title: Smart mirror E-health assistant — Posture analyze algorithm proposed model for upright posture

abstract: Today there exist many different types of smart assistants and devices, such as virtual assistants, smartphones and wearables, which have a purpose to coordinate and optimize the daily activities of the people worldwide. The smart assistants' focus is mainly on basic human needs, e.g. browsing, scheduling, navigating and other similar activities. However, not many smart assistants are concerned with the human health overall. In this paper, we focus on the possibility of using a smart mirror to detect health issues. A new Smart eHealth Mirror model is proposed, that consists of a smart mirror which works on its own algorithm and behaves as smart assistant. This proposed model uses face recognition authentication, posture problem detection, and proper posture guidance, followed with suggestions for preventive healthcare. The algorithm identifies the person's posture and carefully analyses the posture and body changes over time. The obtained results from the analysis satisfied our expectations by improving the upright posture of the tested individual by considerable rate. The benefit of the proposed smart algorithm is proven by the evaluation results, which improved with each new individual analysis.


title: Efficiently represent diverse system field usage in reliability testing

abstract: This paper addresses the problem how to represent diverse field usage of professional systems in an efficient way, so that field usage can be incorporated in reliability tests. With diverse we mean the variability in system use in the field. Operational profiles are constructed from system field data to represent system field use. A clustering technique is introduced and applied in a strategic way to reduce the diversity in describing diverse system use in the field. In this way testing effort could be reduced by a factor 87 while maintaining 70% similarity with the original system field data.


title: Mining questions asked by web developers

abstract: Modern web applications consist of a significant amount of client- side code, written in JavaScript, HTML, and CSS. In this paper, we present a study of common challenges and misconceptions among web developers, by mining related questions asked on Stack Over- flow. We use unsupervised learning to categorize the mined questions and define a ranking algorithm to rank all the Stack Overflow questions based on their importance. We analyze the top 50 questions qualitatively. The results indicate that (1) the overall share of web development related discussions is increasing among developers, (2) browser related discussions are prevalent; however, this share is decreasing with time, (3) form validation and other DOM related discussions have been discussed consistently over time, (4) web related discussions are becoming more prevalent in mobile development, and (5) developers face implementation issues with new HTML5 features such as Canvas. We examine the implications of the results on the development, research, and standardization communities.


title: FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

abstract: Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images to speed up the inference, and adopts orthogonal regularization to enhance the generalization ability. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including (i) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network, (ii) the ability to remove spatially variant noise by specifying a non-uniform noise level map, and (iii) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.


title: Predictors of breastfeeding duration: evidence from a cohort study.

abstract: OBJECTIVE ::: To report the duration of breastfeeding among a population of Australian women and to identify factors that are associated with the duration of full breastfeeding to 6 months and any breastfeeding to 12 months. ::: ::: ::: METHODS ::: Participants were 587 women who were recruited from 2 maternity hospitals in Perth and completed a baseline questionnaire just before or shortly after discharge from the hospital. Women were followed up by telephone interview at 4, 10, 16, 22, 32, 40, and 52 weeks postpartum. Data collected included sociodemographic, biomedical, hospital-related, and psychosocial factors associated with the initiation and the duration of breastfeeding. Cox's proportional hazards model was used to identify factors that were associated with the risk for discontinuing full breastfeeding before 6 months and any breastfeeding before 12 months. ::: ::: ::: RESULTS ::: At 6 months of age, fewer than one half of infants were receiving any breast milk (45.9%), and only 12% were being fully breastfed. By 12 months, only 19.2% of infants were still receiving any breast milk. Breastfeeding duration was independently, positively associated with maternal infant feeding attitudes and negatively associated with breastfeeding difficulties in the first 4 weeks, maternal smoking, introduction of a pacifier, and early return to work. ::: ::: ::: CONCLUSIONS ::: Relatively few women achieved the international recommendations for duration of full and overall breastfeeding. Women should receive anticipatory guidance while still in the hospital on how to prevent or manage common breastfeeding difficulties and should be discouraged from introducing a pacifier before 10 weeks, if at all. Improved maternity leave provisions and more flexible working conditions may help women to remain at home with their infants longer and/or to combine successfully breastfeeding with employment outside the home.


title: A Novel Low-Cost Circularly Polarized Rotated Stacked Dielectric Resonator Antenna

abstract: A novel low-cost method for generating circular polarization in a dielectric resonator antenna is proposed. The antenna comprises four rectangular dielectric layers, each one being rotated by an angle of 30 ° relative to its adjacent layers. Utilizing such an approach has provided a circular polarization over a bandwidth of 6% from 9.55 to 10.15 GHz. This has been achieved in conjunction with a 21% impedance-matching bandwidth over the same frequency range. Also, the radiation efficiency of the proposed circularly polarized dielectric resonator antenna is 93% in this frequency band of operation


title: 6D physical interaction with a fully actuated aerial robot

abstract: This paper presents the design, control, and experimental validation of a novel fully-actuated aerial robot for physically interactive tasks, named Tilt-Hex. We show how the Tilt-Hex, a tilted-propeller hexarotor is able to control the full pose (position and orientation independently) using a geometric control, and to exert a full-wrench (force and torque independently) with a rigidly attached end-effector using an admittance control paradigm. An outer loop control governs the desired admittance behavior and an inner loop based on geometric control ensures pose tracking. The interaction forces are estimated by a momentum based observer. Control and observation are made possible by a precise control and measurement of the speed of each propeller. An extensive experimental campaign shows that the Tilt-Hex is able to outperform the classical underactuated multi-rotors in terms of stability, accuracy and dexterity and represent one of the best choice at date for tasks requiring aerial physical interaction.


title: Parameterized Approximation Schemes for Steiner Trees with Small Number of Steiner Vertices

abstract: We study the Steiner Tree problem, in which a set of terminal vertices needs to be connected in the cheapest possible way in an edge-weighted graph. This problem has been extensively studied from the viewpoint of approximation and also parameterization. In particular, on one hand Steiner Tree is known to be APX-hard, and W[2]-hard on the other, if parameterized by the number of non-terminals (Steiner vertices) in the optimum solution. In contrast to this we give an efficient parameterized approximation scheme (EPAS), which circumvents both hardness results. Moreover, our methods imply the existence of a polynomial size approximate kernelization scheme (PSAKS) for the assumed parameter. ::: We further study the parameterized approximability of other variants of Steiner Tree, such as Directed Steiner Tree and Steiner Forest. For neither of these an EPAS is likely to exist for the studied parameter: for Steiner Forest an easy observation shows that the problem is APX-hard, even if the input graph contains no Steiner vertices. For Directed Steiner Tree we prove that computing a constant approximation for this parameter is W[1]-hard. Nevertheless, we show that an EPAS exists for Unweighted Directed Steiner Tree. Also we prove that there is an EPAS and a PSAKS for Steiner Forest if in addition to the number of Steiner vertices, the number of connected components of an optimal solution is considered to be a parameter.


title: Deep hashing for compact binary codes learning

abstract: In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the deep network: 1) the loss between the original real-valued feature descriptor and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) by including one discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes. Experimental results show the superiority of the proposed approach over the state-of-the-arts.


title: What Value Do Explicit High Level Concepts Have in Vision to Language Problems?

abstract: Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. We propose here a method of incorporating high-level concepts into the very successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art performance in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. In doing so we provide an analysis of the value of high level semantic information in V2L problems.


title: Firewall as a service in SDN OpenFlow network

abstract: Protecting publicly available servers in internet today is a serious challenge, especially when encountering Distributed denial-of-service (DDoS) attacks. In traditional internet, there is narrow scope of choices one can take when ingress traffic overloads physical connection limits. This paper proposes Firewall as a service in internet service providers (ISP) networks allowing end users to request and install match-action rules in ISPs edge routers. In proposed scenario, ISP runs Software Defined Networking environment where control plane is separated from data plane utilizing OpenFlow protocol and ONOS controller. For interaction between end-users and SDN Controller author defines an Application Programming Interface (API) over a secure SSL/TLS connection. The Controller is responsible for translating high-level logics in low-level rules in OpenFlow switches. This study runs experiments in OpenFlow test-bed researching a mechanism for end-user to discard packets on ISP edge routers thus minimizing their uplink saturation and staying on-line.


