title: Design and Development of an automated Robotic Pick&Stow System for an e-Commerce Warehouse

abstract: In this paper, we provide details of a robotic system that can automate the task of picking and stowing objects from and to a rack in an e-commerce fulfillment warehouse. The system primarily comprises of four main modules: (1) Perception module responsible for recognizing query objects and localizing them in the 3-dimensional robot workspace; (2) Planning module generates necessary paths that the robot end- effector has to take for reaching the objects in the rack or in the tote; (3) Calibration module that defines the physical workspace for the robot visible through the on-board vision system; and (4) Gripping and suction system for picking and stowing different kinds of objects. The perception module uses a faster region-based Convolutional Neural Network (R-CNN) to recognize objects. We designed a novel two finger gripper that incorporates pneumatic valve based suction effect to enhance its ability to pick different kinds of objects. The system was developed by IITK-TCS team for participation in the Amazon Picking Challenge 2016 event. The team secured a fifth place in the stowing task in the event. The purpose of this article is to share our experiences with students and practicing engineers and enable them to build similar systems. The overall efficacy of the system is demonstrated through several simulation as well as real-world experiments with actual robots.


title: Time optimal path planning considering acceleration limits

abstract: A robot path planning technique is proposed in the paper. It was developed for robots with differential drive, but with minor modifications it could be used for all types of nonholonomic robots. The path was planned in the way to minimise the time of reaching the end point in desired direction and with desired velocity, starting from the initial state described by the start point, initial direction and initial velocity. The limitation was the grip of the tires that results in the acceleration limits. The path is presented as a spline curve and was optimised by placing the control points through which the curve should pass. © 2003 Elsevier B.V. All rights reserved.


title: On the Complexity of Opinions and Online Discussions

abstract: In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity. Are opinions and online discussions falling into demagoguery? In this work, we aim to provide computational tools to investigate this question and, by doing so, explore the nature and complexity of online discussions and their space of opinions, uncovering where each participant lies. More specifically, we present a modeling framework to construct latent representations of opinions in online discussions which are consistent with human judgments, as measured by online voting. If two opinions are close in the resulting latent space of opinions, it is because humans think they are similar. Our framework is theoretically grounded and establishes a surprising connection between opinion and voting models and the sign-rank of matrices. Moreover, it also provides a set of practical algorithms to both estimate the dimensionality of the latent space of opinions and infer where opinions expressed by the participants of an online discussion lie in this space. Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports, and the Newsroom app show that many discussions are multisided, reveal a positive correlation between the complexity of a discussion, its linguistic diversity and its level of controversy, and show that our framework may be able to circumvent language nuances such as sarcasm or humor by relying on human judgments instead of textual analysis.


title: An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

abstract: Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter.


title: SPIN-ON-GLASS THIN FILMS PREPARED FROM A NOVEL POLYSILSESQUIOXANE BY THERMAL AND ULTRAVIOLET-IRRADIATION METHODS

abstract: New dielectric films are prepared by both pyrolytic and photolytic conversion of β-chloroethyl-silsesquioxane (BCESSQ). Film thickness, refractive index, composition, density and morphology are characterized using a palette of techniques including ellipsometry, Rutherford backscattering and forward recoil spectrometry, X-ray reflectivity, and atomic force and electron microscopies. After annealing at 350°C, BCESSQ films, initially 200 nm thick reach about 55% of their original thickness after 20 min. For films heated in air for 4 h, the atom fractions of carbon and hydrogen monotonically decrease to 10 and 30%, respectively, as annealing temperature increases from 225 to 450°C. The BCESSQ reactivity is reflected in the loss of chlorine at 400°C. At 450°C, the film density is 1.88 g/cm3, or 84% of thermally-grown silicon oxide. Upon exposure to ultraviolet ozone radiation, films ranging from ca. 200 to 700 nm are found to convert to ormosil films within 30 min. Surprisingly, the chlorine concentration is found to decrease more quickly than the hydrogen and carbon concentrations, suggesting that the ormosil film evolves HCl leaving a vinyl group of SiCHCH2. This reaction pathway differs from the thermal case. For films prepared by pyrolytic and photolytic methods, atomic force and electron microscopy studies show that the surface is smooth and featureless, the bulk is void free when view at a magnification of 50 000×, and the ormosil/substrate interface is continuous.


Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network


title: Advancing Muscle-Computer Interfaces with High-Density Electromyography

abstract: In this paper we present our results on using electromyographic (EMG) sensor arrays for finger gesture recognition. Sensing muscle activity allows to capture finger motion without placing sensors directly at the hand or fingers and thus may be used to build unobtrusive body-worn interfaces. We use an electrode array with 192 electrodes to record a high-density EMG of the upper forearm muscles. We present in detail a baseline system for gesture recognition on our dataset, using a naive Bayes classifier to discriminate the 27 gestures. We recorded 25 sessions from 5 subjects. We report an average accuracy of 90% for the within-session scenario, showing the feasibility of the EMG approach to discriminate a large number of subtle gestures. We analyze the effect of the number of used electrodes on the recognition performance and show the benefit of using high numbers of electrodes. Cross-session recognition typically suffers from electrode position changes from session to session. We present two methods to estimate the electrode shift between sessions based on a small amount of calibration data and compare it to a baseline system with no shift compensation. The presented methods raise the accuracy from 59% baseline accuracy to 75% accuracy after shift compensation. The dataset is publicly available.


title: Vehicle routing problem with time-windows for perishable food delivery

abstract: This study has extended a vehicle routing problem, with time-windows (VRPTW), by considering the randomness of the perishable food delivery process, and constructing a SVRPTW model, to obtain optimal delivery routes, loads, fleet dispatching and departure times for delivering perishable food from a distribution center. Our objective was to minimize not only the fixed costs for dispatching vehicles, but also the transportation, inventory, energy and penalty costs for violating time-windows. We also discussed time-dependent travel and time-varying temperatures, during the day, modifying the objective functions as well as the constraints in the above mathematical programming models. Algorithms were developed to solve the proposed models; results indicated that inventory and energy costs can significantly influence total delivery costs. It was found that our proposed models yielded better results than the traditional VRPTW models.


title: A Universal Intelligent System-on-Chip Based Sensor Interface

abstract: The need for real-time/reliable/low-maintenance distributed monitoring systems, e.g., wireless sensor networks, has been becoming more and more evident in many applications in the environmental, agro-alimentary, medical, and industrial fields. The growing interest in technologies related to sensors is an important indicator of these new needs. The design and the realization of complex and/or distributed monitoring systems is often difficult due to the multitude of different electronic interfaces presented by the sensors available on the market. To address these issues the authors propose the concept of a Universal Intelligent Sensor Interface (UISI), a new low-cost system based on a single commercial chip able to convert a generic transducer into an intelligent sensor with multiple standardized interfaces. The device presented offers a flexible analog and/or digital front-end, able to interface different transducer typologies (such as conditioned, unconditioned, resistive, current output, capacitive and digital transducers). The device also provides enhanced processing and storage capabilities, as well as a configurable multi-standard output interface (including plug-and-play interface based on IEEE 1451.3). In this work the general concept of UISI and the design of reconfigurable hardware are presented, together with experimental test results validating the proposed device.


title: Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering

abstract: Defects generated during integrated circuit (IC) fabrication processes are classified into global defects and local defects according to their generation causes. Spatial patterns of locally clustered defects are likely to contain the information related to their defect generation mechanisms. In this paper, we propose a model-based clustering for spatial patterns of local defects to reflect real situations. A flexible two-step approach is proposed to classify the spatial defects patterns via support vector clustering and Bayesian method. Support vector clustering is employed to separate global defects from the local ones to improve both clustering accuracy and computational efficiency in further analysis. A new mixture model is proposed for modeling the distribution of local defects on the wafers. Local defect clusters with amorphous/linear, curvilinear, and ring patterns are modeled by multivariate normal distribution, principal curve, and spherical shell, respectively. A Bayesian inference procedure is then applied for parametric pattern recognition of the local defects. Results from both simulated data and real wafer map data demonstrate potential in applying our approach to analyze general defect patterns in IC manufacturing.


