title: Combinatorial Pure Exploration of Multi-Armed Bandits

abstract: We study the combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting, where a learner explores a set of arms with the objective of identifying the optimal member of a decision class, which is a collection of subsets of arms with certain combinatorial structures such as size-K subsets, matchings, spanning trees or paths, etc. The CPE problem represents a rich class of pure exploration tasks which covers not only many existing models but also novel cases where the object of interest has a non-trivial combinatorial structure. In this paper, we provide a series of results for the general CPE problem. We present general learning algorithms which work for all decision classes that admit offline maximization oracles in both fixed confidence and fixed budget settings. We prove problem-dependent upper bounds of our algorithms. Our analysis exploits the combinatorial structures of the decision classes and introduces a new analytic tool. We also establish a general problem-dependent lower bound for the CPE problem. Our results show that the proposed algorithms achieve the optimal sample complexity (within logarithmic factors) for many decision classes. In addition, applying our results back to the problems of top-K arms identification and multiple bandit best arms identification, we recover the best available upper bounds up to constant factors and partially resolve a conjecture on the lower bounds.


title: Dynamic trajectory generation using continuous-curvature algorithms for door to door assistance vehicles

abstract: In this paper, an algorithm for dynamic path generation in urban environments is presented, taking into account structural and sudden changes in straight and bend segments (e.g. roundabouts and intersections). The results present some improvements in path generation (previously hand plotted) considering parametric equations and continuous-curvature algorithms, which guarantees a comfortable lateral acceleration. This work is focused on smooth and safe path generation using road and obstacle detection information. Finally, some simulation results show a good performance of the algorithm using different ranges of urban curves. The main contribution is an Intelligent Trajectory Generator, which considers infrastructure and vehicle information. This method is recently used in the framework of the project CityMobil2 ::: 1 ::: , for urban autonomous guidance of Cybercars.


title: Emotion regulation through listening to music in everyday situations

abstract: Music is a stimulus capable of triggering an array of basic and complex emotions. We investigated whether and how individuals employ music to induce specific emotional states in everyday situations for the purpose of emotion regulation. Furthermore, we wanted to examine whether specific emotion-regulation styles influence music selection in specific situations. Participants indicated how likely it would be that they would want to listen to various pieces of music (which are known to elicit specific emotions) in various emotional situations. Data analyses by means of non-metric multidimensional scaling revealed a clear preference for pieces of music that were emotionally congruent with an emotional situation. In addition, we found that specific emotion-regulation styles might influence the selection of pieces of music characterised by specific emotions. Our findings demonstrate emotion-congruent music selection and highlight the important role of specific emotion-regulation styles in the selection of music...


title: EEG-based discrimination between imagination of right and left hand movement.

abstract: Abstract Three subjects were asked to imagine either right or left hand movement depending on a visual cue stimulus. The interval between two consecutive imagination tasks was >10 s. Each subject imagined a total of 160 hand movements in each of 3–4 sessions (training) without feedback and 7–8 sessions with feedback. The EEG was recorded bipolarly from left and right central and parietal regions and was sampled at 128 Hz. In the feedback sessions, the EEG from both central channels was classified on-line with a neural network classifier, and the success of the discrimination between left and right movement imagination was given within 1.5 s by means of a visual feedback. For each subject, different frequency components in the alpha and beta band were found which provided best discrimination between left and right hand movement imagination. These frequency bands varied between 9 and 14 Hz and between 18 and 26 Hz. The accuracy of on-line classification was approximately 80% in all 3 subjects and did not improve with increasing number of sessions. By averaging over all training and over all feedback sessions, the EEG data revealed a significant desynchronisation (ERD) over the contralateral central area and synchronisation (ERS) over the ipsilateral side. The ERD/ERS patterns over all sessions displayed a relatively small intra-subject variability with slight differences between sessions with and without feedback.


title: Real time traffic signal control using fuzzy logic controller: Review

abstract: Traffic signal control system is now become very challenging task. It is necessary make road traffic decent, safe, less time and fuel consuming. There is a requirement to improvise the traffic signal control for better traffic control. In past few years traffic control system handles such a situation but not that much effectively because they are static. There should be a system which should be dynamic in nature so that it can handle traffic easily. This system gives improvement in the performance of traffic intersections control. This paper provides overview of fuzzy logic control system used for better control of flow of traffic s at every road intersection.


title: Skew detection for complex document images using robust borderlines in both text and non-text regions

abstract: A new skew detection method for complex document images based on robust borderlines extracted from both text and non-text regions is proposed in this paper. First, borderlines are extracted from the borders of large connected components in a document image by using a run length based method. Second, after filtering out non-linear borderlines, a fast iteration algorithm is applied to optimize each linear borderline's directional angle. Finally, the weighted median value of all the directional angles is calculated as the skew angle of the whole document. Experiments on 2000 various skew document images are implemented. Total correct rate is 95.2%, and the detecting time on average is less than 0.2s for each document. The proposed skew detection method is efficient for complex documents with horizontal and vertical text layout, three kinds of linguistic characters in English, Japanese and Chinese, especially for documents with predominant non-text regions or sparse text regions.


title: RelNet: End-to-end Modeling of Entities&Relations

abstract: We introduce RelNet: a new model for relational reasoning. RelNet is a memory augmented neural network which models entities as abstract memory slots and is equipped with an additional relational memory which models relations between all memory pairs. The model thus builds an abstract knowledge graph on the entities and relations present in a document which can then be used to answer questions about the document. It is trained end-to-end: only supervision to the model is in the form of correct answers to the questions. We test the model on the 20 bAbI question-answering tasks with 10k examples per task and find that it solves all the tasks with a mean error of 0.3%, achieving 0% error on 11 of the 20 tasks.


title: The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network

abstract: This study describes the design of the NTNU-YZU system for the automated evaluation of scientific writing shared task. We employ a convolutional neural network with the Word2Vec/GloVe embedding representation to predict whether a sentence needs language editing. For the Boolean prediction track, our best F-score of 0.6108 ranked second among the ten submissions. Our system also achieved an F-score of 0.7419 for the probabilistic estimation track, ranking fourth among the nine submissions.


title: CUDAlign: using GPU to accelerate the comparison of megabase genomic sequences

abstract: Biological sequence comparison is a very important operation in Bioinformatics. Even though there do exist exact methods to compare biological sequences, these methods are often neglected due to their quadratic time and space complexity. In order to accelerate these methods, many GPU algorithms were proposed in the literature. Nevertheless, all of them restrict the size of the smallest sequence in such a way that Megabase genome comparison is prevented. In this paper, we propose and evaluate CUDAlign, a GPU algorithm that is able to compare Megabase biological sequences with an exact Smith-Waterman affine gap variant. CUDAlign was implemented in CUDA and tested in two GPU boards, separately. For real sequences whose size range from 1MBP (Megabase Pairs) to 47MBP, a close to uniform GCUPS (Giga Cells Updates per Second) was obtained, showing the potential scalability of our approach. Also, CUDAlign was able to compare the human chromosome 21 and the chimpanzee chromosome 22. This operation took 21 hours on GeForce GTX 280, resulting in a peak performance of 20.375 GCUPS. As far as we know, this is the first time such huge chromosomes are compared with an exact method.


title: Inferring TCP connection characteristics through passive measurements

abstract: We propose a passive measurement methodology to infer and keep track of the values of two important variables associated with a TCP connection: the sender's congestion window (cwnd) and the connection round trip time (RTT). Together, these variables provide a valuable diagnostic of end-user-perceived network performance. Our methodology is validated via both simulation and concurrent active measurements, and is shown to be able to handle various flavors of TCP. Given our passive approach and measurement points within a Tier-1 network provider, we are able to analyze more than 10 million connections, with senders located in more than 45% of the autonomous systems in today's Internet. Our results indicate that sender throughput is frequently limited by a lack of data to send, that the TCP congestion control flavor often has minimal impact on throughput, and that the vast majority of connections do not experience significant variations in RTT during their lifetime


title: Satisfaction with Life Scale (SWLS) in Caregivers of Clinically-Referred Youth: Psychometric Properties and Mediation Analysis

abstract: This paper presents the psychometric evaluation of the Satisfaction with Life Scale (SWLS; Diener et al. in J Personal Assess 49:71–75, 1985) used with a large sample (N = 610) of caregivers for youth receiving mental health services. Methods from classical test theory, factor analysis, and item response theory were utilized. Additionally, this paper investigated whether caregiver strain mediates the effect of youth symptom severity on caregiver life satisfaction (N = 356). Bootstrapped confidence intervals were used to determine the significance of the mediated effects. Results indicated that the SWLS is a psychometrically sound instrument to be used with caregivers of clinically-referred youth. Mediation analyses found that the effect of youth symptom severity on caregiver life satisfaction was mediated by caregiver strain but that the mediation effect differed based on the type of youth symptoms: caregiver strain was a partial mediator when externalizing symptoms were measured and a full mediator when internalizing symptoms were measured. Implications for future research and clinical practice are discussed.