title: Fan-Out Chip on Substrate Device Interconnection Reliability Analysis

abstract: Fan-Out (FO) chip on substrate is one of the fan-out solution for package integration. This solution brings the short interconnection between die to die for excellent electrical performance. Fan-Out chip on substrate device provides excellent electrical performance in multi-die connection,. The multiple re-distribution layer (RDL) processing is implemented in advance multi-dies FO chip on substrate device for die to die connection meeting higher density electronic connection need. And for complex function request, the area of side-by-side silicon dies size are very close to FO multi-dies chip size, there is narrow die gap between these side-by-side dies. A large coefficient of thermal expansion (CTE) mismatch between epoxy molding compound (EMC) and silicon dies is a significant contributor to the origin of warpage, and will lead to high thermal-mechanical strain and stress at narrow area of side-by-side die gap. The redistribution layer could be high stress risk site by the high thermal-mechanical stress on narrow side-by-side dies gap area. This study is to build a fan-out chip on substrate package numerical simulation model by finite element method (FEM) and get good warpage and thermal-mechanical strain correlation between simulation and real package measurement result by advance Metrology Analyzer (aMA) system. Then we used this equivalent numerical model to compare the thermal-mechanical performance for different redistribution layer pattern design. Finally, generalizing the redistribution layer pattern design guideline and to enhance the package level reliability performance, especially under temperature cycling test (TCT) condition, of fan-out chip on substrate package. The new redistribution layer pattern layout can pass 1000 temperature cycling test cycles and there is lower stress risk on redistribution layer.


Feature Generation using Ontologies during Induction of Decision Trees on Linked Data


title: A legal cross-references taxonomy for identifying conflicting software requirements

abstract: Companies must ensure their software complies with relevant laws and regulations to avoid the risk of costly penalties, lost reputation, and brand damage resulting from noncompliance. Laws and regulations contain internal cross-references to portions of the same legal text, as well as cross-references to external legal texts. These cross-references introduce ambiguities, exceptions, as well as other challenges to regulatory compliance. Requirements engineers need guidance as to how to address cross-references in order to comply with the requirements of the law. Herein, we analyze each external cross-reference within the U.S. Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule to determine whether a cross-reference either: introduces a conflicting requirement, a conflicting definition, and/or refines an existing requirement. Herein, we propose a legal cross-reference taxonomy to aid requirements engineers in classifying cross-references as they specify compliance requirements. Analyzing cross-references enables us to address conflicting requirements that may otherwise thwart legal compliance. We identify five sets of conflicting compliance requirements and recommend strategies for resolving these conflicts.


title: Concurrency control algorithms for multiversion database systems

abstract: Concurrency control is the activity of synchronizing operations issued by concurrently executing programs on a shared database. The goal is to produce an execution that has the same effect as a serial (noninterleaved) one. In a multiversion database system, each write on a data item produces a new copy (or version ) of that data item. This paper presents a theory for analyzing the correctness of concurrency control algorithms for multiversion database systems. We use the theory to analyze some new algorithms and some previously published ones.


title: Distributed Secondary Control of Droop-Controlled Microgrid Using Averaged Feedback Reward Pinning

abstract: An original method for distributed secondary control (DSC) using averaged feedback reward pinning (AFRP) is proposed to eliminate static error caused by droop controllers in a micorgrid (MG). The method uses local and global rewards to evaluate the frequency and voltage control effects, and it further optimizes the control performance by maximizing the global feedback reward. The AFRP can adaptively adjust the pinning values step-by-step and always converges in the direction that maximizes global feedback. Only certain parts of agents are pinned to the averaged feedback reward, and then all other existing agents converge along with the pinned agents through the neighboring communication coupling. In this way, the DSC can obtain a near-optimal solution, which asymptotically eliminates frequency deviations and optimally regulates voltages in consideration of power losses in the MG. The efficiency and superiority of the AFRP-based DSC are verified by discussion and analysis of the simulation results.


title: How video streaming consumes power in 4G LTE networks

abstract: With the increasing adoption of mobile 4G LTE networks, video streaming as the major contributor of 4G LTE data traffic, has become extremely hot. However, the battery life has become the bottleneck when mobile users are using online video services. In this paper, we deploy a real mobile system for power measurement and profiling of online video streaming in 4G LTE networks. Based on some designed experiments with different configurations, we measure the power consumption for online video streaming, offline video playing, and mobile background. A RRC state study is taken to understand how RRC states impact power consumption. Then, we profile the power consumption of video streaming and show the results with different impact factors. According to our experimental statistics, the power saving room for online video streaming in 4G LTE networks can be up to 69%.


title: Parents’ Intelligence Mindsets Relate to Child Internalizing Problems: Moderation Through Child Gender

abstract: Parental belief systems can strongly influence children’s affect, behavior, and mental health. However, associations between specific kinds of parental beliefs and children’s mental health have not been thoroughly explored. One relevant belief system is parental intelligence mindset: beliefs about the malleability of intelligence. Children of parents who view intelligence as static (known as a fixed intelligence mindset), rather than malleable through effort (known as a growth intelligence mindset), experience more academic, self-regulatory, and motivational difficulty. However, associations between parental intelligence mindset and child mental health problems are unclear. Accordingly, we tested whether parents’ intelligence mindsets related to internalizing problems in their children (N = 131, ages 5–8). Overall, parents with stronger fixed intelligence mindsets had children with greater internalizing problems, particularly social anxiety (characterized by fear of negative evaluation). Results further revealed that parents’ fixed intelligence mindsets were associated with overall internalizing problems and depressive symptoms in boys, but not girls. Results are the first to suggest and parse direct links between parents’ intelligence mindsets and youth internalizing problems.


title: The Mediating Role of Appearance Comparisons in the Relationship Between Media Usage and Self-Objectification in Young Women

abstract: The media’s portrayal of women is often sexually objectifying, and greater exposure to objectifying media is associated with higher levels of self-objectification among young women. One reason why media usage may be associated with self-objectification is because women may be comparing their appearance to others in the media. The present study examined (a) the relationship between the usage of different media types (online social media [Facebook], Internet, television, music videos, and magazines) and self-objectification among young women, (b) whether appearance comparison tendencies in general mediated any observed relationships, and (c) whether appearance comparisons to specific types of women on Facebook (self, family, close friends, distant peers, and celebrities) mediated any relationship between Facebook usage and self-objectification. Female participants (N = 150) aged 17–25 years completed questionnaires about their media usage, appearance comparison tendency in general, appearance comparisons to...


title: Semi-analytical loss model for rotary transformers

abstract: In modern drive systems, electrically excited machines (ESM) have become an interesting alternative to those excited by permanent magnets. The excitation winding is normally supplied by a slip ring system that (in automotive applications) has to be protected against environmental influences like wetness, oil and dust. To increase the power density of ESM, brushless excitation systems based on rotationally symmetric transformers (fig. 1 b) begin to replace slip rings. Their insusceptibility to environmental influences is their major advantage compared to slip rings. To allow the movement of the secondary against the primary side, air gaps are needed. With the focus on vehicle drive applications, they must be designed with (air) gap widths between 0.5 mm and 1.0 mm, or accommodation of tolerances.


Time-Course Transcriptome Analysis of Compatible and Incompatible Pollen-Stigma Interactions in Brassica napus L.


title: Local Search Methods for k-Means with Outliers

abstract: We study the problem of k-means clustering in the presence of outliers. The goal is to cluster a set of data points to minimize the variance of the points assigned to the same cluster, with the freedom of ignoring a small set of data points that can be labeled as outliers. Clustering with outliers has received a lot of attention in the data processing community, but practical, efficient, and provably good algorithms remain unknown for the most popular k-means objective. ::: ::: Our work proposes a simple local search-based algorithm for k-means clustering with outliers. We prove that this algorithm achieves constant-factor approximate solutions and can be combined with known sketching techniques to scale to large data sets. Using empirical evaluation on both synthetic and large-scale real-world data, we demonstrate that the algorithm dominates recently proposed heuristic approaches for the problem.


title: Optic disc region of interest localization in fundus image for Glaucoma detection in ARGALI

abstract: Glaucoma is the second leading cause of permanent blindness worldwide. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Correctly determining the optic disc region of interest (ROI) will produce a smaller initial image which takes much lesser time taken to process compared to the entire image. The earlier ROI localization in the ARGALI system used a grid based method. The new algorithm adds a preprocessing step before analyzing the image. This step significantly improves the performance of the ROI detection. A batch of 1564 retinal images from the Singapore Eye Research Centre was used to compare the performance of the two methods. From the results, the earlier and new algorithm detects the ROI correctly for 88% and 96% of the images respectively. The results indicate potential applicability of the method for automated and objective mass screening for early detection of glaucoma.


