Query: Perspectives to Predict Dropout in University Students with Machine Learning

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A comparative analysis of machine learning techniques for student retention management <=SEP=> Student retention is an essential part of many enrollment management systems. It affects university rankings, school reputation, and financial wellbeing. Student retention has become one of the most important priorities for decision makers in higher education institutions. Improving student retention starts with a thorough understanding of the reasons behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using five years of institutional data along with several data mining techniques (both individuals as well as ensembles), we developed analytical models to predict and to explain the reasons behind freshmen student attrition. The comparative analyses results showed that the ensembles performed better than individual models, while the balanced dataset produced better prediction results than the unbalanced dataset. The sensitivity analysis of the Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

A hybrid data analytic approach to predict college graduation status and its determinative factors <=SEP=> Purpose – The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status. Design/methodology/approach – This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation. Findings – The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model. Originality/value – This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.

Classification and Regression Trees <=SEP=> 

Random Forests <=SEP=> Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

Improved Use of Continuous Attributes in C4.5 <=SEP=> A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modi cations lead to smaller decision trees with higher predictive accuracies. Results also con rm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.

Self-organized computation with unreliable , memristive nanodevices <=SEP=> Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network—a self-organized spatial filter array—that demonstrate its performance as a function of defects and device variation. (Some figures in this article are in colour only in the electronic version) 1. Nanoelectronics and computing paradigms Nanodevices are crummy1. High defect rates, high device variability, device ageing, and limitations on device complexity (e.g., two-terminal devices are much easier to build) are to be expected if we intend to mass produce nanoelectronic systems economically. Not only that, it is almost axiomatic among many researchers that such systems will be built from simple structures, such as crossbars, composed of nanodevices that must be configured to implement the desired functionality (Heath et al 1998, Williams and Kuekes 2000, Kuekes and Williams 2002, DeHon 2003, Stan et al 2003, Ziegler and Stan 2003, Snider et al 2004, DeHon 2005, Ma et al 2005, Snider 2005, Snider et al 2005, Strukov and Likharev 2005). So we are faced with the challenge of computing with devices that are not only crummy, but dynamical as well. Can reliable Boolean logic systems be built from such crummy devices? Yes, but we argue that at some point as 1 ‘Crummy’ was introduced into the technical lexicon by Moore and Shannon (1954). device dimensions scale down, the overhead and complexity become so costly that performance and density improvements will hit a barrier. In the next section we discuss two frequently proposed strategies for implementing Boolean logic with crummy, dynamical devices—reconfiguration and coding theory—and argue that each has severe scalability problems. This is not suggesting that logic at the nanoscale is not worth pursuing. It clearly is, and semiconductor manufacturers have the economic motivation to continue scaling down as aggressively as their profits permit. Rather we are suggesting that the ‘killer app’ for nanoelectronics lies elsewhere. An alternative computational paradigm—adaptive, recurrent networks—is computationally powerful and requires only two types of components, which we call ‘edges’ and ‘nodes’. Edge to node ratios are typically high, hundreds to thousands, and edges are, unfortunately, difficult to implement efficiently. This difficulty has made these networks extremely unpopular; software implementations are impossibly slow, and hardware implementations require far too much area. 0957-4484/07/365202+13$30.00 1 © 2007 IOP Publishing Ltd Printed in the UK Nanotechnology 18 (2007) 365202 G S Snider In this paper we propose using memristive nanodevices to implement edges, conventional analog and digital electronics to implement nodes, and pairs of bipolar pulses, called ‘spikes’, to implement communication. The tiny size of the nanodevices implementing edges would allow, for the first time, a practical hardware implementation of adaptive, recurrent networks. We suggest that such networks are a better architectural fit to nanoelectronics than Boolean logic circuits. They are robust in the presence of device variations and defects; they degrade gracefully as devices become defective over time, and can even ‘self-heal’ in response to internal change; they can be implemented with simple, crossbar-like structures. Just as importantly, they can be structured to self-organize their computations, sidestepping scalability problems with device characterization, compilation and configuration. Such systems can contain large numbers of defective components, but we will not need to discover where they are—in fact, we will not care where they are. The system can adapt and ‘rewire’ itself around them. 2. Boolean logic is