Fabrication of fully transparent nanowire transistors for transparent and flexible electronics


title: Creating Crowdsourced Research Talks at Scale

abstract: There has been a marked shift towards learning and consuming information through video. Most academic research, however, is still distributed only in text form, as researchers often have limited time, resources, and incentives to create video versions of their work. To address this gap, we propose, deploy, and evaluate a scalable, end-to-end system for crowdsourcing the creation of short, 5-minute research videos based on academic papers. Doing so requires solving complex coordination and collaborative video production problems. To assist coordination, we designed a structured workflow that enables efficient delegation of tasks, while also motivating the crowd through a collaborative learning environment. To facilitate video production, we developed an online tool with which groups can make micro-audio recordings that are automatically stitched together to create a complete talk. We tested this approach with a group of volunteers recruited from 52 countries through an open call. This distributed crowd produced over 100 video talks in 12 languages based on papers from top-tier computer science conferences. The produced talks consistently received high ratings from a diverse group of non-experts and experts, including the authors of the original papers. These results indicate that our crowdsourcing approach is a promising method for producing high-quality research talks at scale, increasing the distribution and accessibility of scientific knowledge.


title: Analyzing noise robustness of MFCC and GFCC features in speaker identification

abstract: Automatic speaker recognition can achieve a high level of performance in matched training and testing conditions. However, such performance drops significantly in mismatched noisy conditions. Recent research indicates that a new speaker feature, gammatone frequency cepstral coefficients (GFCC), exhibits superior noise robustness to commonly used mel-frequency cepstral coefficients (MFCC). To gain a deep understanding of the intrinsic robustness of GFCC relative to MFCC, we design speaker identification experiments to systematically analyze their differences and similarities. This study reveals that the nonlinear rectification accounts for the noise robustness differences primarily. Moreover, this study suggests how to enhance MFCC robustness, and further improve GFCC robustness by adopting a different time-frequency representation.


Shopping Hesitation


Taqiyya in Shī'ī Theology and Religion


title: Comparison of Motor Self-Regulatory and Stress Behaviors of Preterm Infants Across Body Positions

abstract: Occupational therapists working with infants in neonatal intensive care units (NICUs) make positioning recommendations to optimize self-regulation, with little published data supporting this practice. In this retrospective descriptive study, 15 hospitalized preterm infants (M gestational age = 32 weeks) were videotaped during noncaregiving periods in order to record the frequency of specific behaviors in relation to six infant positions (prone nested, prone un-nested, side-lying nested, side-lying un-nested, supine nested, and supine un-nested). Behaviors coded were those that suggest infant stress or motor efforts at self-regulation, as defined for the original study. Mixed effects regressions and post-hoc Tukey Honestly Significant Difference tests were used to analyze the data, after the calculation of a ratio of the number of behaviors in each position. Higher ratio values reflect a greater occurrence of behaviors. The number of motor self-regulatory and stress behaviors were related to infant position, with the highest ratios of behaviors observed in side-lying un-nested and the lowest in prone nested. Behavior ratios did not differ between prone un-nested and prone nested, nor between supine un-nested and supine nested. More self-regulatory and stress behaviors were related to longer periods of fussing and crying. Longer periods of light sleep were related to fewer stress behaviors. Infants performed the fewest stress behaviors in prone nested, prone un-nested, or side-lying nested. These positions may benefit infants in the NICU by reducing the need for motor-based self-regulatory behaviors and potentially conserving energy for growth. Grenier, I. R., Bigsby, R., Vergara, E. R., & Lester, B. M. Comparison of motor self-regulatory and stress behaviors of preterm


title: Practical waveform engineering

abstract: RF I-V waveform measurement and engineering systems are now finally enabling practical waveform engineering to be directly undertaken with systems capable of supporting continuous wave stimulus reaching a high level of maturity. On-going research and development activities are presently addressing the multitone requirement. The availability of RF I-V measurement and engineering capability extends the characterization opportunities for both high-frequency/speed transistor technology developers and circuit/system designers; terminal waveforms are the unifying theoretical link between transistor technology, circuit design, and system performance. Design support can involve the direct utilization of such a measurement system in the design investigation/evaluation loop of either power transistor technology or PAs. This eliminates the black box design processes and allows for the development of new intelligent design processes that are completely founded on theoretical waveform analysis. Alternatively, it can also indirectly support the amplifier investigation/evaluation loop by supporting the development or improving the accuracy of nonlinear transistor models or by providing CAD-accessible behavioral or behavioral model parameter datasets. W can now only hope that, triggered by the availability of RF waveform measurement and engineering systems, we will see a renaissance in theoretical waveform mathematical analysis addressing not only power and efficiency but also design sensitivity and linearity.


title: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.


title: Robust inverse perspective mapping based on vanishing point

abstract: Vision-based road signs detection and recognition has been widely used in intelligent robotics and automotive autonomous driving technology. Currently, one-time calibration of inverse perspective mapping (IPM) parameters is employed to eliminate the effect of perspective mapping, but it is not robust to the uphill and downhill road. We propose an automatic inverse perspective mapping method based on vanishing point, which is adaptive to the uphill and downhill road even with slight rotation of the main road direction. The proposed algorithm is composed of the following three steps: detecting the vanishing point, calculating the pitch and yaw angles and adopting inverse perspective mapping to obtain the “bird's eye view” image. Experimental results show that the adaptability of our inverse perspective mapping framework is comparable to existing state-of-the-art methods, which is conducive to the subsequent detection and recognition of road signs.


DeepPlaylist : Using Recurrent Neural Networks to Predict Song Similarity


title: DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

abstract: Personalized predictive medicine necessitates modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, a deep dynamic neural network that reads medical records and predicts future medical outcomes. At the data level, DeepCare models patient health state trajectories with explicit memory of illness. Built on Long Short-Term Memory LSTM, DeepCare introduces time parameterizations to handle irregular timing by moderating the forgetting and consolidation of illness memory. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving upi¾źto the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling and readmission prediction in diabetes, a chronic disease with large economic burden. The results show improved modeling and risk prediction accuracy.


Ecore.fmp: A tool for editing and instantiating class models as feature models,” ECE, Univeristy of Waterloo


title: Development of a Compendium of Energy Expenditures for Youth

abstract: BackgroundThis paper presents a Compendium of Energy Expenditures for use in scoring physical activity questionnaires and estimating energy expenditure levels in youth.Method/ResultsModeled after the adult Compendium of Physical Activities, the Compendium of Energy Expenditures for Youth contains a list of over 200 activities commonly performed by youth and their associated MET intensity levels. A review of existing data collected on the energy cost of youth performing activities was undertaken and incorporated into the compendium. About 35% of the activity MET levels were derived from energy cost data measured in youth and the remaining MET levels estimated from the adult compendium.ConclusionThe Compendium of Energy Expenditures for Youth is useful to researchers and practitioners interested in identifying physical activity and energy expenditure values in children and adolescents in a variety of settings.


Cell Connections by Tunneling Nanotubes: Effects of Mitochondrial Trafficking on Target Cell Metabolism, Homeostasis, and Response to Therapy


AUTOMATIC CALIBRATION OF DIGITAL CAMERAS USING PLANAR CHESS-BOARD PATTERNS


title: Metabolism and Disposition of Ataluren following Oral Administration to Mice, Rats, Dogs, and Humans.

abstract: Ataluren is a unique small molecule developed for the treatment of diseases due to presence of nonsense mutations, which result in premature termination of ribosomal translation and lack of full-length protein production. This study investigated the in vivo metabolism and disposition of ataluren in mice, rats, dogs and humans. Following single oral administration of [14C] ataluren, the overall recovery of radioactivity was ≥ 93.7%, with approximately 39%, 17% to 21%, 12%, and 55% in the urine, and 54%, 70%, 80%, and 47% in the feces from intact mice, rats, dogs, and humans, respectively. In bile-duct cannulated (BDC) rats, approximately 10%, 7%, and 82% of the dose were recovered in the urine, feces, and bile, respectively, suggesting that biliary secretion was a major route for the elimination of ataluren in the rats. Ataluren was extensively metabolized following oral administration and the metabolic profiles of ataluren were quantitatively similar across all species. Unchanged ataluren was the dominant radioactive component in plasma. Ataluren acyl glucuronide was the most prominent metabolite in plasma of all species and the dominant metabolite in BDC rat bile and human urine, while the oxadiazole cleavage products were the major or prominent metabolites in the feces of all species. Overall, the results indicate that, unlike most small molecules, phase I metabolism is negligible, and the pathway largely involves glucuronidation. No other circulatory conjugation metabolite was detected across investigated species. SIGNIFICANCE STATEMENT Ataluren is a novel carboxylic acid containing small molecule drug for treating nonsense mutation Duchenne muscular dystrophy (nmDMD). In vivo metabolism and disposition after a single dose of the drug were investigated in mice, rats, dogs and humans. Unlike most small molecules, phase I metabolism of ataluren was negligible, and the pathway largely involves glucuronidation. No other circulatory conjugation metabolite was detected across investigated species.


