A tutorial on simulation modeling in six dimensions. Simulation involves modeling and analysis of real-world systems. This tutorial will provide a broad overview of the modeling practice within simulation by introducing the reader to modeling choices found using six dimensions: abstraction, complexity, culture, engineering, environment, and process. Modeling can be a daunting task even for the seasoned modeling and simulation professional, and so my goal is to introduce modeling in two ways: 1) to use one specific type of model (Petri Net) as an anchor for cross-dimensional discussion, and 2) to provide a follow up discussion, with additional non Petri Net examples, to clarify the extent of each dimension. For example, in the abstraction dimension, one must think about scale, refinement, and hierarchy when modeling regardless of the type of modeling language. The reader will come away with a broad framework within which to understand the possibilities of models and of modeling within the practice of simulation.

Question: What is a Petri Net?


Distributed generation intelligent islanding detection using governor signal clustering. One of the major protection concerns with distribution networks comprising distributed generation is unintentional islanding phenomenon. Expert diagnosis system is needed to distinguish network cut off from normal occurrences. An important part of synchronous generator is automatic load-frequency controller (ALFC). In this paper, a new approach based on clustering of input signal to governor is introduced. Self-organizing map (SOM) neural network is used to identify and classify islanding and non-islanding phenomena. Simulation results show that input signal to governor has different characteristics concern with islanding conditions and other disturbances. In addition, the SOM is able to identify and classify phenomena satisfactorily. Using proposed method, islanding can be detected after 200 ms.

Question: What is the difference between islanding and non-islanding?


Ambient RF Energy-Harvesting Technologies for Self-Sustainable Standalone Wireless Sensor Platforms. In this paper, various ambient energy-harvesting technologies (solar, thermal, wireless, and piezoelectric) are reviewed in detail and their applicability in the development of self-sustaining wireless platforms is discussed. Specifically, far-field low-power-density energy-harvesting technology is thoroughly investigated and a benchmarking prototype of an embedded microcontroller-enabled sensor platform has been successfully powered by an ambient ultrahigh-frequency (UHF) digital TV signal (512-566 MHz) where a broadcasting antenna is 6.3 km away from the proposed wireless energy-harvesting device. A high-efficiency dual-band ambient energy harvester at 915 MHz and 2.45 GHz and an energy harvester for on-body application at 460 MHz are also presented to verify the capabilities of ambient UHF/RF energy harvesting as an enabling technology for Internet of Things and smart skins applications.

Question: What is the size of the canadian military?


A comparative study of classification algorithms for forecasting rainfall. India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.

Question: What is the accuracy of the classifier?


Adaptive Manifold Learning. Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.

Question: What is manifold learning?


Behavior recognition based on machine learning algorithms for a wireless canine machine interface. Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.

Question: What is a kai ken?


Users of the world , unite ! The challenges and opportunities of Social Media. As of January 2009, the online social networking application Facebook registered more than 175 million active users. To put that number in perspective, this is only slightly less than the population of Brazil (190 million) and over twice the population of Germany (80 million)! At the same time, every minute, 10 hours of content were uploaded to the video sharing platform YouTube. And, the image hosting site Flickr provided access to over 3 billion photographs, making the world-famous Louvre Museum's collection of 300,000 objects seem tiny in comparison. According to Forrester Research, 75% of Internet surfers used ''Social Media'' in the second quarter of 2008 by joining social networks, reading blogs, or contributing reviews to shopping sites; this represents a significant rise from 56% in 2007. The growth is not limited to teenagers, either; members of Generation X, now 35—44 years old, increasingly populate the ranks of joiners, spectators, and critics. It is therefore reasonable to say that Social Media represent a revolutionary new trend that should be of interest to companies operating in online space–—or any space, for that matter. Yet, not overly many firms seem to act comfortably in a world where consumers can speak so freely Business Horizons (2010) 53, 59—68

Question: What is the size of the world population?


Switchboard: a matchmaking system for multiplayer mobile games. Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.

Question: What is the design of a matchmaking system for multiplayer mobile games?


Wireless Body Sensor Network With Adaptive Low-Power Design for Biometrics and Healthcare Applications. A four-levels hierarchical wireless body sensor network (WBSN) system is designed for biometrics and healthcare applications. It also separates pathways for communication and control. In order to improve performance, a communication cycle is constructed for synchronizing the WBSN system with the pipeline. A low-power adaptive process is a necessity for long-time healthcare monitoring. It includes a data encoder and an adaptive power conserving algorithm within each sensor node along with an accurate control switch system for adaptive power control. The thermal sensor node consists of a micro control unit (MCU), a thermal bipolar junction transistor sensor, an analog-to-digital converter (ADC), a calibrator, a data encoder, a 2.4-GHz radio frequency transceiver, and an antenna. When detecting ten body temperature or 240 electrocardiogram (ECG) signals per second, the power consumption is either 106.3 ¿W or 220.4 ¿W. By switching circuits, multi sharing wireless protocol, and reducing transmission data by data encoder, it achieves a reduction of 99.573% or 99.164% in power consumption compared to those without using adaptive and encoding modules. Compared with published research reports and industrial works, the proposed method is 69.6% or 98% lower than the power consumption in thermal sensor nodes which consist only of a sensor and ADC (without MCU, 2.4-GHz transceiver, modulator, demodulator, and data encoder) or wireless ECG sensor nodes which selected Bluetooth, 2.4-GHz transceiver, and Zigbee as wireless protocols.

Question: What is the power consumption of a wireless body sensor network?


EARTh: An Environmental Application Reference Thesaurus in the Linked Open Data cloud. The paper aims at providing a description of EARTh, the Environmental Application Reference Thesaurus. It represents a general-purpose thesaurus for the environment, which has been published as a SKOS dataset in the Linked Open Data cloud. It promises to become a core tool for indexing and discovery environmental resources by refining and extending GEMET, which is considered the de facto standard when speaking of general-purpose thesaurus for the environment in Europe, besides it has been interlinked to popular LOD datasets as AGROVOC, EUROVOC, DBPEDIA and UMTHES. The paper illustrates the main characteristics of EARTh as a guide to its usage. It clarifies (i) the methodology adopted to define the EARTh content; (ii) the design and technological choices made when publishing EARTh as Linked Data; (iii) the information pertaining to its access and maintenance. Descriptions of EARTh applications and future relevance are highlighted. CNR-IIA-EKOLab Environmental Knowledge Organization Laboratory, Area della Ricerca di Roma 1, Via Salaria Km 29,300 C.P. 10,I-00016 Monterotondo stazione RM, {difranco, vds, plini} @iia.cnr.it

Question: What is the environmental application reference thesaurus?


Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference. This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.

Question: What is the difference between Bayesian nonparametric model and Bayesian nonparametric model?


Blockchain and its uses. Blockchain, the technology behind most cryptocurrencies that exist today, offers a paradigm shifting technology that has the potential to transform the way that we record and verify events on the Internet. By offering a decentralized, immutable, community verified record of transactions, regardless of what these transactions represent, blockchain technology promises to transform many industries. In this paper, we provide a survey of this very important technology and discuss its possible use-cases and its effect on society. The purpose of this paper is to familiarize the reader with the current state-of-the-art in the blockchain world both in terms of technology and social impact.