The HoverBot - An Electrically Powered Flying Robot


title: Valkyrie: NASA's First Bipedal Humanoid Robot

abstract: In December 2013, 16 teams from around the world gathered at Homestead Speedway near Miami, FL to participate in the DARPA Robotics Challenge DRC Trials, an aggressive robotics competition partly inspired by the aftermath of the Fukushima Daiichi reactor incident. While the focus of the DRC Trials is to advance robotics for use in austere and inhospitable environments, the objectives of the DRC are to progress the areas of supervised autonomy and mobile manipulation for everyday robotics. NASA's Johnson Space Center led a team comprised of numerous partners to develop Valkyrie, NASA's first bipedal humanoid robot. Valkyrie is a 44 degree-of-freedom, series elastic actuator-based robot that draws upon over 18 years of humanoid robotics design heritage. Valkyrie's application intent is aimed at not only responding to events like Fukushima, but also advancing human spaceflight endeavors in extraterrestrial planetary settings. This paper presents a brief system overview, detailing Valkyrie's mechatronic subsystems, followed by a summarization of the inverse kinematics-based walking algorithm employed at the Trials. Next, the software and control architectures are highlighted along with a description of the operator interface tools. Finally, some closing remarks are given about the competition, and a vision of future work is provided.


title: EDDEEC: Enhanced Developed Distributed Energy-Efficient Clustering for Heterogeneous Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSNs) consist of large number of randomly deployed energy constrained sensor nodes. Sensor nodes have ability to sense and send sensed data to Base Station (BS). Sensing as well as transmitting data towards BS require high energy. In WSNs, saving energy and extending network lifetime are great challenges. Clustering is a key technique used to optimize energy consumption in WSNs. In this paper, we propose a novel clustering based routing technique: Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) for heterogeneous WSNs. Our technique is based on changing dynamically and with more efficiency the Cluster Head (CH) election probability. Simulation results show that our proposed protocol achieves longer lifetime, stability period and more effective messages to BS than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC) in heterogeneous environments.


title: Active inference and epistemic value.

abstract: We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayesoptimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.


title: Compact Printed Wide-Slot UWB Antenna With 3.5/5.5-GHz Dual Band-Notched Characteristics

abstract: A novel compact wide-slot ultrawideband (UWB) antenna with dual band-notched characteristics is presented. The antenna consists of an inverted U-shaped slot on the ground plane and a radiation patch similar to the slot that is fed by a 50-Ω microstrip line. By etching a C-shaped slot on the radiation patch and extruding an L-shaped stub from the ground plane, dual band-notched properties in the WiMAX (3.4-3.69 GHz) and WLAN (5.15-5.825 GHz) are achieved. The proposed antenna has a compact size of 20×27 mm2 and operates from 2.89 to 11.52 GHz. Furthermore, nearly omnidirectional radiation patterns and constant gain are obtained in the working band.


title: Helical antenna over different ground planes

abstract: Axial mode helical antennas have been used in satellite communication for a long time offering the advantage of a high gain in axial direction over a wide range of frequencies [1]. The concept of this antenna was established by Kraus [1], who also developed empirical rules for its design. It is well known that we can enhance the power gain in helical antenna by increasing the number of helical turns, properly choosing the helical circumference. This method, however, forces us to expand the antenna length and decreases the width of the beam unreasonably.


title: Reliability, synchrony and noise

abstract: The brain is noisy. Neurons receive tens of thousands of highly fluctuating inputs and generate spike trains that appear highly irregular. Much of this activity is spontaneous – uncoupled to overt stimuli or motor outputs – leading to questions about the functional impact of this noise. Although noise is most often thought of as disrupting patterned activity and interfering with the encoding of stimuli, recent theoretical and experimental work has shown that noise can play a constructive role – leading to increased reliability or regularity of neuronal firing in single neurons and across populations. These results raise fundamental questions about how noise can influence neural function and computation.


title: vCorfu: a cloud-scale object store on a shared log

abstract: This paper presents vCorfu, a strongly consistent cloudscale object store built over a shared log. vCorfu augments the traditional replication scheme of a shared log to provide fast reads and leverages a new technique, composable state machine replication, to compose large state machines from smaller ones, enabling the use of state machine replication to be used to efficiently in huge data stores. We show that vCorfu outperforms Cassandra, a popular state-of-the art NOSQL stores while providing strong consistency (opacity, read-own-writes), efficient transactions, and global snapshots at cloud scale.


Two effects of inhibition of protein synthesis on the replication of M12 bacteriophage RNA


title: Genomics of cellulosic biofuels

abstract: The development of alternatives to fossil fuels as an energy source is an urgent global priority. Cellulosic biomass has the potential to contribute to meeting the demand for liquid fuel, but land-use requirements and process inefficiencies represent hurdles for large-scale deployment of biomass-to-biofuel technologies. Genomic information gathered from across the biosphere, including potential energy crops and microorganisms able to break down biomass, will be vital for improving the prospects of significant cellulosic biofuel production.


title: Deep convolutional acoustic word embeddings using word-pair side information

abstract: Recent studies have been revisiting whole words as the basic modelling unit in speech recognition and query applications, instead of phonetic units. Such whole-word segmental systems rely on a function that maps a variable-length speech segment to a vector in a fixed-dimensional space; the resulting acoustic word embeddings need to allow for accurate discrimination between different word types, directly in the embedding space. We compare several old and new approaches in a word discrimination task. Our best approach uses side information in the form of known word pairs to train a Siamese convolutional neural network (CNN): a pair of tied networks that take two speech segments as input and produce their embeddings, trained with a hinge loss that separates same-word pairs and different-word pairs by some margin. A word classifier CNN performs similarly, but requires much stronger supervision. Both types of CNNs yield large improvements over the best previously published results on the word discrimination task.


Assessment in the service of learning


A soft suction based end effector for endoluminal tissue manipulation


title: Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

abstract: Abstract Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods—the particle Metropolis-Hastings algorithm—which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis-Hastings algorithm is that it is guaranteed to converge to the “true solution” under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods—including particle Metropolis-Hastings—to a large group of users without requiring them to know all the underlying mathematical details.


title: Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions

abstract: The author compares the multi-attribute and subjective expected utility (SEU) models popular in research on consumer behavior to the approach offered by the theory of planed behavior (TPB). Unlike the multi-attribute and SEU models, the TPB relies not on revealed preferences to infer the underlying decision process but instead on direct assessment of its theoretical constructs. According to the theory, the consumer’s behavior is a function of intention to perform the behavior in question; the intention is based on attitude, subjective norm, and perceived behavioral control with respect to the behavior; and these factors are determined, respectively, by behavioral, normative, and control beliefs. The theory allows us to predict intentions and behavior with respect to the purchase or use of a single brand or product as well as in relation to choice among different brands or products.


title: The basal ganglia: learning new tricks and loving it

abstract: The field of basal ganglia research is exploding on every level - from discoveries at the molecular level to those based on human brain imaging. A remarkable series of new findings support the view that the basal ganglia are essential for some forms of learning-related plasticity. Other new findings are challenging some of the basic tenets of the field as it now stands. Combined with the new evidence on learning-related functions of the basal ganglia, these studies suggest that the basal ganglia are parts of a brain-wide set of adaptive neural systems promoting optimal motor and cognitive control.


title: Perceptions of facebook's value as an information source

abstract: Facebook has become an increasingly important tool for people engaging in a range of communication behaviors, including requesting help from their social network to address information needs. Through a study of 614 staff members at a large university, we show how social capital, network characteristics, and use of Facebook are related to how useful individuals find Facebook to be for informational purposes and their propensity to seek different types of information on the site. We find that bridging social capital and engagement with one's network through directed communication behaviors are important predictors of these dimensions of information seeking; furthermore, a number of demographic and usage behavior differences exist between those who choose to engage in information-seeking behaviors on Facebook and those who do not. Finally, when predicting information-seeking behaviors, we identify a significant interaction between users' perceptions of Facebook as appropriate for purposes beyond the purely social and their engagement with their network.


