat Nav, we could so be able to localize with a certain probability meeting points in a criminal organization. By this way of attacking, we can deduce lots of things by cross-checking information obtained on devices. Thereby, this paper will aim to show the potential offered by such attacks. Firstly, this paper will focus on localization data. Furthermore, an implementation of an Android botnet and its server side part will be presented for illustrative purposes. Besides, the major part of the source code used will be included step by step in this paper. This paper aims to be technical because the author does not want to show any theory without trying some practicals tests with real and technical constraints.


title: Rotary diode failure detection in brushless exciter system of power plant synchronous generator

abstract: Reliability and availability of synchronous generator in power plant is related to performance of exciter system that is coupled mechanically and electrically to synchronous generator. This paper focused on the condition monitoring of rotating rectifier diodes in brushless exciter. Two common types of diode failures such as diode open-circuit and diode short-circuit on the one branch of rotating rectifier are simulated during no load and full load conditions. All accessible signals of main generator and exciter are analyzed by FFT method. In the end best signals for detection fault condition are introduced and suitable criteria to distinguish between diode failures are presented.


title: Mirror-touch synesthesia is linked with empathy

abstract: Watching another person being touched activates a similar neural circuit to actual touch and, for some people with 'mirror-touch' synesthesia, can produce a felt tactile sensation on their own body. In this study, we provide evidence for the existence of this type of synesthesia and show that it correlates with heightened empathic ability. This is consistent with the notion that we empathize with others through a process of simulation.


title: Design and Implementation of a True Random Number Generator Based on

abstract: There are many applications for true, unpredictable random numbers. For example the strength of numerous cryptographic operations is often dependent on a source of truly random numbers. Sources of random information are available in nature but are often hard to access in integrated circuits. In some specialized applications, analog noise sources are used in digital circuits at great cost in silicon area and power consumption. These analog circuits are often influenced by periodic signal sources that are in close proximity to the random number generator. We present a random number generator comprised entirely of digital circuits, which utilizes electronic noise. Unlike earlier work [11], only standard digital gates without regard to precise layout were used.


title: Pedestrian Detection with Deep Convolutional Neural Network

abstract: The problem of pedestrian detection in image and video frames has been extensively investigated in the past decade. However, the low performance in complex scenes shows that it remains an open problem. In this paper, we propose to cascade simple Aggregated Channel Features (ACF) and rich Deep Convolutional Neural Network (DCNN) features for efficient and effective pedestrian detection in complex scenes. The ACF based detector is used to generate candidate pedestrian windows and the rich DCNN features are used for fine classification. Experiments show that the proposed approach achieved leading performance in the INRIA dataset and comparable performance to the state-of-the-art in the Caltech and ETH datasets.


title: The EU’s growing pains in negotiating international food standards

abstract: This article relates the effectiveness of the European Union�s (EU) effectiveness to its international actorness in negotiations on international food standards taking place in the Codex Alimentarius Commission (CAC). Actorness is taken to result from EU competence, preference homogeneity and processes of socialisation among EU Member State representatives. In the 2009 negotiations on growth promoters for livestock, whose use the EU opposes, the Commission took the lead. It was trusted and supported by the EU Member States, but its dominant role resulted in them being rather passive. As a result, the EU�s potential to negotiate effectively in the CAC was not used in its full potential.


Impact of Service Oriented Architecture on ERP Implementations in Technical Education


title: Uplink PHY Design with Shortened TTI for Latency Reduction

abstract: Shortening the transmit time interval (TTI) has been considered in 3GPP LTE as an effective way for latency reduction. In this paper, we discuss the physical layer design aspects for uplink data transmissions with short TTI (sTTI). Here, an sTTI consists of one or a few SC-FDMA symbols, and its time duration is less than 1 ms. Different methods of demodulation reference signals (DMRS) design, DMRS sharing and DMRS multiplexing, are introduced to reduce the reference signal overhead when shortening the TTI length. Our link level simulation results show that both methods can efficiently reduce the DMRS overhead, and thereby improve the system throughput. Moreover, we show that due to better channel estimates, a shorter TTI, e.g., 2-symbol TTI, is more robust towards high Doppler scenarios. In contrast, for a 7-symbol TTI, which is 0.5 ms, more than one reference symbols can be inserted in order to improve the channel estimates in high Doppler scenarios. Finally, a discussion is given on how several short TTIs can be multiplexed into the frame format used for LTE.


