title: Agreement on target-bidirectional LSTMs for sequence-to-sequence learning

abstract: Recurrent neural networks, particularly the long short-term memory networks, are extremely appealing for sequence-to-sequence learning tasks. Despite their great success, they typically suffer from a fundamental shortcoming: they are prone to generate unbalanced targets with good prefixes but bad suffixes, and thus performance suffers when dealing with long sequences. We propose a simple yet effective approach to overcome this shortcoming. Our approach relies on the agreement between a pair of target-directional LSTMs, which generates more balanced targets. In addition, we develop two efficient approximate search methods for agreement that are empirically shown to be almost optimal in terms of sequence-level losses. Extensive experiments were performed on two standard sequence-to-sequence transduction tasks: machine transliteration and grapheme-to-phoneme transformation. The results show that the proposed approach achieves consistent and substantial improvements, compared to six state-of-the-art systems. In particular, our approach outperforms the best reported error rates by a margin (up to 9% relative gains) on the grapheme-to-phoneme task. Our toolkit is publicly available on https://github.com/lemaoliu/Agtarbidir.


An Approach to the Definition of a Core Enterprise Ontology: CEO


title: Tighter Undecidability Bounds for Matrix Mortality, Zero-in-the-Corner Problems, and More

abstract: We study the decidability of three well-known problems related to integer matrix multiplication: Mortality (M), Zero in the Left-Upper Corner (Z), and Zero in the Right-Upper Corner (R). Let d and k be positive integers. Define M(k, d x d) as the following special case of the Mortality problem: given a set X of d -by-d integer matrices such that the cardinality of X is not greater than k, decide whether the d-by-d zero matrix belongs to X^+, where X^+ denotes the closure of X under the usual matrix multiplication. In the same way, define the Z(k, d x d) problem as: given an instance X of M(k, d x d) (the instances of Z(k, d x d) are the same as those of M(k, d x d)), decide whether at least one matrix in X^+ has a zero in the left-upper corner. Define R(k, d x d) as the variant of Z(k, d x d) where"left-upper corner"is replaced with"right-upper corner". In the paper, we prove that M(6, 3 x 3), M(4, 5 x 5), M(3, 9 x 9), M(2, 15 x 15), Z(5, 3 x 3), Z(3, 5 x 5), Z(2, 9 x 9), R(6, 3 x 3), R(5, 4 x 4), and R(3, 6 x 6) are undecidable. The previous best comparable results were the undecidabilities of M(7, 3 x 3), M(3, 13 x 13), M(2, 21 x 21), Z(7, 3 x 3), Z(2, 13 x 13), R(7, 3 x 3), and R(2, 10 x 10).


Interpolation for Polygon Texture Mapping and Shading


title: Research for Uyghur-Chinese Neural Machine Translation

abstract: The problem of rare and unknown words is an important issue in Uyghur-Chinese machine translation, especially using neural machine translation model. We propose a novel way to deal with the rare and unknown words. Based on neural machine translation of using pointers over input sequence, our approach which consists of preprocess and post-process can be used in all neural machine translation model. Pre-process modify the Uyghur-Chinese corpus to extend the ability of pointer network, and the post- process retranslating the raw translation by a phrase-based machine translation model or a wordlist. Experiment show that neural machine translation model used the approach proposed by this paper get a higher BLEU score than the phrase-based model in Uyghur-Chinese MT.


title: Robust non-linear smoothing for vehicle state estimation

abstract: This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions.


title: A hazard based approach to user return time prediction

abstract: In the competitive environment of the internet, retaining and growing one's user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good which makes it crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we address this problem by proposing a new retention metric for web services by concentrating on the rate of user return. We further apply predictive analysis to the proposed retention metric on a service, as a means for characterizing lost customers. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox's proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to be superior.


title: Antioxidant and anticancer activities of Moringa oleifera leaves

abstract: Moringa oleifera leaves extracted with methanol and dichloromethane were screened for antioxidant activity. The in vitro cancer antiproliferative and chemopreventive properties were also investigated. Radical scavenging assays with 1, 1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2´-azino-bis 3- ethylbenzothiazoline-6-sulfonic acid (ABTS) were used to determine the antioxidant activity. The antiproliferative assay was evaluated on three types of cancer cell lines: hepatocarcinoma (HepG2), colorectal adenocarcinoma (Caco-2) and breast adenocarcinoma (MCF-7), using 3-(4,5-dimethylthiazol- 2-yl)-2,5-diphenyl tetrazolium bromide (MTT) reduction assay. The in vitro cancer chemoprevention was performed using quinone reductase (QR) induction assay on hepatoma (Hepa-1c1c7). The chemopreventive activity of the extracts was expressed as concentration to double QR activity (CD value). The methanol extract showed higher free radical scavenging activity than the dichloromethane extract (IC50 = 1.60±0.03 mg/ml in DPPH assay and IC50 = 1.02±0.06 mg/ml in ABTS assay). In the antiproliferative assay, the IC50 of dichloromethane extract varied from 112 to 133 µg/ml for HepG2, Caco-2 and MCF-7 cancer cells, but became more than 250 µg/ml for the methanol extract. In the chemopreventive assay, the dichloromethane extract had capacity to induce QR activity significantly (CD value = 91.36±1.26 μg/ml), while the methanol extract had no inductive effect. This study provides evidence that M. oleifera leaves possess antioxidant activity, as well as cytotoxic and chemopreventive properties. Therefore, it might be beneficial as a medicinal plant for alternative novel anticancer drugs and nutraceutical products.


title: Effective next-items recommendation via personalized sequential pattern mining

abstract: Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capability to recommend the best next-items to each specific target user. In this paper, we introduce a personalized sequential pattern mining-based recommendation framework. Using a novel Competence Score measure, the proposed framework effectively learns user-specific sequence importance knowledge, and exploits this additional knowledge for accurate personalized recommendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users.


title: Big data mining and business intelligence trends

abstract: The conventional databases are not capable of coping with the high capacity data due to different forms of these data?s and fast production speed. In this context, The Big Data structure comes into the scene. The Big Data has been stated as the gold of our age by many authorities. Today, large sizes of data can be analyzed and this led to changes in the lives of people, companies, states, and researchers. The companies develop effective and efficient solutions by analyzing large size of data through big data solutions for their strategic decisions, operational processes, campaign management and marketing techniques. In this research, the introduction has been made to the Big Data architecture, along with daily increasing data mining techniques and methods which will be a solution for accumulating data and current advancements in big data solutions have been addressed. In addition, some well-known companies? tendency to implement business intelligence systems have been examined. The effects of potential threads which are the results of the big data in the business world are analyzed and a couple of suggestions for the future have been presented.


title: Pocketsphinx: A Free, Real-Time Continuous Speech Recognition System for Hand-Held Devices

abstract: The availability of real-time continuous speech recognition on mobile and embedded devices has opened up a wide range of research opportunities in human-computer interactive applications. Unfortunately, most of the work in this area to date has been confined to proprietary software, or has focused on limited domains with constrained grammars. In this paper, we present a preliminary case study on the porting and optimization of CMU Sphinx-11, a popular open source large vocabulary continuous speech recognition (LVCSR) system, to hand-held devices. The resulting system operates in an average 0.87 times real-time on a 206 MHz device, 8.03 times faster than the baseline system. To our knowledge, this is the first hand-held LVCSR system available under an open-source license


title: Deep Neural Net with Attention for Multi-channel Multi-touch Attribution

abstract: Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka. conversion). The marketers track all the customer journey data and try to measure the effectiveness of each advertising channel. The inference about the influence of each channel plays an important role in budget allocation and inventory pricing decisions. Several simplistic rule-based strategies and data-driven algorithmic strategies have been widely used in marketing field, but they do not address the issues, such as channel interaction, time dependency, user characteristics. In this paper, we propose a novel attribution algorithm based on deep learning to assess the impact of each advertising channel. We present Deep Neural Net With Attention multi-touch attribution model (DNAMTA) model in a supervised learning fashion of predicting if a series of events leads to conversion, and it leads us to have a deep understanding of the dynamic interaction effects between media channels. DNAMTA also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects. We used computational experiment of large real world marketing dataset to demonstrate that our proposed model is superior to existing methods in both conversion prediction and media channel influence evaluation.


