Multisensor Fusion and Integration: A Review on Approaches and Its Applications in Mechatronics. The objective of this paper is to review the theories and approaches of multisensor fusion and integration (MFI) with its application in mechatronics. MFI helps the system perceiving changes of the environment and monitoring the system itself. Since each individual sensor has its own inherent defects and limitations, MFI merges the redundant information acquired by multiple sensors synergistically to provide a more accurate perception and make an optimal decision in further. The wide application spectrum of MFI in mechatronics includes the industrial automation, the development of intelligent robots, military applications, biomedical applications, etc. In this paper, the architecture and algorithms of MFI are reviewed, and some implementation examples in industrial automation and robotic applications are presented. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, the most frequently used algorithms in previous researches with their advantages and limitations are summarized. Applications of MFI in robotics and mechatronics are discussed. Future perspectives of MFI deployment are included in the concluding remarks.

Question: What is the difference between multisensor fusion and integration?


Brain tumor segmentation from MRI data sets using region growing approach. Brain segmentation is an important part of medical image processing. Most commonly, it aims at locating various lesions and pathologies inside the human brain. In this paper, a new brain segmentation algorithm is proposed. The method is seeded region growing approach which was developed to segment the area of the brain affected by a tumor. The proposed algorithm was described. Results of testing the developed method on real MRI data set are presented and discussed.

Question: How to segment brain tumor from MRI data set?


Zero-Shot Adaptive Transfer for Conversational Language Understanding. Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments. Extensive experimentation over a dataset of 10 domains relevant to our commercial personal digital assistant shows that our model outperforms previous state-of-the-art systems by a large margin, and achieves an even higher improvement in the low data regime.

Question: What is the zero-shot adaptive transfer for slot tagging?


Mutual Information, Fisher Information, and Population Coding. In the context of parameter estimation and model selection, it is only quite recently that a direct link between the Fisher information and information-theoretic quantities has been exhibited. We give an interpretation of this link within the standard framework of information theory. We show that in the context of population coding, the mutual information between the activity of a large array of neurons and a stimulus to which the neurons are tuned is naturally related to the Fisher information. In the light of this result, we consider the optimization of the tuning curves parameters in the case of neurons responding to a stimulus represented by an angular variable.

Question: What is the Fisher information?


Microwave Doherty Power Amplifier for High Efficiency and Linearity. This paper explains the basic circuit configuration of microwave Doherty amplifier and additional design methods for wide bandwidth and high power applications. This paper also presents the recent experimental results of the group using the uneven power drive. Most of the previous works about microwave Doherty amplifier have employed identical input drives. In this works, two 1:1(2-way) and a 1:2(3-way) Doherty amplifiers are implemented at 2.14GHz center frequency using LDMOS with a peak envelope power of 180 watt and 285 watt, respectively. The 2-way Doherty amplifiers are optimized at 25 watt average output power for high efficiency and linearity and the 3-way Doherty amplifier at 16 watt average output power for high linearity. The amplifiers are tested using WCDMA 4FA signal. The performances of the 2-way Doherty amplifier are compared with that of the comparable class AB amplifier and the Doherty amplifier with even power drive. The performances of the 3-way Doherty amplifier are compared with that of the comparable class AB amplifier. The experimental results show the superior performances of our Doherty amplifiers

Question: What is the difference between a Doherty amplifier and a class AB amplifier?


A multilevel relaxation algorithm for simultaneous localization and mapping. This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.

Question: What is a multilevel relaxation algorithm?


Identifying Users With Alternate Behaviors of Lurking and Active Participation in Multilayer Social Networks. With the growing complexity of scenarios relating to online social networks (OSNs), there is an emergence of effective models and methods for understanding the characteristics and dynamics of multiple interconnected types of user relations. Profiles on different OSNs belonging to the same user can be linked using the multilayer structure, opening to unprecedented opportunities for user behavior analysis in a complex system. In this paper, we leverage the importance of studying the dichotomy between information-producers (contributors) and information-consumers (lurkers), and their interplay over a multilayer network, in order to effectively analyze such different roles a user may take on different OSNs. In this respect, we address the novel problem of identification and characterization of opposite behaviors that users may alternately exhibit over multiple layers of a complex network. We propose the first ranking method for alternate lurker-contributor behaviors on a multilayer OSN, dubbed mlALCR. Performance of mlALCR has been assessed quantitatively as well as qualitatively, and comparatively against methods designed for ranking either contributors or lurkers, on four real-world multilayer networks. Empirical evidence shows the significance and uniqueness of mlALCR in being able to mine alternate lurker-contributor behaviors over different layer networks.

Question: What is the first large-scale Canadian peacekeeping mission?


Constraint Programming in OPL. OPL is a modeling language for mathematical programming and combinatorial optimization problems. It is the rst modeling language to combine high-level algebraic and set notations from model-ing languages with a rich constraint language and the ability to specify search procedures and strategies that is the essence of constraint programming. In addition, OPL models can be controlled and composed using OPLSCRIPT, a script language that simpliies the development of applications that solve sequences of models, several instances of the same model, or a combination of both as in column-generation applications. This paper illustrates some of the functionalities of OPL for constraint programming using frequency allocation, sport-scheduling, and job-shop scheduling applications. It also illustrates how OPL models can be composed using OPLSCRIPT on a simple connguration example.

Question: What is constraint programming?