hard with crummy, dynamical devices 2.1. Reconfiguration to the rescue? Several researchers have proposed using a combination of device characterization, defect avoidance, and configuration to handle initial static defects (Stan et al 2003, DeHon 2003, Snider et al 2004, Strukov and Likharev 2005, Snider and Williams 2007). The strategy is a three-pass algorithm: (1) Characterization. Analyze every nanowire and nanodevice in the system and compile a list of resources which are defective (stuck open or stuck closed, shorted, broken, out-of-spec, etc). Such analysis algorithms were used in the Teramac system (Culbertson et al 1997). (2) Defect avoidance. Give the list of defects from pass 1 to a compiler which maps a desired circuit onto the defective fabric, routing around defective components (Culbertson et al 1997, Snider et al 2004, Strukov and Likharev 2005, Snider and Williams 2007). (3) Configuration. Give the mapping determined in pass 2 to a controller that electrically configures each of the mapped components. Since every chip will have a unique set of defects, the above process must be applied to each and every chip. This presents some interesting challenges for manufacturing, since the time required to perform the above steps will contribute to production cost. Characterization (pass 1) is problematic due to device variability—the functional state of a device (or wire) is not necessarily discrete (working versus nonworking) but can lie on a continuum. And characterizing, say, 1012 nanodevices (DeHon et al 2005) in a reasonable amount of time is not likely to be trivial, especially given the bottleneck of pins on the chip. It is not clear if existing characterization algorithms could be applied to systems like this, or how well they would scale. Compilation (pass 2) also presents considerable risk. Compiling circuits onto configurable chips (such as FPGAs) today is a time-consuming process, due to the NP-hard placement and routing problems that lie at the compiler’s core. Even circuits comprising only a few tens of thousands of gates can require several minutes to compile, depending on the degree of optimization needed—and that’s assuming a defect-free target, where a single compilation can be used to manufacture thousands of parts. One proposal for minimizing this problem requires an initial ‘ideal’ compilation onto a hypothetical defect-free fabric, laying out components a little more sparsely than optimal. This would be done only once for each (circuit type, chip type) combination, so one could afford to spend enormous amounts of compute time on this to arrive at this ideal configuration. The configuration of an individual chip on the production line would then be viewed as a ‘perturbation’ of the ideal configuration, with resource allocation shifted as necessary to avoid defects. One might even combine the characterization pass with this pass for further speed improvements. This strategy might be workable. But it is not clear how well this would scale, or how robust this would be in the presence of defect clustering. Configuration (pass 3) is the most straightforward, with the most significant risk being configuration time restrictions due to the pin bottleneck and power dissipation. Note that the above approach of characterize, compile, configure does not handle device death. What happens when a nanodevice stops working and the chip starts computing nonsense? If the nanodevices are reconfigurable, the system can be stopped and reconfigured to work around the newly formed defects. But that assumes additional circuitry to detect the malfunction (e.g. self-checking circuits (Wakerly 1978)), and a companion host processor capable of implementing the three passes in order to reconfigure the chip. Such a processor would have to be fast, reliable (which probably means it would not be implemented with nanodevices), and likely would require a significant amount of memory. Implementing such a coprocessor would seem to negate the benefits that one was presumably getting from the nanoscale circuit in the first place. 2.2. Coding theory to the rescue? Coding theory has been used for decades to robustly transmit information over noisy channels by adding a small amount of redundant information. Can coding theory do the same for logic circuits by adding a small number of redundant gates or components in order to achieve reliable operation? von Neumann (1956), looking forward to nanoscale computation, was perhaps the first to address this question. His approach used a code that replicated each logic gate, and combined the replicated gate outputs in a clever way so that the entire system achieved a desired level of reliability. Although his primary concern was correcting errors induced by transient faults, the approach could also compensate for initially defective devices or ‘device deaths’ as long as the failing devices were randomly distributed and the number did not exceed a threshold (the trade-off being that device deaths would reduce the system’s tolerance of transient faults). The overhead for his scheme could be enormous, though. Replication factors for Boolean logic went to infinity as fault rates approached about 1%. This bound has been improved by later researchers, with

Controllability of complex networks via pinning. <=SEP=> We study the problem of controlling a general complex network toward an assigned synchronous evolution by means of a pinning control strategy. We define the pinning controllability of the network in terms of the spectral properties of an extended network topology. The roles of the control and coupling gains, as well as of the number of pinned nodes, are also discussed.

Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition <=SEP=> In this paper, we show that learning features with convolutional neural networks is better than using hand-crafted features for handwritten word recognition. We consider two kinds of systems: a grapheme based segmentation and a sliding window segmentation. In both cases, the combination of a convolutional neural network with a HMM outperform a state-of-the art HMM system based on explicit feature extraction. The experiments are conducted on the Rimes database. The systems obtained with the two kinds of segmentation are complementary: when they are combined, they outperform the systems in isolation. The system based on grapheme segmentation yields lower recognition rate but is very fast, which is suitable for specific applications such as document classification.

Design and implementation of 0.7 to 7 GHz broadband double-ridged horn antenna <=SEP=> In this paper, simulation and measurement results of 0.7-7 GHz double-ridged guide horn (DRGH) antenna with coaxial input feed section is presented. This antenna, due to the large frequency band required by standards, is appropriate to be used in electromagnetic compatibility (EMC) testing and antenna measurement as a transmitter. A step by step method for designing DRGH antenna is given. A suitable taper for the ridges in the horn is designed and its impedance variations along the horn are shown. In addition, a new structure for the electrical field probe in the feed section is introduced by which a shift-down of lower frequency to 0.7 GHz is achieved. A sensitivity analysis is done on the parameters of the proposed structure. Other parameters of the feed section are also investigated and optimized values are obtained. Finally, the proposed antenna has been fabricated and measurement results show good agreement with the simulation.

An empirical study of the mechanisms of mindfulness in a mindfulness-based stress reduction program. <=SEP=> S. L. Shapiro and colleagues (2006) have described a testable theory of the mechanisms of mindfulness and how it affects positive change. They describe a model in which mindfulness training leads to a fundamental change in relationship to experience (reperceiving), which leads to changes in self-regulation, values clarification, cognitive and behavioral flexibility, and exposure. These four variables, in turn, result in salutogenic outcomes. Analyses of responses from participants in a mindfulness-based stress-reduction program did not support the mediating effect of changes in reperceiving on the relationship of mindfulness with those four variables. However, when mindfulness and reperceiving scores were combined, partial support was found for the mediating effect of the four variables on measures of psychological distress. Issues arising in attempts to test the proposed theory are discussed, including the description of the model variables and the challenges to their assessment.

Second screen applications and tablet users: constellation, awareness, experience, and interest <=SEP=> This study investigates how tablet users incorporate multiple media in their television viewing experience. Three patterns are found: (a) only focusing on television, (b) confounding television viewing with other screen media (e.g. laptop, tablet) and (c) confounding television viewing with various media, including print and screen media. Furthermore, we question how the incorporation of screen media in this experience affects the practice of engaging in digital commentary on television content. Also, we inquire the uptake and interest in so-called 'second screen applications'. These applications allow extensions of the primary screen experience on secondary screens (e.g. tablet). The results, based on a sample of 260 tablet users, indicate that there is only a modest uptake and interest in using secondary screens to digitally share opinions. However, the use of second screen interaction with television content is not discarded: although there is still little awareness and experience, we notice a moderate interest in these apps.

Muscle simulation for facial animation in Kong: Skull Island <=SEP=> For Kong: Skull Island, Industrial Light & Magic created an anatomically motivated facial simulation model for Kong that includes the facial skeleton and musculature. We applied a muscle simulation framework that allowed us to target facial shapes while maintaining desirable physical properties to ensure that the simulations stayed on-model. This allowed muscle simulations to be used as a powerful tool for adding physical detail to and improving the anatomical validity of both blendshapes and blendshape animations in order to achieve more realistic facial animation with less hand sculpting.

Essential Layers, Artifacts, and Dependencies of Enterprise Architecture <=SEP=> After a period where implementation speed was more important than integration, consistency and reduction of complexity, architectural considerations have become a key issue of information management in recent years again. Enterprise architecture is widely accepted as an essential mechanism for ensuring agility and consistency, compliance and efficiency. Although standards like TOGAF and FEAF have developed, however, there is no common agreement on which architecture layers, which artifact types and which dependencies constitute the essence of enterprise architecture. This paper contributes to the identification of essential elements of enterprise architecture by (1) specifying enterprise architecture as a hierarchical, multilevel system comprising aggregation hierarchies, architecture layers and views, (2) discussing enterprise architecture frameworks with regard to essential elements, (3) proposing interfacing requirements of enterprise architecture with other architecture models and (4) matching these findings with current enterprise architecture practice in several large companies.