title: Image Processing and Image Mining using Decision Trees

abstract: Valuable information can be hidden in images, however, few research discuss data mining on them. In this paper, we propose a general framework based on the decision tree for mining and processing image data. Pixel-wised image features were extracted and transformed into a database-like table which allows various data mining algorithms to make explorations on it. Each tuple of the transformed table has a feature descriptor formed by a set of features in conjunction with the target label of a particular pixel. With the label feature, we can adopt the decision tree induction to realize relationships between attributes and the target label from image pixels, and to construct a model for pixel-wised image processing according to a given training image dataset. Both experimental and theoretical analyses were performed in this study. Their results show that the proposed model can be very efficient and effective for image processing and image mining. It is anticipated that by using the proposed model, various existing data mining and image processing methods could be worked on together in different ways. Our model can also be used to create new image processing methodologies, refine existing image processing methods, or act as a powerful image filter.


title: Decoronation of an ankylosed tooth for preservation of alveolar bone prior to implant placement*

abstract: sites demonstrated complete preservation of the height and width of ankylosis; bone preservation the alveolar bone at the decoronation site, whereas the tooth ex


title: CT Dental Artifact: Comparison of an Iterative Metal Artifact Reduction Technique with Weighted Filtered Back-Projection

abstract: BackgroundDental hardware produces streak artifacts on computed tomography (CT) images reconstructed with the standard weighted filtered back projection (wFBP) method.PurposeTo perform a preliminar...


title: Motion Blur Kernel Estimation via Deep Learning

abstract: The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering-based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges. Facilitated by the learned sharp edges, the proposed deblurring algorithm does not require any coarse-to-fine strategy or edge selection, thereby significantly simplifying kernel estimation and reducing computation load. Extensive experimental results on challenging blurry images demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of visual quality and run-time.


title: Switching clozapine responders to olanzapine

abstract: Background: Clozapine is an atypical antipsychotic indicated for the management of severely ill patients with schizophrenia who have failed to respond adequately to standard drug treatment. The significant risk of agranulocytosis and seizure associated with clozapine has led to the restrictions in its use. Additionally, drug-induced sedation, sialorrhea, enuresis, and weight gain are often cited as problematic consequences of clozapine treatment. Our primary objective was to determine the effectiveness and safety of a method of slow cross-titration from clozapine to olanzapine among patients responsive to clozapine treatment but experiencing medication-induced adverse events. Method: Changes in symptomatology, mood, subjective response, and safety were examined in 20 outpatients meeting DSM-IV criteria for schizophrenia or schizoaffective disorder who converted from clozapine to olanzapine. Patients were considered clozapine-responsive as evidenced by improved social function and decreased symptoms with clozapine therapy; however, they were interested in alternative pharmacologic treatment because of clozapine-related side effects. Results: Equivalent efficacy of olanzapine to clozapine was found in 90% of the patients (18/20) in the study group, without rehospitalization or suicidal behavior in any of the patients. Also notable was a reduction in drug-induced side effects and improved subjective response to pharmacotherapy. Conclusion: The successful conversion from clozapine to olanzapine has the potential to provide great benefits for the patient, including reducing drug-induced side effects while maintaining symptom control. These preliminary results suggest that further research on converting clozapine responders to olanzapine is warranted.


title: Stretchable and Reversibly Deformable Radio Frequency Antennas Based on Silver Nanowires

abstract: We demonstrate a class of microstrip patch antennas that are stretchable, mechanically tunable, and reversibly deformable. The radiating element of the antenna consists of highly conductive and stretchable material with screen-printed silver nanowires embedded in the surface layer of an elastomeric substrate. A 3-GHz microstrip patch antenna and a 6-GHz 2-element patch array are fabricated. Radiating properties of the antennas are characterized under tensile strain and agree well with the simulation results. The antenna is reconfigurable because the resonant frequency is a function of the applied tensile strain. The antenna is thus well suited for applications like wireless strain sensing. The material and fabrication technique reported here could be extended to achieve other types of stretchable antennas with more complex patterns and multilayer structures.


title: Learning assistive strategies for exoskeleton robots from user-robot physical interaction

abstract: Abstract Social demand for exoskeleton robots that physically assist humans has been increasing in various situations due to the demographic trends of aging populations. With exoskeleton robots, an assistive strategy is a key ingredient. Since interactions between users and exoskeleton robots are bidirectional, the assistive strategy design problem is complex and challenging. In this paper, we explore a data-driven learning approach for designing assistive strategies for exoskeletons from user-robot physical interaction. We formulate the learning problem of assistive strategies as a policy search problem and exploit a data-efficient model-based reinforcement learning framework. Instead of explicitly providing the desired trajectories in the cost function, our cost function only considers the user’s muscular effort measured by electromyography signals (EMGs) to learn the assistive strategies. The key underlying assumption is that the user is instructed to perform the task by his/her own intended movements. Since the EMGs are observed when the intended movements are achieved by the user’s own muscle efforts rather than the robot’s assistance, EMGs can be interpreted as the “cost” of the current assistance. We applied our method to a 1-DoF exoskeleton robot and conducted a series of experiments with human subjects. Our experimental results demonstrated that our method learned proper assistive strategies that explicitly considered the bidirectional interactions between a user and a robot with only 60 seconds of interaction. We also showed that our proposed method can cope with changes in both the robot dynamics and movement trajectories.


title: Hemitransdomal versus Dome‐Binding Suture

abstract: The dome-binding suture (DBS) and hemitransdomal suture (HTS) are suture techniques used to narrow and define the nasal tip. The DBS can create a pinched, unnatural appearance, while the HTS puts the lateral crus in a more favorable orientation. This allows a natural contour between the nasal tip and alar lobule while maintaining alar margin support. Objective measurement of the rotational axis of the lateral crus between the DBS and the HTS has not been reported in the literature. To determine whether the DBS or HTS technique results in a more favorable rotational axis of the lateral crus as measured by the alar surface septal angle (ASSA). Open rhinoplasty with cephalic trim and placement of a DBS or HTS was performed in 6 cadaveric heads, for a total of 12 lower lateral cartilages at the VirtuOHSU Simulation and Surgical Training Center at Oregon Health and Science University (OHSU). ASSA measurements were taken at baseline and after placement of either a DBS or HTS. A total of 36 ASSA measurements were obtained. The median baseline ASSA prior to suture placement was 142 degrees (interquartile range [IQR]: 131.5-145 degrees), following DBS placement was 141 degrees (IQR: 33-150.5 degrees), and following HTS placement was 112 degrees (IQR: 108-117 degrees). There was no statistically significant difference of ASSA measurements between baseline and DBS placement (p = 0.24), but there was a statistically significant difference between baseline and HTS (p < 0.0001) and between DBS and HTS (p < 0.0001). The HTS technique creates a more favorable rotational axis of the lateral crus as compared with the DBS, as measured by the ASSA. This study provides objective data to support the use of the HTS for nasal tip contouring.


title: Toward GPU-accelerated Database Optimization

abstract: For over three decades, research investigates optimization options in DBMSs. Nowadays, the hardware used in DBMSs become more and more heterogeneous, because processors are bound by a fixed energy budget leading to increased parallelism. Existing optimization approaches in DBMSs do not exploit parallelism for a single optimization task and, hence, can only benefit from the parallelism offered by current hardware by batch-processing multiple optimization tasks.


title: CAPTION-ing the Situation: A Lexically-Derived Taxonomy of Psychological Situation Characteristics

abstract: In comparison with personality taxonomic research, there has been much less advancement toward establishing an integrative taxonomy of psychological situation characteristics (similar to personality characteristics for persons). One of the main concerns has been the limited content coverage of the characteristics being used. To address this issue, we present a collection of 4 lexically based studies using the largest-to-date number of situation characteristics to identify the major dimensions of the psychological situation. These studies each implemented a unique sampling and analytic methodology—namely, a qualitative dimensional exploration; the factor analyses of 2, independent samples of large-scale in situ ratings of situations; and the use of lexical-vector representations from neural-network-based models derived from millions of sources of natural-language usage with a total of 146.7 billion words. Across these studies, a clear 7-dimensional structure emerged: Complexity, Adversity, Positive Valence, Typicality, Importance, Humor, and Negative Valence—collectively referred to as the “CAPTION” model, which parsimoniously integrates the diversity of dimensions found in the extant literature. We then introduce both full- and short-form measures of these CAPTION. Data from 2 additional diverse samples of native English speakers suggest that the measures have good psychometric properties, and are able to predict a broad range of important psychological outcomes (e.g., behaviors, affect, motivation, and need satisfaction), even when pitted against extant situation taxonomic frameworks. We conclude by discussing how the CAPTION framework may serve as a useful tool for conceptualizing and measuring a broad range of psychological situations across all areas of psychology. (PsycINFO Database Record (c) 2017 APA, all rights reserved)


title: Limiting the Impact of Stealthy Attacks on Industrial Control Systems

abstract: While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.