High-Level Motion Processing


title: Toward an Efficient Resolution for a Single-machine Bi-objective Scheduling Problem with Rejection

abstract: Abstract We consider a single-machine bi-objective scheduling problem with rejection. In this problem, it is possible to reject some jobs. Four algorithms are provided to solve this scheduling problem. The two objectives are the total weighted completion time and the total rejection cost. The aim is to determine the set of efficient solutions. Four heuristics are described; they are implicit enumeration algorithms forming a branching tree, each one having two versions according to the root of the tree corresponding either to acceptance or rejection of all the jobs. The algorithms are first illustrated by a didactic example. Then they are compared on a large set of instances of various dimension and their respective performances are analysed.


title: Boosting the Efficiency: Unconventional Waveform Design for Efficient Wireless Power Transfer

abstract: Traditionally, wireless power is delivered through single-carrier, continuous-wave (CW) signals. Most research efforts to enhance the efficiency of wireless power transfer systems have been confined to the circuit-level design. However, in recent years, attention has been paid to the waveform design for wireless power transmission. It has been found that signals featuring a high peak-to-average power ratio (PAPR) can provide efficiency improvement when compared with CW signals. A number of approaches have been proposed, such as multisines/multicarrier orthogonal frequency division multiplex (OFDM) signals, chaotic signals, harmonicsignals, ultrawideband (UWB) signals, intermittent CW (ICW) signals, or white-noise signals. This article reviews these techniques with a focus on multisines/multicarrier signals, harmonic signals, and chaotic signals. A theoretical explanation for efficiency improvement is provided and accompanied by experimental results. Circuit design considerations are presented for the receiver side, and efficient transmission architectures are also described with an emphasis on spatial power combining.


title: Predictors of physical restraint use in Canadian intensive care units

abstract: Physical restraint (PR) use in the intensive care unit (ICU) has been associated with higher rates of self-extubation and prolonged ICU length of stay. Our objectives were to describe patterns and predictors of PR use. We conducted a secondary analysis of a prospective observational study of analgosedation, antipsychotic, neuromuscular blocker, and PR practices in 51 Canadian ICUs. Data were collected prospectively for all mechanically ventilated adults admitted during a two-week period. We tested for patient, treatment, and hospital characteristics that were associated with PR use and number of days of use, using logistic and Poisson regression respectively. PR was used on 374 out of 711 (53%) patients, for a mean number of 4.1 (standard deviation (SD) 4.0) days. Treatment characteristics associated with PR were higher daily benzodiazepine dose (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.00 to 1.11), higher daily opioid dose (OR 1.04, 95% CI 1.01 to 1.06), antipsychotic drugs (OR 3.09, 95% CI 1.74 to 5.48), agitation (Sedation-Agitation Scale (SAS) >4) (OR 3.73, 95% CI 1.50 to 9.29), and sedation administration method (continuous and bolus versus bolus only) (OR 3.09, 95% CI 1.74 to 5.48). Hospital characteristics associated with PR indicated patients were less likely to be restrained in ICUs from university-affiliated hospitals (OR 0.32, 95% CI 0.17 to 0.61). Mainly treatment characteristics were associated with more days of PR, including: higher daily benzodiazepine dose (incidence rate ratio (IRR) 1.07, 95% CI 1.01 to 1.13), daily sedation interruption (IRR 3.44, 95% CI 1.48 to 8.10), antipsychotic drugs (IRR 15.67, 95% CI 6.62 to 37.12), SAS <3 (IRR 2.62, 95% CI 1.08 to 6.35), and any adverse event including accidental device removal (IRR 8.27, 95% CI 2.07 to 33.08). Patient characteristics (age, gender, Acute Physiology and Chronic Health Evaluation II score, admission category, prior substance abuse, prior psychotropic medication, pre-existing psychiatric condition or dementia) were not associated with PR use or number of days used. PR was used in half of the patients in these 51 ICUs. Treatment characteristics predominantly predicted PR use, as opposed to patient or hospital/ICU characteristics. Use of sedative, analgesic, and antipsychotic drugs, agitation, heavy sedation, and occurrence of an adverse event predicted PR use or number of days used.


