title: Learning Race from Face: A Survey

abstract: Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.


title: Patterns of Self-Organizing Agile Security for Resilient Network Situational Awareness and Sensemaking

abstract: The security gap is widening as adversarial communities of all kinds employ more sophisticated techniques and broaden their targets of opportunity. These adversarial communities are structured as self organized proactive collaborators in tight learning loops driving rapid innovation -- preying upon systems protected by wait and see strategies. In this paper we build upon six fundamental characteristics common to adversarial success, suggest that winning security can employ the same characteristics, show two patterns appropriate for resilient network support that fit this criteria, and describe a larger project that is developing a pattern language of next generation agile security. The two patterns described in this paper are modeled on the Biological Immune Systems and on mammalian hierarchical cortical sense making architecture. A technology that appears capable of implementing these patterns in relatively high biological fidelity is briefly introduced to support pattern-employment feasibility.


title: Traffic Estimation And Prediction Based On Real Time Floating Car Data

abstract: The knowledge of the actual current state of the road traffic and its short-term evolution for the entire road network is a basic component of ATIS (advanced traveler information systems) and ATMS (advanced traffic management system) applications. In this view the use of real-time floating-car data (FCD), based on traces of GPS positions, is emerging as a reliable and cost-effective way to gather accurate travel times/speeds in a road network and to improve short-term predictions of travel conditions. The purpose of this paper is to present a large-scale working application of FCD-system, developed and operated by OCTOTelematics, delivering real-time traffic speed information throughout the Italian motorway network and along some important arterial streets located in major Italian metropolitan areas. Traffic speed estimates are deduced at an interval of 3 minutes from GPS traces transmitted in real-time from a large number (and still growing) of privately owned cars (about 600.000) equipped with a specific device covering a range of insurance-related applications. This paper also proposes two algorithms, respectively based on artificial neural networks and pattern-matching, designed to on-line perform short-term (15 to 30 minutes) predictions of link travel speeds by using current and near-past link average speeds estimated by the OCTOTelematics FCD system. The Rome ring road (GRA-Grande Raccordo Anulare) was used for testing the feasibility of the two algorithms. Testing results showed that the proposed approaches for short-term predictions are very promising and effective.


title: Multifunctional Coreless Hall-Effect Current Transformer for the Protection and Measurement of Power Systems

abstract: This paper presents a multifunctional coreless current transformer based on the Hall effect (multifunctional HCT), and designs its actual electronic circuit that can discriminate load and fault currents to perform both metering and protective functions. The multifunctional HCT has two primary circuits: 1) distinguishes the load current from the fault current and 2) delivers the current value using an analog multiplexer. This HCT can eliminate the 2.5±0.05 V offset voltage of commercial Hall sensors. It possesses two amplifiers with different gains. Therefore, this multifunctional HCT accurately measures power currents during the operations in measurement systems and protection systems. Using a low-pass filter, the multifunctional HCT is not only capable of reducing noise interference, but also improves the accuracy of power current measurements. The experimental results show that the multifunctional HCT can exhibit an accuracy class of 0.5 for both the measurement and protection of power systems within IEC standard 60044-8. Because the proposed multifunctional HCT does not have core and saturation problems, the accuracy class and limit can achieve the standard accuracy limit factor grade of 63.


title: Evolutionary affinity of billfishes (Xiphiidae and Istiophoridae) and flatfishes (Plueronectiformes): Independent and trans-subordinal origins of endothermy in teleost fishes.

abstract: Billfishes (Scombroidei) and tunas (Scombridae), both considered part of the suborder Scombroidei, have long been studied by biologists largely because of their remarkable physiological and anatomical muscular adaptations associated with regional endothermy and continuous swimming. These attributes, combined with analyses of other morphological and molecular data, have led to a general perception that tunas and billfishes are close relatives, though this hypothesis has been vigorously debated. Using Bayesian phylogenetic analysis of nine mitochondrial and three nuclear loci (>7000bp), we show that billfishes are only distantly related to tunas, but rather share strong evolutionary affinities with flatfishes (Pleuronectiformes) and jacks (Carangidae). This phylogenetic relationship is striking because of the marked variation in phenotype and niche across these trans-ordinal groups of fishes. Billfishes and flatfishes have each evolved radically divergent morphological and physiological features: elongated bills and extraocular heater organs in billfishes, and cranial asymmetry with complete eye migration during ontogenetic development in flatfishes. Despite this divergence, we identify synapomorphies consistent with the hypothesis of a common billfish/flatfish/jack ancestor.


title: Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards

abstract: We consider an infinite-armed bandit problem with Bernoulli rewards. The mean rewards are independent, uniformly distributed over [0,1]. Rewards 0 and 1 are referred to as a success and a failure, respectively. We propose a novel algorithm where the decision to exploit any arm is based on two successive targets, namely, the total number of successes until the first failure and until the first m failures, respectively, where m is a fixed parameter. This two-target algorithm achieves a long-term average regret in √2n for a large parameter m and a known time horizon n. This regret is optimal and strictly less than the regret achieved by the best known algorithms, which is in 2√n. The results are extended to any mean-reward distribution whose support contains 1 and to unknown time horizons. Numerical experiments show the performance of the algorithm for finite time horizons.


title: Mindfulness-based stress reduction: A literature review and clinician’s guide

abstract: Purpose: To provide nurse practitioners (NPs) with clinical research about Mindfulness-Based Stress Reduction (MBSR) and demonstrate its usefulness for reducing stress in a variety of populations. DataSources: A literature review was conducted using the following databases: EBSCO, Cinahl, Pschyline, and Medline. English language articles published between 2000 and 2006 in peer-reviewed journals were reviewed. Search terms ‘‘mindfulness,’’ ‘‘meditation,’’ and ‘‘stress’’ were used. Additional information was obtained through select, reputable Internet sites. Conclusions: MBSR is an effective treatment for reducing stress and anxiety that accompanies daily life and chronic illness. MBSR is also therapeutic for healthcare providers, enhancing their interactions with patients. No negative side effects from MBSR have been documented. Implications for practice: MBSR is a safe, effective, integrative approach for reducing stress. Patients and healthcare providers experiencing stress or stressrelated symptoms benefit from MBSR programs. NPs can safely and effectively use this intervention in a variety of patient populations.


title: Missing Modalities Imputation via Cascaded Residual Autoencoder

abstract: Affordable sensors lead to an increasing interest in acquiring and modeling data with multiple modalities. Learning from multiple modalities has shown to significantly improve performance in object recognition. However, in practice it is common that the sensing equipment experiences unforeseeable malfunction or configuration issues, leading to corrupted data with missing modalities. Most existing multi-modal learning algorithms could not handle missing modalities, and would discard either all modalities with missing values or all corrupted data. To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities. Specifically, we propose a novel Cascaded Residual Autoencoder (CRA) to impute missing modalities. By stacking residual autoencoders, CRA grows iteratively to model the residual between the current prediction and original data. Extensive experiments demonstrate the superior performance of CRA on both the data imputation and the object recognition task on imputed data.


title: A 10 mm3 Inductive Coupling Radio for Syringe-Implantable Smart Sensor Nodes

abstract: We present a near-field radio system for a millimeter-scale wireless smart sensor node that is implantable through a 14-gauge syringe needle. The proposed system integrates a radio system on chip and a magnetic antenna on a glass substrate within a total dimension of $1 \times 1 \times 10$ mm3. We demonstrate energy-efficient active near-field wireless communication between the millimeter-scale sensor node and a base station device through an RF energy-absorbing tissue. The wireless transceiver, digital baseband controller, wakeup controller, on-chip baseband timer, sleep timer, and MBUS controller are all integrated on the SoC to form a millimeter-scale sensor node, together with a $1 \times 8$ mm2 magnetic antenna fabricated with a 1.5- $\mu \text{m}$ -thickness gold on a 100 $\mu \text{m}$ -thickness glass substrate. An asymmetric link is established pairing the sensor antenna with a codesigned $11\times 11$ cm2 base station antenna to achieve a link distance of up to 50 cm for sensor transmission and 20 cm for sensor reception. The transmitter consumes a 43.5 $\mu \text{W}$ average power at 2 kb/s, while the receiver power consumption is 36 $\mu \text{W}$ with a −54 dBm sensitivity at 100 kb/s. When powered by a $1 \times 2.2$ mm2 thin-film battery (2 $\mu $ Ah, 4.1 V), the designed system has a two week expected lifetime without battery recharging when the system wakes up and transmits and receives 16 b data every 10 min.


title: 4-QAM gray coding for deep neural network based decoder training

abstract: We propose a gray coding method for deep neural network (DNN) based decoder. With multiple resources considered together, DNN can be used to decode corrupted signals. In deep learning training, stochastic gradient descent (SGD) algorithm is used, which means that the cost function must be differentiable. Then, allocating the discrete bits for each symbol is difficult. To solve this problem, the basic gray coding for 4-quadrature amplitude modulation (QAM) is investigated for deep learning training. The performance of the proposed scheme is evaluated by simulation result compared with non-gray coding scheme. The symbol error rate (SER) performance is shown to be equivalent, but the bit error rate (BER) performance of the proposed scheme is shown to be better, which implies the gray coding is successfully done.