title: Extended Bandwidth Instantaneous Current Sharing Scheme for Parallel UPS Systems

abstract: This paper investigates the suitability of instantaneous average current sharing scheme (IACS) for the parallel operation of three-phase uninterruptible power supply systems. A discrete-time model is developed for the analysis and design of the control loops. Some key issues are discussed based on the model and it is found that there is a compromise between system stability and current sharing performance with the conventional IACS scheme when the lengths of interconnecting cables are not negligible. Subsequently, an improved IACS scheme is proposed that ensures proper current sharing by extending the close-loop bandwidth. Its performance is analytically predicted and subsequently validated by experimental results on a 15-kW laboratory prototype.


title: An ellipsometric study of the RF sputter oxidation of lead—indium alloys

abstract: A simple ellipsometer and an inexpensive 13-MHz sputteretch assembly have been used with a conventional evaporator to make in, situ measurements of the growth of thin oxide layers (≤ 10 nm) on pure lead and pure indium films, and also on a series of alloy films containing up to 46 at % In. Our lead results agree with results of earlier workers. For pure indium, we find that equilibrium oxide thicknesses are typically smaller than for lead, and that equilibrium time constants are longer; moreover, increased RF power leads to increased, rather than decreased, oxide thicknesses. Alloy films show an intermediate behavior in general, but in the range 26-36 at % In the oxide thickness rises to a maximum before falling to its equilibrium value. We agree with results obtained by other methods of oxidation and analysis which show the oxide to have a disproportionately large In 2 O 3 content, and, in particular, that there is 100 percent In 2 O 3 for all alloys containing more than 35 at % In. We show that the difference between PbO and In 2 O 3 as regards the dependence of oxide thickness on RF power can be accounted for by slightly modifying the oxide growth model of Greiner, to make the diffusion length of oxygen ions very slightly dependent on RF voltage. To account for the intermediate behavior, we introduce a simple coupled Greiner model involving separate columns of two oxides, in which oxygen ions can interdiffuse between columns as they find their way to the alloy surface. As the parameters of the model are changed, the growth behavior changes from a simple monotonic growth of thickness with time, through a behavior in which there is an intermediate maximum, and then back to a monotonic growth behavior again.


Named Entity Disambiguation for little known referents: a topic-based approach


title: Refinement of Surface Mesh for Accurate Multi-View Reconstruction

abstract: In this paper we propose a pipeline for accurate 3D reconstruction from multiple images that deals with some of the possible sources of inaccuracy present in the input data. Namely, we address the problem of inaccurate camera calibration by including a method adjusting the camera parameters in a global structure-and-motion problem, which is solved with a depth map for representation that is suitable to large scenes. ::: Secondly, we take the triangular mesh and calibration improved by the global method in the first phase to refine the surface both geometrically and radiometrically. Here we propose surface energy which combines photoconsistency with contour matching and minimize it with a gradient descent method. Our main contribution lies in effective computation of the gradient that naturally regularization and data terms by employing scale space approach. The results are demonstrated on standard high-resolution datasets and a complex outdoor scene.


title: UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor

abstract: Human action recognition has a wide range of applications including biometrics, surveillance, and human computer interaction. The use of multimodal sensors for human action recognition is steadily increasing. However, there are limited publicly available datasets where depth camera and inertial sensor data are captured at the same time. This paper describes a freely available dataset, named UTD-MHAD, which consists of four temporally synchronized data modalities. These modalities include RGB videos, depth videos, skeleton positions, and inertial signals from a Kinect camera and a wearable inertial sensor for a comprehensive set of 27 human actions. Experimental results are provided to show how this database can be used to study fusion approaches that involve using both depth camera data and inertial sensor data. This public domain dataset is of benefit to multimodality research activities being conducted for human action recognition by various research groups.


title: Determination of halides using Ag nanoparticles-modified disposable electrodes. A first approach to a wearable sensor for quantification of chloride ions.

abstract: This work reports a simple voltammetric method for the determination of chloride, bromide, and iodide ions using screen-printed carbon electrodes modified with silver nanoparticles electrochemically deposited on the working electrode surface. UV/Vis absorption spectroelectrochemistry was used to study the electrodeposition of silver nanoparticles on the working carbon electrode on PET or Gore-Tex® supports, and their subsequent oxidation in the presence of halide ions. The main figures of merit of the developed sensors, such as reproducibility and detection limit, have been calculated. Reproducibility values of 2.22%, 2.83% and 3.23% were obtained for chloride, bromide and iodide determinations, respectively. Additionally, the lowest detected amount of chloride, bromide and iodide ions were 3.0·10-6 M, 5.0·10-6 M and 5.0·10-6 M, respectively. Taking into account the relevance of the determination of chloride ion concentration in sweat, the voltammetric method for the determination of halides has been successfully transferred to a Gore-Tex® support to build a first approach to a wearable sensor that facilitates the quantification of this ion in sweat samples. The Gore-Tex® sensor provides a good reproducibility (RSD = 1.61%).


title: Resonance phenomena in cylindrical waveguides

abstract: In the following we study aperiodic wave processes, generated by periodic forces, in infinite tubes. Consider the n-dimensional cylinder Q = Sz’ x ( co, co), where Q’ is a bounded (n 1 )-dimensional domain with smooth boundary XT. Sections 24 of this paper are devoted to the initial and boundary value problem (af-A)u=fe-i’~~’ in Q x (0, a), (1.1) u=o on an, (1.2) 44 0) = u,(x), a,t+, 0) = U,(X) for xE52, (1.3) with prescribed data f, u0 and u1 belonging to C;(Q). We are mainly interested in the asymptotic behavior of the solution u(x, t) as t -+ co. Our results are of the following type: If w2 does not coincide with one of the eigenvalues A,, A*,... of the Dirichlet problem (a:+ ... +a:p,)v+w=o in Q’, v=o on a521 (1.4)


title: Diagnosis and phylogenetic analysis of a multifocal cutaneous orf virus with mixed bacterial infection outbreak in goats in Fujian province, China

abstract: Outbreaks of orf virus on goat farms are common in China. In this study, we investigated a severe multifocal cutaneous orf virus outbreak with a correlative mixed bacterial infection which persisted for up to 6 months, and which had a high morbidity (93.7%) and mortality (15%) among kids in a herd of crossbreed goats in Fujian province in China. The disease was diagnosed as an orf virus (ORFV XD strain) infection associating with Streptococcus pluranimalium and Staphylococcus, identified using standard virological and bacteriological techniques. Multiple sequence alignments and phylogenetic analyses of the whole ORFV 011 (B2L), 059 (F1L), 032 and 080 genes revealed that the even though the virus phylogeny was clustered in branches of conventional orf virus strains, it nonetheless evidenced high variation within this subset. Furthermore, the sequences from the ORFV 080 gene allowed us to distinguish between the different strains at a higher resolution and these observations afforded us a comparative view of the ORFV 080 gene. This is the first report describing an outbreak of severe multifocal cutaneous orf virus with associated bacterial infection in China.


title: Monte Carlo localization in hand-drawn maps

abstract: Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80%


title: Learn&Fuzz: Machine learning for input fuzzing

abstract: Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF parser embedded in Microsoft's new Edge browser. We discuss and measure the tension between conflicting learning and fuzzing goals: learning wants to capture the structure of well-formed inputs, while fuzzing wants to break that structure in order to cover unexpected code paths and find bugs. We also present a new algorithm for this learn&fuzz challenge which uses a learnt input probability distribution to intelligently guide where to fuzz inputs.


title: Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation

abstract: We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visual-interpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region of interest in terms of its severity level. We believe this advantage of the proposed deep learning model is highly desired for DR detection because in practice, users are not only interested with high prediction performance, but also keen to understand the insights of DR detection and why the adopted learning model works. In the experiments conducted on a large scale of retina image dataset, we show that the proposed CNN model can achieve high performance on DR detection compared with the state-of-the-art while achieving the merits of providing the RAM to highlight the salient regions of the input image.