title: Scalability of Robotic Controllers: Speech-Based Robotic Controller Evaluation

abstract: Abstract : This study, which took place at Fort Benning, GA, focused on the feasibility of reducing robotic controller size by replacing some of the manual controls with speech-based controls. Eleven Soldiers from the Officers Candidate School served as participants. After training on the operation of the iRobot PackBot small unmanned ground vehicle (SUGV) system, each Soldier teleoperated the SUGV using two controller conditions; a combination of speech and manual control and manual control only. Soldiers were tasked to drive the robot and to perform operations such as surveillance using the robotic arm. Controller type and usability were evaluated based on objective performance data, data collector observations, and Soldier questionnaire responses. Workload for each controller was measured by having the Soldiers complete the National Aeronautics and Space Administration Task Load Index survey after using each controller type. Speech-based control exhibited the potential for benefits beyond controller size reduction. It decreased time and effort when performing multiple tasks simultaneously by allowing speech commands to be given for control of the robotic arm while at the same time maneuvering the robot using manual controls. The speech-based control system also has the potential to provide other benefits beyond those addressed in this study.


title: Improvement of the Global Connectivity using Integrated Satellite-Airborne-Terrestrial Networks with Resource Optimization

abstract: In this paper, we propose a novel wireless scheme that integrates satellite, airborne, and terrestrial networks aiming to support ground users. More specifically, we study the enhancement of the achievable users' throughput assisted with terrestrial base stations, high altitude platforms (HAPs), and satellite station. The goal is to optimize the resource allocations and the HAPs locations in order to maximize the users' throughput. In this context, we propose to solve the optimization problem in two stages; first a short-term stage and then a long-term stage. In the short-term stage, we start by proposing a near optimal solution and low complexity solution to solve the associations and power allocations. In the first solution, we formulate and solve a binary linear optimization problem to find the best associations and then using Taylor expansion approximation to optimally determine the power allocations. While in the second solution, we propose a low complexity approach based on frequency partitioning technique to solve the associations and power allocations. One the other hand, in the long-term stage, we optimize the locations of the HAPs by proposing an efficient algorithm based on a recursive shrink-and-realign process. Finally, selected numerical results show the advantages provided by our proposed optimization scheme.


title: Flexible electronics under strain: a review of mechanical characterization and durability enhancement strategies

abstract: Flexible electronics incorporate all the functional attributes of conventional rigid electronics in formats that have been altered to survive mechanical deformations. Understanding the evolution of device performance during bending, stretching, or other mechanical cycling is, therefore, fundamental to research efforts in this area. Here, we review the various classes of flexible electronic devices (including power sources, sensors, circuits and individual components) and describe the basic principles of device mechanics. We then review techniques to characterize the deformation tolerance and durability of these flexible devices, and we catalogue and geometric designs that are intended to optimize electronic systems for maximum flexibility.


title: Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization

abstract: Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. We propose a novel, accurate tightly-coupled visual-inertial odom- etry pipeline for such cameras that leverages their outstanding properties to estimate the camera ego-motion in challenging conditions, such as high-speed motion or high dynamic range scenes. The method tracks a set of features (extracted on the image plane) through time. To achieve that, we consider events in overlapping spatio-temporal windows and align them using the current camera motion and scene structure, yielding motion-compensated event frames. We then combine these feature tracks in a keyframe- based, visual-inertial odometry algorithm based on nonlinear optimization to estimate the camera’s 6-DOF pose, velocity, and IMU biases. The proposed method is evaluated quantitatively on the public Event Camera Dataset [19] and significantly outperforms the state-of-the-art [28], while being computationally much more efficient: our pipeline can run much faster than real-time on a laptop and even on a smartphone processor. Fur- thermore, we demonstrate qualitatively the accuracy and robustness of our pipeline on a large-scale dataset, and an extremely high-speed dataset recorded by spinning an event camera on a leash at 850 deg/s.


title: A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application

abstract: Abstract Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.


title: A software implementation of a genetic algorithm based approach to network intrusion detection

abstract: With the rapid expansion of Internet in recent years, computer systems are facing increased number of security threats. Despite numerous technological innovations for information assurance, it is still very difficult to protect computer systems. Therefore, unwanted intrusions take place when the actual software systems are running. Different soft computing based approaches have been proposed to detect computer network attacks. This paper presents a genetic algorithm (GA) based approach to network intrusion detection, and the software implementation of the approach. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experimental results show the achievement of acceptable detection rates based on benchmark DARPA data sets on intrusions, while no other complementary techniques or relevant heuristics are applied.


title: A lossless compression algorithm for vibration data of space systems

abstract: Space systems demand some of the most precise and accurate technology. The body of space systems are subjected to many vibrations and shocks. To make sure their stable operation, these vibrations are monitored continuously at a higher sampling rate which results in large amount of data. The data is transmitted over a wireless network to the ground station. Therefore data compression becomes unavoidable, not only for efficient utilization of transmission bandwidth but also for reduced storage requirements. Even the smallest vibration is critical in space applications. So lossless compression techniques are preferred. In this work, a lossless compression technique is proposed where the input data is split into two sets which helps in attaining data redundancy and then the algorithm is applied. Modified Move to front(MTF) coding is done on this data, where positional values of a dictionary are transmitted instead of the sample value. Run length encoding(RLE) is done on the MTF coded data. Next all the successively repeating number patterns in the data set is recognized and RLE coded. The compression ratio obtained for data without and with noise are 4.15 and 2.75 respectively.


title: Comprehensive simulation of quadrotor UAVs using ROS and gazebo

abstract: Quadrotor UAVs have successfully been used both in research and for commercial applications in recent years and there has been significant progress in the design of robust control software and hardware. Nevertheless, testing of prototype UAV systems still means risk of damage due to failures. Motivated by this, a system for the comprehensive simulation of quadrotor UAVs is presented in this paper. Unlike existing solutions, the presented system is integrated with ROS and the Gazebo simulator. This comprehensive approach allows simultaneous simulation of diverse aspects such as flight dynamics, onboard sensors like IMUs, external imaging sensors and complex environments. The dynamics model of the quadrotor has been parameterized using wind tunnel tests and validated by a comparison of simulated and real flight data. The applicability for simulation of complex UAV systems is demonstrated using LIDAR-based and visual SLAM approaches available as open source software.


title: An IGBT Gate Driver for Feed-Forward Control of Turn-on Losses and Reverse Recovery Current

abstract: This paper addresses the problem of turn on performances of an insulated gate bipolar transistor (IGBT) that works in hard switching conditions. The IGBT turn on dynamics with an inductive load is described, and corresponding IGBT turn on losses and reverse recovery current of the associated freewheeling diode are analysed. A new IGBT gate driver based on feed-forward control of the gate emitter voltage is presented in the paper. In contrast to the widely used conventional gate drivers, which have no capability for switching dynamics optimisation, the proposed gate driver provides robust and simple control and optimization of the reverse recovery current and turn on losses. The collector current slope and reverse recovery current are controlled by means of the gate emitter voltage control in feed-forward manner. In addition the collector emitter voltage slope is controlled during the voltage falling phase by means of inherent increase of the gate current. Therefore, the collector emitter voltage tail and the total turn on losses are significantly reduced. The proposed gate driver was experimentally verified and compared to a conventional gate driver, and the results are presented and discussed in the paper.


title: T-Periodic solutions of time dependent Hamiltonian systems with a potential vanishing at infinity

abstract: We are discussing existence and multiplicity of T-periodic solutions of the time dependent Hamiltonian system\( - \ddot x\)= ∇U(x, t) under the assumption that U(x, t) approaches 0 for large |x| meaning that the force ∇U is concentrated in a finite region. Our method is variational and it is shown how the difficulties with the Palais-Smale condition can be overcome in this case.


