title: Multiresonant Frequency-Locked Loop for Grid Synchronization of Power Converters Under Distorted Grid Conditions

abstract: This paper presents a new multiresonant frequency-adaptive synchronization method for grid-connected power converters that allows estimating not only the positive- and negative-sequence components of the power signal at the fundamental frequency but also other sequence components at other harmonic frequencies. The proposed system is called MSOGI-FLL since it is based on both a harmonic decoupling network consisting of multiple second-order generalized integrators (MSOGIs) and a frequency-locked loop (FLL), which makes the system frequency adaptive. In this paper, the MSOGI-FLL is analyzed for single- and three-phase applications, deducing some key expressions regarding its stability and tuning. Moreover, the performance of the MSOGI-FLL is evaluated by both simulations and experiments to show its capability for detecting different harmonic components in a highly polluted grid scenario.


title: Design of a compact high power phased array for 5G FD-MIMO system at 29 GHz

abstract: This paper presents a new design concept of a beam steerable high gain phased array antenna based on WR28 waveguide at 29 GHz frequency for fifth generation (5G) full dimension multiple input multiple output (FD-MIMO) system. The 8×8 planar phased array is fed by a three dimensional beamformer to obtain volumetric beam scanning ranging from −60 to +60 degrees both in azimuth and elevation direction. Beamforming network (BFN) is designed using 16 set of 8×8 Butler matrix beamformer to get 64 beam states, which control the horizontal and vertical angle. This is a new concept to design waveguide based high power three-dimensional beamformer for volumetric multibeam in Ka band for 5G application. The maximum gain of phased array is 28.5 dBi that covers 28.9 GHz to 29.4 GHz frequency band.


title: Using Artificial Intelligence to Improve Hospital Inpatient Care

abstract: AI can be used to address many challenges facing America's healthcare system - from disease detection to building predictive models for treatment - thereby improving the quality and lowering the cost of patient care.


title: Inversibility of rational mappings and structural identifiability in automatics

abstract: We investigate different methods for testing whether a rational mapping ƒ from k n to k m admits a rational inverse, or whether a polynomial mapping admits a polynomial one. We give a new solution, which seems much more efficient in practice than previously known ones using “tag” variables and standard basis, and a majoration for the degree of the standard basis calculations which is valid for both methods in the case of a polynomial map which is birational. We further show that a better bound can be given for our method, under some assumption on the form of ƒ. Our method can also extend to check whether a given polynomial belongs to the subfield generated by a finite set of fractions. We then illustrate our algorithm, with a application to structural identifiability. The implementation has been done in the IBM computer algebra system Scratchpad II.


title: Infrared Patch-Image Model for Small Target Detection in a Single Image

abstract: The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.


title: Enterprise logistics and supply chain structure: the role of fit

abstract: Abstract The emergence of the extended manufacturing enterprise, a globally dispersed collection of strategically aligned organizations, has brought new attention to how organizations coordinate the flow of information and materials across their supply chains. This paper explores and develops the concept of enterprise logistics [Greis, N.P., Kasarda, J.D., 1997. Enterprise logistics in the information age. California Management Review 39 (3), 55–78] as a tool for integrating the logistics activities both within and between the strategically aligned organizations of the extended enterprise. Specifically, this paper examines the fit between an organization's enterprise logistics integration capabilities and its supply chain structure. Using a configurations approach, we test whether globally dispersed network organizations that adopt enterprise logistics practices are able to achieve higher levels of organizational performance. Results indicate that enterprise logistics is a necessary tool for the coordination of supply chain operations that are geographically dispersed around the world. However, for a pure network structure, a high level of enterprise logistics integration alone does not guarantee improved organizational performance. The paper ends with a discussion of managerial implications and directions for future research.


title: Electrochemotherapy in head and neck cancer: A review of an emerging cancer treatment.

abstract: Patients affected by aggressive neoplasms with a high propensity to metastasize to the skin, including some types of head and neck cancer, may benefit from electrochemotherapy, a modality that combines the electroporation of cell membranes and chemotherapy to facilitate the transport of non-permeant molecules into cells; the host immune response consequently participates in achieving the abolition of tumors. Electrochemotherapy can be successfully used for skin metastases of head and neck tumors and, with some limitations, for primary and relapsing neoplasms; it can also be applied on an outpatient basis with a favorable cost-benefit ratio and it is a repeatable treatment that, if necessary, can be followed by traditional antineoplastic therapies. Although still a palliative treatment, the good level of tolerability and the high success rates of electrochemotherapy make it worth consideration among treatment options in selected patients.


The Advanced Compact MOSFET Model and its Application to Inversion Coefficient Based Circuit Design


title: FacetAtlas: Multifaceted Visualization for Rich Text Corpora

abstract: Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.


title: Cross-site Scripting Attacks on Android WebView

abstract: WebView is an essential component in Android and iOS. It enables applications to display content from on-line resources. It simplifies task of performing a network request, parsing the data and rendering it. WebView uses a number of APIs which can interact with the web contents inside WebView. In the current paper, Cross-site scripting attacks or XSS attacks specific to Android WebView are discussed. Cross site scripting (XSS) is a type of vulnerability commonly found in web applications. This vulnerability makes it possible for attackers to run malicious code into victim's WebView,through HttpClient APIs. Using this malicious code, the attackers can steal the victim's credentials, such as cookies. The access control policies (that is,the same origin policy) employed by the browser to protect those credentials can be bypassed by exploiting the XSS vulnerability.


title: Emerging Late Adolescent Friendship Networks and Big Five Personality Traits: A Social Network Approach

abstract: The current study focuses on the emergence of friendship networks among just-acquainted individuals, investigating the effects of Big Five personality traits on friendship selection processes. Soci ...


title: Business intelligence in risk management: Some recent progresses

abstract: Risk management has become a vital topic both in academia and practice during the past several decades. Most business intelligence tools have been used to enhance risk management, and the risk management tools have benefited from business intelligence approaches. This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue.


title: Calibrated, Registered Images of an Extended Urban Area

abstract: We describe a dataset of several thousand calibrated, time-stamped, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination conditions in an outdoor region spanning several hundred meters. The image data is grouped into several regions which have little mutual inter-visibility. For each group, the calibration data is globally consistent on average to roughly five centimeters and 0 1°, or about four pixels of epipolar registration. All image, feature and calibration data is available for interactive inspection and downloading at http://city.lcs.mit.edu/data.Calibrated imagery is of fundamental interest in a variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it of value as a test set for their own image registration algorithms, as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery, and as input for existing GIS applications.


title: Blind rectification of radial distortion by line straightness

abstract: Lens distortion self-calibration estimates the distortion model using arbitrary images captured by a camera. The estimated model is then used to rectify images taken with the same camera. These methods generally use the fact that built environments are line dominated and these lines correspond to lines on the image when distortion is not present. The proposed method starts by detecting groups of lines whose real world correspondences are likely to be collinear. These line groups are rectified, then a novel error function is calculated to estimate the amount of remaining distortion. These steps are repeated iteratively until suitable distortion parameters are found. A feature selection method is used to eliminate the line groups that are not collinear in the real world. The method is demonstrated to successfully rectify real images of cluttered scenes in a fully automatic manner.


title: Weighted parallel SGD for distributed unbalanced-workload training system

abstract: Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel SGD, often require all nodes to have the same performance or to consume equal quantities of data. However, these requirements are difficult to satisfy when the parallel SGD algorithms run in a heterogeneous computing environment; low-performance nodes will exert a negative influence on the final result. In this paper, we propose an algorithm called weighted parallel SGD (WP-SGD). WP-SGD combines weighted model parameters from different nodes in the system to produce the final output. WP-SGD makes use of the reduction in standard deviation to compensate for the loss from the inconsistency in performance of nodes in the cluster, which means that WP-SGD does not require that all nodes consume equal quantities of data. We also analyze the theoretical feasibility of running two other parallel SGD algorithms combined with WP-SGD in a heterogeneous environment. The experimental results show that WP-SGD significantly outperforms the traditional parallel SGD algorithms on distributed training systems with an unbalanced workload.


title: Autoregressive Parameter Estimation for Kalman Filtering Speech Enhancement

abstract: In this paper, autoregressive parameter estimation for Kalman filtering speech enhancement is studied. In conventional Kalman filtering speech enhancement, spectral subtraction is usually used for speech autoregressive (AR) parameter estimation. We propose log spectral amplitude (LSA) minimum mean-square error (MMSE) instead of spectral subtraction for the estimation of speech AR parameters. Based on an observation that full-band Kalman filtering speech enhancement often causes an unbalanced noise reduction between speech and non-speech segments, a spectral solution is proposed to overcome the unbalanced reduction of noise. This is done by shaping the spectral envelopes of the noise through likelihood ratio. Our simulation results show the effectiveness of the proposed method.


title: Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds

abstract: Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.