Geometric Scene Parsing with Hierarchical LSTM <=SEP=> This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires the rich and diverse contextual information. To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM). It contains two coupled sub-networks: the Pixel LSTM (P-LSTM) and the Multi-scale Super-pixel LSTM (MS-LSTM) for handling the surface labeling and relation prediction, respectively. The two sub-networks provide complementary information to each other to exploit hierarchical scene contexts, and they are jointly optimized for boosting the performance. Our extensive experiments show that our model is capable of parsing scene geometric structures and outperforming several state-of-theart methods by large margins. In addition, we show promising 3D reconstruction results from the still images based on the geometric parsing.

Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments <=SEP=> In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot's proprioceptive capabilities and is carried out through the robot's interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map's applicability for the task at hand (e.g. resolving counter candidates by our knowledge processing system).

Human ES cell-derived neural rosettes reveal a functionally distinct early neural stem cell stage. <=SEP=> Neural stem cells (NSCs) yield both neuronal and glial progeny, but their differentiation potential toward multiple region-specific neuron types remains remarkably poor. In contrast, embryonic stem cell (ESC) progeny readily yield region-specific neuronal fates in response to appropriate developmental signals. Here we demonstrate prospective and clonal isolation of neural rosette cells (termed R-NSCs), a novel NSC type with broad differentiation potential toward CNS and PNS fates and capable of in vivo engraftment. R-NSCs can be derived from human and mouse ESCs or from neural plate stage embryos. While R-NSCs express markers classically associated with NSC fate, we identified a set of genes that specifically mark the R-NSC state. Maintenance of R-NSCs is promoted by activation of SHH and Notch pathways. In the absence of these signals, R-NSCs rapidly lose rosette organization and progress to a more restricted NSC stage. We propose that R-NSCs represent the first characterized NSC stage capable of responding to patterning cues that direct differentiation toward region-specific neuronal fates. In addition, the R-NSC-specific genetic markers presented here offer new tools for harnessing the differentiation potential of human ESCs.

Conscious intention and brain activity <=SEP=> The problem of free will lies at the heart of modern scientific studies of consciousness. An influential series of experiments by Libet has suggested that conscious intentions arise as a result of brain activity. This contrasts with traditional concepts of free will, in which the mind controls the body. A more recent study by Haggard and Eimer has further examined the relation between intention and brain processes, concluding that conscious awareness of intention is linked to the choice or selection of a specific action, and not to the earliest initiation of action processes. The exchange of views in this paper further explores the relation between conscious intention and brain activity.

Identifying emotional states using keystroke dynamics <=SEP=> The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.

Efficient deep learning for stereo matching with larger image patches <=SEP=> Stereo matching plays an important role in many applications, such as Advanced Driver Assistance Systems, 3D reconstruction, navigation, etc. However it is still an open problem with many difficult. Most difficult are often occlusions, object boundaries, and low or repetitive textures. In this paper, we propose a method for processing the stereo matching problem. We propose an efficient convolutional neural network to measure how likely the two patches matched or not and use the similarity as their stereo matching cost. Then the cost is refined by stereo methods, such as semiglobal maching, subpixel interpolation, median filter, etc. Our architecture uses large image patches which makes the results more robust to texture-less or repetitive textures areas. We experiment our approach on the KITTI2015 dataset which obtain an error rate of 4.42% and only needs 0.8 second for each image pairs.