title: Rule discovery from time series

abstract: We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule such as "a period of low telephone call activity is usually followed by a sharp rise in call volume". Examples of rules relating two or more time series are "if the Microsoft stock price goes up and Intel falls, then IBM goes up the next day," and "if Microsoft goes up strongly for one day, then declines strongly on the next day, and on the same days Intel stays about level, then IBM stays about level." Our emphasis is in the discovery of local patterns in multivariate time series, in contrast to traditional time series analysis which largely focuses on global models. Thus, we search for rules whose conditions refer to patterns in time series. However, we do not want to define beforehand which patterns are to be used; rather, we want the patterns to be formed from the data in the context of rule discovery. We describe adaptive methods for finding rules of the above type from time-series data. The methods are based on discretizing the sequence by methods resembling vector quantization. We first form subsequences by sliding a window through the time series, and then cluster these subsequences by using a suitable measure of time-series similarity. The discretized version of the time series is obtained by taking the cluster identifiers corresponding to the subsequence. Once the time-series is discretized, we use simple rule finding methods to obtain rules from the sequence. We present empirical results on the behavior of the method.


title: Towards emotional control recognition through handwriting using fuzzy inference

abstract: Emotion control is one of personality characteristics that can be detected through handwriting or graphology. One of the advantages is it may help the counselor that has difficulties in identifying the emotion of their counselee. This study is to explore the fuzzy technique for feature extraction in handwriting and then identify the emotion of person. This study uses baseline or slope of the handwriting in determining the level of emotion control whether it is very low, low, medium, high or very high, through Mamdani inference.


title: Effective techniques for vegetation monitoring of transmission lines right-of-ways

abstract: The monitoring of high-voltage transmission lines right-of-ways, regarding the cleaning and clearing of vegetation is done periodically by visual survey, video surveillance, or by air-borne LiDAR (Light Detection And Ranging) scanners. Geographical Information Systems (GIS) containing the geo-referenced data of lands, assets from where the transmission lines passes, vegetation near the line and the applications utilized are essential tools for the improvement of transmission lines maintenance. This paper proposes a concept of multispectral satellite stereo images to find out the 3D depth of vegetation endangering transmission lines right-of-ways, and also a new Multimedia Wireless Sensor Networks (MWSNs) based cost effective, less time consuming and a more accurate technique for the automated power line inspection against vegetation encroachment.


title: Delaying the onset of Alzheimer disease: Bilingualism as a form of cognitive reserve

abstract: Objectives:There is strong epidemiologic evidence to suggest that older adults who maintain an active lifestyle in terms of social, mental, and physical engagement are protected to some degree against the onset of dementia. Such factors are said to contribute to cognitive reserve, which acts to compensate for the accumulation of amyloid and other brain pathologies. We present evidence that lifelong bilingualism is a further factor contributing to cognitive reserve. Methods:Data were collected from 211 consecutive patients diagnosed with probable Alzheimer disease (AD). Patients' age at onset of cognitive impairment was recorded, as was information on occupational history, education, and language history, including fluency in English and any other languages. Following this procedure, 102 patients were classified as bilingual and 109 as monolingual. Results:We found that the bilingual patients had been diagnosed 4.3 years later and had reported the onset of symptoms 5.1 years later than the monolingual patients. The groups were equivalent on measures of cognitive and occupational level, there was no apparent effect of immigration status, and the monolingual patients had received more formal education. There were no gender differences. Conclusions:The present data confirm results from an earlier study, and thus we conclude that lifelong bilingualism confers protection against the onset of AD. The effect does not appear to be attributable to such possible confounding factors as education, occupational status, or immigration. Bilingualism thus appears to contribute to cognitive reserve, which acts to compensate for the effects of accumulated neuropathology. GLOSSARYAD = Alzheimer disease; MMSE = Mini-Mental State Examination.


title: A hybrid deep learning CNNELM for age and gender classification

abstract: Automatic age and gender classification has been widely used in a large amount of applications, particularly in human-computer interaction, biometrics, visual surveillance, electronic customer, and commercial applications. In this paper, we introduce a hybrid structure which includes Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM), and integrates the synergy of two classifiers to deal with age and gender classification. The hybrid architecture makes the most of their advantages: CNN is used to extract the features from the input images while ELM classifies the intermediate results. We not only give the detailed deployment of our structure including design of parameters and layers, analysis of the hybrid architecture, and the derivation of back-propagation in this system during the iterations, but also adopt several measures to limit the risk of overfitting. After that, two popular datasets, such as, MORPH-II and Adience Benchmark, are used to verify our hybrid structure. Experimental results show that our hybrid architecture outperforms other studies on the same datasets by exhibiting significant performance improvement in terms of accuracy and efficiency.


title: Emotion recognition with multimodal features and temporal models

abstract: This paper presents our methods to the Audio-Video Based Emotion Recognition subtask in the 2017 Emotion Recognition in the Wild (EmotiW) Challenge. The task aims to predict one of the seven basic emotions for short video segments. We extract different features from audio and facial expression modalities. We also explore the temporal LSTM model with the input of frame facial features, which improves the performance of the non-temporal model. The fusion of different modality features and the temporal model lead us to achieve a 58.5% accuracy on the testing set, which shows the effectiveness of our methods.


title: Mesua ferrea L. seed oil‐based hyperbranched shape memory polyurethanes: Effect of multifunctional component

abstract: The bio-based hyperbranched polyurethanes (HBPUs) have generated immense interest as advanced shape memory materials. In the present investigation, HBPUs were synthesized from poly(e-caprolactone)diol as a macroglycol, butanediol as a chain extender, monoglyceride of Mesua ferrea L. seed oil as a bio-based chain extender, triethanolamine as a branch-generating moiety (at different percentages), and toluene diisocyanate by a prepolymerization technique using A2 + B3 approach. The structure of the synthesized HBPU was characterized by different techniques. Nuclear magnetic resonance (proton) study indicated the formation of highly branched structure with degree of branching 0.9. The increment of thermal stability from 225 to 260°C and melting point from 50 to 53.5°C with the increase of triethanolamine content was observed. Tensile strength 4–8 MPa, elongation at break 614–814%, impact resistance 0.8–0.95 m, and scratch hardness 2–6 kg increased with the increase of multifunctional moiety content from 0 to 5 wt%. The shape recovery ratio increased with the increase of multifunctional moiety content from 0.21 to 0.95. Thus, the studied HBPUs have the potential to be used as advanced thermoresponsive shape memory materials. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers


title: Ground penetrating radar utilizing compressive sampling and OFDM techniques

abstract: In this paper, we propose a new GPR design method by combining OFDM (Orthogonal Frequency Division Multiplexing) and compressive sampling (CS) techniques. Comparing with traditional SFCW (step frequency continuous wave) radar, OFDM allows transmitting/receiving multiple frequency tones simultaneously to reduce frequency sweeping time. Compressive sampling reduces the number of frequency tones required to further improve GPR operating efficiency. To show the design effectiveness, SFCW GPR and OFDM-CS continuous wave GPR are both simulated in conjunction with FDTD (finite difference time domain) numerical modeling to characterize a concrete slab model.


title: A Semi-Lagrangian approach for American Asian options under jump diffusion

abstract: A semi-Lagrangian method is presented to price continuously observed fixed strike Asian options. At each timestep a set of one-dimensional partial integrodifferential equations (PIDEs) is solved and the solution of each PIDE is updated using semi-Lagrangian timestepping. Crank--Nicolson and second-order backward differencing timestepping schemes are studied. Monotonicity and stability results are derived. With low volatility values, it is observed that the nonsmoothness at the strike in the payoff affects the convergence rate; a subquadratic convergence rate is observed.


title: Automatic classification of archaeological pottery sherds

abstract: This article presents a novel technique for automatic archaeological sherd classification. Sherds that are found in the field usually have little to no visible textual information such as symbols, graphs, or marks on them. This makes manual classification an extremely difficult and time-consuming task for conservators and archaeologists. For a bunch of sherds found in the field, an expert identifies different classes and indicates at least one representative sherd for each class (training sample). The proposed technique uses the representative sherds in order to correctly classify the remaining sherds. For each sherd, local features based on color and texture information are extracted and are then transformed into a global vector that describes the whole sherd image, using a new bag of words technique. Finally, a feature selection algorithm is applied that locates features with high discriminative power. Extensive experiments were performed in order to verify the effectiveness of the proposed technique and show very promising results.


