title: Multi-Target Tracking by Discrete-Continuous Energy Minimization

abstract: The task of tracking multiple targets is often addressed with the so-called tracking-by-detection paradigm, where the first step is to obtain a set of target hypotheses for each frame independently. Tracking can then be regarded as solving two separate, but tightly coupled problems. The first is to carry out data association, i.e., to determine the origin of each of the available observations. The second problem is to reconstruct the actual trajectories that describe the spatio-temporal motion pattern of each individual target. The former is inherently a discrete problem, while the latter should intuitively be modeled in continuous space. Having to deal with an unknown number of targets, complex dependencies, and physical constraints, both are challenging tasks on their own and thus most previous work focuses on one of these subproblems. Here, we present a multi-target tracking approach that explicitly models both tasks as minimization of a unified discrete-continuous energy function. Trajectory properties are captured through global label costs, a recent concept from multi-model fitting, which we introduce to tracking. Specifically, label costs describe physical properties of individual tracks, e.g., linear and angular dynamics, or entry and exit points. We further introduce pairwise label costs to describe mutual interactions between targets in order to avoid collisions. By choosing appropriate forms for the individual energy components, powerful discrete optimization techniques can be leveraged to address data association, while the shapes of individual trajectories are updated by gradient-based continuous energy minimization. The proposed method achieves state-of-the-art results on diverse benchmark sequences.


Modelling and trading the EUR/USD exchange rate: do neural network models perform better


title: A Gate Driver With Integrated Deadtime Controller

abstract: Deadtimes are required to avoid simultaneous conduction of high-side and low-side power transistors in half-bridge power converters. During these secure times, free-wheeling current flow generates extra power losses. Very short deadtimes are desired but they cannot be safely set in conventional isolated power converters because of a digital input propagation delay mismatch. A specific deadtime management is introduced in this paper to ensure proper operation of a high-voltage synchronous power converter. A controller integrated in each isolated gate driver secures synchronous switching by detecting the opposite switch turn-off before turn-on. With such a selfswitching technique, very short but safe nonoverlap times can be set. A gate driver has been implemented in a 0.35 μm 20-V CMOS process. The monolithically integrated controller consumes only 140 μA and 0.22 mm2 of silicon area. The proposed local deadtime management has been validated in two synchronous buck converters without external free-wheeling diodes: a 500-W 250-V to 55-V converter based on SiC JFETs and a 30-W 45-V to 10-V converter based on eGaN FETs. In either case, the proposed controller allows a higher efficiency from 10% of the rated load with resulting deadtimes as short as 15 ns.


title: Unsupervised Approach for Document Clustering Using Modified Fuzzy C mean Algorithm

abstract: Clustering is one the main area in data mining literature. There are various algorithms for clustering. There are several clustering approaches available in the literature to cluster the document. But most of the existing clustering techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. In recent times, inclusion of fuzzy logic in clustering results in better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. Before clustering, the documents are ranked using Term Frequency-Inverse Document Frequency (TF-IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering results when compared to the existing technique


BiDAF Model for Question Answering


title: ShadowDraw: real-time user guidance for freehand drawing

abstract: We present ShadowDraw, a system for guiding the freeform drawing of objects. As the user draws, ShadowDraw dynamically updates a shadow image underlying the user's strokes. The shadows are suggestive of object contours that guide the user as they continue drawing. This paradigm is similar to tracing, with two major differences. First, we do not provide a single image from which the user can trace; rather ShadowDraw automatically blends relevant images from a large database to construct the shadows. Second, the system dynamically adapts to the user's drawings in real-time and produces suggestions accordingly. ShadowDraw works by efficiently matching local edge patches between the query, constructed from the current drawing, and a database of images. A hashing technique enforces both local and global similarity and provides sufficient speed for interactive feedback. Shadows are created by aggregating the edge maps from the best database matches, spatially weighted by their match scores. We test our approach with human subjects and show comparisons between the drawings that were produced with and without the system. The results show that our system produces more realistically proportioned line drawings.


title: Energy consumption estimation of wireless sensor networks in greenhouse crop production

abstract: The main goal in this paper is to obtain low power consumption of wireless sensor nodes and collected distributed data in environmental parameters monitoring. Communication module and the controller should be in idle state as long as possible when they are not active. In design and development of Wireless Sensor Networks (WSNs), one of the main challenges is to achieve long lasting battery lifetime. The purpose of this work is to develop a low maintenance and low cost wireless sensor network system which would be used for optimization of greenhouse crop production.


AGRA: An Analysis-Generation-Ranking Framework for Automatic Abbreviation from Paper Titles


title: Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy

abstract: This study focuses on the horizontal and vertical accuracy of point-clouds based on unmanned aerial vehicle (UAV) imagery. The DJI Phantom 3 Professional unmanned aerial vehicle and Agisoft PhotoScan Professional software were used for the evaluation. Three test sites with differing conditions (canopy openness, slope, terrain complexity, etc.) were used for comparison. The accuracy evaluation was aimed on positions of points placed on the ground. This is often disregarded under forest conditions as it is not possible to photogrammetrically reconstruct terrain that is covered by a fully-closed forest canopy. Therefore, such a measurement can only be conducted when there are gaps in the canopy or under leaf-off conditions in the case of deciduous forests. The reported sub-decimetre horizontal accuracy and vertical accuracy lower than 20 cm have proven that the method is applicable for survey, inventory, and various other tasks in forests. An analysis of ground control point (GCP) quantity and configuration showed that the quantity had only a minor effect on the accuracy in cases of plots with ~1-hectare area when using the aforementioned software. Therefore, methods increasing quality (precision, accuracy) of GCP positions should be preferred over the increase of quantity alone.


title: A cost-sensitive decision tree approach for fraud detection

abstract: With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.


title: Exam performance and attitudes toward multitasking in six, multimedia-multitasking classroom environments

abstract: Although many colleges and universities have implemented laptop programs, the use of these technologies in the classroom doesn't guarantee increases in exam performance. Used improperly, these technologies can hinder the learning process. An experiment was conducted comparing how the use or non-use of technology affected exam performance between six different classroom environments. Consistent with predictions based on theories of multitasking and multimedia processing, participants performed worst on an exam when distracted with social media. Moreover, having had this experience, participants in five of the six conditions showed a decrease in perceptions of their abilities to efficiently multitask from pre-test to post-test. Results are discussed in terms of theory and recommendations are made for the integration of experiential learning sessions into orientation programs to help promote a healthy classroom learning environment. Unstructured environments with social media are not conducive to learning.Participants performed worst when distracted in unstructured environments.Writing notes by hand had the greatest learning outcome.Attitudes toward perceived abilities to multitask can be changed.


title: Filter Design and Performance Evaluation for Fingerprint Image Segmentation

abstract: Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.


title: A Survey on Ambient-Assisted Living Tools for Older Adults

abstract: In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.


title: Server-Side Code Injection Attacks: A Historical Perspective

abstract: Server-side code injection attacks used to be one of the main culprits for the spread of malware. A vast amount of research has been devoted to the problem of effectively detecting and analyzing these attacks. Common belief seems to be that these attacks are now a marginal threat compared to other attack vectors such as drive-by download and targeted emails. However, information on the complexity and the evolution of the threat landscape in recent years is mostly conjectural. This paper builds upon five years of data collected by a honeypot deployment that provides a unique, long-term perspective obtained by traffic monitoring at the premises of different organizations and networks. Our contributions are twofold: first, we look at the characteristics of the threat landscape and at the major changes that have happened in the last five years; second, we observe the impact of these characteristics on the insights provided by various approaches proposed in previous research. The analysis underlines important findings that are instrumental at driving best practices and future research directions.