title: Fine Grained Citation Span for References in Wikipedia

abstract: \emph{Verifiability} is one of the core editing principles in Wikipedia, editors being encouraged to provide citations for the added content. For a Wikipedia article, determining the \emph{citation span} of a citation, i.e. what content is covered by a citation, is important as it helps decide for which content citations are still missing. ::: We are the first to address the problem of determining the \emph{citation span} in Wikipedia articles. We approach this problem by classifying which textual fragments in an article are covered by a citation. We propose a sequence classification approach where for a paragraph and a citation, we determine the citation span at a fine-grained level. ::: We provide a thorough experimental evaluation and compare our approach against baselines adopted from the scientific domain, where we show improvement for all evaluation metrics.


title: Design Considerations for Optimizing Storyline Visualizations

abstract: Storyline visualization is a technique used to depict the temporal dynamics of social interactions. This visualization technique was first introduced as a hand-drawn illustration in XKCD's “Movie Narrative Charts” [21]. If properly constructed, the visualization can convey both global trends and local interactions in the data. However, previous methods for automating storyline visualizations are overly simple, failing to achieve some of the essential principles practiced by professional illustrators. This paper presents a set of design considerations for generating aesthetically pleasing and legible storyline visualizations. Our layout algorithm is based on evolutionary computation, allowing us to effectively incorporate multiple objective functions. We show that the resulting visualizations have significantly improved aesthetics and legibility compared to existing techniques.


title: Improvements to the Bayesian Topic N-Gram Models

abstract: One of the language phenomena that n-gram language model fails to capture is the topic information of a given situation. We advance the previous study of the Bayesian topic language model by Wallach (2006) in two directions: one, investigating new priors to alleviate the sparseness problem caused by dividing all ngrams into exclusive topics, and two, developing a novel Gibbs sampler that enables moving multiple n-grams across different documents to another topic. Our blocked sampler can efficiently search for higher probability space even with higher order n-grams. In terms of modeling assumption, we found it is effective to assign a topic to only some parts of a document.


title: An Output Buffer for 3.3-V Applications in a 0.13-$\mu\hbox{m}$ 1/2.5-V CMOS Process

abstract: With a 3.3-V interface, such as PCI-X application, high-voltage overstress on the gate oxide is a serious reliability problem in designing I/O circuits by using only 1/2.5-V low-voltage devices in a 0.13-mum CMOS process. Thus, a new output buffer realized with low-voltage (1- and 2.5-V) devices to drive high-voltage signals for 3.3-V applications is proposed in this paper. The proposed output buffer has been fabricated in a 0.13-mum 1/2.5-V 1P8M CMOS process with Cu interconnects. The experimental results have confirmed that the proposed output buffer can be successfully operated at 133 MHz without suffering high-voltage gate-oxide overstress in the 3.3-V interface. In addition, a new level converter that is realized with only 1- and 2.5-V devices that can convert 0/1-V voltage swing to 1/3.3-V voltage swing is also presented in this paper. The experimental results have also confirmed that the proposed level converter can be operated correctly


title: Review on Heart Disease Diagnosis Based on Data Mining Techniques

abstract: The availability of huge amount of data leads to the need of powerful data analysis tool to extract useful knowledge. To manage data analysis on large data sets researchers have long been concerned with statistical and data mining tools. Disease diagnosis is one of the major applications where data mining tools are showing successful results. Heart disease is the major cause of the death all over the world in the last few years. Many researchers are using different data mining tools to help professionals in the diagnosis of heart disease. Using single data mining technique in heart disease diagnosis has been showing great levels of accuracy. In the past few time researchers have been investigating the effect of hybridizing more than one technique showing better result in the diagnosis of heart disease. In this paper the neural network is trained with selected significant pattern for the diagnosis of heart disease and Genetic Algorithm has been used and applied for optimizing the neural network.


title: Hyper-heuristic general video game playing

abstract: In general video game playing, the challenge is to create agents that play unseen games proficiently. Stochastic tree search algorithms, like Monte Carlo Tree Search, perform relatively well on this task. However, performance is non-transitive: different agents perform best in different games, which means that there is not a single agent that is the best in all the games. Rather, some types of games are dominated by a few agents whereas other different agents dominate other types of games. Thus, it should be possible to construct a hyper-agent that selects from a portfolio, in which constituent sub-agents will play a new game best. Since there is no knowledge about the games, the agent needs to use available features to predict the most suitable algorithm. This work constructs such a hyper-agent using the General Video Game Playing Framework (GVGAI). The proposed method achieves promising results that show the applicability of hyper-heuristics in general video game playing and related tasks.


title: The factor structure of the system usability scale. Human Centered Design

abstract: In 2009, we published a paper in which we showed how three independent sources of data indicated that, rather than being a unidimensional measure of perceived usability, the System Usability Scale apparently had two factors: Usability (all items except 4 and 10) and Learnability (Items 4 and 10). In that paper, we called for other researchers to report attempts to replicate that finding. The published research since 2009 has consistently failed to replicate that factor structure. In this paper, we report an analysis of over 9,000 completed SUS questionnaires that shows that the SUS is indeed bidimensional, but not in any interesting or useful way. A comparison of the fit of three confirmatory factor analyses showed that a model in which the SUS's positive-tone (odd-numbered) and negative-tone (even-numbered) were aligned with two factors had a better fit than a unidimensional model (all items on one factor) or the Usability/Learnability model we published in 2009. Because a distinction based on item tone is of little practical or theoretical interest, we recommend that user experience practitioners and researchers treat the SUS as a unidimensional measure of perceived usability, and no longer routinely compute Usability and Learnability subscales.


title: Non-Data-Aided Feedforward Carrier Frequency Offset Estimators for QAM Constellations: A Nonlinear Least-Squares Approach

abstract: This paper performs a comprehensive performance analysis of a family of non-data-aided feedforward carrier frequency offset estimators for QAM signals transmitted through AWGN channels in the presence of unknown timing error. The proposed carrier frequency offset estimators are asymptotically (large sample) nonlinear least-squares estimators obtained by exploiting the fourth-order conjugate cyclostationary statistics of the received signal and exhibit fast convergence rates (asymptotic variances on the order of ::: , where ::: stands for the number of samples). The exact asymptotic performance of these estimators is established and analyzed as a function of the received signal sampling frequency, signal-to-noise ratio, timing delay, and number of symbols. It is shown that in the presence of intersymbol interference effects, the performance of the frequency offset estimators can be improved significantly by oversampling (or fractionally sampling) the received signal. Finally, simulation results are presented to corroborate the theoretical performance analysis, and comparisons with the modified Cramer-Rao bound illustrate the superior performance of the proposed nonlinear least-squares carrier frequency offset estimators.


title: BRIEF: Computing a Local Binary Descriptor Very Fast

abstract: Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.


title: Discrimination in online ad delivery

abstract: A Google search for a person's name, such as “Trevon Jones”, may yield a personalized ad for public records about Trevon that may be neutral, such as “Looking for Trevon Jones? …”, or may be suggestive of an arrest record, such as “Trevon Jones, Arrested?...”. This writing investigates the delivery of these kinds of ads by Google AdSense using a sample of racially associated names and finds statistically significant discrimination in ad delivery based on searches of 2184 racially associated personal names across two websites. First names, previously identified by others as being assigned at birth to more black or white babies, are found predictive of race (88% black, 96% white), and those assigned primarily to black babies, such as DeShawn, Darnell and Jermaine, generated ads suggestive of an arrest in 81 to 86 percent of name searches on one website and 92 to 95 percent on the other, while those assigned at birth primarily to whites, such as Geoffrey, Jill and Emma, generated more neutral copy: the word "arrest" appeared in 23 to 29 percent of name searches on one site and 0 to 60 percent on the other. On the more ad trafficked website, a black-identifying name was 25% more likely to get an ad suggestive of an arrest record. A few names did not follow these patterns: Dustin, a name predominantly given to white babies, generated an ad suggestive of arrest 81 and 100 percent of the time. All ads return results for actual individuals and ads appear regardless of whether the name has an arrest record in the company’s database. Notwithstanding these findings, the company maintains Google received the same ad text for groups of last names (not first names), raising questions as to whether Google's advertising technology exposes racial bias in society and how ad and search technology can develop to assure racial fairness.