DYNAMIC ENERGY OPTIMIZATION TECHNIQUE IN MOBILE CLOUDLET FOR MOBILE CLOUD COMPUTING USING EFFECTIVE OFFLOADING ALGORITHM 1


Fashion Design in ROWE Fashion PLMs


title: Association Analysis of Urotensin II Gene (UTS2) and Flanking Regions with Biochemical Parameters Related to Insulin Resistance

abstract: This work has been supported by grant CENIT-2008 1004 from the Centro para el Desarrollo Tecnologico Industrial (CDTI) and the Ministerio de Ciencia e Innovacion under the Ingenio 2010 program. The work by the Diabetes Research Laboratory, Biomedical Research Foundation, University Hospital Clinico San Carlos, Madrid, Spain (P.I: Manuel Serrano-Rios) is supported by CIBER of Diabetes y Enfermedades Metabolicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Spain. The Group of “Fisiopatologia Cardiovascular” is supported by funding from Spanish Ministry MICINN (BFU2010-21043-C02-02; PS09-02287) and Red RECAVA [RD06-0014-0020]; from the Government of Andalucia (CICE: [P08-CVI-3913; P09-CTS-4715]. Tarik Smani is a Researcher from “Fundacion Publica Andaluza Progreso y Salud”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


title: An Automated Scoring Tool for Korean Short-Answer Questions Based on Semi-Supervised Learning

abstract: Scoring short-answer questions has disadvantages that may take long time to grade and may be an issue on consistency in scoring. To alleviate the disadvantages, automated scoring systems are widely used in America or Europe, but, in Korea, there has been researches regarding the automated scoring. In this paper, we propose an automated scoring tool for Korean short-answer questions using a semisupervised learning method. The answers of students are analyzed and processed through natural language processing and unmarked-answers are automatically scored by machine learning methods. Then scored answers with high reliability are added in the training corpus iteratively and incrementally. Through the pilot experiment, the proposed system is evaluated for Korean and social subjects in Programme for National Student Assessment. We have showed that the processing time and the consistency of grades are promisingly improved. Using the proposed tool, various assessment methods have got to be development before applying to school test fields.


title: EMAP: An Efficient Mutual-Authentication Protocol for Low-Cost RFID Tags

abstract: RFID tags are devices of very limited computational capabilities, which only have 250-3K logic gates that can be devoted to security-related tasks Many proposals have recently appeared, but all of them are based on RFID tags using classical cryptographic primitives such as PRNGs, hash functions, block ciphers, etc We believe this assumption to be fairly unrealistic, as classical cryptographic constructions lie well beyond the computational reach of very low-cost RFID tags A new approach is necessary to tackle this problem, so we propose an extremely efficient lightweight mutual-authentication protocol that offers an adequate security level for certain applications and can be implemented even in the most limited low-cost RFID tags, as it only needs around 150 gates.


title: Learning place-dependant features for long-term vision-based localisation

abstract: This paper presents an alternative approach to the problem of outdoor, persistent visual localisation against a known map. Instead of blindly applying a feature detector/descriptor combination over all images of all places, we leverage prior experiences of a place to learn place-dependent feature detectors (i.e., features that are unique to each place in our map and used for localisation). Furthermore, as these features do not represent low-level structure, like edges or corners, but are in fact mid-level patches representing distinctive visual elements (e.g., windows, buildings, or silhouettes), we are able to localise across extreme appearance changes. Note that there is no requirement that the features posses semantic meaning, only that they are optimal for the task of localisation. This work is an extension on previous work (McManus et al. in Proceedings of robotics science and systems, 2014b) in the following ways: (i) we have included a landmark refinement and outlier rejection step during the learning phase, (ii) we have implemented an asynchronous pipeline design, (iii) we have tested on data collected in an urban environment, and (iv) we have implemented a purely monocular system. Using over 100 km worth of data for training, we present localisation results from Begbroke Science Park and central Oxford.


title: Rare atmospheric river caused record floods across the Middle East

abstract: AbstractAtmospheric rivers (ARs) are responsible for some of the hydroclimatic extremes around the world. Their mechanisms and contribution to flooding in the Middle East are relatively poorly unde...


title: A 7.6 mW, 214-fs RMS jitter 10-GHz phase-locked loop for 40-Gb/s serial link transmitter based on two-stage ring oscillator in 65-nm CMOS

abstract: This paper presents a −245.3 dB FoMJ phase-locked loop based on a ring oscillator and a novel analysis on 2-stage ring oscillator. The proposed PLL generates a 4-phase 10-GHz clock for a 40-Gb/s serial link transmitter. The proposed analysis offers a time-domain insight on 2-stage ring oscillator and a precise prediction on oscillator behavior such as an output frequency and whether the 2-stage ring oscillates or not, based on a simple open-loop approach with a single stage buffer. The prototype chip is fabricated in 65-nm CMOS technology, and the PLL occupies only 0.009 mm2 and dissipates 7.6 mW from 1.2-V supply and 9 mW from 1.3-V supply. The measured integrated jitter of the PLL is 214 fs from 1.2-V supply and 182 fs from 1.3-V supply, which corresponds to −244.6 dB and −245.3 dB FoMJ, re spectively.


title: Adolescent Conscientiousness Predicts Lower Lifetime Unemployment

abstract: Existing research on Big Five personality and unemployment has relied on personality measures elicited after the respondents had already spent years in the labor market, an experience that could change personality. We clarify the direction of influence by using the British Cohort Study (N = 4,206) to examine whether conscientiousness and other Big Five personality traits at age 16-17 predict unemployment over age 16-42. Our hypothesis that higher conscientiousness in adolescence would predict lower unemployment was supported. In analyses controlling for intelligence, gender, and parental socioeconomic status, the less conscientious (-1 SD) had a predicted probability of unemployment twice as high (3.4% vs. 1.7%) as the highly conscientious (+1 SD), an effect size comparable to intelligence. Mediation analysis revealed that academic motivation and educational attainment explained only 8.9% of this association. Fostering conscientiousness in early life may be an effective way to reduce unemployment throughout adulthood.


title: Smart home automation by GSM using android application

abstract: The Home automation plays an important role in modern lifestyle because of its access in using at different places with high quality which will intern save time by decreasing human work automatically. The home automation is electric devices stand alone and do not communicate; it is programmable, such as sensors remote controller and communication system. Home automation use for electronic control devices remotely and automatically. This technology is focused on control household appliances like light, fan, AC, etc. automatically. In uncomfortable condition, it is useful for old aged and handicapped person.we have proposed a home appliances control in automation using GSM. The overall design of smart Home Automation system with low cost and wireless system. So the ON/OFF process of home appliances can be done remotely.


title: Substrate Integrated Waveguide L-Shaped Iris for Realization of Transmission Zero and Evanescent-Mode Pole

abstract: This paper investigates L-shaped iris (LSI) embedded in substrate integrated waveguide (SIW) structures. A lumped element equivalent circuit is utilized to thoroughly discuss the iris behavior in a wide frequency band. This structure has one more degree of freedom and design parameter compared with the conventional iris structures; therefore, it enables design flexibility with enhanced performance. The LSI is utilized to realize a two-pole evanescent-mode filter with an enhanced stopband and a dual-band filter combining evanescent and ordinary modes excitation. Moreover, a prescribed filtering function is demonstrated using the lumped element analysis not only including evanescent-mode pole, but also close-in transmission zero. The proposed LSI promises to substitute the conventional posts in (SIW) filter design.