Question: What is blockchain?


A Two-Handed Interface for Object Manipulation in Virtual Environments. A two-handed direct manipulation VE (virtual environment) interface has been developed as an intuitive manipulation metaphor for graphical objects. A new input device called ChordGloves introduces a simple technique for rapid and repeatable gesture recognition; the Chordgloves emulate a pair of 3-D mice and a keyboard. A drafting table is isomorphically mapped into the VE and provides hand support for 2-D interface techniques, as well as a reference frame for calibrating the mapping between real and virtual worlds. A cursor gravity function is used to grab vertices, edges, or faces and establish precisely aligned differential constraints between objects called anchors. The capability of subjects to translate, rotate, scale, align, and glue objects is tested with a puzzle building task. An approximation of the puzzle task is done in Adobe Illustrator to provide a performance reference. Results and informal user observations as well as topics for future work are presented.

Question: How to build a puzzle in illustrator?


A formal study of information retrieval heuristics. Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. One basic research question is thus what exactly are these "necessary" heuristics that seem to cause good retrieval performance. In this paper, we present a formal study of retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is satisfied only for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies these constraints. Thus the proposed constraints provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.

Question: What is the formal study of information retrieval heuristics?


Frequent Pattern Compression: A Significance-Based Compression Scheme for L2 Caches. With the widening gap between processor and memory speeds, memory system designers ma find cache compression beneficial to increase cache capacity and reduce off-chip bandwidth Most hardware compression algorithms fall into the dictionary-based category, which depend on building a dictionary and using its entries to encode repeated data values. Such algorithms are effective in compressing large data blocks and files. Cache lines, however, are typically short (32-256 bytes), and a per-line dictionary places a significant overhead that limits the compressibility and increases decompression latency of such algorithms. For such short lines significance-based compression is an appealing alternative. We propose and evaluate a simple significance-based compression scheme that has a low com pression and decompression overhead. This scheme, Frequent Pattern Compression (FPC compresses individual cache lines on a word-by-word basis by storing common word patterns in a compressed format accompanied with an appropriate prefix. For a 64-byte cache line, compression can be completed in three cycles and decompression in five cycles, assuming 1 FO4 gate delays per cycle. We propose a compressed cache design in which data is stored in compressed form in the L2 caches, but are uncompressed in the L1 caches. L2 cache lines ar compressed to predetermined sizes that never exceed their original size to reduce decompre sion overhead. This simple scheme provides comparable compression ratios to more comple schemes that have higher cache hit latencies.

Question: What is the significance of frequent pattern compression?


Graphite: Iterative Generative Modeling of Graphs. Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and uses graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. We draw novel connections of our framework with approximate inference via kernel embeddings. Empirically, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.

Question: What is graph neural networks?


Consensus photometric stereo. This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.

Question: What is consensus photometric stereo?


Extension of heuristic evaluation method: a review and reappraisal. As one of the major discount methods, the heuristic evaluation method (HE) is the most commonly used usability inspection method. We introduced the history and procedure of this method, as well as its strengths and weaknesses. We then reviewed the applications of this method to different Human-Computer Interaction systems with the adapted heuristic sets. We also reviewed many studies that extended the traditional HE method in different ways. Finally, the paper ends with a reappraisal of these extension methods and future research direction to improve the HE method.

Question: What is the history of the heuristic evaluation method?


Pedestrian detection in crowded scenes. In this paper, we address the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. Our basic premise is that this problem is too difficult for any type of model or feature alone. Instead, we present an algorithm that integrates evidence in multiple iterations and from different sources. The core part of our method is the combination of local and global cues via probabilistic top-down segmentation. Altogether, this approach allows examining and comparing object hypotheses with high precision down to the pixel level. Qualitative and quantitative results on a large data set confirm that our method is able to reliably detect pedestrians in crowded scenes, even when they overlap and partially occlude each other. In addition, the flexible nature of our approach allows it to operate on very small training sets.

Question: How to detect pedestrians in crowded scenes?


Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia. The growth of open-access technical publications and other open-domain textual information sources means that there is an increasing amount of online technical material that is in principle available to all, but in practice, incomprehensible to most. We propose to address the task of helping readers comprehend complex technical material, by using statistical methods to model the "prerequisite structure" of a corpus — i.e., the semantic impact of documents on an individual reader's state of knowledge. Experimental results using Wikipedia as the corpus suggest that this task can be approached by crowdsourcing the production of ground-truth labels regarding prerequisite structure, and then generalizing these labels using a learned classifier which combines signals of various sorts. The features that we consider relate pairs of pages by analyzing not only textual features of the pages, but also how the containing corpora is connected and created.

Question: How to predict prerequisite structure in wikipedia?


Head-worn displays: a review. Head-worn display design is inherently an interdisciplinary subject fusing optical engineering, optical materials, optical coatings, electronics, manufacturing techniques, user interface design, computer science, human perception, and physiology for assessing these displays. This paper summarizes the state-of-the-art in head-worn display design (HWD) and development. This review is focused on the optical engineering aspects, divided into different sections to explore principles and applications. Building on the guiding fundamentals of optical design and engineering, the principles section includes a summary of microdisplay or laser sources, the Lagrange invariant for understanding the trade-offs in optical design of HWDs, modes of image presentation (i.e., monocular, biocular, and stereo) and operational modes such as optical and video see-through. A brief summary of the human visual system pertinent to the design of HWDs is provided. Two optical design forms, namely, pupil forming and non-pupil forming are discussed. We summarize the results from previous design work using aspheric, diffractive, or holographic elements to achieve compact and lightweight systems. The applications section is organized in terms of field of view requirements and presents a reasonable collection of past designs

Question: What is the difference between a head-worn display and a head-mounted display?


The huge Package for High-dimensional Undirected Graph Estimation in R. We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.

Question: What is the huge package?


Chaffing and Winnowing: Confidentiality without Encryption. • Encryption: transforming the message to a ciphertext such that an adversary who overhears the ciphertext can not determine the message sent. The legitimate receiver possesses a secret decryption key that allows him to reverse the encryption transformation and retrieve the message. The sender may have used the same key to encrypt the message (with symmetric encryption schemes) or used a different, but related key (with public-key schemes). DES and RSA are familiar examples of encryption schemes.

Question: What is encryption?


A visitor's guide in an active museum: Presentations, communications, and reflection. Technology can play a crucial role in supporting museum visitors and enhancing their overall museum visit experiences. Visitors coming to a museum do not want to be overloaded with information, but to receive the relevant information, learn, and have an overall interesting experience. To serve this goal, a user-friendly and flexible system is needed. The design of such a system poses several challenges that need to be addressed in parallel. The user interface should be intuitive and let the visitors focus on the exhibits, not on the technology. Content and delivery must provide relevant information and at the same time allow visitors to get the level of detail and the perspectives in which they are interested. Personalization may play a key role in providing relevant information to individuals. Yet, since visitors tend to visit the museum in small groups, technology should also contribute to and facilitate during-the-visit communication or post-visit group interaction. The PIL project applied at the Hecht museum extended the research results of the PEACH project and tried to address all of these considerations. Evaluation involving users substantiated several aspects of the design.