title: FLIR image segmentation and natural object classification

abstract: In this paper we compare four classification techniques for classifying texture data of various natural objects found in forward-looking infrared (FLIR) images. The techniques compared include linear discriminant analysis, mean classifier and two different models of k-nearest neighbour methods. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across the four classifiers on both unnormalised and normalised data. On unnormalised data, the average best recognition rate obtained using a ten fold cross-validation is 96.5%, and on unnormalised data it is 86.1% with a single nearest neighbour technique.


title: How words can and cannot be learned by observation.

abstract: Three experiments explored how words are learned from hearing them across contexts. Adults watched 40-s videotaped vignettes of parents uttering target words (in sentences) to their infants. Videos were muted except for a beep or nonsense word inserted where each “mystery word” was uttered. Participants were to identify the word. Exp. 1 demonstrated that most (90%) of these natural learning instances are quite uninformative, whereas a small minority (7%) are highly informative, as indexed by participants’ identification accuracy. Preschoolers showed similar information sensitivity in a shorter experimental version. Two further experiments explored how cross-situational information helps, by manipulating the serial ordering of highly informative vignettes in five contexts. Response patterns revealed a learning procedure in which only a single meaning is hypothesized and retained across learning instances, unless disconfirmed. Neither alternative hypothesized meanings nor details of past learning situations were retained. These findings challenge current models of cross-situational learning which assert that multiple meaning hypotheses are stored and cross-tabulated via statistical procedures. Learners appear to use a one-trial “fast-mapping” procedure, even under conditions of referential uncertainty.


title: STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTING

abstract: The rapid growth of users on the cloud service and number of services to the user increases the load on the servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used effectively a load balancing technique not only to balance the resources for servers but also reduce the negative impact to the end-user service. The current, load balancing techniques have solved the various problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The study of improving these parameters have great significance to improving system performance through load balancing. From there, we can propose more effective methods of load balancing, in order to increase system performance. Therefore, in this paper we researched some parameters affecting the performance load balancing on the cloud computing.


title: Monte Carlo Beam Search

abstract: Monte Carlo tree search is the state of the art for multiple games and for solving puzzles such as Morpion Solitaire. Nested Monte Carlo (NMC) search is a Monte Carlo tree search algorithm that works well for solving puzzles. We propose to enhance NMC search with beam search. We test the algorithm on Morpion Solitaire. Thanks to beam search, our program has been able to match the record score of 82 moves. Monte Carlo beam search achieves better scores in less time than NMC search alone.


title: Physicians' attitude toward female genital plastic surgery: a multinational survey.

abstract: INTRODUCTION ::: The demand for female genital plastic surgery (FGPS) has increased over the last few decades. Yet, to date, there are no objective explicit measurements to define "abnormal" appearance of genital organs. Using the results of this study, we aimed to produce a statement of the European Society for Sexual Medicine (ESSM) on FGPS practice. ::: ::: ::: AIMS ::: To evaluate the prevalence of demand for FGPS and to explore the attitudes of sexual medicine specialists toward indications for FGPS. ::: ::: ::: METHODS ::: Attendees of the 2012 Annual Congress of the ESSM in Amsterdam, the Netherlands, were asked to participate in a survey during the congress. ::: ::: ::: MAIN OUTCOME MEASURE ::: A 25-item self-report, closed-question questionnaire subdivided into three sections: sociodemographic data, professional background, and personal attitudes toward FGPS. ::: ::: ::: RESULTS ::: Overall, a total of 360 physicians (mean age 48 years; range 23-72) from different medical disciplines completed the survey. There were diverse responses among participants regarding the definition of abnormal labial appearance and the techniques for labial reduction they perform. Overall, 65% responded that FGPS is frequently or occasionally demanded by the patients they treat. Likewise, most physicians (63%) reported that they never perform FGPS. Conversely, only 14% reported that they either frequently or occasionally perform FGPS. Almost one-third of participants believe that FGPS (labial surgery) improves sexual function. Fifty-two percent of participants answered that they believe that self image is the main reason for women to ask for labial surgery. ::: ::: ::: CONCLUSIONS ::: Self-image was regarded as the main factor in the demand for FGPS. Many practitioners in the field of sexual medicine recommend that women be referred for consultation with a psychiatrist or psychologist before undergoing FGPS.


What are the Gaps in Mobile Patient Portal? Mining Users Feedback Using Topic Modeling


title: Emotional intelligence and its relationship to workplace performance outcomes of leadership effectiveness

abstract: Purpose – This study seeks to investigate the relationship between emotional intelligence (EI), personality, cognitive intelligence and leadership effectiveness.Design/methodology/approach – Senior executives (n=41) completed an ability measure of EI (MSCEIT), a measure of personality (16PF5) and a measure of cognitive ability (the Wechsler abbreviated scale of intelligence (WASI)). Leadership effectiveness was assessed using an objective measure of performance and a 360° assessment involving each leader's subordinates and direct manager (n=149).Findings – Correlational and regression analyses revealed that higher EI was associated with higher leadership effectiveness, and that EI explained variance not explained by either personality or IQ.Originality/value – This paper establishes a link between EI and workplace measures of leadership effectiveness.


title: Development of an ANN-Based Linearization Technique for the VCO Thermistor Circuit

abstract: This paper presents the development of an artificial neural network (ANN)-based linearization technique for the voltage controlled oscillator (VCO) thermistor circuit. VCO thermistor circuit exhibits a linear analog temperature-frequency relation over a temperature range of 0 °C-100 °C with good response and reasonable error. An ANN-based direct modeling technique is used for a nonlinearity estimation of VCO thermistor circuit to further improve the linearity. The performance of the developed technique has been experimentally verified. A linearity of approximately ±0.2% and sensitivity of 5 kHz/°C are achieved over a temperature range of 0 °C-100 °C, with a high thermal stability. A notable feature of the developed technique is linearity and sensitivity of the temperature measurement, which are quite high. The efficacy of the technique is established through simulation studies and its practicality is demonstrated on a prototype unit.


title: Return to Antikythera: Multi-session SLAM Based AUV Mapping of a First Century B.C. Wreck Site

abstract: This paper describes an expedition to map a first century B.C. ship wreck off the coast of the Greek island of Antikythera using an Autonomous Underwater Vehicle (AUV) equipped with a high-resolution stereo imaging system. The wreck, first discovered in 1900, has yielded a wealth of important historical artefacts from two previous interventions, including the renowned Antikythera mechanism. The deployments described in this paper aimed to map the current state of the wreck site prior to further excavation. Over the course of 10 days of operation, the AUV completed multiple dives over the main wreck site and other nearby targets of interest. This paper describes the motivation for returning to the wreck and producing a detailed map, gives an overview of the techniques used for multi-session Simultaneous Localisation and Mapping (SLAM) to stitch data from two dives into a single, composite map of the site and presents preliminary results of the mapping exercise.


title: NAS to NextGen: the evolution of a socio-technical system

abstract: Several legacy Federal Aviation Administration (FAA) programs have shown the need to bridge the gap between the user community and the engineers implementing a system for the National Airspace System (NAS). There is often a challenge between defining requirements and implementing requirements to meet user needs without imposing excessive cost and schedule impacts. Furthermore, as individual systems are developed for the Next Generation (NextGen) Air Transportation System, they most likely serve as part of a thread that provides a service capability. Implementing effective solutions must be assessed and orchestrated within the broader context of the NextGen vision. This paper presents lessons learned and explores attributes and challenges for achieving symbiotic socio-technical systems.


title: A Framework for Optimal Grasp Contact Planning

abstract: We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions under which minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.