Effective Data Cleaning with Continuous Evaluation


title: Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies

abstract: We use single-agent and multi-agent Reinforcement Learning (RL) for learning dialogue policies in a resource allocation negotiation scenario. Two agents learn concurrently by interacting with each other without any need for simulated users (SUs) to train against or corpora to learn from. In particular, we compare the Qlearning, Policy Hill-Climbing (PHC) and Win or Learn Fast Policy Hill-Climbing (PHC-WoLF) algorithms, varying the scenario complexity (state space size), the number of training episodes, the learning rate, and the exploration rate. Our results show that generally Q-learning fails to converge whereas PHC and PHC-WoLF always converge and perform similarly. We also show that very high gradually decreasing exploration rates are required for convergence. We conclude that multiagent RL of dialogue policies is a promising alternative to using single-agent RL and SUs or learning directly from corpora.


title: Fortuna: Model Checking Priced Probabilistic Timed Automata

abstract: Fortuna is the first tool for model checking priced probabilistic timed automata (PPTAs). PPTAs are an important model that can handle the combination of real-time, probabilistic and cost features. Only model checkers that incorporate all these features can address the key design trade-offs that arise in many practical applications such as: the Zeroconf, Bluetooth, IEEE802.11 and Firewire protocols, protocols for sensor networks, and scheduling problems with failures. PPTAs are an extension of probabilistic timed automata (PTAs), by having cost-rates and discrete cost increments on states. Fortuna is able to compute the maximal probability by which a class of states can be reached under a certain cost-bound (and time bound.) Although the problem is undecidable in general, there exists a semi-algorithm that produces a non-decreasing sequence of maximal probabilities. This paper enhances that algorithm. We compared the performance of Fortuna with existing approaches for PTAs. Surprisingly, although PPTAs are more general, our techniques exhibit superior performance.


title: A dual-band unidirectional coplanar antenna for 2.4–5-GHz wireless applications

abstract: A dual-band unidirectional antenna has been developed. The dual-band antenna consists of a long dipole for the lower frequency band and two short dipoles for the higher frequency band. All dipoles are printed coplanar on a thin substrate. The printed dipole antenna is excited by a microstrip line. The higher-order mode in the higher frequency band has been suppressed, leading to a good unidirectional pattern in the both frequency bands. This dual-band unidirectional antenna may find application in base stations and/or access points for 2.4/5- GHz wireless communications.


title: Fast strictness analysis based on demand propagation

abstract: Strictness analysis is a well-known technique used in compilers for optimization of sequential and parallel evaluation of lazy functional programming languages. Ever since Mycroft's pioneering research in strictness analysis, there have been substantial research efforts in advancing the basic technique in several directions such as higher-order functions, nonflat domains, etc. While almost all of these methods define strictness based on denotational semantics, in 1990 we proposed an operational method called ee-analysis. Operational methods directly address the primary aspect of the strictness analysis problem, namely nontermination of evaluation, and can yield simplicity both in formulation and computational algorithms. Moreover, the use of operational approach has led us to a simple and natural treatment of polymorphism. While ee-analysis reasoned about normal-form evaluation herein we extend its power substantially so as to deal with other degrees of evaluation that are intermediate between normal and head-normal form. An interesting aspect of our approach is our formulation of a strictness property as a constraint that relates the demand placed on the output of a function to the demands placed on its arguments. Strictness properties are then computed using symbolic constraint-solving techniques. An important advantage of constraint-driven analysis is that it captures interargument dependencies accurately. Moreover, the analysis performance is relatively insensitive to domain size. Based on our implementation of this method, we show that our analysis techniques are efficient, as well as effective.


title: Paraoxonase 1 and platelet-activating factor acetylhydrolase activities in patients with low hdl-cholesterol levels with or without primary hypertriglyceridemia

abstract: Abstract Background Previous studies have shown that high density lipoprotein (HDL)-deficient states are associated with reduced paraoxonase 1 (PON1) activity. However, HDL reduction caused by primary hypertriglyceridemia has not been fully explored. The aim of the present study was to evaluate whether PON1 and platelet-activating factor acetylhydrolase (PAF-AH), two antioxidant enzymes, were altered in patients with low HDL-cholesterol levels with or without primary hypertriglyceridemia in comparison with control normolipemic subjects. Methods We studied 24 patients with low HDL-cholesterol levels with ( n = 12) or without ( n = 12) primary hypertriglyceridemia in comparison with 12 control subjects who presented normal HDL-cholesterol and triglyceride levels. Paraoxon and phenylacetate were used as substrate for measuring PON1 activities and 1-hexadecyl-2-[ 3 H]acetyl-glycero-3-phosphocholine for platelet-activating factor acetylhydrolase (PAF-AH) activity. Double substrate method was used to assign phenotypes. Lipid, lipoprotein, apolipoprotein, and lipoprotein particles were determined by standardized methods. Results Both PON1 activities were significantly reduced in patients with low HDL-cholesterol levels. This reduction could be selectively attributed to the hypertriglyceridemic subgroup. PAF-AH activity was not different between hypoalphalipoproteinemic patients and controls. PON1 activities correlated positively and significantly with HDL-cholesterol, HDL 2 -cholesterol, HDL 3 -cholesterol, HDL-phospholipids, apo A-I, apo A-II, and LpA-I:A-II. PAF-AH correlated positively and significantly with total and low density lipoprotein-cholesterol. Conclusions Data from this study would suggest that in hypoalphalipoproteinemic syndrome, particularly when associated with hypertriglyceridemia, there is impairment in enzymatic antioxidant activity exclusively related with HDL.


title: Optimal trajectory generation for dynamic street scenarios in a Frenét Frame

abstract: Safe handling of dynamic highway and inner city scenarios with autonomous vehicles involves the problem of generating traffic-adapted trajectories. In order to account for the practical requirements of the holistic autonomous system, we propose a semi-reactive trajectory generation method, which can be tightly integrated into the behavioral layer. The method realizes long-term objectives such as velocity keeping, merging, following, stopping, in combination with a reactive collision avoidance by means of optimal-control strategies within the Frenet-Frame [12] of the street. The capabilities of this approach are demonstrated in the simulation of a typical high-speed highway scenario.


title: Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach

abstract: Society is rapidly accepting the use of video cameras in many new and varied locations, but effective methods to utilize and manage the massive resulting amounts of visual data are only slowly developing. This paper presents a framework for live video analysis in which the behaviors of surveillance subjects are described using a vocabulary learned from recurrent motion patterns, for real-time characterization and prediction of future activities, as well as the detection of abnormalities. The repetitive nature of object trajectories is utilized to automatically build activity models in a 3-stage hierarchical learning process. Interesting nodes are learned through Gaussian mixture modeling, connecting routes formed through trajectory clustering, and spatio-temporal dynamics of activities probabilistically encoded using hidden Markov models. Activity models are adapted to small temporal variations in an online fashion using maximum likelihood regression and new behaviors are discovered from a periodic retraining for long-term monitoring. Extensive evaluation on various data sets, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based activity analysis.


title: A Chopper Current-Feedback Instrumentation Amplifier With a 1 mHz $1/f$ Noise Corner and an AC-Coupled Ripple Reduction Loop

abstract: This paper presents a chopper instrumentation amplifier for interfacing precision thermistor bridges. For high CMRR and DC gain, the amplifier employs a three-stage current-feedback topology with nested-Miller compensation. By chopping both the input and intermediate stages of the amplifier, a 1 mHz 1/f noise corner was achieved at an input-referred noise power spectral density (PSD) of 15 nV/?Hz. To reduce chopper ripple, the amplifier employs a continuous-time AC-coupled ripple reduction loop. Due to its continuous-time nature, the loop causes no noise folding to DC and hence offers improved noise performance over auto-zeroed amplifiers. The loop reduces chopper ripple by more than 60 dB, to levels below the amplifier's own input-referred noise. Furthermore, a maximum input referred offset of 5 ? V and a CMRR greater than 120 dB were measured at a supply current of 230 ?A at 5 V.


title: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach

abstract: Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various interesting topics, such as brands, products or celebrities, etc. Naturally, people may anticipate an approach to receiving the common sentiment tendency towards these topics directly rather than through reading the huge amount of tweets about them. On the other side, Hashtags, starting with a symbol "#" ahead of keywords or phrases, are widely used in tweets as coarse-grained topics. In this paper, instead of presenting the sentiment polarity of each tweet relevant to the topic, we focus our study on hashtag-level sentiment classification. This task aims to automatically generate the overall sentiment polarity for a given hashtag in a certain time period, which markedly differs from the conventional sentence-level and document-level sentiment analysis. Our investigation illustrates that three types of information is useful to address the task, including (1) sentiment polarity of tweets containing the hashtag; (2) hashtags co-occurrence relationship and (3) the literal meaning of hashtags. Consequently, in order to incorporate the first two types of information into a classification framework where hashtags can be classified collectively, we propose a novel graph model and investigate three approximate collective classification algorithms for inference. Going one step further, we show that the performance can be remarkably improved using an enhanced boosting classification setting in which we employ the literal meaning of hashtags as semi-supervised information. Experimental results on a real-life data set consisting of 29,195 tweets and 2,181 hashtags show the effectiveness of the proposed model and algorithms.