Question: What is the Hecht museum?


Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load

Question: What is the optimal residential load control with price prediction in real-time electricity pricing environments?


Visualizing and Understanding Convolutional Networks. Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark (Krizhevsky et al., 2012). However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

Question: What is the function of intermediate feature layers?


Teaching theory of mind: a new approach to social skills training for individuals with autism.. This study examined the effectiveness of a social skills training program for normal-IQ adolescents with autism. Five boys participated in the 4 1/2-month treatment condition; four boys matched on age, IQ, and severity of autism constituted the no-treatment control group. In addition to teaching specific interactional and conversational skills, the training program provided explicit and systematic instruction in the underlying social-cognitive principles necessary to infer the mental states of others (i.e., theory of mind). Pre- and post-intervention assessment demonstrated meaningful change in the treatment group's performance on several false belief tasks, but no improvement in the control sample. No changes, however, were demonstrated on general parent and teacher ratings of social competence for either group.

Question: What is the theory of mind?


Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example. Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.

Question: Customer churn prediction based on support vector machine


Improving static and dynamic registration in an optical see-through HMD. In Augmented Reality, see-through HMDs superimpose virtual 3D objects on the real world. This technology has the potential to enhance a user's perception and interaction with the real world. However, many Augmented Reality applications will not be accepted until we can accurately register virtual objects with their real counterparts. In previous systems, such registration was achieved only from a limited range of viewpoints, when the user kept his head still. This paper offers improved registration in two areas. First, our system demonstrates accurate static registration across a wide variety of viewing angles and positions. An optoelectronic tracker provides the required range and accuracy. Three calibration steps determine the viewing parameters. Second, dynamic errors that occur when the user moves his head are reduced by predicting future head locations. Inertial sensors mounted on the HMD aid head-motion prediction. Accurate determination of prediction distances requires low-overhead operating systems and eliminating unpredictable sources of latency. On average, prediction with inertial sensors produces errors 2-3 times lower than prediction without inertial sensors and 5-10 times lower than using no prediction at all. Future steps that may further improve registration are outlined.

Question: How to improve static and dynamic registration in an optical see-through HMD?


Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map, combining the intuitions of dropout and zoneout while respecting neuron values. This connection suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.

Question: What is the intuition behind the GELU nonlinearity?


To buy or not to buy online: adopters and non-adopters of online shopping in Singapore. The Internet, as a dynamic virtual medium for selling and buying information, services and products, is gaining increasing attention from researchers and practitioners. In this study, we examine the perceptions of adopters and non-adopters of online shopping in terms of demographic profile, consumer expectations of online stores, advantages and problems of online shopping and transaction cost. In addition, we also examine the types of products purchased, frequency of online purchase and the extent of communication with e-commerce vendors. The findings are useful in explaining consumers' buying behaviour in the electronic marketplace. Implications of the results are discussed.

Question: What are the advantages and disadvantages of online shopping?


Developing Maturity Models for IT Management.

Question: What is the difference between a maturity model and a maturity framework?


Parental Socio-Economic Status and Students ' Academic Achievement in Selected Secondary Schools in Urban Informal Settlements in Westlands Division , Nairobi County .. This study sought to investigate critical parental socio-economic factors affecting the academic achievement of students in selected secondary schools in urban informal settlements in Westlands District in Nairobi County. The study was premised on the classical Liberal Theory of Equal Opportunity and social Darwinism proposed by Charles Darwin. A descriptive survey design using a sample of 125 respondents comprising of 91 students, 18 teachers and 16 parents was used for the study. The tools for data collection were questionnaires for students, Focus Group Discussions for teachers and Interview schedules for parents. Quantitative data from the questionnaires was analyzed using descriptive and inferential statistics while the qualitative data from interviews was managed through thematic techniques. The major findings of the study were that; the physical and other critical instructional resources were grossly inadequate or in a pathetic condition not conducive to education provision, there was a strong non-significant negative correlation between the occupation of parents and ability to finance education, that there is a significant positive correlation between good parent-teacher relationship and their involvement in their children's academic achievement. It was concluded that, parental occupation and involvement in learning activities and effective parent-teacher relationship were facilitating factors. Parents' low ability to finance education, coupled with the poor status of physical and instructional resources were inhibiting factors to students' academic achievement in the study locale. It was therefore recommended that the government should strengthen the collaboration between key education development partners to mobilize physical teaching/learning resources and strengthen education in the regio

Question: What is the relationship between parental socio-economic status and students' academic achievement?


Research Paper: Creating an Online Dictionary of Abbreviations from MEDLINE. OBJECTIVE The growth of the biomedical literature presents special challenges for both human readers and automatic algorithms. One such challenge derives from the common and uncontrolled use of abbreviations in the literature. Each additional abbreviation increases the effective size of the vocabulary for a field. Therefore, to create an automatically generated and maintained lexicon of abbreviations, we have developed an algorithm to match abbreviations in text with their expansions.   DESIGN Our method uses a statistical learning algorithm, logistic regression, to score abbreviation expansions based on their resemblance to a training set of human-annotated abbreviations. We applied it to Medstract, a corpus of MEDLINE abstracts in which abbreviations and their expansions have been manually annotated. We then ran the algorithm on all abstracts in MEDLINE, creating a dictionary of biomedical abbreviations. To test the coverage of the database, we used an independently created list of abbreviations from the China Medical Tribune.   MEASUREMENTS We measured the recall and precision of the algorithm in identifying abbreviations from the Medstract corpus. We also measured the recall when searching for abbreviations from the China Medical Tribune against the database.   RESULTS On the Medstract corpus, our algorithm achieves up to 83% recall at 80% precision. Applying the algorithm to all of MEDLINE yielded a database of 781,632 high-scoring abbreviations. Of all the abbreviations in the list from the China Medical Tribune, 88% were in the database.   CONCLUSION We have developed an algorithm to identify abbreviations from text. We are making this available as a public abbreviation server at \url[http://abbreviation.stanford.edu/].

Question: What is the abbreviation for "abbreviation"?


Bipolar transistor gain influence on the high temperature thermal stability of HV-BiGTs. In this paper we present the detailed investigation of the influence of the internal bipolar PNP transistor gain on the thermal stability of high voltage IGBTs and BiGTs. The bipolar gain is controlled by means of anode and buffer design and by the introduction of anode shorts. The influence of the different buffer and anode doping profiles and the different layouts in the case of anode-shorted designs are analyzed. Temperature dependent leakage current measurements confirm that the lowering of the leakage current and its subsequent weak temperature dependency can be achieved by buffer and anode engineering albeit with certain design trade-off restrictions. Nevertheless, another effective approach for suppressing the leakage current and its dependency on temperature is achieved by the introduction of anode shorts as demonstrated in reverse conducting IGBT or BiGT structures. Such designs eliminate to a large extent the internal bipolar transistor action in the BiGT anode shorted designs while allowing different anode and buffer doping profiles for the design trade-offs. Despite the fact that the lifetime control in the BiGT drift region causes the leakage current to increase, the temperature coefficient remains unchanged, hence, making the hard switched BiGT suitable for high temperature operation.