Language may indeed influence thought


title: A universal completion of the ZX-calculus

abstract: In this paper, we give a universal completion of the ZX-calculus for the whole of pure qubit quantum mechanics. This proof is based on the completeness of another graphical language: the ZW-calculus, with direct translations between these two graphical systems.


title: Splatting indirect illumination

abstract: In this paper we present a novel method for plausible real-time rendering of indirect illumination effects for diffuse and non-diffuse surfaces. The scene geometry causing indirect illumination is captured by an extended shadow map, as proposed in previous work, and secondary light sources are distributed on directly lit surfaces. One novelty is the rendering of these secondary lights' contribution by splatting in a deferred shading process, which decouples rendering time from scene complexity. An importance sampling strategy, implemented entirely on the GPU, allows efficient selection of secondary light sources. Adapting the light's splat shape to surface glossiness also allows efficient rendering of caustics. Unlike previous approaches the approximated indirect lighting does barely exhibit coarse artifacts - even under unfavorable viewing and lighting conditions. We describe an implementation on contemporary graphics hardware, show a comparison to previous approaches, and present adaptation to and results in game-typical applications.


title: Health Impacts of Yoga and Pranayama: A State-of-the-Art Review

abstract: Thousands of years ago yoga originated in India, and in present day and age, an alarming awareness was observed in health and natural remedies among people by yoga and pranayama which has been proven an effective method for improving health in addition to prevention and management of diseases. With increasing scientific research in yoga, its therapeutic aspects are also being explored. Yoga is reported to reduce stress and anxiety, improves autonomic functions by triggering neurohormonal mechanisms by the suppression of sympathetic activity, and even, now‑a‑days, several reports suggested yoga is beneficial for physical health of cancer patients. Such global recognition of yoga also testifies to India’s growing cultural influence.


title: Photocurrent in graphene harnessed by tunable intrinsic plasmons

abstract: Graphene's optical properties in the infrared and terahertz can be tailored and enhanced by patterning graphene into periodic metamaterials with sub-wavelength feature sizes. Here we demonstrate polarization sensitive and gate tunable photodetection in graphene nanoribbon arrays. The long-lived hybrid plasmon-phonon modes utilized are coupled excitations of electron density oscillations and substrate (SiO2) surface polar phonons. Their excitation by s-polarization leads to an in-resonance photocurrent an order of magnitude larger than the photocurrent observed for p-polarization, which excites electron-hole pairs. The plasmonic detectors exhibit photoinduced temperature increases up to four times as large as comparable 2D graphene detectors. Moreover, the photocurrent sign becomes polarization sensitive in the narrowest nanoribbon arrays due to differences in decay channels for photoexcited hybrid plasmon-phonons and electrons. Our work provides a path to light sensitive and frequency selective photodetectors based on graphene's plasmonic excitations.


title: Assistive technologies for cognitive disabilities

abstract: Purpose: To provide a comprehensive review of assistive technology (AT) to offset cognitive impairment, including examples, with pros and cons and important considerations for AT selection. Method: Prior research and a literature review identified the critical need for a means to identify key elements known to influence the successful use of AT and other supports by persons with cognitive disabilities. Results: The components of effective and satisfied AT use result from a good match among device features, user goals and preferences, and environmental resources (including trained professionals and providers). Conclusions: As the number of AT options increase, individualized interventions for individuals with cognitive disabilities will be easier to accomplish. The key to successful and optimal use of these products will be a comprehensive yet individualized determination of consumer needs and preferences and the identification of additional accommodations and supports.


Designing Context Sensitive Mobile User Interface


title: Relations among media influence, body image, eating concerns, and sexual orientation in men: A preliminary investigation.

abstract: The current study explored the relation between sexual orientation, media persuasion, and eating and body image concerns among 78 college men (39 gay; 39 straight). Participants completed measures of sexual orientation, eating disorder symptoms, appearance-related anxiety, perceived importance of physical attractiveness, perceptions of media influence, and media exposure. Gay men scored significantly higher on drive for thinness, body dissatisfaction, and body image-related anxiety than their straight counterparts. Additionally, perceptions of media influence were higher for gay men, and significantly mediated the relation between sexual orientation and eating and body image concerns. Sexual orientation also moderated the relation between perceived media influence and beliefs regarding the importance of physical attractiveness, as this relation was significant for gay men, but not straight men. The current findings suggest that gay men's increased vulnerability to media influence partially accounts for the relatively high rate of eating pathology observed in this population.


title: Valuing American options by simulation: A simple least-squares approach

abstract: This article presents a simple yet powerful new approach for approximating the value of American options by simulation. The key to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniqes cannot be used. We illustrate this technique with several realistic examples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an American swaption in a 20-factor string model of the term structure.


title: Bendtroller:: An Exploration of In-Game Action Mappings with a Deformable Game Controller

abstract: We explore controller input mappings for games using a deformable prototype that combines deformation gestures with standard button input. In study one, we tested discrete gestures using three simple games. We categorized the control schemes as binary (button only), action, and navigation, the latter two named based on the game mechanics mapped to the gestures. We found that the binary scheme performed the best, but gesture-based control schemes are stimulating and appealing. Results also suggest that the deformation gestures are best mapped to simple and natural tasks. In study two, we tested continuous gestures in a 3D racing game using the same control scheme categorization. Results were mostly consistent with study one but showed an improvement in performance and preference for the action control scheme.


title: Effect of the Dielectric Constant of the Surrounding Medium and the Substrate on the Surface Plasmon Resonance Spectrum and Sensitivity Factors of Highly Symmetric Systems: Silver Nanocubes

abstract: Silver nanocubes (AgNCs), 60 nm, have four extinction surface plasmon resonance (SPR) peaks. The finite difference time domain (FDTD) simulation method is used to assign the absorption and scattering peaks and also to calculate the plasmon field intensity for AgNCs. Because AgNCs have a highly symmetric cubic shape, there is a uniform distribution of the plasmon field around them, and they are thus sensitive to asymmetric dielectric perturbations. When the dielectric medium around a nanoparticle is changed anisotropically, either by placing the particle on a substrate or by coating it asymmetrically with a solvent, the plasmon field is distorted, and the plasmonic absorption and scattering spectra could shift differently. For the 60 nm AgNC, we found that the scattering resonance peak shifted more than the absorption peak. This changes the extinction bandwidth of these overlapping absorption and scattering bands, and consequently the figure of merit of the nanoparticle, as a localized SPR sensor, no longe...


title: Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?