title: Fast Optimization Based Motion Planning and Path-Tracking Control for Car Parking

abstract: Abstract This paper presents a car parking control concept for real-time application. It utilizes a two-degrees-of-freedom control scheme consisting of a feedforward and a feedback controller. The reference trajectory is constructed in two steps. First a geometric path is planned by solving a local static optimization problem, which is formulated by discretizing the path. Second a pathfollowing problem in the form of an optimal control problem considering the physical limitations is solved. The solution of this optimal control problem yields the time parametrization of the geometric path. In order to account for model uncertainties and disturbances, a Lyapunovbased feedback controller is designed for the trajectory error system. Simulation studies show the applicability and efficiency of the proposed approach.


title: A hybrid fuzzy clustering approach for fertile and unfertile analysis

abstract: Diagnosis of male infertility by the laboratory tests is expensive, and sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision making process, so only in the cases with a high probability of infertility, we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression, and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each method; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.


title: Augmented reality and photogrammetry : A synergy to visualize physical and virtual city environments

abstract: Abstract Close-range photogrammetry is based on the acquisition of imagery to make accurate measurements and, eventually, three-dimensional (3D) photo-realistic models. These models are a photogrammetric product per se. They are usually integrated into virtual reality scenarios where additional data such as sound, text or video can be introduced, leading to multimedia virtual environments. These environments allow users both to navigate and interact on different platforms such as desktop PCs, laptops and small hand-held devices (mobile phones or PDAs). In very recent years, a new technology derived from virtual reality has emerged: Augmented Reality (AR), which is based on mixing real and virtual environments to boost human interactions and real-life navigations. The synergy of AR and photogrammetry opens up new possibilities in the field of 3D data visualization, navigation and interaction far beyond the traditional static navigation and interaction in front of a computer screen. In this paper we introduce a low-cost outdoor mobile AR application to integrate buildings of different urban spaces. High-accuracy 3D photo-models derived from close-range photogrammetry are integrated in real (physical) urban worlds. The augmented environment that is presented herein requires for visualization a see-through video head mounted display (HMD), whereas user’s movement navigation is achieved in the real world with the help of an inertial navigation sensor. After introducing the basics of AR technology, the paper will deal with real-time orientation and tracking in combined physical and virtual city environments, merging close-range photogrammetry and AR. There are, however, some software and complex issues, which are discussed in the paper.


title: Marketing actions can modulate neural representations of experienced pleasantness

abstract: Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.


title: Multi-Scale Dense Convolutional Networks for Efficient Prediction

abstract: We introduce a new convolutional neural network architecture with the ability to adapt dynamically to computational resource limits at test time. Our network architecture uses progressively growing multi-scale convolutions and dense connectivity, which allows for the training of multiple classifiers at intermediate layers of the network. We evaluate our approach in two settings: (1) anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and (2) budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across"easier"and"harder"inputs. Experiments on three image-classification datasets demonstrate that our proposed framework substantially improves the state-of-the-art in both settings.


title: Teaching tools, teachers’ rules: exploring the impact of teaching styles on young children’s programming knowledge in ScratchJr

abstract: Computer programming tools for young children are being created and used in early childhood classrooms more than ever. However, little is known about the relationship between a teacher’s unique instructional style and their students’ ability to explore and retain programming content. In this mixed-methods study, quantitative and qualitative data were collected from N = 6 teachers and N = 222 Kindergarten through second grade students at six schools across the United States. These teachers and students participated in an investigation of the relationship between teaching styles and student learning outcomes. All participants engaged in a minimum of two lessons and a maximum of seven lessons using the ScratchJr programming environment to introduce coding. Teachers reported on their classroom structure, lesson plan, teaching style and comfort with technology. They also administered ScratchJr Solve It assessments to capture various aspects of students’ programming comprehension, which were analyzed for trends in learning outcomes. Results from this descriptive, exploratory study show that all students were successful in attaining foundational ScratchJr programming comprehension. Statistically significant findings revealed higher programming achievement in students whose educators demonstrated flexibility in lesson planning, responsiveness to student needs, technological content expertise, and concern for developing students’ independent thinking. Implications for research in the development of computational thinking strategies are discussed, as well as suggestions for successfully implementing early childhood classroom interventions with ScratchJr.