Question: What is the influence of the internal bipolar transistor gain on the thermal stability of high voltage IGBTs and BiGTs?


Robust TiN HM process to overcome under etch issue for SAV scheme on 14nm node. For advance node such as 14nm technology and beyond, back end of line interconnect has implemented self-aligned via (SAV) schemes for better via-metal short process window [1]. A TiN metal hard mask (MHM) is used for the trench pattern definition, after which via lithography and partial via (PV) etch is performed where the TiN was opened. The via etch condition has very good selectivity so that via is formed in a self-aligned fashion by TiN HM [2]. It is indeed to have significant benefit of via to metal short [3]. However, one of the trade off in SAV scheme is the via under etch that whether or not via can well land on an opened oxide area during PV etch. To define the contact area between via resist hole and opened HM oxide as an effective area for via to open successfully. "Fig. 1" shows the effective area change during process variation due to Via alignment, Via photo resist CD variation (Via ADICD) and post hard mask etch CD variation (AMICD). "Fig. 2" shows the mechanism of this under etch failure mode and typical TEM image from 64nm metal pitch test vehicle. In this work, we try to enlarge the process window by an aggressive AMICD targeting in combining with a higher density TiN material to maintain profile.

Question: How to enlarge the process window?


On the importance of accurate weak classifier learning for boosted weak classifiers. Recent work has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.

Question: What is the accuracy of the weak classifier?


Spatial Planning: A Configuration Space Approach. This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.

Question: How to compute configuration space obstacles?


Urban Physics : Effect of the micro-climate on comfort , health and energy demand. Y-NC-ND license. Abstract The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on energy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a wellestablished discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorology and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics. & 2012. Higher Education Press Limited Company. Production and hosting by Elsevier B.V. Open access under CC BY-NC-ND license.

Question: What is the role of urban physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain?


Automatic removal of manually induced artefacts in ultrasound images of thyroid gland. Manually induced artefacts, like caliper marks and anatomical labels, render an ultrasound (US) image incapable of being subjected to further processes of Computer Aided Diagnosis (CAD). In this paper, we propose a technique to remove these artefacts and restore the image as accurately as possible. The technique finds application as a pre-processing step when developing unsupervised segmentation algorithms for US images that deal with automatic estimation of the number of segments and clustering. The novelty of the algorithm lies in the image processing pipeline chosen to automatically identify the artefacts and is developed based on the histogram properties of the artefacts. The algorithm was able to successfully restore the images to a high quality when it was executed on a dataset of 18 US images of the thyroid gland on which the artefacts were induced manually by a doctor. Further experiments on an additional dataset of 10 unmarked US images of the thyroid gland on which the artefacts were simulated using Matlab showed that the restored images were again of high quality with a PSNR > 38dB and free of any manually induced artefacts.

Question: How to remove manually induced artefacts in ultrasound images of thyroid gland?


Customer Knowledge Contribution Behavior in Social Shopping Communities. Social shopping communities, a special form of social media, have offered fertile ground for customers to communicate their opinions and exchange product information. Although social shopping communities have the potential to transform the way online customers acquire knowledge in everyday life, research in information systems has paid little attention to this emerging type of social media. Thus, the goal of this paper is to enhance our understanding of user behavior in this new form of community. We propose and empirically test an integrative theoretical model of customer knowledge contribution based on social capital theory. By analyzing panel data collected over two weeks from 2,251 customers in a social shopping community, we found that reputation, reciprocity, network centrality, as well as customer expertise have significant impact on customer knowledge contribution. These results contribute significantly to the literature and provide important implications for future research and practice.

Question: What is customer knowledge contribution behavior in social shopping communities?


Problems with the Concept of Video Game "Addiction": Some Case Study Examples. This paper argues that the recent concerns about video game "addiction" have been based less on scientific facts and more upon media hysteria. By examining the literature, it will be demonstrated that the current criteria used for identifying this concept are both inappropriate and misleading. Furthermore, by presenting four case studies as examples it will be demonstrated how such claims of video game addiction can be inaccurately applied. It is concluded that the most likely reasons that people play video games excessively are due to either ineffective time management skills, or as a symptomatic response to other underlying problems that they are escaping from, rather than any inherent addictive properties of the actual games.

Question: What is the difference between a video game and a video game addiction?


MPPT of photovoltaic systems using extremum - seeking control. A stability analysis for a maximum power point tracking (MPPT) scheme based on extremum-seeking control is developed for a photovoltaic (PV) array supplying a dc-to-dc switching converter. The global stability of the extremum-seeking algorithm is demonstrated by means of Lyapunov's approach. Subsequently, the algorithm is applied to an MPPT system based on the "perturb and observe" method. The steady-state behavior of the PV system with MPPT control is characterized by a stable oscillation around the maximum power point. The tracking algorithm leads the array coordinates to the maximum power point by increasing or decreasing linearly with time the array voltage. Off-line measurements are not required by the control law, which is implemented by means of an analog multiplier, standard operational amplifiers, a flip-flop circuit and a pulsewidth modulator. The effectiveness of the proposed MPPT scheme is demonstrated experimentally under different operating conditions.

Question: What is the maximum power point tracking algorithm?


Prediction of Microwave Absorption Behavior of Grading Honeycomb Composites Based on Effective Permittivity Formulas. Microwave absorbing honeycomb composites with grading coating thickness are prepared by impregnation of aramid paper frame within water solutions of conductive carbon blacks. Based on effective medium method, grading impedance in the composites can be theoretically expressed by a linear approximation of the inversely tapered effective permittivity at wave propagation direction, which provides a feasible way to optimize the design of microwave absorbing honeycomb structures. The consistent result of calculations and experimental measurements for positive impedance grading design confirms the broadband absorption effect in high frequencies.

Question: What is the microwave absorbing honeycomb composite?


Collaborative Recommendation via Adaptive Association Rule Mining. Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among users' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are ine cient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm speci cally designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be speci ed in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a speci c target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is signi cantly better than that of traditional correlationbased approaches. Corresponding author. Present a liation: Department of Computer Science, Wellesley College, 106 Central Street, Wellesley, MA 02481 USA, e-mail: salvarez@wellesley.edu

Question: What is the minimum support for an association rule?


Representations of recent and remote autobiographical memories in hippocampal subfields. The hippocampus has long been implicated in supporting autobiographical memories, but little is known about how they are instantiated in hippocampal subfields. Using high-resolution functional magnetic resonance imaging (fMRI) combined with multivoxel pattern analysis we found that it was possible to detect representations of specific autobiographical memories in individual hippocampal subfields. Moreover, while subfields in the anterior hippocampus contained information about both recent (2 weeks old) and remote (10 years old) autobiographical memories, posterior CA3 and DG only contained information about the remote memories. Thus, the hippocampal subfields are differentially involved in the representation of recent and remote autobiographical memories during vivid recall.

