title: Face detection using discriminating feature analysis and support vector machine in video

abstract: This work presents a novel face detection method in video by using discriminating feature analysis (DFA) and support vector machine (SVM). Our method first incorporates temporal and skin color information to locate the field of interests. Then the face class is modelled using a small training set and the nonface class is defined by choosing nonface images that lie close to the face class. Finally, the SVM classifier together with Bayesian statistical analysis procedure applies the efficient features defined by DFA for face and nonface classification. Experiments using both still images and video streams show the feasibility of our new face detection method. In particular, when using 92 images (containing 282 faces) from the MIT-CMU test sets, our method achieves 98.2% correct face detection accuracy with 2 false detections. When using video streams, our method detects faces reliably with computational efficiency of more than 20 frames per second.


title: Economic and Optimal Operation of a Combined Heat and Power Microgrid with Renewable Energy Resources

abstract: Featured by its energy saving capability,environment friendliness and economic benefit,the combined heat and power(CHP) system has a great prospect for further development and wider applications.A mathematical model is developed to achieve economic and optimal operation of a CHP microgrid which consists of wind turbine driven generators,photovoltaic cells,fuel cells,heat recovery and gas fired boilers,battery storage systems and thermal as well as electrical loads.The model uses chance constrained programming(CCP) method to consider the stochastic nature of wind power,photovoltaic power,thermal and electrical loads.A stochastic simulation based particle swarm optimization(PSO) algorithm is proposed to solve the optimization model.The optimal operation scheme is studied under different ways of deployment of micro-sources.Simulation results show that the proposed method can be adopted to find the optimal deployment of micro-sources,such that the dynamic economic dispatch of the microgrid is possible to reduce the operation cost effectively.


A refutation of Metcalfe ’ s Law and a better estimate for the value of networks and network interconnections


title: A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average

abstract: Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.


title: Go and no-go learning in reward and punishment: Interactions between affect and effect

abstract: Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.


title: Far but Near or Near but Far?: The Effects of Perceived Distance on the Relationship between Geographic Dispersion and Perceived Diversity

abstract: Geographic dispersion has been proposed as one means to promote cooperation and coordination in teams high in perceived diversity. However, research has found mixed support for this assertion. This study proposes that the inclusion of perceived distance helps to explain these mixed results. To test this assertion, we examined 121 teams-62 collocated and 59 geographically dispersed. Results demonstrate that perceived distance explains when geographic dispersion benefits teams high in perceived diversity. Results also indicate that the type of perceived diversity matters (surface-level vs. deep-level diversity). This study contributes to our understanding of distance and diversity in teams.


title: Information cascades in complex networks

abstract: Information cascades are important dynamical processes in complex networks. An information cascade can describe the spreading dynamics of rumour, disease, memes, or marketing campaigns, which initially start from a node or a set of nodes in the network. If conditions are right, information cascades rapidly encompass large parts of the network, thus leading to epidemics or epidemic spreading. Certain network topologies are particularly conducive to epidemics, while others decelerate and even prohibit rapid information spreading. Here we review models that describe information cascades in complex networks, with an emphasis on the role and consequences of node centrality. In particular, we present simulation results on sample networks that reveal just how relevant the centrality of initiator nodes is on the latter development of an information cascade, and we define the spreading influence of a node as the fraction of nodes that is activated as a result of the initial activation of that node. A systemic review of existing results shows that some centrality measures, such as the degree and betweenness, are positively correlated with the spreading influence, while other centrality measures, such as eccentricity and the information index, have negative correlation. A positive correlation implies that choosing a node with the highest centrality value will activate the largest number of nodes, while a negative correlation implies that the node with the lowest centrality value will have the same effect.We discuss possible applications of these results, and we emphasize how information cascades can help us identify nodes with the highest spreading capability in complex networks.


title: Off-line signature verification and forgery detection using fuzzy modeling

abstract: Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance.


title: Autonomous repositioning and localization of an in situ fabricator

abstract: Despite the prevalent use of robotic technologies in industrial manufacturing, their use on building construction sites is still very limited. This is mainly due to the unstructured nature of construction sites and the fact that the structures built must be larger than the machines which build them. This paper addresses these difficulties by presenting a repositioning and localization system which, using purely on-board sensing, allows a mobile robot to maintain a high degree of end-effector positioning accuracy while moving among numerous building positions during a building task. We introduce a newly developed machine, called the In situ Fabricator (IF), whose goal is to bring digital fabrication to the construction site. The capabilities of the repositioning and localization system is demonstrated through the construction of a vertical stack of bricks, in which the IF repositions itself after placing each brick. With this experiment, we demonstrate that the IF can build with sub-centimeter accuracy over long building sequences.


title: O$^2$TD: (Near)-Optimal Off-Policy TD Learning

abstract: Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their objective functions is optimal w.r.t approximating the true value function $V$. Two novel algorithms are proposed to approximate the true value function $V$. This paper makes the following contributions: (1) A batch algorithm that can help find the approximate optimal off-policy prediction of the true value function $V$. (2) A linear computational cost (per step) near-optimal algorithm that can learn from a collection of off-policy samples. (3) A new perspective of the emphatic temporal difference learning which bridges the gap between off-policy optimality and off-policy stability.


title: Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?

abstract: When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world.


title: Embedding Spatio-Temporal Information into Maps by Route-Zooming

abstract: Analysis and exploration of spatio-temporal data such as traffic flow and vehicle trajectories have become important in urban planning and management. In this paper, we present a novel visualization technique called route-zooming that can embed spatio-temporal information into a map seamlessly for occlusion-free visualization of both spatial and temporal data. The proposed technique can broaden a selected route in a map by deforming the overall road network. We formulate the problem of route-zooming as a nonlinear least squares optimization problem by defining an energy function that ensures the route is broadened successfully on demand while the distortion caused to the road network is minimized. The spatio-temporal information can then be embedded into the route to reveal both spatial and temporal patterns without occluding the spatial context information. The route-zooming technique is applied in two instantiations including an interactive metro map for city tourism and illustrative maps to highlight information on the broadened roads to prove its applicability. We demonstrate the usability of our spatio-temporal visualization approach with case studies on real traffic flow data. We also study various design choices in our method, including the encoding of the time direction and choices of temporal display, and conduct a comprehensive user study to validate our embedded visualization design.


Data-Driven Ghosting using Deep Imitation Learning


title: Complex trauma and mental health in children and adolescents placed in foster care: findings from the National Child Traumatic Stress Network.

abstract: Many children in the child welfare system (CWS) have histories of recurrent interpersonal trauma perpetrated by caregivers early in life often referred to as complex trauma. Children in the CWS also experience a diverse range of reactions across multiple areas of functioning that are associated with such exposure. Nevertheless, few CWSs routinely screen for trauma exposure and associated symptoms beyond an initial assessment of the precipitating event. This study examines trauma histories, including complex trauma exposure (physical abuse, sexual abuse, emotional abuse, neglect, domestic violence), posttraumatic stress, and behavioral and emotional problems of 2,251 youth (age 0 to 21; M = 9.5, SD = 4.3) in foster care who were referred to a National Child Traumatic Stress Network site for treatment. High prevalence rates of complex trauma exposure were observed: 70.4% of the sample reported at least two of the traumas that constitute complex trauma; 11.7% of the sample reported all 5 types. Compared to youth with other types of trauma, those with complex trauma histories had significantly higher rates of internalizing problems, posttraumatic stress, and clinical diagnoses, and differed on some demographic variables. Implications for child welfare practice and future research are discussed.