title: An ultra low-cost 32-element 28 GHz phased-array transceiver with 41 dBm EIRP and 1.0–1.6 Gbps 16-QAM link at 300 meters

abstract: This paper presents a 32-element phased-array architecture suitable for fifth-generation (5G) communication links. A 28–32 GHz silicon core chip is designed with 4 transmit/receive elements each with 14 dB gain control, 6-bit phase control, 4.6 dB measured noise figure (NF) in the RX mode and 10 dBm output 1 dB compression point (OP1dB) in the TX mode. Eight of these chips are flipped on a low-cost printed circuit board (PCB) with integrated antennas and Wilkinson combiners. The 32-element array has a measured EIRP of 41 dBm at P1dB, can scan to ±20° and ±50° in E- and H-planes, and comsumes 4.2 W and 6.4 W in RX and TX modes, respectively. The array is used in a 300 meter wireless link and achieves a data rate of 1.0–1.6 Gbps at 16-QAM for all scan angles with <12% EVM. To our knowledge, this represents state-of-the art in 28 GHz phased-arrays in terms of chip performance and integration level.


title: Attribute Based Access Control for Big Data Applications by Query Modification

abstract: We present concepts which can be used for the efficient implementation of Attribute Based Access Control (ABAC) in large applications using maybe several data storage technologies, including Hadoop, NoSQL and relational database systems. The ABAC authorization process takes place in two main stages. Firstly a sequence of permissions is derived which specifies permitted data to be retrieved for the user's transaction. Secondly, query modification is used to augment the user's transaction with code which implements the ABAC controls. This requires the storage technologies to support a high-level language such as SQL or similar. The modified user transactions are then optimized and processed using the full functionality of the underlying storage systems. We use an extended ABAC model (TCM2) which handles negative permissions and overrides in a single permissions processing mechanism. We illustrate these concepts using a compelling electronic health records scenario.


title: Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network

abstract: Audio source separation is a difficult machine learning problem and performance is measured by comparing extracted signals with the component source signals. However, if separation is motivated by the ultimate goal of re-mixing then complete separation is not necessary and hence separation difficulty and separation quality are dependent on the nature of the re-mix. Here, we use a convolutional deep neural network (DNN), trained to estimate 'ideal' binary masks for separating voice from music, to perform re-mixing of the vocal balance by operating directly on the individual magnitude components of the musical mixture spectrogram. Our results demonstrate that small changes in vocal gain may be applied with very little distortion to the ultimate re-mix. Our method may be useful for re-mixing existing mixes.


title: The University of Maryland CLPsych 2015 Shared Task System

abstract: The 2015 ACL Workshop on Computational Linguistics and Clinical Psychology included a shared task focusing on classification of a sample of Twitter users according to three mental health categories: users who have self-reported a diagnosis of depression, users who have self-reported a diagnosis of post-traumatic stress disorder (PTSD), and control users who have done neither (Coppersmith et al., 2015; Coppersmith et al., 2014). Like other shared tasks, the goal here was to assess the state of the art with regard to a challenging problem, to advance that state of the art, and to bring together and hopefully expand the community of researchers interested in solving it.


title: Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs

abstract: Wireless positioning has attracted much research attention and has become increasingly important in recent years. Wireless positioning has been found very useful for other applications besides E911 service, ranging from vehicle navigation and network optimization to resource management and automated billing. Although many positioning devices and services are currently available, it is necessary to develop an integrated and seamless positioning platform to provide a uniform solution for different network configurations. This article surveys the state-of-the-art positioning designs, focusing specifically on signal processing techniques in network-aided positioning. It serves as a tutorial for researchers and engineers interested in this rapidly growing field. It also provides new directions for future research for those who have been working in this field for many years.


title: Associations between Dietary Intake and Academic Achievement in College Students: A Systematic Review

abstract: The impact of diet on academic achievement is a growing area of research. The aim of this systematic review was to evaluate the current evidence examining dietary intake and academic achievement in college/university students. Eight electronic databases were searched for studies published in English to January 2016. To be included, studies must have been conducted in higher education (i.e., college, university) students, reported measures of dietary intake and academic achievement, and reported the association between these. Data were extracted using a standardised tool, and studies were assessed for methodological quality. Seven studies were included, with four rated as positive quality, and the remaining three rated as neutral. Most studies were cross-sectional (n = 4), and conducted in America (n = 5). The most common dietary outcomes were fruit and vegetable (n = 3), and breakfast consumption (n = 3). Standardised grade point average (GPA) was the most common measure of academic achievement (n = 4). Five studies reported small to moderate significant positive associations between diet and academic achievement, including for breakfast, regular meal consumption, and meeting national recommendations for fruit intake. This review examines the current evidence regarding diet and academic achievement in college/university students. The results demonstrate that few studies exist in this population group. Future studies should consider the use of validated dietary assessment methods, comprehensive measures of overall diet, and use standardised assessment and reporting of academic outcomes.


title: Approximation algorithms and hardness results for cycle packing problems

abstract: The cycle packing number νe(G) of a graph G is the maximum number of pairwise edge-disjoint cycles in G. Computing νe(G) is an NP-hard problem. We present approximation algorithms for computing νe(G) in both undirected and directed graphs. In the undirected case we analyze a variant of the modified greedy algorithm suggested by Caprara et al. [2003] and show that it has approximation ratio Θ(&sqrt;log n), where n = |V(G)|. This improves upon the previous O(log n) upper bound for the approximation ratio of this algorithm. In the directed case we present a &sqrt;n-approximation algorithm. Finally, we give an O(n2/3)-approximation algorithm for the problem of finding a maximum number of edge-disjoint cycles that intersect a specified subset S of vertices. We also study generalizations of these problems. Our approximation ratios are the currently best-known ones and, in addition, provide upper bounds on the integrality gap of standard LP-relaxations of these problems. In addition, we give lower bounds for the integrality gap and approximability of νe(G) in directed graphs. Specifically, we prove a lower bound of Ω(log n/loglog n) for the integrality gap of edge-disjoint cycle packing. We also show that it is quasi-NP-hard to approximate νe(G) within a factor of O(log1 − ϵ n) for any constant ϵ > 0. This improves upon the previously known APX-hardness result for this problem.


title: A High Throughput List Decoder Architecture for Polar Codes

abstract: While long polar codes can achieve the capacity of arbitrary binary-input discrete memoryless channels when decoded by a low complexity successive-cancellation (SC) algorithm, the error performance of the SC algorithm is inferior for polar codes with finite block lengths. The cyclic redundancy check (CRC)-aided SC list (SCL) decoding algorithm has better error performance than the SC algorithm. However, current CRC-aided SCL decoders still suffer from long decoding latency and limited throughput. In this paper, a reduced latency list decoding (RLLD) algorithm for polar codes is proposed. Our RLLD algorithm performs the list decoding on a binary tree, whose leaves correspond to the bits of a polar code. In existing SCL decoding algorithms, all the nodes in the tree are traversed, and all possibilities of the information bits are considered. Instead, our RLLD algorithm visits much fewer nodes in the tree and considers fewer possibilities of the information bits. When configured properly, our RLLD algorithm significantly reduces the decoding latency and, hence, improves throughput, while introducing little performance degradation. Based on our RLLD algorithm, we also propose a high throughput list decoder architecture, which is suitable for larger block lengths due to its scalable partial sum computation unit. Our decoder architecture has been implemented for different block lengths and list sizes using the TSMC 90-nm CMOS technology. The implementation results demonstrate that our decoders achieve significant latency reduction and area efficiency improvement compared with the other list polar decoders in the literature.


title: Sensory over-responsivity and social cognition in ASD: Effects of aversive sensory stimuli and attentional modulation on neural responses to social cues

abstract: Abstract Sensory over-responsivity (SOR) is a common condition in autism spectrum disorders (ASD) that is associated with greater social impairment. However, the mechanisms through which sensory stimuli may affect social functioning are not well understood. This study used fMRI to examine brain activity while interpreting communicative intent in 15 high-functioning youth with ASD and 16 age- and IQ-matched typically-developing (TD) controls. Participants completed the task with and without a tactile sensory distracter, and with and without instructions directing their attention to relevant social cues. When completing the task in the presence of the sensory distracter, TD youth showed increased activity in auditory language and frontal regions whereas ASD youth showed decreased activation in these areas. Instructions mitigated this effect such that ASD youth did not decrease activation during tactile stimulation; instead, the ASD group showed increased medial prefrontal activity. SOR severity modulated the effect of the tactile stimulus on social processing. Results demonstrate for the first time a neural mechanism through which sensory stimuli cause disruption of social cognition, and that attentional modulation can restore neural processing of social cues through prefrontal regulation. Findings have implications for novel, integrative interventions that incorporate attentional directives to target both sensory and social symptoms.


