title: noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts

abstract: We present noWorkflow, an open-source tool that systematically and transparently collects provenance from Python scripts, including data about the script execution and how the script evolves over time. During the demo, we will show how noWorkflow collects and manages provenance, as well as how it supports the analysis of computational experiments. We will also encourage attendees to use noWorkflow for their own scripts.


title: Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition

abstract: Based on the progress of image recognition, video recognition has been extensively studied recently. However, most of the existing methods are focused on short-term but not long-term video recognition, called contextual video recognition. To address contextual video recognition, we use convolutional recurrent neural networks (ConvRNNs) having a rich spatio-temporal information processing capability, but ConvRNNs requires extensive computation that slows down training. In this paper, inspired by the normalization and detrending methods, we propose adaptive detrending (AD) for temporal normalization in order to accelerate the training of ConvRNNs, especially for convolutional gated recurrent unit (ConvGRU). AD removes internal covariate shift within a sequence of each neuron in recurrent neural networks (RNNs) by subtracting a trend. In the experiments for contextual recognition on ConvGRU, the results show that (1) ConvGRU clearly outperforms the feed-forward neural networks, (2) AD consistently offers a significant training acceleration and generalization improvement, and (3) AD is further improved by collaborating with the existing normalization methods.


title: An examination of "nonleadership": From laissez-faire leadership to leader reward omission and punishment omission.

abstract: Laissez-faire leadership has received much less attention than have the 3 transactional leadership dimensions of the Multifactor Leadership Questionnaire (MLQ). However, laissez-faire leadership has shown strong negative relationships with various leadership criteria, and the absence of leadership (laissez-faire leadership) may be just as important as is the presence of other types of leadership. This article focuses on a single type of laissez-faire leadership (i.e., the lack of response to subordinate performance). Using a reinforcement perspective, the authors developed measures and examined the effects of the lack of performance-contingent reinforcement in 2 forms: reward omission (leader nonreinforcement of good subordinate performance) and punishment omission (leader nonreinforcement of poor subordinate performance). They found strong evidence in support of the construct validity of the new measures and found that omission was related to follower satisfaction with the leader, subordinate-rated leader effectiveness, subordinate-perceived role clarity, and supervisor-rated subordinate performance.


title: Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds

abstract: Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.


title: Special Relativity properties from Minkowski diagrams

abstract: This paper has pedagogical motivation. It is not uncommon that students have great difficulty in accepting the new concepts of standard special relativity, since these seem contrary to common sense. Experience shows that geometrical or graphically exposition of the basic ideas of relativity theory improves student understanding of the algebraic expressions of the theory. What we suggest here may complement standard textbook approaches.


title: Area under the curve as a measure of discounting

abstract: We describe a novel approach to the measurement of discounting based on calculating the area under the empirical discounting function. This approach avoids some of the problems associated with measures based on estimates of the parameters of theoretical discounting functions. The area measure may be easily calculated for both individual and group data collected using any of a variety of current delay and probability discounting procedures. The present approach is not intended as a substitute for theoretical discounting models. It is useful, however, to have a simple, univariate measure of discounting that is not tied to any specific theoretical framework.


title: Efficacy of the third wave of behavioral therapies: a systematic review and meta-analysis.

abstract: During the last two decades a number of therapies, under the name of the third wave of cognitive behavior therapy (CBT), have been developed: acceptance and commitment therapy (ACT), dialectical behavior therapy (DBT), cognitive behavioral analysis system of psychotherapy (CBASP), functional analytic psychotherapy (FAP), and integrative behavioral couple therapy (IBCT). The purposes of this review article of third wave treatment RCTs were: (1) to describe and review them methodologically, (2) to meta-analytically assess their efficacy, and (3) to evaluate if they currently fulfil the criteria for empirically supported treatments. There are 13 RCTs both in ACT and DBT, 1 in CBASP, 2 in IBCT, and none in FAP. The conclusions that can be drawn are that the third wave treatment RCTs used a research methodology that was significantly less stringent than CBT studies; that the mean effect size was moderate for both ACT and DBT, and that none of the third wave therapies fulfilled the criteria for empirically supported treatments. The article ends with suggestions on how to improve future RCTs to increase the possibility of them becoming empirically supported treatments.


title: Speech enhancement using a minimum mean-square error log-spectral amplitude estimator

abstract: In this correspondence we derive a short-time spectral amplitude (STSA) estimator for speech signals which minimizes the mean-square error of the log-spectra (i.e., the original STSA and its estimator) and examine it in enhancing noisy speech. This estimator is also compared with the corresponding minimum mean-square error STSA estimator derived previously. It was found that the new estimator is very effective in enhancing the noisy speech, and it significantly improves its quality.


Comparison Some of Ontology Editors


title: Residence Time Distribution for Tubular Reactors

abstract: In the core of Chemical Engineering is the reactor design that includes most of all scientific disciplines. The reactor, in general, are treated ideally: mixed and plug-flow patterns. Unfortunately, it is observed in the real world a very different behavior from that expected. Thus, to characterize nonideal reactors is used, among others, residence time distribution function E(t), mean residence time tm and cumulative distribution function F(t). The aim of this present work is to determine in the Comsol Multiphysics a distribution of residence time of a tubular reactor that is used, didactically, in the Chemical Engineering Laboratory in Federal University of Parana. The results were compared with experimental dates that concluded the modeling of the reactor showed good agreement according to the correlation coefficient of 0.97. At last, it was purposed a modification in the reactor's geometry to take note with its hydrodynamic would get better.


title: CPW-Fed Dual-Band Dual-Sense Circularly Polarized Monopole Antenna

abstract: A novel design of a dual-band and dual-sense circularly polarized coplanar-waveguide-fed monopole antenna with two rectangular parasitic elements and an I-shape grounded stub is presented here. The monopole consists of a vertical and a horizontal section. Lower band is obtained due to 90° phase difference between currents in the two orthogonal branches of the monopole. Upper band is obtained due to the I-shape stub. Parasitic elements help in input impedance matching. The dual-band and dual-sense antenna provides wide 10-dB impedance bandwidth of 71.63% at 2.82 GHz. The 3-dB axial-ratio bandwidths are 27.45% at 2.55 GHz (right-hand circular polarization) and 7.1% at 3.53 GHz (left-hand circular polarization).


A Comparative Study of IEEE 802 . 11 Standards for Non-Safety Applications on Vehicular Ad Hoc Networks : A Congestion Control Perspective


title: Wave attenuation across a tidal marsh in San Francisco Bay

abstract: Abstract Wave attenuation is a central process in the mechanics of a healthy salt marsh. Understanding how wave attenuation varies with vegetation and hydrodynamic conditions informs models of other marsh processes that are a function of wave energy (e.g. sediment transport) and allows for the incorporation of marshes into coastal protection plans. Here, we examine the evolution of wave height across a tidal salt marsh in San Francisco Bay. Instruments were deployed along a cross-shore transect, starting on the mudflat and crossing through zones dominated by Spartina foliosa and Salicornia pacifica. This dataset is the first to quantify wave attenuation for these vegetation species, which are abundant in the intertidal zone of California estuaries. Measurements were collected in the summer and winter to assess seasonal variation in wave attenuation. Calculated drag coefficients of S. foliosa and S. pacifica were similar, indicating equal amounts of vegetation would lead to similar energy dissipation; however, S. pacifica has much greater biomass close to the bed (