title: Toward preprototype user acceptance testing of new information systems: implications for software project management

abstract: Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system's perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system's functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that preprototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly predictive of usage intentions and behavior up to six months after workplace implementation. The present findings open the door toward research on how user acceptance testing may be done much earlier in the system development process than has traditionally been the case. Such preprototype user acceptance tests have greater informational value than their postprototype counterparts because they are captured when only a relatively small proportion of project costs have been incurred and there is greater flexibility to modify a new system's design attributes. Implications are discussed for future research to confirm the robustness of the present findings and to better understand the practical potential and limitations of preprototype user acceptance testing.


title: A class-feature-centroid classifier for text categorization

abstract: Automated text categorization is an important technique for many web applications, such as document indexing, document filtering, and cataloging web resources. Many different approaches have been proposed for the automated text categorization problem. Among them, centroid-based approaches have the advantages of short training time and testing time due to its computational efficiency. As a result, centroid-based classifiers have been widely used in many web applications. However, the accuracy of centroid-based classifiers is inferior to SVM, mainly because centroids found during construction are far from perfect locations. We design a fast Class-Feature-Centroid (CFC) classifier for multi-class, single-label text categorization. In CFC, a centroid is built from two important class distributions: inter-class term index and inner-class term index. CFC proposes a novel combination of these indices and employs a denormalized cosine measure to calculate the similarity score between a text vector and a centroid. Experiments on the Reuters-21578 corpus and 20-newsgroup email collection show that CFC consistently outperforms the state-of-the-art SVM classifiers on both micro-F1 and macro-F1 scores. Particularly, CFC is more effective and robust than SVM when data is sparse.


title: GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

abstract: Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s Dest and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. ::: ::: Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. ::: ::: Contact: rod.peakall@anu.edu.au


How Much Light Do We Really Need


title: Long-Term Identity-Aware Multi-Person Tracking for Surveillance Video Summarization

abstract: Multi-person tracking plays a critical role in the analysis of surveillance video. However, most existing work focus on shorter-term (e.g. minute-long or hour-long) video sequences. Therefore, we propose a multi-person tracking algorithm for very long-term (e.g. month-long) multi-camera surveillance scenarios. Long-term tracking is challenging because 1) the apparel/appearance of the same person will vary greatly over multiple days and 2) a person will leave and re-enter the scene numerous times. To tackle these challenges, we leverage face recognition information, which is robust to apparel change, to automatically reinitialize our tracker over multiple days of recordings. Unfortunately, recognized faces are unavailable oftentimes. Therefore, our tracker propagates identity information to frames without recognized faces by uncovering the appearance and spatial manifold formed by person detections. We tested our algorithm on a 23-day 15-camera data set (4,935 hours total), and we were able to localize a person 53.2% of the time with 69.8% precision. We further performed video summarization experiments based on our tracking output. Results on 116.25 hours of video showed that we were able to generate a reasonable visual diary (i.e. a summary of what a person did) for different people, thus potentially opening the door to automatic summarization of the vast amount of surveillance video generated every day.


title: Indoor navigation system using optical mouse sensor and smartphone

abstract: In this paper, we propose a low cost indoor positioning system that uses an ADNS-2610 optical flow sensor from an old Compaq PS/2 optical mouse. We built a prototype for a navigational kit around the proposed indoor positioning system. The navigational path is determined by collecting displacement data from the optical flow sensor and composing the same with the rotational data from the inbuilt magnetometer of an Android based smart phone. The user's position is then updated on a map accordingly. The collected displacement data is initially sent to an Arduino microprocessor, which in turn sends this data to a Bluetooth module for onward transmission to the Android based smart phone. The proposed system has wide applicability not only in accurate positioning but in accurate odometry.


title: Selene: Voting with Transparent Verifiability and Coercion-Mitigation

abstract: End-to-end verifiable voting schemes typically involve voters handling an encrypted ballot in order to confirm that their vote is accurately included in the tally. While this may be technically valid, from a public acceptance standpoint it may be problematic: many voters may not really understand the purpose of the encrypted ballot and the various checks that they can perform. In this paper we take a different approach and revisit an old idea: to provide each voter with a private tracking number. Votes are posted on a bulletin board in the clear along with their associated tracking number. This is appealing in that it provides voters with a very simple, intuitive way to verify their vote, in the clear. However, there are obvious drawbacks: we must ensure that no two voters are assigned the same tracker and we need to keep the link between voters and trackers private.


title: Total Cluster: A person agnostic clustering method for broadcast videos

abstract: The goal of this paper is unsupervised face clustering in edited video material – where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. This is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. The novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material – the shots, threads and scenes that are standard in edited video; second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors; third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. The method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works.


title: Multi-Drive Stacked-FET Power Amplifiers at 90 GHz in 45 nm SOI CMOS

abstract: Gate resistance significantly limits the output power and power-added efficiency of stacked-FET power amplifiers in 45 nm SOI CMOS above 60 GHz. A multi-drive stacked-FET approach is proposed to improve the output power and efficiency. An analysis of conventional and multi-drive stacked-FET PAs demonstrates the performance improvement. A multi-drive three-stack PA is implemented in 45 nm SOI CMOS for 90 GHz operation occupying 0.23 mm 2 . This PA achieves more than 19 dBm output power with peak PAE of 14% and 12 dB gain at 90 GHz using a 3.4 V power supply.


Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery


title: Temporal and Real-Time Databases: A Survey

abstract: A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two previously separate areas. First we characterize the time domain; then we investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. We examine temporal and real-time DBMS implementation. Finally, we summarize major research accomplishments to date and list several unanswered research questions. >


title: Multispectral palmprint recognition using steerable filter

abstract: This paper presents a multispectral palmprint recognition approach based on palm line orientation feature extracted with high order steerable filter. Gaussian function is used as isotropic window to design a high order steerable filter. The orientation features are selected as per dominant filter response for a particular orientation. Optimum values for parameters, i.e., standard deviation and number of orientations are found experimentally in order to obtain low equal error rate (EER) and high correct identification rate (CIR). Weighted score level fusion strategy is applied to combine the score of all spectral palmprints. A recognition rate of 99.97% is achieved with high decidability index (DI) and low EER. Further, the proposed approach is compared with traditional competitive code method for multispectral PolyU palmprint database.


title: Preconditioned Stochastic Gradient Descent

abstract: Stochastic gradient descent (SGD) still is the workhorse for many practical problems. However, it converges slow, and can be difficult to tune. It is possible to precondition SGD to accelerate its convergence remarkably. But many attempts in this direction either aim at solving specialized problems, or result in significantly more complicated methods than SGD. This paper proposes a new method to adaptively estimate a preconditioner, such that the amplitudes of perturbations of preconditioned stochastic gradient match that of the perturbations of parameters to be optimized in a way comparable to Newton method for deterministic optimization. Unlike the preconditioners based on secant equation fitting as done in deterministic quasi-Newton methods, which assume positive definite Hessian and approximate its inverse, the new preconditioner works equally well for both convex and nonconvex optimizations with exact or noisy gradients. When stochastic gradient is used, it can naturally damp the gradient noise to stabilize SGD. Efficient preconditioner estimation methods are developed, and with reasonable simplifications, they are applicable to large-scale problems. Experimental results demonstrate that equipped with the new preconditioner, without any tuning effort, preconditioned SGD can efficiently solve many challenging problems like the training of a deep neural network or a recurrent neural network requiring extremely long-term memories.