title: Combining tactile sensing and vision for rapid haptic mapping

abstract: We consider the problem of enabling a robot to efficiently obtain a dense haptic map of its visible surroundings using the complementary properties of vision and tactile sensing. Our approach assumes that visible surfaces that look similar to one another are likely to have similar haptic properties. We present an iterative algorithm that enables a robot to infer dense haptic labels across visible surfaces when given a color-plus-depth (RGB-D) image along with a sequence of sparse haptic labels representative of what could be obtained via tactile sensing. Our method uses a color-based similarity measure and connected components on color and depth data. We evaluated our method using several publicly available RGBD image datasets with indoor cluttered scenes pertinent to robot manipulation. We analyzed the effects of algorithm parameters and environment variation, specifically the level of clutter and the type of setting, like a shelf, table top, or sink area. In these trials, the visible surface for each object consisted of an average of 8602 pixels, and we provided the algorithm with a sequence of haptically-labeled pixels up to a maximum of 40 times the number of objects in the image. On average, our algorithm correctly assigned haptic labels to 76.02% of all of the object pixels in the image given this full sequence of labels. We also performed experiments with the humanoid robot DARCI reaching in a cluttered foliage environment while using our algorithm to create a haptic map. Doing so enabled the robot to reach goal locations using a single plan after a single greedy reach, while our previous tactile-only mapping method required 5 or more plans to reach each goal.


title: Correlation-based Feature Selection for Machine Learning

abstract: A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. Further experiments compared CFS with a wrapper—a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets. Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates iii feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.


title: A Family of Droids-Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis

abstract: Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MaMaDroid, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AuntieDroid and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F-measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.


title: Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention

abstract: Video-based person re-identification (ReID) is a challenging problem, where some video tracks of people across non-overlapping cameras are available for matching. Feature aggregation from a video track is a key step for video-based person ReID. Many existing methods tackle this problem by average/maximum temporal pooling or RNNs with attention. However, these methods cannot deal with temporal dependency and spatial misalignment problems at the same time. We are inspired by video action recognition that involves the identification of different actions from video tracks. Firstly, we use 3D convolutions on video volume, instead of using 2D convolutions across frames, to extract spatial and temporal features simultaneously. Secondly, we use a non-local block to tackle the misalignment problem and capture spatial-temporal long-range dependencies. As a result, the network can learn useful spatial-temporal information as a weighted sum of the features in all space and temporal positions in the input feature map. Experimental results on three datasets show that our framework outperforms state-of-the-art approaches by a large margin on multiple metrics.


User Experience in Human-Robot Interactions


title: Are we Done with Object Recognition? The iCub robot's Perspective

abstract: Abstract We report on an extensive study of the benefits and limitations of current deep learning approaches to object recognition in robot vision scenarios, introducing a novel dataset used for our investigation. To avoid the biases in currently available datasets, we consider a natural human–robot interaction setting to design a data-acquisition protocol for visual object recognition on the iCub humanoid robot. Analyzing the performance of off-the-shelf models trained off-line on large-scale image retrieval datasets, we show the necessity for knowledge transfer. We evaluate different ways in which this last step can be done, and identify the major bottlenecks affecting robotic scenarios. By studying both object categorization and identification problems, we highlight key differences between object recognition in robotics applications and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. In a nutshell, our results confirm the remarkable improvements yield by deep learning in this setting, while pointing to specific open challenges that need be addressed for seamless deployment in robotics.


ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization.


title: Physically reconfigurable antennas: Concepts and automation

abstract: This paper discusses concepts of three physically reconfigurable antennas that are software controlled and fully automated. Each of the three antennas reconfigures its operating frequency or radiation pattern based on a physical movement of some of its radiating parts. The antennas discussed vary from a frequency reconfigurable quadrifilar helix to a frequency reconfigurable sector monopole as well as a radiation pattern reconfigurable patch. The antennas discussed are fabricated and measured where a good analogy is found with the predicted data.


title: A hybrid face recognition method using Markov random fields

abstract: We propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a Markov random field (MRF) model. The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's is then inferred using the belief propagation (BP) algorithm. Finally, the ID of the test image is determined by a voting scheme from the estimated patch ID's. Experimental results on several face datasets indicate the significant potential of our method.


Tracing territorial capital: a landscape based approach to institutional innovation


title: A 2W, 92% efficiency and 0.01% THD+N class-D audio power amplifier for mobile applications, based on the novel SCOM architecture

abstract: This paper presents a high power efficient class-D audio power amplifier. The proposed class-D amplifier adopts the synchronized controlled oscillation modulator (SCOM), which is suitable for mobile applications. The THD+N at half the maximum output power is below 0.01% and the efficiency is better than 92% in an 8 /spl Omega/ speaker load. The one-chip integrated circuit is implemented in a 0.35 /spl mu/m CMOS technology, with a supply voltage range of 1.6 V-3.6 V. it occupies a chip area of 1.2/spl times/1.2 mm/sup 2/ and dissipates only 1.3 mA at idle.


title: How to structure online discussions for meaningful discourse: a case study

abstract: This study examined the impact of structuredness of asynchronous online discussion protocols and evaluation rubrics on meaningful discourse. Transcripts of twelve online discussions involving 87 participants from four sections of a graduate course entitled Instructional Technology Foundations and Learning Theory were analysed across four semesters. Protocols and evaluation rubrics guiding online discussions in this course ranged from minimal structure or loosely defined protocols in the first section, to high sructure or well defined and comprehensive protocols and evaluation criteria in the fourth section. The analyses revealed that some elements of structure had a significant impact on meaningful discourse. Particularly, guidelines that assisted the facilitation and evaluation of online discussions increased the cognitive quality of student postings promoting a deeper and more meaningful understanding of course content.


title: Mobile Edge Computing Empowers Internet of Things

abstract: In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods are validated via extensive simulations.


title: Roots and Lexicality in Distributed Morphology

abstract: Fifth York-Essex Morphology Meeting (YEMM), 9th February and 10th February 2008, Department of Language and Linguistic ::: Science, University of York


title: Exposing private information by timing web applications

abstract: We show that the time web sites take to respond to HTTP requests can leak private information, using two different types of attacks. The first, direct timing, directly measures response times from a web site to expose private information such as validity of an username at a secured site or the number of private photos in a publicly viewable gallery. The second, cross-site timing, enables a malicious web site to obtain information from the user's perspective at another site. For example, a malicious site can learn if the user is currently logged in at a victim site and, in some cases, the number of objects in the user's shopping cart. Our experiments suggest that these timing vulnerabilities are wide-spread. We explain in detail how and why these attacks work, and discuss methods for writing web application code that resists these attacks.


title: Impending Facial Skin Necrosis and Ocular Involvement After Dermal Filler Injection: A Case Report

abstract: Demand for non-surgical rejuvenating procedure is constantly increasing due to the aging population, increasing expense of aesthetics and beauty procedures, introduction of new applications and rising demand for noninvasive aesthetic procedures over surgical procedures. Skin necrosis is a rare but severe potential complication. It is caused by impediment of the blood supply to the skin area by compression and/or obstruction of the vessel with filler material, and/or direct injury to the vessel. We report the case of a young patient who presented an acute and severe complication after a dermal filler injection by an unlicensed therapist. High-dose corticosteroids i.v. therapy among others helped in the process of healing. Skin necrosis left the patient with a full thickness scar on the forehead region. Dermal fillers are to be considered safe only when handled by trained doctors. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