abstract: Qualitative research methods are enjoying unprecedented popularity. Although checklists have undoubtedly contributed to the wider acceptance of such methods, these can be counterproductive if used prescriptively. The uncritical adoption of a range of “technical fixes” (such as purposive sampling, grounded theory, multiple coding, triangulation, and respondent validation) does not, in itself, confer rigour. ::: ::: In this article I discuss the limitations of these procedures and argue that there is no substitute for systematic and thorough application of the principles of qualitative research. Technical fixes will achieve little unless they are embedded in a broader understanding of the rationale and assumptions behind qualitative research. ::: ::: #### Summary points ::: ::: Checklists can be useful improving qualitative research methods, but overzealous and uncritical use can be counterproductive ::: ::: Reducing qualitative research to a list of technical procedures (such as purposive sampling, grounded theory, multiple coding, triangulation, and respondent validation) is overly prescriptive and results in “the tail wagging the dog” ::: ::: None of these “technical fixes” in itself confers rigour; they can strengthen the rigour of qualitative research only if embedded in a broader understanding of qualitative research design and data analysis ::: ::: Otherwise we risk compromising the unique contribution that systematic qualitative research can make to health services research ::: ::: In medical research the question is no longer whether qualitative methods are valuable but how rigour can be ensured or enhanced. Checklists have played an important role in conferring respectability on qualitative research and in convincing potential sceptics of its thoroughness.1–3 They have equipped those unfamiliar with this approach to evaluate or review qualitative work (by providing guidance on crucial questions that need to be asked) and in reminding qualitative researchers of the need for a systematic approach (by providing an aide-memoire of the various stages involved in research design and data analysis4). ::: ::: Qualitative researchers stress the …


title: On inverse kinematics with fractional calculus

abstract: In this paper an application of the fractional calculus to path control is studied. The integer-order derivative and integral are replaced with the fractional-order ones in order to solve the inverse kinematics problem. The proposed algorithm is a modification of the existing one. In order to maintain the accuracy and to lower the memory requirements a history limit and varying order for derivation are proposed. An impact of the parameters upon a generated path is studied. Obtained results prove that using the fractional calculus may improve well known methods of solving the inverse kinematics problem.


title: CorePhrase: Keyphrase Extraction for Document Clustering

abstract: The ability to discover the topic of a large set of text documents using relevant keyphrases is usually regarded as a very tedious task if done by hand. Automatic keyphrase extraction from multi-document data sets or text clusters provides a very compact summary of the contents of the clusters, which often helps in locating information easily. We introduce an algorithm for topic discovery using keyphrase extraction from multi-document sets and clusters based on frequent and significant shared phrases between documents. The keyphrases extracted by the algorithm are highly accurate and fit the cluster topic. The algorithm is independent of the domain of the documents. Subjective as well as quantitative evaluation show that the algorithm outperforms keyword-based cluster-labeling algorithms, and is capable of accurately discovering the topic, and often ranking it in the top one or two extracted keyphrases.


title: Inverse Procedural Modelling of Trees

abstract: Procedural tree models have been popular in computer graphics for their ability to generate a variety of output trees from a set of input parameters and to simulate plant interaction with the environment for a realistic placement of trees in virtual scenes. However, defining such models and their parameters is a difficult task. We propose an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input. Our framework is based on a novel parametric model for tree generation and uses Monte Carlo Markov Chains to find the optimal set of parameters. We demonstrate our approach on a variety of input models obtained from different sources, such as interactive modelling systems, reconstructed scans of real trees and developmental models.


title: Cactis: a self-adaptive, concurrent implementation of an object-oriented database management system

abstract: Cactis is an object-oriented, multiuser DBMS developed at the University of Colorado. The system supports functionally-defined data and uses techniques based on attributed graphs to optimize the maintenance of functionally-defined data. The implementation is self-adaptive in that the physical organization and the update algorithms dynamically change in order to reduce disk access. The system is also concurrent. At any given time there are some number of computations that must be performed to bring the database up to date; these computations are scheduled independently and are performed when the expected cost to do so is minimal. The DBMS runs in the Unix/C Sun workstation environment. Cactis is designed to support applications that require rich data modeling capabilities and the ability to specify functionally-defined data, but that also demand good performance. Specifically, Cactis is intended for use in the support of such applications as VLSI and PCB design, and software environments.


Design of BPSK/QPSK Modulator using Verilog HDL and Matlab


title: Dual-Band Multi-Pole Directional Filter for Microwave Multiplexing Applications

abstract: A novel microstrip directional filter for multiplexing applications is presented. This device uses composite right-left-handed transmission lines and resonators to achieve dual-band frequency response. In addition, by cascading two or more stages using dual frequency immitance inverters, multi-pole configurations can be obtained. Simulation and experimental results are presented with good agreement.


title: Hollow metal microneedles for insulin delivery to diabetic rats

abstract: The goal of this study was to design, fabricate, and test arrays of hollow microneedles for minimally invasive and continuous delivery of insulin in vivo. As a simple, robust fabrication method suitable for inexpensive mass production, we developed a modified-LIGA process to micromachine molds out of polyethylene terephthalate using an ultraviolet laser, coated those molds with nickel by electrodeposition onto a sputter-deposited seed layer, and released the resulting metal microneedle arrays by selectively etching the polymer mold. Mechanical testing showed that these microneedles were sufficiently strong to pierce living skin without breaking. Arrays containing 16 microneedles measuring 500 /spl mu/m in length with a 75 /spl mu/m tip diameter were then inserted into the skin of anesthetized, diabetic, hairless rats. Insulin delivery through microneedles caused blood glucose levels to drop steadily to 47% of pretreatment values over a 4-h insulin delivery period and were then approximately constant over a 4-h postdelivery monitoring period. Direct measurement of plasma insulin levels showed a peak value of 0.43 ng/ml. Together, these data suggest that microneedles can be fabricated and used for in vivo insulin delivery.


title: A 2-GHz 6.1-mA Fully-Differential CMOS Phase-Locked Loop

abstract: A fully-differential 2-GHz phase-locked loop (PLL) was designed and fabricated in 0.18-mum CMOS process. The PLL rejects the common noise due to fully-differential VCO and differential charge pump. The VCO has a 16.15% tuning range (from 1.8998GHz to 2.2335GHz) due to a combination of analog and digital tuning technique (4-bit binary switch-capacitor array). With the pn-junction varactors, the phase noise of the VCO varies only about 2dB in the tuning range. The current consumption of the PLL is only about 6.1mA from a 1.8 V power supply. It is comparable to the results reported in recent literatures. The phase noise of the PLL at 2.033 GHz can achieve -117.17dBc/Hz at 1 MHz frequency offset from the carrier.


title: Near-Field Sound-Source Localization Based on a Signed Binary Code

abstract: This paper proposes near-field sound-source localization based on crosscorrelation of a signed binary code. The signed binary code eliminates multibit signal processing for simpler implementation. Explicit formulae with near-field assumption are derived for a two microphone scenario and extended to a three microphone case with front-rear discrimination. Adaptive threshold for enabling and disabling source localization is developed for robustness in noisy environment. The proposed sound-source localization algorithm is implemented on a fixed-point DSP. Evaluation results in a robot scenario demonstrate that near-field assumption and front-rear discrimination provides almost 40% improvement in DOA estimation. A correct detection rate of 85% is obtained by a robot in a home environment.


title: Probabilistically Bounded Staleness for Practical Partial Quorums

abstract: Data store replication results in a fundamental trade-off between operation latency and data consistency. In this paper, we examine this trade-off in the context of quorum-replicated data stores. Under partial, or non-strict quorum replication, a data store waits for responses from a subset of replicas before answering a query, without guaranteeing that read and write replica sets intersect. As deployed in practice, these configurations provide only basic eventual consistency guarantees, with no limit to the recency of data returned. However, anecdotally, partial quorums are often "good enough" for practitioners given their latency benefits. In this work, we explain why partial quorums are regularly acceptable in practice, analyzing both the staleness of data they return and the latency benefits they offer. We introduce Probabilistically Bounded Staleness (PBS) consistency, which provides expected bounds on staleness with respect to both versions and wall clock time. We derive a closed-form solution for versioned staleness as well as model real-time staleness for representative Dynamo-style systems under internet-scale production workloads. Using PBS, we measure the latency-consistency trade-off for partial quorum systems. We quantitatively demonstrate how eventually consistent systems frequently return consistent data within tens of milliseconds while offering significant latency benefits.