Loading of the knee joint during activities of daily living measured in vivo in five subjects. <=SEP=> Detailed knowledge about loading of the knee joint is essential for preclinical testing of implants, validation of musculoskeletal models and biomechanical understanding of the knee joint. The contact forces and moments acting on the tibial component were therefore measured in 5 subjects in vivo by an instrumented knee implant during various activities of daily living. Average peak resultant forces, in percent of body weight, were highest during stair descending (346% BW), followed by stair ascending (316% BW), level walking (261% BW), one legged stance (259% BW), knee bending (253% BW), standing up (246% BW), sitting down (225% BW) and two legged stance (107% BW). Peak shear forces were about 10-20 times smaller than the axial force. Resultant forces acted almost vertically on the tibial plateau even during high flexion. Highest moments acted in the frontal plane with a typical peak to peak range -2.91% BWm (adduction moment) to 1.61% BWm (abduction moment) throughout all activities. Peak flexion/extension moments ranged between -0.44% BWm (extension moment) and 3.16% BWm (flexion moment). Peak external/internal torques lay between -1.1% BWm (internal torque) and 0.53% BWm (external torque). The knee joint is highly loaded during daily life. In general, resultant contact forces during dynamic activities were lower than the ones predicted by many mathematical models, but lay in a similar range as measured in vivo by others. Some of the observed load components were much higher than those currently applied when testing knee implants.

An overview of pricing models for revenue management <=SEP=> This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

TrendLearner: Early Prediction of Popularity Trends of User Generated Content <=SEP=> Accurately predicting the popularity of user generated content (UGC) is very valuable to content providers, online advertisers, as well as social media and social network researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. We here focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible, as a step towards building more accurate popularity prediction methods. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Moreover, given the heterogeneity in popularity dynamics across objects in most UGC applications, this tradeoff has to be solved on a per-object basis, which makes the prediction task harder. We propose to tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. The first step exploits a time series clustering algorithm to represent each trend by a time series centroid. We propose to treat the second step as a classification problem. First, we extract a set of features of the target object corresponding to the distances of its early popularity curve to the previously identified centroids. We then combine these features with content features (e.g., incoming links, category), using them to train classifiers for prediction. Our experimental results for YouTube datasets show that we can achieve Micro and Macro F1 scores between 0.61 and 0.71 (a gain of up to 38% when compared to alternative approaches), with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. We also show that our approach can be applied to produce predictions of content popularity at a future date that are much more accurate than recently proposed regression-based and state-space based models, with accuracy improvements of at least 33% and 59%, on average.

Blur detection for digital images using wavelet transform <=SEP=> With the prevalence of digital cameras, the number of digital images increases quickly, which raises the demand for image quality assessment in terms of blur. Based on the edge type and sharpness analysis, using the Harr wavelet transform, a new blur detection scheme is proposed in this paper, which can determine whether an image is blurred or not and to what extent an image is blurred. Experimental results demonstrate the effectiveness of the proposed scheme.

Time-driven activity-based costing. <=SEP=> In the classroom, activity-based costing (ABC) looks like a great way to manage a company's limited resources. But executives who have tried to implement ABC in their organizations on any significant scale have often abandoned the attempt in the face of rising costs and employee irritation. They should try again, because a new approach sidesteps the difficulties associated with large-scale ABC implementation. In the revised model, managers estimate the resource demands imposed by each transaction, product, or customer, rather than relying on time-consuming and costly employee surveys. This method is simpler since it requires, for each group of resources, estimates of only two parameters: how much it costs per time unit to supply resources to the business's activities (the total overhead expenditure of a department divided by the total number of minutes of employee time available) and how much time it takes to carry out one unit of each kind of activity (as estimated or observed by the manager). This approach also overcomes a serious technical problem associated with employee surveys: the fact that, when asked to estimate time spent on activities, employees invariably report percentages that add up to 100. Under the new system, managers take into account time that is idle or unused. Armed with the data, managers then construct time equations, a new feature that enables the model to reflect the complexity of real-world operations by showing how specific order, customer, and activity characteristics cause processing times to vary. This Tool Kit uses concrete examples to demonstrate how managers can obtain meaningful cost and profitability information, quickly and inexpensively. Rather than endlessly updating and maintaining ABC data,they can now spend their time addressing the deficiencies the model reveals: inefficient processes, unprofitable products and customers, and excess capacity.

Visualization in Meteorology—A Survey of Techniques and Tools for Data Analysis Tasks <=SEP=> This article surveys the history and current state of the art of visualization in meteorology, focusing on visualization techniques and tools used for meteorological data analysis. We examine characteristics of meteorological data and analysis tasks, describe the development of computer graphics methods for visualization in meteorology from the 1960s to today, and visit the state of the art of visualization techniques and tools in operational weather forecasting and atmospheric research. We approach the topic from both the visualization and the meteorological side, showing visualization techniques commonly used in meteorological practice, and surveying recent studies in visualization research aimed at meteorological applications. Our overview covers visualization techniques from the fields of display design, 3D visualization, flow dynamics, feature-based visualization, comparative visualization and data fusion, uncertainty and ensemble visualization, interactive visual analysis, efficient rendering, and scalability and reproducibility. We discuss demands and challenges for visualization research targeting meteorological data analysis, highlighting aspects in demonstration of benefit, interactive visual analysis, seamless visualization, ensemble visualization, 3D visualization, and technical issues.