Question: How can we detect representations of specific autobiographical memories in individual hippocampal subfields?


Alzheimer's Disease and the Impact of Music Therapy: A Systematic Literature Review. Currently ranked as the sixth leading cause of death in the United States, Alzheimer's disease (AD) has become the most prevalent form of dementia, a term commonly associated with memory loss and other progressive cognitive deficits that compromise patients' lives. What may begin as a mindlessly misplaced object or momentary inability to recall newly learned information will eventually advance to a loss of personal identity, forgotten loved ones, and utter misperception of reality. Although a cure for AD has yet to be discovered, there are several nonpharmacological treatments that can improve patients' quality of life and provide temporary relief from the disabling manifestations, one of which is music therapy, the topic of this literature review. Music can be deeply connected with emotional processing and memory recall, and, when utilized as an interventional therapy for AD patients, can yield numerous cognitive and behavioral symptomatic benefits. The purpose of this project is to conduct a systematic literature review that evaluates the therapeutic relationship between AD and music therapy with a narrowed focus on familiar music therapy, the potential mechanisms of action that explain the efficacy of this intervention, and the resulting nursing implications that may be utilized in practice. MUSIC THERAPY AND ALZHEIMER'S DISEASE 6 Methods An exhaustive literature review was conducted using the database CINAHL (EBSCO). The search terms included: "Alzheimer's disease AND music therapy", "Alzheimer's AND music therapy", "Alzheimer's disease symptoms AND music therapy", "Alzheimer's disease AND music therapy AND familiar music", and "Alzheimer's disease AND active music therapy." The literature search included full text articles from academic journals in the English language, including international articles. Only articles published between 2007 through 2017 were reviewed, with the e

Question: What is the impact of music therapy on Alzheimer's disease?


Post-Punching Behavior of Flat Slabs by. Reinforced concrete flat slabs are a common structural system for cast-in-place concrete slabs. Failures in punching shear near the column regions are typically governing at ultimate. In case no punching shear or integrity reinforcement is placed, failures in punching develop normally in a brittle manner with almost no warning signs. Furthermore, the residual strength after punching is, in general, significantly lower than the punching load. Thus, punching of a single column of a flat slab overloads adjacent columns and can potentially lead to their failure on punching, thus triggering the progressive collapse of the structure. Over the past decades, several collapses have been reported due to punching shear failures, resulting in human casualties and extensive damage. Other than placing conventional punching shear reinforcement, the deformation capacity and residual strength after punching can also be enhanced by placing integrity reinforcement to avoid progressive collapses of flat slabs. This paper presents the main results of an extensive experimental campaign performed at the Ecole Polytechnique Fédérale de Lausanne (EPFL) on the role of integrity reinforcement by means of 20 slabs with dimensions of 1500 x 1500 x 125 mm (≈5 ft x 5 ft x 5 in.) and various integrity reinforcement layouts. The performance and robustness of the various solutions is investigated to obtain physical explanations and a consistent design model for the load-carrying mechanisms and strength after punching failures. INTRODUCTION Over the past decades, several collapses due to punching shear failure have been reported in Europe and America,

Question: What is the role of integrity reinforcement in punching shear?


Sharp Finite-Time Iterated-Logarithm Martingale Concentration. We give concentration bounds for martingales that are uniform over finite times and extend classical Hoeffding and Bernstein inequalities. We also demonstrate our concentration bounds to be optimal with a matching anti-concentration inequality, proved using the same method. Together these constitute a finite-time version of the law of the iterated logarithm, and shed light on the relationship between it and the central limit theorem.

Question: What is the sharp finite-time martingale concentration inequality?


The individual and social dynamics of knowledge sharing: an exploratory study. Purpose – This paper aims to examine how knowledge sharing behavior is influenced by three sets of dynamics: a rational calculus that weighs the costs and benefits of sharing; a dispositional preference that favors certain patterns of sharing outcomes; and a relational effect based on working relationships. Design/methodology/approach – Concepts from social exchange theory, social value orientation, and leader-member exchange theory are applied to analyze behavioral intentions to share knowledge. The study population consists of employees of a large pension fund in Canada. Participants answered a survey that used allocation games and situational vignettes to measure social value orientation, propensity to share knowledge, and perception of cost and benefit. Findings – The results suggest that personal preferences about the distribution of sharing outcomes, individual perceptions about costs and benefits, and structural relationship with knowledge recipients, all affect knowledge sharing behavior significantly. Notably, it was found that propensity to share knowledge is positively related to perceived benefit to the recipient, thus suggesting that evaluation of cost and benefit in social exchange is not limited to self-interest, but is also influenced by perceived recipient benefit. Moreover, it was found that the relationship with the sharing target (superior or colleague) also influenced sharing. Originality/value – Most studies emphasize the organizational benefits of knowledge sharing. This study examines knowledge sharing from the perspective of the individual who approaches knowledge sharing as a social exchange that involves perceptions of costs and benefits, preferences about sharing outcomes, and relationship with the sharing target. The study also introduces innovative methods to measure social value orientation and propensity to share knowledge.

Question: What is the relationship between knowledge sharing and social value orientation?


Building a Virtual HPC Cluster with Auto Scaling by the Docker. Solving the software dependency issue under the HPC environment has always been a difficult task for both computing system administrators and application scientists. This work would like to tackle the issue by introducing the modern container technology, the Docker, to be specific. By integrating the auto-scaling feature of service discovery with the light-weight virtualization tool, the Docker, the construction of a virtual cluster on top of physical cluster hardware is attempted. Thus, through the isolation of computing environment, a remedy of software dependency of HPC environment is possible. Keywords—HPC; Docker; auto-scaling; service discovery; MPI; Consul;

Question: How to build a virtual HPC cluster with auto-scaling by the Docker?


Sub-sentencial Paraphrasing by Contextual Pivot Translation. The ability to generate or to recognize paraphrases is key to the vast majority of NLP applications. As correctly exploiting context during translation has been shown to be successful, using context information for paraphrasing could also lead to improved performance. In this article, we adopt the pivot approach based on parallel multilingual corpora proposed by (Bannard and Callison-Burch, 2005), which finds short paraphrases by finding appropriate pivot phrases in one or several auxiliary languages and back-translating these pivot phrases into the original language. We show how context can be exploited both when attempting to find pivot phrases, and when looking for the most appropriate paraphrase in the original subsentential "envelope". This framework allows the use of paraphrasing units ranging from words to large sub-sentential fragments for which context information from the sentence can be successfully exploited. We report experiments on a text revision task, and show that in these experiments our contextual sub-sentential paraphrasing system outperforms a strong baseline system.

Question: How can we exploit context information to paraphrase?