title: Research on continuous auditing: A bibliometric analysis

abstract: This paper presents the results of a bibliometric study about the evolution of research on Continuous Auditing. This study has as main motivation to find reasons for the very slow evolvement of research on this topic. In addition, Continuous Auditing is one of the features of the emerging concept of Continuous Assurance. Thus, considering that Continuous Assurance represents numerous advantages for the organizational performance, this study also intends to understand if there is a relation between the evolution of research on Continuous Auditing and the still very low maturity levels of continuous assurance solutions. This study shows that the number of publications is considerably reduced and that the slow growth of research on Continuous Auditing may be contributing to the lack of maturity of Continuous Assurance.


title: Direct liquid cooling For IGBT power module

abstract: Power electronics systems trend to decrease size, increase switching frequency and voltage ratings. This has resulted in higher power dissipation dnesities of IGBT modules and higher junction temperature of IGBT devices. Thermal management becomes an important package design criteria. From the thermal resistance network of conventional indirect IGBT power module, the thermal interface material between IGBT power module and system cooler occupies a significant part. The present paper studies the thermal effects of IGBT power module with direct liquid cooling design. The microchannel cold plate is bonded with DBC substrate directly and elimitation thermal interface material. Thermal simulations of IGBT power with indirect liquid cooling and direct liquid cooling are complished and compared. The direct liquid cooling using microchannel cold plate can reduce thermal resistance and cooler size of IGBT power module effectively. Additionally, the microchannel cold plate can decrease the warpage and increase reliability of IGBT power module.


title: Wavelet Trees: From Theory to Practice

abstract: The \emph{wavelet tree} data structure is a space-efficient technique for rank and select queries that generalizes from binary characters to an arbitrary multicharacter alphabet. It has become a key tool in modern full-text indexing and data compression because of its capabilities in compressing, indexing, and searching. We present a comparative study of its practical performance regarding a wide range of options on the dimensions of different coding schemes and tree shapes. Our results are both theoretical and experimental: (1)~We show that the run-length $\delta$ coding size of wavelet trees achieves the 0-order empirical entropy size of the original string with leading constant 1, when the string's 0-order empirical entropy is asymptotically less than the logarithm of the alphabet size. This result complements the previous works that are dedicated to analyzing run-length $\gamma$-encoded wavelet trees. It also reveals the scenarios when run-length $\delta$ encoding becomes practical. (2)~We introduce a full generic package of wavelet trees for a wide range of options on the dimensions of coding schemes and tree shapes. Our experimental study reveals the practical performance of the various modifications.


title: Synchronized Video and Motion Analysis for the Assessment of Procedures in the Operating Theater

abstract: HYPOTHESIS ::: Objective assessment of surgical skill has recently been shown to be possible through the use of dexterity-based and video analysis systems. The aim of this study was to synchronize these 2 modalities to produce a comprehensive surgical assessment tool. ::: ::: ::: DESIGN ::: The Imperial College Surgical Assessment Device is a dexterity-based motion analysis device that has been developed in the Department of Surgical Oncology and Technology by the Surgical Computing and Imaging Research Group. Further advances to this system have been made to enable synchronized acquisition of hand kinematics and video from real procedures, and their concurrent analysis. To test the feasibility of the system, 10 laparoscopic cholecystectomies performed by 5 different surgeons on consenting patients were recorded. Analysis focused on the entire procedure and also on specific parts of the operation such as the clipping and cutting of the cystic duct and artery. ::: ::: ::: RESULTS ::: Dexterity analysis was performed using the objective measures of time, path length, number of movements, velocities, and trajectories. Comparative analysis of a surgeon's dexterity was carried out on the whole procedure and by using the synchronized zoom facility in the software. Kinematic signals revealed rapid changes in velocity caused by alternating between different instruments or occurring after complications such as bleeding. ::: ::: ::: CONCLUSION ::: This new motion analysis system has been shown to be an effective tool for the comprehensive assessment of operative procedures.


title: Efficient Estimation of Word Representations in Vector Space

abstract: We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.


title: A Linear Programming Approach to Novelty Detection

abstract: Novelty detection involves modeling the normal behaviour of a system hence enabling detection of any divergence from normality. It has potential applications in many areas such as detection of machine damage or highlighting abnormal features in medical data. One approach is to build a hypothesis estimating the support of the normal data i.e. constructing a function which is positive in the region where the data is located and negative elsewhere. Recently kernel methods have been proposed for estimating the support of a distribution and they have performed well in practice - training involves solution of a quadratic programming problem. In this paper we propose a simpler kernel method for estimating the support based on linear programming. The method is easy to implement and can learn large datasets rapidly. We demonstrate the method on medical and fault detection datasets.


title: A study on development of home Mess-Cleanup Robot McBot

abstract: Autonomous and automatic home Mess-Cleanup Robot (Mcbot) is newly developed in this paper. So far, the vacuum-cleaner had made the burden of house chore lighten but the operational labor of a vacuum-cleaner had been so severe. Recently, the cleaning robot was commercialized to solve home cleaning labor problem but it also was not successful because it still had a problem of mess-cleanup, which was the clean-up of big trash and the arrangement of newspapers, clothes, etc. The cleaning robot just vacuum dust and so small trash but has no function to arrange the mess-up and throw away the big trash in house. For this reason, the market for the cleaning robot is not yet actively built up. So, we develop a new home mess-cleanup robot McBot to perfectly overcome this problem. It needs functions of agile navigation and novel manipulation system for mess-cleanup. The autonomous navigation system has to be controlled for the full scanning of living room and for the precise tracking of the desired path, It must be also able to recognize the absolute position and orientation of the self and to distinguish the mess object to clean-up from obstacle object to just avoid. The manipulator, which is not needed in the vacuum-cleaning robot, has the functions to distinguish big trash to clean from mess objects to arrange, to use its discretion in according to the form of mess objects and to properly carry them to the destination. We use the RFID system to sense them in this paper and propose the new disposition algorithm to have RFID tags installed on the floor and objects indoor. Then, it should be an intelligent system so that the messed-cleanup task can be autonomously performed in a wide variety of situations and environments. It needs to also have an intelligent interaction function for the human-friendly communication. Finally, the good performance of the developed McBot is confirmed through the results of the mess-cleanup experiments.


title: Low-level software security: Attacks and defenses

abstract: This tutorial paper considers the issues of low-level software security from a language-based perspective, with the help of concrete examples. Four examples of low-level software attacks are covered in full detail; these examples are representative of the major types of attacks on C and C++ software that is compiled into machine code. Six examples of practical defenses against those attacks are also covered in detail; these defenses are selected because of their effectiveness, wide applicability, and low enforcement overhead.


Understanding Regularization by Virtual Adversarial Training, Ladder Networks and Others


title: Blockchain Practices, Potentials, and Perspectives in Greening Supply Chains

abstract: Blockchain technology is an inchoate technology whose current popularity is peaking. Some of the most pervasive blockchain technology use cases exist for supply chains. Sustainable, and especially green, supply chains can benefit from blockchain technology, but there are also caveats. The sustainability and environmental management research and academic literature is only starting to investigate this emergent field. This paper seeks to help advance the discussion and motivate additional practice and research related to green supply chains and blockchain technology. This viewpoint paper provides insight into some of the main dimensions of blockchain technology, an overview of the use cases and issues, and some general research areas for further investigation.


title: COMPUTER SCIENCE | RESEARCH ARTICLE A comparative analysis on artificial neural network-based two-stage clustering

abstract: 1 and Ssu-Han Chen 2 * Abstract: The artificial neural network (ANN), which is capable of noise removal and data complexity reduction, has been regarded as one of outstanding intermediaries in the two-stage clustering procedures. Various ANN-based two-stage clustering procedures have been individually proposed; however, the performance among those methods has not been examined yet. In this study, a preliminary comparative analysis is conducted in four benchmark data-sets and a real-world market data-set, which are used to simulate various conditions for evaluation purposes. The experiment results suggest that high-accuracy self-organizing feature map can potentially improve the effectiveness of decision-making.


title: FRACTIONAL PEARSON DIFFUSIONS.