title: Sleep and Athletic Performance

abstract: Sleep is an essential component of health and well-being, with significant impacts on physical development, emotional regulation, cognitive performance, and quality of life. Along with being an integral part of the recovery and adaptive process between bouts of exercise, accumulating evidence suggests that increased sleep duration and improved sleep quality in athletes are associated with improved performance and competitive success. In addition, better sleep may reduce the risk of both injury and illness in athletes, not only optimizing health but also potentially enhancing performance through increased participation in training. Despite this, most studies have found that athletes fail to obtain the recommended amount of sleep, threatening both performance and health. Athletes face a number of obstacles that can reduce the likelihood of obtaining proper sleep, such as training and competition schedules, travel, stress, academic demands, and overtraining. In addition, athletes have been found to demonstrate poor self-assessment of their sleep duration and quality. In light of this, athletes may require more careful monitoring and intervention to identify individuals at risk and promote proper sleep to improve both performance and overall health. This review attempts to highlight the recent literature regarding sleep issues in athletes, the effects of sleep on athletic performance, and interventions to enhance proper sleep in athletes.


title: Reprogramming in vivo produces teratomas and iPS cells with totipotency features

abstract: Reprogramming of adult cells to generate induced pluripotent stem cells (iPS cells) has opened new therapeutic opportunities; however, little is known about the possibility of in vivo reprogramming within tissues. Here we show that transitory induction of the four factors Oct4, Sox2, Klf4 and c-Myc in mice results in teratomas emerging from multiple organs, implying that full reprogramming can occur in vivo. Analyses of the stomach, intestine, pancreas and kidney reveal groups of dedifferentiated cells that express the pluripotency marker NANOG, indicative of in situ reprogramming. By bone marrow transplantation, we demonstrate that haematopoietic cells can also be reprogrammed in vivo. Notably, reprogrammable mice present circulating iPS cells in the blood and, at the transcriptome level, these in vivo generated iPS cells are closer to embryonic stem cells (ES cells) than standard in vitro generated iPS cells. Moreover, in vivo iPS cells efficiently contribute to the trophectoderm lineage, suggesting that they achieve a more plastic or primitive state than ES cells. Finally, intraperitoneal injection of in vivo iPS cells generates embryo-like structures that express embryonic and extraembryonic markers. We conclude that reprogramming in vivo is feasible and confers totipotency features absent in standard iPS or ES cells. These discoveries could be relevant for future applications of reprogramming in regenerative medicine.


title: Design, implementation, and experimental validation of a cascaded trajectory tracking controller for nonholonomic car-like wheeled mobile robots with velocity and steering controllers in the loops

abstract: This paper presents a cascaded trajectory tracking controller and a control gains selection procedure for nonholonomic car-like wheeled mobile robots with steering and velocities controllers in the tracking control loops. The trajectory tracking controller enables stable tracking control of trajectories for the car-like mobile robots in the presence of the robot's velocity and steering control latencies. Also, the proposed tracking control solution provides a systematic approach to implementing the tracking controller onto the real car-like mobile robots without requiring any dynamical model of the robot's steering and velocities control systems. This feature avoids the need for ad-hoc control parameters tuning when implementing the tracking controller. The trajectory tracking controller was validated in both simulations and experimentations. The tracking controller has been applied on a car-like pickup truck for experimentations in both on-road and off-road environments. The controller was tested up to the speed of 5.4m/sec in off-road environments. The obtained results confirm the effectiveness of the proposed tracking controller.


title: Semantic search

abstract: Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called 'Semantic Search' which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.


title: Correlating events with time series for incident diagnosis

abstract: As online services have more and more popular, incident diagnosis has emerged as a critical task in minimizing the service downtime and ensuring high quality of the services provided. For most online services, incident diagnosis is mainly conducted by analyzing a large amount of telemetry data collected from the services at runtime. Time series data and event sequence data are two major types of telemetry data. Techniques of correlation analysis are important tools that are widely used by engineers for data-driven incident diagnosis. Despite their importance, there has been little previous work addressing the correlation between two types of heterogeneous data for incident diagnosis: continuous time series data and temporal event data. In this paper, we propose an approach to evaluate the correlation between time series data and event data. Our approach is capable of discovering three important aspects of event-timeseries correlation in the context of incident diagnosis: existence of correlation, temporal order, and monotonic effect. Our experimental results on simulation data sets and two real data sets demonstrate the effectiveness of the algorithm.


title: MelanoGANs: High Resolution Skin Lesion Synthesis with GANs

abstract: Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images. Unfortunately, they usually require large training datasets, which are often scarce in the medical field, and to the best of our knowledge GANs have been only applied for medical image synthesis at fairly low resolution. However, many state-of-the-art machine learning models operate on high resolution data as such data carries indispensable, valuable information. In this work, we try to generate realistically looking high resolution images of skin lesions with GANs, using only a small training dataset of 2000 samples. The nature of the data allows us to do a direct comparison between the image statistics of the generated samples and the real dataset. We both quantitatively and qualitatively compare state-of-the-art GAN architectures such as DCGAN and LAPGAN against a modification of the latter for the task of image generation at a resolution of 256x256px. Our investigation shows that we can approximate the real data distribution with all of the models, but we notice major differences when visually rating sample realism, diversity and artifacts. In a set of use-case experiments on skin lesion classification, we further show that we can successfully tackle the problem of heavy class imbalance with the help of synthesized high resolution melanoma samples.


title: Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation

abstract: Density evolution is an algorithm for computing the capacity of low-density parity-check (LDPC) codes under message-passing decoding. For memoryless binary-input continuous-output additive white Gaussian noise (AWGN) channels and sum-product decoders, we use a Gaussian approximation for message densities under density evolution to simplify the analysis of the decoding algorithm. We convert the infinite-dimensional problem of iteratively calculating message densities, which is needed to find the exact threshold, to a one-dimensional problem of updating the means of the Gaussian densities. This simplification not only allows us to calculate the threshold quickly and to understand the behavior of the decoder better, but also makes it easier to design good irregular LDPC codes for AWGN channels. For various regular LDPC codes we have examined, thresholds can be estimated within 0.1 dB of the exact value. For rates between 0.5 and 0.9, codes designed using the Gaussian approximation perform within 0.02 dB of the best performing codes found so far by using density evolution when the maximum variable degree is 10. We show that by using the Gaussian approximation, we can visualize the sum-product decoding algorithm. We also show that the optimization of degree distributions can be understood and done graphically using the visualization.


title: Crowd-sourcing NLG Data: Pictures Elicit Better Data

abstract: Recent advances in corpus-based Natural Language Generation (NLG) hold the promise of being easily portable across domains, but require costly training data, consisting of meaning representations (MRs) paired with Natural Language (NL) utterances. In this work, we propose a novel framework for crowdsourcing high quality NLG training data, using automatic quality control measures and evaluating different MRs with which to elicit data. We show that pictorial MRs result in better NL data being collected than logic-based MRs: utterances elicited by pictorial MRs are judged as significantly more natural, more informative, and better phrased, with a significant increase in average quality ratings (around 0.5 points on a 6-point scale), compared to using the logical MRs. As the MR becomes more complex, the benefits of pictorial stimuli increase. The collected data will be released as part of this submission.


title: Cue strength as a moderator of the testing effect: the benefits of elaborative retrieval.

abstract: The current study explored the elaborative retrieval hypothesis as an explanation for the testing effect: the tendency for a memory test to enhance retention more than restudying. In particular, the retrieval process during testing may activate elaborative information related to the target response, thereby increasing the chances that activation of any of this information will facilitate later retrieval of the target. In a test of this view, participants learned cue-target pairs, which were strongly associated (e.g., Toast: Bread) or weakly associated (e.g., Basket: Bread), through either a cued recall test (Toast: _) or a restudy opportunity (Toast: Bread). A final test requiring free recall of the targets revealed that tested items were retained better than restudied items, and although strong cues facilitated recall of tested items initially, items recalled from weak cues were retained better over time, such that this advantage was eliminated or reversed at the time of the final test. Restudied items were retained at similar rates on the final test regardless of the strength of the cue-target relationship. These results indicate that the activation of elaborative information-which would occur to a greater extent during testing than restudying—may be one mechanism that underlies the testing effect.