title: Adolescent risky decision-making: Neurocognitive development of reward and control regions

abstract: Recent models hypothesize that adolescents' risky behavior is the consequence of increased sensitivity to rewards in the ventral medial (VM) prefrontal cortex (PFC) and the ventral striatum (VS), paired with immature cognitive control abilities due to slow maturation of the dorsal anterior cingulate cortex (ACC) and lateral PFC. We tested this hypothesis with fMRI using a gambling task in which participants chose between Low-Risk gambles with a high probability of obtaining a small reward (1 Euro) and High-Risk gambles with a smaller probability of obtaining a higher reward (2, 4, 6, or 8 Euro). We examined neural responses during choice selection and outcome processing in participants from 4 age groups (pre-pubertal children, early adolescents, older adolescents and young adults). High-Risk choices increased with rewards for all ages, but risk-taking decreased with age for low reward gambles. The fMRI results confirmed that High-Risk choices were associated with activation in VMPFC, whereas Low-Risk choices were associated with activation in lateral PFC. Activation in dorsal ACC showed a linear decrease with age, whereas activation in VMPFC and VS showed an inverted U-shaped developmental pattern, with a peak in adolescence. In addition, behavioral differences in risk-taking propensity modulated brain activation in all age groups. These findings support the hypothesis that risky behavior in adolescence is associated with an imbalance caused by different developmental trajectories of reward and regulatory brain circuitry.


title: On-line trust: concepts, evolving themes, a model

abstract: Trust is emerging as a key element of success in the on-line environment. Although considerable research on trust in the offline world has been performed, to date empirical study of on-line trust has been limited. This paper examines on-line trust, specifically trust between people and informational or transactional websites. It begins by analysing the definitions of trust in previous offline and on-line research. The relevant dimensions of trust for an on-line context are identified, and a definition of trust between people and informational or transactional websites is presented. We then turn to an examination of the causes of on-line trust. Relevant findings in the human-computer interaction literature are identified. A model of on-line trust between users and websites is presented. The model identifies three perceptual factors that impact on-line trust: perception of credibility, ease of use and risk. The model is discussed in detail and suggestions for future applications of the model are presented.


title: Comparison of baseline estimation algorithms for chromatographic signals

abstract: Baseline in signals is a relatively complicated problem in analysis of signals obtained in various analytical techniques such as chromatography and spectroscopy. In this article there are presented results of tests on four algorithms for a baseline estimation in chromatographic signals. Two of them are based on a polynomial fitting in a region of detected peaks. Another two algorithms, i.e. assymetric least squares method and iterative polynomial fitting with the automatic treshold, estimate the baseline under the whole signal and generally does not require peak detection. The tests of the algorithms are performed on both simulated and experimental signals.


title: Trust in news on social media

abstract: This paper investigates trust in news on a social media platform. The paper is motivated by the finding that social media is the primary news source for a large group of people, especially young adults. Considering the challenges posed by online misinformation and fake news, an understanding of how users quantify trust in news and what factors influence this trust is needed. In a study with 108 participants, German high-school students provided trust ratings for online news including quality media and fake news. The study shows that users can quantify their trust in news items and that these trust ratings correspond to rankings of the sources by experts. The paper finds that psychometric scales that measure interpersonal trust are predictive of a user's mean trust rating across different news items. We show how this can be used to provide interventions for those prone to false trust and false distrust.


title: Beamforming in Non-Regenerative Two-Way Multi-Antenna Relay Networks for Simultaneous Wireless Information and Power Transfer

abstract: Simultaneous wireless information and power trans- fer (SWIPT) is able to prolong the lifetime of energy constrained wireless networks. In this paper, we consider the relay beam- forming design problem for SWIPT scheme in a non-regenerative two-way multi-antenna relay network. Our objective is to max- imize the sum rate of two-way relay network under the trans- mit power constraint at relay and the energy harvesting (EH) constraint at EH receiver. For the non-convex EH-constrained relay beamforming optimization problem, we propose an iterative algorithm to find the global optimal solution based on semidefinite programming and rank-one decomposition theorem. To reduce computational complexity of global optimal solution, we transform the EH-constrained optimization problem to a difference of convex programming and propose a constrained concave convex proce- dure based iterative algorithm to find a local optimum. To further reduce the complexity, we propose a suboptimal solution based on the generalized eigenvectors method. When the case of multi-an- tenna sources is considered, we propose the alternating optimiza- tion based iterative algorithms. It is shown from simulations that considering the EH constraint, our proposed schemes outperform conventional relay beamforming schemes in the literature.


title: Residual Parameter Transfer for Deep Domain Adaptation

abstract: The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representations that are invariant to the changes that occur when going from one domain to the other, which means using the same network parameters in both domains. While some recent algorithms explicitly model the changes by adapting the network parameters, they either severely restrict the possible domain changes, or significantly increase the number of model parameters. ::: By contrast, we introduce a network architecture that includes auxiliary residual networks, which we train to predict the parameters in the domain with little annotated data from those in the other one. This architecture enables us to flexibly preserve the similarities between domains where they exist and model the differences when necessary. We demonstrate that our approach yields higher accuracy than state-of-the-art methods without undue complexity.


title: Wireless sensor-based control of mobile robots motion

abstract: The paper deals with the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Bluetooth wireless transmission and Sun SPOT technology. The Sun SPOT is designed to be a flexible development platform, capable of hosting widely differing application modules. Web technologies are changing the education in robotics. A feature of remote control laboratories is that users can interact with real mobile robot motion processes through the Internet. Motion control of mobile robots is very important research field today, because mobile robots are a interesting subject both in scientific research and practical applications. In this paper the object of the remote control is the Boe-Bot mobile robot from Parallax. This Boe-Bot mobile robot is the simplest, low-cost platform and the most suitable for the small-sized, light, battery-driven autonomous vehicle. The vehicle has two driving wheels and the angular velocities of the two wheels are independently controlled. When the vehicle is moving towards the target in an unknown environment with obstacles, an avoiding strategy is necessary. A remote control program has been implemented.


title: Cross-Modal Attentional Context Learning for RGB-D Object Detection

abstract: Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications, such as robot grasping and autonomous driving. In this paper, we address this problem by developing a cross-modal attentional context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data. Compared to existing RGB-D object detection frameworks, our approach has several appealing properties. First, it consists of an attention-based global context model for exploiting adaptive contextual information and incorporating this information into a region-based CNN (e.g., fast RCNN) framework to achieve improved object detection performance. Second, our CMAC framework further contains a fine-grained object part attention module to harness multiple discriminative object parts inside each possible object region for superior local feature representation. While greatly improving the accuracy of RGB-D object detection, the effective cross-modal information fusion as well as attentional context modeling in our proposed model provide an interpretable visualization scheme. Experimental results demonstrate that the proposed method significantly improves upon the state of the art on all public benchmarks.


title: Video Question Answering via Hierarchical Dual-Level Attention Network Learning

abstract: Video question answering is a challenging task in visual information retrieval, which provides the accurate answer from the referenced video contents according to the given question. However, the existing visual question answering approaches mainly tackle the problem of static image question answering, which may be ineffectively applied for video question answering directly, due to the insufficiency of modeling the video temporal dynamics. In this paper, we study the problem of video question answering from the viewpoint of hierarchical dual-level attention network learning. We obtain the object appearance and movement information in the video based on both frame-level and segment-level feature representation methods. We then develop the hierarchical duallevel attention networks to learn the question-aware video representations with word-level and question-level attention mechanisms. We next devise the question-level fusion attention mechanism for our proposed networks to learn the questionaware joint video representation for video question answering. We construct two large-scale video question answering datasets. The extensive experiments validate the effectiveness of our method.