abstract: Pearson diffusions are governed by diffusion equations with polynomial coefficients. Fractional Pearson diffusions are governed by the corresponding time-fractional diffusion equation. They are useful for modeling sub-diffusive phenomena, caused by particle sticking and trapping. This paper provides explicit strong solutions for fractional Pearson diffusions, using spectral methods. It also presents stochastic solutions, using a non-Markovian inverse stable time change.


title: Data Mining: A prediction for performance improvement using classification

abstract: Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.


title: Friend or Foe: Cyberbullying in Social Network Sites

abstract: As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.


title: Periodicity, directionality, and randomness: Wold features for image modeling and retrieval

abstract: One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as database retrieval, it is important that image features used in pattern comparison provide good measures of image perceptual similarities. We present an image model with a new set of features that address the challenge of perceptual similarity. The model is based on the 2D Wold decomposition of homogeneous random fields. The three resulting mutually orthogonal subfields have perceptual properties which can be described as "periodicity," "directionality," and "randomness," approximating what are indicated to be the three most important dimensions of human texture perception. The method presented improves upon earlier Wold-based models in its tolerance to a variety of local inhomogeneities which arise in natural textures and its invariance under image transformation such as rotation. An image retrieval algorithm based on the new texture model is presented. Different types of image features are aggregated for similarity comparison by using a Bayesian probabilistic approach. The, effectiveness of the Wold model at retrieving perceptually similar natural textures is demonstrated in comparison to that of two other well-known pattern recognition methods. The Wold model appears to offer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria. Examples of natural scene Wold texture modeling are also presented.


title: Evaluating and improving adaptive educational systems with learning curves

abstract: Personalised environments such as adaptive educational systems can be evaluated and compared using performance curves. Such summative studies are useful for determining whether or not new modifications enhance or degrade performance. Performance curves also have the potential to be utilised in formative studies that can shape adaptive model design at a much finer level of granularity. We describe the use of learning curves for evaluating personalised educational systems and outline some of the potential pitfalls and how they may be overcome. We then describe three studies in which we demonstrate how learning curves can be used to drive changes in the user model. First, we show how using learning curves for subsets of the domain model can yield insight into the appropriateness of the model’s structure. In the second study we use this method to experiment with model granularity. Finally, we use learning curves to analyse a large volume of user data to explore the feasibility of using them as a reliable method for fine-tuning a system’s model. The results of these experiments demonstrate the successful use of performance curves in formative studies of adaptive educational systems.


title: Cashless payment and economic growth

abstract: Abstract Background This study examines the effect of adopting cashless payment in five European Union (EU) countries, namely, Austria, Belgium, France, Germany, and Portugal, for the period of 2000-2012. Methods The within and between effect of adopting cheque payment, telegraphic transfer, card payment and electronic money on these EU’s economy are examined by applying the Pedroni residual cointergration and Panel Vector Error Correction Model (VECM). Results There is short run causality running from cheque payment to telegraphic transfer and card payment, as well as causality running telegraphic transfer to card payment. In the long run, there is significant effect of adopting cashless payment on the economy of the five EU countries. Conclusions The adoption of one type of cashless payment will affect another type of cashless payment in the short run. The impact of adopting cashless payment on economic growth can only be significantly observed in the long run. Hence, any policy that promotes cashless payment will not affect the economy immediately.


title: "Snapchat is more personal": An exploratory study on Snapchat behaviors and young adult interpersonal relationships

abstract: Snapchat is a social media platform that allows users to send images, videos, and text with a specified amount of time for the receiver(s) to view the content before it becomes permanently inaccessible to the receiver. Using focus group methodology and in-depth interviews, the current study sought to understand young adult (18-23 years old; nź=ź34) perceptions of how Snapchat behaviors influenced their interpersonal relationships (family, friends, and romantic). Young adults indicated that Snapchat served as a double-edged sword-a communication modality that could lead to relational challenges, but also facilitate more congruent communication within young adult interpersonal relationships. Young adults report perceived influence of Snapchat on relationships.Snapchat perceived to facilitate both positive and negative relational interactions.Snapchat use reserved for closest relationships, not strangers.


title: Response time of VSWR protection for GaN HEMT based power amplifiers

abstract: Protection against high voltage-standing-wave-ratios (VSWR) is of great importance in many power amplifier applications. Despite excellent thermal and voltage breakdown properties even gallium nitride devices may need such measures. This work focuses on the timing aspect when using barium-strontium-titanate (BST) varactors to limit power dissipation and gate current. A power amplifier was designed and fabricated, implementing a varactor and a GaN-based voltage switch as varactor modulator for VSWR protection. The response time until the protection is effective was measured by switching the voltages at varactor, gate and drain of the transistor, respectively. It was found that it takes a minimum of 50 μs for the power amplifier to reach a safe condition. Pure gate pinch-off or drain voltage reduction solutions were slower and bias-network dependent. For a thick-film BST MIM varactor, optimized for speed and power, a switching time of 160 ns was achieved.


title: Towards amharic semantic search engine

abstract: Recently the amount of documents written in Amharic language has been dramatically increasing. Searching such content using localized and regional version of general search engine such as google.com.et returns documents containing search key terms while excluding specific characteristics of Amharic Language. In this paper, we present the design and implementation of Semantic Search Engine for Amharic documents. The search engine has Crawler, Ontology/Knowledge base, Indexer and Query Processor that consider characteristics of Amharic language. The ontology provides shared concepts Sport. This ontology is built manually by language and sport domain experts and it is used in building semantic indexer, ranker and query engine. In addition, we show how the system facilitate meaning based searching, document relevant and popularity based documents ranking.


title: Advanced gate driver for IGBT devices with dv/dt and peak voltage limitation based on active gate-emitter voltage control

abstract: An advanced gate drive unit for 1.2 kV IGBT modules has been developed. It includes two main features: limitation of the collector-emitter voltage slope and of the collector-emitter peak voltage. This is achieved through a feedback loop with a capacitive/resistive voltage divider coupled to the gate-emitter voltage of the IGBT. For the studied case a reduction of the switching losses of around 25% is achieves. An experimental verification and comparison to a commercially available gate drive unit with active clamping based on a series connections of TVS diodes is presented. The proposed gate drive unit can be used in applications with high stray inductance to ensure optimum switching behaviour of every switching transient.


title: On code reuse from StackOverflow : An exploratory study on Android apps

abstract: Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from StackOverflow.Objective: To better understand the impact of code reuse from StackOverflow, we perform an exploratory study focusing on code reuse from StackOverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse StackOverflow code.Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from StackOverflow. We then apply different quantitative and qualitative methods to answer our research questions.Results: Our findings indicate that 1) the amount of reused StackOverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from StackOverflow, 3) mid-age and older apps reuse StackOverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from StackOverflow.Conclusion: Our results provide insights on the potential impact of code reuse from StackOverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from StackOverflow.


title: Personal Authentication Using Finger Knuckle Surface

abstract: This paper investigates a new approach for personal authentication using fingerback surface imaging. The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. The finger geometry features can be simultaneously acquired from the same image at the same time and integrated to further improve the user-identification accuracy of such a system. The fingerback surface images from each user are normalized to minimize the scale, translation, and rotational variations in the knuckle images. This paper details the development of such an approach using peg-free imaging. The experimental results from the proposed approach are promising and confirm the usefulness of such an approach for personal authentication.


title: Low-light image enhancement using variational optimization-based Retinex model

abstract: This paper presents an optimization-based low-light image enhancement method using spatially adaptive �������� -norm based Retinex model. The proposed method adaptively enforces the regularization parameter using the spatially adaptive weight map, which is generated using the bright channel prior (BCP) and local variance map. Since the proposed weight map assigns the smaller weight value at the bright and edge region, the proposed method can perform weak noise reduction to preserve the edges and textures. In addition, the simplified version of the proposed method is presented using the FFT and quantized weight values for the application to consumer devices. Experimental results show that the proposed method can provide better enhanced result without the ι2 -norm minimization artifacts at the low computational cost.