Optical Music Recognition of Early Typographic Prints using Hidden Markov Models


title: Molecular Boxes on a Molecular Printboard: Encapsulation of Anionic Dyes in Immobilized Dendrimers

abstract: Fifth-generation poly(propylene imine) dendrimers, modified with 64 apolar adamantyl groups, have been immobilized on cyclodextrin host monolayers (molecular printboards) on glass by supramolecular microcontact printing. The immobilized dendrimers retain their guest-binding properties and function as molecular boxes that can be filled with fluorescent dye molecules from solution. Alternatively, part of the immobilized dendrimers were filled with dye molecules by cross-microcontact printing while the remaining, empty dendrimers were filled with a different dye from solution, resulting in alternating patterns of dye molecules. In addition, we demonstrate that encapsulation of dyes in immobilized dendrimers is reversible: immobilized molecular boxes can be filled with a dye, emptied, and subsequently refilled with a different dye.


title: Reviewing emergency care systems I: insights from system dynamics modelling

abstract: OBJECTIVES ::: To describe the components of an emergency and urgent care system within one health authority and to investigate ways in which patient flows and system capacity could be improved. ::: ::: ::: METHODS ::: Using a qualitative system dynamics (SD) approach, data from interviews were used to build a conceptual map of the system illustrating patient pathways from entry to discharge. The map was used to construct a quantitative SD model populated with demographic and activity data to simulate patterns of demand, activity, contingencies, and system bottlenecks. Using simulation experiments, a range of scenarios were tested to determine their likely effectiveness in meeting future objectives and targets. ::: ::: ::: RESULTS ::: Emergency hospital admissions grew at a faster annual rate than the national average for 1998-2001. Without intervention, and assuming this trend continued, acute hospitals were likely to have difficulty sustaining levels of elective work, in reaching elective admission targets and in achieving bed occupancy targets. General practice admissions exerted the greatest influence on occupancy rates. Prevention of emergency admissions for older people (3%-6% each year) reduced bed occupancy in both hospitals by 1% per annum over five years. Prevention of emergency admissions for patients with chronic respiratory disease affected occupancy less noticeably, but because of the seasonal pattern of admissions, had an effect on peak winter occupancy. ::: ::: ::: CONCLUSIONS ::: Modelling showed the potential consequences of continued growth in demand for emergency care, but also considerable scope to intervene to ameliorate the worst case scenarios, in particular by increasing the care management options available in the community.


Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring


title: Protein folding and misfolding

abstract: The manner in which a newly synthesized chain of amino acids transforms itself into a perfectly folded protein depends both on the intrinsic properties of the amino-acid sequence and on multiple contributing influences from the crowded cellular milieu. Folding and unfolding are crucial ways of regulating biological activity and targeting proteins to different cellular locations. Aggregation of misfolded proteins that escape the cellular quality-control mechanisms is a common feature of a wide range of highly debilitating and increasingly prevalent diseases.


title: A high efficiency 140W power amplifier based on a single GaN HEMT device for space applications in L-band

abstract: This paper deals with the design, manufacture and test of a high efficiency power amplifier for L-band space borne applications. The circuit operates with a single 36 mm gate periphery GaN HEMT power bar die allowing both improved integration and performance as compared with standard HPA design in a similar RF power range. A huge effort dedicated to the device's characterization and modeling has eased the circuit optimization leaning on the multi-harmonics impedances synthesis. Test results demonstrate performance up to 140 W RF output power with an associated 60% PAE for a limited 3.9 dB gain compression under 50 V supply voltage using a single GaN power bar.


title: Security and usability of authenticating process of online banking: User experience study

abstract: Bank websites are secure websites considered high risk, so security is a prime concern. Authentication is extremely important because it serves as the entry point for customers to access their personal and sensitive information. To be considered effective and desirable, a banking website should provide its users with secure and usable authentication mechanisms. Otherwise, the authentication process likely will become unsafe and frustrating for users. This issue in turn compromises the bank’s objective to make online transactions convenient for its users. Focusing on this dilemma, this paper sought to examine the security and usability of single and multifactor authentication methods, which are used by most banks to strengthen the security process. However, the security and usability of multifactor authentication have not been closely investigated from the user’s perspective. In a survey of 302 e-banking customers who owned at least two international bank accounts, multifactor authentication methods were perceived as secure and trustworthy, with a high rate of usability. Token-based authentication was perceived as more usable than SMS-based authentication, during which a SMS is sent via mobile means.


title: Drone remote sensing for forestry research and practices

abstract: Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing initiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, measuring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it is important to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.


title: Sudden cardiac death in a child affected by Prader-Willi syndrome

abstract: A case of sudden cardiac death in a 3-year-old young male affected by Prader-Willi syndrome, clinically diagnosed and confirmed by means of DNA methylation, is presented. The infant suddenly collapsed at home and was taken apparently unconsciousness by his mother to the emergency clinic where he was pronounced dead. A complete postmortem examination was performed and the histological findings led to the definition of cardiac death with a typical picture of contraction band necrosis. Pulmonary hypoxic alterations are frequently reported as the primary cause of death in PWS cases. In this fatal case according to the macroscopic and microscopic findings, the cause of death was most likely cardiac and possibly related to contraction band necrosis linked with ventricular fibrillation and sudden death.


title: Signal processing techniques for interpolation in graph structured data

abstract: In this paper, we propose a novel algorithm to interpolate data defined on graphs, using signal processing concepts. The interpolation of missing values from known samples appears in various applications, such as matrix/vector completion, sampling of high-dimensional data, semi-supervised learning etc. In this paper, we formulate the data interpolation problem as a signal reconstruction problem on a graph, where a graph signal is defined as the information attached to each node (scalar or vector values mapped to the set of vertices/edges of the graph). We use recent results for sampling in graphs to find classes of bandlimited (BL) graph signals that can be reconstructed from their partially observed samples. The interpolated signal is obtained by projecting the input signal into the appropriate BL graph signal space. Additionally, we impose a `bilateral' weighting scheme on the links between known samples, which further improves accuracy. We use our proposed method for collaborative filtering in recommendation systems. Preliminary results show a very favorable trade-off between accuracy and complexity, compared to state of the art algorithms.


title: CORDS: automatic discovery of correlations and soft functional dependencies

abstract: The rich dependency structure found in the columns of real-world relational databases can be exploited to great advantage, but can also cause query optimizers---which usually assume that columns are statistically independent---to underestimate the selectivities of conjunctive predicates by orders of magnitude. We introduce CORDS, an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies between columns. CORDS searches for column pairs that might have interesting and useful dependency relations by systematically enumerating candidate pairs and simultaneously pruning unpromising candidates using a flexible set of heuristics. A robust chi-squared analysis is applied to a sample of column values in order to identify correlations, and the number of distinct values in the sampled columns is analyzed to detect soft functional dependencies. CORDS can be used as a data mining tool, producing dependency graphs that are of intrinsic interest. We focus primarily on the use of CORDS in query optimization. Specifically, CORDS recommends groups of columns on which to maintain certain simple joint statistics. These "column-group" statistics are then used by the optimizer to avoid naive selectivity estimates based on inappropriate independence assumptions. This approach, because of its simplicity and judicious use of sampling, is relatively easy to implement in existing commercial systems, has very low overhead, and scales well to the large numbers of columns and large table sizes found in real-world databases. Experiments with a prototype implementation show that the use of CORDS in query optimization can speed up query execution times by an order of magnitude. CORDS can be used in tandem with query feedback systems such as the LEO learning optimizer, leveraging the infrastructure of such systems to correct bad selectivity estimates and ameliorating the poor performance of feedback systems during slow learning phases.


title: KM implementation in a public sector accounting organization: an empirical investigation

abstract: Purpose – This study aims to investigate how accountants working in a large public sector accounting organization perceive KM implementation issues in their organization. An integrated KM framework that interconnects KM enablers, knowledge sharing process, and organizational performance derived from the literature is proposed and adopted in this study.Design/methodology/approach – The accountants working at the Accountant‐General's Department (AGD) under the Ministry of Finance, Malaysia are selected for an in‐depth study through the use of a self‐reporting questionnaire. The model's validity was confirmed through factor analysis. The data were then analyzed using multiple regression analyses.Findings – The statistical results provide strong support for the KM framework. There are positive effects of KM enablers and knowledge sharing process towards organizational performance. When both the variables are regarded as antecedents of organizational performance, knowledge sharing process and technology resour...


title: N4SID method applied to obtain a discrete-time linear state space system as a mathematical model of a jaw crusher prototype

abstract: This paper presents results of the use of the N4SID method, belonging to the subspaces identification techniques, in its deterministic case, applied to a prototype of a crusher, where this unit operation is part of the mineral processing chain. A mathematical model in discrete-time linear state space system is obtained, where the result indicates that the estimation of the model is satisfactory for use in model-based design control systems, aiming to improve performance and efficiency in plants that involves the use of this unit operation.