A CASE STUDY OF CONDUCTIVITY IN MINERAL WATER


title: Identification of methylation-dependent regulatory elements for intergenic miRNAs in human H4 cells.

abstract: MicroRNAs (miRNAs) are important post-transcriptional regulators of various biological processes. Although our knowledge of miRNA expression and regulation has been increased considerably in recent years, the regulatory elements for miRNA gene expression (especially for intergenic miRNAs) are not fully understood. In this study, we identified differentially methylated regions (DMRs) within 1000 bp upstream from the start site of intergenic miRNAs in human neuroglioma cells using microarrays. Then we identified a unique sequence pattern, C[N](6)CT, within the DMRs using motif analysis. Interestingly, treatment of cells with a methyl transferase inhibitor (5-aza-2-deoxycytidine, DAC) significantly increased expression of miRNA genes with a high frequency of the C[N](6)CT motif in DMRs. Statistical analysis showed that the frequency of the C[N](6)CT motif in DMRs is highly correlated with intergenic miRNA gene expression, suggesting that C[N](6)CT motifs associated with DNA methylation regions play a role as regulatory elements for intergenic miRNA gene expression.


title: A fundamental critique of the fascial distortion model and its application in clinical practice

abstract: Abstract Introduction The therapeutic techniques used in the fascial distortion model (FDM) have become increasingly popular among manual therapists and physical therapists. The reasons for this trend remain to be empirically explored. Therefore this paper pursues two goals: first, to investigate the historical and theoretical background of FDM, and second, to discuss seven problems associated with the theory and practice of FDM. Materials and methods The objectives of this paper are based on a review of the literature. The research mainly focuses on clinical proofs of concept for FDM treatment techniques in musculoskeletal medicine. Results FDM as a treatment method was founded and developed in the early 1990s by Stephen Typaldos. It is based on the concept that all musculoskeletal complaints can be traced back to three-dimensional deformations or distortions of the fasciae. The concept is that these distortions can be undone through direct application of certain manual techniques. A literature review found no clinical trials or basic research studies to support the empirical foundations of the FDM contentions. Discussion Based on the absence of proof of concept for FDM treatment techniques along with certain theoretical considerations, seven problems emerge, the most striking of which include (1) diagnostic criteria for FDM, (2) the biological implausibility of the model, (3) the reduction of all such disorders to a single common denominator: the fasciae, (4) the role of FDM research, and (5) potentially harmful consequences related to FDM treatment. Conclusion The above problems can only be invalidated through high-quality clinical trials. Allegations that clinical experience is sufficient to validate therapeutic results have been abundantly refuted in the literature.


title: HSI-Based Color Error-Aware Subpixel Rendering Technique

abstract: In this letter, a new subpixel rendering algorithm using the hue-saturation-intensity (HSI)-based color error analysis is proposed for matrix display devices. The proposed method converts an RGB input image into a HSI image, which is a color space based on human color perception, in order to analyze the sampling errors that are caused by subpixel rendering. Then, it computes the difference between the directionally displaced pixels and selects the direction with the minimum difference. Finally, the proposed method adaptively assigns weights to current and neighboring subpixels based on the directional difference. In experiments for various types of test images, the proposed method enhanced the average luminance sharpness by up to 0.224 and the average chrominance blending by up to 7.311 dB, as compared to the benchmark methods.


title: 45° Linearly Polarized Resonant Slot Array Antenna Based on Substrate Integrated Waveguide

abstract: In this paper, a new configuration of integrated 45deg linearly polarized slot array antenna based on substrate integrated waveguide (SIW) technology is proposed, designed and fabricated with normal printed circuit board (PCB) process. The antenna consists of eight 45deg inclined slots and the measured gain is higher than 10.27 dBi at X-band. The measured radiation patterns and the return loss are in agreement with the simulated results.


title: Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation

abstract: Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.