title: Coagulation and ablation patterns of high-intensity focused ultrasound on a tissue-mimicking phantom and cadaveric skin

abstract: High-intensity focused ultrasound (HIFU) can be applied noninvasively to create focused zones of tissue coagulation on various skin layers. We performed a comparative study of HIFU, evaluating patterns of focused tissue coagulation and ablation upon application thereof. A tissue-mimicking (TM) phantom was prepared with bovine serum albumin and polyacrylamide hydrogel to evaluate the geometric patterns of HIFU-induced thermal injury zones (TIZs) for five different HIFU devices. Additionally, for each device, we investigated histologic patterns of HIFU-induced coagulation and ablation in serial sections of cadaveric skin of the face and neck. All HIFU devices generated remarkable TIZs in the TM phantom, with different geometric values of coagulation for each device. Most of the TIZs seemed to be separated into two or more tiny parts. In cadaveric skin, characteristic patterns of HIFU-induced ablation and coagulation were noted along the mid to lower dermis at the focal penetration depth of 3 mm and along subcutaneous fat to the superficial musculoaponeurotic system or the platysma muscle of the neck at 4.5 mm. Additionally, remarkable pre-focal areas of tissue coagulation were observed in the upper and mid dermis at the focal penetration depth of 3 mm and mid to lower dermis at 4.5 mm. For five HIFU devices, we outlined various patterns of HIFU-induced TIZ formation along pre-focal, focal, and post-focal areas of TM phantom and cadaveric skin of the face and neck.


title: Longevity of colonies of Anaplasma marginale in midgut epithelial cells of Dermacentor andersoni.

abstract: Colonies of Anaplasma marginale in midgut epithelial cells of experimentally infected Dermacentor andersoni were studied in adult ticks 1, 3, and 6 months old. Longevity of the parasite in ticks was assessed by evaluating its infectivity for splenectomized calves; calves were exposed by feeding ticks and by inoculation of tick gut homogenates. Longevity was also evaluated by determining size, type, and density of colonies in male and female ticks. The effect of incubation (2.5 days at 37 C) on colony density was also examined for ticks at each age period. All methods used to assess longevity of A marginale in ticks (tick transmission, calf inoculation, and histologic studies) indicated a decrease of the numbers of organisms in 6-month-old ticks. Furthermore, when tick gut homogenates from 6-month-old nonincubated ticks were not infectious for susceptible calves, incubation of ticks before dissection restored infectivity of homogenates. Colonies of A marginale were detected in gut tissues of 6-month-old ticks that were not infective; therefore, infectivity of ticks could not be confirmed merely by presence of A marginale colonies.


title: Challenges and solutions to FinFET gate etch process

abstract: When CMOS technologies entered nanometer scales, FinFET has become one of the most promising devices because of its superior electrical characteristics. To accommodate the 3D topography, gate etch needs soft landing on the top of Fin while removing the extra poly-si around Fin. Its over etch is more aggressive than conventional planar gate to avoid poly-si residue. Fin loss should be well controlled because it will adversely affect device performance. Besides, the gate profile control is also one big challenge for FinFET gate etch. We found the amorphous poly-si etching with plasma pulsing can balance Fin top loss and gate profile control. The optimized pulsing parameters could deliver the preferred slightly notched profile. This will benefit the following metal gate fabrication. Furthermore, Line end-to-end space CD shrink is another challenge for FinFET gate etch. SO2 based plasma for mask opening exhibits the promising CD shrinkage capability.


title: COSAR: hybrid reasoning for context-aware activity recognition

abstract: Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module.


title: PV Battery Charger Using an $L3C$ Resonant Converter for Electric Vehicle Applications

abstract: In electric vehicles with rooftop PV panels, the solar irradiance and surface temperature can affect their performance and output voltages (e.g., $V_{\mathrm{ pv}} =24$ –45 $\text{V}_{\mathrm{ dc}}$ ). In these systems, the maximum energy must be extracted from the variable input voltage (PV panel), boosted by different gains, and stored in high-voltage battery packs. Furthermore, depending on the battery state of charge, the charger should operate in constant voltage, constant current, or constant power modes, all the way from complete discharge condition, up to the charged floating voltage phase ( $V_{\mathrm{ bat}}=230$ –430 $\text{V}_{\mathrm{ dc}}$ ). This combination of the variable PV input voltage and different states of charge creates a significant regulation challenge for the converter. In this paper, a high-efficiency fourth-order $L3C$ resonant converter is proposed with an extreme voltage regulation capability that can effectively extract the maximum power from the PV panels and respond to the battery states of charge at different voltage and current levels. The experimental results from a 350-W prototype prove the features of the proposed $L3C$ resonant converter and demonstrate its ability to track the maximum input power while responding to the battery various states of charge.


Chapter 2 Heterogeneous Wireless Networks


A survey on computer vision tools for action recognition , crowd surveillance and suspect retrieval


Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks


title: OVERVIEW OF PLATOONING SYSTEMS

abstract: This paper presents an overview of current projects that deal with vehicle platooning. The platooning concept can be defined as a collection of vehicles that travel together, actively coordinated in formation. Some expected advantages of platooning include increased fuel and traffic efficiency, safety and driver comfort. There are many variations of the details of the concept such as: the goals of platooning, how it is implemented, mix of vehicles, the requirements on infrastructure, what is automated (longitudinal and lateral control) and to what level. The following projects are presented: SARTRE – a European platooning project; PATH – a California traffic automation program that includes platooning; GCDC – a cooperative driving initiative, SCANIA platooning and; Energy ITS – a Japanese truck platooning project.


title: Detection of Sudoku puzzle using image processing and solving by Backtracking, Simulated Annealing and Genetic Algorithms: A comparative analysis

abstract: In this paper, we propose the digital detection and decryption of a sudoku puzzle using vision based techniques and subsequent solving of the puzzle using three algorithms-Backtracking, Simulated Annealing and Genetic Algorithm. The proposed method can recognize any sudoku puzzle captured from a digital camera and after employing appropriate pre-processing algorithms which include adaptive thresholding, Hough Transform and geometric transformation, the digits are recognized using Optical Character Recognition (OCR), and based on their pixel locations in the image, they are stored in corresponding locations in the 9×9 matrix. The detected puzzles of varying complexity levels are then solved using the three algorithms and the results are compared and contrasted, indicating the relative efficiencies of the three techniques in accurately solving Sudoku puzzles. Simulated Annealing performed the best amongst the three algorithms, whereas, genetic algorithm performed the worst in the comparison.


title: Depth synthesis and local warps for plausible image-based navigation

abstract: Modern camera calibration and multiview stereo techniques enable users to smoothly navigate between different views of a scene captured using standard cameras. The underlying automatic 3D reconstruction methods work well for buildings and regular structures but often fail on vegetation, vehicles, and other complex geometry present in everyday urban scenes. Consequently, missing depth information makes Image-Based Rendering (IBR) for such scenes very challenging. Our goal is to provide plausible free-viewpoint navigation for such datasets. To do this, we introduce a new IBR algorithm that is robust to missing or unreliable geometry, providing plausible novel views even in regions quite far from the input camera positions. We first oversegment the input images, creating superpixels of homogeneous color content which often tends to preserve depth discontinuities. We then introduce a depth synthesis approach for poorly reconstructed regions based on a graph structure on the oversegmentation and appropriate traversal of the graph. The superpixels augmented with synthesized depth allow us to define a local shape-preserving warp which compensates for inaccurate depth. Our rendering algorithm blends the warped images, and generates plausible image-based novel views for our challenging target scenes. Our results demonstrate novel view synthesis in real time for multiple challenging scenes with significant depth complexity, providing a convincing immersive navigation experience.


title: Wave energy devices with compressible volumes

abstract: We present an analysis of wave energy devices with air-filled compressible submerged volumes, where variability of volume is achieved by means of a horizontal surface free to move up and down relative to the body. An analysis of bodies without power take-off (PTO) systems is first presented to demonstrate the positive effects a compressible volume could have on the body response. Subsequently, two compressible device variations are analysed. In the first variation, the compressible volume is connected to a fixed volume via an air turbine for PTO. In the second variation, a water column separates the compressible volume from another volume, which is fitted with an air turbine open to the atmosphere. Both floating and bottom-fixed, axisymmetric, configurations are considered, and linear analysis is employed throughout. Advantages and disadvantages of each device are examined in detail. Some configurations with displaced volumes less than 2000 m3 and with constant turbine coefficients are shown to be capable of achieving 80% of the theoretical maximum absorbed power over a wave period range of about 4 s.