title: On the Complexity of Some Enumeration Problems for Matroids

abstract: Let $M$ be a matroid defined by an independence oracle on ground set $S$, and let $A\subseteq S$. We present an incremental polynomial-time algorithm for enumerating all minimal (maximal) subsets of $S$ which span (do not span) $A$. Special cases of these problems include the generation of bases, circuits, hyperplanes, flats of given rank, circuits through a given element, generalized Steiner trees, and multiway cuts in graphs, as well as some other applications. We also consider some tractable and NP-hard generation problems related to systems of polymatroid inequalities and (generalized) packing and spanning in matroids.


title: A Location-Based VR Museum

abstract: This poster presents a novel type of Virtual Reality (VR) application for education and culture: a location-based VR Museum, which is a large-room scale multi-user multi-zone virtual museum. This VR museum was designed to support over 100 simultaneous users, walking in a large tracking system (600 m2) and sharing a ten times bigger virtual space (7000 m2) containing indoor and outdoor dinosaur exhibitions. This poster is giving an overview of the system and its main features as well as discussing its potential benefits and future evaluation.


title: US, CT and MR imaging characteristics of nephroblastomatosis

abstract: OBJECTIVES ::: To describe the imaging features of nephroblastomatosis with US, CT and MR, to point out characteristics of differentiation between nephrogenic rests (NR) and Wilms' tumour (WT) and to determine the most appropriate imaging modality. ::: ::: ::: MATERIALS AND METHODS ::: We reviewed the US, CT and MR images of 29 cases of histopathologically confirmed nephroblastomatosis sent to our department for reference evaluation (German nephroblastoma study). The series included 17 kidneys with NR, 6 kidneys with WT and 32 kidneys with both NR and WT. ::: ::: ::: RESULTS ::: NR presented as multinodular, peripheral, cortical lesions, the diffuse form of distribution being less common. Foci were homogeneous and of low echogenicity, density or signal intensity. The lesions were most clearly depicted with contrast-enhanced CT and T1-weighted (T1-W) MR images. Lesions smaller than 1 cm were rarely identified by US. The most reliable criterion to differentiate NR from WT was their homogeneity. ::: ::: ::: CONCLUSIONS ::: Contrast-enhanced CT and T1-W MR images are of similar potential and superior to US in the diagnosis of nephroblastomatosis. Due to the significant radiation dose of serial CT, MR imaging should be the method of choice wherever it is available. The cost-effectiveness and availability of US makes it ideal for serial follow-up of known lesions.


title: Cultural Heritage in CLEF CHiC 2013

abstract: The Cultural Heritage in CLEF 2013 lab comprised three tasks: multilingual ad-hoc retrieval and semantic enrichment in 13 languages Dutch, English, German, Greek, Finnish, French, Hungarian, Italian, Norwegian, Polish, Slovenian, Spanish, and Swedish, Polish ad-hoc retrieval and the interactive task, which studied user behavior via log analysis and questionnaires. For the multilingual and Polish sub-tasks, more than 170,000 documents were assessed for relevance on a tertiary scale. The multilingual task had 7 participants submitting 30 multilingual and 41 monolingual runs. The Polish task comprised 3 participating groups submitting manual and automatic runs. The interactive task had 4 participating research groups and 208 user participants in the study. For the multilingual task, results show that more participants are necessary in order to provide comparative analyses. The interactive task created a rich data set comprising of questionnaire of log data. Further analysis of the data is planned in the future.


title: Alcohol and the adolescent brain: human studies

abstract: Many people begin to drink alcohol during adolescence and young adulthood. Alcohol consumption during this developmental period may have profound effects on brain structure and function. Heavy drinking has been shown to affect the neuropsychological performance (e.g., memory functions) of young people and may impair the growth and integrity of certain brain structures. Furthermore, alcohol consumption during adolescence may alter measures of brain functioning, such as blood flow in certain brain regions and electrical brain activities. Not all adolescents and young adults are equally sensitive to the effects of alcohol consumption, however. Moderating factors—such as family history of alcohol and other drug use disorders, gender, age at onset of drinking, drinking patterns, use of other drugs, and co-occurring psychiatric disorders—may influence the extent to which alcohol consumption interferes with


title: Trends in steganography

abstract: Methods for embedding secret data are more sophisticated than their ancient predecessors, but the basic principles remain unchanged.


title: A Hybrid Abnormal Advertising Traffic Detection Method

abstract: Abnormal traffic is pervasive in the online advertising market. There are various cheating approaches while traditional anti-fraud methods are only effective for specific patterns. Combining the rule-based methods with supervised classification methods, we propose an abnormal traffic detection framework on both user layer and traffic layer. On the user layer, rule-based filters are designed to detect malicious users with duplicate clicks. We extract hybrid features under multi-granular time windows and train a user classifier to filter cheaters and complex spams indirectly. On traffic layer, we apply traffic filters to detect explicit fraudulent clicks and use a prediction model to detect malicious traffic with a higher precision. Extensive experiments on ground-truth data demonstrate the effectiveness of our detection method.


title: Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving

abstract: For the design and test of functional modules of an automated vehicle, it is essential to define interfaces. While interfaces on the perception side, like object lists, point clouds or occupancy grids, are to a certain degree settled already, they are quite vague in the consecutive steps of context modeling and in particular on the side of driving execution. The authors consider the scene as the central interface between perception and behavior planning a control. Within the behavior planning a control block, a situation is a central data container. A scenario is a common approach to substantiate test cases for functional modules and can be used to detail the functional description of a system. However, definitions of these terms are often-at best-vague or even contradictory. This paper will review these definitions and come up with a consistent definition for each term. Moreover, we present an example for the implementation of each of these interfaces.


title: Overlapping community detection via bounded nonnegative matrix tri-factorization

abstract: Complex networks are ubiquitous in our daily life, with the World Wide Web, social networks, and academic citation networks being some of the common examples. It is well understood that modeling and understanding the network structure is of crucial importance to revealing the network functions. One important problem, known as community detection, is to detect and extract the community structure of networks. More recently, the focus in this research topic has been switched to the detection of overlapping communities. In this paper, based on the matrix factorization approach, we propose a method called bounded nonnegative matrix tri-factorization (BNMTF). Using three factors in the factorization, we can explicitly model and learn the community membership of each node as well as the interaction among communities. Based on a unified formulation for both directed and undirected networks, the optimization problem underlying BNMTF can use either the squared loss or the generalized KL-divergence as its loss function. In addition, to address the sparsity problem as a result of missing edges, we also propose another setting in which the loss function is defined only on the observed edges. We report some experiments on real-world datasets to demonstrate the superiority of BNMTF over other related matrix factorization methods.


title: Visual Simulation of Hydraulic Erosion

abstract: A new algorithm for hydraulic terrain erosion is introduced. The main goal of the paper is to provide a technique which is inspired by physics and which allows for high level of control. We divided the erosion process into four independent steps that can be applied independently to achieve high level of realism. The erosion algorithm is based on the ability of water to dissolve material that is then transported to another locations. Because of the evaporation the sediment capacity of water volume is exceeded and the material is deposited. This kind of material transport significantly influences the terrain morphology. The algorithm has wide area of applications; we describe here water sources, drying up the plashes, as well as fictive rain on the surface of Mars.


title: 5.10 A 1.4V 10.5MHz swing-boosted differential relaxation oscillator with 162.1dBc/Hz FOM and 9.86psrms period jitter in 0.18µm CMOS

abstract: Relaxation oscillators have a profound scope as on-chip reference clock sources or sensor front-ends in comparison to ring oscillators due to their superior frequency stability, control linearity, and wide tuning range. However, despite a better fundamental limit predicted in theory, the phase noise performance of relaxation oscillators trails behind that of ring oscillators. Furthermore, there is a huge gap between the maximum achievable 1/f2 phase noise FOM (169dBc/Hz) [1] and those achieved by recently proposed low-power relaxation oscillator implementations [1–4].