Comparing Open-Source Speech Recognition Toolkits ⋆


title: Effects of leucine and its metabolite β-hydroxy-β-methylbutyrate on human skeletal muscle protein metabolism

abstract: Maintenance of skeletal muscle mass is contingent upon the dynamic equilibrium (fasted losses-fed gains) in protein turnover. Of all nutrients, the single amino acid leucine (Leu) possesses the most marked anabolic characteristics in acting as a trigger element for the initiation of protein synthesis. While the mechanisms by which Leu is 'sensed' have been the subject of great scrutiny, as a branched-chain amino acid, Leu can be catabolized within muscle, thus posing the possibility that metabolites of Leu could be involved in mediating the anabolic effect(s) of Leu. Our objective was to measure muscle protein anabolism in response to Leu and its metabolite HMB. Using [1,2-(13)C2]Leu and [(2)H5]phenylalanine tracers, and GC-MS/GC-C-IRMS we studied the effect of HMB or Leu alone on MPS (by tracer incorporation into myofibrils), and for HMB we also measured muscle proteolysis (by arteriovenous (A-V) dilution). Orally consumed 3.42 g free-acid (FA-HMB) HMB (providing 2.42 g of pure HMB) exhibited rapid bioavailability in plasma and muscle and, similarly to 3.42 g Leu, stimulated muscle protein synthesis (MPS; HMB +70% vs. Leu +110%). While HMB and Leu both increased anabolic signalling (mechanistic target of rapamycin; mTOR), this was more pronounced with Leu (i.e. p70S6K1 signalling 90 min vs. 30 min for HMB). HMB consumption also attenuated muscle protein breakdown (MPB; -57%) in an insulin-independent manner. We conclude that exogenous HMB induces acute muscle anabolism (increased MPS and reduced MPB) albeit perhaps via distinct, and/or additional mechanism(s) to Leu.


title: Data hiding in binary image for authentication and annotation

abstract: This paper proposes a new method to embed data in binary images, including scanned text, figures, and signatures. The method manipulates "flippable" pixels to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. Shuffling is applied before embedding to equalize the uneven embedding capacity from region to region. The hidden data can be extracted without using the original image, and can also be accurately extracted after high quality printing and scanning with the help of a few registration marks. The proposed data embedding method can be used to detect unauthorized use of a digitized signature, and annotate or authenticate binary documents. The paper also presents analysis and discussions on robustness and security issues.


title: Paving the way for CRM success: The mediating role of knowledge management and organizational commitment

abstract: Data from an international sample of 312 hotels (from UK and Spain) is analyzed.The process through which CRM technology translates into organizational performance is described.A CRM technology, when properly implemented, shows a positive effect on performance.Knowledge management and organizational commitment acted as relevant mediators.Organizational commitment probed to be the main determinant of CRM success. Customer relationship management (CRM) has become one of the most influential technologies in the world, and companies are increasingly implementing it to create value. However, despite significant investment in CRM technology infrastructure, empirical research offers inconsistent support for its positive impact on performance. This study develops and tests a research model analyzing the process through which CRM technology infrastructure translates into organizational performance, drawing on the resource-based view (RBV) and the knowledge-based view (KBV) of the firm. Based on an international sample of 125 hotels, the results suggest that organizational commitment and knowledge management fully mediate this process.


Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task


title: Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

abstract: Fluvial flooding is one of the most catastrophic natural disasters threatening people’s lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.


title: The role of social networks in students' learning experiences

abstract: The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students' learning experiences. The construction of students' social networks, the evolution of these networks, and their effects on the students' learning experience in a university environment are examined.


title: A 0.3 GHz to 1.4 GHz N-path mixer-based code-domain RX with TX self-interference rejection

abstract: A code-domain N-path RX is proposed based on PN-code modulated LO pulses for concurrent reception of two code-modulated signals. Additionally, a combination of Walsh-Function and PN sequence is proposed to translate in-band TX self-interference (SI) to out-of-band at N-path RX output enabling frequency filtering for high SI rejection. A 0.3 GHz–1.4 GHz 65-nm CMOS implementation has 35 dB gain for desired signals and concurrently receives two RX signals while rejecting mismatched spreading codes at RF input. Proposed TX SI mitigation approach results in 38.5 dB rejection for −11.8dBm 1.46 Mb/s QPSK modulated SI at RX input. The RX achieves 23.7dBm OP1dB for in-band SI, while consuming ∼35mW and occupies 0.31mm2.


title: Content-based Text Categorization using Wikitology

abstract: A major computational burden, while performing document clustering, is the calculation of similarity measure between a pair of documents. Similarity measure is a function that assign a real number between 0 and 1 to a pair of documents, depending upon the degree of similarity between them. A value of zero means that the documents are completely dissimilar whereas a value of one indicates that the documents are practically identical. Traditionally, vector-based models have been used for computing the document similarity. The vector-based models represent several features present in documents. These approaches to similarity measures, in general, cannot account for the semantics of the document. Documents written in human languages contain contexts and the words used to describe these contexts are generally semantically related. Motivated by this fact, many researchers have proposed semantic-based similarity measures by utilizing text annotation through external thesauruses like WordNet (a lexical database). In this paper, we define a semantic similarity measure based on documents represented in topic maps. Topic maps are rapidly becoming an industrial standard for knowledge representation with a focus for later search and extraction. The documents are transformed into a topic map based coded knowledge and the similarity between a pair of documents is represented as a correlation between the common patterns. The experimental studies on the text mining datasets reveal that this new similarity measure is more effective as compared to commonly used similarity measures in text clustering.


title: Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval

abstract: We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.


title: Using Computer Vision In Security Applications

abstract: In this paper we present projects developed in the Computer Vision Laboratory, which address the issue of safety. First, we present the Internet Video Server (IVS) monitoring system [5] that sends live video stream over the Internet and enables remote camera control. Its extension GlobalView [1,6], which incorporates intuitive user interface for remote camera control, is based on panoramic image. Then we describe our method for automatic face detection [3] based on color segmentation and feature extraction. Finally, we introduce our SecurityAgent system [4] for automatic surveillance of observed location.


title: Fast analysis of FSS radome for antenna RCS reduction

abstract: This work presents a method for the analysis of large frequency selective radomes relying on the combination of a high frequency approach, based on a locally planar approximation, and the PRM method for an efficient characterization of the planar selective structure. The proposed approach combines efficiency and conceptual simplicity with a reasonable accuracy, thus providing a tool for a fast analysis of antennas enclosed by large arbitrarily shaped frequency selective radomes.


title: Video-based facial expression recognition using histogram sequence of local Gabor binary patterns from three orthogonal planes

abstract: Video-based facial expression recognition has received significant attention in recent years due to its widespread applications. One key issue for video-based facial expression analysis in practice is how to extract dynamic features. In this paper, a novel approach is presented using histogram sequence of local Gabor binary patterns from three orthogonal planes (LGBP-TOP). In this approach, every facial expression sequence is firstly convolved with the multi-scale and multi-orientation Gabor filters to extract the Gabor Magnitude Sequences (GMSs). Then, we use local binary patterns from three orthogonal planes (LBP-TOP) on each GMS to further enhance the feature extraction. Finally, the facial expression sequence is modeled as a histogram sequence by concatenating the histogram pieces of all the local regions of all the LGBP-TOP maps. For recognition, Support Vector Machine (SVM) is exploited. Our experimental results on the extended Cohn-Kanade database (CK+) demonstrate that the proposed method has achieved the best results compared to other methods in recent years.