title: Designing Groundless Body Channel Communication Systems: Performance and Implications

abstract: Novel interactions that capacitively couple electromagnetic (EM) fields between devices and the human body are gaining more attention in the human-computer interaction community. One class of these techniques is Body Channel Communication (BCC), a method that overlays physical touch with digital information. Despite the number of published capacitive sensing and communication prototypes, there exists no guideline on how to design such hardware or what are the application limitations and possibilities. Specifically, wearable (groundless) BCC has been proven in the past to be extremely challenging to implement. Additionally, the exact behavior of the human body as an EM-field medium is still not fully understood today. Consequently, the application domain of BCC technology could not be fully explored. This paper addresses this problem. Based on a recently published general purpose wearable BCC system, we first present a thorough evaluation of the impact of various technical parameter choices and an exhaustive channel characterization of the human body as a host for BCC. Second, we discuss the implications of these results for the application design space and present guidelines for future wearable BCC systems and their applications. Third, we point out an important observation of the measurements, namely that BCC can employ the whole body as user interface (and not just hands or feet). We sketch several applications with these novel interaction modalities.


title: TD Learning with Constrained Gradients

abstract: Temporal Difference Learning with function approximation is known to be unstable. Previous work like \citet{sutton2009fast} and \citet{sutton2009convergent} has presented alternative objectives that are stable to minimize. However, in practice, TD-learning with neural networks requires various tricks like using a target network that updates slowly \citep{mnih2015human}. In this work we propose a constraint on the TD update that minimizes change to the target values. This constraint can be applied to the gradients of any TD objective, and can be easily applied to nonlinear function approximation. We validate this update by applying our technique to deep Q-learning, and training without a target network. We also show that adding this constraint on Baird's counterexample keeps Q-learning from diverging.


title: Integrating Classification and Association Rule Mining: A Concept Lattice Framework

abstract: Concept lattice is an efficient tool for data analysis. In this paper we show how classification and association rule mining can be unified under concept lattice framework. We present a fast algorithm to extract association and classification rules from concept lattice.


title: SNS Based Predictive Model for Depression

abstract: Worldwide the Mental illness is a primary cause of disability. It affects millions of people each year and whom of few receives cure. We found that social networking sites (SNS) can be used as a screening tool for discovering an affective mental illness in individuals. SNS posting truly depicts user’s current behavior, thinking style, and mood. We consider a set of behavioral attributes concerning to socialization, socioeconomics, familial, marital status, feeling, language use, and references of antidepressant treatments. We take advantage of these behavioral attributes to envision a tool that can provide prior alerts to an individual based on their SNS data regarding Major Depression Disorder (MDD). We propose a method, to automatically classify individuals into displayer and non-displayer depression using ensemble learning techniquefrom theirFacebook profile. Our developed tool is used for MDD diagnosis of individuals in additional to questioner techniques such as Beck Depression Inventory (BDI) and CESD-R.


title: Planetary-scale views on a large instantmessaging network

abstract: We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.


title: Benzo[b]thiophene derivatives. XX. The sulfur isostere of 5,6-Dihydroxytryptamine†

abstract: 3β-Aminoethyl-5,6-dihydroxybenzo[b]thiophene, the sulfur bioisostere of 5,6-dihydroxytryptamine, a selective neurotoxin, has been synthesized and shown to have significant effect on biogenic amine levels both centrally and peripherally. A derivative, 3β-aminoethyl-5,6-isopropylidenedioxybenzo[b]thiophene, showed significant but opposite activity. The syntheses and preliminary pharmacology are reported.


title: Consistency in synesthetic experience to vowels and consonants: five case studies

abstract: The present study attempted to specify the actual degree of consistency in synesthetic experience in two synesthetes and three non-synesthetes. By comparing colour chip selections, and size and density ratings, to the same set of stimulus letters on separate occasions, it was found that the synesthetic subjects showed a high consistency in responding over a short (24 hours) as well as a long (one year) time span. In comparison, the non-synesthetic subjects demonstrated less consistent responding when retested after 24 hours. However, the differences between the two groups were smaller than expected. Consistency to vowels and consonants was compared, and it was found that consistency to consonants was comparable to, or even higher than, vowel consistency in all subjects.


title: Automatic fairing algorithm for B-spline curves

abstract: Abstract An algorithm is presented for locally fairing B-spline curves. The algorithm is based on repeatedly removing and reinserting knots of the spline. These knots are selected automatically by means of a fairness criterion. The proposed scheme involves a new knot removal algorithm that compares favourably to existing ones.


title: Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched Arabic Data

abstract: We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data. The process involves developing clear annotation standards and Guidelines, streamlining the annotation process, and implementing quality control measures. We used two main protocols for annotation: in-lab gold annotations and crowd sourcing annotations. We developed a web-based annotation tool to facilitate the management of the annotation process. The current version of the repository contains a total of 886,252 tokens that are tagged into one of sixteen code-switching tags. The data exhibits code switching between Modern Standard Arabic and Egyptian Dialectal Arabic representing three data genres: Tweets, commentaries, and discussion fora. The overall Inter-Annotator Agreement is 93.1%.


title: Foodness Proposal for Multiple Food Detection by Training of Single Food Images

abstract: We propose a CNN-based "food-ness" proposal method which requires neither pixel-wise annotation nor bounding box annotation. Some proposal methods have been proposed to detect regions with high "object-ness" so far. However, many of them generated a large number of candidates to raise the recall rate. Considering the recent advent of the deeper CNN, these methods to generate a large number of proposals have difficulty in processing time for practical use. Meanwhile, a fully convolutional network (FCN) was proposed the network of which localizes target objects directly. FCN saves computational cost, although FCN is essentially equivalent to the sliding window search. This approach made large progress and achieved significant success in various tasks. Then, in this paper we propose an intermediate approach between the traditional proposal approach and the fully convolutional approach. Especially we propose a novel proposal method which generates high "food-ness" regions by fully convolutional networks and back-propagation based approach with training food images gathered from the Web.


title: Representing the existence and the location of hidden objects: Object permanence in 6- and 8-month-old infants

abstract: The present experiment investigated two facets of object permanence in young infants: the ability to represent the existence and the location of a hidden stationary object, and the ability to ‘represent the existence and the trajectory of a hidden moving object. Six- and B-month-old infants sat in front of a screen; to the left of the screen was an inclined ramp. The infants watched the following event: the screen was raised and lowered, and a toy car rolled down the ramp, passed behind the screen, and exited the apparatus to the right. After the infants habituated to this event, they saw two test events. These were identical to the habituation event, except that a box was placed behind the screen. In one event (possible event), the box stood in back of the car’s tracks; in the other (impossible event), it stood on top of the tracks, blocking the car’s path. Infants looked longer at the impossible than at the possible event, indicating that they were surprised to see the car reappear from behind the screen when the box stood in its path. A control experiment in which the box was placed in front (possible event) or on top (impossible event) of the car’s tracks yielded similar results. Together, the results of these experiments suggest that infants understood that (I) the box continued to exist, in its same location, after it was occluded by the screen; (2) the car continued to exist, and pursued its trajectory, after it disappeared behind the screen; and (3) the car could not roll through the space occupied by the box. These results have implications for theory and research on the development of infants’ knowledge about objects and infants’ reasoning abilities.


title: Automatic Learning of Combat Models for RTS Games

abstract: Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or "simulator") of the game at hand. However, in some games such forward model is not readily available. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several approaches to learn such combat model from replay data to models generated by hand. We use StarCraft, a Real-Time Strategy (RTS) game, as our application domain. Specifically, we use a large collection of already collected replays, and focus on learning a combat model for tactical combats.


title: Intelligent UAV in smart cities using IoT

abstract: The concept of a Smart City is becoming an increasingly important research area around the world. The amount of information collected and the number of stationary sensors, surveillance cameras and other devices that need to be places in a smart city is so vast that using a mobile platform to replace them can reduce the cost in both energy and resources. This paper proposes an open source intelligent IoT platform that would allow implementation of various Smart City services such as: smart home, smart unmanned aerial vehicle, smart energy grid, smart transportation systems and other possible future services that will evolve along with the consumer demand. A smart platform is enabled by Machine-to-Machine communications and multi-agent system architecture. Such a platform will allow solving multiple issues with the smart city implementation such as scalability, integration and interoperation of different IoT services and networks that are run on different hardware. We discuss the structure of such a platform.