title: Mindfulness, self-compassion, and happiness in non-meditators: A theoretical and empirical examination

abstract: Abstract This study examined relationships between mindfulness and indices of happiness and explored a five-factor model of mindfulness. Previous research using this mindfulness model has shown that several facets predicted psychological well-being (PWB) in meditating and non-meditating individuals. The current study tested the hypothesis that the prediction of PWB by mindfulness would be augmented and partially mediated by self-compassion. Participants were 27 men and 96 women (mean age = 20.9 years). All completed self-report measures of mindfulness, PWB, personality traits (NEO-PI-R), and self-compassion. Results show that mindfulness is related to psychologically adaptive variables and that self-compassion is a crucial attitudinal factor in the mindfulness–happiness relationship. Findings are interpreted from the humanistic perspective of a healthy personality.


title: An Evaluation of Non-Equijoin Algorithms

abstract: A non-equijoin of relations R and S is R bnnd join if the join predicate requires valnes in the join att.ribr1t.e of R 10 fall within a specified hand ahcmt the valnrs in the join r.tt.rihnte of S. We propose a new algorithm. t.ermed a partitionerl hnnd join, for evaluating band joins. We present a comparison between the partitioned band ,join algorithm and the classical sort-merge join algorit.hm (op(.imixed for band ,jnins) using bot,h an analytical model and an implemenlaCon on top of the WiSS storage system. The results show that the partitioned ba.nd join algorithm outperforms sort.-merge unlrsp memory is scarce and t.he opernnda of t,he join are of equal size. We also describe a parallel implementation of the pnrtitioned band join on the Gamma database machine. and present data from speedup and scalcup experiments demonstrating that the partitioned hand join is efficiently paral-


MEDICAL IMAGE SEGMENTATION


title: Smart refrigerator: A next generation refrigerator connected to the IoT

abstract: The Internet of Things (IoT) refers to the set of devices and systems that interconnect real world sensors and actuators to the Internet. This includes many different systems, including smart objects, smart monitoring devices, home automation systems, smartphones (which are increasingly being used to measure the world around them) and many more. This paper presents the concept, architecture, building process and functionality of such a device that incorporates the systems stated above: a refrigerator that is exposed as an IoT object and interacts with the items stored within, gathers information about them, process this information into relevant data that is later conveyed through an IoT platform to its owners, in other words, a smart refrigerator.


title: A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems

abstract: BackgroundText mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task.ResultsA relation extraction system derived using the proposed framework achieved overall F-scores of 72.66% for the Simple events and 55.57% for the Binding events on the BioNLP-ST 2011 GE test set, comparing favorably with the top performing systems that participated in the BioNLP-ST 2011 GE task. We obtained similar results on the BioNLP-ST 2013 GE test set (80.07% and 60.58%, respectively). We conducted additional experiments on the training and development sets to provide a more detailed analysis of the system and its individual modules. This analysis indicates that without increasing the number of patterns, simplification and referential relation linking play a key role in the effective extraction of biomedical relations.ConclusionsIn this paper, we present a novel framework for fast development of relation extraction systems. The framework requires only a list of triggers as input, and does not need information from an annotated corpus. Thus, we reduce the involvement of domain experts, who would otherwise have to provide manual annotations and help with the design of hand crafted patterns. We demonstrate how our framework is used to develop a system which achieves state-of-the-art performance on a public benchmark corpus.


title: Reaction of dithiocarbamates with malononitrile dimer; simple synthesis of new 1,4-dihydropyridine-2-thiols

abstract: Dithiacarbamates reacted with malononitrile dimer to give 1,4-dihydropyridine-2-thiols. The structures of the obtained products were proven by IR, mass, and NMR spectra and elemental analyses. The reaction mechanism is also discussed.


title: Overview of sciDB: large scale array storage, processing and analysis

abstract: SciDB [4, 3] is a new open-source data management system intended primarily for use in application domains that involve very large (petabyte) scale array data; for example, scientific applications such as astronomy, remote sensing and climate modeling, bio-science information management, risk management systems in financial applications, and the analysis of web log data. In this talk we will describe our set of motivating examples and use them to explain the features of SciDB. We then briefly give an overview of the project 'in flight', explaining our novel storage manager, array data model, query language, and extensibility frameworks.


title: Smart-cap for pedestrian safety

abstract: In the last few decades, the use of mobile phones have increased rapidly. Safety experts have considered dependency on mobile phones as a threat to the pedestrian safety. In this paper, a wearable smart-cap has been proposed to detect an obstacle in the path of a pedestrian mobile user. The vision of the proposed system is to provide a user-friendly, cost effective and a quick way to avoid pedestrian accidents due to mobile phones. The volunteers' feedback and testing results have been outstanding. The volunteers have reported that the proposed system is user-friendly and accurate.


title: A Virtual Coach for Active Ageing Based on Sentient Computing and m-health

abstract: As life expectancy increases it has become more necessary to find ways to support healthy ageing. A number of active ageing initiatives are being developed nowadays to foster healthy habits in the population. This paper presents our contribution to these initiatives in the form of a conversational agent that acts as a coach for physical activities. The agent can be developed as an Android app running on smartphones and coupled with cheap widely available sport sensors in order to provide meaningful coaching. It can be employed to prepare exercise sessions, provide feedback during the sessions, and to discuss the results after the exercise. It incorporates an affective component that informs dynamic user models to produce adaptive interaction strategies.


title: Unsupervised Multilingual Sentence Boundary Detection

abstract: In this article, we present a language-independent, unsupervised approach to sentence boundary detection. It is based on the assumption that a large number of ambiguities in the determination of sentence boundaries can be eliminated once abbreviations have been identified. Instead of relying on orthographic clues, the proposed system is able to detect abbreviations with high accuracy using three criteria that only require information about the candidate type itself and are independent of context: Abbreviations can be defined as a very tight collocation consisting of a truncated word and a final period, abbreviations are usually short, and abbreviations sometimes contain internal periods. We also show the potential of collocational evidence for two other important subtasks of sentence boundary disambiguation, namely, the detection of initials and ordinal numbers. The proposed system has been tested extensively on eleven different languages and on different text genres. It achieves good results without any further amendments or language-specific resources. We evaluate its performance against three different baselines and compare it to other systems for sentence boundary detection proposed in the literature.


title: Stair climbing for humanoid robots using stereo vision

abstract: For the fully autonomous navigation in a 3 dimensional world, a humanoid robot must be capable of stepping up and down staircases and other obstacle where a sufficient large flat surface can support the robot's feet. This paper presents methods for the recognition of stairs and a control architecture that enable the humanoid robot QRIO to safely climb up and down in its environment The approach is based on data captured by a stereo vision system and segmented into planar surfaces. From the segmented planes, stairs that can be climbed by the robot are extracted and fed to a control system which decides the action to be taken next. Experimental results on a staircase as well as climbing up and down a sill are presented.


title: Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

abstract: This paper presents a new method - adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.


title: The Koch monopole: a small fractal antenna

abstract: Fractal objects have some unique geometrical properties. One of them is the possibility to enclose in a finite area an infinitely long curve. The resulting curve is highly convoluted being nowhere differentiable. One such curve is the Koch curve. In this paper, the behavior the Koch monopole is numerically and experimentally analyzed. The results show that as the number of iterations on the small fractal Koch monopole are increased, the Q of the antenna approaches the fundamental limit for small antennas.


title: A Rule-Based Relation Extraction System using DBpedia and Syntactic Parsing

abstract: In this paper, we present a rule-based relation extraction approach which uses DBpedia and linguistic information provided by the syntactic parser Fips. Our goal is twofold: (i) the morpho-syntactic patterns are defined using the syntactic parser Fips to identify relations between named entities (ii) the RDF triples extracted from DBpedia are used to improve RE task by creating gazetteer relations.


title: Landscape genetics: where are we now?