title: Booster in High Dimensional Data Classification

abstract: Classification problems in high dimensional data with a small number of observations are becoming more common especially in microarray data. During the last two decades, lots of efficient classification models and feature selection (FS) algorithms have been proposed for higher prediction accuracies. However, the result of an FS algorithm based on the prediction accuracy will be unstable over the variations in the training set, especially in high dimensional data. This paper proposes a new evaluation measure Q-statistic that incorporates the stability of the selected feature subset in addition to the prediction accuracy. Then, we propose the Booster of an FS algorithm that boosts the value of the Q-statistic of the algorithm applied. Empirical studies based on synthetic data and 14 microarray data sets show that Booster boosts not only the value of the Q-statistic but also the prediction accuracy of the algorithm applied unless the data set is intrinsically difficult to predict with the given algorithm.


title: New technology trends in education: Seven years of forecasts and convergence

abstract: Each year since 2004, a new Horizon Report has been released. Each edition attempts to forecast the most promising technologies likely to impact on education along three horizons: the short term (the year of the report), the mid-term (the next 2 years) and the long term (the next 4 years). This paper analyzes the evolution of technology trends from 2004 to 2014 that correspond to the long-term predictions of the most recent Horizon Report. The study analyzes through bibliometric analysis which technologies were successful and became a regular part of education systems, which ones failed to have the predicted impact and why, and the shape of technology flows in recent years. The study also shows how the evolution and maturity of some technologies allowed the revival of expectations for others. The analysis here, which focuses on educational applications, offers guidelines that may be helpful to those seeking to invest in new research areas.


title: Diasporicity in the City of Portsmouth (UK): Local and Global Connections of Black Britishness

abstract: This article engages with the theoretical premise of diasporicity - the local/regional specificities and workings of a given diaspora. Diasporicity is an attempt to extend the vocabulary of the concept of diaspora as an intervention against fixed ideas of race and nation. The article tests the usefulness of some aspects of 'diasporicity' by applying them to the settlement of African, Caribbean and South Asian Black British groups in Portsmouth, UK. The article draws on qualitative research, including extended interviews, and offers a social commentary on Black British diasporic connections that are distinctive to this city and, at the same time, contribute to an overall idea of Black Britishness.


title: Investigating semantic similarity measures across the Gene Ontology:

abstract: Motivation: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or ‘semantic similarity’ between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. Am easure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repetoire of analyses. Results: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. Availability: Software available from http://www.russet.


title: A Fully-Convolutional Framework for Semantic Segmentation

abstract: In this paper we propose a deep learning technique to improve the performance of semantic segmentation tasks. Previously proposed algorithms generally suffer from the over-dependence on a single modality as well as a lack of training data. We made three contributions to improve the performance. Firstly, we adopt two models which are complementary in our framework to enrich field-of-views and features to make segmentation more reliable. Secondly, we repurpose the datasets form other tasks to the segmentation task by training the two models in our framework on different datasets. This brings the benefits of data augmentation while saving the cost of image annotation. Thirdly, the number of parameters in our framework is minimized to reduce the complexity of the framework and to avoid over- fitting. Experimental results show that our framework significantly outperforms the current state-of-the-art methods with a smaller number of parameters and better generalization ability.


title: A new look at the essence of the imaging photoplethysmography

abstract: Photoplethysmography (PPG) is a noninvasive optical method accepted in the clinical use for measurements of arterial oxygen saturation. It is widely believed that the light intensity after interaction with the biological tissue in vivo is modulated at the heartbeat frequency mainly due to pulsatile variations of the light absorption caused by arterial blood-volume pulsations. Here we report experimental observations, which are not consistent with this model and demonstrate the importance of elastic deformations of the capillary bed in the formation of the PPG waveform. These results provide new insight on light interaction with live tissue. To explain the observations we propose a new model of PPG in which pulse oscillations of the arterial transmural pressure deform the connective-tissue components of the dermis resulting in periodical changes of both the light scattering and absorption. These local changes of the light-interaction parameters are detected as variations of the light intensity returned to a photosensitive camera. Therefore, arterial pulsations can be indirectly monitored even by using the light, which slightly penetrates into the biological tissue.


title: Bayesian Grammar Learning for Inverse Procedural Modeling

abstract: Within the fields of urban reconstruction and city modeling, shape grammars have emerged as a powerful tool for both synthesizing novel designs and reconstructing buildings. Traditionally, a human expert was required to write grammars for specific building styles, which limited the scope of method applicability. We present an approach to automatically learn two-dimensional attributed stochastic context-free grammars (2D-ASCFGs) from a set of labeled building facades. To this end, we use Bayesian Model Merging, a technique originally developed in the field of natural language processing, which we extend to the domain of two-dimensional languages. Given a set of labeled positive examples, we induce a grammar which can be sampled to create novel instances of the same building style. In addition, we demonstrate that our learned grammar can be used for parsing existing facade imagery. Experiments conducted on the dataset of Haussmannian buildings in Paris show that our parsing with learned grammars not only outperforms bottom-up classifiers but is also on par with approaches that use a manually designed style grammar.


title: Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition

abstract: Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-art performance on many speech recognition tasks, as they are able to provide the learned dynamically changing contextual window of all sequence history. On the other hand, the convolutional neural networks (CNNs) have brought significant improvements to deep feed-forward neural networks (FFNNs), as they are able to better reduce spectral variation in the input signal. In this paper, a network architecture called as convolutional recurrent neural network (CRNN) is proposed by combining the CNN and LSTM RNN. In the proposed CRNNs, each speech frame, without adjacent context frames, is organized as a number of local feature patches along the frequency axis, and then a LSTM network is performed on each feature patch along the time axis. We train and compare FFNNs, LSTM RNNs and the proposed LSTM CRNNs at various number of configurations. Experimental results show that the LSTM CRNNs can exceed state-of-the-art speech recognition performance.


title: Computed muscle control for an anthropomimetic elbow joint

abstract: The soft robotics approach is widely considered to enable human-friendly robots which are able to work in our future homes and factories. Furthermore, achieving the smooth and natural movements of humans has become a hot topic in robotics, especially when robots are supposed to work in close proximity to humans. The anthropomimetic principle aims at mimicking not only the outside but also the inner mechanisms of the human body in humanoid robots. However, for this class of robots there exist as yet no scalable controllers that might make it possible to control a full body, or even several joints. A very similar problem is ongoing research in biomechanics which is the computation of muscle excitation patterns for coordinated movements. For this purpose, biomechanicists have developed computed muscle control which has proven a very scalable technique. In this paper, we demonstrate the adaptation of computed muscle control for a tendon-driven robot, comparing different methods for obtaining the muscle kinematics, as well as different low-level controllers. Results are shown for the implementation on a distributed control architecture and a single revolute elbow joint.


title: Deep convolutional neural networks for pedestrian detection with skip pooling

abstract: With the big success of deep convolutional neural networks (CNN) in image classification task, many proposal based networks are proposed to detect given objects in an image. Faster R-CNN is such a network that uses a region proposal network (RPN) to generate nearly cost-free region proposals, which has shown excellent performance in ILSVRC and MS COCO datasets. However, Faster R-CNN does not behave so well for the task of pedestrian detection since the images in popular pedestrian detection datasets have more complicated background and contain a lot of small foreground objects. In this work, we leverage the RPN architecture of Faster R-CNN and extend it to a multi-layer version combined with skip pooling to tackle the pedestrian detection problem. Skip pooling is a kind of network connection that combines multiple ROI pooling results from lower layers to form a single input to a higher layer while bypassing intermediate layers. We comprehensively evaluate our network, referred to as SP-CNN, on the Caltech pedestrian detection benchmark and KITTI object detection benchmark. Our method achieves state-of-the-art accuracy on Caltech dataset and presents a comparable result on KITTI dataset while maintaining a good speed.