title: A Deep Learning Solution to Named Entity Recognition

abstract: Identifying named entities is vital for many Natural Language Processing (NLP) applications. Much of the earlier work for identifying named entities focused on using handcrafted features and knowledge resources (feature engineering). This is a barrier for resource-scarce languages as many resources are not readily available. Recently, Deep Learning techniques have been proposed for various NLP tasks requiring little/no hand-crafted features and knowledge resources, instead the features are learned from the data. Many proposed deep learning solutions for Named Entity Recognition (NER) still rely on feature engineering as opposed to feature learning. However, it is not clear whether the deep learning system or the engineered features are responsible for the positive results reported. This is in contrast with the goal of deep learning systems i.e., to learn the features from the data itself. In this study, we show that a feature learned deep learning system is a viable solution to NER task. We test our deep learning systems on CoNLL English and Spanish NER datasets. Our system is able to give comparable results with the existing state-of-the-art feature engineered systems for English. We report the best performance of 89.27 F-Score for English when comparing with systems which do not use any handcrafted features or knowledge resources. Evaluation of our trained system on out-of-domain data indicate that the results are promising with the reported results. Our system when tested on Spanish NER achieves the best reported F-Score of 82.59 indicating its applicability to other languages.


title: Libra: a library operating system for a jvm in a virtualized execution environment

abstract: If the operating system could be specialized for every application, many applications would run faster. For example, Java virtual machines (JVMs) provide their own threading model and memory protection, so general-purpose operating system implementations of these abstractions are redundant. However, traditional means of transforming existing systems into specialized systems are difficult to adopt because they require replacing the entire operating system. This paper describes Libra, an execution environment specialized for IBM's J9 JVM. Libra does not replace the entire operating system. Instead, Libra and J9 form a single statically-linked image that runs in a hypervisor partition. Libra provides the services necessary to achieve good performance for the Java workloads of interest but relies on an instance of Linux in another hypervisor partition to provide a networking stack, a filesystem, and other services. The expense of remote calls is offset by the fact that Libra's services can be customized for a particular workload; for example, on the Nutch search engine, we show that two simple customizations improve application throughput by a factor of 2.7.


title: Video interactions in online video social networks

abstract: This article characterizes video-based interactions that emerge from YouTube's video response feature, which allows users to discuss themes and to provide reviews for products or places using much richer media than text. Based on crawled data covering a representative subset of videos and users, we present a characterization from two perspectives: the video response view and the interaction network view. In addition to providing valuable statistical models for various characteristics, our study uncovers typical user behavioral patterns in video-based environments and shows evidence of opportunistic behavior.


Melatonin implantation improved the egg-laying rate and quality in hens past their peak egg-laying age


Modeling Discourse Segments in Lyrics Using Repeated Patterns


title: A honey bees mating optimization algorithm for the open vehicle routing problem

abstract: Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Open Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Open Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm and the Expanding Neighborhood Search (ENS) algorithm. Two set of benchmark instances is used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen instances proposed by Christofides, the average quality is 0.35% when a hierarchical objective function is used, where, first, the number of vehicles is minimized and, afterwards, the total travel distance is minimized and the average quality is 0.42% when only the travel distance is minimized, while for the eight instances proposed by Li et al. when a hierarchical objective function is used the average quality is 0.21%.


Artificial Immune Systems: A New Computational Intelligence Paradigm


title: Minimizing Flow Completion Times using Adaptive Routing over Inter-Datacenter Wide Area Networks

abstract: Inter-datacenter networks connect dozens of geographically dispersed datacenters and carry traffic flows with highly variable sizes and different classes. Adaptive flow routing can improve efficiency and performance by assigning paths to new flows according to network status and flow properties. A popular approach widely used for traffic engineering is based on current bandwidth utilization of links. We propose an alternative that reduces bandwidth usage by up to at least 50% and flow completion times by up to at least 40% across various scheduling policies and flow size distributions.


title: Learning and Transferring IDs Representation in E-commerce

abstract: Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc. The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones. In this paper, we propose an embedding based framework to learn and transfer the representation of IDs. As the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions. By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space. Subsequently, the learned representations are utilized and transferred in four scenarios: (i) measuring the similarity between items, (ii) transferring from seen items to unseen items, (iii) transferring across different domains, (iv) transferring across different tasks. We deploy and evaluate the proposed approach in Hema App and the results validate its effectiveness.


title: Inducing Grammars with and for Neural Machine Translation

abstract: Previous work has demonstrated the benefits of incorporating additional linguistic annotations such as syntactic trees into neural machine translation. However the cost of obtaining those syntactic annotations is expensive for many languages and the quality of unsupervised learning linguistic structures is too poor to be helpful. In this work, we aim to improve neural machine translation via source side dependency syntax but without explicit annotation. We propose a set of models that learn to induce dependency trees on the source side and learn to use that information on the target side. Importantly, we also show that our dependency trees capture important syntactic features of language and improve translation quality on two language pairs En-De and En-Ru.


title: Active instance matching with pairwise constraints and its application to Chinese knowledge base construction

abstract: Instance matching is the problem of determining whether two instances describe the same real-world entity or not. Instance matching plays a key role in data integration and data cleansing, especially for building a knowledge base. For example, we can regard each article in encyclopedias as an instance, and a group of articles which refers to the same real-world object as an entity. Therefore, articles about Washington should be distinguished and grouped into different entities such as Washington, D.C (the capital of the USA), George Washington (first president of the USA), Washington (a state of the USA), Washington (a village in West Sussex, England), Washington F.C. (a football club based in Washington, Tyne and Wear, England), Washington, D.C. (a novel). In this paper, we proposed a novel instance matching approach Active Instance Matching with Pairwise Constraints, which can bring the human into the loop of instance matching. The proposed approach can generate candidate pairs in advance to reduce the computational complexity, and then iteratively select the most informative pairs according to the uncertainty, influence, connectivity and diversity of pairs. We evaluated our approach one two publicly available datasets AMINER and WIDE-NUS and then applied our approach to the two large-scale real-world datasets, Baidu Baike and Hudong Baike, to build a Chinese knowledge base. The experiments and practice illustrate the effectiveness of our approach.


title: in Simple Mediation Models When the Constituent Paths Are Nonlinear

abstract: Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) for estimating indirect effects in models of mediators and outcomes that are nonlinear functions but linear in their parameters. We introduce the concept of the instantaneous indirect effect of X on Y through M and illustrate its computation and describe a bootstrapping procedure for inference. Mplus code as well as SPSS and SAS macros are provided to facilitate the adoption of this approach and ease the computational burden on the researcher.


title: Transcranial magnetic stimulation and the human brain

abstract: Transcranial magnetic stimulation (TMS) is rapidly developing as a powerful, non-invasive tool for studying the human brain. A pulsed magnetic field creates current flow in the brain and can temporarily excite or inhibit specific areas. TMS of motor cortex can produce a muscle twitch or block movement; TMS of occipital cortex can produce visual phosphenes or scotomas. TMS can also alter the functioning of the brain beyond the time of stimulation, offering potential for therapy.