Enhanced 3GPP system for Machine Type Communications and Internet of Things. The Third Generation Partnership Project (3GPP) has been working on developing specifications on Machine Type Communications (MTC), also known as Machine to Machine Communications (M2M), which is all part of Internet of Things (IoT), a technology that enables machines and devices to be inter-connected via the internet. This paper presents recent M2M/IoT feature enhancements in 3GPP. These features include architectural enhancements for inclusion of a service exposure framework, group based IoT management, efficient monitoring mechanisms for IoT devices, optimization of IoT device power consumption, and high latency communications. These M2M/IoT related functionalities and enhancements are a continuing effort within 3GPP to make the mobile network fulfil the ever changing M2M/IoT requirements, and in so doing allow operators/service providers to offer unique services.

Question: What is the latest 3GPP specification for MTC?


Multimodal analysis of the implicit affective channel in computer-mediated textual communication. Computer-mediated textual communication has become ubiquitous in recent years. Compared to face-to-face interactions, there is decreased bandwidth in affective information, yet studies show that interactions in this medium still produce rich and fulfilling affective outcomes. While overt communication (e.g., emoticons or explicit discussion of emotion) can explain some aspects of affect conveyed through textual dialogue, there may also be an underlying implicit affective channel through which participants perceive additional emotional information. To investigate this phenomenon, computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures. Analyses demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. Additionally, bodily expressions of posture and gesture correlated with student cognitive-affective states that were perceived by tutors through the implicit affective channel. Finally, posture and gesture complement each other in multimodal predictive models of student cognitive-affective states, explaining greater variance than either modality alone. This approach of empirically studying the implicit affective channel may identify details of human behavior that can inform the design of future textual dialogue systems modeled on naturalistic interaction.

Question: What is the implicit affective channel?


BUSINESS-TO-BUSINESS E-BUSINESS MODELS : CLASSIFICATION AND TEXTILE INDUSTRY IMPLICATIONS. Since the introduction of the Internet and e-commerce in the mid-1990s, there has been a lot of hype surrounding e-business, the impact that it will have on the way that companies do business, and how it will change the global economy as a whole. Since the crash of the dotcom companies in 2001, there has been much less hype surrounding the use of the Internet for business. There seems to have been a realization that e-business may not be the answer to all of a company's problems, but can be a great asset in the struggle to increase efficiencies in daily business dealings, and that the Web is primarily a new way of relating to customers and suppliers. This paper categorizes and discusses the different types of business-to-business electronic business models currently being used by businesses and discussed in the academic literature, and shows how these business models are being implemented within the textile industry. This paper is divided into three parts. Part I gives an overview and some important definitions associated with businessto-business e-business, and discusses some characteristics that are unique to doing business on the Internet. Risks and benefits associated with doing business online are also discussed. Part II analyzes the different types of e-business models seen in the academic literature. Based on the analysis of the literature, a taxonomy of e-business models was developed. This new classification system organized e-business models into the following categories: sourcing models, ownership models, service-based models, customer relationship management models, supply chain models, interaction Models and revenue models. Part III reviews how these e-business models are currently being used within the textile industry. A preliminary analysis of 79 textile manufacturing companies was conducted to identify the applications of e-business.

Question: What is the difference between a business-to-business e-business model and a business-to-consumer e-business model?


Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG. This paper proposed a multimodal fusion between brain and peripheral signals for emotion detection. The input signals were electroencephalogram, galvanic skin resistance, temperature, blood pressure and respiration, which can reflect the influence of emotion on the central nervous system and autonomic nervous system respectively. The acquisition protocol is based on a subset of pictures which correspond to three specific areas of valance-arousal emotional space (positively excited, negatively excited, and calm). The features extracted from input signals, and to improve the results, correlation dimension as a strong nonlinear feature is used for brain signals. The performance of the Quadratic Discriminant Classifier has been evaluated on different feature sets: peripheral signals, EEG's, and both. In comparison among the results of different feature sets, EEG signals seem to perform better than other physiological signals, and the results confirm the interest of using brain signals as peripherals in emotion assessment. According to the improvement in EEG results compare in each raw of the table, it seems that nonlinear features would lead to better understanding of how emotional activities work.

Question: How to improve the results of EEG?


ADHD comorbidity findings from the MTA study: comparing comorbid subgroups.. OBJECTIVES Previous research has been inconclusive whether attention-deficit/hyperactivity disorder (ADHD), when comorbid with disruptive disorders (oppositional defiant disorder [ODD] or conduct disorder [CD]), with the internalizing disorders (anxiety and/or depression), or with both, should constitute separate clinical entities. Determination of the clinical significance of potential ADHD + internalizing disorder or ADHD + ODD/CD syndromes could yield better diagnostic decision-making, treatment planning, and treatment outcomes.   METHOD Drawing upon cross-sectional and longitudinal information from 579 children (aged 7-9.9 years) with ADHD participating in the NIMH Collaborative Multisite Multimodal Treatment Study of Children With Attention-Deficit/Hyperactivity Disorder (MTA), investigators applied validational criteria to compare ADHD subjects with and without comorbid internalizing disorders and ODD/CD.   RESULTS Substantial evidence of main effects of internalizing and externalizing comorbid disorders was found. Moderate evidence of interactions of parent-reported anxiety and ODD/CD status were noted on response to treatment, indicating that children with ADHD and anxiety disorders (but no ODD/CD) were likely to respond equally well to the MTA behavioral and medication treatments. Children with ADHD-only or ADHD with ODD/CD (but without anxiety disorders) responded best to MTA medication treatments (with or without behavioral treatments), while children with multiple comorbid disorders (anxiety and ODD/CD) responded optimally to combined (medication and behavioral) treatments.   CONCLUSIONS Findings indicate that three clinical profiles, ADHD co-occurring with internalizing disorders (principally parent-reported anxiety disorders) absent any concurrent disruptive disorder (ADHD + ANX), ADHD co-occurring with ODD/CD but no anxiety (ADHD + ODD/CD), and ADHD with both anxiety and ODD/

Question: What is the difference between ADHD and ADHD comorbid with internalizing disorders?


Factorized Geometrical Autofocus for Synthetic Aperture Radar Processing. This paper describes a factorized geometrical autofocus (FGA) algorithm, specifically suitable for ultrawideband synthetic aperture radar. The strategy is integrated in a fast factorized back-projection chain and relies on varying track parameters step by step to obtain a sharp image; focus measures are provided by an object function (intensity correlation). The FGA algorithm has been successfully applied on synthetic and real (Coherent All RAdio BAnd System II) data sets, i.e., with false track parameters introduced prior to processing, to set up constrained problems involving one geometrical quantity. Resolution (3 dB in azimuth and slant range) and peak-to-sidelobe ratio measurements in FGA images are comparable with reference results (within a few percent and tenths of a decibel), demonstrating the capacity to compensate for residual space variant range cell migration. The FGA algorithm is finally also benchmarked (visually) against the phase gradient algorithm to emphasize the advantage of a geometrical autofocus approach.

Question: What is the difference between synthetic aperture radar and radar?