title: From Conceptual Mash-ups to Bad-ass Blends: A Robust Computational Model of Conceptual Blending

abstract: Conceptual blending is a complex cognitive phenomenon whose instances range from the humdrum to the pyrotechnical. Most remarkable of all is the ease with which we humans regularly understand and produce complex blends. While this facility will doubtless elude our best efforts at computational modeling for some time to come, there are practical forms of conceptual blending that are amenable to computational exploitation right now. In this chapter we introduce the notion of a conceptual mash-up, a robust form of blending that allows a computer to creatively reuse and extend its existing commonsense knowledge of a topic. We show also how a repository of such knowledge can be harvested automatically from the web, by targeting the casual questions that we pose to ourselves and to others every day. By acquiring its world knowledge from the questions of others, a computer can eventually learn to pose introspective questions of its own, in the service of its own creative mash-ups.


title: Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

abstract: Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge: mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in the context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at the same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in a highly dynamic 3D mapping and tracking scenario with centimeter accuracy over a 1.5km trajectory.


title: Query to Knowledge: Unsupervised Entity Extraction from Shopping Queries using Adaptor Grammars

abstract: Web search queries provide a surprisingly large amount of information, which can be potentially organized and converted into a knowledgebase. In this paper, we focus on the problem of automatically identifying brand and product entities from a large collection of web queries in online shopping domain. We propose an unsupervised approach based on adaptor grammars that does not require any human annotation efforts nor rely on any external resources. To reduce the noise and normalize the query patterns, we introduce a query standardization step, which groups multiple search patterns and word orderings together into their most frequent ones. We present three different sets of grammar rules used to infer query structures and extract brand and product entities. To give an objective assessment of the performance of our approach, we conduct experiments on a large collection of online shopping queries and intrinsically evaluate the knowledgebase generated by our method qualitatively and quantitatively. In addition, we also evaluate our framework on extrinsic tasks on query tagging and chunking. Our empirical studies show that the knowledgebase discovered by our approach is highly accurate, has good coverage and significantly improves the performance on the external tasks.


title: Beta Regression in R

abstract: The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.


Trade and reciprocity among the river Bushmen of northern Botswana


title: Lane Recognition Using On-vehicle LIDAR

abstract: This paper presents an approach to the lane recognition using on-vehicle LIDAR. It detests the objects by 2-dimensional scanning and collects the range and reflectivity data in each scanning direction. We developed the lane recognition algorithm with these data, in which the lane curvature, yaw angle and offset are calculated by using the Hough transformation, and the lane width is calculated by statistical procedure. Next the lane marks are tracked by the Extended Kalman Filter. Then we test the performance of the lane recognition and the good results are achieved. Finally we show the result of the road environment recognition applying the lane recognition by LIDAR.


title: A probabilistic framework for stereo-vision based 3D object search with 6D pose estimation

abstract: This paper presents a method whereby an autonomous mobile robot can search for a 3-dimensional (3D) object using an on-board stereo camera sensor mounted on a pan-tilt head. Search efficiency is realized by the combination of a coarse-scale global search coupled with a fine-scale local search. A grid-based probability map is initially generated using the coarse search, which is based on the color histogram of the desired object. Peaks in the probability map are visited in sequence, where a local (refined) search method based on 3D SIFT features is applied to establish or reject the existence of the desired object, and to update the probability map using Bayesian recursion methods. Once found, the 6D object pose is also estimated. Obstacle avoidance during search can be naturally integrated into the method. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation.


title: Locally Normalized Filter Banks Applied to Deep Neural-Network-Based Robust Speech Recognition

abstract: This letter describes modifications to locally normalized filter banks (LNFB), which substantially improve their performance on the Aurora-4 robust speech recognition task using a Deep Neural Network-Hidden Markov Model (DNN-HMM)-based speech recognition system. The modified coefficients, referred to as LNFB features, are a filter-bank version of locally normalized cepstral coefficients (LNCC), which have been described previously. The ability of the LNFB features is enhanced through the use of newly proposed dynamic versions of them, which are developed using an approach that differs somewhat from the traditional development of delta and delta–delta features. Further enhancements are obtained through the use of mean normalization and mean–variance normalization, which is evaluated both on a per-speaker and a per-utterance basis. The best performing feature combination (typically LNFB combined with LNFB delta and delta–delta features and mean–variance normalization) provides an average relative reduction in word error rate of 11.4% and 9.4%, respectively, compared to comparable features derived from Mel filter banks when clean and multinoise training are used for the Aurora-4 evaluation. The results presented here suggest that the proposed technique is more robust to channel mismatches between training and testing data than MFCC-derived features and is more effective in dealing with channel diversity.


title: Stressors and Anxiety in Patients Undergoing Coronary Artery Bypass Surgery

abstract: BACKGROUND ::: Patients undergoing coronary artery bypass surgery who have increased anxiety levels have poorer outcomes than patients with lower levels, yet few studies have identified the concerns associated with this anxiety. ::: ::: ::: OBJECTIVE ::: To describe the concerns of patients undergoing coronary artery bypass surgery and to identify concerns that were associated with higher levels of anxiety. ::: ::: ::: METHOD ::: Patients (n = 172) were interviewed to determine their concerns and anxiety levels before surgery, before discharge, and 10 days after discharge. Multiple regression was used to determine the predictors of anxiety. ::: ::: ::: RESULTS ::: Although individual concerns changed over time, anxiety levels did not change from before to after surgery, remaining low to moderate. Being female and having more concerns about waiting for the surgery, being in pain/discomfort, and resuming lifestyle were predictors of increased anxiety before surgery. Predictors of increased anxiety while hospitalized after the surgery included taking anxiolytic or antidepressant medications, higher anxiety levels before surgery, concerns about personal things being inaccessible, and difficulty sleeping. Patients with higher anxiety levels after discharge were older, more anxious before surgery, and had concerns about being in pain/discomfort. ::: ::: ::: CONCLUSION ::: Patients waiting for coronary artery bypass surgery should be routinely assessed for anxiety before the procedure, and interventions to prevent or reduce anxiety should be provided. Interventions must be multifactorial, including information and support for pain management and realistic information about surgery schedules and resuming lifestyle after the surgery. Women and older patients may need to be targeted for intervention.


title: Impact of periodontal maintenance on tooth survival in patients with removable partial dentures.

abstract: AIM ::: Removable partial dentures (RPDs) may have a negative impact on oral health and have the potential to cause further tooth loss, especially of abutment teeth. However, no evidence indicates the effective interval of regular periodontal maintenance after RPD provision. This practice-based cohort study aimed to examine the impact of regular periodontal maintenance visits on survival of RPD abutment teeth. ::: ::: ::: MATERIALS AND METHODS ::: One hundred and ninety-two patients had been previously provided with 304 new clasp-retained RPDs at Osaka University Dental Hospital, Japan. Using the Kaplan-Meier method and log-rank test, 1094 abutments were analysed to illustrate survival curves and to compare each curve. According to the frequency of periodontal maintenance, study samples were divided into three groups; every 3-6 months (3-6M) group, 1-year (1Y) group and no-maintenance (NM) group. ::: ::: ::: RESULTS ::: Seven-year cumulative survival rates were 83.7% (3-6M), 75.5% (1Y) and 71.9% (NM) respectively. Survival of abutment teeth in the 3-6M group was significantly better than both 1Y (p = 0.005) and NM (p < 0.001) groups. ::: ::: ::: CONCLUSION ::: These longitudinal clinical data indicates that periodontal maintenance at least once in 6 months had the most favourable outcome. Frequent periodontal maintenance after RPD provision could be effective in preventing further tooth loss.


title: Robust Smith Predictor (RSP)

abstract: As is well known, control of industrial processes with significant dead time by using the conventional Smith Predictor (SP) results better performance in comparison to PI controllers. The SP has a main disadvantage which is a lack of robustness to variations in process parameters. To eliminate this deficiency, this paper presents the Robust Smith Predictor which is based on a modified disturbance observer to estimate disturbance and a filter instead of the Smith controller. The conducted simulations show that the proposed Robust Smith Predictor can eliminate the Smith predictor deficiency in robustness and provide an elegant trade-off between disturbance rejection and robustness.


title: Design and implementation of a street parking system using wireless sensor networks

abstract: Recently, with the explosive increase of automobiles in China, people park their cars on streets without following the rules and police has hard time to conduct law enforcement without introducing effective street parking system. To solve the problem, we propose a SPS (street parking system) based on wireless sensor networks. For accurately detecting a parking car, a parking algorithm based on state machine is also proposed. As the vehicle detection in SPS is absolutely critical, we use a Honeywell 3-axis magnetic sensor to detect vehicle. However, the magnetic sensor may be affected by the fluctuation of the outside temperature. To solve the problem, we introduce a moving drift factor. On the parking lot in Shenzhen Institutes of Advanced Technology (SIAT), 62 sensor devices are deployed to evaluate the performance of SPS. By running the system for several months, we observe the vehicle detection accurate rate of the SPS is nearly 99%. The proposed SPS is energy efficient. The end device can run about 7 years with one 2400mAh AA battery.