abstract: Landscape genetics has seen rapid growth in number of publications since the term was coined in 2003. An extensive literature search from 1998 to 2008 using keywords associated with landscape genetics yielded 655 articles encompassing a vast array of study organisms, study designs and methodology. These publications were screened to identify 174 studies that explicitly incorporated at least one landscape variable with genetic data. We systematically reviewed this set of papers to assess taxonomic and temporal trends in: (i) geographic regions studied; (ii) types of questions addressed; (iii) molecular markers used; (iv) statistical analyses used; and (v) types and nature of spatial data used. Overall, studies have occurred in geographic regions proximal to developed countries and more commonly in terrestrial vs. aquatic habitats. Questions most often focused on effects of barriers and/or landscape variables on gene flow. The most commonly used molecular markers were microsatellites and amplified fragment length polymorphism (AFLPs), with AFLPs used more frequently in plants than animals. Analysis methods were dominated by Mantel and assignment tests. We also assessed differences among journals to evaluate the uniformity of reporting and publication standards. Few studies presented an explicit study design or explicit descriptions of spatial extent. While some landscape variables such as topographic relief affected most species studied, effects were not universal, and some species appeared unaffected by the landscape. Effects of habitat fragmentation were mixed, with some species altering movement paths and others unaffected. Taken together, although some generalities emerged regarding effects of specific landscape variables, results varied, thereby reinforcing the need for species-specific work. We conclude by: highlighting gaps in knowledge and methodology, providing guidelines to authors and reviewers of landscape genetics studies, and suggesting promising future directions of inquiry.


title: Clustering of the Self-Organizing Map

abstract: The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time.


title: Ultra-Wideband Power Divider With Good In-Band Splitting and Isolation Performances

abstract: An ultra-wideband (UWB) power divider on microstrip line is proposed, analyzed and designed. This divider is formed by installing a pair of stepped-impedance open-circuited stubs and parallel-coupled lines to two symmetrical output ports. In addition, a single resistor is properly placed between two output ports. After simple transmission line theory analysis, it is demonstrated that 3 dB power splitting from one input to two output ports, good impedance matching at all the three ports and excellent isolation between two output ports are achieved over the specified 3.1-10.6 GHz UWB range. Finally, a prototype divider is fabricated and measured to provide an experimental verification on the predicted attractive features.


title: Dimethyltryptamine (DMT): Prevalence, user characteristics and abuse liability in a large global sample

abstract: This paper presents original research on prevalence, user characteristics and effect profile of N,N-dimethyltryptamine (DMT), a potent hallucinogenic which acts primarily through the serotonergic s...


title: Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals

abstract: We present an unsupervised approach that generates a diverse, ranked set of bounding box and segmentation video object proposals—spatio-temporal tubes that localize the foreground objects—in an unannotated video. In contrast to previous unsupervised methods that either track regions initialized in an arbitrary frame or train a fixed model over a cluster of regions, we instead discover a set of easy-togroup instances of an object and then iteratively update its appearance model to gradually detect harder instances in temporally-adjacent frames. Our method first generates a set of spatio-temporal bounding box proposals, and then refines them to obtain pixel-wise segmentation proposals. We demonstrate state-of-the-art segmentation results on the SegTrack v2 dataset, and bounding box tracking results that perform competitively to state-of-the-art supervised tracking methods.


title: An innovative web-based collaborative platform for video annotation

abstract: Large scale labeled datasets are of key importance for the development of automatic video analysis tools as they, from one hand, allow multi-class classifiers training and, from the other hand, support the algorithms' evaluation phase. This is widely recognized by the multimedia and computer vision communities, as witnessed by the growing number of available datasets; however, the research still lacks in annotation tools able to meet user needs, since a lot of human concentration is necessary to generate high quality ground truth data. Nevertheless, it is not feasible to collect large video ground truths, covering as much scenarios and object categories as possible, by exploiting only the effort of isolated research groups. In this paper we present a collaborative web-based platform for video ground truth annotation. It features an easy and intuitive user interface that allows plain video annotation and instant sharing/integration of the generated ground truths, in order to not only alleviate a large part of the effort and time needed, but also to increase the quality of the generated annotations. The tool has been on-line in the last four months and, at the current date, we have collected about 70,000 annotations. A comparative performance evaluation has also shown that our system outperforms existing state of the art methods in terms of annotation time, annotation quality and system's usability.


title: Frequency-reconfigurable antennas for multiradio wireless platforms

abstract: Looking to increase the functionality of current wireless platforms and to improve their quality of service, we have explored the merits of using frequency-reconfigurable antennas as an alternative for multiband antennas. Our study included an analysis of various reconfigurable and multiband structures such as patches, wires, and combinations. Switches, such as radio-frequency microelectromechanical systems (RFMEMS) and p-i-n diodes, were also studied and directly incorporated onto antenna structures to successfully form frequency-reconfigurable antennas.


title: A Parasitic-Insensitive Charge Transfer Circuit for Capacitive Sensing using Active Output Voltage Feedback Technique

abstract: Abstract This paper introduces a new parasitic-insensitive charge transfer circuit using active output voltage feedback. The proposed parasitic-insensitive charge transfer circuit uses a passive charge transfer circuit for maximum output voltage dynamic range. A feedback loop between the transfer and output nodes is formed using a unity-gain opamp to make the two nodes’ potentials same, which prevents the charges stored at parasitic capacitors from transferring to the output. With those two characteristics, the proposed parasitic-insensitive charge transfer circuit offers full supply output voltage dynamic range for capacitive sensing without degradation of the linearity from the effects of parasitic components. The proposed parasitic-insensitive charge transfer circuit’s performances were simulated with standard 0.35μm CMOS technology and compared with those of a passive charge transfer circuit. It can be concluded from the results that the proposed parasitic-insensitive charge transfer circuit can be effectively used as a capacitive sensor with improved touch sensitivity for wider range of touch devices which include larger parasitic components.


title: Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning

abstract: Semi-supervised learning based on the low-density separation principle such as the cluster and manifold assumptions has been extensively studied in the last decades. However, such semi-supervised learning methods do not always perform well due to violation of the cluster and manifold assumptions. In this paper, we propose a novel approach to semi-supervised learning that does not require such restrictive assumptions. Our key idea is to combine learning from positive and negative data (standard supervised learning) and learning from positive and unlabeled data (PU learning), the latter is guaranteed to be able to utilize unlabeled data without the cluster and manifold assumptions. We theoretically and experimentally show the usefulness of our approach.


title: Transporter pharmacogenetics: transporter polymorphisms affect normal physiology, diseases, and pharmacotherapy.

abstract: Drug transporters mediate the movement of endobiotics and xenobiotics across biological membranes in multiple organs and in most tissues. As such, they are involved in physiology, development of disease, drug pharmacokinetics, and ultimately the clinical response to myriad medications. Genetic variants in transporters cause population-specific differences in drug transport and are responsible for considerable inter-individual variation in physiology and pharmacotherapy. The purpose of this review is to provide a broad overview of how inherited variants in transporters are associated with disease etiology, disease state, and the pharmacological treatment of diseases. Given that there are thousands of published papers related to the interplay between transporter genetics and medicine, this review will provide examples that exemplify the broader focus of the literature.


title: An Efficient Anonymous Scheme for Secure Micropayments

abstract: Micropayment systems allow payments of low value. Low cost for transaction is the main requirement for micropayments. For this reason, anonymity, a desired feature, is difficult to implement in micropayment systems. This paper presents an anonymous and untraceable micropayment system that offers low transactional costs while the use of secure protocols avoids financial risks. The efficient anonymity is achieved thanks to the use of a double spending prevention technique. Finally, the bank behavior can be verified.