title: A combined statistical and biomechanical model for estimation of intra-operative prostate deformation

abstract: An approach for estimating the deformation of the prostate caused by transrectal ultrasound (TRUS) probe insertion is presented. This work is particularly useful during brachytherapy procedures, in which planning for radioactive seed insertion is performed on preoperative scans, and significant deformation of the prostate can occur during the procedure. The approach makes use of a patient specific biomechanical model to run simulations for TRUS probe insertion, extract the main modes of the deformation of the prostate, and use this information to establish a deformable registration between 2 orthogonal cross-sectional ultrasound images and the preoperative prostate. In the work presented here, the approach is tested on an anatomy-realistic biomechanical phantom for the prostate and results are reported for 5 test simulations. More than 73% of maximum deformation of the prostate was recovered, with the estimation error mostly attributed to the relatively small number of biomechanical simulations used for training.


title: Experimental evaluation of cyber attacks on Automatic Generation Control using a CPS Security Testbed

abstract: Cyber-Physical Security Testbeds serve as valuable experimental platforms to implement and evaluate realistic, complex cyber attack-defense experiments. Testbeds, unlike traditional simulation platforms, capture communication, control and physical system characteristics and their interdependencies adequately in a unified environment. In this paper, we show how the PowerCyber CPS testbed at Iowa State was used to implement and evaluate cyber attacks on one of the fundamental Wide-Area Control applications, namely, the Automatic Generation Control (AGC). We provide a brief overview of the implementation of the experimental setup on the testbed. We then present a case study using the IEEE 9 bus system to evaluate the impacts of cyber attacks on AGC. Specifically, we analyzed the impacts of measurement based attacks that manipulated the tie-line and frequency measurements, and control based attacks that manipulated the ACE values sent to generators. We found that these attacks could potentially create under frequency conditions and could cause unnecessary load shedding. As part of future work, we plan to extend this work and utilize the experimental setup to implement other sophisticated, stealthy attack vectors and also develop attack-resilient algorithms to detect and mitigate such attacks.


title: Acceptance and mindfulness-based therapy: New wave or old hat?

abstract: Abstract Some contemporary theorists and clinicians champion acceptance and mindfulness-based interventions, such as Acceptance and Commitment Therapy (ACT), over cognitive–behavioral therapy (CBT) for the treatment of emotional disorders. The objective of this article is to juxtapose these two treatment approaches, synthesize, and clarify the differences between them. The two treatment modalities can be placed within a larger context of the emotion regulation literature. Accordingly, emotions can be regulated either by manipulating the evaluation of the external or internal emotion cues (antecedent-focused emotion regulation) or by manipulating the emotional responses (response-focused emotion regulation). CBT and ACT both encourage adaptive emotion regulation strategies but target different stages of the generative emotion process: CBT promotes adaptive antecedent-focused emotion regulation strategies, whereas acceptance strategies of ACT counteract maladaptive response-focused emotion regulation strategies, such as suppression. Although there are fundamental differences in the philosophical foundation, ACT techniques are fully compatible with CBT and may lead to improved interventions for some disorders. Areas of future treatment research are discussed.


title: Qualitative research sample design and sample size: resolving and unresolved issues and inferential imperatives.

abstract: ► Resolved, resolving and unresolved issues for qualitative sample design. ► Successful models for qualitative sampling for NIH quality research. ► Inferential issues: differences in qualitative quantitative sampling paradigms. ► Epistemology of qualitative sample size considerations.


title: Branch vanguard: decomposing branch functionality into prediction and resolution instructions

abstract: While control speculation is highly effective for generating good schedules in out-of-order processors, it is less effective for in-order processors because compilers have trouble scheduling in the presence of unbiased branches, even when those branches are highly predictable. In this paper, we demonstrate a novel architectural branch decomposition that separates the prediction and deconvergence point of a branch from its resolution, which enables the compiler to profitably schedule across predictable, but unbiased branches. We show that the hardware support for this branch architecture is a trivial extension of existing systems and describe a simple code transformation for exploiting this architectural support. As architectural changes are required, this technique is most compelling for a dynamic binary translation-based system like Project Denver. We evaluate the performance improvements enabled by this transformation for several in-order configurations across the SPEC 2006 benchmark suites. We show that our technique produces a Geomean speedup of 11% for SPEC 2006 Integer, with speedups as large as 35%. As floating point benchmarks contain fewer unbiased, but predictable branches, our Geomean speedup on SPEC 2006 FP is 7%, with a maximum speedup of 26%.


title: Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization

abstract: Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. Our key contribution is significantly improving the prediction hitting rate (or prediction accuracy) for each data point based on its nearby data values along multiple dimensions. We derive a series of multilayer prediction formulas and their unified formula in the context of data compression. One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn. We explore the best layer for the prediction by considering the impact of compression errors on the prediction accuracy. Moreover, we propose an adaptive error-controlled quantization encoder, which can further improve the prediction hitting rate considerably. The data size can be reduced significantly after performing the variable-length encoding because of the uneven distribution produced by our quantization encoder. We evaluate the new compressor on production scientific data sets and compare it with many other state-of-the-art compressors: GZIP, FPZIP, ZFP, SZ-1.1, and ISABELA. Experiments show that our compressor is the best in class, especially with regard to compression factors (or bit-rates) and compression errors (including RMSE, NRMSE, and PSNR). Our solution is better than the second-best solution by more than a 2x increase in the compression factor and 3.8x reduction in the normalized root mean squared error on average, with reasonable error bounds and user-desired bit-rates.


title: Analysis of modular biped wall climbing robot with high mobility and effective transit mechanism

abstract: Model and analysis of a wall climbing robot based on the link and suction mechanism. Many wall climbing robots are designed and shaped for few decades, but still, the underlying problems for the wall climbing are faced by the designers. Wall-climbing robots consist of a two mechanism, one is link and other is suction mechanism. In link mechanism there are two joints I-joint and T-joint, Where I-joint can rotate about its axis, T-joint can rotate about 180, Both the joints give the ability to rotate in clockwise and anticlockwise direction, which helps the robot to have a good transient mechanism between the horizontal wall to vertical wall. Based on these link mechanisms static and dynamic analysis were carried out to find the stress and strain developed in the joint during the motion. To achieve the suction mechanism, suction cups are placed on the suction base in a triangle shape and three suction cups are employed. Based on the weight of the robot load distribution is calculated. Both the static and dynamic analysis is performed using the software Ansys R15 and Inventry15.


title: QoS-aware middleware for Web services composition

abstract: The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.


title: Context-aware web search abandonment prediction

abstract: Web search queries without hyperlink clicks are often referred to as abandoned queries. Understanding the reasons for abandonment is crucial for search engines in evaluating their performance. Abandonment can be categorized as good or bad depending on whether user information needs are satisfied by result page content. Previous research has sought to understand abandonment rationales via user surveys, or has developed models to predict those rationales using behavioral patterns. However, these models ignore important contextual factors such as the relationship between the abandoned query and prior abandonment instances. We propose more advanced methods for modeling and predicting abandonment rationales using contextual information from user search sessions by analyzing search engine logs, and discover dependencies between abandoned queries and user behaviors. We leverage these dependency signals to build a sequential classifier using a structured learning framework designed to handle such signals. Our experimental results show that our approach is 22% more accurate than the state-of-the-art abandonment-rationale classifier. Going beyond prediction, we leverage the prediction results to significantly improve relevance using instances of predicted good and bad abandonment.


title: Characterizing User Behavior and Information Propagation on a Social Multimedia Network

abstract: An increasing portion of modern socializing takes place via online social networks. Members of these communities often play distinct roles that can be deduced from observations of users' online activities. One such activity is the sharing of multimedia, the popularity of which can vary dramatically. Here we discuss our initial analysis of anonymized, scraped data from consenting Facebook users, together with associated demographic and psychological profiles. We present five clusters of users with common observed online behaviors, where these users also show correlated profile characteristics. Finally, we identify some common properties of the most popular multimedia content.


title: Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition

abstract: Fine-grained recognition is challenging mainly because the inter-class differences between fine-grained classes are usually local and subtle while intra-class differences could be large due to pose variations. In order to distinguish them from intra-class variations, it is essential to zoom in on highly discriminative local regions. In this work, we introduce a reinforcement learning-based fully convolutional attention localization network to adaptively select multiple task-driven visual attention regions. We show that zooming in on the selected attention regions significantly improves the performance of fine-grained recognition. Compared to previous reinforcement learning-based models, the proposed approach is noticeably more computationally efficient during both training and testing because of its fully-convolutional architecture, and it is capable of simultaneous focusing its glimpse on multiple visual attention regions. The experiments demonstrate that the proposed method achieves notably higher classification accuracy on three benchmark fine-grained recognition datasets: Stanford Dogs, Stanford Cars, and CUB-200-2011.