Using Serious Games to Improve the Preparation of Pre-Service Teachers in Bulgaria


title: Single Sample Face Recognition via Learning Deep Supervised Autoencoders

abstract: This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervised autoencoder, which is a new type of building block for deep architectures. There are two features distinct our supervised autoencoder from standard autoencoder. First, we enforce the faces with variants to be mapped with the canonical face of the person, for example, frontal face with neutral expression and normal illumination; Second, we enforce features corresponding to the same person to be similar. As a result, our supervised autoencoder extracts the features which are robust to variances in illumination, expression, occlusion, and pose, and facilitates the face recognition. We stack such supervised autoencoders to get the deep architecture and use it for extracting features in image representation. Experimental results on the AR, Extended Yale B, CMU-PIE, and Multi-PIE data sets demonstrate that by coupling with the commonly used sparse representation-based classification, our stacked supervised autoencoders-based face representation significantly outperforms the commonly used image representations in single sample per person face recognition, and it achieves higher recognition accuracy compared with other deep learning models, including the deep Lambertian network, in spite of much less training data and without any domain information. Moreover, supervised autoencoder can also be used for face verification, which further demonstrates its effectiveness for face representation.


ABSTRACT Real-Time Fog using Post-processing in OpenGL


Sigma- Delta Modulator Simulation and Analysis using MatLab


title: Wireless Communication for Factory Automation: an opportunity for LTE and 5G systems

abstract: The evolution of wireless communication from 4G toward 5G is driven by application demands and business models envisioned for 2020 and beyond. This requires network support for novel use cases in addition to classical mobile broadband services. Wireless factory automation is an application area with highly demanding communication requirements. We classify these requirements and identify the opportunities for the current LTE air interface for factory automation applications. Moreover, we give an outlook on the relevant design considerations to be addressed by 5G communication systems.


title: Association of cytoplasmic dynein with manchette microtubules and spermatid nuclear envelope during spermiogenesis in rats

abstract: During spermiogenesis, the shape of the spermatid nucleus, which is spherical, changes and it becomes the sperm head. A microtubular structure called a manchette is thought to be involved in this morphogenetic process. In this report, we demonstrate the localization of cytoplasmic dynein and manchette development by a double immunofluorescence technique using anti-bovine brain MAP 1C and anti-tubulin. Before step 6 of the Leblond and Clermont staging, the microtubules showed a fine reticular network, and the dynein staining was homogeneous. In step 6, the microtubular network was concentrated around the nucleus. The manchette developed in step 7 spermatids, and was fully formed, with a skirt-like appearance, covering the nuclear surface in step 8. Dynein fluorescence was associated with the microtubular manchette in steps 7–10. During these steps, the nucleus was protruded from the cytoplasm. In steps 11–13, the most active stages in nuclear shaping, the dynein was densely localized at the nuclear surface covered by the manchette. As the nucleus acquired a shape similar to the mature spermatozoon at step 14, the dynein fluorescence was localized only at the concave side of the nuclear caudal edge. The manchette became narrower and elongated. In step 15, the manchette extended into the elongated cytoplasm, diminishing during steps 16–18. The localization of the dynein was limited to the ventral aspect of the caudal head in these steps. There was little dynein fluorescence in mature spermatozoa. Immunoelectron microscopy showed positive reactions in the nuclear envelope and the inner region of the microtubular manchette. These observations suggest that cytoplasmic dynein, possibly bound to the nuclear envelope, and manchette microtubules are involved in the protrusion of the spermatid nucleus from the cytoplasm.


title: Policy-Based Hybrid Approach to Service Provisioning in Federations of Personal Networks

abstract: A Personal Network (PN) is a network of geographically distributed personal devices, which creates a distributed environment of a user and provides access to personal resources and services regardless the location of the user. A Federation of Personal Networks (Fednet) is a peer-to-peer cooperation of PNs of different users to share personal resources and services. Until now little research has been conducted on the service provisioning in Fednets. Based on the current status of the research on Fednets, the service provisioning can be done directly at the personal device (network overlay approach), or at a specific entry point of a PN, called a service proxy (service proxy approach). While the network overlay approach provides quick service access and provisioning, the service proxy approach provides a more elegant solution for service provisioning by preventing direct access of other PNs to the PN’s services and by enabling the control over the access to personal services at a single point of a PN. In this paper, to leverage the advantages of both approaches, we propose a hybrid approach to service provisioning in Fednets, which combines both approaches to provide an optimal and flexible solution for service provisioning in Fednets.


title: Mental Health of Transgender and Gender Nonconforming Youth Compared With Their Peers

abstract: BACKGROUND: Understanding the magnitude of mental health problems, particularly life-threatening ones, experienced by transgender and/or gender nonconforming (TGNC) youth can lead to improved management of these conditions. METHODS: Electronic medical records were used to identify a cohort of 588 transfeminine and 745 transmasculine children (3–9 years old) and adolescents (10–17 years old) enrolled in integrated health care systems in California and Georgia. Ten male and 10 female referent cisgender enrollees were matched to each TGNC individual on year of birth, race and/or ethnicity, study site, and membership year of the index date (first evidence of gender nonconforming status). Prevalence ratios were calculated by dividing the proportion of TGNC individuals with a specific mental health diagnosis or diagnostic category by the corresponding proportion in each reference group by transfeminine and/or transmasculine status, age group, and time period before the index date. RESULTS: Common diagnoses for children and adolescents were attention deficit disorders (transfeminine 15%; transmasculine 16%) and depressive disorders (transfeminine 49%; transmasculine 62%), respectively. For all diagnostic categories, prevalence was severalfold higher among TGNC youth than in matched reference groups. Prevalence ratios (95% confidence intervals [CIs]) for history of self-inflicted injury in adolescents 6 months before the index date ranged from 18 (95% CI 4.4–82) to 144 (95% CI 36–1248). The corresponding range for suicidal ideation was 25 (95% CI 14–45) to 54 (95% CI 18–218). CONCLUSIONS: TGNC youth may present with mental health conditions requiring immediate evaluation and implementation of clinical, social, and educational gender identity support measures.