title: Evaluation and design for an integrated modular motor drive (IMMD) with GaN devices

abstract: This paper explores the use of GaN MOSFETs and series-connected inverter segments to realize an IMMD. The proposed IMMD topology reduces the segment voltage and offers an opportunity to utilize wide bandgap 200V GaN MOSFETs. Consequently, a reduction in IMMD size is achieved by eliminating inverter heat sink and optimizing the choice of DC-link capacitors. Gate signals of the IMMD segments are shifted (interleaved) to cancel the capacitor voltage ripple and further reduce the capacitor size. Motor winding configuration and coupling effect are also investigated to match with the IMMD design. An actively controlled balancing resistor is programmed to balance the voltages of series connected IMMD segments. Furthermore, this paper presents simulation results as well as experiment results to validate the proposed design.


title: An effective and efficient method for query by humming system based on multi-similarity measurement fusion

abstract: Since it is the most natural way for people to search a specific melody in large music database, query by humming/singing is attracting more and more researcherspsila attention in the field of content-based music information retrieval. In this task, note-based and frame-based similarity measures are two commonly used methods. However, in previous works, researchers always focus on one of the two methods alone. In this paper, we propose a novel scheme taking advantage of two different similarity measurements to improve not only the retrieval accuracy but also the retrieving speed. First, Earth Moverpsilas Distance (EMD), which is note-based and much faster, is adopted to eliminate most unlikely candidate. Then, Dynamic Time Warping (DTW), which is frame-based and more accurate, is executed on these surviving candidates. Finally, fusion strategies of these two similarity measurements are employed to improve the performance of whole system. Experiments show our approach can achieve 92.9% accuracy on the database used in MIREX 2006 QBH contest, which is better than those systems participated in that task.


title: Multiple Structure-View Learning for Graph Classification

abstract: Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.


title: Omni-directional microstrip monopole antenna for UWB microwave imaging system

abstract: A miniaturized ultra-wideband antenna is presented for using in microwave imaging systems. This antenna structure consists of a radiating patch, a microstrip transmission line, and a partial ground plane which provides a wide bandwidth covering from 3.65 GHz to more than 11 GHz frequency. It has overall dimensions of 16 mm × 21 mm× 1.6 mm with a compact size. Negligible distortion is observed from the time domain characteristic of the proposed UWB antenna. It is appropriate for using in the field of microwave imaging systems due to its short pulse operation. The proposed antenna shows good directional radiation pattern, considerable gain level, surface current distribution and correlation coefficient for using in the microwave imaging systems.


title: Using Bayes Network for Prediction of Type-2 diabetes

abstract: Diabetes mellitus is a chronic disease and a major public health challenge worldwide. Using data mining methods to aid people to predict diabetes has become a popular topic. In this paper, Bayes Network was proposed to predict patients with developing Type-2 diabetes. The dataset used is the Pima Indians Diabetes Data Set, which collects the information of patients with and without developing Type-2 diabetes. Weka software was used throughout this study. Accurate results have been obtained which proves using the proposed Bayes network to predict Type-2 diabetes is effective.


title: A Non-Parametric Bayesian Method for Inferring Hidden Causes

abstract: We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hidden causes is unbounded, but only a finite number influence observable variables. This makes it possible to use a Gibbs sampler to approximate the distribution over causal structures. We evaluate the performance of both approaches in discovering hidden causes in simulated data, and use our non-parametric approach to discover hidden causes in a real medical dataset.


title: Intelligent Tutoring Systems: New Challenges and Directions

abstract: Intelligent Tutoring Systems (ITS) is the interdisciplinary field that investigates how to devise educational systems that provide instruction tailored to the needs of individual learners, as many good teachers do. Research in this field has successfully delivered techniques and systems that provide adaptive support for student problem solving in a variety of domains. There are, however, other educational activities that can benefit from individualized computer-based support, such as studying examples, exploring interactive simulations and playing educational games. Providing individualized support for these activities poses unique challenges, because it requires an ITS that can model and adapt to student behaviors, skills and mental states often not as structured and welldefined as those involved in traditional problem solving. This paper presents a variety of projects that illustrate some of these challenges, our proposed solutions, and future opportunities.


title: Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures

abstract: This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images


title: Self-organization of teams for free/libre open source software development,” Inf

abstract: This paper provides empirical evidence about how free/libre open source software development teams self-organize their work, specifically, how tasks are assigned to project team members. Following a case study methodology, we examined developer interaction data from three active and successful FLOSS projects using qualitative research methods, specifically inductive content analysis, to identify the task-assignment mechanisms used by the participants. We found that 'self-assignment' was the most common mechanism across three FLOSS projects. This mechanism is consistent with expectations for distributed and largely volunteer teams. We conclude by discussing whether these emergent practices can be usefully transferred to mainstream practice and indicating directions for future research.


title: Understanding in-video dropouts and interaction peaks inonline lecture videos

abstract: With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.


title: Soft-Switched CCM Boost Converters With High Voltage Gain for High-Power Applications

abstract: This paper proposes a new soft-switched continuous-conduction-mode (CCM) boost converter suitable for high-power applications such as power factor correction, hybrid electric vehicles, and fuel cell power conversion systems. The proposed converter achieves zero-voltage-switched (ZVS) turn-on of active switches in CCM and zero-current-switched turn-off of diodes leading to negligible reverse-recovery loss. The components' voltage ratings and energy volumes of passive components of the proposed converter are greatly reduced compared to the conventional zero-voltage-transition converter. Voltage conversion ratio is almost doubled compared to the conventional boost converter. Extension of the proposed concept to realize multiphase dc-dc converters is discussed. Experimental results from a 1.5-kW prototype are provided to validate the proposed concept.


Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques


title: Efficiently evolving programs through the search for novelty

abstract: A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic programming a method that originated in neuroevolution (i.e. the evolution of artificial neural networks) that circumvents the problem of deceptive local optima. The main idea is to search only for behavioral novelty instead of for higher fitness values. Although such novelty search abandons following the gradient of the fitness function, if such gradients are deceptive they may actually occlude paths through the search space towards the objective. Because there are only so many ways to behave, the search for behavioral novelty is often computationally feasible and differs significantly from random search. Counterintuitively, in both a deceptive maze navigation task and the artificial ant benchmark task, genetic programming with novelty search, which ignores the objective, outperforms traditional genetic programming that directly searches for optimal behavior. Additionally, novelty search evolves smaller program trees in every variation of the test domains. Novelty search thus appears less susceptible to bloat, another significant problem in genetic programming. The conclusion is that novelty search is a viable new tool for efficiently solving some deceptive problems in genetic programming.


Chemical sedimentology of some Permo-Jurassic and Tertiary bedded cherts in central Honshu, Japan.


title: Inverse procedural modeling of 3D models for virtual worlds

abstract: This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.


title: Integration of Historic Building Information Modeling (HBIM) and 3D GIS for recording and managing cultural heritage sites

abstract: This paper outlines a two stage approach for digitally recording cultural heritage sites. This approach involves a 3D modeling stage and the integration of the 3D model into a 3D GIS for further management and analysis. The modeling stage is carried out using a new concept; Historic Building Information Modeling (HBIM) which has been developed at the Dublin Institute of Technology [12]. Historic Building Information Modeling is a system for modeling historic structures from laser scan and photogrammetric data using Building Information Modeling (BIM) software. The HBIM process involves a reverse engineering solution whereby parametric objects representing architectural elements are mapped onto laser scan or photogrammetric survey data. A library of parametric architectural objects has been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded scripting language within the BIM software called Geometric Descriptive Language (GDL). These objects are combined and mapped onto the survey data to build the entire model. After the 3D model has been created the next stage involves integrating the 3D model into a 3D GIS for further analysis. The international framework for 3D city modeling, CityGML has been adopted for this purpose. CityGML provides an interoperable framework for modeling 3D geometries, semantics, topology and appearance properties [13]. The aim of this research is to bridge the gap between parametric CAD modeling and 3D GIS while using benefits from both systems to help document and analyze cultural heritage sites.