title: Cataract diagnosis by measurement of backscattered light

abstract: We present a portable optical cataract assessment technology which measures with a circular photodetector the fraction of light scattered backwards by the human eye lens when illuminated by a laser diode. As our signal arises directly from the fundamental pathology—increased scattering in the lens—it directly assesses cataract extent and progression. Initial clinical results in undilated human eyes show device reading correlations in agreement with clinical examination and Scheimpflug photography.


title: Subsidence feature discrimination using deep convolutional neural networks in synthetic aperture radar imagery

abstract: Effective detection and discrimination of surface deformation features in Synthetic Aperture Radar imagery is one of the most important applications of the data. Areas that undergo surface deformation can pose health and safety risks which necessitates an automatic and reliable means of surface deformation discrimination. Due to the similarities between subsidence features and false positives, advanced discrimination methods are necessary in order to obtain meaningful results. Convolutional neural networks have shown to be effective discriminators in other image processing tasks by making use of the spatial relations and underlying characteristics of images in order to classify inputs into classes. This paper presents a Convolutional Neural Network tailored to process interferometric Synthetic Aperture Radar imagery to identify subsidence features. Initial results indicate that the network and its trained kernel weights are able to effectively identify false positives in a small dataset due to careful network selection. Future work includes improving the initial detections to reduce false alarms and making use of multi-channel Synthetic Aperture Radar data directly into the network.


A process mining driven framework for clinical guideline improvement in critical care


title: Understanding the Perpetration of Employee Computer Crime in the

abstract: While hackers and viruses fuel the IS security concerns for organisations, the problems posed by employee computer crime should not be underestimated. Indeed, a number of IS security researchers have turned their attention to the 'insider' threat. Of this group, several focus on the offender, either in terms of a series of attributes required for perpetration, or with reference to forms of safeguards aimed at negating such behaviour. These studies are complemented by those texts which examine the organisational context in which rogue employees commit computer crime. Currently, however, there has been a lack of insight into the relationship between the offender and the context, during the commission process. To address this deficiency, two criminological theories are advanced. This paper illustrates how the theories, entitled the Rational Choice Perspective and Situational Crime Prevention, can be applied to the IS domain, thereby offering a theoretical basis by which to analyse the offender/context relationship during perpetration. By so doing, practitioners may use these insights to inform and enhance the selection of safeguards in a bid to improve prevention programmes. Furthermore, the importation of the Rational Choice Perspective and Situational Crime Prevention into the IS field opens up potentially new areas for future research.


title: Swimming with stiff legs at low Reynolds number.

abstract: Locomotion at low Reynolds number is not possible with cycles of reciprocal motion, an example being the oscillation of a single pair of rigid paddles or legs. Here, I demonstrate the possibility of swimming with two or more pairs of legs. They are assumed to oscillate collectively in a metachronal wave pattern in a minimal model based on slender-body theory for Stokes flow. The model predicts locomotion in the direction of the traveling wave, as commonly observed along the body of free-swimming crustaceans. The displacement of the body and the swimming efficiency depend on the number of legs, the amplitude, and the phase of oscillations. This study shows that paddling legs with distinct orientations and phases offers a simple mechanism for driving flow.


Agile for Everyone Else: Using Triggers and Checks to Create Agility Outside of Software Development


title: Detecting information-dense texts in multiple news domains

abstract: We introduce the task of identifying information-dense texts, which report important factual information in direct, succinct manner. We describe a procedure that allows us to label automatically a large training corpus of New York Times texts. We train a classifier based on lexical, discourse and unlexicalized syntactic features and test its performance on a set of manually annotated articles from business, U.S. international relations, sports and science domains. Our results indicate that the task is feasible and that both syntactic and lexical features are highly predictive for the distinction. We observe considerable variation of prediction accuracy across domains and find that domain-specific models are more accurate.


title: Modular Construction of Time-Delay Neural Networks for Speech Recognition

abstract: Several strategies are described that overcome limitations of basic network models as steps towards the design of large connectionist speech recognition systems. The two major areas of concern are the problem of time and the problem of scaling. Speech signals continuously vary over time and encode and transmit enormous amounts of human knowledge. To decode these signals, neural networks must be able to use appropriate representations of time and it must be possible to extend these nets to almost arbitrary sizes and complexity within finite resources. The problem of time is addressed by the development of a Time-Delay Neural Network; the problem of scaling by Modularity and Incremental Design of large nets based on smaller subcomponent nets. It is shown that small networks trained to perform limited tasks develop time invariant, hidden abstractions that can subsequently be exploited to train larger, more complex nets efficiently. Using these techniques, phoneme recognition networks of increasing complexity...


title: Game theory and human evolution: a critique of some recent interpretations of experimental games. Theoretical Population Biology 69

abstract: Abstract Economists and psychologists have been testing Nash equilibrium predictions of game theory models of human behavior. In many instances, humans do not conform to the predictions. These results are of great interest to biologists because they also raise questions about well-known ESS models of cooperation. Cooperation in certain one-shot, anonymous interactions, and a willingness to punish others at a net cost to oneself are some of the most intriguing deviations from standard theory. One proposed explanation for these results that is receiving increasing attention invokes the cultural group selection of ‘other regarding’ social norms. We critically review this explanation. We conclude that experimental results reveal limits in two implicit models of cognitive structure commonly employed by economists and evolutionary biologists.


title: Make new friends, but keep the old: recommending people on social networking sites

abstract: This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.


title: Semantic description, discovery and integration for the Internet of Things

abstract: To share and publish the domain knowledge of IoT objects, the development of a semantic IoT model based directory system that manages meta-data and relationships of IoT objects is required. Many researches focus on static relationships between IoT objects. However, because complex relationships between various resources change with time in an IoT environment, an efficient method for updating the meta-data is required. Thus, we propose an IoT-DS as the IoT directory that supports semantic description, discovery, and integration of IoT objects. Firstly, we introduce a semantic IoT component model to establish a shared conceptualization. Secondly, we present general cases of relationships to efficiently interact between IoT-DS and IoT objects. Thirdly, we construct IoT-DS as a Web portal. Finally, we verify in our evaluation study that the query processing time and communication workload imposed by the proposed approach are reduced.


title: Study of the therapeutic effects of a hippotherapy simulator in children with cerebral palsy: a stratified single-blind randomized controlled trial

abstract: OBJECTIVE ::: To investigate whether hippotherapy (when applied by a simulator) improves postural control and balance in children with cerebral palsy. ::: ::: ::: DESIGN ::: Stratified single-blind randomized controlled trial with an independent assessor. Stratification was made by gross motor function classification system levels, and allocation was concealed. ::: ::: ::: SUBJECTS ::: Children between 4 and 18 years old with cerebral palsy. ::: ::: ::: INTERVENTIONS ::: Participants were randomized to an intervention (simulator ON) or control (simulator OFF) group after getting informed consent. Treatment was provided once a week (15 minutes) for 10 weeks. ::: ::: ::: MAIN MEASURES ::: Gross Motor Function Measure (dimension B for balance and the Total Score) and Sitting Assessment Scale were carried out at baseline (prior to randomization), end of intervention and 12 weeks after completing the intervention. ::: ::: ::: RESULTS ::: Thirty-eight children participated. The groups were balanced at baseline. Sitting balance (measured by dimension B of the Gross Motor Function Measure) improved significantly in the treatment group (effect size = 0.36; 95% CI 0.01-0.71) and the effect size was greater in the severely disabled group (effect size = 0.80; 95% CI 0.13-1.47). The improvements in sitting balance were not maintained over the follow-up period. Changes in the total score of the Gross Motor Function Measure and the Sitting Assessment Scale were not significant. ::: ::: ::: CONCLUSION ::: Hippotherapy with a simulator can improve sitting balance in cerebral palsy children who have higher levels of disability. However, this did not lead to a change in the overall function of these children (Gross Motor Function Classification System level V).