Wear Behavior of Ceramic CAD/CAM Crowns and Natural Antagonists


Dynamic difficulty adjustment through parameter manipulation for Space Shooter game


title: Onychomatricoma in the light of the microanatomy of the normal nail unit.

abstract: Onychomatricoma (OM) is an uncommon benign tumor of the nail thought to exhibit differentiation limited toward the nail matrix. Four recent articles from our laboratory have shown, in some respect, a morphological and immunohistochemical homology between the nail unit and the hair follicle at the level of the matrix and isthmus. The purposes of this article are as follows: to investigate whether the sequential pattern of hair keratin expression in the normal nail matrix is maintained in OM, to compare and contrast follicular tumors with matrix differentiation in OM, and to furnish morphological and immunohistochemical markers of the onychogenic capacity of OM. Formalin-fixed paraffin sections from 6 OM were examined using specific keratin (K) antibodies for the matrix, nail bed, and nail isthmus. Hair keratins were expressed in a sequential pattern similar to normal nail matrix. In 3 cases where the cavities were completely lined by the fibroepithelial projections, the morphological aspect and the pattern of expression of K5, K17, K6, K16, and K75 suggested a differentiation toward the nail bed and the nail isthmus. This study shows for the first time that OM can recapitulate the entire nail unit with differentiation toward the nail bed and the nail isthmus. We have identified new histopathological and immunohistochemical features in OM, and we have abridged the diversity of its histological presentation in 2 main patterns: a lobulated or foliated pattern, observed principally on transverse section, and a "glove-finger" mono- or multidigitate pattern, observed mainly on longitudinal section. We have also concluded that OM is not a nail variant of trichoblastoma, pilomatricoma, or other pilar tumors. The concept of epithelial onychogenic tumor with onychogenic mesenchyme could shed more light about the true nature of this peculiar mixed tumor. However, the term OM is short and sanctioned by usage, which justifies keeping it.


title: Cyber Attacks Prediction Model Based on Bayesian Network

abstract: Cyber attacks prediction is an important part of risk management. Existing cyber attacks prediction methods did not fully consider the specific environment factors of the target network, which may make the results deviate from the true situation. In this paper, we propose a cyber attacks prediction model based on Bayesian network. We use attack graphs to represent all the vulnerabilities and possible attack paths. Then we capture the using environment factors using Bayesian network model. Cyber attacks predictions are performed on the constructed Bayesian network. Experimental analysis shows that our method gets more accurate results.


title: Imaging retinotopic maps in the human brain

abstract: A quarter-century ago visual neuroscientists had little information about the number and organization of retinotopic maps in human visual cortex. The advent of functional magnetic resonance imaging (MRI), a non-invasive, spatially-resolved technique for measuring brain activity, provided a wealth of data about human retinotopic maps. Just as there are differences amongst non-human primate maps, the human maps have their own unique properties. Many human maps can be measured reliably in individual subjects during experimental sessions lasting less than an hour. The efficiency of the measurements and the relatively large amplitude of functional MRI signals in visual cortex make it possible to develop quantitative models of functional responses within specific maps in individual subjects. During this last quarter-century, there has also been significant progress in measuring properties of the human brain at a range of length and time scales, including white matter pathways, macroscopic properties of gray and white matter, and cellular and molecular tissue properties. We hope the next 25 years will see a great deal of work that aims to integrate these data by modeling the network of visual signals. We do not know what such theories will look like, but the characterization of human retinotopic maps from the last 25 years is likely to be an important part of future ideas about visual computations.


title: The Genia Event Extraction Shared Task, 2013 Edition - Overview

abstract: The Genia Event Extraction task is organized for the third time, in BioNLP Shared Task 2013. Toward knowledge based construction, the task is modified in a number of points. As the final results, it received 12 submissions, among which 2 were withdrawn from the final report. This paper presents the task setting, data sets, and the final results with discussion for possible future directions.


title: Immunotherapy for Multiple Sclerosis: Basic Insights for New Clinical Strategies

abstract: Multiple Sclerosis (MS) is a chronic inflammatory degenerative disease of the central nervous system (CNS), first described over 100 years ago. MS is a clinically heterogeneous disease with an increasing incidence over time, and a population prevalence that increases with distance from the equator. It is postulated that environmental factors such as diet and population-specific genetics, influence the distribution of MS. Diagnosis is based on established clinical criteria, aided by magnetic resonance imaging, evoked potential recordings and cerebrospinal fluid examination. Whichever the type of clinical manifestation, MS is considered an organ-specific autoimmune disease, characterized by T-cell and macrophage infiltrates, triggered by CNS-specific CD4 T-cells. The prominent autoimmune etiology of MS is considered to be the aberrant activation of IFN-gamma-producing Th1 cells that recognize self-peptides of the myelin sheath, such as myelin basic protein (MBP) and proteolipid protein (PLP). Current treatments for MS aim primarily to suppress T-cell-mediated immune responses, albeit non-specifically. Experimental approaches towards the therapeutic management of MS involve the use of peptide analogs of disease-associated myelin epitopes or vaccines, to help shift T helper cell responses from Type-1 (secreting pro-inflammatory cytokines) to Type-2 (secreting anti-inflammatory cytokines) or induce peripheral T-cell tolerance. Animal models of MS have been useful to dissect disease mechanisms and evaluate new therapies. Experimental clinical trials, although few, are valuable to assess new treatment regimens and clarify possible conceptual mistakes about the disease. This review attempts to link the current knowledge of MS pathogenesis with clinical and experimental protocols of immunotherapy for MS.


title: Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants

abstract: After a fast photovoltaic (PV) expansion in the past decade supported by many governments in Europe, in this postincentive era, one of the most significant open issues in the PV sector is to find appropriate inspection methods to evaluate real PV plant performance and failures. In this context, PV modules are surely the key components affecting the overall system performance; therefore, there is a main concern about the occurrence of any kind of failure in PV modules. This paper aims to propose a novel concept for monitoring PV plants by using light unmanned aerial vehicles (UAVs) or systems (UASs) during their operation and maintenance. The main objectives of this study are to explore and evaluate the use of different UAV technologies and to propose a reliable, cost-effective, and time-saving method for the inspection of PV plants. In this research, different UAVs were employed to inspect a PV array field. For this purpose, some thermal imaging cameras and a visual camera were chosen as monitoring tools to suitably scan PV modules. The first results show that the procedure of utilizing UAV was effective in the detection of different failures of PV modules. Moreover, such a process was much faster and cost effective than traditional methods.


title: Developmental differences in hyperoxia-induced oxidative stress and cellular responses in the murine lung

abstract: Abstract Exposure of newborn mice to high inspired oxygen elicits a distinct phenotype of compromised alveolar and vascular development, although lethality during long-term exposure is lower in newborns compared to adults. As the effects of hyperoxia are mediated by excessive reactive oxygen species (ROS) generation, we hypothesized that newborn mice may exhibit enhanced expression of antioxidant defenses or attenuated ROS generation compared with adults. We measured subcellular oxidant responses to acute hyperoxia in lung slices and alveolar epithelial cells at varying time points during postnatal murine lung development. Oxidant stress was assessed using RoGFP, a ratiometric protein thiol redox sensor, targeted to the cytosol or the mitochondrial matrix. In contrast to newborn resistance to oxygen-induced mortality, cells of lung slices from younger mice demonstrated exaggerated mitochondrial matrix oxidant stress compared to adults, whereas oxidant stress responses in the cytosol were absent. Cell death in lung slices from newborn mice exposed to 48 h of hyperoxia was also greater than for adults. Consistent with these findings, expression of antioxidant enzymes in newborn lungs was lower than in adults, and induction of antioxidant levels and activity during 24 h of in vivo exposure was absent. However, expression of the reactive oxygen species-generating enzyme NADPH oxidase 1 was increased with hyperoxic exposure in the young but not the adult lung. Collectively, these results suggest that the greater lethality in adult animals may be more likely attributed to processes such as inflammation than to differences in antioxidant defenses. Therapies for neonatal and adult oxidative lung injury should therefore consider and address developmental differences in oxidative stress responses.


title: Initial trust, perceived risk, and the adoption of internet banking

abstract: Studies on the adoption of business-to-consumer e-commerce have not simultaneously considered trust and risk as important determinants of adoption behavior. Further, trust in information technology has not been addressed to a great extent in the context of e-commerce. This research explicitly encompasses the electronic channel and the firm as objects to be trusted in e-commerce. Our conceptual model leads us to believe that trust in the electronic channel and perceived risks of e-commerce are the major determinants of the adoption behavior. Based on the social network theory and the trust theory, determinants of trust in the electronic channel are included in the research model. This research is expected to provide both theoretical explanations and empirical validation on the adoption of e-commerce. We will also be able to offer specific recommendations on marketing strategies for practitioners, regarding the adoption of Internet banking.


title: Development of a Nonlinear Piezoelectric Energy Harvester for Alternating Air Load

abstract: The demand for energy-harvesting technology is steadily growing in the field of self-powered wireless sensor systems for use in pneumatic systems. The purpose of this research was to study an energy harvester excited by alternating air load in a pneumatic system. The harvester was designed to consist of a power chamber and a compressed chamber, and to the bottom of the power chamber a piezoelectric patch as been affixed. The harvester is excited by the changing pressure, which can be adjusted through changing volume, and the alternating air pressure energy can be harvested through the deformation of the piezoelectric patch. A test system was built and a prototype device was tested under various experimental conditions. The test results show that the energy generation performance of the harvester can be influenced by varying the volume compression parameters, with the output voltage increasing when the flow increases. The maximal output voltage and power are 24.7 V and 1.06 mW, respectively. An effective power of 0.28 mW was measured across the 200 kΩ resistor at a pressure of 200 kPa and a cycle time of 2.5 s with a flow of 150 L/min.