Pronunciation Error Detection for New Language Learners


title: A comparison of tactile , auditory , and visual feedback in a pointing task using a mouse-type device

abstract: A mouse was modified to add tactile feedback via a solenoid-driven pin projecting through a hole in the left mouse button. An experiment is described using a target selection task under five different sensory feedback conditions (‘normal’, auditory, colour, tactile, and combined). No differences were found in overall response times, error rates, or bandwidths; however, significant differences were found in the final positioning times (from the cursor entering the target to selecting the target). For the latter, tactile feedback was the quickest, normal feedback was the slowest. An examination of the spatial distributions in responses showed a peaked, narrow distribution for the normal condition, and a flat, wide distribution for the tactile (and combined) conditions. It is argued that tactile feedback allows subjects to use a wider area of the target and to select targets more quickly once the cursor is inside the target. Design considerations for human-computer interfaces are discussed.


Towards a Sentient Object Model


An Improved PSO Clustering Algorithm with Entropy-based Fuzzy Clustering


title: Flaming on YouTube

abstract: In this explorative study, flaming on YouTube was studied using surveys of YouTube users. Flaming is defined as displaying hostility by insulting, swearing or using otherwise offensive language. Three general conclusions were drawn. First, although many users said that they themselves do not flame, flaming appears to be very common on YouTube. Second, views on flaming varied but were more often negative than positive. Some people refrain from uploading videos as a result of flaming, but most users do not think of flaming as a problem for themselves. Third, several explanations of flaming were found to be plausible, among which were perceived flaming norms and the reduced awareness of other people's feelings. Although some YouTube users flame for entertainment, flaming is more often intended to express disagreement or as a response to a perceived offense by others.


title: Product Perceived Quality and Purchase Intention with Consumer Satisfaction

abstract: The purpose of this study is to measure the impact of product perceive quality on purchase intention with level of satisfaction, for meeting this purpose the data was collected by individually through 122 questionnaires by adopting the convenience techniques. Using statistical software hypothesis shows that these variables have positive significant relationship. Practical contribution shows that this study can be used as a guideline to management and marketers to improve the product quality.


title: Developmental kinesiology: three levels of motor control in the assessment and treatment of the motor system.

abstract: Three levels of sensorimotor control within the central nervous system (CNS) can be distinguished. During the neonatal stage, general movements and primitive reflexes are controlled at the spinal and brain stem levels. Analysis of the newborn's spontaneous general movements and the assessment of primitive reflexes is crucial in the screening and early recognition of a risk for abnormal development. Following the newborn period, the subcortical level of the CNS motor control emerges and matures mainly during the first year of life. This allows for basic trunk stabilization, a prerequisite for any phasic movement and for the locomotor function of the extremities. At the subcortical level, orofacial muscles and afferent information are automatically integrated within postural-locomotor patterns. Finally, the cortical (the highest) level of motor control increasingly becomes activated. Cortical control is important for the individual qualities and characteristics of movement. It also allows for isolated segmental movement and relaxation. A child with impaired cortical motor control may be diagnosed with developmental dyspraxia or developmental coordination disorder. Human ontogenetic models, i.e., developmental motor patterns, can be used in both the diagnosis and treatment of locomotor system dysfunction.


title: Prognostic value of exercise radionuclide angiography in low risk acute myocardial infarction survivors.

abstract: Patients with an uneventful course during hospital stay, which represent from 30 to 50% of all myocardial infarction survivors, still have an incidence of new coronary events up to 7% during the first year of follow-up. To assess the value of radionuclide angiography in predicting new coronary events in this low risk population, 93 patients without evidence of left ventricular failure or recurrent postinfarction angina underwent rest and exercise radionuclide angiography and treadmill exercise testing before hospital discharge. During follow-up (16 +/- 5 months, range 12 to 32) 14 patients developed new coronary events: two patients died, four had a new myocardial infarction and the remaining eight had unstable angina. There were no differences regarding clinical variables, the results of the exercise test and the resting ejection fraction, between patients with or without new coronary events; however, patients without events during follow-up exercised longer during both exercise treadmill test and exercise radionuclide angiography. Resting end-diastolic and end-systolic volume indexes were higher in patients presenting coronary events (122 +/- 50 vs 92 +/- 32 ml.m-2, P < 0.05, 69 +/- 47 vs 47 +/- 26 ml.m-2, P < 0.05). These patients also had a higher incidence of wall motion abnormalities in more than one area (64 vs 28%, P < 0.02). During exercise, ejection fraction increased significantly in patients with an uneventful outcome (49 +/- 13 to 56 +/- 14%, P < 0.01), while it did not change in their counterparts (46 +/- 14 to 45 +/- 14%, NS).(ABSTRACT TRUNCATED AT 250 WORDS)


title: Aligning the Parasite Experiment Ontology and the Ontology for Biomedical Investigations Using AgreementMaker

abstract: Tremendous amounts of data exist in life sciences along with many bio-ontologies. Though these databases contain important information about gene, proteins, functions, etc., this information is not well utilized due to the heterogeneous formats of these databases. Therefore, ontology alignment (OA) is now very critical for life science domain. Our work utilizes AgreementMaker for OA and describes results, difficulties faced in the process, and lessons learned. We aligned two real-world ontologies, the Parasite Experiment Ontology (PEO) and the Ontology for Biomedical Investigations (OBI). The former is more applicationoriented and the latter is a reference ontology for any biomedical or clinical investigations. Our study led to several enhancements to AgreementMaker: annotation profiling, mapping provenance information, and tailored lexicon building. These enhancements, which are applicable to any OA system, greatly improved the alignment of these real world ontologies, producing 90% precision with 60% recall from the BSM, the Base Similarity Matcher, and 57% precision with 67% recall from the PSM, the Parametric String Matcher, both using lexicon lookup for synonyms. The mappings obtained through this study are posted on BioPortal site for public use. † Partially supported by NSF Awards IIS–0513553 and IIS-0812258 and by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL) contract number FA8650-10-C-7061. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.


title: SOA maturity model a frame of reference

abstract: Service Oriented Architecture (SOA) is an architectural style that supports service orientation. In reality, SOA is much more than architecture. SOA adoption is prerequisite for organization to excel their service deliveries, as the delivery platforms are shifting to mobile, cloud and social media. A maturity model is a tool to accelerate enterprise SOA adoption, however it depends on how it should be applied. This paper presents a literature review of existing maturity models and proposes 5 major aspects that a maturity model has to address to improve SOA practices of an enterprise. A maturity model can be used as: (i) a roadmap for SOA adoption, (ii) a reference guide for SOA adoption, (iii) a tool to gauge maturity of process execution, (iv) a tool to measure the effectiveness of SOA motivations, and (v) a review tool for governance framework. This paper also sheds light on how SOA maturity assessment can be modeled. A model for SOA process execution maturity and perspective maturity assessment has been proposed along with a framework to include SOA scope of adoption.


title: Design of Full Adder Circuit Using Double Gate MOSFET

abstract: This paper presents a design of a one bit full adder cell based on degenerate pass transistor logic (PTL) using Double Gate MOSFET. The design cell is degenerate 5-T XOR-XNOR module. This design has been compared with existing one-bit full adder cell based on degenerate pass transistor logic (PTL) designed using Single Gate MOSFET. In this paper, the proposed circuit has been analyzed for parameters like- power consumption and power delay product. The simulations of the proposed Full Adder have been performed using Tanner EDA Tool version 13.0. All the proposed design simulations are carried out at 45nm technology for various inputs like supply voltage, temperature and frequency. The decrease of 24% in power consumption has observed in proposed circuit. The results show a validity of double gate MOSFETs for designing for low power full adder circuit.


title: The influence of brand image and company reputation where manufacturers market to small firms: A customer value perspective

abstract: Abstract Branding research has largely focused on consumer goods markets and only recently has attention been given to business markets. In many business markets the company's reputation has a strong influence on buying decisions which may differ from the more specific product related influence of the brand's image. In this paper we investigate these differences by testing the hypotheses about the influences of brand image and company reputation on customers' perceptions of product and service quality, customer value, and customer loyalty in a business market where there are three manufacturers marketing their brands directly to a large number of small firms. The results indicate that the brand's image has a more specific influence on the customers' perceptions of product and service quality while the company's reputation has a broader influence on perceptions of customer value and customer loyalty.