title: Ankle and hip balance control strategies with transitions

abstract: A method for implementing the ankle and hip balance control strategies, well known from studies on human balance control, is suggested. The moment of the acting disturbance force is evaluated continuously in real time via the difference between the ZMP and the ground projection of the center of mass. Compliant response to the continuous disturbance is ensured by attaching a virtual spring-damper in an appropriate way for each strategy. Further on, whenever the limit of the ankle strategy is reached, a smooth transition toward the hip strategy is initialized and compliance is ensured in a continuous way and in agreement with the disturbance. After the disturbance is removed, the humanoid switches first back to the ankle strategy, and then returns to the initial equilibrium (erect) posture. Experimental data taken with a small humanoid robot (HOAP-2) are presented to validate the method. See also the accompanying video clip.


title: On the need for time series data mining benchmarks: a survey and empirical demonstration

abstract: In the last decade there has been an explosion of interest in mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in the case of classification and clustering, model accuracy in the case of segmentation) offer an amount of “improvement” that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets, or the variance that would have been observed by changing minor (unstated) implementation details. ::: ::: To illustrate our point, we have undertaken the most exhaustive set of time series experiments ever attempted, re-implementing the contribution of more than two dozen papers, and testing them on 50 real world, highly diverse datasets. Our empirical results strongly support our assertion, and suggest the need for a set of time series benchmarks and more careful empirical evaluation in the data mining community.


title: An Analytical Model for the Contention Access Period of the Slotted IEEE 802.15.4 with Service Differentiation

abstract: The IEEE 802.15.4 standard is poised to become the global standard for low data rate, low energy consumption wireless sensor networks (WSN). By assigning the same sets of contention access parameters for all data frames and nodes, the contention access period (CAP) of the slotted IEEE 802.15.4 medium access control (MAC) currently provides a priorityindependent channel access functionality and no service diiTerentiation. Several recent WSN applications such as wireless body sensor networks, however, may require service dilTerentiation and traf6e prioritization support to accommodate potential highpriority traffic (e.g., alarms or emergency alerts). By allowing dilTerent sets of access parameters and data frame lengths for differentpriority classes, this paper develops a Markov-chain-based analytical model of the CAP of the IEEE 802.15.4 MAC with service dilTerentiatlon, under unsaturated traffic conditions. In particular, given two priority classes, our analytical model is used to evaluate the performance of a simple, yet eiTective, contentionwindow-based service dilTerentiation strategy, in terms of the resulting throughput, average frame service time and access priority for each priority class. The accuracy of the analytical model is validated by extensive ns-2 simulation.


title: Achieving sustainable gains in happiness: Change your actions, not your circumstances

abstract: Although attaining happiness is a nearly universal goal, surprisingly little research has focused on how happiness can be increased and then sustained. Three studies test predictions of a model (Lyubomirsky et al., 2005) positing that sustainable happiness is possible through intentional activity changes, more so than through circumstantial changes. Study 1 shows that less hedonic adaptation is reported in response to activity changes than to circumstantial changes. Study 2 tests a dynamic process model, showing that while both positive activity changes and positive circumstantial changes predict rank-order increases in subjective well-being from Time 1 to Time 2, only activity changes predict maintained gains at Time 3. Study 3 replicates the Study 2 findings and extends them to psychological well-being (Ryff and Keyes, 1995). Implications for positive psychology and “the pursuit of happiness” are discussed.


title: Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

abstract: Digital images are often corrupted by impulsive noise during data acquisition, transmission, and processing. This paper presents a turbulent particle swarm optimization (PSO) (TPSO)-based fuzzy filtering (or TPFF for short) approach to remove impulse noise from highly corrupted images. The proposed fuzzy filter contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy composition process. To a certain extent, the TPFF is an improved and online version of those genetic-based algorithms which had attracted a number of works during the past years. As the PSO is renowned for its ability of achieving success rate and solution quality, the superiority of the TPFF is almost for sure. In particular, by using a no-reference Q metric, the TPSO learning is sufficient to optimize the parameters necessitated by the TPFF. Therefore, the proposed fuzzy filter can cope with practical situations where the assumption of the existence of the "ground-truth" reference does not hold. The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.


title: Listening to Mozart enhances spatial-temporal reasoning: towards a neurophysiological basis

abstract: Abstract Motivated by predictions of a structured neuronal model of the cortex, we performed a behavioral experiment which showed that listening to a Mozart piano sonata produced significant short-term enhancement of spatial-temporal reasoning in college students. Here we present results from an experiment which replicates these findings, and shows that (i) ‘repetitive’ music does not enhance reasoning; (ii) a taped short story does not enhance reasoning; and (iii) short-term memory is not enhanced. We propose experiments designed to explore the neurophysiological bases of this causal enhancement of spatial-temporal reasoning by music, and begin to search for quantitative measures of further higher ognitive effects of music.


A Study On Image Segmentation Techniques


A Behavioral Reasoning Analysis of Multichannel Consumers' Intention to Use Online Order/In-store Pickup Service


title: Deep learning to extract laboratory mouse ultrasonic vocalizations from scalograms

abstract: We tested two techniques that can assist in the automatic extraction and analysis of laboratory mouse ultrasonic vocalizations. First, we substituted a Morlet-wavelet-based scalogram in place of the commonly used Fourier-based spectrogram. The frequencies in a scalogram are logarithmically scaled, which likely makes it better model a mouse's pitch perception based on the structure of its inner ear. Works that use the linear spectrogram are more likely to overemphasize frequency change at high frequencies, and so might misclassify calls when partitioning them according to frequency modulation. We showed that laboratory mice do indeed modulate their calls more when making high-frequency calls, which we would expect if they perceive pitch logarithmically. Secondly, we used “deep” convolutional neural networks to automatically identify calls within the scalogram. A convolutional neural network performs much like the animal visual cortex, and has enjoyed recent success in computer-vision and related problems. We compared the convolutional network to a conventional fully-connected neural network and the Song filter used by recent papers, and found that our convolutional network identifies calls with greater precision and recall.


title: Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm

abstract: As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.


title: Adapting to a Market Shock: Optimal Sequential Market-Making

abstract: We study the profit-maximization problem of a monopolistic market-maker who sets two-sided prices in an asset market. The sequential decision problem is hard to solve because the state space is a function. We demonstrate that the belief state is well approximated by a Gaussian distribution. We prove a key monotonicity property of the Gaussian state update which makes the problem tractable, yielding the first optimal sequential market-making algorithm in an established model. The algorithm leads to a surprising insight: an optimal monopolist can provide more liquidity than perfectly competitive market-makers in periods of extreme uncertainty, because a monopolist is willing to absorb initial losses in order to learn a new valuation rapidly so she can extract higher profits later.


title: A Linear Approximation for Graph-based Simultaneous Localization and Mapping

abstract: This article investigates the problem of Simultaneous Localization and Mapping (SLAM) from the perspective of linear estimation theory. The problem is first formulated in terms of graph embedding: a graph describing robot poses at subsequent instants of time needs be embedded in a three-dimensional space, assuring that the estimated configuration maximizes measurement likelihood. Combining tools belonging to linear estimation and graph theory, a closed-form approximation to the full SLAM problem is proposed, under the assumption that the relative position and the relative orientation measurements are independent. The approach needs no initial guess for optimization and is formally proven to admit solution under the SLAM setup. The resulting estimate can be used as an approximation of the actual nonlinear solution or can be further refined by using it as an initial guess for nonlinear optimization techniques. Finally, the experimental analysis demonstrates that such refinement is often unnecessary, since the linear estimate is already accurate.


title: Book Recommendation System through content based and collaborative filtering method

abstract: The online recommendation system has become a trend. Now a days rather than going out and buying items for themselves, reason being, online recommendation provides an easier and quicker way to buy items and transactions are also quick when it is done online. Recommended systems are powerful new technology and it helps users to find items which they want to buy. A recommendation system is broadly used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days is competing with each other by considering many attributes. A recommendation system is one of the strongest tools to increase profits and retaining buyer. The existing systems lead to extraction of irrelevant information and lead to lack of user satisfaction. This paper presents Book Recommendation System (BRS) based on combined features of content based filtering (CBF), collaborative filtering (CF) and association rule mining to produce efficient and effective recommendation. For this we are proposing a hybrid algorithm in which we combine two or more algorithms, so it helps the recommendation system to recommend the book based on the buyer's interest.