title: A Similarity Measure for Collaborative Filtering with Implicit Feedback

abstract: Collaborative Filtering(CF) is a widely accepted method of creating recommender systems. CF is based on the similarities among users or items. Measures of similarity including the Pearson Correlation Coefficient and the Cosine Similarity work quite well for explicit ratings, but do not capture real similarity from the ratings derived from implicit feedback. This paper identifies some problems that existing similarity measures have with implicit ratings by analyzing the characteristics of implicit feedback, and proposes a new similarity measure called Inner Product that is more appropriate for implicit ratings. We conducted experiments on user-based collaborative filtering using the proposed similarity measure for two e-commerce environments. Empirical results show that our similarity measure better captures similarities for implicit ratings and leads to more accurate recommendations. Our inner product-based similarity measure could be useful for CF-based recommender systems using implicit ratings in which negative ratings are difficult to be incorporated.


title: Efficient FPGA acceleration of Convolutional Neural Networks using logical-3D compute array

abstract: Convolutional Deep Neural Networks (DNNs) are reported to show outstanding recognition performance in many image-related machine learning tasks. DNNs have a very high computational requirement, making accelerators a very attractive option. These DNNs have many convolutional layers with different parameters in terms of input/output/kernel sizes as well as input stride. Design constraints usually require a single design for all layers of a given DNN. Thus a key challenge is how to design a common architecture that can perform well for all convolutional layers of a DNN, which can be quite diverse and complex. In this paper we present a flexible yet highly efficient 3D neuron array architecture that is a natural fit for convolutional layers. We also present our technique to optimize its parameters including onchip buffer sizes for a given set of resource constraint for modern FPGAs. Our experimental results targeting a Virtex-7 FPGA demonstrate that our proposed technique can generate DNN accelerators that can outperform the state-of-the-art solutions, by 22% for 32-bit floating-point MAC implementations, and are far more scalable in terms of compute resources and DNN size.


title: FSM based FM0 and Miller encoder for UHF RFID Tag Emulator

abstract: The Radio Frequency Identification System (RFID) is becoming very popular system wireless technologies. The UHF RFID tag emulator is a part of RFID testing tools. The UHF RFID tag Emulator would be imitating the behavior of RFID Tag. The UHF RFID tag emulator (860Mhz to 960 MHz) is aimed for testing the RFID systems and also as a general-purpose communication link to other electronic devices. In this work, we have presented high-level architecture of tag emulator and the design of FM0 encoder and Miller encoder. As motivated by Finite State Machine, encoders are discussed with particular focus to use the RFID Emulator as data transport device and debugging tool. The synthesis result shows that FSM design is efficient and we have achieved operating frequency of 192.641MHz and 188.644MHz for FMO and Miller encoders.


title: MEASURING CUSTOMER SATISFACTION: MUST OR NOT?

abstract: Customer satisfaction and measurement issues have vital roles for businesses in providing and maintaining a competitive advantage. It is recognized that the businesses forming components of marketing mix by acknowledging the customers’ expectations, receive customer loyalty and profit in return. Via measuring customer satisfaction, organizations do not only have customer knowledge also have competitors’ knowledge in the market.


title: Development of a deep silicon phase Fresnel lens using Gray-scale lithography and deep reactive ion etching

abstract: We report the first fabrication and development of a deep phase Fresnel lens (PFL) in silicon through the use of gray-scale lithography and deep-reactive ion etching (DRIE). A Gaussian tail approximation is introduced as a method of predicting the height of photoresist gray levels given the relative amount of transmitted light through a gray-scale optical mask. Device mask design is accomplished through command-line scripting in a CAD tool to precisely define the millions of pixels required to generate the appropriate profile in photoresist. Etch selectivity during DRIE pattern transfer is accurately controlled to produce the desired scaling factor between the photoresist and silicon profiles. As a demonstration of this technology, a 1.6-mm diameter PFL is etched 43 /spl mu/m into silicon with each grating profile designed to focus 8.4 keV photons a distance of 118 m.