title: Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output Networks

abstract: Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size. This makes them difficult to train, due to the limited memory of graphical processing units, and difficult to deploy on mobile devices with limited hardware. To address these difficulties, we propose Bloom embeddings, a compression technique that can be applied to the input and output of neural network models dealing with sparse high-dimensional binary-coded instances. Bloom embeddings are computationally efficient, and do not seriously compromise the accuracy of the model up to 1/5 compression ratios. In some cases, they even improve over the original accuracy, with relative increases up to 12%. We evaluate Bloom embeddings on 7 data sets and compare it against 4 alternative methods, obtaining favorable results. We also discuss a number of further advantages of Bloom embeddings, such as 'on-the-fly' constant-time operation, zero or marginal space requirements, training time speedups, or the fact that they do not require any change to the core model architecture or training configuration.


title: Frontomaxillary facial angle at 11+0 to 13+6 weeks' gestation-reproducibility of measurements.

abstract: OBJECTIVE ::: To assess the intra- and interobserver reproducibility in the measurement of the frontomaxillary facial (FMF) angle at 11+0 to 13+6 weeks' gestation and to investigate the effect of deviations from the exact mid-sagittal view on these measurements. ::: ::: ::: METHODS ::: Three-dimensional (3D) volumes of the fetal face were used by two operators to measure the FMF angle in 50 chromosomally normal and 50 trisomy 21 fetuses. The measurements were taken in the exact mid-sagittal view and repeated after lateral rotation of the head by 5 degrees, 10 degrees and 15 degrees away from the vertical position of the occipitofrontal diameter axis. Mean difference and 95% limits of agreement between paired measurements of FMF angle by the same and by two different sonographers were determined. ::: ::: ::: RESULTS ::: In the mid-sagittal plane the maxillary bone was rectangular shaped. Rotation away from this plane became easily recognizable because at a mean of 7 degrees (range, 4-10 degrees) the shape of the maxilla changed with the appearance of the zygomatic process of the maxilla and at a mean of 8 degrees (range, 4-12 degrees) the tip of the nose became invisible. In both the normal and trisomy 21 fetuses the FMF angle measured at 5-15 degrees was not significantly different from the one measured in the mid-sagittal plane. In 95% of the cases, the difference between paired measurements of the FMF angle by the same sonographer at the mid-sagittal plane was between -2.3 degrees and 3.0 degrees and at 15 degrees it was -1.0 degrees to 6.8 degrees. At the mid-sagittal plane, the difference in measurements between two sonographers was -3.1 to 3.0 degrees. ::: ::: ::: CONCLUSION ::: The landmarks that define the mid-sagittal plane of the fetal face are the tip of the nose and the rectangular shaped maxilla. Measurement of the FMF angle is highly reproducible.


title: Micro-Baseline Stereo

abstract: Tradeoffs exist between the baseline or distance between cameras and the difficulty of matching corresponding points in stereo and structure from motion. Smaller baselines result in reduced disparities reducing the accuracy of depth estimation. Larger baselines increase the range of observed disparities, but also increase the difficulty of finding corresponding points. In this paper, we explore the use of very small baselines, called micro-baselines. Microbaselines, typically just a few millimeters, provide the advantage that they can be captured using a single camera. That is, a “static” camera that is either hand-held or mounted on a tripod will typically vibrate some small amount while capturing video. We take advantage of the vibrating motion to compute depth information. For hand-held cameras a small amount of motion is generally always present, while many surveillance applications involve cameras mounted outside or on high poles that exhibit this type of motion. Even indoor cameras mounted on tripods move due to human traffic and machine vibrations.


title: Simple Triplet Loss Based on Intra/Inter-Class Metric Learning for Face Verification

abstract: Recently, benefiting from the advances of the deep convolution neural networks (CNNs), significant progress has been made in the field of the face verification and face recognition. Specially, the performance of the FaceNet has overpassed the human level performance in terms of the accuracy on the datasets "Labeled Faces in the Wild (LFW)" and "Youtube Faces in the Wild (YTF)". The triplet loss used in the FaceNet has proved its effectiveness for face verification. However, the number of the possible triplets is explosive when using a large scale dataset to train the model. In this paper, we propose a simple class-wise triplet loss based on the intra/inter-class distance metric learning which can largely reduce the number of the possible triplets to be learned. However the simplification of the classic triplet loss function has not degraded the performance of the proposed approach. The experimental evaluations on the most widely used benchmarks LFW and YTF show that the model with the proposed class-wise simple triplet loss can reach the state-of-the-art performance. And the visualization of the distribution of the learned features based on the MNIST dataset has also shown the effectiveness of the proposed method to better separate the classes and make the features more discriminative in comparison with the other state-of-the-art loss function.


title: A Novel Dual-Feed Slot-Coupling Feeding Technique for Circularly Polarized Patch Arrays

abstract: In this letter, a novel slot-coupling feeding technique has been adopted to realize a circularly polarized (CP) 2 × 2 microstrip array. Each array element is fed through two microstrip lines that are excited 90° out of phase (dual-feed technique) and coupled to a square patch by means of a square-ring slot realized in the feeding network ground plane. Design procedure, simulation results, and measurement data are presented for a 2 × 2 array working in the WiMax 3.3-3.8 GHz frequency band (14% percentage bandwidth). Due to both the symmetry properties of the novel slot-coupling feeding configuration and the implementation of a sequential rotation technique, excellent axial ratio (AR) performance is achieved in the WiMax band (AR < 1.35 dB at broadside) and for any direction in the antenna main beam (AR < 2.25 dB at 3.55 GHz). Actually, the 3-dB AR bandwidth is larger than the WiMax frequency band, as it goes up to about 30%.


title: Extrinsic 6DoF calibration of 3D LiDAR and radar

abstract: Environment perception is a key component of any autonomous system and is often based on a heterogeneous set of sensors and fusion thereof, for which extrinsic sensor calibration plays fundamental role. In this paper, we tackle the problem of 3D LiDAR-radar calibration which is challenging due to low accuracy and sparse informativeness of the radar measurements. We propose a complementary calibration target design suitable for both sensors, thus enabling a simple, yet reliable calibration procedure. The calibration method is composed of correspondence registration and a two-step optimization. The first step, reprojection error based optimization, provides initial estimate of the calibration parameters, while the second step, field of view optimization, uses additional information from the radar cross section measurements and the nominal field of view to refine the parameters. In the end, results of the experiments validated the proposed method and demonstrated how the two steps combined provide an improved estimate of extrinsic calibration parameters.


title: Awareness 2.0: staying aware of projects, developers and tasks using dashboards and feeds

abstract: Software development teams need to maintain awareness of various different aspects ranging from overall project status and process bottlenecks to current tasks and incoming artifacts. Currently, there is a lack of theoretical foundations to guide tool selection and tool design to best support awareness tasks. In this paper, we explore how the combination of highly configurable project, team and contributor dashboards along with individual event feeds is used to accomplish extensive awareness. Our results stem from an empirical study of several large development teams, with a detailed study of a team of 150 developers and additional data from another four project teams. We present how dashboards become pivotal to task prioritization in critical project phases and how they stir competition while feeds are used for short term planning. Our findings indicate that the distinction between high-level and low-level awareness is often unclear and that integrated tooling could improve development practices.


Group Linguistic Bias Aware Neural Response Generation


title: Comparative study of two 3-CRU translational parallel manipulators

abstract: Two types of 3-CRU translational parallel manipulators (TPM) are compared and investigated in this paper. The two 3-CRU TPMs have identical kinematics, but some differences exist in terms of the chain arrangement, one is in fully symmetrical chain arrangement, called symmetrical 3-CRU TPM, the other one is in asymmetrical chain arrangement, called asymmetrical 3-CRU TPM. This paper focuses on discussing the differences between the two 3-CRU TPMs in kinematics, workspace and performance. This study provides insights into parallel manipulators with identical kinematics arranged differently.


title: A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

abstract: Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed. In order to address this need, we present an extensive review and performance evaluation of single- and multi-shot re-id algorithms. The experimental protocol incorporates the most recent advances in both feature extraction and metric learning. To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques. All approaches were evaluated using a new large-scale dataset that closely mimics a real-world problem setting, in addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR, DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03, RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will be made publicly available for community use.