title: A mixed equation-based and simulation-based design methodology for continuous-time sigma-delta modulators

abstract: This paper presents a design automation tool for continuous-time sigma-delta modulators from high level specifications down to layout. System level synthesis, circuit synthesis as well as layout synthesis are performed in the CAIRO+ design environment. Strong interaction between the system level and the circuit level is used to optimize the final design. The design of a complete third order current-mode continuous-time sigma-delta modulator is taken as an example to show the effectiveness of the proposed design methodology.


title: Development of a high-throughput microwave imaging system for concealed weapons detection

abstract: A video-rate microwave imaging aperture for concealed threat detection can serve as a useful tool in securing crowded, high foot traffic environments. Realization of such a system presents two major technical challenges: 1) implementation of an electrically large antenna array for capture of a moving subject, and 2) fast image reconstruction on cost-effective computing hardware. This paper presents a hardware-efficient multistatic array design to address the former challenge, and a compatible fast imaging technique to address the latter. Prototype hardware which forms a partition of an imaging aperture is discussed. Using this hardware, it is shown that the proposed array design can be used to form high-fidelity 3D images, and that the presented image reconstruction technique can form an image of a human-sized domain in ≤0.1s with low cost computing hardware.


title: A Double Layer Bayesian Classifier

abstract: Numerous approaches have been proposed to relax the conditional independence assumption of naive Bayes, the accuracy performance was indeed improved relative to naive Bayes when the assumption is violated. But most of the previous approaches treated the attribute relation in the same way for all class labels. In practice, this relation may be different for different class labels. This paper proposes a novel approach, by which the posterior probability of different class label is evaluated using different attribute relation. Experiment results indicate that the new approach obtains comparative performance relative to other modern Bayesian classifiers on some datasets, and on some other datasets it outperforms the others.


title: Interactive Social Recommendation

abstract: Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is beneficial for improving recommendation accuracy, especially when dealing with cold-start users who lack sufficient past behavior information for accurate recommendation. However, it is nontrivial to use such information, since some of a person's friends may share similar preferences in certain aspects, but others may be totally irrelevant for recommendations. Thus one challenge is to explore and exploit the extend to which a user trusts his/her friends when utilizing social information to improve recommendations. On the other hand, most existing social recommendation models are non-interactive in that their algorithmic strategies are based on batch learning methodology, which learns to train the model in an offline manner from a collection of training data which are accumulated from users? historical interactions with the recommender systems. In the real world, new users may leave the systems for the reason of being recommended with boring items before enough data is collected for training a good model, which results in an inefficient customer retention. To tackle these challenges, we propose a novel method for interactive social recommendation, which not only simultaneously explores user preferences and exploits the effectiveness of personalization in an interactive way, but also adaptively learns different weights for different friends. In addition, we also give analyses on the complexity and regret of the proposed model. Extensive experiments on three real-world datasets illustrate the improvement of our proposed method against the state-of-the-art algorithms.


Enhanced Harmonic Content and Vocal Note Based Predominant Melody Extraction from Vocal Polyphonic Music Signals.


title: Social skills deficits in schizotypal personality disorder

abstract: Abstract Evidence of long-standing social difficulties has been well documented in persons with schizophrenia. These deficits are often so rudimentary that a person with schizophrenia may never have developed the skills necessary to present as socially competent. Given the cognitive, biological, and neuroanatomical links between schizophrenia and schizotypal personality disorder (SPD), a study of social skills in persons with SPD may reveal a behavioral link. This study examined persons with SPD and their ability to label emotions in a recognition task, to display socially competent behaviors in a social role-play task, and to select appropriate behaviors from a multiple choice measure of social behavior. Results indicated that the performance of persons with SPD was similar to previously published findings in persons with schizophrenia. In terms of emotion recognition, the SPD group’s ability to label positive emotions was significantly worse than their ability to label other emotions. Persons with SPD performed significantly worse than matched control participants on a social role-play task. However, the groups were equivalent in their ability to select socially appropriate behavior from a multiple choice measure. These results suggest that persons with SPD display social skills which mirror those previously reported in persons with schizophrenia.


title: Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography

abstract: In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning models extended to 3-D inputs, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection and extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning models produces a mean 5-year mortality prediction AUC in [68.8%,69.8%] and accuracy in [64.5%,66.5%], while radiomics produces a mean AUC of 64.6% and accuracy of 64.6%. The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.


Human-Aware Planning Revisited : A Tale of Three Models


title: The role of Artificial Intelligence in Software Engineering

abstract: There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE.


title: Sensorless Speed And Position Estimation Of PMSM Based On Sliding Mode Observer With Tan Hyperbolic Function

abstract: Sinusoidal Permanent Magnet synchronous motors have become more popular for new drives. Advantages of PMSMs includes high torque to inertia ratio, high efficiency and high power density. For the PMSMs it is necessary to estimate the rotor position and speed as well. A good error filtering, low angle error and dynamic performance can be obtained with feedback observers. The sliding mode observer (SMO) is one of the observers with a system model suitable for PM synchronous motors. The study deals with the analysis based on simulation of PMSM with space vector pulse width modulation in MATLAB environment. In this paper sensorless estimation of speed and position using SMO with tan hyperbolic function achieved. The result is compared with SMO using sigmoidal function. The simulation results shows that sliding mode observer with tan hyperbolic function gives smooth performance. Keywords: PMSM, Sliding Mode observer,Sigmoidal function, tan hyperbolic function


title: The theory of planned behavior

abstract: Research dealing with various aspects of* the theory of planned behavior (Ajzen, 1985, 1987) is reviewed, and some unresolved issues are discussed. In broad terms, the theory is found to be well supported by empirical evidence. Intentions to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior. Attitudes, subjective norms, and perceived behavioral control are shown to be related to appropriate sets of salient behavioral, normative, and control beliefs about the behavior, but the exact nature of these relations is still uncertain. Expectancy— value formulations are found to be only partly successful in dealing with these relations. Optimal rescaling of expectancy and value measures is offered as a means of dealing with measurement limitations. Finally, inclusion of past behavior in the prediction equation is shown to provide a means of testing the theory*s sufficiency, another issue that remains unresolved. The limited available evidence concerning this question shows that the theory is predicting behavior quite well in comparison to the ceiling imposed by behavioral reliability. © 1991 Academic Press. Inc.


Analysis of Footwear Impression Evidence


Axel Rovers for Exploring Extreme Planetary Terrains


title: State-of-charge and remaining charge estimation of series-connected lithium-ion batteries for cell balancing scheme

abstract: This paper proposes state-of-charge (SOC) and remaining charge estimation algorithm of each cell in series-connected lithium-ion batteries. SOC and remaining charge information are indicators for diagnosing cell-to-cell variation; thus, the proposed algorithm can be applied to SOC- or charge-based balancing in cell balancing controller. Compared to voltage-based balancing, SOC and remaining charge information improve the performance of balancing circuit but increase computational complexity which is a stumbling block in implementation. In this work, a simple current sensor-less SOC estimation algorithm with estimated current equalizer is used to achieve aforementioned object. To check the characteristics and validate the feasibility of the proposed method, a constant current discharging/charging profile is applied to a series-connected battery pack (twelve 2.6Ah Li-ion batteries). The experimental results show its applicability to SOC- and remaining charge-based balancing controller with high estimation accuracy.