Combining perspiration- and morphology-based static features for fingerprint liveness detection <=SEP=> 0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.01.009 ⇑ Corresponding author at: Dipartimento di Informa of Naples Federico II, via Claudio 21, 80125 Naples, It E-mail addresses: emanuela.marasco@unina.it (E. (C. Sansone). It has been showed that, by employing fake fingers, the existing fingerprint recognition systems may be easily deceived. So, there is an urgent need for improving their security. Software-based liveness detection algorithms typically exploit morphological and perspiration-based characteristics separately to measure the vitality. Both such features provide discriminant information about live and fake fingers, then, it is reasonable to investigate also their joint contribution. In this paper, we combine a set of the most robust morphological and perspiration-based measures. The effectiveness of the proposed approach has been assessed through a comparison with several state-ofthe-art techniques for liveness detection. Experiments have been carried out, for the first time, by adopting standard databases. They have been taken from the Liveness Detection Competition 2009 whose data have been acquired by using three different optical sensors. Further, we have analyzed how the performance of our algorithm changes when the material employed for the spoof attack is not available during the training of the system. 2012 Elsevier B.V. All rights reserved.

Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network <=SEP=> In this paper, the recognition of pollen bearing honey bees from videos of the entrance of the hive is presented. This computer vision task is a key component for the automatic monitoring of honeybees in order to obtain large scale data of their foraging behavior and task specialization. Several approaches are considered for this task, including baseline classifiers, shallow Convolutional Neural Networks, and deeper networks from the literature. The experimental comparison is based on a new dataset of images of honeybees that was manually annotated for the presence of pollen. The proposed approach, based on Convolutional Neural Networks is shown to outperform the other approaches in terms of accuracy. Detailed analysis of the results and the influence of the architectural parameters, such as the impact of dedicated color based data augmentation, provide insights into how to apply the approach to the target application.

A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA <=SEP=> A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.

Psychophysiological contributions to phantom limbs. <=SEP=> Recent studies of amputees reveal a remarkable diversity in the qualities of experiences that define the phantom limb, whether painless or painful. This paper selectively reviews evidence of peripheral, central and psychological processes that trigger or modulate a variety of phantom limb experiences. The data show that pain experienced prior to amputation may persist in the form of a somatosensory memory in the phantom limb. It is suggested that the length and size of the phantom limb may be a perceptual marker of the extent to which sensory input from the amputation stump have re-occupied deprived cortical regions originally subserving the amputated limb. A peripheral mechanism involving a sympathetic-efferent somatic-afferent cycle is presented to explain fluctuations in the intensity of paresthesias referred to the phantom limb. While phantom pain and other sensations are frequently triggered by thoughts and feelings, there is no evidence that the painful or painless phantom limb is a symptom of a psychological disorder. It is concluded that the experience of a phantom limb is determined by a complex interaction of inputs from the periphery and widespread regions of the brain subserving sensory, cognitive, and emotional processes.

Experimental personality designs: analyzing categorical by continuous variable interactions. <=SEP=> Theories hypothesizing interactions between a categorical and one or more continuous variables are common in personality research. Traditionally, such hypotheses have been tested using nonoptimal adaptations of analysis of variance (ANOVA). This article describes an alternative multiple regression-based approach that has greater power and protects against spurious conclusions concerning the impact of individual predictors on the outcome in the presence of interactions. We discuss the structuring of the regression equation, the selection of a coding system for the categorical variable, and the importance of centering the continuous variable. We present in detail the interpretation of the effects of both individual predictors and their interactions as a function of the coding system selected for the categorical variable. We illustrate two- and three-dimensional graphical displays of the results and present methods for conducting post hoc tests following a significant interaction. The application of multiple regression techniques is illustrated through the analysis of two data sets. We show how multiple regression can produce all of the information provided by traditional but less optimal ANOVA procedures.