title: Automatic tracking and labeling of human activities in a video sequence",Joint

abstract: This paper presents a novel approach for tracking multiple objects and a statistical learning approach for detection of human activities in a video sequence. For the tracking, a rigid transformation invariant appearance model combining color and edge information of the detected blob is proposed. For the activity detection, each activity label is regarded as a hypothesis. Given some labeled sequences, a group of features are first extracted from motion trajectories of each detected object and the likelihood of each feature under that hypothesis is calculated. A dynamic programming-based training algorithm is applied to get an optimal classifier for each feature. Then it selects the classifiers with the most discriminative power and combines them to form a stronger classifier. This algorithm complies with criterion so that it is guaranteed to achieve a specified detection rate as well as a minimized false alarm rate. Results on dataset 1show the effectiveness of the proposed algorithm.


title: Evaluation of SQL Injection Detection and Prevention Techniques

abstract: Database driven web application are threaten by SQL Injection Attacks (SQLIAs) because this type of attack can compromise confidentiality and integrity of information in databases. Actually, an attacker intrudes to the web application database and consequently, access to data. For stopping this type of attack different approaches have been proposed by researchers but they are not enough because usually they have limitations. Indeed, some of these approaches have not implemented yet and also most of implemented approaches cannot stop all type of attacks. In this paper all type of SQL injection attack and also different approaches which can detect or prevent them are presented. Finally we evaluate these approaches against all types of SQL injection attacks and deployment requirements.


title: An Ultra-Miniature SIW Cavity-Backed Slot Antenna

abstract: A metamaterial-inspired ultra-miniature substrate integrated waveguide (SIW) cavity-backed slot antenna (CBSA) is presented in this letter. A ramp-shaped slot is used in an SIW CBSA to force the structure to work in the first negative-order resonance. Other miniaturization elements such as ramp-shaped strips as the ground plane around the antenna and another loading patch underneath the slot antenna are used to achieve an 87% miniature antenna working at 2.1 GHz. Close agreement is observed for the proposed simulated and measured performance results and suggests maximum gain of 3.6 dBi with an 80% efficiency.


title: Coarse and Fine Grain Programmable Overlay Architectures for FPGAs

abstract: Overlay architectures are programmable logic systems that are compiled on top of a traditional FPGA. These architectures give designers flexibility, and have a number of benefits, such as being designed or optimized for specific application domains, making it easier or more efficient to implement solutions, being independent of platform, allowing the ability to do partial reconfiguration regardless of the underlying architecture, and allowing compilation without using vendor tools, in some cases with fully open source tool chains. This thesis describes the implementation of two FPGA overlay architectures, ZUMA and CARBON. These overlay implementations include optimizations to reduce area and increase speed which may be applicable to many other FPGAs and also ASIC systems. ZUMA is a fine-grain overlay which resembles a modern commercial FPGA, and is compatible with the VTR open source compilation tools. The implementation includes a number of novel features tailored to efficient FPGA implementation, including the utilization of reprogrammable LUTRAMs, a novel two-stage local routing crossbar, and an area efficient configuration controller. CARBON


title: MODELLING AND PID CONTROL DESIGN OF NONLINEAR EDUCATIONAL MODEL BALL & PLATE

abstract: This paper is focused on modelling and co ntrol of nonlinear dynamical system Ball & Plate in language Matlab/Simulink. PID/PSD controller is used in closed loop feedback control structure for the purpose of contr ol. The verification of designed PID control algorithms, the same as nonlinear model of dynamical system, is performed with functional blocks of Simulink environment. This pap er includes testing of designed PID control algorithms on real model Ball & Plate using multifunction I/O card MF 614, which communicates with PC by the functions of Real Time Toolbox. Visualization of the simulation results is realized by internet appl ications, which use Matlab Web Server.


title: Healthy food subsidies and unhealthy food taxation: A systematic review of the evidence.

abstract: The Global Burden of Disease Study and related studies report unhealthy diet is the leading risk for death and disability globally. Given the evidence associating diet and non-communicable diseases (NCDs), international and national health bodies including the World Health Organization and United Nations have called for population health interventions to improve diet as a means to target NCDs. One of the proposed interventions is to ensure healthy foods/beverages are more accessible to purchasers and unhealthy ones less accessible via fiscal policy, namely taxation and subsidies. The objective of this systematic review was to evaluate the evidence base to assess the effect of healthy food/beverage subsidies and unhealthy food/beverage taxation. A comprehensive review was conducted by searching PubMed, Medline, and Google Scholar for peer-reviewed publications and seventy-eight studies were identified for inclusion in this review. This review was performed in keeping with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance. Although moderate in quality, there was consistent evidence that taxation and subsidy intervention influenced dietary behaviors. The quality, level and strength of evidence along with identified gaps in research support the need for further policies and ongoing evaluation of population-wide food/beverage subsidies and taxation. To maximize success and effect, this review suggests that food taxes and subsidies should be a minimum of 10 to 15% and preferably used in tandem. Implementation of population-wide polices for taxation and subsides with ongoing evaluation of intended and unintended effects are supported by this review.


SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning


title: Tumor Cell Metabolism: Cancer's Achilles' Heel

abstract: The essential hallmarks of cancer are intertwined with an altered cancer cell-intrinsic metabolism, either as a consequence or as a cause. As an example, the resistance of cancer mitochondria against apoptosis-associated permeabilization and the altered contribution of these organelles to metabolism are closely related. Similarly, the constitutive activation of signaling cascades that stimulate cell growth has a profound impact on anabolic metabolism. Here, we review the peculiarities of tumor cell metabolism that might be taken advantage of for cancer treatment. Specifically, we discuss the alterations in signal transduction pathways and/or enzymatic machineries that account for metabolic reprogramming of transformed cells.


title: Analyzing Massive Machine Maintenance Data in a Computing Cloud

abstract: We present a novel framework, CloudView, for storage, processing and analysis of massive machine maintenance data, collected from a large number of sensors embedded in industrial machines, in a cloud computing environment. This paper describes the architecture, design, and implementation of CloudView, and how the proposed framework leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. A case-based reasoning (CBR) approach is adopted for machine fault prediction, where the past cases of failure from a large number of machines are collected in a cloud. A case-base of past cases of failure is created using the global information obtained from a large number of machines. CloudView facilitates organization of sensor data and creation of case-base with global information. Case-base creation jobs are formulated using the MapReduce parallel data processing model. CloudView captures the failure cases across a large number of machines and shares the failure information with a number of local nodes in the form of case-base updates that occur in a time scale of every few hours. At local nodes, the real-time sensor data from a group of machines in the same facility/plant is continuously matched to the cases from the case-base for predicting the incipient faults-this local processing takes a much shorter time of a few seconds. The case-base is updated regularly (in the time scale of a few hours) on the cloud to include new cases of failure, and these case-base updates are pushed from CloudView to the local nodes. Experimental measurements show that fault predictions can be done in real-time (on a timescale of seconds) at the local nodes and massive machine data analysis for case-base creation and updating can be done on a timescale of minutes in the cloud. Our approach, in addition to being the first reported use of the cloud architecture for maintenance data storage, processing and analysis, also evaluates several possible cloud-based architectures that leverage the advantages of the parallel computing capabilities of the cloud to make local decisions with global information efficiently, while avoiding potential data bottlenecks that can occur in getting the maintenance data in and out of the cloud.


title: Toward harnessing user feedback for machine learning

abstract: There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.


title: Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)

abstract: A composition comprising a thermoplastic resin in admixture with a mold release effective amount of an acrylate.