Data Encryption and Decryption by Using Triple DES and Performance Analysis of Crypto System


title: Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network

abstract: Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to study labeled cyberbullying incidents in the Instagram social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, profanity, social graph features, temporal commenting behavior, linguistic content, and image content.


title: Probabilistic aggregation for data dissemination in VANETs

abstract: We propose an algorithm for the hierarchical aggregation of observations in dissemination-based, distributed traffic information systems. Instead of carrying specific values (e.g., the number offree parking places in a given area), our aggregates contain a modified Flajolet-Martin sketch as a probabilistic approximation. The main advantage of this approach is that the aggregates are duplicate insensitive. This overcomes two central problems of existing aggregation schemes for VANET applications. First, when multiple aggregates of observations for the same area are available, it is possible to combine them into an aggregate containing all information from the original aggregates. This is fundamentally different from existing approaches where typically one of the aggregates is selected for further use while the rest is discarded. Second, any observation or aggregate can be included into higher level aggregates, regardless if it has already been previously - directly or indirectly - added. As a result of those characteristics the quality of the aggregates is high, while their construction is very flexible. We demonstrate these traits of our approach by a simulation study.


title: Visual correspondence using energy minimization and mutual information

abstract: We address visual correspondence problems without assuming that scene points have similar intensities in different views. This situation is common, usually due to nonLambertian scenes or to differences between cameras. We use maximization of mutual information, a powerful technique for registering images that requires no a priori model of the relationship between scene intensities in different views. However, it has proven difficult to use mutual information to compute dense visual correspondence. Comparing fixed-size windows via mutual information suffers from the well-known problems of fixed windows, namely poor performance at discontinuities and in low-texture regions. In this paper, we show how to compute visual correspondence using mutual information without suffering from these problems. Using a simple approximation, mutual information can be incorporated into the standard energy minimization framework used in early vision. The energy can then be efficiently minimized using graph cuts, which preserve discontinuities and handle low-texture regions. The resulting algorithm combines the accurate disparity maps that come from graph cuts with the tolerance for intensity changes that comes from mutual information.


title: Progress in bioethanol processing

abstract: Production of ethanol (bioethanol) from biomass is one way to reduce both consumption of crude oil and environmental pollution. Bioethanol is appropriate for the mixed fuel in the gasoline engine because of its high octane number, and its low cetane number and high heat of vaporization impede self-ignition in the diesel engine. So, ignition improver, glow-plug, surface ignition, and pilot injection are applied to promote self-ignition by using diesel-bioethanol-blended fuel. Disadvantages of bioethanol include its lower energy density than gasoline, its corrosiveness, low flame luminosity, lower vapor pressure (making cold starts difficult), miscibility with water, and toxicity to ecosystems. Bioethanol can be produced from cellulosic feedstocks. One major problem with bioethanol production is the availability of raw materials for the production. The availability of feedstocks for bioethanol can vary considerably from season to season and depends on geographic locations. Lignocellulosic biomass is the most promising feedstock considering its great availability and low cost, but the large-scale commercial production of fuel bioethanol from lignocellulosic materials has still not been implemented. Conversion technologies for producing bioethanol from cellulosic biomass resources such as forest materials, agricultural residues and urban wastes are under development and have not yet been demonstrated commercially. For designing fuel bioethanol production processes, assessment of utilization of different feedstocks (i.e. sucrose containing, starchy materials, lignocellulosic biomass) is required considering the big share of raw materials in bioethanol costs. In this work a review of the biological and thermochemical methods that could be used to produce bioethanol is made and an analysis of its global production trends is carried out.


Prevalence and Outcome of Severe Acute Malnutrition among Children 6- 59 Months, in Magumeri Local Government Area, Borno State, Attending General Hospital Magumeri, Borno State, Nigeria


title: Title Benchmarking Short Text Semantic Similarity

abstract: Short text semantic similarity measurement is a new and rapidly growing field of research. 'Short texts' are typically sentence length but are not required to be grammatically correct. There is great potential for applying these measures in fields such as information retrieval, dialogue management and question answering. A dataset of 65 sentence pairs, with similarity ratings, produced in 2006 has become adopted as a de facto gold standard benchmark. This paper discusses the adoption of the 2006 dataset, lays down a number of criteria that can be used to determine whether a dataset should be awarded a 'gold standard' accolade and illustrates its use as a benchmark. Procedures for the generation of further gold standard datasets in this field are recommended.


title: Review of e-Learning Systems Convergence from Traditional Systems to Services based Adaptive and Intelligent Systems

abstract: Service Oriented Architecture (SOA) as a design pattern that presents systems as collection of reusable services that can be exposed and consumed on the Internet with standard interfaces has many advantages that can be achieved on technical, managerial, and implementation aspects of the system. Integrating University Management Information Systems (UMIS) and Learning Management Systems (LMS) can be achieved effectively, efficiently, and with minor modifications of both systems via SOA utilization. SOA can also be useful in integrating long decades and efforts of adaptive and intelligent features of eLearning in today’s information systems. Both UMIS and LMS; as the two major components of learning information systems can make use of adaptive and intelligent features became available over decades. Adaptive and intelligent features in information systems are many, and they can be utilized in new learning systems. Presenting adaptive and intelligent features as services with standard interfaces will allow different e-Learning systems to adopt them, so they will be reusable and newly introduced information systems will not have to redo the work again, besides, wrapping adaptive and intelligent features with standard interfaces will present a separation of interests that help adaptive and intelligent features’ researchers and developers to focus more on their target and transfer the responsibility of utilizing those features in different information systems to information systems specialists. Today’s e-Learning information systems’ architects can make use of SOA in integrating major e-Learning components together, side by side with adaptive and intelligent features to provide students with personalized learning environment. This article reviews the e-Learning different models and the challenges facing e-Learning nowadays, supposing solution to those challenges. Reviewing the e-Learning proposed systems, architectures, and frameworks starting from traditional e-Learning systems through adaptive e-Learning systems supported by intelligent eLearning features, methods, and techniques yields eLearning researchers to Services based Adaptive and Intelligent e-Learning Systems.


title: Boosting instance selection algorithms

abstract: Instance selection is one of the most important preprocessing steps in many machine learning tasks. Due to the increasing size of the problems, removing useless, erroneous or noisy instances is frequently an initial step that is performed before other data mining algorithms are applied. Instance selection as part of this data reduction task is one of the most relevant problems in current data mining research. Over the past decades, many different instance selection algorithms have been proposed, each with its own strengths and weaknesses. However, as in the case of classification, it is unlikely that a single instance selection algorithm would be able to achieve good results across many different datasets and application fields. In classification, one of the most successful ways of consistently improving the performance of a single learner is the construction of ensembles using boosting methods. In this paper, we propose a novel approach for instance selection based on boosting instance selection algorithms in the same way boosting is applied to classification. The proposed approach opens a new field of research in which to apply the many techniques developed for boosting classifiers, for instance selection and other data reduction techniques such as feature selection and simultaneous instance and feature selection. Using 60 datasets for balanced problems and 45 datasets for class-imbalanced problems, the experiments reported show a clear improvement in several state-of-the-art instance selection algorithms using the proposed methodology.


title: Modeling a Retweet Network via an Adaptive Bayesian Approach

abstract: Twitter (and similar microblogging services) has become a central nexus for discussion of the topics of the day. Twitter data contains rich content and structured information on users' topics of interest and behavior patterns. Correctly analyzing and modeling Twitter data enables the prediction of the user behavior and preference in a variety of practical applications, such as tweet recommendation and followee recommendation. Although a number of models have been developed on Twitter data in prior work, most of these only model the tweets from users, while neglecting their valuable retweet information in the data. Models would enhance their predictive power by incorporating users' retweet content as well as their retweet behavior. In this paper, we propose two novel Bayesian nonparametric models, URM and UCM, on retweet data. Both of them are able to integrate the analysis of tweet text and users' retweet behavior in the same probabilistic framework. Moreover, they both jointly model users' interest in tweet and retweet. As nonparametric models, URM and UCM can automatically determine the parameters of the models based on input data, avoiding arbitrary parameter settings. Extensive experiments on real-world Twitter data show that both URM and UCM are superior to all the baselines, while UCM further outperforms URM, confirming the appropriateness of our models in retweet modeling.


title: Toward locality-aware scheduling for containerized cloud services

abstract: The state-of-the-art scheduler of containerized cloud services considers load-balance as the only criterion and neglects many others such as application performance. In the era of Big Data, however, applications have evolved to be highly data-intensive thus perform poorly in existing systems. This particularly holds for Platform-as-a-Service environments that encourage an application model of stateless application instances in containers reading and writing data to services storing states, e.g., key-value stores. To this end, this work strives to improve today's cloud services by incorporating sensitivity to both load-balance and application performance. We built and analyzed theoretical models that respect both dimensions, and unlike prior studies, our model abstracts the dilemma between load-balance and application performance into an optimization problem and employs a statistical method to meet the discrepant requirements. Using heuristic algorithms and approaches we try to solve the abstracted problems. We implemented the proposed approach in Diego (an open-source cloud service scheduler) and demonstrate that it can significantly boost the performance of containerized applications while preserving a relatively high load-balance.


title: Collective behavior of place and non-place neurons in the hippocampal network

abstract: Summary Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state.