Accessible Maps for the Visually Impaired


title: Characterizing and improving the use of demand-fetched caches in GPUs

abstract: Initially introduced as special-purpose accelerators for games and graphics code, graphics processing units (GPUs) have emerged as widely-used high-performance parallel computing platforms. GPUs traditionally provided only software-managed local memories (or scratchpads) instead of demand-fetched caches. Increasingly, however, GPUs are being used in broader application domains where memory access patterns are both harder to analyze and harder to manage in software-controlled caches. In response, GPU vendors have included sizable demand-fetched caches in recent chip designs. Nonetheless, several problems remain. First, since these hardware caches are quite new and highly-configurable, it can be difficult to know when and how to use them; they sometimes degrade performance instead of improving it. Second, since GPU programming is quite distinct from general-purpose programming, application programmers do not yet have solid intuition about which memory reference patterns are amenable to demand-fetched caches. In response, this paper characterizes application performance on GPUs with caches and provides a taxonomy for reasoning about different types of access patterns and locality. Based on this taxonomy, we present an algorithm which can be automated and applied at compile-time to identify an application's memory access patterns and to use that information to intelligently configure cache usage to improve application performance. Experiments on real GPU systems show that our algorithm reliably predicts when GPU caches will help or hurt performance. Compared to always passively turning caches on, our method can increase the average benefit of caches from 5.8% to 18.0% for applications that have significant performance sensitivity to caching.


title: A Full-Duplex Line Driver for Gigabit Ethernet With Rail-to-Rail Class-AB Output Stage in 28 nm CMOS

abstract: Gigabit Ethernet PHY (GPHY) transceivers find wide use in SoCs and standalone PHY chips with hundreds of millions of ports shipped every year. Transceiver design has recently focused on power reduction driven by the need for higher port density and throughput with minimum energy and thermal cost. The line drivers that deliver power from a high voltage supply to remote 100Ω differential loads dominate the GPHY power consumption. The supply voltage determined by the transmit amplitude specs (e.g., 2Vppdiff for 1000BASE-T/100BASE-TX Ethernet) does not scale with technology. This paper presents an architecture that enables rail-to-rail full-duplex operation for high voltage efficiency resulting in a 2.5V GPHY driver in 28nm CMOS that saves 24% power from the mainstream 3.3V drivers.


title: Dependency Parsing with Dilated Iterated Graph CNNs

abstract: Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs' capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.


title: Dynamic Proofs of Retrievability for Coded Cloud Storage Systems

abstract: Cloud storage allows users to store their data in a remote server to get rid of expensive local storage and management costs and then access data of interest anytime anywhere. A number of solutions have been proposed to tackle the verification of remote data integrity and retrievability in cloud storage systems. Most of existing schemes, however, do not support efficient data dynamics and/or suffer from security vulnerabilities when involving dynamic data operations. In this paper, we propose a dynamic proof of retrievability scheme supporting public auditability and communication-efficient recovery from data corruptions. To this end, we split up the data into data blocks and encode each data block individually using outer code and inner code before outsourcing so that i) an update inside any data block only affects a few codeword symbols and ii) communication-efficient data repair for a breakdown server can be achieved and communication overhead for small data corruptions within a server can be eliminated. Based on the encoded data blocks, we utilize rb23Tree to enforce the data sequence for dynamic operations, preventing the cloud service provider from manipulating data block to pass the integrity check in the dynamic scenario. Formal security analysis and extensive experimental evaluations are conducted, showing that the proposed scheme is practical for use in cloud storage systems.


title: Debiasing representations by removing unwanted variation due to protected attributes

abstract: We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficient than known approaches. Further, we show that this approach leads to debiased representations that satisfy a first order approximation of conditional parity. Finally, we demonstrate the efficacy of the proposed approach by reducing racial bias in recidivism risk scores.


title: Learning to Parse Wireframes in Images of Man-Made Environments

abstract: In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. The wireframe (see Fig. 1) contains all salient straight lines and their junctions of the scene that encode efficiently and accurately large-scale geometry and object shapes. To this end, we have built a very large new dataset of over 5,000 images with wireframes thoroughly labelled by humans. We have proposed two convolutional neural networks that are suitable for extracting junctions and lines with large spatial support, respectively. The networks trained on our dataset have achieved significantly better performance than state-of-the-art methods for junction detection and line segment detection, respectively. We have conducted extensive experiments to evaluate quantitatively and qualitatively the wireframes obtained by our method, and have convincingly shown that effectively and efficiently parsing wireframes for images of man-made environments is a feasible goal within reach. Such wireframes could benefit many important visual tasks such as feature correspondence, 3D reconstruction, vision-based mapping, localization, and navigation. The data and source code are available at https://github.com/huangkuns/wireframe.


title: Integrating word embeddings and traditional NLP features to measure textual entailment and semantic relatedness of sentence pairs

abstract: Recent years the distributed representations of words (i.e., word embeddings) have been shown to be able to significantly improve performance in many natural language processing tasks, such as pos-of-tag tagging, chunking, named entity recognition and sentiment polarity judgement, etc. However, previous tasks only involve a single sentence. In contrast, this paper evaluates the effectiveness of word embeddings in sentence pair classification or regression problems. Specifically, we propose novel simple yet effective features based on word embeddings and extract many traditional linguistic features. Then these features serve as input of a classification/regression algorithm in isolation and in combination. Evaluations are conducted on three sentence pair classification/regression tasks, i.e., textual entailment, cross-lingual textual entailment and semantic relatedness estimation. Experiments on benchmark datasets provided by Semantic Evaluation 2013 and 2014 showed that using word embeddings is able to significantly improve the performance and our results outperform the best achieved results so far.


title: Resilience and social support promote posttraumatic growth of women with infertility: The mediating role of positive coping

abstract: Abstract According to previous research, clinical experience with individuals facing infertility has demonstrated that positive psychological changes can arise from the struggle involved ( Paul et al., 2010 ), which is called posttraumatic growth (PTG). However, little knowledge has been gained about the relationships between PTG and its facilitating factors. The present study examined whether resilience and social support could predict PTG in women with infertility. The role of positive coping as a potential mediator was also assessed. Using a cross-sectional design, all members of a convenience sample of 182 women with infertility completed self-report measures of PTG, resilience, perceived social support, positive coping and background information. It was found that resilience, social support and positive coping positively correlated with PTG, which explained 34.0% of the total variance. The results suggested that positive coping partially mediated the impact of resilience on PTG while it totally mediated the relationship between social support and PTG. These findings demonstrated that, in clinical settings, improving positive coping in women with infertility may be helpful for the attainment of PTG.


title: Monolithically Integrated μLEDs on Silicon Neural Probes for High-Resolution Optogenetic Studies in Behaving Animals

abstract: We report a scalable method to monolithically integrate microscopic light emitting diodes (μLEDs) and recording sites onto silicon neural probes for optogenetic applications in neuroscience. Each μLED and recording site has dimensions similar to a pyramidal neuron soma, providing confined emission and electrophysiological recording of action potentials and local field activity. We fabricated and implanted the four-shank probes, each integrated with 12 μLEDs and 32 recording sites, into the CA1 pyramidal layer of anesthetized and freely moving mice. Spikes were robustly induced by 60 nW light power, and fast population oscillations were induced at the microwatt range. To demonstrate the spatiotemporal precision of parallel stimulation and recording, we achieved independent control of distinct cells ∼ 50 μm apart and of differential somato-dendritic compartments of single neurons. The scalability and spatiotemporal resolution of this monolithic optogenetic tool provides versatility and precision for cellular-level circuit analysis in deep structures of intact, freely moving animals.


Roadmakers pavage, pulse reformation framework and image segmentation in the discrete pulse transform


title: Fall detection system using Kinect’s infrared sensor

abstract: This paper presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred. The key novelty of our approach is measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. By explicitly using the 3D bounding box, our algorithm requires no pre-knowledge of the scene (i.e. floor), as the set of detected actions are adequate to complete the process of fall detection.


poLCA : Polytomous Variable Latent Class Analysis Version 1 . 2


title: Spell corrector to social media datasets in message filtering systems

abstract: We develop a spell checker and corrector to check word errors in the social media datasets, which will be used in message filtering systems specially for cyberbullying detection. We use the dictionary techniques to check words and there are ten word spell error checking and correction approaches. If there are more than one corrected word we get from each approach, we use Levenshtein distance to choose the corrected word from the words in the dictionary. The spell correction results were around 90%. Moreover the percentage of each approach highlighted the efficiency of adding letters in the word.