title: Certolizumab Pegol: A Review of Its Use in Patients with Axial Spondyloarthritis or Psoriatic Arthritis

abstract: Certolizumab pegol (Cimzia®) is a polyethylene glycolylated antigen-binding fragment of a recombinant human monoclonal antibody that binds to and selectively neutralizes tumour necrosis factor (TNF) α. In the EU, subcutaneous certolizumab pegol is indicated for the treatment of adults with severe active axial spondyloarthritis (axSpA), comprising ankylosing spondylitis (AS) and non-radiographic axSpA (nr-axSpA), and for adults with active psoriatic arthritis (PsA). In the USA it is indicated for the treatment of adults with active AS or active PsA. This article reviews the efficacy and tolerability of certolizumab pegol in these patients and briefly summarizes its pharmacology. In two ongoing, well-designed studies, data at 12 and 24 weeks showed that treatment with certolizumab pegol (200 mg every 2 weeks or 400 mg every 4 weeks) was effective in improving the clinical signs and symptoms of disease, health-related quality of life and productivity in patients with axSpA (the RAPID-axSpA study) or PsA (the RAPID-PsA study), with the improvements maintained during longer-term (48 weeks) treatment. Within the axSpA population, clinical benefits with certolizumab pegol were seen both in patients with AS and in those with nr-axSpA. In addition, 12 weeks’ treatment with certolizumab pegol reduced inflammation in the sacroiliac joints and spine in patients with axSpA and 24 weeks’ treatment with the agent slowed radiographic disease progression in patients with PsA. Certolizumab pegol was generally well tolerated in these studies, with a tolerability profile consistent with that seen in previous clinical trials in other indications. Although additional long-term and comparative data are needed to position certolizumab pegol with respect to other TNFα antagonists, current evidence indicates that certolizumab pegol is an effective option for the treatment of axSpA (including AS and nr-axSpA) and PsA.


title: Certifying algorithms for recognizing interval graphs and permutation graphs

abstract: A certifying algorithm for a decision problem is an algorithm that provides a certificate with each answer that it produces. The certificate is a piece of evidence that proves that the answer has not been compromised by a bug in the implementation. We give linear-time certifying algorithms for recognition of interval graphs and permutation graphs. Previous algorithms fail to provide supporting evidence when they claim that the input graph is not a member of the class. We show that our certificates of non-membership can be authenticated in O(vVv) time.


title: A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface&Modified k-Means Clustering

abstract: Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of human face based on facial expression can be an important component of natural human-machine interface. It may also be used in behavioural science. Although human can recognize the face practically without any effort, but reliable face recognition by machine is a challenge. This paper presents a new approach for recognizing the face of a person considering the expressions of the same human face at different instances of time. This methodology is developed combining Eigenface method for feature extraction and modified k-Means clustering for identification of the human face. This method endowed the face recognition without using the conventional distance measure classifiers. Simulation results show that proposed face recognition using perception of k-Means clustering is useful for face images with different facial expressions.


title: Magnetic Resonance Imaging (MRI) of Sexual Intercourse: Second Experience in Missionary Position and Initial Experience in Posterior Position

abstract: Our objective was to confirm that it is feasible to take images of the male and female genitals during coitus and to compare this present study with previous theories and recent radiological studies of the anatomy during sexual intercourse. Magnetic resonance imaging was used to study the anatomy of the male and female genitals during coitus. Three experiments were performed with one couple in two positions and after male ejaculation. The images obtained confirmed that during intercourse in the missionary position, the penis reaches the anterior fornix with preferential contact of the anterior vaginal wall. The posterior bladder wall was pushed forward and upward and the uterus was pushed upward and backward. The images obtained from the rear-entry position showed for the first time that the penis seems to reach the posterior fornix with preferential contact of the posterior vaginal wall. In this position, the bladder and uterus were pushed forward. A different preferential contact of the penis with the female genitals was observed with each position. These images could contribute to a better understanding of the anatomy of sexual intercourse.


title: Child Poverty and Its Lasting Consequence

abstract: One in six newborns were born poor over the past 40 years, and nearly half remained poor half their childhoods. These persistently poor children are nearly 90 percent more likely than never-poor children to enter their 20s without completing high school and are four times more likely to give birth outside of marriage during their teenage years. Children whose parents did not complete high school are less likely to complete high school themselves. This paper examines the magnitude of child poverty, family characteristics related to childhood poverty persistence, and childhood poverty’s lasting consequences.


The American Academy of Ambulatory Care Nursing's Invitational Summit on Care Coordination and Transition Management: An Overview


title: GOSSIPY: A distributed localization system for Internet of Things using RFID technology

abstract: The popularity of smart objects in our daily life fosters a new generation of applications under the umbrella of the Internet of Things (IoT). Such applications are built on a distributed network of heterogeneous context-aware devices, where localization is a key issue. The localization problem is further magnified by IoT challenges such as scalability, mobility and the heterogeneity of objects. In existing localization systems using RFID technology, there is a lack of systems that localize mobile tags using heterogeneous mobile readers in a distributed manner. In this paper, we propose the GOSSIPY system for localizing mobile RFID tags using a group of ad hoc heterogeneous mobile RFID readers. The system depends on cooperation of mobile readers through time-constrained interleaving processes. Readers in a neighborhood share interrogation information, estimate tag locations accordingly and employ both proactive and reactive protocols to ensure timely dissemination of location information. We evaluate the proposed system and present its performance through extensive simulation experiments using ns-3.


title: CeMNet: Self-supervised learning for accurate continuous ego-motion estimation

abstract: In this paper, we propose a novel self-supervised learning model for estimating continuous ego-motion from video. Our model learns to estimate camera motion by watching RGBD or RGB video streams and determining translational and rotation velocities that correctly predict the appearance of future frames. Our approach differs from other recent work on self-supervised structure-from-motion in its use of a continuous motion formulation and representation of rigid motion fields rather than direct prediction of camera parameters. To make estimation robust in dynamic environments with multiple moving objects, we introduce a simple two-component segmentation process that isolates the rigid background environment from dynamic scene elements. We demonstrate state-of-the-art accuracy of the self-trained model on several benchmark ego-motion datasets and highlight the ability of the model to provide superior rotational accuracy and handling of non-rigid scene motions.


title: A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and in Vivo Bioavailability

abstract: A biopharmaceutics drug classification scheme for correlating in vitro drug product dissolution and in vivo bioavailability is proposed based on recognizing that drug dissolution and gastrointestinal permeability are the fundamental parameters controlling rate and extent of drug absorption. This analysis uses a transport model and human permeability results for estimating in vivo drug absorption to illustrate the primary importance of solubility and permeability on drug absorption. The fundamental parameters which define oral drug absorption in humans resulting from this analysis are discussed and used as a basis for this classification scheme. These Biopharmaceutic Drug Classes are defined as: Case 1. High solubility-high permeability drugs, Case 2. Low solubility-high permeability drugs, Case 3. High solubility-low permeability drugs, and Case 4. Low solubility-low permeability drugs. Based on this classification scheme, suggestions are made for setting standards for in vitro drug dissolution testing methodology which will correlate with the in vivo process. This methodology must be based on the physiological and physical chemical properties controlling drug absorption. This analysis points out conditions under which no in vitro-in vivo correlation may be expected e.g. rapidly dissolving low permeability drugs. Furthermore, it is suggested for example that for very rapidly dissolving high solubility drugs, e.g. 85% dissolution in less than 15 minutes, a simple one point dissolution test, is all that may be needed to insure bioavailability. For slowly dissolving drugs a dissolution profile is required with multiple time points in systems which would include low pH, physiological pH, and surfactants and the in vitro conditions should mimic the in vivo processes.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects

abstract: Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally invariant structures in the sensorimotor experience of an agent. We take inspiration from the Sensorimotor Contingencies Theory to define a computational model of this mechanism through a sensorimotor, unsupervised and predictive approach. Our model is based on processing the unsupervised interaction of an artificial agent with its environment. We show how spatio-temporally invariant structures in the environment induce regularities in the sensorimotor experience of an agent, and how this agent, while building a predictive model of its sensorimotor experience, can capture them as densely connected subgraphs in a graph of sensory states connected by motor commands. Our approach is focused on elementary mechanisms, and is illustrated with a set of simple experiments in which an agent interacts with an environment. We show how the agent can build an internal model of moving but spatio-temporally invariant structures by performing a Spectral Clustering of the graph modeling its overall sensorimotor experiences. We systematically examine properties of the model, shedding light more globally on the specificities of the paradigm with respect to methods based on the supervised processing of collections of static images.


title: Chiral Separations Using a Modified Water Stationary Phase in Supercritical Fluid Chromatography

abstract: A novel means of achieving chiral separations in supercritical fluid chromatography (SFC) using a water stationary phase is presented. By adding various chiral selectors to the phase, different chiral analytes can be readily separated using neat CO2 as a mobile phase. For example, by adding β-cyclodextrin, it is found that certain flavanone enantiomers can be separated, while using the antibiotic vancomycin as a selector provides separation of some chiral phenoxypropionic acids. Other additives such as sodium chloride and triethylamine are also explored and found to enhance certain separations when also present in the water phase. While column pressure has a moderate impact on chiral analyte retention and separation in this SFC method, column temperature has a comparatively larger influence. In particular, relatively cooler temperatures below about 5 °C are found to markedly increase resolution and selectivity. For instance, notably large resolution of 4.7 is achieved for a phenoxypropionic acid pair at 0 °C and 150 atm CO2. Since the method does not require modifier to elute such polar species, it is also readily compatible with FID detection and does not generate organic waste. Therefore, results indicate that this approach could be a potentially simple and flexible means of achieving chiral separations in SFC.