Explorer Beyond a simple “ yes ” and “ no ”


title: Artificial muscle technology: physical principles and naval prospects

abstract: The increasing understanding of the advantages offered by fish and insect-like locomotion is creating a demand for muscle-like materials capable of mimicking nature's mechanisms. Actuator materials that employ voltage, field, light, or temperature driven dimensional changes to produce forces and displacements are suggesting new approaches to propulsion and maneuverability. Fundamental properties of these new materials are presented, and examples of potential undersea applications are examined in order to assist those involved in device design and in actuator research to evaluate the current status and the developing potential of these artificial muscle technologies. Technologies described are based on newly explored materials developed over the past decade, and also on older materials whose properties are not widely known. The materials are dielectric elastomers, ferroelectric polymers, liquid crystal elastomers, thermal and ferroelectric shape memory alloys, ionic polymer/metal composites, conducting polymers, and carbon nanotubes. Relative merits and challenges associated with the artificial muscle technologies are elucidated in two case studies. A summary table provides a quick guide to all technologies that are discussed.


title: Pragmatic application of a clinical prediction rule in primary care to identify patients with low back pain with a good prognosis following a brief spinal manipulation intervention

abstract: BackgroundPatients with low back pain are frequently encountered in primary care. Although a specific diagnosis cannot be made for most patients, it is likely that sub-groups exist within the larger entity of nonspecific low back pain. One sub-group that has been identified is patients who respond rapidly to spinal manipulation. The purpose of this study was to examine the association between two factors (duration and distribution of symptoms) and prognosis following a spinal manipulation intervention.MethodsData were taken from two previously published studies. Patients with low back pain underwent a standardized examination, including assessment of duration of the current symptoms in days, and the distal-most distribution of symptoms. Based on prior research, patients with symptoms of <16 days duration and no symptoms distal to the knee were considered to have a good prognosis following manipulation. All patients underwent up to two sessions of spinal manipulation treatment and a range of motion exercise. Oswestry disability scores were recorded before and after treatment. If ≥ 50% improvement on the Oswestry was achieved, the intervention was considered a success. Sensitivity, specificity, and positive likelihood ratio were calculated for the association of the two criteria with the outcome of the treatment.Results141 patients (49% female, mean age = 35.5 (± 11.1) years) participated. Mean pre- and post-treatment Oswestry scores were 41.9 (± 10.9) and 24.1 (± 14.2) respectively. Sixty-three subjects (45%) had successful treatment outcomes. The sensitivity of the two criteria was 0.56 (95% CI: 0.43, 0.67), specificity was 0.92 (95% CI: 0.84, 0.96), and the positive likelihood ratio was 7.2 (95% CI: 3.2, 16.1).ConclusionThe results of this study demonstrate that two factors; symptom duration of less than 16 days, and no symptoms extending distal to the knee, were associated with a good outcome with spinal manipulation.


No-Reference Image Quality Assessment using Blur and Noise


title: SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

abstract: For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer’s attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.


title: Techniques for obtaining high performance in Java programs

abstract: This survey describes research directions in techniques to improve the performance of programs written in the Java programming language. The standard technique for Java execution is interpretation, which provides for extensive portability of programs. A Java interpreter dynamically executes Java bytecodes, which comprise the instruction set of the Java Virtual Machine (JVM). Execution time performance of Java programs can be improved through compilation, possibly at the expense of portability. Various types of Java compilers have been proposed, including Just-In-Time (JIT) compilers that compile bytecode into native processor instructions on the fly; direct compilers that directly translate the Java source code into the target processor's native language; and bytecode-to-source translators that generate either native code or an intermediate language, such as C, from the bytecodes. Additional techniques, including bytecode optimization, dynamic compilation, and executing Java programs in parallel, attempt to improve Java run-time performance while maintaining Java's portability. Another alternative for executing Java programs is a Java processor that implements the JVM directly in hardware. In this survey, we discuss the basis features, and the advantages and disadvantages, of the various Java execution techniques. We also discuss the various Java benchmarks that are being used by the Java community for performance evaluation of the different techniques. Finally, we conclude with a comparison of the performance of the alternative Java execution techniques based on reported results.


title: Lexical Chains as Document Features

abstract: Document clustering and classiﬁcation is usually done by representing the documents using a bag of words scheme. This scheme ignores many of the linguistic and semantic features contained in text documents. We propose here an alternative representation for documents using Lexical Chains. We compare the performance of the new representation against the old one on a clustering task. We show that Lexical Chain based features give better results than the Bag of Words based features, while achieving almost 30% reduction in the dimensionality of the feature vectors resulting in faster execution of the algorithms.


A Survey on Identification of Motifs and Ontology in Medical Database


title: Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images

abstract: In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.


title: A Quantitative and Qualitative Assessment of Automatic Text Summarization Systems

abstract: Text summarization is the process of automatically creating a shorter version of one or more text documents. This paper presents a qualitative and quantitative assessment of the 22 state-of-the-art extractive summarization systems using the CNN corpus, a dataset of 3,000 news articles.


title: Visual analytics for supporting manufacturers and distributors in online sales.

abstract: This article presents the basic concepts of OPTOSA, a Visual Analytics solution for the optimization of online sales, designed to support manufacturers in all phases of the online sales process from the product specification to the price fixing and more. OPTOSA combines a data processing module that builds and constantly updates operational knowledge related to sales positioning with a decision assistant that uses relevant aspects of the knowledge for helping the tasks of the different teams along the integrated chain of the sales. After reviewing the challenges of the approach, we discuss the first significant experiments with OPTOSA on some formalized use-cases.


title: α-Amylase, a flour additive: An important cause of protein contact dermatitis in bakers

abstract: Background: α-Amylase, an enzyme commonly used in flour additives, has been reported to be an important cause of rhinitis and asthma in bakers. Objective: Our purpose was to determine whether this enzyme could also cause dermatitis. We tested it routinely in bakers with hand eczema. Methods: Patch tests were administered with the International Contact Dermatitis Research Group standard series and a bakery series and scratch-chamber or prick tests were performed with the bakers' own material and with α-amylase powder. Results: Of 32 bakers tested, seven had an immediate wheal-and-flare reaction and two also had a delayed eczematous reaction. High dilutions of the α-amylase powder still gave strong reactions. Conclusion: α-Amylase is an important cause of skin reactions in bakers and should be tested routinely if a contact allergy is suspected.


title: Dual-state parametric eye tracking

abstract: Most eye trackers work well for open eyes. However blinking is a physiological necessity for humans. More over, for applications such as facial expression analysis and driver awareness systems, we need to do more than tracking of the locations of the person's eyes but obtain their detailed description. We need to recover the state of the eyes (i.e., whether they are open or closed), and the parameters of an eye model (e.g., the location and radius of the iris, and the corners and height of the eye opening). We develop a dual-state model-based system for tracking eye features that uses convergent tracking techniques and show how it can be used to detect whether the eyes are open or closed, and to recover the parameters of the eye model. Processing speed on a Pentium II 400 MHz PC is approximately 3 frames/second. In experimental tests on 500 image sequences from child and adult subjects with varying colors of skin and eye, accurate tracking results are obtained in 98% of image sequences.