title: Simulation of Blood Vessels for Surgery Simulators

abstract: Laparoscopic virtual reality based surgery simulators are becoming a ubiquitous tool in resident training and assessment. Blood vessels on abdominal organ surface are major visual cue of laparoscopic imagery, so it makes sense to develop algorithms of generation of blood vessels. Stochastic parameter Lindenmayer systems (L-systems) are formulated to generate blood vessel structures that can incorporate the physiological laws of blood vessel branching. With appropriate rewriting rules and choice of parameters, they can represent different organ blood vessels, and can exhibit the overall statistical character and the local stochastic character of the physiological system. Experiment results show that proposed method is feasible and effective.


title: A Bayesian regularized artificial neural network for stock market forecasting

abstract: In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. The accurate prediction of stock price movements could play an important role in helping investors improve stock returns. The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. Experiments were performed with Microsoft Corp. and Goldman Sachs Group Inc. stock to determine the effectiveness of the model. The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis.


title: Statistical Significance, Power, and Sample Sizes: A Systematic Review of SIGIR and TOIS, 2006-2015

abstract: We conducted a systematic review of 840 SIGIR full papers and 215 TOIS papers published between 2006 and 2015. The original objective of the study was to identify IR effectiveness experiments that are seriously underpowered (i.e., the sample size is far too small so that the probability of missing a real difference is extremely high) or overpowered (i.e., the sample size is so large that a difference will be considered statistically significant even if the actual effect size is extremely small). However, it quickly became clear to us that many IR effectiveness papers either lack significance testing or fail to report p-values and/or test statistics, which prevents us from conducting power analysis. Hence we first report on how IR researchers (fail to) report on significance test results, what types of tests they use, and how the reporting practices may have changed over the last decade. From those papers that reported enough information for us to conduct power analysis, we identify extremely overpowered and underpowered experiments, as well as appropriate sample sizes for future experiments. The raw results of our systematic survey of 1,055 papers and our R scripts for power analysis are available online. Our hope is that this study will help improve the reporting practices and experimental designs of future IR effectiveness studies.


Big Sensing Data Curation in Cloud Data Center for Next Generation IoT and WSN


title: An in-Depth Survey of Visible Light Communication Based Positioning Systems

abstract: While visible light communication (VLC) has become the candidate for the wireless technology of the 21st century due to its inherent advantages, VLC based positioning also has a great chance of becoming the standard approach to positioning. Within the last few years, many studies on VLC based positioning have been published, but there are not many survey works in this field. In this paper, an in-depth survey of VLC based positioning systems is provided. More than 100 papers ranging from pioneering papers to the state-of-the-art in the field were collected and classified based on the positioning algorithms, the types of receivers, and the multiplexing techniques. In addition, current issues and research trends in VLC based positioning are discussed.


title: A boosted classifier tree for hand shape detection

abstract: The ability to detect a persons unconstrained hand in a natural video sequence has applications in sign language, gesture recognition and HCl. This paper presents a novel, unsupervised approach to training an efficient and robust detector which is capable of not only detecting the presence of human hands within an image but classifying the hand shape. A database of images is first clustered using a k-method clustering algorithm with a distance metric based upon shape context. From this, a tree structure of boosted cascades is constructed. The head of the tree provides a general hand detector while the individual branches of the tree classify a valid shape as belong to one of the predetermined clusters exemplified by an indicative hand shape. Preliminary experiments carried out showed that the approach boasts a promising 99.8% success rate on hand detection and 97.4% success at classification. Although we demonstrate the approach within the domain of hand shape it is equally applicable to other problems where both detection and classification are required for objects that display high variability in appearance.


title: Continuous stress detection using a wrist device: in laboratory and real life

abstract: Continuous exposure to stress is harmful for mental and physical health, but to combat stress, one should first detect it. In this paper we propose a method for continuous detection of stressful events using data provided from a commercial wrist device. The method consists of three machine-learning components: a laboratory stress detector that detects short-term stress every 2 minutes; an activity recognizer that continuously recognizes user's activity and thus provides context information; and a context-based stress detector that exploits the output of the laboratory stress detector and the user's context in order to provide the final decision on 20 minutes interval. The method was evaluated in a laboratory and a real-life setting. The accuracy on 55 days of real-life data, for a 2-class problem, was 92%. The method is currently being integrated in a smartphone application for managing mental health and well-being.


title: A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation

abstract: We propose a fast algorithm for solving the $\ell_1$-regularized minimization problem $\min_{x\in\mathbb{R}^n}\mu\|x\|_1+\|Ax-b\|^2_2$ for recovering sparse solutions to an undetermined system of linear equations $Ax=b$. The algorithm is divided into two stages that are performed repeatedly. In the first stage a first-order iterative “shrinkage” method yields an estimate of the subset of components of $x$ likely to be nonzero in an optimal solution. Restricting the decision variables $x$ to this subset and fixing their signs at their current values reduces the $\ell_1$-norm $\|x\|_1$ to a linear function of $x$. The resulting subspace problem, which involves the minimization of a smaller and smooth quadratic function, is solved in the second phase. Our code FPC_AS embeds this basic two-stage algorithm in a continuation (homotopy) approach by assigning a decreasing sequence of values to $\mu$. This code exhibits state-of-the-art performance in terms of both its speed and its ability to recover sparse signals.


Active Reinforcement Learning : Observing Rewards at a Cost


title: Cannibalism and psychopathology

abstract: Eating members of one's own species is one of the few remaining taboos in modern human societies. In humans, aggression cannibalism has been associated with mental illness. The objective of this report is to examine the unique set of circumstances and characteristics revealing the underlying etiology leading to such an act and the type of psychological effect it has for the perpetrator. A case report of a patient with paranoid schizophrenia who committed patricide and cannibalism is presented. The psychosocial implications of anthropophagy on the particular patient management are outlined.


title: The Measure of Pareto Optima. Applications to Multi-objective Metaheuristics

abstract: This article describes a set function that maps a set of Pareto optimal points to a scalar. A theorem 1 is presented that shows that the maximization of this scalar value constitutes the necessary and sufficient condition for the function's arguments to be maximally diverse Pareto optimal solutions of a discrete, multi-objective, optimization problem. This scalar quantity, a hypervolume based on a Lebesgue measure, is therefore the best metric to assess the quality of multiobjective optimization algorithms. Moreover, it can be used as the objective function in simulated annealing (SA) to induce convergence in probability to the Pareto optima. An efficient, polynomial-time algorithm for calculating this scalar and an analysis of its complexity is also presented.


title: Effects of androgenization on the white matter microstructure of female-to-male transsexuals. A diffusion tensor imaging study

abstract: Summary Diffusion tensor imaging (DTI) can sensitively detect white matter sex differences and the effects of pharmacological treatments. Before cross-sex hormone treatment, the white matter microstructure of several brain bundles in female-to-male transsexuals (FtMs) differs from those in females but not from that in males. The purpose of this study was to investigate whether cross-sex hormone treatment (androgenization) affects the brain white matter microstructure. Using a Siemens 3 T Trio Tim Magneton, DTI was performed twice, before and during cross-sex hormonal treatment with testosterone in 15 FtMs scanned. Fractional anisotropy (FA) was analyzed on white matter of the whole brain, and the latter was spatially analyzed using Tract-Based Spatial Statistics. Before each scan the subjects were assessed for serum testosterone, sex hormone binding globulin level (SHBG), and their free testosterone index. After at least seven months of cross-gender hormonal treatment, FA values increased in the right superior longitudinal fasciculus (SLF) and the right corticospinal tract (CST) in FtMs compared to their pretreatment values. Hierarchical regression analyses showed that the increments in the FA values in the SLF and CST are predicted by the free testosterone index before hormonal treatment. All these observations suggest that testosterone treatment changes white matter microstructure in FtMs.


title: An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems

abstract: Adopting mobile electronic medical record (MEMR) systems is expected to be one of the superior approaches for improving nurses’ bedside and point of care services. However, nurses may use the functions for far fewer tasks than the MEMR supports. This may depend on their technological personality associated to MEMR acceptance. The purpose of this study is to investigate nurses’ personality traits in regard to technology readiness toward MEMR acceptance. The study used a self-administered questionnaire to collect 665 valid responses from a large hospital in Taiwan. Structural Equation modeling was utilized to analyze the collected data. Of the four personality traits of the technology readiness, the results posit that nurses are optimistic, innovative, secure but uncomfortable about technology. Furthermore, these four personality traits were all proven to have a significant impact on the perceived ease of use of MEMR while the perceived usefulness of MEMR was significantly influenced by the optimism trait only. The results also confirmed the relationships between the perceived components of ease of use, usefulness, and behavioral intention in the Technology Acceptance Model toward MEMR usage. Continuous educational programs can be provided for nurses to enhance their information technology literacy, minimizing their stress and discomfort about information technology. Further, hospital should recruit, either internally or externally, more optimistic nurses as champions of MEMR by leveraging the instrument proposed in this study. Besides, nurses’ requirements must be fully understood during the development of MEMR to ensure that MEMR can meet the real needs of nurses. The friendliness of user interfaces of MEMR and the compatibility of nurses’ work practices as these will also greatly enhance nurses’ willingness to use MEMR. Finally, the effects of technology personality should not be ignored, indicating that hospitals should also include more employees’ characteristics beyond socio-demographic profiles in their personnel databases.