title: Once-per-step control of ankle-foot prosthesis push-off work reduces effort associated with balance during walking

abstract: BackgroundIndividuals with below-knee amputation have more difficulty balancing during walking, yet few studies have explored balance enhancement through active prosthesis control. We previously used a dynamical model to show that prosthetic ankle push-off work affects both sagittal and frontal plane dynamics, and that appropriate step-by-step control of push-off work can improve stability. We hypothesized that this approach could be applied to a robotic prosthesis to partially fulfill the active balance requirements of human walking, thereby reducing balance-related activity and associated effort for the person using the device.MethodsWe conducted experiments on human participants (N = 10) with simulated amputation. Prosthetic ankle push-off work was varied on each step in ways expected to either stabilize, destabilize or have no effect on balance. Average ankle push-off work, known to affect effort, was kept constant across conditions. Stabilizing controllers commanded more push-off work on steps when the mediolateral velocity of the center of mass was lower than usual at the moment of contralateral heel strike. Destabilizing controllers enforced the opposite relationship, while a neutral controller maintained constant push-off work regardless of body state. A random disturbance to landing foot angle and a cognitive distraction task were applied, further challenging participants’ balance. We measured metabolic rate, foot placement kinematics, center of pressure kinematics, distraction task performance, and user preference in each condition. We expected the stabilizing controller to reduce active control of balance and balance-related effort for the user, improving user preference.ResultsThe best stabilizing controller lowered metabolic rate by 5.5% (p = 0.003) and 8.5% (p = 0.02), and step width variability by 10.0% (p = 0.009) and 10.7% (p = 0.03) compared to conditions with no control and destabilizing control, respectively. Participants tended to prefer stabilizing controllers. These effects were not due to differences in average push-off work, which was unchanged across conditions, or to average gait mechanics, which were also unchanged. Instead, benefits were derived from step-by-step adjustments to prosthesis behavior in response to variations in mediolateral velocity at heel strike.ConclusionsOnce-per-step control of prosthetic ankle push-off work can reduce both active control of foot placement and balance-related metabolic energy use during walking.


title: Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach

abstract: A spatial time series dataset is a collection of time series, each referencing a location in a common spatial framework. Correlation analysis is often used to identify pairs of potentially interacting elements from the cross product of two spatial time series datasets. However, the computational cost of correlation analysis is very high when the dimension of the time series and the number of locations in the spatial frameworks are large. The key contribution of this paper is the use of spatial autocorrelation among spatial neighboring time series to reduce computational cost. A filter-and-refine algorithm based on coning, i.e. grouping of locations, is proposed to reduce the cost of correlation analysis over a pair of spatial time series datasets. Cone-level correlation computation can be used to eliminate (filter out) a large number of element pairs whose correlation is clearly below (or above) a given threshold. Element pair correlation needs to be computed for remaining pairs. Using experimental studies with Earth science datasets, we show that the filter-and-refine approach can save a large fraction of the computational cost, particularly when the minimal correlation threshold is high.


title: Correlation Hashing Network for Efficient Cross-Modal Retrieval

abstract: Hashing is widely applied to approximate nearest neighbor search for large-scale multimodal retrieval with storage and computation efficiency. Cross-modal hashing improves the quality of hash coding by exploiting semantic correlations across different modalities. Existing cross-modal hashing methods first transform data into low-dimensional feature vectors, and then generate binary codes by another separate quantization step. However, suboptimal hash codes may be generated since the quantization error is not explicitly minimized and the feature representation is not jointly optimized with the binary codes. This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error. The proposed CHN is a hybrid deep architecture that constitutes a convolutional neural network for learning good image representations, a multilayer perception for learning good text representations, two hashing layers for generating compact binary codes, and a structured max-margin loss that integrates all things together to enable learning similarity-preserving and high-quality hash codes. Extensive empirical study shows that CHN yields state of the art cross-modal retrieval performance on standard benchmarks.


title: Machine Learning Based Session Drop Prediction in LTE Networks and Its SON Aspects

abstract: Abnormal bearer session release (i.e. bearer session drop) in cellular telecommunication networks may seriously impact the quality of experience of mobile users. The latest mobile technologies enable high granularity real-time reporting of all conditions of individual sessions, which gives rise to use data analytics methods to process and monetize this data for network optimization. One such example for analytics is Machine Learning (ML) to predict session drops well before the end of session. In this paper a novel ML method is presented that is able to predict session drops with higher accuracy than using traditional models. The method is applied and tested on live LTE data offline. The high accuracy predictor can be part of a SON function in order to eliminate the session drops or mitigate their effects.


title: A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation

abstract: Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain. We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality. We first do this directly by combining a recent factor method known as a shared response model with searchlight analysis. Then we design a multi-view convolutional autoencoder for the same task. Both approaches preserve spatial locality and have competitive or better performance compared with standard searchlight analysis and the shared response model applied across the whole brain. We also report a system design to handle the computational challenge of training the convolutional autoencoder.


title: A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm

abstract: The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao’s algorithms.


title: Different time courses of learning-related activity in the prefrontal cortex and striatum

abstract: The ability to follow arbitrary associations, such as “stop at red”, is integral to everyday behaviour and the primate brain is quick to learn them. Both the prefrontal complex and the striatum have been implicated in this process, but their respective roles are largely unknown. An experiment in which brain activity in these two areas was recorded while monkeys learned new associations shows that the learning-related changes occur much more quickly in the striatum, but that changes in the prefrontal cortex were more closely related to improved performance of the new task. It is possible that the striatum learns first, then ‘trains’ the prefrontal cortex. To navigate our complex world, our brains have evolved a sophisticated ability to quickly learn arbitrary rules such as ‘stop at red’. Studies in monkeys using a laboratory test of this capacity—conditional association learning—have revealed that frontal lobe structures (including the prefrontal cortex) as well as subcortical nuclei of the basal ganglia are involved in such learning1,2,3,4,5. Neural correlates of associative learning have been observed in both brain regions6,7,8,9,10,11,12,13,14, but whether or not these regions have unique functions is unclear, as they have typically been studied separately using different tasks. Here we show that during associative learning in monkeys, neural activity in these areas changes at different rates: the striatum (an input structure of the basal ganglia) showed rapid, almost bistable, changes compared with a slower trend in the prefrontal cortex that was more in accordance with slow improvements in behavioural performance. Also, pre-saccadic activity began progressively earlier in the striatum but not in the prefrontal cortex as learning took place. These results support the hypothesis that rewarded associations are first identified by the basal ganglia, the output of which ‘trains’ slower learning mechanisms in the frontal cortex15.


title: Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing

abstract: Machine Learning has been the quintessential solution for many AI problems, but learning is still heavily dependent on the specific training data. Some learning models can be incorporated with a prior knowledge in the Bayesian set up, but these learning models do not have the ability to access any organised world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with News20, DBPedia datasets and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained well with substantially less amount of labeled training data, when it has access to organised world knowledge in the form of knowledge graph.


title: inFORM: dynamic physical affordances and constraints through shape and object actuation

abstract: Past research on shape displays has primarily focused on rendering content and user interface elements through shape output, with less emphasis on dynamically changing UIs. We propose utilizing shape displays in three different ways to mediate interaction: to facilitate by providing dynamic physical affordances through shape change, to restrict by guiding users with dynamic physical constraints, and to manipulate by actuating physical objects. We outline potential interaction techniques and introduce Dynamic Physical Affordances and Constraints with our inFORM system, built on top of a state-of-the-art shape display, which provides for variable stiffness rendering and real-time user input through direct touch and tangible interaction. A set of motivating examples demonstrates how dynamic affordances, constraints and object actuation can create novel interaction possibilities.


title: Graph Query Reformulation with Diversity

abstract: We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Given a query issued by the user, the system, apart from returning the result patterns, also proposes a number of specializations (i.e., supergraphs) of the original query to facilitate the exploration of the results. We formalize the problem of finding a set of reformulations of the input query by maximizing a linear combination of coverage (of the original query's answer set) and diversity among the specializations. We prove that our problem is hard, but also that a simple greedy algorithm achieves a (1/2)-approximation guarantee. The most challenging step of the greedy algorithm is the computation of the specialization that brings the maximum increment to the objective function. To efficiently solve this step, we show how to compute the objective-function increment of a specialization linearly in the number of its results and derive an upper bound that we exploit to devise an efficient search-space visiting strategy. An extensive evaluation on real and synthetic databases attests high efficiency and accuracy of our proposal.