title: Sparse, guided feature connections in an Abstract Deep Network

abstract: We present a technique for developing a network of re-used features, where the topology is formed using a coarse learning method, that allows gradient-descent fine tuning, known as an Abstract Deep Network (ADN). New features are built based on observed co-occurrences, and the network is maintained using a selection process related to evolutionary algorithms. This allows coarse ex- ploration of the problem space, effective for irregular domains, while gradient descent allows pre- cise solutions. Accuracy on standard UCI and Protein-Structure Prediction problems is comparable with benchmark SVM and optimized GBML approaches, and shows scalability for addressing large problems. The discrete implementation is symbolic, allowing interpretability, while the continuous method using fine-tuning shows improved accuracy. The binary multiplexer problem is explored, as an irregular domain that does not support gradient descent learning, showing solution to the bench- mark 135-bit problem. A convolutional implementation is demonstrated on image classification, showing an error-rate of 0.79% on the MNIST problem, without a pre-defined topology. The ADN system provides a method for developing a very sparse, deep feature topology, based on observed relationships between features, that is able to find solutions in irregular domains, and initialize a network prior to gradient descent learning.


title: A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference

abstract: Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning an HASMM from the EHR data is achieved via a novel forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reverse-time direction while conditioning on the future states. Real-time inferences are drawn via a forward- filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the di- agnostic and prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center.


title: Which battery charger components should be included in power system stability studies of controllable load strategies?

abstract: The impact of converter-based loads on the power system is evaluated based on stability studies. This requires adequate load models. This paper deals with a time-domain model of an electric vehicle battery charger with grid support functionality (so-called controllable load). The dynamic properties of the components of a unidirectional 1,2 kW battery charger with input diode rectifier are discussed with respect to transients and oscillatory modes in power systems. Simulation results are shown for the battery charger model for a low-frequency oscillation. These results are compared to an induction motor load, which is directly connected to the mains.


title: A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

abstract: It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.


title: The emotional responses of browsing Facebook: Happiness, envy, and the role of tie strength

abstract: Positive emotions are more prevalent than negative emotions while browsing Facebook.Users are happier when a positive post comes from a strong tie rather than a weak tie.Similarly, users experience more benign envy when a post comes from a strong tie.The experience of malicious envy is independent of tie strength. On Facebook, users are exposed to posts from both strong and weak ties. Even though several studies have examined the emotional consequences of using Facebook, less attention has been paid to the role of tie strength. This paper aims to explore the emotional outcomes of reading a post on Facebook and examine the role of tie strength in predicting happiness and envy. Two studies - one correlational, based on a sample of 207 American participants and the other experimental, based on a sample of 194 German participants - were conducted in 2014. In Study 2, envy was further distinguished into benign and malicious envy. Based on a multi-method approach, the results showed that positive emotions are more prevalent than negative emotions while browsing Facebook. Moreover, tie strength is positively associated with the feeling of happiness and benign envy, whereas malicious envy is independent of tie strength after reading a (positive) post on Facebook.


title: Conversational AI: The Science Behind the Alexa Prize

abstract: Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as socialbots, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes. The Alexa Prize offers the academic community a unique opportunity to perform research with a live system used by millions of users. The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team. This enabled teams to effectively iterate and make improvements throughout the competition while being evaluated in real-time through live user interactions. To build their socialbots, university teams combined state-of-the-art techniques with novel strategies in the areas of Natural Language Understanding, Context Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To support the efforts of participating teams, the Alexa Prize team made significant scientific and engineering investments to build and improve Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice User Experience, and tools for traffic management and scalability. This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational AI.


title: Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?

abstract: Predicting knowledge levels from user's implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events KIE. We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.


title: Combinatorial Energy Learning for Image Segmentation

abstract: We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.


title: Performance Comparison of Deep Learning Techniques for Recognizing Birds in Aerial Images

abstract: In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.


title: ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines

abstract: Abstract : The recent success of deep learning is driving a trend towards structurally complex computer vision models that combine feature extraction with predictive elements into integrated pipelines. While some of these models have achieved breakthrough results in applications like object recognition, they are difficult to design and tune, impeding progress. We feel that visual analysis can be a powerful tool to aid iterative development of deep model pipelines. Building on feature evaluation work in the computer vision community, we introduce ML-o-scope, an interactive visualization system for exploratory analysis of convolutional neural networks, a prominent type of pipelined model. We present ML-o-scope's time-lapse engine that provides views into model dynamics during training, and evaluate the system as a support for tuning large scale object-classification pipelines.


title: Axial Coding and the Grounded Theory Controversy

abstract: The purpose of this article is to describe the similarities and differences between two approaches to grounded theory research: grounded theory as espoused by Glaser and grounded theory as espoused by Strauss and Corbin. The focus of the article is the controversy surrounding the use of axial coding. The author proposes a resolution to the controversy by suggesting that one does not need to view either approach as right or wrong; rather, the qualitative and grounded theory researcher can choose an approach, and that choice is based on the goal of the researcher’s study. Examples of both approaches, from the author’s research study on the experiences of living in a family with a child with attention deficit hyperactivity disorder (ADHD), are provided.


title: Online Kernel Slow Feature Analysis for Temporal Video Segmentation and Tracking

abstract: Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how visual brain cells work. In recent years, the SFA was adopted for computer vision tasks. In this paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite kernels in Krein space. We then formulate an online KSFA which employs a reduced set expansion. Finally, by utilizing a special kind of kernel family, we formulate exact online KSFA for which no reduced set is required. We apply the proposed system to develop a SFA-based change detection algorithm for stream data. This framework is employed for temporal video segmentation and tracking. We test our setup on synthetic and real data streams. When combined with an online learning tracking system, the proposed change detection approach improves upon tracking setups that do not utilize change detection.


title: W-Climbot: A modular biped wall-climbing robot

abstract: Although a large number of wall-climbing robots have been developed in the past decades, most of them suffer from shortcomings such as poor ability to omni-directional locomotion, lower capability to transit between walls and to negotiate obstacles on the walls. To overcome these drawbacks, we have developed a novel biped wall-climbing robot - W-Climbot using modularization method. Consisting of an arm as the main body and two vacuum suckers at the two ends, W-Climbot has great mobility on walls and potential manipulation function. In this paper, the development of W-Climbot is briefly introduced, and then the suction force model for suction module design is presented. The superior ability to transit between walls and to traverse and step over obstacles is analyzed. Experiments are carried out to verify the effectiveness of the system design and to demonstrate basic features of the novel robot.


title: The Role of Soil Microbes in Promoting Plant Growth

abstract: More and more food is needed all over the world in order to meet rapidly growing population demands, while the modern agriculture on increasing output has already reached its limit. It is badly in need to cultivate new crop varieties for increase the yield and resistant to environmental stress and insects. However, crops are still need to provide the necessary fertilizer nutrients which is insufficient in soil. Recently, many evidences showed that soil microbes provide an opportunity for reducing agricultural demand in inorganic fertilizer. Microorganisms, due to their huge gene pool, are also used for a potential resource in biochemical reactions, which recycle nutrients for plant growth. Therefore, we need to modify and better use of soil microbiota to promote plant growth. In this review, we mainly summarized the role of soil microbiota by explaining the ecological balance, nitrogen fixation, nutrient elements, interactions and communication medium.


title: Acute Zonal Cone Photoreceptor Outer Segment Loss

abstract: Importance The diagnostic path presented narrows down the cause of acute vision loss to the cone photoreceptor outer segment and will refocus the search for the cause of similar currently idiopathic conditions. Objective To describe the structural and functional associations found in a patient with acute zonal occult photoreceptor loss. Design, Setting, and Participants A case report of an adolescent boy with acute visual field loss despite a normal fundus examination performed at a university teaching hospital. Main Outcomes and Measures Results of a complete ophthalmic examination, full-field flash electroretinography (ERG) and multifocal ERG, light-adapted achromatic and 2-color dark-adapted perimetry, and microperimetry. Imaging was performed with spectral-domain optical coherence tomography (SD-OCT), near-infrared (NIR) and short-wavelength (SW) fundus autofluorescence (FAF), and NIR reflectance (REF). Results The patient was evaluated within a week of the onset of a scotoma in the nasal field of his left eye. Visual acuity was 20/20 OU, and color vision was normal in both eyes. Results of the fundus examination and of SW-FAF and NIR-FAF imaging were normal in both eyes, whereas NIR-REF imaging showed a region of hyporeflectance temporal to the fovea that corresponded with a dense relative scotoma noted on light-adapted static perimetry in the left eye. Loss in the photoreceptor outer segment detected by SD-OCT co-localized with an area of dense cone dysfunction detected on light-adapted perimetry and multifocal ERG but with near-normal rod-mediated vision according to results of 2-color dark-adapted perimetry. Full-field flash ERG findings were normal in both eyes. The outer nuclear layer and inner retinal thicknesses were normal. Conclusions and Relevance Localized, isolated cone dysfunction may represent the earliest photoreceptor abnormality or a distinct entity within the acute zonal occult outer retinopathy complex. Acute zonal occult outer retinopathy should be considered in patients with acute vision loss and abnormalities on NIR-REF imaging, especially if multimodal imaging supports an intact retinal pigment epithelium and inner retina but an abnormal photoreceptor outer segment.