title: An approach based on ANFIS input selection and modeling for supplier selection problem

abstract: Supplier selection is a key task for firms, enabling them to achieve the objectives of a supply chain. Selecting a supplier is based on multiple conflicting factors, such as quality and cost, which are represented by a multi-criteria description of the problem. In this article, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to overcome the supplier selection problem. First, criteria that are determined for the problem are reduced by applying ANFIS input selection method. Then, the ANFIS structure is built using data related to selected criteria and the output of the problem. The proposed method is illustrated by a case study in a textile firm. Finally, results obtained from the ANFIS approach we developed are compared with the results of the multiple regression method, demonstrating that the ANFIS method performed well.


title: From Awareness to Empowerment: Using Design Fiction to Explore Paths towards a Sustainable Energy Future

abstract: This paper presents a novel application of participatory design fiction for sustainable domestic energy consumption. In our study we examine how social practices relate to a newly developed energy management system introduced to households. We explore how design fiction can be used to identify current design limitations and to showcase future design directions to overcome these limitations. In particular, we investigated how energy consumption feedback is leaving users in a state of "helplessness" despite raising awareness of their environmental impact. Through participatory fictional future story-writing workshops we identified five levels of empowerment that design for sustainability practitioners can engage with. Our proposal is to use those levels of empowerment to reframe sustainable design by shifting from behaviour to social practices and from awareness to empowerment.


title: Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing

abstract: We present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of factor graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.


title: ' s personal copy The prediction and diagnosis of wind turbine faults

abstract: The rapid expansion of wind farms has drawn attention to operations and maintenance issues. Condition monitoring solutions have been developed to detect and diagnose abnormalities of various wind turbine subsystems with the goal of reducing operations and maintenance costs. This paper explores fault data provided by the supervisory control and data acquisition system and offers fault prediction at three levels: (1) fault and no-fault prediction; (2) fault category (severity); and (3) the specific fault prediction. For each level, the emerging faults are predicted 5–60 min before they occur. Various data-mining algorithms have been applied to develop models predicting possible faults. Computational results validating the models are provided. The research limitations are discussed.


title: Geometries and materials for subwavelength surface plasmon modes

abstract: Plasmonic waveguides can guide light along metal–dielectric interfaces with propagating wave vectors of greater magnitude than are available in free space and hence with propagating wavelengths shorter than those in vacuum. This is a necessary, rather than sufficient, condition for subwavelength confinement of the optical mode. By use of the reflection pole method, the two-dimensional modal solutions for single planar waveguides as well as adjacent waveguide systems are solved. We demonstrate that, to achieve subwavelength pitches, a metal–insulator–metal geometry is required with higher confinement factors and smaller spatial extent than conventional insulator–metal–insulator structures. The resulting trade-off between propagation and confinement for surface plasmons is discussed, and optimization by materials selection is described.


title: Clustering Affine Subspaces: Hardness and Algorithms

abstract: We study a generalization of the famous k-center problem where each object is an affine subspace of dimension Δ, and give either the first or significantly improved algorithms and hardness results for many combinations of parameters. This generalization from points (Δ = 0) is motivated by the analysis of incomplete data, a pervasive challenge in statistics: incomplete data objects in


title: A Survey of Software Quality for Machine Learning Applications

abstract: Machine learning (ML) is now widespread. Traditional software engineering can be applied to the development ML applications. However, we have to consider specific problems with ML applications in therms of their quality. In this paper, we present a survey of software quality for ML applications to consider the quality of ML applications as an emerging discussion. From this survey, we raised problems with ML applications and discovered software engineering approaches and software testing research areas to solve these problems. We classified survey targets into Academic Conferences, Magazines, and Communities. We targeted 16 academic conferences on artificial intelligence and software engineering, including 78 papers. We targeted 5 Magazines, including 22 papers. The results indicated key areas, such as deep learning, fault localization, and prediction, to be researched with software engineering and testing.


title: CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations

abstract: Social media has quickly risen to prominence as a news source, yet lingering doubts remain about its ability to spread rumor and misinformation. Systematically studying this phenomenon, however, has been difficult due to the need to collect large-scale, unbiased data along with in-situ judgements of its accuracy. In this paper we present CREDBANK, a corpus designed to bridge this gap by systematically combining machine and human computation. Specifically, CREDBANK is a corpus of tweets, topics, events and associated human credibility judgements. It is based on the real-time tracking of more than 1 billion streaming tweets over a period of more than three months, computational summarizations of those tweets, and intelligent routings of the tweet streams to human annotators — within a few hours of those events unfolding on Twitter. In total CREDBANK comprises more than 60 million tweets grouped into 1049 real-world events, each annotated by 30 human annotators. As an example, with CREDBANK one can quickly calculate that roughly 24% of the events in the global tweet stream are not perceived as credible. We have made CREDBANK publicly available, and hope it will enable new research questions related to online information credibility in fields such as social science, data mining and health.


title: A comparison of classification techniques for the P300 Speller

abstract: This study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.


title: Automatic mechanical assembly planning for a flexible assembly system

abstract: The authors introduce a method for generating all feasible assembly plans for a flexible assembly system from a computer model of a product. The major objectives are the following: to construct realistic assumptions on an assembly task and an assembly plan; to introduce representation methods of assembly tasks, assembly constraints, and assembly plans in the component domain: to develop an efficient algorithm for acquiring assembly constraints from a computer model of a product under the assumption of an assembly task; and to develop an efficient algorithm for generating all feasible assembly plans of a product under the assumptions of an assembly task and an assembly plan. The focus is on the parallelism of assembly tasks at a workplace as well as between the workplaces. >


title: Recurrent Ladder Networks

abstract: We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The architecture shows close-to-optimal results on temporal modeling of video data, competitive results on music modeling, and improved perceptual grouping based on higher order abstractions, such as stochastic textures and motion cues. We present results for fully supervised, semi-supervised, and unsupervised tasks. The results suggest that the proposed architecture and principles are powerful tools for learning a hierarchy of abstractions, learning iterative inference and handling temporal information.


title: On the design of low-power CMOS comparators with programmable hysteresis

abstract: We compare designs of low-power CMOS comparators with programmable hysteresis. We chose two baseline comparators: a two-stage CMOS op-amp with output inverter and a folded-cascode op-amp with output inverter. To these baseline circuits, we add programmable hysteresis using two methods. The first method uses positive feedback to unbalance the input differential pair. The second method uses positive feedback to steer current through a fixed-value resistor. The comparators were implemented in a 0.5µm CMOS process and operate with ±1.25V supplies. For an input voltage that is 10mV beyond the switching point, the propagation delays of the two-stage comparator with programmable hysteresis are measured as 932ns for the first method biased at 3.1µA and 672ns for the second method biased at 4µA.