An overview of topic modeling and its current applications in bioinformatics


title: Executive functions and their disorders

abstract: The term executive function defines complex cognitive processing requiring the co-ordination of several subprocesses to achieve a particular goal. Neuropsychological evidence suggests that executive processing is intimately connected with the intact function of the frontal cortices. Executive dysfunction has been associated with a range of disorders, and is generally attributed to structural or functional frontal pathology. Neuroimaging, with PET and fMRI, has confirmed the relationship; however, attempts to link specific aspects of executive functioning to discrete prefrontal foci have been inconclusive. Instead, the emerging view suggests that executive function is mediated by dynamic and flexible networks, that can be characterised using functional integration and effective connectivity analyses. This view is compatible with the clinical presentation of executive dysfunction associated with a range of pathologies, and also with evidence that recovery of executive function can occur after traumatic brain injury, perhaps due to functional reorganisation within executive networks.


title: The role of working memory in sport

abstract: The concept of working memory has received a great deal of attention in the last couple of decades and discussions of working memory are now common in almost all branches of psychology, including cognitive, clinical, social, developmental, and educational settings. Therefore, it is surprising that the concept of working memory has received a lot less attention in the field of sport psychology compared to other branches of psychology, especially since research in sport psychology has increasingly incorporated cognitive concepts such as attention, perception and decision-making, which are purported to rely heavily on working memory. Thus, it is essential, in our opinion, to systematically investigate the working memory system in the field of sports, which offers a fruitful domain to explore the validity of models developed in other fields. This review provides an overview of working memory theory and discusses its relevance in sport psychology. We end the review by giving an outlook of potentially fruitful ...


title: Topic Modelling of Empirical Text Corpora: Validity, Reliability, and Reproducibility in Comparison to Semantic Maps

abstract: Using the 6,638 case descriptions of societal impact submitted for evaluation in the Research Excellence Framework (REF 2014), we replicate the topic model (Latent Dirichlet Allocation or LDA) made in this context and compare the results with factor-analytic results using a traditional word-document matrix (Principal Component Analysis or PCA). Removing a small fraction of documents from the sample, for example, has on average a much larger impact on LDA than on PCA-based models to the extent that the largest distortion in the case of PCA has less effect than the smallest distortion of LDA-based models. In terms of semantic coherence, however, LDA models outperform PCA-based models. The topic models inform us about the statistical properties of the document sets under study, but the results are statistical and should not be used for a semantic interpretation - for example, in grant selections and micro-decision making, or scholarly work-without follow-up using domain-specific semantic maps.


title: Multichannel Power Allocation for Maximizing Energy Efficiency in Wireless Networks

abstract: This paper aims at solving two classes of energy efficiency (EE) maximization problems in multiple channels wireless communication systems. First, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and second, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large-scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low-degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency.


title: Secure and Efficient Skyline Queries on Encrypted Data

abstract: Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Furthermore, we demonstrate two optimizations, data partitioning and lazy merging, to further reduce the computation load. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.


title: Evidential Deep Learning to Quantify Classification Uncertainty

abstract: Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of out-of-distribution queries and endurance against adversarial perturbations.


title: Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

abstract: To establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in information retrieval, natural language processing, artificial intelligence, etc. The challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans. Along with the prosperity of Web 2.0, we are now able to collect extremely massive conversational data, which are publicly available. It casts a great opportunity to launch automatic conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will be able to find at least some responses from the massive repository for any user inputs. Given a human issued message, i.e., query, our system would provide a reply after adequate training and learning of how to respond. In this paper, we propose a retrieval-based conversation system with the deep learning-to-respond schema through a deep neural network framework driven by web data. The proposed model is general and unified for different conversation scenarios in open domain. We incorporate the impact of multiple data inputs, and formulate various features and factors with optimization into the deep learning framework. In the experiments, we investigate the effectiveness of the proposed deep neural network structures with better combinations of all different evidence. We demonstrate significant performance improvement against a series of standard and state-of-art baselines in terms of p@1, MAP, nDCG, and MRR for conversational purposes.


title: Dielectric resonator antenna phased array with liquid crystal based phase shifters

abstract: The work presents for the first time a combination of liquid crystal phase shifter technology and dielectric resonator antenna, in an array configuration. The electromagnetic energy is coupled into the rectangular dielectric resonator from the slots in the ground plane which is then radiated from the top face of the dielectric resonator antennas. The antenna consists of 4×1 dielectric resonators as radiating elements, which have been constructed from bulk-glass ceramic material with permittivity of ϵ ::: r ::: =22 and tanδ=0.005. The phase shifters are implemented in inverted microstrip line topology by filling liquid crystal between two borosilicate glass substrates (ϵ ::: r ::: =2.4, tan<;5=0.006). Reflection loss data measured with network analyzer and radiation characteristics for different beam directions measured in anechoic chamber are presented and discussed here.


title: Fingerprint recognition with ridge features and minutiae on distortion

abstract: In this paper we present a new fingerprint matching method which combines different features, including minutiae and ridge features. The ridge features contain ridge count, ridge length, ridge curvature direction and ridge frequency. All ridge features are extracted in blocks around each minutia. The similarity scores of the features are summed with different weighting values as the final score of two fingerprints. Experiments are conducted on the FVC2002 database to compare the proposed method with other fingerprint methods on equal error rate (EER). The proposed method achieves better performance than other methods. The average EER value of the proposed method is 0.82 whereas the average EER value of the conventional matching method is 8.12.


title: Safety climate and injuries: an examination of theoretical and empirical relationships.

abstract: Our purpose in this study was to meta-analytically address several theoretical and empirical issues regarding the relationships between safety climate and injuries. First, we distinguished between extant safety climate-->injury and injury-->safety climate relationships for both organizational and psychological safety climates. Second, we examined several potential moderators of these relationships. Meta-analyses revealed that injuries were more predictive of organizational safety climate than safety climate was predictive of injuries. Additionally, the injury-->safety climate relationship was stronger for organizational climate than for psychological climate. Moderator analyses revealed that the degree of content contamination in safety climate measures inflated effects, whereas measurement deficiency attenuated effects. Additionally, moderator analyses showed that as the time period over which injuries were assessed lengthened, the safety climate-->injury relationship was attenuated. Supplemental meta-analyses of specific safety climate dimensions also revealed that perceived management commitment to safety is the most robust predictor of occupational injuries. Contrary to expectations, the operationalization of injuries did not meaningfully moderate safety climate-injury relationships. Implications and recommendations for future research and practice are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved). Language: en


title: Syntactic skeleton-based translation

abstract: In this paper we propose an approach to modeling syntactically-motivated skeletal structure of source sentence for machine translation. This model allows for application of high-level syntactic transfer rules and low-level non-syntactic rules. It thus involves fully syntactic, non-syntactic, and partially syntactic derivations via a single grammar and decoding paradigm. On large-scale Chinese-English and English-Chinese translation tasks, we obtain an average improvement of +0.9 BLEU across the newswire and web genres.


title: Finding community through information and communication technology in disaster response

abstract: Disasters affect not only the welfare of individuals and family groups, but also the well-being of communities, and can serve as a catalyst for innovative uses of information and communication technology (ICT). In this paper, we present evidence of ICT use for re-orientation toward the community and for the production of public goods in the form of information dissemination during disasters. Results from this study of information seeking practices by members of the public during the October 2007 Southern California wildfires suggest that ICT use provides a means for communicating community-relevant information especially when members become geographically dispersed, leveraging and even building community resources in the process. In the presence of pervasive ICT, people are developing new practices for emergency response by using ICT to address problems that arise from information dearth and geographical dispersion. In doing so, they find community by reconnecting with others who share their concern for the locale threatened by the hazard.


title: Surgical consideration of the anatomic origin of the risorius in relation to facial planes.

abstract: BACKGROUND ::: Confusion exists as to the plane of the risorius with respect to the superficial musculoaponeurotic system (SMAS), the parotid fascia, and the masseteric fascia, which generally are considered origins of the risorius. ::: ::: ::: OBJECTIVES ::: The authors attempted to clarify the origin of the risorius by topographic examination and dissection, which would provide valuable anatomic information for flap dissection in facelift surgery. ::: ::: ::: METHODS ::: Detailed dissection was performed in the perioral region of 46 cadaveric specimens to discern the origin of the risorius in relation to the fascial layer. The anatomic aspects of the muscular arrangement and origin of the risorius were classified according to the location of attachment. ::: ::: ::: RESULTS ::: The risorius originated solely from the fascial layer superficial to the SMAS in 27 specimens (58.7%; type A). It originated solely from the masseter tendon in 3 specimens (6.5%; type B) and from the fascial layers, both superficial and deep to the SMAS, in 16 specimens (34.8%; type C). ::: ::: ::: CONCLUSIONS ::: The patterns of risorius origination identified in this study represent important anatomic reference information for flap dissection in facelift surgery.