title: An ultra-wideband horizontally polarized omnidirectional connected vivaldi array antenna

abstract: An omnidirectional horizontally polarized antenna with an ultra-wideband impedance and radiation bandwidth is proposed in this paper. The connected Vivaldi antenna array is firstly applied to realize a UWB horizontally polarized antenna with good omnidirectional radiation properties. The designed antenna exhibits a relative impedance bandwidth of 163.17% for |S11|<-10dB (1.45–14.3GHz). Meanwhile, good omnidirectional radiation patterns with a gain variation of less than 1.5dB has been obtained within the frequency band of 1.45–12GHz (156.88%).


title: 3D Pick&Mix: Object Part Blending in Joint Shape and Image Manifolds

abstract: We present 3D Pick & Mix, a new 3D shape retrieval system that provides users with a new level of freedom to explore 3D shape and Internet image collections by introducing the ability to reason about objects at the level of their constituent parts. While classic retrieval systems can only formulate simple searches such as "find the 3D model that is most similar to the input image" our new approach can formulate advanced and semantically meaningful search queries such as: "find me the 3D model that best combines the design of the legs of the chair in image 1 but with no armrests, like the chair in image 2". Many applications could benefit from such rich queries, users could browse through catalogues of furniture and pick and mix parts, combining for example the legs of a chair from one shop and the armrests from another shop.


title: Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

abstract: The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user’s musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.


Self-Regulated Learning : The Educational Legacy of


title: SketchInsight: Natural data exploration on interactive whiteboards leveraging pen and touch interaction

abstract: In this work, we advance research efforts in combining the casual sketching approach of whiteboards with the machine's computing power. We present SketchInsight, a system that applies the familiar and collaborative features of a whiteboard interface to the accurate data exploration capabilities of interactive visualizations. SketchInsight enables data analysis with more fluid interaction, allowing people to visually explore their data by drawing simple charts and directly manipulating them. In addition, we report results from a qualitative study conducted to evaluate user experience in exploring data with SketchInsight, expanding our understanding on how people use a pen- and touch-enabled digital whiteboard for data exploration. We also discuss the challenges in building a working system that supports data analytic capabilities with pen and touch interaction and freeform annotation.


title: Learning Policies for First Person Shooter Games using Inverse Reinforcement

abstract: The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the gaming industry. However, a secondary consideration is whether the autonomous bots behave like human players; this is especially important for simulation/training applications which aim to instruct participants in real-world tasks. Bots often compensate for a lack of combat acumen with advantages such as accurate targeting, predefined navigational networks, and perfect world knowledge, which makes them challenging but often predictable opponents. In this paper, we examine the problem of teaching a bot to play like a human in first-person shooter game combat scenarios. Our bot learns attack, exploration and targeting policies from data collected from expert human player demonstrations in Unreal Tournament. ::: ::: We hypothesize that one key difference between human players and autonomous bots lies in the relative valuation of game states. To capture the internal model used by expert human players to evaluate the benefits of different actions, we use inverse reinforcement learning to learn rewards for different game states. We report the results of a human subjects' study evaluating the performance of bot policies learned from human demonstration against a set of standard bot policies. Our study reveals that human players found our bots to be significantly more human-like than the standard bots during play. Our technique represents a promising stepping-stone toward addressing challenges such as the Bot Turing Test (the CIG Bot 2K Competition).


title: Merging the senses into a robust percept

abstract: To perceive the external environment our brain uses multiple sources of sensory information derived from several different modalities, including vision, touch and audition. All these different sources of information have to be efficiently merged to form a coherent and robust percept. Here we highlight some of the mechanisms that underlie this merging of the senses in the brain. We show that, depending on the type of information, different combination and integration strategies are used and that prior knowledge is often required for interpreting the sensory signals.


title: Adaptive Sampling for Human-aware Path Planning in Dynamic Environments

abstract: Nowadays, robots are increasingly used in densely populated dynamic environments. Robots not only need to complete the navigation tasks quickly, but also need to take into account the human trajectories and the constraints of social rules. In order to avoid the robot going into the crowed areas and improve robot acceptance in the crowded public environment, we propose a human-aware motion planning algorithm that is based on sampling method. Firstly, human will be annoyed and stressed if robots disturb them during operation. To alleviate this uncomfort brought by the robot, we use probabilistic representations to build the Human Domain Zone (HDZ) of individual or crowd behaviors. Besides, we propose a sampling strategy that is capable of biasing the sampling in the area where human feel comfortable or crowd is sparse. Moreover, we put forward an evaluation function to select the optimum trajectory. This function can avoid robot falling into the crowded area through VDM which is used to model the relationship between human and robot. The proposed approach is verified with extensive experiments in simulated environments. The results show that our method has the promising performance in crowded environment. It can also generate a smooth path with higher success rate.


title: Cross-lingual link discovery with TR-ESA

abstract: Cross-lingual data linking is the problem of establishing links between resources, such as places, services, or movies, which are described in different languages. In cross-lingual data linking it is often the case that very short descriptions have to be matched, which makes the problem even more challenging. This work presents a method named TRanslation-based Explicit Semantic Analysis (tr-esa) to represent and match short textual descriptions available in different languages. tr-esa translates short descriptions in any given language into a pivot language by exploiting a machine translation tool. Then, it generates a Wikipedia-based representation of the translated text by using the Explicit Semantic Analysis technique. The resulting representations are used to match short descriptions in different languages. The method is incorporated in CroSeR (Cross-lingual Service Retrieval), an interactive data linking tool that recommends potential matches to users. We compared results coming from an in-vitro evaluation on a gold standard consisting of five datasets in different languages, with an in-vivo experiment that involved human experts supported by CroSeR. The in-vivo evaluation confirmed the results of the in-vitro evaluation and the overall effectiveness of the proposed method.


title: On stable piecewise linearization and generalized algorithmic differentiation

abstract: It is shown how functions that are defined by evaluation programs involving the absolute value function abs besides smooth elementals can be approximated locally by piecewise-linear models in the style of algorithmic or automatic differentiation AD. The model can be generated by a minor modification of standard AD tools and it is Lipschitz continuous with respect to the base point at which it is developed. The discrepancy between the original function, which is piecewise differentiable, and the piecewise linear model is of second order in the distance to the base point. Consequently, successive piecewise linearization yields bundle type methods for unconstrained minimization and Newton-type equation solvers. As a third fundamental numerical task we consider the integration of ordinary differential equations, for which we examine generalizations of the midpoint and the trapezoidal rule for the case of Lipschitz continuous right hand sides RHSs. As a by-product of piecewise linearization, we show how to compute at any base point some generalized Jacobians of the original function, namely those that are conically active as defined by Khan and Barton. This subset of the Clarke Jacobian is never empty, independent of the particular function representation in terms of elementals, and also invariant with respect to linear transformations on domain and range. However, like all generalized derivatives the conically active Jacobians reduce almost everywhere to the singleton formed by the proper Jacobian, which may approximate the original function only in a minuscule neighbourhood. Since the piecewise linearization always reflects kinks in the vicinity, we illustrate how it can be used to approximate generalized Jacobians at nearby points along a user specified preferred direction.