title: Inquiry Based-Computational Experiment, Acquisition of Threshold Concepts and Argumentation in Science and Mathematics Education

abstract: Introduction Inquiry based learning and modelling in science and mathematics education Inquiry based learning has officially been promoted as a pedagogy for improving STEM learning in many countries (Bybee et al., 2008) and can be defined as "the deliberate process of diagnosing problems, planning experiments, distinguishing alternatives, setting up investigations, researching conjectures, sharing information, constructing models, forming coherent arguments, collecting and analyzing data" (Bell et al., 2004). Significant parts of scientific research are carried out on models rather than on the real phenomena because by studying a model we can discover features of and ascertain facts about the system the model stands for. This cognitive function of models has been widely recognized in the literature, and some researchers even suggest that models give rise to a new form of reasoning, the so-called "model based reasoning" (Magnani & Nersessian, 2002) while modelling ability is also associated to model-based reasoning (Chittleborough & Treagust, 2007). It is well known that scientific theories are developed through a process of continuous elaboration and modification in which scientific models are developed and transformed to account for new phenomena that are uncovered. Similar processes are involved in students' learning of STEM concepts when they develop conceptual models (e.g., Bell et al., 2010). In a similar fashion, inquiry based learning requires from students to make successive refinements to their mental models in order to transform them to conceptual models that align to scientific theories. The Computational Experiment (CE) Computational Science can be considered as a third independent scientific methodology (the other two are the theoretical science and the experimental science), has arisen over the last twenty years and shares characteristics with both theory and experiment, while it demands interdisciplinary skills in Science, Mathematics, Engineering and Computer Science. According to (Landau, Paez, & Bordeianu, 2008) Computational Science focuses on the form of a problem to solve, with the components that compose the solution separated according to the scientific problem-solving paradigm: (a) Problem (from science); (b) Modelling (Mathematical relations between selected entities and variables); (c) Simulation Method (time dependence of the state variables, discrete, continuous or stochastic processes like e.g., Monte Carlo simulation); (d) Development of the algorithm based on numerical analysis methods, (e) Implementation of the algorithm (using Java, Mathematica, Fortran etc.); and (f) Assessment and Visualization through exploration of the results and comparison with real data in authentic phenomena. In this framework, being able to transform a theory into an algorithm, requires significant theoretical insight, concrete Physical and Mathematical understanding as well as a mastery of the art of programming. In order to describe inquiry based learning as a search process, (Klahr & Dunbar, 1998) introduced two spaces, the hypothesis and the experimental spaces. (Psycharis, 2013) added one more space, the prediction space, in order to address the introduction of modelling and the comparison of data produced by the model with real data. In the "prediction space" the Computational Science methodology is implemented through the development of models of simulations that favor the so called "Computational Thinking (CT)." According to (Psycharis, 2013), the three spaces of the Computational Science methodology should include issues form (CT), namely: (a) logically organizing and analyzing data; (b) representation of data through abstractions such as models and simulations; and (c) algorithmic thinking. In this context, the three spaces of the Computational Science methodology include: * The hypotheses space, where the students, in cooperation with the teacher, decide, clarify and state the hypotheses of the problem to be studied, as well as the variables included in the problem and the possible relations between the variables. …


User Evaluation of Websites: From First Impression to Recommendation


Exploring Design Space of 3 D NVM and eDRAM Caches Using DESTINY Tool


title: Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture

abstract: A decision support system based on the combination of the wireless sensor and actuation network technology and the fuzzy logic theory is proposed to support the irrigation management in agriculture. The farmers’ experience and the irrigation best practices are modeled through fuzzy rule sets, and the outputs of numerical soil and crop models are used to provide a context-aware and optimized irrigation schedule. The suggested actions are devoted to reduce the waste of water and to maximize the crop yield according to the weather conditions and the real water needs. The proposed methodology is embedded in the network gateway making the system, a truly smart and autonomous wireless decision support system. The numerical validation and the experiments performed in a vineyard in the north of Italy point out a considerable water saving respect to other state-of-the-art methods based on parameters thresholding, and an improved exploitation of the irrigated water thanks to the reduction of the percolation phenomenon without affecting the quality of the crops.


title: Decoding with Value Networks for Neural Machine Translation

abstract: Neural Machine Translation (NMT) has become a popular technology in recent years, and beam search is its de facto decoding method due to the shrunk search space and reduced computational complexity. However, since it only searches for local optima at each time step through one-step forward looking, it usually cannot output the best target sentence. Inspired by the success and methodology of AlphaGo, in this paper we propose using a prediction network to improve beam search, which takes the source sentence $x$, the currently available decoding output $y_1,\cdots, y_{t-1}$ and a candidate word $w$ at step $t$ as inputs and predicts the long-term value (e.g., BLEU score) of the partial target sentence if it is completed by the NMT model. Following the practice in reinforcement learning, we call this prediction network \emph{value network}. Specifically, we propose a recurrent structure for the value network, and train its parameters from bilingual data. During the test time, when choosing a word $w$ for decoding, we consider both its conditional probability given by the NMT model and its long-term value predicted by the value network. Experiments show that such an approach can significantly improve the translation accuracy on several translation tasks.


title: 3D Materials Image Segmentation by 2D Propagation: A Graph-Cut Approach Considering Homomorphism

abstract: Segmentation propagation, similar to tracking, is the problem of transferring a segmentation of an image to a neighboring image in a sequence. This problem is of particular importance to materials science, where the accurate segmentation of a series of 2D serial-sectioned images of multiple, contiguous 3D structures has important applications. Such structures may have distinct shape, appearance, and topology, which can be considered to improve segmentation accuracy. For example, some materials images may have structures with a specific shape or appearance in each serial section slice, which only changes minimally from slice to slice, and some materials may exhibit specific inter-structure topology that constrains their neighboring relations. Some of these properties have been individually incorporated to segment specific materials images in prior work. In this paper, we develop a propagation framework for materials image segmentation where each propagation is formulated as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Our framework makes three key contributions: 1) a homomorphic propagation approach, which considers the consistency of region adjacency in the propagation; 2) incorporation of shape and appearance consistency in the propagation; and 3) a local non-homomorphism strategy to handle newly appearing and disappearing substructures during this propagation. To show the effectiveness of our framework, we conduct experiments on various 3D materials images, and compare the performance against several existing image segmentation methods.


title: Ka-band Fresnel lens antenna fed with an active linear microstrip patch array

abstract: The design of a linear microstrip array and Fresnel lens combination as a potential candidate for Ka-band satellite communications is presented. Medium-power amplifiers are distributed within the array to achieve a high EIRP required for satellite communications. This architecture may be used as an alternative to the conventional horn-fed reflector antenna with a single high-power amplifier.


title: Fascial Disorders: Implications for Treatment

abstract: In the past 15 years, multiple articles have appeared that target fascia as an important component of treatment in the field of physical medicine and rehabilitation. To better understand the possible actions of fascial treatments, there is a need to clarify the definition of fascia and how it interacts with various other structures: muscles, nerves, vessels, organs. Fascia is a tissue that occurs throughout the body. However, different kinds of fascia exist. In this narrative review, we demonstrate that symptoms related to dysfunction of the lymphatic system, superficial vein system, and thermoregulation are closely related to dysfunction involving superficial fascia. Dysfunction involving alterations in mechanical coordination, proprioception, balance, myofascial pain, and cramps are more related to deep fascia and the epimysium. Superficial fascia is obviously more superficial than the other types and contains more elastic tissue. Consequently, effective treatment can probably be achieved with light massage or with treatment modalities that use large surfaces that spread the friction in the first layers of the subcutis. The deep fasciae and the epymisium require treatment that generates enough pressure to reach the surface of muscles. For this reason, the use of small surface tools and manual deep friction with the knuckles or elbows are indicated. Due to different anatomical locations and to the qualities of the fascial tissue, it is important to recognize that different modalities of approach have to be taken into consideration when considering treatment options.


title: Statistical Phrase-Based Translation

abstract: We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models out-perform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.


title: Food Recognition using Fusion of Classifiers based on CNNs

abstract: With the arrival of Convolutional Neural Networks, the complex problem of food recognition has experienced an important improvement recently. The best results have been obtained using methods based on very deep Convolutional Neural Networks, which show that the deeper the model, the better the classification accuracy is. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on Convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on 2 public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent Convolutional Neural Networks models.


title: Release of glucagon-like peptide 1 (GLP-1 [7-36 amide]), gastric inhibitory polypeptide (GIP) and insulin in response to oral glucose after upper and lower intestinal resections.