title: Evaluating and selecting software packages: A review

abstract: Evaluating and selecting software packages that meet an organization's requirements is a difficult software engineering process. Selection of a wrong software package can turn out to be costly and adversely affect business processes. The aim of this paper is to provide a basis to improve the process of evaluation and selection of the software packages. This paper reports a systematic review of papers published in journals and conference proceedings. The review investigates methodologies for selecting software packages, software evaluation techniques, software evaluation criteria, and systems that support decision makers in evaluating software packages. The key findings of the review are: (1) analytic hierarchy process has been widely used for evaluation of the software packages, (2) there is lack of a common list of generic software evaluation criteria and its meaning, and (3) there is need to develop a framework comprising of software selection methodology, evaluation technique, evaluation criteria, and system to assist decision makers in software selection.


title: NR2A subunit expression shortens NMDA receptor synaptic currents in developing neocortex

abstract: NMDA receptors play important roles in learning and memory and in sculpting neural connections during development. After the period of peak cortical plasticity, NMDA receptor-mediated EPSCs (NMDAR EPSCs) decrease in duration. A likely mechanism for this change in NMDA receptor properties is the molecular alteration of NMDA receptor structure by regulation of NMDA receptor subunit gene expression. The four modulatory NMDAR2A–D (NR2A–D) NMDA receptor subunits are known to alter NMDA receptor properties, and the expression of these subunits is regulated developmentally. It is unclear, however, how the four NR2 subunits are expressed in individual neurons and which NR2 subunits are important to the regulation of NMDA receptor properties during development in vivo. Analysis of NR2 subunit gene expression in single characterized neurons of postnatal neocortex revealed that cells expressing NR2A subunit mRNA had faster NMDAR EPSCs than cells not expressing this subunit, regardless of postnatal age. Expression of NR2A subunit mRNA in cortical neurons at even low levels seemed sufficient to alter the NMDA receptor time course. The proportion of cells expressing NR2A and displaying fast NMDAR EPSCs increased developmentally, thus providing a molecular basis for the developmental change in mean NMDAR EPSC duration.


title: Reasoning about Independence in Probabilistic Models of Relational Data

abstract: Bayesian networks leverage conditional independence to compactly encode joint probability distributions. Many learning algorithms exploit the constraints implied by observed conditional independencies to learn the structure of Bayesian networks. The rules of d-separation provide a theoretical and algorithmic framework for deriving conditional independence facts from model structure. However, this theory only applies to Bayesian networks. Many realworld systems, such as social or economic systems, are characterized by interacting heterogeneous entities and probabilistic dependencies that cross the boundaries of entities. Consequently, researchers have developed extensions to Bayesian networks that can represent these relational dependencies. We show that the theory of d-separation inaccurately infers conditional independence when applied directly to the structure of probabilistic models of relational data. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally ecient method for answering d-separation queries about relational models, and we present empirical results that demonstrate eectiveness.


title: Guidelines for safe handling of hazardous drugs: A systematic review

abstract: OBJECTIVE ::: To review the scientific literature related to the safe handling of hazardous drugs (HDs). ::: ::: ::: METHOD ::: Critical analysis of works retrieved from MEDLINE, the Cochrane Library, Scopus, CINHAL, Web of Science and LILACS using the terms "Hazardous Substances", "Antineoplastic Agents" and "Cytostatic Agents", applying "Humans" and "Guidelines" as filters. Date of search: January 2017. ::: ::: ::: RESULTS ::: In total, 1100 references were retrieved, and from those, 61 documents were selected based on the inclusion and exclusion criteria: 24 (39.3%) documents related to recommendations about HDs; 27 (44.3%) about antineoplastic agents, and 10 (33.3%) about other types of substances (monoclonal antibodies, gene medicine and other chemical and biological agents). In 14 (23.3%) guides, all the stages in the manipulation process involving a risk due to exposure were considered. Only one guide addressed all stages of the handling process of HDs (including stages with and without the risk of exposure). The most described stages were drug preparation (41 guides, 67.2%), staff training and/or patient education (38 guides, 62.3%), and administration (37 guides, 60.7%). No standardized informatics system was found that ensured quality management, traceability and minimization of the risks associated with these drugs. ::: ::: ::: CONCLUSIONS ::: Most of the analysed guidelines limit their recommendations to the manipulation of antineoplastics. The most frequently described activities were preparation, training, and administration. It would be convenient to apply ICTs (Information and Communications Technologies) to manage processes involving HDs in a more complete and simpler fashion.


title: Biometric cryptosystem based on discretized fingerprint texture descriptors

abstract: This paper focuses on a biometric cryptosystem implementation and evaluation based on a number of fingerprint texture descriptors. The texture descriptors, namely, the Gabor filter-based FingerCode, a local binary pattern (LBP), and a local direction pattern (LDP), and their various combinations are considered. These fingerprint texture descriptors are binarized using a biometric discretization method and used in a fuzzy commitment scheme (FCS). We constructed the biometric cryptosystems, which achieve a good performance, by fusing discretized fingerprint texture descriptors and using effective error-correcting codes. We tested the proposed system on a FVC2000 DB2a fingerprint database, and the results demonstrate that the new system significantly improves the performance of the FCS for texture-based fingerprints.


title: Permanent-magnets linear actuators applicability in automobile active suspensions

abstract: Significant improvements in automobile suspension performance are achieved by active systems. However, current active suspension systems are too expensive and complex. Developments occurring in power electronics, permanent magnet materials, and microelectronic systems justifies analysis of the possibility of implementing electromagnetic actuators in order to improve the performance of automobile suspension systems without excessively increasing complexity and cost. In this paper, the layouts of hydraulic and electromagnetic active suspensions are compared. The actuator requirements are calculated, and some experimental results proving that electromagnetic suspension could become a reality in the future are shown.


title: Trimming and Improving Skip-thought Vectors

abstract: The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to trim and improve it. First, we validate a hypothesis that, given a current sentence, inferring the previous and inferring the next sentence provide similar supervision power, therefore only one decoder for predicting the next sentence is preserved in our trimmed skip-thought model. Second, we present a connection layer between encoder and decoder to help the model to generalize better on semantic relatedness tasks. Third, we found that a good word embedding initialization is also essential for learning better sentence representations. We train our model unsupervised on a large corpus with contiguous sentences, and then evaluate the trained model on 7 supervised tasks, which includes semantic relatedness, paraphrase detection, and text classification benchmarks. We empirically show that, our proposed model is a faster, lighter-weight and equally powerful alternative to the original skip-thought model.


title: Writing for synthesis of evidence in empirical software engineering

abstract: Context: Systematic literature reviews have become common in software engineering in the last decade, but challenges remain. Goal: Given the challenges, the objective is to describe improvement areas in writing primary studies, and hence provide a good basis for researchers aiming at synthesizing research evidence in a specific area. Method: The results presented are based on a literature review with respect to synthesis of research results in software engineering with a particular focus on empirical software engineering. The literature review is complemented and exemplified with experiences from conducting systematic literature reviews and working with research methodologies in empirical software engineering. Results: The paper presents three areas where improvements are needed to become more successful in synthesizing empirical evidence. These three areas are: terminology, paper content and reviewing. Conclusion: It is concluded that it must be possible to improve the primary studies, but it requires that researchers start having synthesis in mind when writing their research papers.