title: The Management of Peyronie's Disease: Evidence-based 2010 Guidelines

abstract: INTRODUCTION ::: The field of Peyronie's disease is evolving and there is need for a state-of-the-art information in this area. ::: ::: ::: AIM ::: To develop an evidence-based state-of-the-art consensus report on the management of Peyronie's disease. ::: ::: ::: METHODS ::: To provide state-of-the-art knowledge regarding the prevalence, etiology, medical and surgical management of Peyronie's Disease, representing the opinion of leading experts developed in a consensus process over a 2-year period. ::: ::: ::: MAIN OUTCOME MEASURES ::: Expert opinion was based on grading of evidence-based medical literature, widespread internal committee discussion, public presentation, and debate. ::: ::: ::: CONCLUSIONS ::: The real etiology of Peyronie's disease and the mechanisms of formation of the plaque still remain obscure. Although conservative management is obtaining a progressively larger consensus among the experts, surgical correction still remains the mainstay treatment for this condition.


title: Holoprosencephaly: Two Case Reports

abstract: Holoprosencephaly (HPE) is the most frequent malformation of the prosencephalon. It represents the absence or incomplete division of the prosencephalon during the 4th and 8th week of gestation. Its incidence is estimated to be 1 in 16000 live births and 1 in 250 spontaneous abortions (1). It is classified in 3 types, according to the degree of cerebral involvement: alobar, semilobar and lobar. The clinical features vary very much, depending on the severity of holoprosencephaly. We present in this paper two cases of newborns diagnosed with holoprosencephaly and a brief discussion on the pathogenesis, clinical features, management and prognosis of holoprosencephaly. ::: ::: Case no. 1: a preterm male sex infant was born at 32 weeks gestational age by a 26 year old mother. The birth weight was 1800 grams. The infant died immediately after birth. The pregnancy, which was partially investigated, had an apparently uncomplicated course. The mother had a previous cesarean section in 2002, her first child having a normal neurologic development until now. ::: ::: At 29 weeks of gestation alobar holoprosencephaly was diagnosed by brain scan. The sonography showed: fused cerebral hemispheres in their anterior and middle regions, major dilatation of the cerebral ventricles, proboscis, microcephaly, bilateral microftalmia and hypotelorism; the orbits were situated between the mouth and the proboscis. At birth, the infant presented with: cyclopia, proboscis and macroglossia. The autopsy showed, besides the facial malformations, fused cerebral hemispheres, a single ventricle, and fused thalamic nuclei in their caudal portion and suprarenal glands agenesis (Figures (Figures11--55). ::: ::: ::: ::: Figure 1 ::: ::: Proboscis, cyclopia, macroglossia ::: ::: ::: ::: ::: ::: Figure 5 ::: ::: Single ventricle, fused thalamic nuclei – coronal section ::: ::: ::: ::: ::: ::: Figure 2 ::: ::: Fused cerebral hemispheres ::: ::: ::: ::: ::: ::: Figure 3 ::: ::: Proboscis, cyclopia ::: ::: ::: ::: ::: ::: Figure 4 ::: ::: Single ventricle, choroid plexuses - coronal section ::: ::: ::: ::: Case no. 2: an infant with lobar holoprosencephaly and craniosynostosis, born at 29 weeks of gestation. The mother, age 32 years old, had in the past one spontaneous abortion and one spontaneous delivery and she received good prenatal care during this pregnancy. At 19 weeks gestation, the diagnosis of a choroids plexus cyst was made. ::: ::: At 28 weeks of gestation ultrasound scan revealed posterior pole of cranial cavity occupied by a translucent, asymmetrically disposed structure, with anatomically normal cerebellum, compressing the posterior horn of the left cerebral hemisphere, which was considered to be an arachnoid cyst. Lateral ventricles, had also a large communication in their middle with incomplete fused thalami. ::: ::: At 29 weeks gestation, following a premature labor, she gave birth to a live baby, female sex, weighting 1350 grams, who died 5 days after birth. Physical findings at birth included microcephaly and palatoschisis. The autopsy report confirmed ultrasound findings: lobar holoprosencephaly, midline fusion of cingulate gyrus, craniosynostosis, and the remains of the supposed arachnoid cyst (Figures (Figures66--9).9). ❑ ::: ::: ::: ::: Figure 6 ::: ::: Arahnoid cyst compressing the occipital lobe of the left cerebral hemisphere, above tentorium cerebelli, intact cerebellum – sagital section ::: ::: ::: ::: ::: ::: Figure 9 ::: ::: Arahnoid cyst compressing the occipital lobe of the left cerebral hemisphere, above tentorium cerebelli, intact cerebellum – oblique section through the posterior fossa ::: ::: ::: ::: ::: ::: Figure 7 ::: ::: Corpus callosum agenesis, midline fusion of cingulate girus, lateral ventricles with communication in their middle with incomplete fused thalami ::: ::: ::: ::: ::: ::: Figure 8 ::: ::: Corpus callosum agenesis, midline fusion of cingulate girus, lateral ventricles with communication in their middle with incomplete fused thalami – coronal section, ultrasound image