abstract: UNLABELLED ::: Glucagon-like peptide 1 (GLP-1[7-36 amide]) is an incretin hormone primarily synthesized in the lower gut (ileum, colon/rectum). Nevertheless, there is an early increment in plasma GLP-1 immediately after ingesting glucose or mixed meals, before nutrients have entered GLP-1 rich intestinal regions. The responsible signalling pathway between the upper and lower gut is not clear. It was the aim of this study to see, whether small intestinal resection or colonectomy changes GLP-1[7-36 amide] release after oral glucose. In eight healthy controls, in seven patients with inactive Crohn's disease (no surgery), in nine patients each after primarily jejunal or ileal small intestinal resections, and in six colonectomized patients not different in age (p = 0.10), body-mass-index (p = 0.24), waist-hip-ratio (p = 0.43), and HbA1c (p = 0.22), oral glucose tolerance tests (75 g) were performed in the fasting state. GLP-1[7-36 amide], insulin C-peptide, GIP and glucagon (specific (RIAs) were measured over 240 min. ::: ::: ::: STATISTICS ::: Repeated measures ANOVA, t-test (significance: p < 0.05). A clear and early (peak: 15-30 min) GLP-1[7-36 amide] response was observed in all subjects, without any significant difference between gut-resected and control groups (p = 0.95). There were no significant differences in oral glucose tolerance (p = 0.21) or in the suppression of pancreatic glucagon (p = 0.36). Colonectomized patients had a higher insulin (p = 0.011) and C-peptide (p = 0.0023) response in comparison to all other groups. GIP responses also were higher in the colonectomized patients (p = 0.0005). Inactive Crohn's disease and resections of the small intestine as well as proctocolectomy did not change overall GLP-1[7-36 amide] responses and especially not the early increment after oral glucose. This may indicate release of GLP-1[7-36 amide] after oral glucose from the small number of GLP-1[7-36 amide] producing L-cells in the upper gut rather than from the main source in the ileum, colon and rectum. Colonectomized patients are characterized by insulin hypersecretion, which in combination with their normal oral glucose tolerance possibly indicates a reduced insulin sensitivity in this patient group. GIP may play a role in mediating insulin hypersecretion in these patients.


title: A Problem of Dimensionality: A Simple Example

abstract: In pattern recognition problems it has been noted that beyond a certain point the inclusion of additional parameters (that have been estimated) leads to higher probabilities of error. A simple problem has been formulated where the probability of error approaches zero as the dimensionality increases and all the parameters are known; on the other hand, the probability of error approaches one-half as the dimensionality increases and parameters are estimated.


title: Searchable Encryption over Feature-Rich Data

abstract: Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on real-world high-dimensional datasets, and achieved a search quality of 99 percent recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.


title: A Review on Plagiarism Detection Tools

abstract: Plagiarism has become an increasingly serious problem in the academic world. It is aggravated by the easy access to and the ease of cutting and pasting from a wide range of materials available on the internet. It constitutes academic theft the offender has 'stolen' the work of others and presented the stolen work as if it were his or her own. It goes to the integrity and honesty of a person. It stifles creativity and originality, and defeats the purpose of education The plagiarism is a widespread and growing problem in the academic process. The traditional manual detection of plagiarism by human is difficult, not accurate, and time consuming process as it is difficult for any person to verify with the existing data. The main purpose of this paper is to present existing tools about in regards with plagiarism detection. Plagiarism detection tools are useful to the academic community to detect plagiarism of others and avoid such unlawful activity. This paper describes some of the plagiarism detection tools available for plagiarism checking and types of plagiarism.


title: Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

abstract: Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.


title: StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

abstract: Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for spatial-temporal modeling in videos. In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatialtemporal network (StNet) architecture for both local and global modeling in videos. Particularly, StNet stacks N successive video frames into a super-image which has 3N channels and applies 2D convolution on super-images to capture local spatial-temporal relationship. To model global spatialtemporal structure, we apply temporal convolution on the local spatial-temporal feature maps. Specifically, a novel temporal Xception block is proposed in StNet, which employs a separate channel-wise and temporal-wise convolution over the feature sequence of a video. Extensive experiments on the Kinetics dataset demonstrate that our framework outperforms several state-of-the-art approaches in action recognition and can strike a satisfying trade-off between recognition accuracy and model complexity. We further demonstrate the generalization performance of the leaned video representations on the UCF101 dataset.


title: PSCAD simulation of grid-tied photovoltaic systems and Total Harmonic Distortion analysis

abstract: With the increasing fears of the impacts of the high penetration rates of Photovoltaic (PV) systems, a technical study about their effects on the power quality metrics of the utility grid is required. Since such study requires a complete modeling of the PV system in an electromagnetic transient software environment, PSCAD was chosen. This paper investigates a grid-tied PV system that is prepared in PSCAD. The model consists of PV array, DC link capacitor, DC-DC buck converter, three phase six-pulse inverter, AC inductive filter, transformer and a utility grid equivalent model. The paper starts with investigating the tasks of the different blocks of the grid-tied PV system model. It also investigates the effect of variable atmospheric conditions (irradiation and temperature) on the performance of the different components in the model. DC-DC converter and inverter in this model use PWM and SPWM switching techniques, respectively. Finally, total harmonic distortion (THD) analysis on the inverter output current at PCC will be applied and the obtained THD values will be compared with the limits specified by the regulating standards such as IEEE Std 519-1992.


title: Medical Image Fusion: A survey of the state of the art

abstract: Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.


title: Information Content of Unsolicited Credit Ratings and Incentives of Rating Agencies: A Theory

abstract: Unsolicited credit ratings are issued solely at the discretion of rating agencies based on public information. This paper analyzes firms' incentives to solicit credit ratings to signal their quality and rating agencies' incentives to issue unsolicited ratings. Conditions for two types of equilibria are derived. When rating agencies are compensated for the accuracy of ratings under the subscriber-fee scheme, a quasi-separating equilibrium is likely to occur, in which firm quality is revealed through unsolicited ratings. When rating agencies are compensated only for solicited ratings under the issuer-fee scheme, a separating equilibrium results, in which high-quality firms signal through solicited ratings while low-quality firms are revealed through unsolicited ratings. We show that rating agencies have strong incentives to selectively issue unsolicited ratings in order to induce more fee-based solicited ratings under the issuer-fee scheme.


title: Language independent web news extraction system based on text detection framework

abstract: Web news provides a direct and efficient way to construct large text corpora. The creation of text data requires an understanding of HTML code and the preparation of customized parsing rules to identify text content in a webpage. Typically, parsing rules are written manually and cannot be applied to pages with different layouts. In this study, we present a web news extraction system that is based on a text detection framework. The proposed method scans the input HTML page and creates text statistics as a projection profile. Then, text block identification is applied to determine a set of content candidates. To filter noise, text verification determines whether a given text block can be included with content. We evaluate the proposed approach with the L3S-GN1 corpus and 3506 multilingual news data items randomly sampled from 325 websites (15 geographic regions and 11 distinct languages). We also compare the proposed method to 23 well-known state-of-the-art techniques. The experimental results show that the proposed method outperforms the second best method (NReadability) by 7.30% in the macro F-measure rate and is 16.91 times faster than NReadability. In terms of the perfect rate, the proposed method demonstrates 46.38% accuracy, whereas the Boilerpipe algorithm demonstrates only 21.54% accuracy. The proposed method is very useful for constructing a multilingual corpus because it requires no language-specific processing component.