When learning about the real world is better done virtually : A study of substituting computer simulations for laboratory equipment. This paper examines the effects of substituting a computer simulation for real laboratory equipment in the second semester of a large-scale introductory physics course. The direct current circuit laboratory was modified to compare the effects of using computer simulations with the effects of using real light bulbs, meters, and wires. Two groups of students, those who used real equipment and those who used a computer simulation that explicitly modeled electron flow, were compared in terms of their mastery of physics concepts and skills with real equipment. Students who used the simulated equipment outperformed their counterparts both on a conceptual survey of the domain and in the coordinated tasks of assembling a real circuit and describing how it worked.

Question: What is the difference between a real and a virtual world?


LDS-OFDM an Efficient Multiple Access Technique. In this paper LDS-OFDM is introduced as an uplink multicarrier multiple access scheme. LDS-OFDM uses Low Density Signature (LDS) structure for spreading the symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams up to 400% more than the number of subcarriers (overloaded condition). The performance of LDS-OFDM is evaluated and compared with conventional OFDMA systems over multipath fading channel. Monte Carlo based simulations for various loading conditions indicate significant performance improvement over OFDMA system.

Question: What is LDS-OFDM?


Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer. This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%.

Question: What is the modified convolutional neural network based on dropout and the stochastic gradient descent optimizer?


Social Media Use and Mental Health among Young Adults. In recent years many parents, advocates and policy makers have expressed concerns regarding the potential negative impact of social media use. Some studies have indicated that social media use may be tied to negative mental health outcomes, including suicidality, loneliness and decreased empathy. Other studies have not found evidence for harm, or have indicated that social media use may be beneficial for some individuals. The current correlational study examined 467 young adults for their time spent using social media, importance of social media in their lives and tendency to engage in vaguebooking (posting unclear but alarming sounding posts to get attention). Outcomes considered included general mental health symptoms, suicidal ideation, loneliness, social anxiety and decreased empathy. Results indicated that social media use was not predictive of impaired mental health functioning. However, vaguebooking was predictive of suicidal ideation, suggesting this particular behavior could be a warning sign for serious issues. Overall, results from this study suggest that, with the exception of vaguebooking, concerns regarding social media use may be misplaced.

Question: What is vaguebooking?


Review of axial flux induction motor for automotive applications. Hybrid and electric vehicles have been the focus of many academic and industrial studies to reduce transport pollution; they are now established products. In hybrid and electric vehicles, the drive motor should have high torque density, high power density, high efficiency, strong physical structure and variable speed range. An axial flux induction motor is an interesting solution, where the motor is a double sided axial flux machine. This can significantly increase torque density. In this paper a review of the axial flux motor for automotive applications, and the different possible topologies for the axial field motor, are presented.

Question: What is the axial flux motor?


VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking. system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it VT uses domain-specific knowledge to guide its backtracking search for successful refinements The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool SALT was used to build VT and provides an analysis of VT's knowledge base to assess its potential for convergence on a solution VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking Sandra Marcus, Jeffrey Stout, John McDermott

Question: What is the name of the knowledge base?


A survey of socially interactive robots. This paper reviews "socially interactive robots": robots for which social human–robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of "social robots". We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report [T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots: concepts, design and applications, Technical Report No. CMU-RI-TR-02-29, Robotics Institute, Carnegie Mellon University, 2002]. © 2003 Elsevier Science B.V. All rights reserved.

Question: What is a survey of socially interactive robots?


A Descriptive Content Analysis of Trust-Building Measures in B2B Electronic Marketplaces. Because business-to-business (B2B) electronic marketplaces (e-marketplaces) facilitate transactions between buyers and sellers, they strive to foster a trustworthy trading environment with a variety of trust-building measures. However, little research has been undertaken to explore trust-building measures used in B2B e-marketplaces, or to determine to what extent these measures are applied in B2B e-marketplaces and how they are applied. Based on reviews of the scholarly, trade, and professional literature on trust in electronic commerce, we identified 11 trustbuilding measures used to create trust in B2B e-marketplaces. Zucker's trust production theory [1986] was applied to understand how these trust-building measures will enhance participants' trust in buyers and sellers in B2B e-marketplaces or in B2B e-marketplace providers. A descriptive content analysis of 100 B2B e-marketplaces was conducted to survey the current usage of the 11 trust-building measures. Many of the trust-building measures were found to be widely used in the B2B e-marketplaces. However, although they were proven to be effective in building trust-related beliefs in online business environments, several institutional-based trustbuilding measures, such as escrow services, insurance and third-party assurance seals, are not widely used in B2B e-marketplaces.

Question: What are the trust-building measures used in B2B e-marketplaces?


A Quick Tour of BabelNet 1.1. In this paper we present BabelNet 1.1, a brand-new release of the largest "encyclopedic dictionary", obtained from the automatic integration of the most popular computational lexicon of English, i.e. WordNet, and the largest multilingual Web encyclopedia, i.e. Wikipedia. BabelNet 1.1 covers 6 languages and comes with a renewed Web interface, graph explorer and programmatic API. BabelNet is available online at http://www.babelnet.org.

Question: What is the largest "encyclopedic dictionary"?


Temporal Summaries of News Topics. We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.

Question: How to monitor changes in news coverage over time?


A Theoretic Framework of K-Means-Based Consensus Clustering. Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the Kmeans based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-meansbased Consensus Clustering (KCC). We first formulate the general definition of KCC, and then reveal a necessary and sufficient condition for utility functions that work for KCC, on both complete and incomplete basic partitionings. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with substantial missing values.

Question: What is the difference between K-means and K-means based consensus clustering?


OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems. In this paper, we present an approach for modeling 3D environments based on octrees using a probabilistic occupancy estimation. Our technique is able to represent full 3D models including free and unknown areas. It is available as an open-source library to facilitate the development of 3D mapping systems. We also provide a detailed review of existing approaches to 3D modeling. Our approach was thoroughly evaluated using different real-world and simulated datasets. The results demonstrate that our approach is able to model the data probabilistically while, at the same time, keeping the memory requirement at a minimum.

Question: What is a probabilistic occupancy estimation?


Data Base Sublanguage Founded on the Relational Calculus. Three principal types of language for data base manipulation are identified: the low-level, procedure-oriented (typified by the CODASYL-proposed DML), the intermediate level, algebraic (typified by the Project MAC MacAIMS language), and the high level, relational calculus-based data sublanguage, an example of which is described in this paper. The language description is informal and stresses concepts and principles. Following this, arguments are presented for the superiority of the calculus-based type of data base sub-language over the algebraic, and for the algebraic over the low-level procedural. These arguments are particularly relevant to the questions of inter-system compatibility and standardization.

Question: What is the data base sublanguage?