Intensifi ed chemotherapy with ACVBP plus rituximab versus standard CHOP plus rituximab for the treatment of diff use large B-cell lymphoma ( LNH 032 B ) : an open-label randomised phase 3 trial


title: Effectiveness of a 'Global Postural Reeducation' program for persistent Low Back Pain: a non-randomized controlled trial

abstract: BackgroundThe aim of this non-randomized controlled trial was to evaluate the effectiveness of a Global Postural Reeducation (GPR) program as compared to a Stabilization Exercise (SE) program in subjects with persistent low back pain (LBP) at short- and mid-term follow-up (ie. 3 and 6 months).MethodsAccording to inclusion and exclusion criteria, 100 patients with a primary complaint of persistent LBP were enrolled in the study: 50 were allocated to the GPR group and 50 to the SE group. Primary outcome measures were Roland and Morris Disability Questionnaire (RMDQ) and Oswestry Disability Index (ODI). Secondary outcome measures were lumbar Visual Analogue Scale (VAS) and Fingertip-to-floor test (FFT). Data were collected at baseline and at 3/6 months by health care professionals unaware of the study. An intention to treat approach was used to analyze participants according to the group to which they were originally assigned.ResultsOf the 100 patients initially included in the study, 78 patients completed the study: 42 in the GPR group and 36 in the SE group. At baseline, the two groups did not differ significantly with respect to gender, age, BMI and outcome measures. Comparing the differences between groups at short- and mid-term follow-up, the GPR group revealed a significant reduction (from baseline) in all outcome measures with respect to the SE group.The ordered logistic regression model showed an increased likelihood of definitive improvement (reduction from baseline of at least 30% in RMDQ and VAS scores) for the GPR group compared to the SE group (OR 3.9, 95% CI 2.7 to 5.7).ConclusionsOur findings suggest that a GPR intervention in subjects with persistent LBP induces a greater improvement on pain and disability as compared to a SE program. These results must be confirmed by further studies with higher methodological standards, including randomization, larger sample size, longer follow-up and subgrouping of the LBP subjects.Trial registrationNCT00789204


Leveraging Multilingual Training for Limited Resource Event Extraction


title: Automatic Classification of Non-Functional Requirements from Augmented App User Reviews

abstract: Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. Research shows that user reviews contain abundant useful information which may help developers to improve their Apps. Extracting and considering Non-Functional Requirements (NFRs), which describe a set of quality attributes wanted for an App and are hidden in user reviews, can help developers to deliver a product which meets users' expectations. Objective: Developers need to be aware of the NFRs from massive user reviews during software maintenance and evolution. Automatic user reviews classification based on an NFR standard provides a feasible way to achieve this goal. Method: In this paper, user reviews were automatically classified into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others. We combined four classification techniques BoW, TF-IDF, CHI2, and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging to classify user reviews. We conducted experiments to compare the F-measures of the classification results through all the combinations of the techniques and algorithms. Results: We found that the combination of AUR-BoW with Bagging achieves the best result (a precision of 71.4%, a recall of 72.3%, and an F-measure of 71.8%) among all the combinations. Conclusion: Our finding shows that augmented user reviews can lead to better classification results, and the machine learning algorithm Bagging is more suitable for NFRs classification from user reviews than Naive Bayes and J48.


title: SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation

abstract: We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly quantifies similarity rather than association or relatedness so that pairs of entities that are associated but not actually similar Freud, psychology have a low rating. We show that, via this focus on similarity, SimLex-999 incentivizes the development of models with a different, and arguably wider, range of applications than those which reflect conceptual association. Second, SimLex-999 contains a range of concrete and abstract adjective, noun, and verb pairs, together with an independent rating of concreteness and free association strength for each pair. This diversity enables fine-grained analyses of the performance of models on concepts of different types, and consequently greater insight into how architectures can be improved. Further, unlike existing gold standard evaluations, for which automatic approaches have reached or surpassed the inter-annotator agreement ceiling, state-of-the-art models perform well below this ceiling on SimLex-999. There is therefore plenty of scope for SimLex-999 to quantify future improvements to distributional semantic models, guiding the development of the next generation of representation-learning architectures.


title: Mobile Edge Caching: An Optimal Auction Approach

abstract: With the explosive growth of wireless data, the sheer size of the mobile traffic is challenging the capacity of current wireless systems. To tackle this challenge, mobile edge caching has emerged as a promising paradigm recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality.


title: The influence of social media interactions on consumer-brand relationships: A three-country study of brand perceptions and marketing behaviors

abstract: Companies are increasingly allocating more of their marketing spending to social media programs. Yet there is little research about how social media use is associated with consumer–brand relationships. We conducted three studies to explore how individual and national differences influence the relationship between social media use and customer brand relationships. The first study surveyed customers in France, the U.K. and U.S. and compared those who engage with their favorite brands via social media with those who do not. The findings indicated that social media use was positively related with brand relationship quality and the effect was more pronounced with high anthropomorphism perceptions (the extent to which consumers' associate human characteristics with brands). Two subsequent experiments further validated these findings and confirmed that cultural differences, specifically uncertainty avoidance, moderated these results. We obtained robust and convergent results from survey and experimental data using both student and adult consumer samples and testing across three product categories (athletic shoes, notebook computers, and automobiles). The results offer cross-national support for the proposition that engaging customers via social media is associated with higher consumer–brand relationships and word of mouth communications when consumers anthropomorphize the brand and they avoid uncertainty.


Evaluating string comparator performance for record linkage


title: Deeply-Supervised Nets

abstract: Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce "companion objective" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performance gain over existing methods (e.g. all state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and SVHN).


Objective video quality assessment


title: Equivalent circuit model of the IC-Stripline coupling to IC package

abstract: As the operating frequency of integrated circuit (IC) is rapidly increased, the importance of electromagnetic compatibility (EMC) performance test in the design stage of IC development process is becoming more significant. The new EMC test method, IC-Stripline enables fast and accurate EMC test with smaller form factor and lower cost compared with transverse electromagnetic (TEM) cell which is the most widely used for radiated electromagnetic interference test at the moment. This paper presents the equivalent lumped-element circuit model to capture capacitive and inductive coupling between open version of IC-Stripline and an IC package. Also, the equations to calculate the circuit components with geometrical structure of IC-Stripline are proposed. The S-parameter results from presented circuit models are compared against those obtained by 3D EM solver. The presented models are very accurate up to 3 GHz which is the upper limit of effective frequency range of IC-Stripline described in IEC standards [1], [2]. Therefore, the proposed IC-Stripline models can be effectively used to predict radiated emission and immunity of ICs using circuit level simulation tools.


title: A Wide-Band CMOS to Waveguide Transition at mm-Wave Frequencies With Wire-Bonds

abstract: In this paper a two-step transition between a CMOS chip and a WR-10 waveguide is investigated. The transition between a coplanar waveguide (CPW) on Duroid to W-band aluminum waveguide is studied and measured results show a bandwidth of 49% (67–110 GHz), an average insertion loss of 0.35 dB, and return loss below ${-}{\hbox{10}}$ dB throughout the entire band. Wire-bonding is then investigated as a connection between on-chip CMOS ground–signal–ground (GSG) pads to the Duroid CPW where the CMOS to printed circuit board transition measured results show an average 1-dB loss (including the CMOS GSG launcher) in the W-band. The full transition is also used to demonstrate packaging of a 100-GHz voltage-controlled oscillator in 90-nm CMOS where the RF signal was transmitted successfully with an average 2.5-dB loss (which includes a 2-cm Duroid CPW line) compared to on-chip probing measurement results.