title: Temporal indexing of medical entity in Chinese clinical notes

abstract: The goal of temporal indexing is to select an occurred time or time interval for each medical entity in clinical notes, so that all medical entities can be indexed on a united timeline, which could assist the understanding of clinical notes and the further application of medical entities. Some temporal relation shared tasks for the medical entity in English clinical notes have been organized in the past few years, such as the 2012 i2b2 NLP challenge, 2015 and 2016 clinical TempEval challenges. In these tasks, many heuristics rule-based and machine learning-based systems have been developed. In recent years, the deep neural network models have shown great potential on many problems including the relation classification. In this paper, we propose a recurrent convolutional neural network (RNN-CNN) model for the temporal indexing task, which consists of four layers: input layer – generates representation for each context word of medical entities or temporal expressions; LSTM (long-short term memory) layer – learns the context information of each word in a sentence and outputs a new word representation sequence; CNN layer – extracts meaningful features from a sentence and outputs a new representation for medical entity or temporal expression; Output layer – takes the representations of medical entity, temporal expression and relation features as input and classifies the temporal relation. Finally, the time or time interval for each medical entity can be directly selected according to the probability of each temporal relation predicted by above model. To investigate the performance of our RNN-CNN model for the temporal indexing task, several baseline methods were also employed, such as the rule-based, support vector machine (SVM), convolutional neural network (CNN) and recurrent neural network (RNN) methods. Experiments conducted on a manually annotated corpus (including 563 clinical notes with 12,611 medical entities and 4006 temporal expressions) show that RNN-CNN model achieves the best F1-score of 75.97% for temporal relation classification and the best accuracy of 71.96% for temporal indexing. Neural network methods perform much better than the traditional rule-based and SVM-based method, which can capture more semantic information from the context of medical entities and temporal expressions. Besides, all our methods perform much better for the accurate time indexing than the time interval indexing, so how to improve the performance for time interval indexing will be the main focus in our future work.


title: End-to-end Anchored Speech Recognition

abstract: Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device-directed speech in the presence of interfering background speech, i.e., background noise and interfering speech from another person or media device in proximity need to be ignored. We propose two end-to-end models to tackle this problem with information extracted from the anchored segment. The anchored segment refers to the wake-up word part of an audio stream, which contains valuable speaker information that can be used to suppress interfering speech and background noise. The first method is called Multi-source Attention where the attention mechanism takes both the speaker information and decoder state into consideration. The second method directly learns a frame-level mask on top of the encoder output. We also explore a multi-task learning setup where we use the ground truth of the mask to guide the learner. Given that audio data with interfering speech is rare in our training data, we also propose a way to synthesize "noisy" speech from "clean" speech to mitigate the mismatch between training and test data. Our proposed methods show up to 15% relative reduction in WER for Amazon Alexa live data with interfering background speech without significantly degrading on clean speech.


title: The ROI of Systems Engineering: Some Quantitative Results

abstract: This paper presents quantitative results on the return on investment of systems engineering (SE-ROI) from an analysis of the 161 software projects in the COCOMO II database. The analysis shows that, after normalizing for the effects of other cost drivers, the cost difference between projects doing a minimal job of software systems engineering - as measured by the thoroughness of its architecture definition and risk resolution - and projects doing a very thorough job was 18% for small projects and 92% for very large software projects as measured in lines of code.


title: Learning Deep Structured Models

abstract: Many problems in real-world applications involve predicting several random variables that are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such dependencies. The goal of this paper is to combine MRFs with deep learning to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as tagging of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.


title: Towards CT-quality Ultrasound Imaging using Deep Learning

abstract: The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflected ultrasound radio-frequency(RF) data obtained by simulation from real CT scans of a human body. We also show that CNN is able to imitate existing computationally heavy despeckling methods, thereby saving orders of magnitude in computations and making them amenable to real-time applications.


title: Design and Performance Study of a Dual-Element Multiband Printed Monopole Antenna Array for MIMO Terminals

abstract: This letter presents a study on linearly polarized compact multiband multiple-input-multiple-output (MIMO) antenna system for small mobile terminals. The MIMO antenna system consists of two symmetric printed monopole antennas with edge-to-edge separation of 0.097 λ0 at 900 MHz. Each antenna element has a capacitive feed and is composed of two twisted lines, a parasitic loop, and a shorting trip that generate five resonant modes around 900, 1800, 2100, 3500, and 5400 MHz, covering GSM850/900, DCS, PCS, UMTS, WLAN, and WiMAX frequency bands. Two inverted-L shaped branches and a rectangular slot with one circular end, etched on the ground plane, were introduced to improve the isolation between antenna elements. The isolation achieved is higher than 15 dB in the lower band and 20 dB in the upper bands, leading to an envelope correlation coefficient of less than 0.025. The simulated performance of the designed antenna system has been verified in the experiment.


title: Fuzzy sets in Data mining- A Review

abstract: Data mining, also called knowledge discovery in databases, is regarded as a non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable knowledge in large-scale data. This paper briefly reviews some typical applications and highlights potential contributions that fuzzy set theory can make to data mining. In this connection, some advantages of fuzzy methods for representing and mining vague patterns in data are especially emphasized. The aim of this paper is to convey an impression of the current status and prospects of fuzzy set in data mining, especially highlighting potential features and advantages of fuzzy in comparison with non-fuzzy approaches.


title: Analysis of agriculture data using data mining techniques: application of big data

abstract: In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning. Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using data mining techniques like PAM, CLARA, DBSCAN and Multiple Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change.


title: An Adaptive Clipping Approach for Proximal Policy Optimization

abstract: Very recently proximal policy optimization (PPO) algorithms have been proposed as first-order optimization methods for effective reinforcement learning. While PPO is inspired by the same learning theory that justifies trust region policy optimization (TRPO), PPO substantially simplifies algorithm design and improves data efficiency by performing multiple epochs of \emph{clipped policy optimization} from sampled data. Although clipping in PPO stands for an important new mechanism for efficient and reliable policy update, it may fail to adaptively improve learning performance in accordance with the importance of each sampled state. To address this issue, a new surrogate learning objective featuring an adaptive clipping mechanism is proposed in this paper, enabling us to develop a new algorithm, known as PPO-$\lambda$. PPO-$\lambda$ optimizes policies repeatedly based on a theoretical target for adaptive policy improvement. Meanwhile, destructively large policy update can be effectively prevented through both clipping and adaptive control of a hyperparameter $\lambda$ in PPO-$\lambda$, ensuring high learning reliability. PPO-$\lambda$ enjoys the same simple and efficient design as PPO. Empirically on several Atari game playing tasks and benchmark control tasks, PPO-$\lambda$ also achieved clearly better performance than PPO.


title: Effective face landmark localization via single deep network

abstract: In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data. Rather than using a max-pooling layer followed one convolutional layer in typical convolutional neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group layer contains two convolutional layers and a max-pooling layer, which can extract the features hierarchically. Moreover, an effective data augmentation strategy and corresponding training skills are also proposed to over-come the lack of training images on COFW and 300-W da-tasets. The experiment results show that our method outper-forms state-of-the-art methods in both detection accuracy and speed.


title: Kinodynamic trajectory optimization and control for car-like robots

abstract: This paper presents a novel generic formulation of Timed-Elastic-Bands for efficient online motion planning of car-like robots. The planning problem is defined in terms of a finite-dimensional and sparse optimization problem subject to the robots kinodynamic constraints and obstacle avoidance. Control actions are implicitly included in the optimized trajectory. Reliable navigation in dynamic environments is accomplished by augmenting the inner optimization loop with state feedback. The predictive control scheme is real-time capable and responds to obstacles within the robot's perceptual field. Navigation in large and complex environments is achieved in a pure pursuit fashion by requesting intermediate goals from a global planner. Requirements on the initial global path are fairly mild, compliance with the robot kinematics is not required. A comparative analysis with Reeds and Shepp curves and investigation of prototypical car maneuvers illustrate the advantages of the approach.


title: Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis

abstract: The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.


title: Making Sense of Primary Headship: The surprises awaiting new heads

abstract: A model of the gains and losses of job change taken from the occupational mobility literature was used to underpin research on new headteachers and deputies which examined the gains and losses accompanying the transition from deputy to headteacher, the degree to which these were anticipated and the seriousness of their impact. Results from two studies indicate that many of the 87 deputies were deterred by the burdens of headship, though those most likely to apply for headship were prepared to take these in their stride. The 37 new heads (in post 1-3 years), though well prepared for headship, were more overwhelmed by the job than they (or the likely-to-apply deputies) had anticipated and outlined the systems (general, local authority and school) causing initial overload and confusion. The implications for training and support are explored.