Safety in shipping: the human element.. INTRODUCTION There are numerous diverse papers that have addressed issues within maritime safety; to date there has been no comprehensive review of this literature to aggregate the causal factors within accidents in shipping and surmise current knowledge.   METHODS This paper reviewed the literature on safety in three key areas: common themes of accidents, the influence of human error, and interventions to make shipping safer. The review included 20 studies of seafaring across the following areas: fatigue, stress, health, situation awareness, teamwork, decision-making, communication, automation, and safety culture.   RESULTS The review identifies the relative contributions of individual and organizational factors in shipping accidents, and also presents the methodological issues with previous research.   CONCLUSIONS The paper concludes that monitoring and modifying the human factors issues presented in this paper could contribute to maritime safety performance.   IMPACT ON INDUSTRY This review illustrates which human factors issues are prevalent in incidents therefore this gives shipping practitioners a focus for interventions.

Question: What is the human element in shipping?


Focused Trajectory Planning for autonomous on-road driving. On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.

Question: What is the difference between a spatiotemporal planner and a kinodynamic planner?


Efficient Exploration in Monte Carlo Tree Search using Human Action Abstractions. Monte Carlo Tree Search (MCTS) is a family of methods for planning in large domains. It focuses on finding a good action for a particular state, making its complexity independent of the size of the state space. However such methods are exponential with respect to the branching factor. Effective application of MCTS requires good heuristics to arbitrate action selection during learning. In this paper we present a policy-guided approach that utilizes action abstractions, derived from human input, with MCTS to facilitate efficient exploration. We draw from existing work in hierarchical reinforcement learning, interactive machine learning and show how multi-step actions, represented as stochastic policies, can serve as good action selection heuristics. We demonstrate the efficacy of our approach in the PacMan domain and highlight its advantages over traditional MCTS.

Question: What is the branching factor?


Efficient Estimation of Word Representations in Vector Space. We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

Question: What is the size of the data set?


Question: What is a neural probabilistic language model?


Neural Machine Translation Incorporating Named Entity. This study proposes a new neural machine translation (NMT) model based on the encoderdecoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models' ability to translate compound words seems challenging because the encoder receives a word, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore, the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.

Question: What is the neural machine translation model incorporating named entity?


On Two Metaphors for Learning and the Dangers of Choosing Just One. The upshots of the former section can be put as follows: All our concepts and beliefs have their roots in a limited number of fundamental ideas that cross disciplinary boundaries and are carried from one domain to another by the language we use. One glance at the current discourse on learning should be enough to realize that nowadays educational research is caught between two metaphors that, in this article, will be called the acquisition metaphor and the participation metaphor. Both of these metaphors are simultaneously present in most recent texts, but while the acquisition metaphor is likely to be more prominent in older writings, more recent studies are often dominated by the participation metaphor.

Question: What is the difference between the acquisition and participation metaphors?


Enterprise architecture modelling--the issue of integration. The problem of aligning and integrating business and IT is hampering many companies in their strategic and tactical development. Constructing integrated architecture models contributes to tackling this problem. Unfortunately, no enterprise architecture description language currently exists that fully enables integrated enterprise modelling. A variety of architectural domains are commonly distinguished for which architects use their own modelling techniques and concepts, tool support, visualisation techniques, etc. In this paper we outline such an integrated language and describe concepts that relate architectural domains. This language also serves as a bridge between other existing modelling languages. Furthermore, we present the design of a workbench for enterprise architecture that serves as a modelling tool and a tool integration environment at the same time: it supports both the integration of models in existing modelling languages and the integration of existing modelling tools. q 2005 Elsevier Ltd. All rights reserved.

Question: What is an integrated language?


Algorithmic Authority: the Ethics, Politics, and Economics of Algorithms that Interpret, Decide, and Manage. This panel will explore algorithmic authority as it manifests and plays out across multiple domains. Algorithmic authority refers to the power of algorithms to manage human action and influence what information is accessible to users. Algorithms increasingly have the ability to affect everyday life, work practices, and economic systems through automated decision-making and interpretation of "big data". Cases of algorithmic authority include algorithmically curating news and social media feeds, evaluating job performance, matching dates, and hiring and firing employees. This panel will bring together researchers of quantified self, healthcare, digital labor, social media, and the sharing economy to deepen the emerging discourses on the ethics, politics, and economics of algorithmic authority in multiple domains.

Question: What is the ethics of algorithmic authority?


NAIDS design using ChiMIC-KGS. The tasks of IDS is to detect activities in the form of intrusion by unauthorized parties which attempt to gain access to information unlawfully in a network environment. Changes in intrusion strategies affect the behavior of networks where the representation of network traffic data changes over time. The nature of the network data has high volume and dimension and contains redundancy. This study proposes the use of ChiMIC as a method to reduce redundancy features and select the relevant features to be used in detecting anomalies on the network, where KGS is used as an anomaly detection engine. Both approach are used to build NAIDS that has capabilities to detect the emergence of new attacks over time.

Question: What is the difference between ChiMIC and KGS?


Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression. Reliable prediction of affective states in real world scenarios is very challenging and a significant amount of ongoing research is targeted towards improvement of existing systems. Major problems include the unreliability of labels, variations of the same affective states amongst different persons and in different modalities as well as the presence of sensor noise in the signals. This work presents a framework for adaptive fusion of input modalities incorporating variable degrees of certainty on different levels. Using a strategy that starts with ensembles of weak learners, gradually, level by level, the discriminative power of the system is improved by adaptively weighting favorable decisions, while concurrently dismissing unfavorable ones. For the final decision fusion the proposed system leverages a trained Kalman filter. Besides its ability to deal with missing and uncertain values, in its nature, the Kalman filter is a time series predictor and thus a suitable choice to match input signals to a reference time series in the form of ground truth labels. In the case of affect recognition, the proposed system exhibits superior performance in comparison to competing systems on the analysed dataset.

Question: How to predict depression?


Image biomarker standardisation initiative version 1 . 4.

Question: What is the image biomarker standardisation initiative?


Decision Tree Discovery. We describe the two most commonly used systems for induction of decision trees for classi cation: C4.5 and CART. We highlight the methods and di erent decisions made in each system with respect to splitting criteria, pruning, noise handling, and other di erentiating features. We describe how rules can be derived from decision trees and point to some di erence in the induction of regression trees. We conclude with some pointers to advanced techniques, including ensemble methods, oblique splits, grafting, and coping with large data.

Question: What is decision tree discovery?


A Reinforcement Learning Approach to Automated GUI Robustness Testing. Graphical User Interfaces (GUIs) can be found in almost all modern desktop, tablet and smartphone applications. Since they are the glue between an application's components, they lend themselves to system level testing. Unfortunately, although many tools promise automation, GUI testing is still an inherently difficult task and involves great manual labor. However, tests that aim at critical faults, like crashes and excessive response times, are completely automatable and can be very effective. These robustness tests often apply random algorithms to select the actions to be executed on the GUI. This paper proposes a new approach to fully automated robustness testing of complex GUI applications with the goal to enhance the fault finding capabilities. The approach uses a well-known machine learning algorithm called Q-Learning in order to combine the advantages of random and coverage-based testing. We will explain how it operates, how we plan to implement it and provide arguments for its usefulness.

Question: What is a reinforcement learning algorithm?


ADMIRAL: A Data Mining Based Financial Trading System. This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions

Question: What is a data mining based financial trading system?