Analysis in Amazon Reviews Using Probabilistic Machine Learning


title: Accurate Shadow Detection From High-Resolution Satellite Images

abstract: High-resolution satellite images contain a huge amount of information. Shadows in such images generate real problems in classifying and extracting the required information. Although signals recorded in shadow area are weak, it is still possible to recover them. Significant work is already done in shadow detection direction but, classifying shadow pixels from vegetation pixels correctly is still an issue as dark vegetation areas are still misclassified as shadow in some cases. In this letter, a new image index is developed for shadow detection employing multiple bands. Shadow pixels are classified from the index histogram by an automatic threshold identification procedure. The whole approach is applied on different study areas and high accuracies are achieved (average of 97%). The linear correlation method is then applied to compensate the classified shadow pixels. Two standard approaches of shadow detection are then applied to the same study areas to validate the proposed approach. The results show that the proposed approach achieves the best results. It also gives robust shadow detection results in classifying shadow from vegetation pixels comparable to the other two considered standard approaches.


title: An Economic Perspective of Disk vs. Flash Media in Archival Storage

abstract: For three decades, Kryder's law correctly predicted an exponential increase in bit density on disk platters, leading to an exponential drop in cost per gigabyte, and thus to an entrenched expectation that if data could be stored for a few years the incremental cost of storing it forever would be minimal. However, disk now is over 7 times as expensive as Kryder's law would have predicted, and industry projections suggest that in 2020 the gap will reach 200 times, disrupting this expectation. Our model shows that archives based upon alternative media are surprisingly cost competitive with archives based upon traditional disk media over the long-term. We propose using Archival Flash for long-term data preservation, with the trade off between longer data retention period and lower write cycles.


title: Exploring the Structure of Complex Software Designs : An Empirical Study of Open Source and Proprietary Code

abstract: This paper reports data from a study that seeks to characterize the differences in design structure between complex software products. We use design structure matrices (DSMs) to map dependencies between the elements of a design and define metrics that allow us to compare the structures of different designs. We use these metrics to compare the architectures of two software products---the Linux operating system and the Mozilla Web browser---that were developed via contrasting modes of organization: specifically, open source versus proprietary development. We then track the evolution of Mozilla, paying attention to a purposeful “redesign” effort undertaken with the intention of making the product more “modular.” We find significant differences in structure between Linux and the first version of Mozilla, suggesting that Linux had a more modular architecture. Yet we also find that the redesign of Mozilla resulted in an architecture that was significantly more modular than that of its predecessor and, indeed, than that of Linux. Our results, while exploratory, are consistent with a view that different modes of organization are associated with designs that possess different structures. However, they also suggest that purposeful managerial actions can have a significant impact in adapting a design's structure. This latter result is important given recent moves to release proprietary software into the public domain. These moves are likely to fail unless the product possesses an “architecture for participation.”


title: Efficient Light Field Images Compression Method Based on Depth Estimation and Optimization

abstract: Massive recent investigations from both industry and academia have been poured into autostereoscopic display. One of the emerging techniques, light field image (LFI), can provide more immersive perception by increasing the number of views and the spatial resolution. However, these advantages restrict the storage and transmission due to such dense-view image simultaneously. To solve this problem, we propose to compress the LFI using multiview video plus depth (MVD) coding architecture. In this paper, we preliminarily estimate the depth based on the concept of epipolar plane image. To achieve a depth value, we design an optimal slope decision algorithm to determine the best slope with the minimal cost. While this rough estimation produces some error points within initial depth map, therefore, we present a depth optimization algorithm using the characteristic of the associated texture image. Ultimately, a small number of selected viewpoint images are encoded with their corresponding depth maps using the MVD framework, and then, the unselected viewpoint images are synthesized by depth image-based rendering technique. To verify the validity of the proposed LFI compression scheme, extensive experiments are conducted. The simulated results demonstrate that the proposed depth map estimation algorithm is superior to other state-of-the-art methods for the LFI. Meanwhile, our LFI compression method outperforms other LFI compression algorithms significantly.


title: On the use of words and n-grams for Chinese information retrieval

abstract: In the processing of Chinese documents and queries in information retrieval (IR), one has to identify the units that are used as indexes. Words and n-grams have been used as indexes in several previous studies, which showed that both kinds of indexes lead to comparable IR performances. In this study, we carry out more experiments on different ways to segment documents and queries, and to combine words with n-grams. Our experiments show that a combination of the longest-matching algorithm with single characters is the best choice.


title: Prototype-based learning on concept-drifting data streams

abstract: Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drift in data streams, PCA and statistics based heuristic approaches are employed. SyncStream has several attractive benefits: (a) It is capable of dynamically modeling evolving concepts from even a small set of prototypes and is robust against noisy examples. (b) Owing to synchronization-based constrained clustering and the P-Tree, it supports an efficient and effective data representation and maintenance. (c) Gradual and abrupt concept drift can be effectively detected. Empirical results shows that our method achieves good predictive performance compared to state-of-the-art algorithms and that it requires much less time than another instance-based stream mining algorithm.


title: Extraction of temporal facts and events from Wikipedia

abstract: Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases. In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base. We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90% for extraction from semi-structured data and almost 70% for extraction from text.


title: DC-Link Stabilization and Voltage Sag Ride-Through of Inverter Drives

abstract: Previous results concerning instability of the dc link in inverter drives fed from a dc grid or via a rectifier are extended. It is shown that rectifier-inverter drives equipped with small (film) dc-link capacitors may need active stabilization. The impact of limited bandwidth and switching frequency in the inverter-motor current control loop is considered, and recommendations for selection of the dc-link capacitor, the switching frequency, and the dc-link stabilization control law in relation to each other are given. This control law is incorporated in a field-weakening (to enhance voltage sag ride-through) current controller for which design recommendations are presented


title: Live Metric 3D Reconstruction on Mobile Phones

abstract: In this paper, we propose a complete on-device 3D reconstruction pipeline for mobile monocular hand-held devices, which generates dense 3D models with absolute scale on-site while simultaneously supplying the user with real-time interactive feedback. The method fills a gap in current cloud-based mobile reconstruction services as it ensures at capture time that the acquired image set fulfills desired quality and completeness criteria. In contrast to existing systems, the developed framework offers multiple innovative solutions. In particular, we investigate the usability of the available on-device inertial sensors to make the tracking and mapping process more resilient to rapid motions and to estimate the metric scale of the captured scene. Moreover, we propose an efficient and accurate scheme for dense stereo matching which allows to reduce the processing time to interactive speed. We demonstrate the performance of the reconstruction pipeline on multiple challenging indoor and outdoor scenes of different size and depth variability.


title: M-invariance: towards privacy preserving re-publication of dynamic datasets

abstract: The previous literature of privacy preserving data publication has focused on performing "one-time" releases. Specifically, none of the existing solutions supports re-publication of the microdata, after it has been updated with insertions and deletions. This is a serious drawback, because currently a publisher cannot provide researchers with the most recent dataset continuously. This paper remedies the drawback. First, we reveal the characteristics of the re-publication problem that invalidate the conventional approaches leveraging k-anonymity and l-diversity. Based on rigorous theoretical analysis, we develop a new generalization principle m-invariance that effectively limits the risk of privacy disclosure in re-publication. We accompany the principle with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata. Our theoretical results are confirmed by extensive experiments with real data.


title: Counting the Founders: The Matrilineal Genetic Ancestry of the Jewish Diaspora

abstract: The history of the Jewish Diaspora dates back to the Assyrian and Babylonian conquests in the Levant, followed by complex demographic and migratory trajectories over the ensuing millennia which pose a serious challenge to unraveling population genetic patterns. Here we ask whether phylogenetic analysis, based on highly resolved mitochondrial DNA (mtDNA) phylogenies can discern among maternal ancestries of the Diaspora. Accordingly, 1,142 samples from 14 different non-Ashkenazi Jewish communities were analyzed. A list of complete mtDNA sequences was established for all variants present at high frequency in the communities studied, along with high-resolution genotyping of all samples. Unlike the previously reported pattern observed among Ashkenazi Jews, the numerically major portion of the non-Ashkenazi Jews, currently estimated at 5 million people and comprised of the Moroccan, Iraqi, Iranian and Iberian Exile Jewish communities showed no evidence for a narrow founder effect, which did however characterize the smaller and more remote Belmonte, Indian and the two Caucasus communities. The Indian and Ethiopian Jewish sample sets suggested local female introgression, while mtDNAs in all other communities studied belong to a well-characterized West Eurasian pool of maternal lineages. Absence of sub-Saharan African mtDNA lineages among the North African Jewish communities suggests negligible or low level of admixture with females of the host populations among whom the African haplogroup (Hg) L0-L3 sub-clades variants are common. In contrast, the North African and Iberian Exile Jewish communities show influence of putative Iberian admixture as documented by mtDNA Hg HV0 variants. These findings highlight striking differences in the demographic history of the widespread Jewish Diaspora.