title: More effective boilerplate removal – the GoldMiner algorithm

abstract: The ever-increasing web is an important source for building large-scale corpora. However, dynamically generated web pages often contain much irrelevant and duplicated text, which impairs the quality of the corpus. To ensure the high quality of web-based corpora, a good boilerplate removal algorithm is needed to extract only the relevant content from web pages. In this article, we present an automatic text extraction procedure, GoldMiner, which by enhancing a previously published boilerplate removal algorithm, minimizes the occurrence of irrelevant duplicated content in corpora, and keeps the text more coherent than previous tools. The algorithm exploits similarities in the HTML structure of pages coming from the same domain. A new evaluation document set (CleanPortalEval) is also presented, which can demonstrate the power of boilerplate removal algorithms for web portal pages.


title: Estimating Taxi-out times with a reinforcement learning algorithm

abstract: Flight delays have a significant impact on the nationpsilas economy. Taxi-out delays in particular constitute a significant portion of the block time of a flight. In the future, it can be expected that accurate predictions of dasiawheels-offpsila time may be used in determining whether an aircraft can meet its allocated slot time, thereby fitting into an en-route traffic flow. Without an accurate taxi-out time prediction for departures, there is no way to effectively manage fuel consumption, emissions, or cost. Dynamically changing operations at the airport makes it difficult to accurately predict taxi-out time. This paper describes a method for estimating average taxi-out times at the airport in 15 minute intervals of the day and at least 15 minutes in advance of aircraft scheduled gate push-back time. A probabilistic framework of stochastic dynamic programming with a learning-based solution strategy called Reinforcement Learning (RL) has been applied. Historic data from the Federal Aviation Administrationpsilas (FAA) Aviation System Performance Metrics (ASPM) database were used to train and test the algorithm. The algorithm was tested on John F. Kennedy International airport (JFK), one of the busiest, challenging, and difficult to predict airports in the United States that significantly influences operations across the entire National Airspace System (NAS). Due to the nature of departure operations at JFK the prediction accuracy of the algorithm for a given day was analyzed in two separate time periods (1) before 4:00 P.M and (2) after 4:00 P.M. On an average across 15 days, the predicted average taxi-out times matched the actual average taxi-out times within plusmn5 minutes for about 65 % of the time (for the period before 4:00 P.M) and 53 % of the time (for the period after 4:00 P.M). The prediction accuracy over the entire day within plusmn5 minutes range of accuracy was about 60 %. Further, application of the RL algorithm to estimate taxi-out times at airports with multi-dependent static surface surveillance data will likely improve the accuracy of prediction. The implications of these results for airline operations and network flow planning are discussed.


title: Association between regional differences in muscle activation in one session of resistance exercise and in muscle hypertrophy after resistance training

abstract: The purpose of this study was to examine if the regional difference in muscle hypertrophy after chronic resistance training is associated with muscle activation after one session of resistance exercise. Twelve men performed one session of resistance exercise of elbow extensors. Before and immediately after the exercise, transverse relaxation time (T2)-weighted magnetic resonance (MR) images of upper arm were recorded to evaluate the muscle activation along its length. In the MR images, T2 for the pixels within the triceps brachii muscle was quantified. The number of pixels with T2 greater than the threshold (mean + 1SD of T2 before the exercise) was expressed as the ratio to the number of pixels occupied by the muscle (%activated area). Another 12 subjects completed 12 weeks of training intervention (3 days per week), which consisted of the same program variables as used in the experiment for the T2 measurement. The cross-sectional areas of the triceps brachii before and after the training intervention were measured from MR images of upper arm. The %activated area of the triceps brachii induced by one session of the exercise was found to be significantly lower in the distal region than the middle and proximal regions. Similarly, the relative increase in muscle cross-sectional area after the 12 weeks of training intervention was significantly less in the distal region than the middle and proximal regions. The results suggest that the regional difference in muscle hypertrophy after chronic resistance training is attributable to the regional difference in muscle activation during the exercise.


title: Developing an innovation smart meter based on CS5490

abstract: This paper proposes developing an innovation smart meter based on CS5490. It uses to measure the energy data and send to PIC 32-bit microcontrollers via RS232 standard. Then it records energy data into SD card that consumer can monitor their energy usage and cost forecasting. The experiment result shows that energy measurement circuit can record the energy usage into SD card. It can be used for NILM system.


title: Conjoined twin's cephalothoracopagus janiceps monoymmetros: a case report

abstract: We present a report on a case of conjoined twin/s (cephalothoracopagus janiceps monosymmetros) diagnosed by ultrasonography and X-ray at 27 weeks' gestation.


title: A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

abstract: Several issues related to the vulnerability of fingerprint recognition systems to attacks have been highlighted in the biometrics literature. One such vulnerability involves the use of artificial fingers, where materials such as Play-Doh, silicone, and gelatin are inscribed with fingerprint ridges. Researchers have demonstrated that some commercial fingerprint recognition systems can be deceived when these artificial fingers are placed on the sensor; that is, the system successfully processes the ensuing fingerprint images, thereby allowing an adversary to spoof the fingerprints of another individual. However, at the same time, several countermeasures that discriminate between live fingerprints and spoof artifacts have been proposed. While some of these antispoofing schemes are hardware based, several software-based approaches have been proposed as well. In this article, we review the literature and present the state of the art in fingerprint antispoofing.


title: Implicit slicing for functionally tailored additive manufacturing

abstract: One crucial component of the additive manufacturing software toolchain is a class of geometric algorithms known as "slicers." The purpose of the slicer is to compute a parametric toolpath and associated commands, which direct an additive manufacturing system to produce a physical realization of a three-dimensional input model. Existing slicing algorithms operate by application of geometric transformations upon the input geometry in order to produce the toolpath. In this paper we introduce a new implicit slicing algorithm based on the computation of toolpaths derived from the level sets of arbitrary heuristics-based or physics-based fields defined over the input geometry. This enables computationally efficient slicing of arbitrarily complex geometries in a straight forward fashion. Additionally, the calculation of component "infill" (as a process control parameter) is explored due to its crucial effect on functional performance fields of interest such as strain and stress distributions. Several examples of the application of the proposed implicit slicer are presented. Finally, an example demonstrating improved structural performance during physical testing is presented. We conclude with remarks regarding the strengths of the implicit approach relative to existing explicit approaches, and discuss future work required in order to extend the methodology. A novel implicitly-defined slicer for additive manufacturing (AM) is developed.The implict formulation allows for slicing based on design intent.The slicer is intended to produce components with tailored functional properties.A large improvement in the mechanical properties of AM components is demonstrated.


title: A three-axis magnetic sensor array system for permanent magnet tracking

abstract: Orientations and positions of a permanent magnet can be acquired via a three-axis magnetic sensor array system. Thus, the position and orientation of an object embedded with the permanent magnet can be tracked in real-time. This magnet tracking system can be adapted to medical and industrial applications. How to detect the weak magnetic signal and the sensor fusion algorithm are vitally important for the accuracy performance of the tracking system. In this study, we present a novel magnet tracking system based on nine three-axis digital magnetic sensors, instead of analog output, with the valid tracking space of 0.5m×0.5m × 0.2m. Before calibration, the system achieved average localization error at 6.62mm and 2.98°. This system has the advantages such as low power consumption, portable, and convenient configuration via a SPI bus.


title: Using interpolation to improve path planning: The Field D* algorithm

abstract: We present an interpolation-based planning and replanning algorithm for generating low-cost paths through uniform and nonuniform resolution grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent's motion to a small set of possible headings (e.g., 0, π/4, π/2, etc.). As a result, even “optimal” grid-based planners produce unnatural, suboptimal paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and produce paths with a range of continuous headings. Consequently, it is particularly well suited to planning low-cost trajectories for mobile robots. In this paper, we introduce a version of the algorithm for uniform resolution grids and a version for nonuniform resolution grids. Together, these approaches address two of the most significant shortcomings of grid-based path planning: the quality of the paths produced and the memory and computational requirements of planning over grids. We demonstrate our approaches on a number of example planning problems, compare them to related algorithms, and present several implementations on real robotic systems.