title: Applying parallel design techniques to template matching with GPUs

abstract: Designing algorithms for data parallelism can create significant gains in performance on SIMD architectures. The performance of General Purpose GPUs can also benefit from careful analysis of memory usage and data flow due to their large throughput and system memory bottlenecks. In this paper we present an algorithm for template matching that is designed from the beginning for the GPU architecture and achieves greater than an order of magnitude speedup over traditional algorithms designed for the CPU and reimplemented on the GPU. This shows that it is not only desirable to adapt existing algorithms to run on GPUs, but also that future algorithms should be designed with the GPU architecture in mind.


title: Shape Segmentation by Approximate Convexity Analysis

abstract: We present a shape segmentation method for complete and incomplete shapes. The key idea is to directly optimize the decomposition based on a characterization of the expected geometry of a part in a shape. Rather than setting the number of parts in advance, we search for the smallest number of parts that admit the geometric characterization of the parts. The segmentation is based on an intermediate-level analysis, where first the shape is decomposed into approximate convex components, which are then merged into consistent parts based on a nonlocal geometric signature. Our method is designed to handle incomplete shapes, represented by point clouds. We show segmentation results on shapes acquired by a range scanner, and an analysis of the robustness of our method to missing regions. Moreover, our method yields results that are comparable to state-of-the-art techniques evaluated on complete shapes.


title: STN-OCR: A single Neural Network for Text Detection and Text Recognition

abstract: Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In re- cent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present STN-OCR, a step towards semi-supervised neural networks for scene text recognition, that can be optimized end-to-end. In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way. STN-OCR is a network that integrates and jointly learns a spatial transformer network, that can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We investigate how our model behaves on a range of different tasks (detection and recognition of characters, and lines of text). Experimental results on public benchmark datasets show the ability of our model to handle a variety of different tasks, without substantial changes in its overall network structure.


title: Key detection for a virtual piano teacher

abstract: We propose a method for identifying a piano keyboard present in the video footage of a standard webcam with the goal of teaching chords, scales and suggested finger positions to a beginner pianist. Our keyboard identification method makes use of binary thresholding, Sobel operators and Hough transforms, as well as proposed algorithms specific to this application, to first find an area resembling a piano keyboard before narrowing the search to detect individual keys. Through the use of our method the keys of a piano keyboard were successfully identified from webcam video footage, with a tolerance to camera movement and occluded keys demonstrated. This result allowed the augmented reality style highlighting of individual keys, and the display of suggested fingering, for various chords and scales - which demonstrates the potential for our piano teacher program as a learning tool. The demo application achieved an average frame rate of 25.1 frames per second when run on a 2.20GHz dual-core laptop with 4GB RAM; a suitable rate for real-time use.


Comparative Analysis of Lean and Six Sigma in Building Construction: Benefits, Obstacles and Opportunities


title: The impact of polices on government social media usage: Issues, challenges, and recommendations

abstract: Abstract Government agencies are increasingly using social media to connect with those they serve. These connections have the potential to extend government services, solicit new ideas, and improve decision-making and problem-solving. However, interacting via social media introduces new challenges related to privacy, security, data management, accessibility, social inclusion, governance, and other information policy issues. The rapid adoption of social media by the population and government agencies has outpaced the regulatory framework related to information, although the guiding principles behind many regulations are still relevant. This paper examines the existing regulatory framework and the ways in which it applies to social media use by the U.S. federal government, highlighting opportunities and challenges agencies face in implementing them, as well as possible approaches for addressing these challenges.


title: A narrative review of binge eating disorder in adolescence: prevalence, impact, and psychological treatment strategies

abstract: Binge eating disorder (BED) represents one of the most problematic clinical conditions among youths. Research has shown that the developmental stage of adolescence is a critical stage for the onset of eating disorders (EDs), with a peak prevalence of BED at the age of 16-17 years. Several studies among adults with BED have underlined that it is associated with a broad spectrum of negative consequences, including higher concern about shape and weight, difficulties in social functioning, and emotional-behavioral problems. This review aimed to examine studies focused on the prevalence of BED in the adolescent population, its impact in terms of physical, social, and psychological outcomes, and possible strategies of psychological intervention. The review of international literature was made on paper material and electronic databases ProQuest, PsycArticles, and PsycInfo, and the Scopus index were used to verify the scientific relevance of the papers. Epidemiological research that examined the prevalence of BED in adolescent samples in accordance with Diagnostic and Statistical Manual of Mental Disorders, 4th Edition showed a prevalence ranging from 1% to 4%. More recently, only a few studies have investigated the prevalence of BED, in accordance with the Diagnostic and Statistical Manual of Disorders, Fifth Edition criteria, reporting a prevalence of ~1%-5%. Studies that focused on the possible impact that BED may have on physical, psychological, and social functioning showed that adolescents with BED have an increased risk of developing various adverse consequences, including obesity, social problems, substance use, suicidality, and other psychological difficulties, especially in the internalizing area. Despite the evidence, to date, reviews on possible and effective psychological treatment for BED among young population are rare and focused primarily on adolescent females.


title: A radiographic method for assessing changes in alveolar bone height following periodontal therapy

abstract: A method is described for obtaining reproducible radiographs of several groups of teeth in the same jaw. The method was applied to assess changes in the alveolar bone height following periodontal treatment. An oral device was designed to permit direction of the central rays of the beam perpendicularly to the alveolar process in different parts of the jaws. The exposures were made at 60 kVp and 10mA, and standardized procedures were used for developing, fixing and rinsing the films. Alterations in the marginal alveolar bone level were determined in a stereocomparator system. The reproducibility of the method was determined. A pilot experiment was performed on 5 patients with periodontal disease. Following conservative treatment, including scaling and root planning, all patients were subjected to periodontal surgery, including alveolar bone denudation. Radiographs of the alveolar bone were obtained before and 2 months after the operation. By measuring in the stereocomparator parallaxes of both control points and points of the alveolar bone crest on paired radiographs, a mean decrease in interproximal alveolar bone height of 0.69 mm was found. Taking into account the small errors which are inherent in the method, this change in bone height could be regarded as a reliable measure of actual loss of bone.


title: Learning Object Grasping for Soft Robot Hands

abstract: We present a three-dimensional deep convolutional neural network (3D CNN) approach for grasping unknown objects with soft hands. Soft hands are compliant and capable of handling uncertainty in sensing and actuation, but come at the cost of unpredictable deformation of the soft fingers. Traditional model-driven grasping approaches, which assume known models for objects, robot hands, and stable grasps with expected contacts, are inapplicable to such soft hands, since predicting contact points between objects and soft hands is not straightforward. Our solution adopts a deep CNN approach to find good caging grasps for previously unseen objects by learning effective features and a classifier from point cloud data. Unlike recent CNN models applied to robotic grasping which have been trained on 2D or 2.5D images and limited to a fixed top grasping direction, we exploit the power of a 3D CNN model to estimate suitable grasp poses from multiple grasping directions (top and side directions) and wrist orientations, which has great potential for geometry-related robotic tasks. Our soft hands guided by the 3D CNN algorithm show 87% successful grasping on previously unseen objects. A set of comparative evaluations shows the robustness of our approach with respect to noise and occlusions.


title: A Decade of Automatic Content Evaluation of News Summaries: Reassessing the State of the Art

abstract: How good are automatic content metrics for news summary evaluation? Here we provide a detailed answer to this question, with a particular focus on assessing the ability of automatic evaluations to identify statistically significant differences present in manual evaluation of content. Using four years of data from the Text Analysis Conference, we analyze the performance of eight ROUGE variants in terms of accuracy, precision and recall in finding significantly different systems. Our experiments show that some of the neglected variants of ROUGE, based on higher order n-grams and syntactic dependencies, are most accurate across the years; the commonly used ROUGE-1 scores find too many significant differences between systems which manual evaluation would deem comparable. We also test combinations of ROUGE variants and find that they considerably improve the accuracy of automatic prediction.