title: Mate preferences do predict attraction and choices in the early stages of mate selection.

abstract: Although mate preference research has firmly established that men value physical attractiveness more than women do and women value social status more than men do, recent speed-dating studies have indicated mixed evidence (at best) for whether people's sex-differentiated mate preferences predict actual mate choices. According to an evolutionary, mate preference priority model (Li, Bailey, Kenrick, & Linsenmeier, 2002; Li & Kenrick, 2006; Li, Valentine, & Patel, 2011), the sexes are largely similar in what they ideally like, but for long-term mates, they should differ on what they most want to avoid in early selection contexts. Following this model, we conducted experiments using online messaging and modified speed-dating platforms. Results indicate that when a mating pool includes people at the low end of social status and physical attractiveness, mate choice criteria are sex-differentiated: Men, more than women, chose mates based on physical attractiveness, whereas women, more than men, chose mates based on social status. In addition, individuals who more greatly valued social status or physical attractiveness on paper valued these traits more in their actual choices. In particular, mate choices were sex-differentiated when considering long-term relationships but not short-term ones, where both sexes shunned partners with low physical attractiveness. The findings validate a large body of mate preferences research and an evolutionary perspective on mating, and they have implications for research using speed-dating and other interactive contexts. (PsycINFO Database Record (c) 2013 APA, all rights reserved)


title: A collocation of IRIS flower using neural network clustering tool in MATLAB

abstract: Background/Objectives: Organizing large amount of data is the biggest challenge in domains of data mining. One way to deal with these kind of issues is by grouping data taking into account the matter of group of files. Clustering is a widely used technique in data mining application for discovering patterns in original data. Many of the algorithms almost all customized clustering algorithms are restricted in taking care and managing datasets that consists of unconditional characteristics and attributes. However, datasets with unconditional types of attributes are common in real life data mining problem. Clustering is a fundamental data analysis method. It is a way to form natural groupings in the given set of data. K-means is a simple unsupervised clustering techniques. Neural network clustering tool is best for obtaining optimal clustering of large data set as it uses unsupervised competitive technique and clusters by liner dicrimant. It is a form of unsupervised learning where generally we do not have examples demonstrating how the data should be grouped together [7]. Methodology: Existing iris flower dataset is preloaded in MATLAB and is used for clustering into three different species. The dataset is clustered using the k-means algorithm and neural network clustering tool in MATLAB. Neural network clustering tool is mainly used for clustering large data set without any supervision. It is also used for pattern recognition, feature extraction, vector quantization, image segmentation, function approximation, and data mining. Results/Findings: The results include the clustered iris dataset into three species without any supervision. The neighboring cluster's/neuron's distances and weights have also been plotted using self organizing map for better understanding of unsupervised clustering. As an unsupervised classification technique, clustering - 2 - identifies some inherent structures present in a set of objects based on a similarity measure. Clustering techniques are formed on the basis of statistical model identification or competitive learning. The research paper shows detailed outline of competitive learning formed on clustering methods like k-means and obtaining optimal clustering of iris dataset using neural network clustering tool.


title: Common Capacitor Multiphase LLC Converter With Passive Current Sharing Ability

abstract: In high-power applications, parallel operation is required to improve both efficiency and reliability. In an LLC converter, it is difficult to achieve parallel operation due to the gain sensitivity to component tolerance. This paper proposes a new structure of a common resonant capacitor for a multiphase LLC converter, which can achieve very good current-sharing performance with no additional components or control. To evaluate the current-sharing performance, a new first harmonic approximation (FHA) model is built in this paper to analyze the LLC converter with coupled resonant tanks. Both FHA analysis and powersim (PSIM) simulation results demonstrate the load-sharing improvement of the proposed common capacitor LLC resonant converter over the conventional two-phase LLC converter. The current-sharing performance from the FHA analysis and the PSIM simulation are compared, and the error of the FHA analysis is evaluated and explained. A 600 W two-phase LLC converter prototype based on the proposed method is also built to verify the feasibility. Excellent current-sharing performance (6% resonant current sharing error at a wide load range) has been achieved.


title: Recognition-by-components: A theory of human image understanding

abstract: The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N £ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensiona l image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position an$ image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configurations to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimensional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but instead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its particular category. But it is only with rare exceptions that an image fails to be rapidly and readily classified, either as an instance of a familiar object category or as an instance that cannot be so classified (itself a form of classification).


title: A review of rapid serial visual presentation-based brain-computer interfaces.

abstract: Rapid serial visual presentation (RSVP) combined with the detection of event-related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited, but significant, literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice.


title: Notes on Nondeterminism in Message Passing Programs

abstract: Nondeterministic program behavior can lead to different results in subsequent program runs based on the same input data. This kind of problem can be seen in any program, but is even magnified in a parallel execution context due to the existence of several independent but communicating tasks. Even though this kind of nondeterminism is commonplace and in many cases even useful for the implementation of applications, it often leads to sporadically occurring errors. These bugs are difficult to reproduce and represent a heavy challenge during testing and debugging. The biggest problem, however, may be the unawareness of users about the existence of nondeterministic choices and their consequences. In order to raise the awareness and to provoke discussions about this serious problem, this paper provides an exemplary overview of nondeterministic behavior in message passing programs. With simple examples, it is demonstrated how nondeterminism can vigorously affect the behavior and the final results of software and how the behavior can change between different architectures.


title: The Cetus Source-to-Source Compiler Infrastructure: Overview and Evaluation

abstract: This paper provides an overview and an evaluation of the Cetus source-to-source compiler infrastructure. The original goal of the Cetus project was to create an easy-to-use compiler for research in automatic parallelization of C programs. In meantime, Cetus has been used for many additional program transformation tasks. It serves as a compiler infrastructure for many projects in the US and internationally. Recently, Cetus has been supported by the National Science Foundation to build a community resource. The compiler has gone through several iterations of benchmark studies and implementations of those techniques that could improve the parallel performance of these programs. These efforts have resulted in a system that favorably compares with state-of-the-art parallelizers, such as Intel's ICC. A key limitation of advanced optimizing compilers is their lack of runtime information, such as the program input data. We will discuss and evaluate several techniques that support dynamic optimization decisions. Finally, as there is an extensive body of proposed compiler analyses and transformations for parallelization, the question of the importance of the techniques arises. This paper evaluates the impact of the individual Cetus techniques on overall program performance.


title: The role of the basal ganglia in discovering novel actions

abstract: Our interest is in the neural circuitry which supports the discovery and encoding of novel actions. We discuss the significant existing literature which identifies the basal ganglia, a complex of subcortical nuclei, as important in both the selection of actions and in reinforcement learning. We discuss the complementarity of these problems of action selection and action learning. Two basic mechanisms of biasing action selection are identified: (a) adjusting the relative strengths of competing inputs and (b) adjusting the relative sensitivity of the receiver of reinforced inputs. We discuss the particular importance of the phasic dopamine signal in the basal ganglia and its proposed role in conveying a reward prediction error. Temporal constraints of this signal limit the information it can convey to immediately surprising sensory events, thus—we argue—making it inappropriate to convey information regarding the economic value of actions (as proposed by the reward prediction error hypothesis). Rather, we suggest this signal is ideal to support the identification of novel actions and their encoding via the biasing of future action selection.


title: ID3 Decision Tree in Fraud Detection Application

abstract: In this paper, data mining technology is applied to fraud detection, established the fraud detection model, described the process of creating the fraud detection model, then established data model with ID3 decision tree, and established example of fraud detection model by using this model. At last, pointed out the application fields of fraud detection model.


title: Consensus ranking - An ICT security awareness case study

abstract: There are many disciplines where the problem of consensus ranking plays a vital role. Decision-makers are frequently asked to express their preferences for a group of objects, e.g. new projects, new products, candidates in an election, etc. The basic problem then becomes one of combining the individual rankings into a group choice or consensus ranking. The objective of this paper is to report on the application of two management science methodologies to the problem of identifying the most important areas to be included in an Information Communications Technology (ICT) security awareness program. The first methodology is based on the concept of minimizing the distance (disagreement) between individual rankings, while the second one employs a heuristic approach. A real-world case study from the mining industry is presented to illustrate the methods.