title: Estimation of QoE of video traffic using a fuzzy expert system

abstract: Quality of experience (QoE) in multimedia traffic has been the focus of extensive research in the last decade. The estimation of the QoE provides valuable input in order to measure the user satisfaction of a particular service. QoE estimation is challenging as it tries to measure a subjective metric where the user experience depends on a number of factors that cannot simply be measured. In this work, we present a methodology and a system based on fuzzy expert system to estimate the impact of network conditions (QoS) on the QoE of video traffic. At first, we conducted subjective tests to correlate network QoS metrics with participants' perceived QoE of video traffic. Second, we propose a No Reference method based on fuzzy expert system to estimate the network impact on the video QoE. The membership functions of the proposed fuzzy system are derived from normalized probability distributions correlating the QoS metrics with QoE. We propose a simple methodology to build the fuzzy inference rules. We evaluated our system in two different sets of experiments. The estimated video quality showed high correlation with the subjective QoE obtained from the participants in a controlled test. We integrated our system as part of a monitoring tool in an industrial IPTV test bed and compared its output with standard Video Quality Monitoring (VQM). The evaluation results show that the proposed video quality estimation method based on fuzzy expert system can effectively measure the network impact on the QoE.


title: A Light Transport Framework for Lenslet Light Field Cameras

abstract: Light field cameras capture full spatio-angular information of the light field, and enable many novel photographic and scientific applications. It is often stated that there is a fundamental trade-off between spatial and angular resolution, but there has been limited understanding of this trade-off theoretically or numerically. Moreover, it is very difficult to evaluate the design of a light field camera because a new design is usually reported with its prototype and rendering algorithm, both of which affect resolution. In this article, we develop a light transport framework for understanding the fundamental limits of light field camera resolution. We first derive the prefiltering model of lenslet-based light field cameras. The main novelty of our model is in considering the full space-angle sensitivity profile of the photosensor—in particular, real pixels have nonuniform angular sensitivity, responding more to light along the optical axis rather than at grazing angles. We show that the full sensor profile plays an important role in defining the performance of a light field camera. The proposed method can model all existing lenslet-based light field cameras and allows to compare them in a unified way in simulation, independent of the practical differences between particular prototypes. We further extend our framework to analyze the performance of two rendering methods: the simple projection-based method and the inverse light transport process. We validate our framework with both flatland simulation and real data from the Lytro light field camera.


Raspberry Drone : Unmanned Aerial Vehicle


title: Timed motor tests can detect subtle motor dysfunction in early Parkinson's disease.

abstract: Early diagnosis of Parkinson's disease (PD) is important for putative neuroprotective therapies to be initiated in the earliest stage of the disease. We investigated whether a previously validated timed motor test (TMT) battery could detect subtle motor dysfunction in early PD patients and even in clinically unaffected limbs of strictly hemiparkinsonian patients. We assessed 107 PD patients (symptom duration <or=2 years; dopa-naive) and 100 healthy, age-matched controls with eight simple TMTs based on aspects of (a) walking, (b) writing, (c) single and double-handed pegboard performance, (d) finger tapping, and (e) diadochokinesis. We evaluated the ability of individual and combined TMTs to discriminate patients from controls using ROC curves. Second, we investigated whether these TMTs could identify motor dysfunction of the clinically unaffected limb in 42 strictly hemiparkinsonian patients. The pegboard dexterity test had the best ROC curve (AUC 0.97; 95% sensitivity, 89% specificity) for patients versus controls. It retained reasonable accuracy when testing the clinically unaffected limb of hemiparkinsonian patients versus the mean of right and left-hand scores in controls (AUC 0.73). The pegboard dexterity test is a sensitive and inexpensive instrument to detect motor dysfunction in early PD. Therefore, it may be worth evaluating as a diagnostic tool in everyday clinical practice to assess patients with early symptomatic PD, or as part of a more elaborate screening battery in a defined population at risk.


title: Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

abstract: With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.


permission. Browsing and Analyzing the Command-Level Structure of Large Collections of Image Manipulation Tutorials


title: Review of Childhood Obesity: From Epidemiology, Etiology, and Comorbidities to Clinical Assessment and Treatment

abstract: Abstract Childhood obesity has emerged as an important public health problem in the United States and other countries in the world. Currently 1 in 3 children in the United States is afflicted with overweight or obesity. The increasing prevalence of childhood obesity is associated with emergence of comorbidities previously considered to be "adult" diseases including type 2 diabetes mellitus, hypertension, nonalcoholic fatty liver disease, obstructive sleep apnea, and dyslipidemia. The most common cause of obesity in children is a positive energy balance due to caloric intake in excess of caloric expenditure combined with a genetic predisposition for weight gain. Most obese children do not have an underlying endocrine or single genetic cause for their weight gain. Evaluation of children with obesity is aimed at determining the cause of weight gain and assessing for comorbidities resulting from excess weight. Family-based lifestyle interventions, including dietary modifications and increased physical activity, are the cornerstone of weight management in children. A staged approach to pediatric weight management is recommended with consideration of the age of the child, severity of obesity, and presence of obesity-related comorbidities in determining the initial stage of treatment. Lifestyle interventions have shown only modest effect on weight loss, particularly in children with severe obesity. There is limited information on the efficacy and safety of medications for weight loss in children. Bariatric surgery has been found to be effective in decreasing excess weight and improving comorbidities in adolescents with severe obesity. However, there are limited data on the long-term efficacy and safety of bariatric surgery in adolescents. For this comprehensive review, the literature was scanned from 1994 to 2016 using PubMed using the following search terms: childhood obesity , pediatric obesity , childhood overweight , bariatric surgery , and adolescents .


title: Animal model and neurobiology of suicide

abstract: Animal models are formidable tools to investigate the etiology, the course and the potential treatment of an illness. No convincing animal model of suicide has been produced to date, and despite the intensive study of thousands of animal species naturalists have not identified suicide in nonhuman species in field situations. When modeling suicidal behavior in the animal, the greatest challenge is reproducing the role of will and intention in suicide mechanics. To overcome this limitation, current investigations on animals focus on every single step leading to suicide in humans. The most promising endophenotypes worth investigating in animals are the cortisol social-stress response and the aggression/impulsivity trait, involving the serotonergic system. Astroglia, neurotrophic factors and neurotrophins are implied in suicide, too. The prevention of suicide rests on the identification and treatment of every element increasing the risk.


Tackling the digitalization challenge : how to benefit from digitalization in practice


title: A Scalable Distributed Graph Partitioner

abstract: We present Scalable Host-tree Embeddings for Efficient Partitioning (Sheep), a distributed graph partitioning algorithm capable of handling graphs that far exceed main memory. Sheep produces high quality edge partitions an order of magnitude faster than both state of the art offline (e.g., METIS) and streaming partitioners (e.g., Fennel). Sheep's partitions are independent of the input graph distribution, which means that graph elements can be assigned to processing nodes arbitrarily without affecting the partition quality. ::: ::: Sheep transforms the input graph into a strictly smaller elimination tree via a distributed map-reduce operation. By partitioning this tree, Sheep finds an upper-bounded communication volume partitioning of the original graph. ::: ::: We describe the Sheep algorithm and analyze its space-time requirements, partition quality, and intuitive characteristics and limitations. We compare Sheep to contemporary partitioners and demonstrate that Sheep creates competitive partitions, scales to larger graphs, and has better runtime.