Analysis of the PeerRank Method for Peer Grading


title: Tapped-inductor buck converter for high-step-down DC-DC conversion

abstract: The narrow duty cycle in the buck converter limits its application for high-step-down dc-dc conversion. With a simple structure, the tapped-inductor buck converter shows promise for extending the duty cycle. However, the leakage inductance causes a huge turn-off voltage spike across the top switch. Also, the gate drive for the top switch is not simple due to its floating source connection. This paper solves all these problems by modifying the tapped-inductor structure. A simple lossless clamp circuit can effectively clamp the switch turn-off voltage spike and totally recover the leakage energy. Experimental results for 12V-to-1.5V and 48V-to-6V dc-dc conversions show significant improvements in efficiency.


title: Under-exposed image enhancement using exposure compensation

abstract: Exposure is critical in the imaging process, since if the lighting condition is poor, the quality and visibility of images will be severely degraded. In this paper, we propose a fully automatic image enhancement algorithm that can efficiently solve the under-exposed problem. The basic idea behind this method comes from the common technique of photography: the exposure compensation. For poorly exposed images like nighttime images and backlit images, we first examine the level of exposure by adapting the Zone system used in photography, and then use exposure compensation to correct the insufficient lightness. Our experiments demonstrate compared to existing methods, the proposed algorithm not only presents more nature color and visible details, but also leads to less unpleasing artifacts. It is particularly useful for image quality enhancement for a video recorded by event data recorder (EDR) or by a video-based driving assistance system in a vehicle.


title: A sensor cluster to monitor body kinematics

abstract: Several different factors have been proposed to contribute to the development of chronic low back pain (LBP). Specifically, researchers and clinicians have proposed that impairments of low back posture and movement, particularly during functional activities, are important to address during intervention. However, objective measures of posture and movement are typically only measured in the laboratory setting. Observation of posture and movement in laboratory is limited because people with LBP may not perform naturally when they are being observed, and observation in a single session does not provide information about the duration of postures or frequency of movements across the day. In this paper, we present a wireless body sensor cluster formed by up to seven sensors in order to monitor spine posture and movement both in absolute and relative coordinate systems. The Body Kinematics Monitoring (BKM) system measures the magnitude and frequency of spine movements, and duration of spine postures in 3D, without impeding natural movement. The BKM node developed in this study is 3.0cm in diameter, and contains a 9-axis motion processor that records the raw inertial information of different spine regions. The system offers a standard Bluetooth Low Energy (BLE) protocol to communicate with mobile or fixed hosts. The BKM system has been validated in the laboratory by measuring lumbar spine postures on a mechanical spine testing platform across a known range of angles.


title: A Real-Time Signal Processing Framework of Musical Expressive Feature Extraction Using Matlab

abstract: In this paper we propose a real-time signal processing framework for musical audio that 1) aligns the audio with an existing music score or creates a musical score by automated music transcription algorithms; and 2) obtains the expressive feature descriptors of music performance by comparing the score with the audio. Real-time audio segmentation algorithms are implemented to identify the onset points of music notes in the incoming audio stream. The score related features and musical expressive features are extracted based on these segmentation results. In a realtime setting, these audio segmentation and feature extraction operations have to be accomplished at (or shortly after) the note onset points, when an incomplete length of audio signal is captured. To satisfy real-time processing requirements while maintaining feature accuracy, our proposed framework combines the processing stages of prediction, estimation, and updating in both audio segmentation and feature extraction algorithms in an integrated refinement process. The proposed framework is implemented in a MATLAB real-time signal processing framework.


title: A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images

abstract: This paper presents a cost-sensitive Question-Answering (QA) framework for learning a nine-layer And-Or graph (AoG) from web images, which explicitly represents object categories, poses, parts, and detailed structures within the parts in a compositional hierarchy. The QA framework is designed to minimize an overall risk, which trades off the loss and query costs. The loss is defined for nodes in all layers of the AoG, including the generative loss (measuring the likelihood for the images) and the discriminative loss (measuring the fitness to human answers). The cost comprises both human labor of answering questions and the computational cost of model learning. The cost-sensitive QA framework iteratively selects different storylines of questions to update different nodes in the AoG. Experiments showed that our method required much less human supervision (e.g., labeling parts on 3--10 training objects for each category) and achieved better performance than baseline methods.


title: CGM: A biomedical text categorization approach using concept graph mining

abstract: Text Categorization is used to organize and manage biomedical text databases that are growing at an exponential rate. Feature representations for documents are a crucial factor for the performance of text categorization. Most of the successful existing techniques use a vector representation based on key entities extracted from the text. In this paper we investigate a new direction where we represent a document as a graph. In this representation we identify high level concepts and build a rich graph structure that contains additional concepts and relationships. We then use graph kernel techniques to perform text categorization. The results show a significant improvement in accuracy when compared to categorization based on only the extracted concepts.


title: A comprehensive review of recent advances on deep vision systems

abstract: Real-time video objects detection, tracking, and recognition are challenging issues due to the real-time processing requirements of the machine learning algorithms. In recent years, video processing is performed by deep learning (DL) based techniques that achieve higher accuracy but require higher computations cost. This paper presents a recent survey of the state-of-the-art DL platforms and architectures used for deep vision systems. It highlights the contributions and challenges from over numerous research studies. In particular, this paper first describes the architecture of various DL models such as AutoEncoders, deep Boltzmann machines, convolution neural networks, recurrent neural networks and deep residual learning. Next, deep real-time video objects detection, tracking and recognition studies are highlighted to illustrate the key trends in terms of cost of computation, number of layers and the accuracy of results. Finally, the paper discusses the challenges of applying DL for real-time video processing and draw some directions for the future of DL algorithms.


title: Multisource Coordination Energy Management Strategy Based on SOC Consensus for a PEMFC–Battery–Supercapacitor Hybrid Tramway

abstract: For the sake of coordinating multiple energy sources appropriately from power demand and guarantee stage of charge (SOC) consensus of the energy storage systems in different operation conditions, a multisource coordination energy management strategy based on self-convergence droop control is proposed for a large-scale and high-power hybrid tramway. A hybrid powertrain configuration that includes multiple proton exchange membrane fuel cell systems, batteries, and supercapacitors is designed for a 100% low-floor light rail vehicle (LF-LRV) tramway. According to the hybrid system model of LF-LRV tramway developed with commercial equipment, this proposed multisource coordination energy management strategy is assessed with a real driving cycle of tramway. The results obtained from RT-LAB platform testify that the proposed strategy is capable of coordinating multiple energy sources, guaranteeing the SOC consensus and improving the efficiency of overall tramway.


2 Application : Persistent Homology and Brain Data


title: Complete Triaxis Magnetometer Calibration in the Magnetic Domain

abstract: Article deposited according to Hindawi Publishing Corporation policy in SHERPA/RoMEO, June 14, 2011.


title: A Creative Computing Based Inspiration Assistant to Poem Generation

abstract: This paper presents a process of poem generation for users who need creative inspiration. Rather than enumerating existing poems, users are able to discover possible creative changes of existing poems, and then develop their own poems through our App. Creativity can re-shape the way that people look at things, which has already been studied in many disciplines. When collaborating with most widely used facility, computers, as a joining force of innovation, can be formed by researching creative computing. In order to explain how computers can enhance human creativity, a general framework of creative computing has been proposed. For the purpose of evaluating creative computing in practice, this paper proposes a creative approach to inspiring users and a supplemental poem modification case will be presented to show how knowledge combination can contribute and help the inspiration process in the form of software development. In order to achieve this purpose, the intrinsic motivation and external stimulation for creativity from psychology will be abstracted to a word linking algorithm in the proposed research and the semantic web techniques will be used to make software understand the context of input. Then a feedback loop will be constructed to make software adaption.


title: Comparison of classification techniques applied for network intrusion detection and classification

abstract: Abstract In a previous research, a multi-agent artificial immune system for network intrusion detection and classification was proposed and tested, where a multi-layer detection and classification process was executed on each agent, for each host in the network. In this paper, we show the experiments that were held to chose the appropriate classifiers by testing different classifiers and comparing them to increase the detection accuracy and obtain more information on the detected anomalies. It will be shown that no single classifier should be used for all types of attacks, due to different classification rates obtained. This is due to attacks representations in the train set and dependency between features used to detect them. It will also be shown that a basic and simple classifier such as Naive Bayes has better classification results in the case of low-represented attacks, and the basic decision trees such as Naive-Bayes Tree and Best-First Tree give very good results compared to well-known J48 (Weka implementation of C4.5) and Random Forest decision trees. Based on these experiments and their results, Naive Bayes and Best-First tree classifiers were selected to classify the anomaly-detected traffic. It was shown that in the detection phase, 90% of anomalies were detected, and in the classification phase, 88% of false positives were successfully labeled as normal traffic connections, and 79% of DoS and Probe attacks were labeled correctly, mostly by NB, NBTree, and BFTree classifiers.


title: A 6LoWPAN Accelerator for Internet of Things Endpoint Devices

abstract: The Internet of Things (IoT) is revolutionizing the Internet of the future and the way smart devices, people, and processes interact with each other. Challenges in endpoint devices (EDs) communication are due not only to the security and privacy-related requirements but also due to the ever-growing amount of data transferred over the network that needs to be handled, which can considerably reduce the system availability to perform other tasks. This paper presents an IPv6 over low power wireless personal area networks (6LoWPAN) accelerator for the IP-based IoT networks, which is able to process and filter IPv6 packets received by an IEEE 802.15.4 radio transceiver without interrupting the microcontroller. It offers nearly 13.24% of performance overhead reduction for the packet processing and the address filtering tasks, while guaranteeing full system availability when unwanted packets are received and discarded. The contribution is meant to be deployed on heterogeneous EDs at the network edge, which include on the same architecture, field-programmable gate array (FPGA) technology beside a microcontroller and an IEEE 802.15.4-compliant radio transceiver. Performed evaluations include the low FPGA hardware resources utilization and the energy consumption analysis when the processing and accepting/rejection of a received packet is performed by the 6LoWPAN accelerator.