title: QRPp1-4: Characterizing Quality of Time and Topology in a Time Synchronization Network

abstract: As Internet computing gains speed, complexity, and becomes ubiquitous, the need for precise and accurate time synchronization increases. In this paper, we present a characterization of a clock synchronization network managed by Network Time Protocol (NTP), composed by thousands of nodes, including hundreds of Stratum 1 servers, based on data collected recently by a robot. NTP is the most common protocol for time synchronization in the Internet. Many aspects that define the quality of timekeeping are analyzed, as well as topological characteristics of the network. The results are compared to previous characterizations of the NTP network, showing the evolution of clock synchronization in the last fifteen years.


title: Phase Correction of Single-Look Complex Radar Images for User-Friendly Efficient Interferogram Formation

abstract: We present a new interferometric synthetic aperture radar processing approach that removes topography–dependent phase from single-look complex (SLC) radar images, making interferogram formation more efficient. We first adopt motion compensation techniques to resample SLC images with respect to an ideal reference orbit and then separate the residual topographic phase contributions into parts dependent only on individual SLC acquisitions, and generate topography-compensated images directly in latitude–longitude coordinates. Since the number of interferograms is typically much larger than the number of SLC images, our approach greatly reduces needed computational resources. Furthermore, we move the need for precise knowledge of imaging geometry upstream from the end user to the data provider. We demonstrate our approach for both preprocessed SLC images and raw data using COSMO-SkyMed L1A and ALOS L0 products. The performance of our method depends on the quality of the digital elevation model (DEM) used—DEM error affects the correction phase proportionally to the baseline between radar scenes and the reference orbital path. With a 1000-m baseline and a nominal 30° incidence angle, we find that the uncertainty of estimated deformation increases by approximately 1 cm with every 3 m increase in the DEM error.


title: Spontaneous speech dialogue system TOSBURG II—the user‐centered multimodal interface

abstract: This paper considers the user-centered spontaneous speech dialogue system TOSBURG-II (Taskoriented dialogue system based on speech understanding and response generation), and discusses the design from the viewpoint of media technology and a multimodal interface. The authors have developed element techniques, including spontaneous speech understanding, user-center dialogue control, multimodal response generation and speech response cancelling, all based on the noise-immune word-spotting and key-words. The concept is that “no constraint is imposed on the user.” By integrating these techniques, the realtime speech dialogue system for an unspecified user is developed. The speech dialogue data acquisition/ evaluation system is constructed on the real system. The system can record real speech data as well as the intermediate result of processing in the dialogue system, such as keyword spotting, speech understanding and dialogue processing. The system can also be utilized in the construction of the speech dialogue corpus and the evaluation/improvement of the human factor aspect, in addition to the evaluation of the system performance. As a result of trial use and evaluation experiment for the real system for unspecified users, it is verified that spontaneous speech understanding based on the interruption function by the user and the multimodal response and keywords, is useful in improving the naturalness of the dialogue and robustness.


title: A simulation-based software design framework for network-centric and parallel systems

abstract: In this paper we discuss a software design framework that is capable of realizing network-centricity and the rising multicore technology. Instead of producing static design documents in the form of UML diagrams, we propose automatic generation of a visual simulation model, which represents the target system design. We discuss a design environment that is responsible for the generation and execution of the simulation model.


Animation Killed the Video Star


title: Using machine learning techniques to detect metamorphic relations for programs without test oracles

abstract: Much software lacks test oracles, which limits automated testing. Metamorphic testing is one proposed method for automating the testing process for programs without test oracles. Unfortunately, finding the appropriate metamorphic relations required for use in metamorphic testing remains a labor intensive task, which is generally performed by a domain expert or a programmer. In this work we present a novel approach for automatically predicting metamorphic relations using machine learning techniques. Our approach uses a set of features developed using the control flow graph of a function for predicting likely metamorphic relations. We show the effectiveness of our method using a set of real world functions often used in scientific applications.


title: Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook

abstract: The ongoing development of mobile communication networks to support a wide range of superfast broadband services has led to massive capacity demand. This problem is expected to be a significant concern during the deployment of the 5G wireless networks. The demand for additional spectrum to accommodate mobile services supporting higher data rates and having lower latency requirements, as well as the need to provide ubiquitous connectivity with the advent of the Internet of Things sector, is likely to considerably exceed the supply, based on the current policy of exclusive spectrum allocation to mobile cellular systems. Hence, the imminent spectrum shortage has introduced a new impetus to identify practical solutions to make the most efficient use of scarce licensed bands in a shared manner. Recently, the concept of dynamic spectrum sharing has received considerable attention from regulatory bodies and governments globally, as it could potentially open new opportunities for mobile operators to exploit spectrum bands whenever they are underutilized by their owners, subject to service level agreements. Although various sharing paradigms have been proposed and discussed, the impact and performance gains of different schemes can be scenario-specific, and may vary depending on the nature of the sharing players, the level of sharing and spectrum access scheme. In this survey, we study the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing of licensed bands. Finally, we conclude this survey with open research challenges and suggest some future research directions.


title: Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF

abstract: Current inductive machine learning algorithms typically use greedy search with limited lookahead. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELIEFF, an extension of RELIEF developed by Kira and Rendell l10, 11r, for heuristic guidance of inductive learning algorithms. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems and the results are compared with some other well known machine learning algorithms. Excellent results on artificial data sets and two real world problems show the advantage of the presented approach to inductive learning.


title: Deep neural architectures for large scale android malware analysis

abstract: Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.


Linguistically Regularized LSTM for Sentiment Classification


title: A scalable modular antenna configuration to extend the detection volume of a near-field UHF-RFID desktop reader

abstract: A modular combination of a resonating circular ring slot antenna and a spiral Travelling Wave Antenna (TWA) is proposed in this paper for NF UHF RFID desktop reader applications. The spiral TWA is able to generate strong and uniform fields just above the antenna surface. The circular ring slot antenna is serially-fed by the TWA and it is used to extend the read range up to the radiative near field region. In order to make the overall antenna scalable, the spiral TWA is extended beyond the slot antenna, so allowing to cover a large and arbitrarily shaped area.


title: Nested or Networked? Future Directions for Ecological Systems Theory

abstract: Bronfenbrenner's ecological systems theory (EST) is among the most widely adopted theoretical frameworks for studying individuals in ecological contexts. In its traditional formulation, different levels of ecological systems are viewed as nested within one another. In this article, we use Simmel's notion of intersecting social circles and Bronfenbrenner's earlier writing on social networks to develop an alternative ‘networked’ model that instead views ecological systems as an overlapping arrangement of structures, each directly or indirectly connected to the others by the direct and indirect social interactions of their participants. We redefine each of the systems discussed by EST—micro, meso, exo, macro, and chrono—based on patterns of social interaction, and then illustrate how this alternative model might be applied in the classic context of the developing child. We conclude by discussing future directions for how the networked model of EST can be applied as a conceptual framework, arguing that this approach offers developmental researchers with a more precise and flexible way to think about ecological contexts. We also offer some initial suggestions for moving a networked EST model from theory to method.


title: Information Security and Privacy—Rethinking Governance Models

abstract: Concerns about information security and privacy continue to make headlines in the media and rate as serious issues in business and public surveys. Conventional thinking suggests that enhancements to governance models and business practices improve the performance of individual businesses. However, is this enough? Good governance is based on clear rights and responsibilities, and this panel will address the contention that such clarity is lacking in the treatment of personal information. As a result, certain types of business innovation may be constrained and good practices may remain tick box exercises, disconnected from wider business objectives. More radical thinking would go beyond incremental improvements to the status quo and recognize the profound challenges to privacy and information security created by digital technology. This panel will bring together a range of research disciplines and senior business representatives to critique current practice and develop a future research agenda.


title: Identifying Player\'s Strategies in No Limit Texas Hold\'em Poker through the Analysis of Individual Moves

abstract: The development of competitive artificial Poker playing agents has proven to be a challenge, because agents must deal with unreliable information and deception which make it essential to model the opponents in order to achieve good results. This paper presents a methodology to develop opponent modeling techniques for Poker agents. The approach is based on applying clustering algorithms to a Poker game database in order to identify player types based on their actions. First, common game moves were identified by clustering all players' moves. Then, player types were defined by calculating the frequency with which the players perform each type of movement. With the given dataset, 7 different types of players were identified with each one having at least one tactic that characterizes him. The identification of player types may improve the overall performance of Poker agents, because it helps the agents to predict the opponent's moves, by associating each opponent to a distinct cluster.