title: ORIGA-light: An online retinal fundus image database for glaucoma analysis and research

abstract: Retinal fundus image is an important modality to document the health of the retina and is widely used to diagnose ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. However, the enormous amount of retinal data obtained nowadays mostly stored locally; and the valuable embedded clinical knowledge is not efficiently exploited. In this paper we present an online depository, ORIGA-light, which aims to share clinical groundtruth retinal images with the public; provide open access for researchers to benchmark their computer-aided segmentation algorithms. An in-house image segmentation and grading tool is developed to facilitate the construction of ORIGA-light. A quantified objective benchmarking method is proposed, focusing on optic disc and cup segmentation and Cup-to-Disc Ratio (CDR). Currently, ORIGA-light contains 650 retinal images annotated by trained professionals from Singapore Eye Research Institute. A wide collection of image signs, critical for glaucoma diagnosis, are annotated. We will update the system continuously with more clinical ground-truth images. ORIGA-light is available for online access upon request.


title: Minimization of Torque Ripple in the DFIG-DC System Via Predictive Delay Compensation

abstract: Torque ripple caused by stator current and flux harmonics is one of the main issues in the doubly fed induction generator (DFIG)-dc system, which inherently has to operate with distorted waveforms produced by the diode commutation. This paper proposes a torque-ripple mitigation strategy based on a predictive estimation of the reciprocal of flux linkage. The predictive estimation compensates for the intrinsic delay in the actuation of the torque-ripple rejection signal through the rotor current control loops. Unlike other approaches relying on complex current regulators with selective harmonic tracking, this strategy is based on well-established proportional-integral (PI) controllers for the rotor currents. PI current controllers can then still have bandwidth values typical of usual DFIG systems. Simulations and experiments on a test-rig show that the compensation strategy achieves a strong torque ripple reduction and is very robust against stator frequency variations.


title: A novel three-phase DC/DC converter for high-power applications

abstract: This paper proposes a new three-phase series resonant converter. The principle working of the converter is described analytically in detail for switching frequencies equal to the resonant frequency and for switching frequencies greater than the resonant frequency. Additionally, detailed simulations are performed to analyse the converter. Based on the analysis, design criteria for a 5 kW breadboard are derived. The 5 kW breadboard version of the converter has been built to validate the analytical investigations and simulations experimentally. Moreover, the breadboard is used to investigate the ZVS and ZCS possibilities of the topology and the influence of the deadtime and the switching frequency on the overall efficiency.


title: Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

abstract: Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficiently generate object shapes in the form of dense point clouds. We use 2D convolutional operations to predict the 3D structure from multiple viewpoints and jointly apply geometric reasoning with 2D projection optimization. We introduce the pseudo-renderer, a differentiable module to approximate the true rendering operation, to synthesize novel depth maps for optimization. Experimental results for single-image 3D object reconstruction tasks show that we outperforms state-of-the-art methods in terms of shape similarity and prediction density.


title: Nonlinear robust tracking control of a quadrotor UAV on SE(3)

abstract: This paper provides nonlinear tracking control systems for a quadrotor unmanned aerial vehicle (UAV) that are robust to bounded uncertainties. A mathematical model of a quadrotor UAV is defined on the special Euclidean group, and nonlinear output-tracking controllers are developed to follow (1) an attitude command, and (2) a position command for the vehicle center of mass. The controlled system has the desirable properties that the tracking errors are uniformly ultimately bounded, and the size of the ultimate bound can be arbitrarily reduced by control system parameters. Numerical examples illustrating complex maneuvers are provided.


title: Combined Therapy Using Q-Switched Ruby Laser and Bleaching Treatment with Tretinoin and Hydroquinone for Periorbital Skin Hyperpigmentation in Asians

abstract: Background:Periorbital skin hyperpigmentation, so-called dark circles, is of major concern for many people. However, only a few reports refer to the morbidity and treatment, and as far as the authors know, there are no reports of the condition in Asians.Methods:A total of 18 Japanese patients underw


title: Coding of predicted reward omission by dopamine neurons in a conditioned inhibition paradigm

abstract: Animals learn not only about stimuli that predict reward but also about those that signal the omission of an expected reward. We used a conditioned inhibition paradigm derived from animal learning theory to train a discrimination between a visual stimulus that predicted reward (conditioned excitor) and a second stimulus that predicted the omission of reward (conditioned inhibitor). Performing the discrimination required attention to both the conditioned excitor and the inhibitor; however, dopamine neurons showed very different responses to the two classes of stimuli. Conditioned inhibitors elicited considerable depressions in 48 of 69 neurons (median of 35% below baseline) and minor activations in 29 of 69 neurons (69% above baseline), whereas reward-predicting excitors induced pure activations in all 69 neurons tested (242% above baseline), thereby demonstrating that the neurons discriminated between conditioned stimuli predicting reward versus nonreward. The discriminative responses to stimuli with differential reward-predicting but common attentional functions indicate differential neural coding of reward prediction and attention. The neuronal responses appear to reflect reward prediction errors, thus suggesting an extension of the correspondence between learning theory and activity of single dopamine neurons to the prediction of nonreward.


title: Modeling Cyber-Physical Systems with Semantic Agents

abstract: The development of accurate models for cyber-physical systems (CPSs) is hampered by the complexity of these systems, fundamental differences in the operation of cyber and physical components, and significant interdependencies among these components. Agent-based modeling shows promise in overcoming these challenges, due to the flexibility of software agents as autonomous and intelligent decision-making components. Semantic agent systems are even more capable, as the structure they provide facilitates the extraction of meaningful content from the data provided to the software agents. In this paper, we present a multi-agent model for a CPS, where the semantic capabilities are underpinned by sensor networks that provide information about the physical operation to the cyber infrastructure. This model is used to represent the static structure and dynamic behavior of an intelligent water distribution network as a CPS case study.


title: Learning to Reweight Examples for Robust Deep Learning

abstract: Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.


title: Foveated 3D graphics

abstract: We exploit the falloff of acuity in the visual periphery to accelerate graphics computation by a factor of 5-6 on a desktop HD display (1920x1080). Our method tracks the user's gaze point and renders three image layers around it at progressively higher angular size but lower sampling rate. The three layers are then magnified to display resolution and smoothly composited. We develop a general and efficient antialiasing algorithm easily retrofitted into existing graphics code to minimize "twinkling" artifacts in the lower-resolution layers. A standard psychophysical model for acuity falloff assumes that minimum detectable angular size increases linearly as a function of eccentricity. Given the slope characterizing this falloff, we automatically compute layer sizes and sampling rates. The result looks like a full-resolution image but reduces the number of pixels shaded by a factor of 10-15. We performed a user study to validate these results. It identifies two levels of foveation quality: a more conservative one in which users reported foveated rendering quality as equivalent to or better than non-foveated when directly shown both, and a more aggressive one in which users were unable to correctly label as increasing or decreasing a short quality progression relative to a high-quality foveated reference. Based on this user study, we obtain a slope value for the model of 1.32-1.65 arc minutes per degree of eccentricity. This allows us to predict two future advantages of foveated rendering: (1) bigger savings with larger, sharper displays than exist currently (e.g. 100 times speedup at a field of view of 70° and resolution matching foveal acuity), and (2) a roughly linear (rather than quadratic or worse) increase in rendering cost with increasing display field of view, for planar displays at a constant sharpness.