title: A New Variable-Frequency Optimal Direct Power Control Algorithm

abstract: This paper presents a new formulation for direct power control (DPC) with several improvements over previous DPC formulations such as an exact discrete-time expression for the predicted power variations yielded by a given inverter switching state and the inclusion of the computing delay time in the model. Based on this formulation, a variable-frequency optimal DPC algorithm is presented that selects the inverter switching state that minimizes the power error. This algorithm shows an excellent precision in estimating power variations, therefore reducing power ripple and unwanted current harmonics, while retaining the fast dynamics inherent to variable-frequency DPC. This makes this algorithm suitable for interfacing distributed generation to microgrids, where a fast and accurate power control is desirable in order to perform voltage and frequency regulation. Additionally, studies are carried out regarding the average switching frequency and the influence of the model parameters on the algorithm robustness.


title: Deep Neural Network Language Models

abstract: In recent years, neural network language models (NNLMs) have shown success in both peplexity and word error rate (WER) compared to conventional n-gram language models. Most NNLMs are trained with one hidden layer. Deep neural networks (DNNs) with more hidden layers have been shown to capture higher-level discriminative information about input features, and thus produce better networks. Motivated by the success of DNNs in acoustic modeling, we explore deep neural network language models (DNN LMs) in this paper. Results on a Wall Street Journal (WSJ) task demonstrate that DNN LMs offer improvements over a single hidden layer NNLM. Furthermore, our preliminary results are competitive with a model M language model, considered to be one of the current state-of-the-art techniques for language modeling.


title: Understanding deep convolutional networks

abstract: Deep convolutional networks provide state-of-the-art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and nonlinearities. A mathematical framework is introduced to analyse their properties. Computations of invariants involve multiscale contractions with wavelets, the linearization of hierarchical symmetries and sparse separations. Applications are discussed.


title: SentiML: functional annotation for multilingual sentiment analysis

abstract: Sentiment Analysis is the task of automatically identifying whether a text or a single sentence is intended to carry a positive or negative connotation. The commonly used Bag-of-Words approach that relies on counting positive and negative words, whose connotation is indicated by specially crafted sentiment dictionaries, is not ideal because it does not take into account the relations between words and how the connotation of single words changes according to the context. This paper proposes a way of identifying and analysing the targets of the opinions and their modifiers, along with their linkage (appraisal group) through an annotation schema called SentiML. Such schema has been developed in order to facilitate the identification of these elements and the annotation of their sentiment, along with advanced linguistic features such as their appraisal type according to the Appraisal Framework. The schema is XML-based and has been also designed to be language-independent. Preliminary results show that the schema allows more coverage than a sentiment dictionary, while achieving reasonably fast and reliable annotation in spite of its fine granularity.


title: GMTI radar data analysis and simulation

abstract: GMTI radars produce a huge amount of data with measurements of up to several thousands of objects. So the analysis and realistic simulation of GMTI data is a big challenge, since the sensor dynamics, its moving platform and the radar characteristics also have to be taken into account as the data load. Still the land and sea environment of the targets, including models using road maps or straits, increase the complexity of GMTI data interpretation and simulation as well. Finally, with the NATO STANAG 4607 there exists a standard but also very generic and complex GMTI radar interface which describes the data encoding and gives furthermore the ability to map the sensor dynamics into the data. Violations of this standard are sometimes difficult to find though they have a great impact on GMTI tracker performance. This paper describes the problems that can occur with a GMTI radar interface and presents the principles of a tooling suite for GMTI radar data analysis and simulation.


title: LinkSCAN*: Overlapping community detection using the link-space transformation

abstract: In this paper, for overlapping community detection, we propose a novel framework of the link-space transformation that transforms a given original graph into a link-space graph. Its unique idea is to consider topological structure and link similarity separately using two distinct types of graphs: the line graph and the original graph. For topological structure, each link of the original graph is mapped to a node of the link-space graph, which enables us to discover overlapping communities using non-overlapping community detection algorithms as in the line graph. For link similarity, it is calculated on the original graph and carried over into the link-space graph, which enables us to keep the original structure on the transformed graph. Thus, our transformation, by combining these two advantages, facilitates overlapping community detection as well as improves the resulting quality. Based on this framework, we develop the algorithm LinkSCAN that performs structural clustering on the link-space graph. Moreover, we propose the algorithm LinkSCAN* that enhances the efficiency of LinkSCAN by sampling. Extensive experiments were conducted using the LFR benchmark networks as well as some real-world networks. The results show that our algorithms achieve higher accuracy, quality, and coverage than the state-of-the-art algorithms.


Classifier Comparisons On Credit Approval Prediction


title: Narrowband Internet of Things: Implementations and Applications

abstract: Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.


title: The extended contact effect: Knowledge of cross-group friendships and prejudice.

abstract: The extended contact hypothesis proposes that knowledge that an in-group member has a close relationship with an out-group member can lead to more positive intergroup attitudes. Proposed mechanisms are the in-group or out-group member serving as positive exemplars and the inclusion of the out-group member's group membership in the self. In Studies I and 2, respondents knowing an in-group member with an out-group friend had less negative attitudes toward that out-group, even controlling for disposition.il variables and direct out-group friendships. Study 3, with constructed intergroup-conflict situations (on the robbers cave model). found reduced negative out-group attitudes after participants learned of cross-group friendships. Study 4, a minimal group experiment, showed less negative out-group attitudes for participants observing an apparent in-group-out-group friendship. The intergroup contact hypothesis (Allport, 1954; Williams, 1947) proposes that under a given set of circumstances contact between members of different groups reduces existing negative intergroup attitudes. Some recent research (reviewed below) suggests that the effect may be most clearly associated with the specific contact of a friendship relationship. The extended contact hypothesis, which we introduce here, proposes that knowledge that an in-group member has a close relationship with an out-group member can lead to more positive intergroup attitudes. This article presents the rationale for the extended contact effect, including three mechanisms by which it may operate, and four methodologically diverse studies to demonstrate the phenomenon.


title: Automated water usage monitoring system

abstract: Advances in wireless communication devices and systems have a significant impact on various fields like agriculture, healthcare, smart homes etc. One of its important applications is the development of monitoring system for various purposes. Automated water usage monitoring system proposes an effective way of controlling the wastage of water at home/Industries by means of Wireless sensor nodes and LabVIEW software. At many places water is luxuriously wasted while many areas don't have enough. Sensor nodes are placed at every water outlet pointes sensing the flow of water. The concept of internet of things (IOT) is used to continuously monitor and track water usage via the wireless sensor nodes. Server collects the data through Wi-Fi/LAN to process and track usage and wastage of water at every outlet. When water is used at excess it is indicated and an alert is sent to the user. The user can continuously keep a track of the water usage or wastage through a mobile or laptop with an internet connection. This proposed water monitoring system helps the individuals to keep a track on their water bills and helps the government to manage the water supply and modify billing scheme based on the water usage.


title: Fuzzy sets, advanced fuzzy sets and hybrids

abstract: Fuzzy Sets were proposed several years ago with various extensions in later years. Each extension has advantages over the fuzzy sets. Rough sets are used to handle incomplete information. Interval-valued fuzzy sets deal with uncertainty and vagueness. Intuitionistic fuzzy sets contain a sub-interval hesitation degree that lies between membership and non-membership degrees. Soft sets overcome the problem of insufficiency of parameterization. Advanced fuzzy sets have myriad number of advantages due to their applications in realistic examples. This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids. Advanced fuzzy sets include rough sets, intuitionistic fuzzy sets, interval-valued fuzzy sets and soft sets.