title: Ellipse Detection in Digital Image Data Using Geometric Features

abstract: Ellipse detection is an important task in vision based systems because many real world objects can be described by this primitive. This paper presents a fast data driven four stage filtering process which uses geometric features in each stage to synthesize ellipses from binary image data with the help of lines, arcs, and extended arcs. It can cope with partially occluded and overlapping ellipses, works fast and accurate and keeps memory consumption to a minimum.


title: Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers

abstract: Ensuring the quality of service (QoS) for latency-sensitive applications while allowing co-locations of multiple applications on servers is critical for improving server utilization and reducing cost in modern warehouse-scale computers (WSCs). Recent work relies on static profiling to precisely predict the QoS degradation that results from performance interference among co-running applications to increase the number of "safe" co-locations. However, these static profiling techniques have several critical limitations: 1) a priori knowledge of all workloads is required for profiling, 2) it is difficult for the prediction to capture or adapt to phase or load changes of applications, and 3) the prediction technique is limited to only two co-running applications. To address all of these limitations, we present Bubble-Flux, an integrated dynamic interference measurement and online QoS management mechanism to provide accurate QoS control and maximize server utilization. Bubble-Flux uses a Dynamic Bubble to probe servers in real time to measure the instantaneous pressure on the shared hardware resources and precisely predict how the QoS of a latency-sensitive job will be affected by potential co-runners. Once "safe" batch jobs are selected and mapped to a server, Bubble-Flux uses an Online Flux Engine to continuously monitor the QoS of the latency-sensitive application and control the execution of batch jobs to adapt to dynamic input, phase, and load changes to deliver satisfactory QoS. Batch applications remain in a state of flux throughout execution. Our results show that the utilization improvement achieved by Bubble-Flux is up to 2.2x better than the prior static approach.


title: Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?

abstract: The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.


title: T*: A data-centric cooling energy costs reduction approach for Big Data analytics cloud

abstract: Explosion in Big Data has led to a surge in extremely large-scale Big Data analytics platforms, resulting in burgeoning energy costs. Big Data compute model mandates strong data-locality for computational performance, and moves computations to data. State-of-the-art cooling energy management techniques rely on thermal-aware computational job placement/migration and are inherently data-placement-agnostic in nature. T* takes a novel, data-centric approach to reduce cooling energy costs and to ensure thermal-reliability of the servers. T* is cognizant of the uneven thermal-profile and differences in thermal-reliability-driven load thresholds of the servers, and the differences in the computational jobs arrival rate, size, and evolution life spans of the Big Data placed in the cluster. Based on this knowledge, and coupled with its predictive file models and insights, T* does proactive, thermal-aware file placement, which implicitly results in thermal-aware job placement in the Big Data analytics compute model. Evaluation results with one-month long real-world Big Data analytics production traces from Yahoo! show up to 42% reduction in the cooling energy costs with T* courtesy of its lower and more uniform thermal-profile and 9x better performance than the state-of-the-art data-agnostic cooling techniques.


title: Query Expansion with Locally-Trained Word Embeddings

abstract: Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships. We study the use of term relatedness in the context of query expansion for ad hoc information retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when trained globally, underperform corpus and query specific embeddings for retrieval tasks. These results suggest that other tasks benefiting from global embeddings may also benefit from local embeddings.


title: Cassandra and SQL database comparison for near real-time Twitter data warehouse

abstract: In the era of Big Data, social media analysis has grown extremely popular. Twitter, one of the most popular social media, is believed to contain many user opinion in its message. Thus, leading organizations start utilizing its data using social media analytic tools to get in-sight of their markets in real-time. However, many Twitter analytic tools are still specified only in some specific tasks. Therefore, in order to enhance the possibility of doing many analysis on Twitter, a data warehouse technology can be utilized to receive, process, and store a real-time Twitter streams. Nonetheless, data warehouse development using relational database start to show its limit on storing big data let alone real-time. Hence, NoSQL (Not Only SQL) technology has emerged as an alternative solution. In this paper, we try to develop near real-time Twitter data warehouse using NoSQL database, Cassandra, and compare its storing and querying performance with that developed using relational databases. The results show that Cassandra performs significantly better in storing data than the relational databases. Meanwhile, in its querying performance, Cassandra is slower while using small data but way faster on vast data.


title: Rumination and overgeneral memory in depression: effects of self-focus and analytic thinking.

abstract: Previous research has shown that, compared with a rumination induction, a brief distraction procedure reduces overgeneral autobiographical memory in depression. The authors investigated whether this effect depends on reductions in analytic thinking or reductions in self-focus. Focus of attention (high vs. low self-focus) and thinking style (high vs. low analytical thinking) were independently manipulated in depressed patients in a 2 x 2 design. Autobiographical recall was measured pre- and postmanipulation. Thinking style significantly affected overgeneral memory, whereas focus of attention significantly affected despondent mood. Reducing analytical self-focus reduced overgeneral memory, suggesting that high levels of naturally occurring ruminative analytic thinking may be important in the maintenance of overgeneral memory. Overgeneral memory in depression may be associated with chronic ruminative attempts to make sense of current or past difficulties.


title: A mobile-based home automation system

abstract: The rapidly advancing mobile communication technology and the decrease in costs make it possible to incorporate mobile technology into home automation systems. We propose a mobile-based home automation system that consists of a mobile phone with Java capabilities, a cellular modem, and a home server. The home appliances are controlled by the home server, which operates according to the user commands received from the mobile phone via the cellular modem. In our proposed system the home server is built upon an SMS/GPRS (short message service/general packet radio service) mobile cell module and a microcontroller, allowing a user to control and monitor any variables related to the home by using any Java capable cell phone. This paper presents the design and implementation of AT modem driver, text based command processing software, and power failure resilient output for a microcontroller to facilitate in sending and receiving data via the cell module, together with the design of Java application to enable the cell phone to send commands and receive alerts through the cell module


title: UX Heatmaps: Mapping User Experience on Visual Interfaces

abstract: In this paper, we present an off-the-shelf UX evaluation tool which contextualizes users' physiological and behavioral signals while interacting with a system. The proposed tool triangulates users' gaze data with inferred users' cognitive and emotional states to produce user experience (UX) heatmaps, which show where users were looking when they experienced specific cognitive and emotional states. Results show that for a given cognitive state (i.e., cognitive load), the proposed UX heatmap was able to effectively highlight the areas where users experienced different levels of cognitive load on an interface. The proposed tool enables the visual analysis of users' various emotional and cognitive states for specific areas on a given interface, and also to compare users' states across multiple interfaces, which should be useful for both UX researchers and practitioners.


title: Large-Scale Online Feature Selection for Ultra-High Dimensional Sparse Data

abstract: Feature selection (FS) is an important technique in machine learning and data mining, especially for large-scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, we present a novel second-order OFS algorithm that is simple yet effective, very fast and extremely scalable to deal with large-scale ultra-high dimensional sparse data streams. The basic idea is to exploit the second-order information to choose the subset of important features with high confidence weights. Unlike existing OFS methods that often suffer from extra high computational cost, we devise a novel algorithm with a MaxHeap-based approach, which is not only more effective than the existing first-order algorithms, but also significantly more efficient and scalable. Our extensive experiments validated that the proposed technique achieves highly competitive accuracy as compared with state-of-the-art batch FS methods, meanwhile it consumes significantly less computational cost that is orders of magnitude lower. Impressively, on a billion-scale synthetic dataset (1-billion dimensions, 1-billion non-zero features, and 1-million samples), the proposed algorithm takes less than 3 minutes to run on a single PC.


title: Automatic speech recognition and speech variability: A review

abstract: Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge. ::: ::: Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.