MetaPhlAn2 for enhanced metagenomic taxonomic profiling


title: When Does Maluma/Takete Fail? Two Key Failures and a Meta-Analysis Suggest That Phonology and Phonotactics Matter

abstract: Eighty-seven years ago, Kohler reported that the majority of students picked the same answer in a quiz: Which novel word form (‘maluma’ or ‘takete’) went best with which abstract line drawing (one curved, one angular). Others have consistently shown the effect in a variety of contexts, with only one reported failure by Rogers and Ross. In the spirit of transparency, we report our own failure in the same journal. In our study, speakers of Syuba, from the Himalaya in Nepal, do not show a preference when matching word forms ‘kiki’ and ‘bubu’ to spiky versus curvy shapes. We conducted a meta-analysis of previous studies to investigate the relationship between pseudoword legality and task effects. Our combined analyses suggest a common source for both of the failures: ‘wordiness’ – We believe these tests fail when the test words do not behave according to the sound structure of the target language.


title: Augmenting geospatial data provenance through metadata tracking in geospatial service chaining

abstract: In a service-oriented environment, heterogeneous data from distributed data archiving centers and various geo-processing services are chained together dynamically to generate on-demand data products. Creating an executable service chain requires detailed specification of metadata for data sets and service instances. Using metadata tracking, semantics-enabled metadata are generated and propagated through a service chain. This metadata can be employed to validate a service chain, e.g. whether metadata preconditions on the input data of services can be satisfied. This paper explores how this metadata can be further exploited to augment geospatial data provenance, i.e., how a geospatial data product is derived. Provenance information is automatically captured during the metadata tracking process. Semantic Web technologies, including OWL and SPARQL, are used for representation and query of this provenance information. The approach can not only contribute to the automatic recording of geospatial data provenance, but also provide a more informed understanding of provenance information using Semantic Web technologies.


title: Vision-based localization and mapping for an autonomous mower

abstract: This paper presents a vision-based localization and mapping algorithm for an autonomous mower. We divide the task for robotic mowing into two separate phases, a teaching phase and a mowing phase. During the teaching phase, the mower estimates the 3D positions of landmarks and defines a boundary in the lawn with an estimate of its own trajectory. During the mowing phase, the location of the mower is estimated using the landmark and boundary map acquired from the teaching phase. Of particular interest for our work is ensuring that the estimator for landmark mapping will not fail due to the nonlinearity of the system during the teaching phase. A nonlinear observer is designed with pseudo-measurements of each landmark's depth to prevent the map estimator from diverging. Simultaneously, the boundary is estimated with an EKF. Measurements taken from an omnidirectional camera, an IMU, and a ground speed sensor are used for the estimation. Numerical simulations and offline teaching phase experiments with our autonomous mower demonstrate the potential of our algorithm.


title: Teaching by demonstration on dual-arm robot using variable stiffness transferring

abstract: Teaching by demonstration (TbD) techniques have been extensively investigated in the recent decades to enable transferring various task skills from human to robots. The traditional TbD techniques focus on teaching motion trajectories that may be sufficient for routine tasks with fixed objects. While for interactive tasks in contact with dynamic environment and objects, e.g., the payload of a robot manipulator may change from one to another, teaching robot only by motion demonstration may cause undesired contact force and inefficiency in the task execution. In this paper, we present a novel TbD method enhanced by transferring the stiffness profile during human robot interaction (HRI). The method is developed on a bimanual robot, whereas one slave arm plays the role of the tutee, and the other master arm coupled with human demonstrator plays the role of tutor. A rendering algorithm is employed to provide demonstrator with force feedback via a purposely built coupling device according to the motion disparity between the two arms. The muscle surface electromyography (sEMG) signals collected during HRI is processed to extract the demonstrator's variable stiffness as well as hand grasping patterns. Comparative tests have been carried out on a bimanual Baxter robot for a lifting task with three different set-ups: i) TbD with predefined fixed stiffness; ii) TbD with demonstrator transferred variable stiffness without force feedback; and iii) TbD with demonstrator transferred variable stiffness with force feedback. Results show that the proposed TbD method performs best by transferring the demonstrator's physical interactive skill to the robot in a natural and efficient manner.


title: Hidden Structure: Using Network Methods to Map System Architecture

abstract: In this paper, we describe an operational methodology for characterizing the architecture of complex technical systems and demonstrate its application to a large sample of software releases. Our methodology is based upon directed network graphs, which allows us to identify all of the direct and indirect linkages between the components in a system. We use this approach to define three fundamental architectural patterns, which we label core–periphery, multi-core, and hierarchical. Applying our methodology to a sample of 1286 software releases from 17 applications, we find that the majority of releases possess a “core–periphery” structure. This architecture is characterized by a single dominant cyclic group of components (the “Core”) that is large relative to the system as a whole as well as to other cyclic groups in the system. We show that the size of the Core varies widely, even for systems that perform the same function. These differences appear to be associated with different models of development – open, distributed organizations develop systems with smaller Cores, while closed, co-located organizations develop systems with larger Cores. Our findings establish some “stylized facts” about the fine-grained structure of large, real-world technical systems, serving as a point of departure for future empirical work.


title: An open source tool for simulation and supervision of underwater intervention missions

abstract: This paper presents UWSim: a new software tool for visualization and simulation of underwater robotic missions. The software visualizes an underwater virtual scenario that can be configured using standard modeling software. Controllable underwater vehicles, surface vessels and robotic manipulators, as well as simulated sensors, can be added to the scene and accessed externally through network interfaces. This allows to easily integrate the simulation and visualization tool with existing control architectures, thus allowing hardware-in-the-loop simulations (HIL). UWSim has been successfully used for simulating the logics of underwater intervention missions and for reproducing real missions from the captured logs. The software is offered as open source, thus filling a gap in the underwater robotics community, where commercial simulators oriented to ROV pilot training predominate.


title: Spatial Sound Rendering Using Measured Room Impulse Responses

abstract: Spatial sound rendering has many applications such as music production, movies, electronic gaming and teleconferencing. Each of the applications may have different quality and complexity requirements. This paper presents a new spatial sound rendering framework that aims at producing realistic multichannel audio while being flexible and scalable so that is can be extended and adopted by various applications. The proposed framework uses multi-channel measured room impulse response (MMRIR) as the basis for building a room acoustic model which is used to synthesize multi-channel audio. The proposed framework has been evaluated by informal listening tests.


title: The Electoral Effect of Credit Claiming for Pork Barrel Projects in Congress

abstract: This study examines the electoral connection between members of Congress and their constituents through credit claiming for traditional pork barrel projects, defined as categorical and discretionary grant awards, and legislative earmarks to congressional districts. Previous research on the electoral impact of pork has employed federal spending in congressional districts as the measure of pork; often implicit in that work is the assumption that members claim credit for that spending and therefore receive electoral benefits. We argue that it is important directly to measure credit claiming, which is central to the theory of the electoral benefits to be derived from pork. We collect from members’ websites data on legislators’ credit claiming for earmarks and federal grants. Using individual level survey data from the 2006 Cooperative Congressional Election Study we examine the impact of credit claiming for distributive benefits on voters’ electoral support for incumbent House members in the 2006 election. We find that credit claiming did not unconditionally endear members to their constituents. The effects of pork on voters’ support for the incumbent were frequently negative and mediated by both the voter’s and the candidate’s party. However, those effects were not always as expected: Republican voters tended to reward Democrats for earmarks, while voters of both parties sometimes punished incumbents for credit claiming for grants.


Automatic Pixel-Level Land-use Prediction Using Deep Convolutional Neural Networks


title: Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning

abstract: We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient’s risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of breast lesions (i.e., masses and micro-calcifications). This is a holistic methodology that can classify a whole mammographic exam, containing the CC and MLO views and the segmentation maps, as opposed to the classification of individual lesions, which is the dominant approach in the field. We also demonstrate that the proposed system is capable of using the segmentation maps generated by automated mass and micro-calcification detection systems, and still producing accurate results. The semi-automated approach (using manually defined mass and micro-calcification segmentation maps) is tested on two publicly available data sets (INbreast and DDSM), and results show that the volume under ROC surface (VUS) for a 3-class problem (normal tissue, benign, and malignant) is over 0.9, the area under ROC curve (AUC) for the 2-class “benign versus malignant” problem is over 0.9, and for the 2-class breast screening problem (malignancy versus normal/benign) is also over 0.9. For the fully automated approach, the VUS results on INbreast is over 0.7, and the AUC for the 2-class “benign versus malignant” problem is over 0.78, and the AUC for the 2-class breast screening is 0.86.