Using Educational Video in the Classroom: Theory, Research and Practice Multimodal Learning Styles Dual-channel Learning Motivation and Affective Learning


title: Process integration of a 27nm, 16Gb Cu ReRAM

abstract: A 27nm 16Gb Cu based NV Re-RAM chip has been demonstrated. Novel process introduction to enable this technology include a Damascene Cell, Line-SAC Digit Lines filled with Cu, exhumed-silicided array contacts, raised epitaxial arrays, and high-drive buried access devices.


title: Robust Speech Recognition Using Generative Adversarial Networks

abstract: This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained with the proposed approach enjoy improved invariance by learning to map noisy audio to the same embedding space as that of clean audio. Unlike previous methods, the new framework does not rely on domain expertise or simplifying assumptions as are often needed in signal processing, and directly encourages robustness in a data-driven way. We show the new approach improves simulated far-field speech recognition of vanilla sequence-to-sequence models without specialized front-ends or preprocessing.


title: HoMonit: Monitoring Smart Home Apps from Encrypted Traffic

abstract: Smart home is an emerging technology for intelligently connecting a large variety of smart sensors and devices to facilitate automation of home appliances, lighting, heating and cooling systems, and security and safety systems. Our research revolves around Samsung SmartThings, a smart home platform with the largest number of apps among currently available smart home platforms. The previous research has revealed several security flaws in the design of SmartThings, which allow malicious smart home apps (or SmartApps) to possess more privileges than they were designed and to eavesdrop or spoof events in the SmartThings platform. To address these problems, this paper leverages side-channel inference capabilities to design and develop a system, dubbed HoMonit, to monitor SmartApps from encrypted wireless traffic. To detect anomaly, HoMonit compares the SmartApps activities inferred from the encrypted traffic with their expected behaviors dictated in their source code or UI interfaces. To evaluate the effectiveness of HoMonit, we analyzed 181 official SmartApps and performed evaluation on 60 malicious SmartApps, which either performed over-privileged accesses to smart devices or conducted event-spoofing attacks. The evaluation results suggest that HoMonit can effectively validate the working logic of SmartApps and achieve a high accuracy in the detection of SmartApp misbehaviors.


title: Low Complexity MMSE Vector Precoding Using Lattice Reduction for MIMO Systems

abstract: In this paper, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) vector precoding (VP) is proposed for multiple input multiple output (MIMO) systems. Three schemes are provided for the perturbation vector design by Babai's approximation procedures based on lattice reduction method to reduce the complexity. Performance and complexity analysis are provided. Simulation results show that the proposed schemes significantly outperform the conventional MMSE Tomlinson-Harashima precoding (THP) and the zero forcing (ZF) VP. Compared with the MMSE VP via closest-point search, our LRA approach provides a simple alternative with little performance loss.


title: Digital Forensics Evidence Acquisition and Chain of Custody in Cloud Computing

abstract: The new cloud computing concept delivers an adaptable service to many users. This is due to the fact that cloud computing offers an economic solution based on pay-per use idea. At the same time, digital forensics is a relatively new discipline born out due to the growing use of computing and digital solution. Digital forensics in cloud computing brings new technical and legal challenges (e.g. the remote nature of the evidence, trust required in the integrity and authenticity, and lack of physical access.) Digital forensics difficulties in cloud computing comprise acquisition of remote data, chain of custody, distributed and elastic data, big data volumes, and ownership. In the literature, there are many schemes that deal with these issues. In 2013, Hou et al. proposed a scheme to verify data authenticity and integrity in server-aided confidential forensic investigation. The authenticity and integrity are two essential requirements for the evidence admitted in court. The aim of this paper is twofold. First, to introduce a new concept for digital artifacts acquisition in cloud computing as a consolidation between digital forensic and cloud computing. This concept guarantees safe investigation to trusted digital evidence. Secondly, to analyze Hou et al.’s scheme with respect to its claimed integrity and authenticity properties. Our analysis shows that Hou et al.’s scheme does not satisfy the claimed integrity and authenticity in server-aided confidential forensics investigation. To achieve the authenticity, confidentiality and integrity of evidence in cloud, we illustrate how encryption and digital signature algorithms could be used within different designs to ensure confidentiality and chain of custody for the digital forensics process in the cloud.


title: A Dependency-Based Neural Network for Relation Classification

abstract: Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency information. We first propose a new structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop dependency-based neural networks (DepNN): a recursive neural network designed to model the subtrees, and a convolutional neural network to capture the most important features on the shortest path. Experiments on the SemEval-2010 dataset show that our proposed method achieves state-of-art results.


title: HYPERSHELL: a practical hypervisor layer guest OS shell for automated in-VM management

abstract: To direct the operation of a computer, we often use a shell, a user interface that provides accesses to the OS kernel services. Traditionally, shells are designed atop an OS kernel. In this paper, we show that a shell can also be designed below an OS. More specifically, we present HYPERSHELL, a practical hypervisor layer guest OS shell that has all of the functionality of a traditional shell, but offers better automation, uniformity and centralized management. This will be particularly useful for cloud and data center providers to manage the running VMs in a large scale. To overcome the semantic gap challenge, we introduce a reverse system call abstraction, and we show that this abstraction can significantly relieve the painful process of developing software below an OS. More importantly, we also show that this abstraction can be implemented transparently. As such, many of the legacy guest OS management utilities can be directly reused in HYPERSHELL without any modification. Our evaluation with over one hundred management utilities demonstrates that HYPERSHELL has 2.73X slowdown on average compared to their native in-VM execution, and has less than 5% overhead to the guest OS kernel.


abstract: INTRODUCTION ::: Evidence was recently provided for vaginal orgasm, orgasm triggered purely by penile-vaginal intercourse (PVI), being associated with better psychological functioning. Common sex education and sexual medicine approaches might undermine vaginal orgasm benefits. ::: ::: ::: AIMS ::: To examine the extent to which women's vaginal orgasm consistency is associated with (i) being told in childhood or adolescence that the vagina was the important zone for inducing female orgasm; (ii) how well they focus mentally on vaginal sensations during PVI; (iii) greater PVI duration; and (iv) preference for above-average penis length. ::: ::: ::: METHODS ::: In a representative sample of the Czech population, 1,000 women reported their vaginal orgasm consistency (from never to almost every time; only 21.9% never had a vaginal orgasm), estimates of their typical foreplay and PVI durations, what they were told in childhood and adolescence was the important zone for inducing female orgasm, their degree of focus on vaginal sensations during PVI, and whether they were more likely to orgasm with a longer than average penis. ::: ::: ::: MAIN OUTCOME MEASURES ::: The association of vaginal orgasm consistency with the predictors noted above. ::: ::: ::: RESULTS ::: Vaginal orgasm consistency was associated with all hypothesized correlates. Multivariate analysis indicated the most important predictors were being educated that the vagina is important for female orgasm, being mentally focused on vaginal sensations during PVI, and in some analyses duration of PVI (but not foreplay) and preferring a longer than average penis. ::: ::: ::: CONCLUSIONS ::: Focusing attention on penile-vaginal sensation supports vaginal orgasm and the myriad benefits thereof. Brody S, and Weiss P. Vaginal orgasm is associated with vaginal (not clitoral) sex education, focusing mental attention on vaginal sensations, intercourse duration, and a preference for a longer penis.