Biomimetic Approaches To Architectural Design For Increased Sustainability


title: Computational Complexity of a Pop-up Book ∗

abstract: Origami is the centuries-old art of folding paper, and recently, it is investigated as computer science: Given an origami with creases, the problem to determine if it can be flat after folding all creases is NP-hard. Another hundreds-old art of folding paper is a pop-up book. A model for the pop-up book design problem is given, and its computational complexity is investigated. We show that both of the opening book problem and the closing book problem are NP-hard.


title: Parasocial attributes and YouTube personalities: Exploring content trends across the most subscribed YouTube channels

abstract: This study seeks to analyze the relationships between content features, video attributes, and parasocial attributes the characteristics that could lead to the creation of parasocial relationshipsamong the top most subscribed YouTube channels. A quantitative content analysis was utilized in order to explore the videos of the most popular YouTube personalities. A stratified random sample was used to select 24 videos from each the top ten most subscribed YouTube channels. The findings of this study illuminate the relationship between content features, production features, and parasocial attributes. The most common videos are vlogs, followed by gaming videos, sketches, and tutorials.Vlogs are more likely to contain self-disclosure than any other video genre.Self-disclosure is associated with higher levels of authenticity and realism.The effect of self-disclosure does not depend on disclosure valence.


Graph Matching : Theoretical Foundations , Algorithms , and Applications


Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge


title: Box Pleating is Hard

abstract: Flat foldability of general crease patterns was first claimed to be hard for over twenty years. In this paper we prove that deciding flat foldability remains NP-complete even for box pleating, where creases form a subset of a square grid with diagonals. In addition, we provide new terminology to implicitly represent the global layer order of a flat folding, and present a new planar reduction framework for grid-aligned gadgets.


title: The intestinal microbiome, probiotics and prebiotics in neurogastroenterology.

abstract: The brain-gut axis allows bidirectional communication between the central nervous system (CNS) and the enteric nervous system (ENS), linking emotional and cognitive centers of the brain with peripheral intestinal functions. Recent experimental work suggests that the gut microbiota have an impact on the brain-gut axis. A group of experts convened by the International Scientific Association for Probiotics and Prebiotics (ISAPP) discussed the role of gut bacteria on brain functions and the implications for probiotic and prebiotic science. The experts reviewed and discussed current available data on the role of gut microbiota on epithelial cell function, gastrointestinal motility, visceral sensitivity, perception and behavior. Data, mostly gathered from animal studies, suggest interactions of gut microbiota not only with the enteric nervous system but also with the central nervous system via neural, neuroendocrine, neuroimmune and humoral links. Microbial colonization impacts mammalian brain development in early life and subsequent adult behavior. These findings provide novel insights for improved understanding of the potential role of gut microbial communities on psychological disorders, most particularly in the field of psychological comorbidities associated with functional bowel disorders like irritable bowel syndrome (IBS) and should present new opportunity for interventions with pro- and prebiotics.


title: Intrinsic Distortion of a Fully Differential BD-Modulated Class-D Amplifier With Analog Feedback

abstract: This paper presents a mathematical analysis of a fully differential BD-modulated Class-D amplifier with analog feedback, i.e., one having a bridge-tied-load output configuration with negative feedback and ternary PWM signal. Notwithstanding the highly nonlinear nature of the amplifier's operation, an extremely accurate closed-form expression for the audible output signal is derived and verified based on computer simulations. This expression demonstrates that there exist larger high-order intrinsic distortions (e.g., 5th-order harmonic distortion and intermodulation distortion) for BD-modulation, compared to that for AD-modulation (binary PWM signal). Furthermore, the 3rd-order harmonic distortion has a roughly parabolic response as a function of the magnitude of the input signal and reaches its peak when the modulation index of the input signal is around 0.7. Overall, the BD-modulated Class-D amplifier has a larger intrinsic distortion for small input signal but a smaller intrinsic distortion for large input signal, compared to AD-modulated designs.


title: © 2009 Science Publications A Prototype Design for Enhancing Customer Trust in Online Payments

abstract: Problem statement: The adoption of mobile device technology can contribute significantly to enhance customers trust in online payment systems. Approach: The perceptions and preferences of online shoppers are influenced by several key factors which serve to both enhance and compromise this trust and in turn affect a customer intentions and behavior in relation to use online payment systems. The first part of the research was a quantitative study to investigate these factors. In the second part of this research, a mobile payment model for online payment systems was proposed. In this model, the customers do not need to trust merchants during the transaction because merchants will not act as an intermediary between customers and the acquirer. Customers can therefore send their financial details without concern of disclosure, or potential misuse by the merchant. Results: In this study, the key factors influencing to adopt mobile payment systems were identify. The proposed model was developed and an analysis of the model architecture against conventional online payment systems was discussed. Conclusion/Recommendations: The significance of this research comes from providing a practical mobile payment model as a possible step towards increasing customer acceptance of online shopping and increasing their trust in online payment systems. The new model focuses on enhancing the feeling of security of the use of an online payment system and satisfying the security requirements.


title: Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition

abstract: Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and 2D texture appearance cues of the face which are beneficial for Facial Expression Recognition (FER). However, accurate facial parts extraction as well as their fusion are challenging tasks. In this paper, a novel system for 3D FER is designed based on accurate facial parts extraction and deep feature fusion of facial parts. In particular, each textured 3D face scan is firstly represented as a 2D texture map and a depth map with one-to-one dense correspondence. Then, the facial parts of both texture map and depth map are extracted using a novel 4-stage process consists of facial landmark localization, facial rotation correction, facial resizing, facial parts bounding box extraction and post-processing procedures. Finally, deep fusion Convolutional Neural Networks (CNNs) features of all facial parts are learned from both texture maps and depth maps, respectively and nonlinear SVMs are used for expression prediction. Experiments are conducted on the BU-3DFE database, demonstrating the effectiveness of combing different facial parts, texture and depth cues and reporting the state-of-the-art results in comparison with all existing methods under the same setting.


title: A Divide-and-Conquer Algorithm for the Symmetric TridiagonalEigenproblem

abstract: The authors present a stable and efficient divide-and-conquer algorithm for computing the spectral decomposition of an $N \times N$ symmetric tridiagonal matrix. The key elements are a new, stable method for finding the spectral decomposition of a symmetric arrowhead matrix and a new implementation of deflation. Numerical results show that this algorithm is competitive with bisection with inverse iteration, Cuppen's divide-and-conquer algorithm, and the QR algorithm for solving the symmetric tridiagonal eigenproblem.


title: What do cMOOC participants talk about in social media?: a topic analysis of discourse in a cMOOC

abstract: Creating meaning from a wide variety of available information and being able to choose what to learn are highly relevant skills for learning in a connectivist setting. In this work, various approaches have been utilized to gain insights into learning processes occurring within a network of learners and understand the factors that shape learners' interests and the topics to which learners devote a significant attention. This study combines different methods to develop a scalable analytic approach for a comprehensive analysis of learners' discourse in a connectivist massive open online course (cMOOC). By linking techniques for semantic annotation and graph analysis with a qualitative analysis of learner-generated discourse, we examined how social media platforms (blogs, Twitter, and Facebook) and course recommendations influence content creation and topics discussed within a cMOOC. Our findings indicate that learners tend to focus on several prominent topics that emerge very quickly in the course. They maintain that focus, with some exceptions, throughout the course, regardless of readings suggested by the instructor. Moreover, the topics discussed across different social media differ, which can likely be attributed to the affordances of different media. Finally, our results indicate a relatively low level of cohesion in the topics discussed which might be an indicator of a diversity of the conceptual coverage discussed by the course participants.


title: Learning to Speak and Act in a Fantasy Text Adventure Game

abstract: We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.


title: A low power IoT network for smart agriculture

abstract: Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has on average 83% prolonged lifetime at a lower cost compared to previously proposed network in the agriculture field.


title: A Methodology for the Development of Web-based Information Systems: Web Development Team Perspective

abstract: Web engineering has evolved through software engineering due to the technological changes from traditional information systems to web-based services. It has powered the Internet and World Wide Web technologies in which it has reflected the use of web-based information systems to reach broader audiences, new markets, access more information and interact in real time across barriers. The need for a methodology to develop such web-based information systems is inevitable, to enable adaptation of the traditional approach of systems analysis and design that is the software development life cycle (SDLC) to shift into a new paradigm namely the web-based development life cycle (WDLC). In this paper, the literature review has discovered a general methodology for WDLC that differs in terms of the number of stages and the nature of the development environment compared to the SDLC approach. However, from the insights of web development team through case studies, it is found that there is a collective agreement that having comprehensive development stages can satisfy their clients. The reason is their clients seem to believe that, if a smaller number of development stages exist to build web-based information systems, this can really reduce the development costs and time leading to faster delivery to the market.