title: A faceted approach to building ontologies

abstract: An ontology can be defined as a conceptualization of a domain or subject area typically captured in an abstract model of how people think about things in the domain. Humans have been producing ontologies for millennia to understand and explain our rationale and environment. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. This paper describes how a domain analysis method based on faceted classification can be used for building ontologies. It relates domain analysis and ontologies, illustrates a step in the domain analysis method for identifying and categorizing concepts, and describes how this step, borrowed from library science, is incorporated into the domain analysis method. The paper also gives an overview of the method and describes a tool for automating parts of the process.


title: Agile methods for open source safety-critical software

abstract: The introduction of software technology in a life-dependent environment requires the development team to execute a process that ensures a high level of software reliability and correctness. Despite their popularity, agile methods are generally assumed to be inappropriate as a process family in these environments due to their lack of emphasis on documentation, traceability, and other formal techniques. Agile methods, notably Scrum, favor empirical process control, or small constant adjustments in a tight feedback loop. This paper challenges the assumption that agile methods are inappropriate for safety-critical software development. Agile methods are flexible enough to encourage the rightamount of ceremony; therefore if safety-critical systems require greater emphasis on activities like formal specification and requirements management, then an agile process will include these as necessary activities. Furthermore, agile methods focus more on continuous process management and code-level quality than classic software engineering process models. We present our experiences on the image-guided surgical toolkit (IGSTK) project as a backdrop. IGSTK is an open source software project employing agile practices since 2004. We started with the assumption that a lighter process is better, focused on evolving code, and only adding process elements as the need arose. IGSTK has been adopted by teaching hospitals and research labs, and used for clinical trials. Agile methods have matured since the academic community suggested they are not suitable for safety-critical systems almost a decade ago, we present our experiences as a case study for renewing the discussion.


Fibrinolytic Activity of Purified Thrombin


UBIQUITOUS LEARNING: DETERMINANTS IMPACTING LEARNERS' SATISFACTION AND PERFORMANCE WITH SMARTPHONES


MoocRec.com : Massive Open Online Courses Recommender System


title: Reliability Fusion of Time-of-Flight Depth and Stereo Geometry for High Quality Depth Maps

abstract: Time-of-flight range sensors have error characteristics, which are complementary to passive stereo. They provide real-time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes where stereo excels. We explore their complementary characteristics and introduce a method for combining the results from both methods that achieve better accuracy than either alone. In our fusion framework, the depth probability distribution functions from each of these sensor modalities are formulated and optimized. Robust and adaptive fusion is built on a pixel-wise reliability weighting function calculated for each method. In addition, since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturer's calibration. We demonstrate that our proposed techniques lead to improved accuracy and robustness on an extensive set of experimental results.


title: Cloud-based harvest management information system for hand-harvested specialty crops

abstract: A cloud-based Harvest Management Information System (HMIS) has been developed.It credits pickers for the fruit they have harvested without impeding the process.The HMIS provides real-time tracking of harvest, yield mapping, and traceability.It simplifies data analysis process and visualizes the harvest and labor data.It improves accuracy of payroll and enhances decision making and logistics. The harvest process for specialty crops is generally one of intensive activity because many people are required for harvest and packing, and the harvest window is brief due to the high perishability of the produce. Herein we present a cloud-based Harvest Management Information System (HMIS) that combines a novel real-time Portable Labor Monitoring System (PLMS) with a cloud-based harvest management software. The PLMS comprised of three key elements (1) a self-leveling scale, (2) electronic control box, and (3) a frame that supports all hardware. The electronic control box includes: (i) a RFID reader, (ii) a LCD display, (iii) a thermal printer, (iv) a GPS module, and (v) a communication system. RFID tags, containing unique ID numbers, embedded within rubber wrist bands, are worn by pickers. This system can read a picker's ID (RFID bracelet), measure the weight of fruit, and record the time and location (optional) of every fruit 'transaction' (i.e., every time a picker brings a bucket of fruit to the collection bin). The collected data can be transmitted wirelessly to the server in real-time. The cloud-based software receives and processes the PLMS data on labor activities, visualizes the collected data, and can extract the data necessary for management information and automated filling of documents (e.g. payroll, yield maps). The HMIS is unique in its ability to: (1) accurately credit pickers for the fruit they have harvested in the field without impeding or altering the harvest process, (2) streamline data entry to payroll, (3) provide real-time tracking of harvest, yield mapping, and traceability, and, (4) generate precise and reliable harvest efficiency data. This integrated system was evaluated in sweet cherry, blueberry and apple orchards in Washington, USA. The weight of harvested fruit, time and location of every fruit drop were calculated accurately; all the data were transmitted wirelessly to the server and no errors were recorded.


European Land Robot Trial (ELROB) Towards a Realistic Benchmark for Outdoor Robotics


title: Autoencoder-based holographic image restoration

abstract: We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and quick response codes by using the proposed method.


title: How many participants are really enough for usability studies?

abstract: The growth of the Internet and related technologies has enabled the development of a new breed of dynamic websites, applications and software products that are growing rapidly in use and that have had a great impact on many businesses. These technologies need to be continuously evaluated by usability evaluation methods (UEMs) to measure their efficiency and effectiveness, to assess user satisfaction, and ultimately to improve their quality. However, estimating the sample sizes for these methods has become the source of considerable debate at usability conferences. This paper aims to determine an appropriate sample size through empirical studies on the social network and educational domains by employing three types of UEM; it also examines further the impact of sample size on the findings of usability tests. Moreover, this paper quantifies the sample size required for the Domain Specific-to-context Inspection (DSI) method, which itself is developed through an adaptive framework. The results show that there is no certain number of participants for finding all usability problems; however, the rule of 16 4 users gains much validity in user testing. The magic number of five evaluators fails to find 80% of problems in heuristic evaluation, whereas three evaluators are enough to find 91% of usability problems in the DSI method.


title: Deep tracking with objectness

abstract: Visual tracking is a fundamental problem in computer vision. However, due to the (sometimes) ambiguous target information given at the first frame, it has also been criticized as less well-posed compared with other tasks with clearly-defined targets, such as object detection and semantic segmentation. In this paper, we try to evaluate the importance of object category in visual tracking by tracking objects with known object types. The proposed algorithm, termed Deep-Track with Objectness (DTO), naturally combines the state-of-the-art deep-learning-based detectors and trackers, which essentially share a large part of the network. In DTO, a deep tracker, which is scale-fixed and sensitive to small translations tracks the object in a relative short lifespan. A deep detector, which is scale-changeable and robust to pose or illumination changes guides the deep tracker in a longer lifespan. As the deep tracker and detector share the main part of their networks, no much extra computation is imposed while the performance gain is significant. We test the proposed algorithm on two well-accepted benchmarks and on both of them, the proposed method increases the tracking accuracies remarkably compared with state-of-the-art visual trackers.


title: Practical Secure Computation Outsourcing: A Survey

abstract: The rapid development of cloud computing promotes a wide deployment of data and computation outsourcing to cloud service providers by resource-limited entities. Based on a pay-per-use model, a client without enough computational power can easily outsource large-scale computational tasks to a cloud. Nonetheless, the issue of security and privacy becomes a major concern when the customer’s sensitive or confidential data is not processed in a fully trusted cloud environment. Recently, a number of publications have been proposed to investigate and design specific secure outsourcing schemes for different computational tasks. The aim of this survey is to systemize and present the cutting-edge technologies in this area. It starts by presenting security threats and requirements, followed with other factors that should be considered when constructing secure computation outsourcing schemes. In an organized way, we then dwell on the existing secure outsourcing solutions to different computational tasks such as matrix computations, mathematical optimization, and so on, treating data confidentiality as well as computation integrity. Finally, we provide a discussion of the literature and a list of open challenges in the area.