title: Single image super resolution based on sparse representation using discrete wavelet transform

abstract: Single image super resolution (SR) based on sparse representation is a promising technique where the SR problem is solved by searching for the most robust representation of a signal in terms of atoms in a dictionary. However, first and second-order derivatives are always used as features for patches to be trained as dictionaries and super-resolved patches are reconstructed using dictionaries and sparse representation of these features. In this paper, a novel single image SR algorithm based on sparse representation with considering the effect of significant features is proposed. Therefore, high frequency details are preserved using discrete wavelet transform (DWT). In addition, an intermediate process is also proposed to learn finer dictionaries and thereby estimate the sharper and more detailed super-resolved image. The dictionaries are constructed from the distinctive features using K-SVD dictionary learning algorithm. The intermediate process uses approximation subband. Thus, constructed dictionaries contain so much more significant information and the interpolated high frequency components are corrected. Therefore, the intermediate process restores the high frequency details better in super-resolved images. The proposed algorithm was tested on ‘Set14’ dataset. Owing to DWT, the proposed algorithm recovers the edges better as well as improving the computational efficiency. The quantitative and visual results show the superiority and competitiveness of the proposed method over the simplest techniques and state-of-art SR algorithms. Experimental time comparisons with the state-of-art algorithms validate the effectiveness of the proposed approach.


title: Learning Automata as a Basis for Multi Agent Reinforcement Learning

abstract: A control system for the stroke limitation of a machine tool element in which the element is connected with the slider of a potentiometer so that the voltage on the potentiometer slider varies in a respective direction with each direction of movement of the tool element. A pair of adjustable resistors are provided which form voltage sources with voltages conforming substantially to that of the potentiometer slider at the desired points of reversal of the tool element. The slider voltage is compared with the voltage at the adjustable resistors and the tool element is reversed when the slider voltage bears a predetermined relation to the voltage with which it is compared. A selectively adjustable delay could also be provided to delay the reversing of the tool element at at least one of the limits of travel thereof.


title: On-line Handwritten Signature Verification using Machine Learning Techniques with a Deep Learning Approach

abstract: The problem to be solved in this project is to distinguish two signatures from each other, with help of machine learning techniques. The main technique used is the comparison between two signatures and classifying if they are written by the same person (match) or not (no-match). The binary classication problem is then tackled with a few alternatives to better understand it. First by a simple engineered feature, then by the machine learning techniques as logistic regression, multi-layer perceptron and nally a deep learning approach with a convolutional neural network. ::: The evaluation method for the dierent algorithms was a plot of true positive rate (sensitivity) versus false positive rate (fall-out). The results of the alternative algorithms gave a dierent understanding of the problem. The engineered feature performed unexpectedly well. The logistic regression and multi-layer perceptron performed similarly. The main results from the nal model, which was a max-pooling, convolutional neural network, were a true positive rate of 96.7 % and a false positive rate of 0.6 %. ::: The deep learning approach on the signature verication problem shows ::: promising results but there is still room for improvement.


title: Design and control of a soft and continuously deformable 2D robotic manipulation system

abstract: In this paper we describe the design, fabrication, control, and experimental validation of a soft and highly compliant 2D manipulator. The arm consists of several body segments actuated using bi-directional fluidic elastomer actuators and is fabricated using a novel composite molding process. We use a cascaded PI and PID computation and novel fluidic drive cylinders to provide closed-loop control of curvature for each soft and highly compliant body segment. Furthermore, we develop algorithms to compute the arm’s forward and inverse kinematics in a manner consistent with piece-wise constant curvature continuum manipulators. These computation and control systems enable this highly compliant robot to autonomously follow trajectories. Experimental results with a robot consisting of six segments show that controlled movement of a soft and highly compliant manipulator is feasible.