title: Trust and TAM in online shopping: an integrated model

abstract: A separate and distinct interaction with both the actual e-vendor and with its IT Web site interface is at the heart of online shopping. Previous research has established, accordingly, that online purchase intentions are the product of both consumer assessments of the IT itself-specifically its perceived usefulness and ease-of-use (TAM)-and trust in the e-vendor. But these perspectives have been examined independently by IS researchers. Integrating these two perspectives and examining the factors that build online trust in an environment that lacks the typical human interaction that often leads to trust in other circumstances advances our understanding of these constructs and their linkages to behavior. ::: ::: Our research on experienced repeat online shoppers shows that consumer trust is as important to online commerce as the widely accepted TAM use-antecedents, perceived usefulness and perceived ease of use. Together these variable sets explain a considerable proportion of variance in intended behavior. The study also provides evidence that online trust is built through (1) a belief that the vendor has nothing to gain by cheating, (2) a belief that there are safety mechanisms built into the Web site, and (3) by having a typical interface, (4) one that is, moreover, easy to use.


title: A compact antenna-in-package 60-GHz SiGe BiCMOS radio

abstract: A small form-factor 60-GHz SiGe BiCMOS radio with two antennas-in-package is presented. The fully-integrated feature-rich transceiver provides a complete RF solution for mobile WiGig/IEEE 802.11ad applications.


title: Tough and Water‐Insensitive Self‐Healing Elastomer for Robust Electronic Skin

abstract: An electronic (e-) skin is expected to experience significant wear and tear over time. Therefore, self-healing stretchable materials that are simultaneously soft and with high fracture energy, that is high tolerance of damage or small cracks without propagating, are essential requirements for the realization of robust e-skin. However, previously reported elastomers and especially self-healing polymers are mostly viscoelastic and lack high mechanical toughness. Here, a new class of polymeric material crosslinked through rationally designed multistrength hydrogen bonding interactions is reported. The resultant supramolecular network in polymer film realizes exceptional mechanical properties such as notch-insensitive high stretchability (1200%), high toughness of 12 000 J m-2 , and autonomous self-healing even in artificial sweat. The tough self-healing materials enable the wafer-scale fabrication of robust and stretchable self-healing e-skin devices, which will provide new directions for future soft robotics and skin prosthetics.


title: The Bobath (NDT) concept in adult neurological rehabilitation: what is the state of the knowledge? A scoping review. Part I: conceptual perspectives

abstract: AbstractObjective: The study’s purpose was to describe the range of knowledge pertaining to the Bobath concept/NDT in adult neurological rehabilitation, synthesize the findings, identify knowledge gaps and develop empirically based recommendations for future research. This article explores the conceptual literature. Methods: A scoping review of research and non-research articles published from 2007 to 2012. Two independent reviewers selected studies based on a systematic procedure. Inclusion criteria for studies were: electronically accessible English language literature with Bobath and/or neurodevelopmental therapy (NDT) as the subject heading in the title/keyword/abstract/intervention comparison with respect to adult neurological conditions. Data were abstracted and summarized with respect to study purpose, defining and operationalizing the Bobath concept, therapist demographics, recruitment, discussion and conclusions. Results: Of the 33 publications identified, 14 publications sought to define the the...


title: Performance evaluation of a face detection algorithm running on general purpose operating systems

abstract: Face detection is an important area of research in robotics and mechatronics applications as an essential subsystem in the interaction with human subjects. As face detection-based applications are moving into mobile and embedded systems, processing performance, among other characteristics, plays a major role. This paper reviews the current status concerning the development of face detection algorithms in order to evaluate the performance evaluation of a set of algorithms. A face detection algorithm based on the Viola-Jones face detector is implemented such that to run on three different operating systems and two hardware platforms in order to represent the basis for the development of a computing environment to run on robotic devices and on mechatronics applications.


title: Extractive document summarization based on convolutional neural networks

abstract: Extractive summarization aims to generate a summary by ranking sentences, whose performance relies heavily on the quality of sentence features. In this paper, a document summarization framework based on convolutional neural networks is successfully developed to learn sentence features and perform sentence ranking jointly. We adapt the original CNN model to address a regression process for sentence ranking. Pre-trained word vectors are used to enhance the performance of our model. We evaluate our proposed method on the DUC 2002 and 2004 datasets covering single and multi-document summarization tasks respectively. The proposed system achieves competitive or even better performance compared with state-of-the-art document summarization systems.


title: Meta-Learning for Semi-Supervised Few-Shot Classification

abstract: In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We evaluate these methods on versions of the Omniglot and miniImageNet benchmarks, adapted to this new framework augmented with unlabeled examples. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would.


title: Opportunistic data structures with applications

abstract: We address the issue of compressing and indexing data. We devise a data structure whose space occupancy is a function of the entropy of the underlying data set. We call the data structure opportunistic since its space occupancy is decreased when the input is compressible and this space reduction is achieved at no significant slowdown in the query performance. More precisely, its space occupancy is optimal in an information-content sense because text T[1,u] is stored using O(H/sub k/(T))+o(1) bits per input symbol in the worst case, where H/sub k/(T) is the kth order empirical entropy of T (the bound holds for any fixed k). Given an arbitrary string P[1,p], the opportunistic data structure allows to search for the occurrences of P in T in O(p+occlog/sup /spl epsiv//u) time (for any fixed /spl epsiv/>0). If data are uncompressible we achieve the best space bound currently known (Grossi and Vitter, 2000); on compressible data our solution improves the succinct suffix array of (Grossi and Vitter, 2000) and the classical suffix tree and suffix array data structures either in space or in query time or both. We also study our opportunistic data structure in a dynamic setting and devise a variant achieving effective search and update time bounds. Finally, we show how to plug our opportunistic data structure into the Glimpse tool (Manber and Wu, 1994). The result is an indexing tool which achieves sublinear space and sublinear query time complexity.


title: Adversarial Learning for Neural Dialogue Generation

abstract: In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator---analagous to the human evaluator in the Turing test--- to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. ::: In addition to adversarial training we describe a model for adversarial {\em evaluation} that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.


title: Patellar Taping, Patellofemoral Pain Syndrome, Lower Extremity Kinematics, and Dynamic Postural Control

abstract: Abstract Context: Patellar taping has been a part of intervention for treatment of patellofemoral pain syndrome (PFPS). However, research on the efficacy of patellar taping on lower extremity kinematics and dynamic postural control is limited. Objective: To evaluate the effects of patellar taping on sagittal-plane hip and knee kinematics, reach distance, and perceived pain level during the Star Excursion Balance Test (SEBT) in individuals with and without PFPS. Design: Repeated-measures design with 2 within-subjects factors and 1 between-subjects factor. Setting: The University of Toledo Athletic Training Research Laboratory. Patients or Other Participants: Twenty participants with PFPS and 20 healthy participants between the ages of 18 and 29 years. Intervention(s): The participants performed 3 reaches of the SEBT in the anterior direction under tape and no-tape conditions on both legs. Main Outcome Measure(s): The participants' hip and knee sagittal-plane kinematics were measured using the electromagnet...


title: Age and outcomes following guided de-escalation of antiplatelet treatment in acute coronary syndrome patients undergoing percutaneous coronary intervention: results from the randomized TROPICAL-ACS trial

abstract: Aims ::: Guided de-escalation of P2Y12-inhibitor treatment was recently identified as an effective alternative treatment strategy in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention. Safety and efficacy of this strategy may differ in relation to patient's age. This pre-specified analysis of the TROPICAL-ACS trial aimed to assess the impact of age on clinical outcomes following guided de-escalation of antiplatelet treatment in ACS patients. ::: ::: ::: Methods and results ::: Patients were randomly assigned in a 1:1 fashion to either standard treatment with prasugrel for 12 months (control group) or to a guided de-escalation regimen (1 week prasugrel followed by 1 week clopidogrel and platelet function testing guided maintenance therapy with clopidogrel or prasugrel from day 14 after hospital discharge; guided de-escalation group). We used Cox regression models to assess the associations of age on clinical endpoints and interactions. In younger patients (age ≤70, n = 2240), the 1 year incidence of the primary endpoint (cardiovascular death, myocardial infarction, stroke, or bleeding ≥ grade 2 according to Bleeding Academic Research Consortium criteria) was significantly lower in guided de-escalation vs. control group [5.9% vs. 8.3%; hazard ratio (HR) 0.70, 95% confidence interval (CI) 0.51-0.96; P = 0.03, number needed to treat = 42]. In elderly patients (age >70, n = 370), the absolute risk of events was higher without significant differences between guided de-escalation vs. control group (15.5% vs. 13.6%; HR 1.17, 95% CI 0.69-2.01; P = 0.56). When the impact of age, as a continuous variable, was analysed on outcomes after guided de-escalation vs. control treatment, an increasing relative risk reduction was observed in the primary endpoint by decreasing age (Pint = 0.02), due to significant reductions in bleeding. ::: ::: ::: Conclusion ::: Treatment effects of guided de-escalation for P2Y12 inhibitors depend on patient's age with younger patients deriving a significant net clinical benefit. Although the safety and efficacy of guided de-escalation in the elderly was similar to uniform prasugrel therapy, this should be further investigated due to the limited sample size of this group.


title: A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges

abstract: Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today. Although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.


title: A Simple , but NP-Hard , Motion Planning Problem

abstract: Determining the existence of a collision-free path between two points is one of the most fundamental questions in robotics. However, in situations where crossing an obstacle is costly but not impossible, it may be more appropriate to ask for the path that crosses the fewest obstacles. This may arise in both autonomous outdoor navigation (where the obstacles are rough but not completely impassable terrain) or indoor navigation (where the obstacles are doors that can be opened if necessary). This problem, the minimum constraint removal problem, is at least as hard as the underlying path existence problem. In this paper, we demonstrate that the minimum constraint removal problem is NP-hard for navigation in the plane even when the obstacles are all convex polygons, a case where the path existence problem is very easy.


title: The visual attention span deficit in dyslexia is visual and not verbal

abstract: The visual attention (VA) span deficit hypothesis of dyslexia posits that letter string deficits are a consequence of impaired visual processing. Alternatively, some have interpreted this deficit as resulting from a visual-to-phonology code mapping impairment. This study aims to disambiguate between the two interpretations by investigating performance in a non-verbal character string visual categorization task with verbal and non-verbal stimuli. Results show that VA span ability predicts performance for the non-verbal visual processing task in normal reading children. Furthermore, VA span impaired dyslexic children are also impaired for the categorization task independently of stimuli type. This supports the hypothesis that the underlying impairment responsible for the VA span deficit is visual, not verbal.


title: Got issues? Who cares about it? A large scale investigation of issue trackers from GitHub

abstract: Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering such questions by performing an empirical study on a hundred thousands of open source projects. After filtering relevant trackers, the study used about 20,000 projects. We investigate and answer various research questions on the popularity and impact of issue trackers.


title: Compact Circularly-Polarized Patch Antenna Loaded With Metamaterial Structures

abstract: A metamaterial-inspired low-profile patch antenna is proposed and studied for circularly-polarized (CP) radiation. The present antenna, which has a single-fed configuration, is loaded with the composite right/left-handed (CRLH) mushroom-like structures and a reactive impedance surface (RIS) for miniaturization purpose. The CP radiation is realized by exciting two orthogonally-polarized modes simultaneously which are located in the left-handed (LH) region. The detailed antenna radiation characteristics are examined and illustrated with both simulated and experimental results. The CP performance can be controlled in several different ways. This antenna exhibits an overall size of 0.177λ0 × 0.181λ0 × 0.025λ0 at 2.58 GHz and a radiation efficiency around 72%. Finally, based on the proposed CP patch antenna, a compact dual-band dual linearly-polarized patch antenna has also been designed and fabricated. Promising experimental results are observed.


title: MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

abstract: We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip-gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine-tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.


abstract: CUDA by Example: An Introduction to General-Purpose GPU Programming Jason Sanders and Edward Kandrot ISBN-13: 978-0131387683 Addison-Wesley Professional; 1 edition (July 29, 2010) Introduction This book is designed for readers who are interested in studying how to develop general parallel applications on graphics processing unit (GPU) by using CUDA C. CUDA C is a programming language, which combines industry standard programming C language and some more features which can exploit CUDA architecture. With proper introduction to NVIDA's CUDA architecture and in depth explanation for setting up development environment, this book is an easy to read, easy to understand, and hands on book. Readers of this book are assumed to have at least C language as background. Through this book, readers will not only gain experience in CUDA C development languages, but also will understand a lot of important underlying hardware knowledge, which in return can help software developers develop more efficient and effective applications. Outline of the Book This book is very well organized. Each chapter consists of general introduction, chapter objectives and Chapter Review. Both Sanders and Kandrot are senior software engineers in the CUDA Platform group and CUDA Algorithm team in NVIDIA Company, respectively. First chapter provide users background about history of GPU and CUDA architecture. Special features in CUDA architecture enable GPU to perform general purpose computation in addition to carry out traditional graphic computation. Readers can easily understand the benefit of CUDA architecture by reading though three different applications varying from medical field to environmental filed. In Chapter 2, Sanders and Kandrot equip users with complete lists of hardware and software support for running CUDA C applications. All software can be downloaded for free from websites suggested from authors. Then by a familiar Hello world program in Chapter 3, authors demystified that the CUDA C fundamentally is a standard C language with additional features which can allow application developer to specify which code can be run on device (GPU and its memory) or host (CPU and system memory). After setting all of proper background, the use of CUDA C to run parallel programs on GPU are discussed from Chapter 4 to Chapter 7. In Chapter 8, authors try to illustrate how to incorporate rendering and general purpose computation by using CUDA C. Readers without background in OpenGL or DirectX, can skip this chapter and go to the next. However, this chapter is a great addition to the book since it gives readers complete view of CUDA C. Even though CUDA C turns complicated application with single thread execution into easier case by parallel processing, there are some situation that special care should be taken when simple single thread application are tried to implement on massively parallel architecture; Chapter 9 discusses this topic. Compared to parallelism discussed in above chapters, which refers to parallel execution of a function on different sets of data, in Chapter 10, readers are exposed to a different class of parallelism on GPU, which refers to two or more completely independent tasks to be performed in parallel. Chapter 11 covers how to develop CUDA C application on Multiple GPUS. For further study, Chapter 12 shows more tools to aid CUDA C development and more resources to enhance reader's CUBA C development skills to another level. Summary Jason Sanders and Edward Kandrot wrote this book in such a way that is very easy to read and follow. Also, Sanders and Kandrot never forget the great sense of humor throughout the book. Reading this book is not only a discovery about CUDA C but also a joyful journal. It is highly recommended for students who are interested to learn CUDA C application development as Computer Science major. This book is recommended to be adopted as textbook for undergraduate students studying parallel programming. Jie Cheng, University of Hawaii Hilo


AMMON: A Speech Analysis Library for Analyzing Affect, Stress, and Mental Health on Mobile Phones


title: An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation

abstract: This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.


title: Low-Cost Laser Scanning System Design

abstract: 3D data is the main element of three-dimensional models that are commonly used in many different application areas such as architecture, archeology, medicine, film production, reverse engineering, etc. In this study, we discuss designing and utilizing opportunities for a low-cost triangulation-based laser scanner. To evaluate the system, a test object is specially designed, and the accuracy and operation of the system are verified by comparing the data output of the laser scanner with the terrestrial photogrammetric method. The designed system is also verified using different objects. At the end of the study, we calculate the precision of the scanned sample models to be 0.1–0.2 mm and RMS, 0.59 mm.


title: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients

abstract: This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.


title: TopicTiling: A Text Segmentation Algorithm based on LDA

abstract: This work presents a Text Segmentation algorithm called TopicTiling. This algorithm is based on the well-known TextTiling algorithm, and segments documents using the Latent Dirichlet Allocation (LDA) topic model. We show that using the mode topic ID assigned during the inference method of LDA, used to annotate unseen documents, improves performance by stabilizing the obtained topics. We show significant improvements over state of the art segmentation algorithms on two standard datasets. As an additional benefit, TopicTiling performs the segmentation in linear time and thus is computationally less expensive than other LDA-based segmentation methods.