The twisted string actuation system: Modeling and control. This paper describes a novel actuation system for very compact and light-weight robotic devices, like artificial hands. The actuation concept presented here allows the implementation of powerful tendon-based driving systems, using as actuators small-size DC motors characterized by high speed and low torque. After the presentation of the basic concept of this novel actuation system, the constitutive equations of the system are given, validated by means of laboratory tests. Moreover, the problem of tracking a desired actuation force profile is taken into account, considering as load a mass-spring-damper system. A control algorithm based on a second-order sliding manifold has been firstly evaluated by means of simulations, and then validated by experiments. This output-feedback controller has been chosen to guarantee a high level of robustness against disturbances, parameter variations and uncertainties while maintaining a low computational burden.

Question: What is the twisted string actuation system?


Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz. The radar signatures of automotive targets were measured from 22 to 29 GHz and 76 to 81 GHz inside an anechoic chamber using a vector network analyzer. Radar cross section maps of a sedan, truck, van, pedestrian, motorcycle, and bicycle as a function of effective radar sensor bandwidth, center frequency, and polarization are presented.

Question: What is the radar cross section of a van?


Elderly people at home: technological help in everyday activities. The aim of this paper is to understand to what extent elderly people are likely to accept a technological aid in performing everyday activities. In this perspective, the present research focused on elderly people's strategies in performing everyday activities at home, in order to understand in what domestic domains technology can be considered an acceptable help. We administered a questionnaire focusing on preferred strategies in carrying out common domestic tasks, and on attitudes towards new technologies and home modification to a sample of 123 elderly people living in Rome. Results show that the adoption of a strategy, including the introduction of technological devices, is highly problem-specific, while personal factors are relevant only in particular situations. With increasing age, people are more inclined to give up, and higher educational levels correspond to more frequent technological solutions.

Question: What are the most common strategies elderly people use to perform everyday activities at home?


Power factor improvement of a transverse flux machine with high torque density. Design of a permanent magnet transverse flux machine optimized for high torque density and high power factor is presented. The optimization process focuses on performance enhancement through improvement of power factor without sacrificing torque density. Simple magnetostatic simulation based method is proposed for the optimization process. Magnetic couplings among axially separated phases are also discussed in the article. Experimental results demonstrating the correlation of measured inductances with that of simulation results are provided.

Question: How to improve the power factor of a transverse flux machine?


An Intrusion-Detection Model. A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. The model includes profiles for representing the behavior of subjects with respect to objects in terms of metrics and statistical models, and rules for acquiring knowledge about this behavior from audit records and for detecting anomalous behavior. The model is independent of any particular system, application environment, system vulnerability, or type of intrusion, thereby providing a framework for a general-purpose intrusion-detection expert system.

Question: What is an intrusion-detection expert system?


The More You Know: Using Knowledge Graphs for Image Classification. One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.

Question: What is the difference between a neural network and a graph neural network?


Child maltreatment and the developing brain : A review of neuroscience perspectives ☆. a r t i c l e i n f o Keywords: Child maltreatment Neuroscience Brain plasticity Stress system dysregulation Brain development In this article we review neuroscience perspectives on child maltreatment to facilitate understanding of the rapid integration of neuroscience knowledge into the academic, clinical, and lay literature on this topic. Seminal articles from developmental psychology and psychiatry, a discussion of brain plasticity, and a summary of recent reviews of research on stress system dysregulation are presented with some attention to methodological issues. A common theme is that maltreatment during childhood is an experience that may affect the course of brain development, potentially leading to differences in brain anatomy and functioning with lifelong consequences for mental health. The design of prevention and intervention strategies for child maltreatment may benefit from considering neuroscience perspectives along with those of other disciplines.

Question: What is the difference between a review and a summary?


Object discovery in 3D scenes via shape analysis. We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its "objectness" - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.

Question: How does our method work?


Practical Design and Implementation of Metamaterial-Enhanced Magnetic Induction Communication. The wireless communications in complex environments, such as underground and underwater, can enable various applications in the environmental, industrial, homeland security, law enforcement, and military fields. However, conventional electromagnetic wave-based techniques do not work due to the lossy media and complicated structures. Magnetic induction (MI) has been proved to achieve reliable communication in such environments. However, due to the small antenna size, the communication range of MI is still very limited, especially for the portable mobile devices. To this end, Metamaterial-enhanced MI (M2I) communication has been proposed, where the theoretical results predict that it can significantly increase the data rate and range. Nevertheless, there exists a significant gap between the theoretical prediction and the practical realization of M2I; the theoretical model relies on an ideal spherical metamaterial, while it does not exist in nature. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M2I communication. The full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M2I communication, the communication range can be significantly extended, exactly as we predicted in the ideal M2I model. Finally, the proposed M2I communication is implemented and tested in various environments.

Question: How to design a practical M2I communication system?


Linear Object Classes and Image Synthesis From a Single Example Image. The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and°object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced [11, 6, 5] techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter [12]. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.

Question: What is the linear class?


PPG-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks. Non invasive and continuous measurement of blood pressure can enable an effective 24/7 monitoring of hypertensive patients to timely prevent cardiovascular problems and precisely regulate anti-hypertension cures. Unfortunately, to date, blood pressure can be only measured through either cuff-based or invasive instruments that cannot be continuously used due to their characteristics. This paper proposes an overview of techniques and approaches based on the photoplethysmographic (PPG) signal for non invasive and continuous measurement of blood pressure. In particular, the PPG signal can be easily acquired from an optical sensor applied on the epidermis and used, alone or integrated with the ECG signal, to estimate the blood pressure. On the basis of such methods new instruments and sensor-based systems can be developed and integrated with computer-based health care systems that aim at supporting continuous and remote monitoring of assisted livings.

Question: What is the difference between a non invasive and continuous blood pressure measurement and a non invasive and continuous blood pressure measurement?


Stronger Security of Authenticated Key Exchange. Recent work by Krawczyk [12] and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols. In this paper we focus on security models for authenticated key exchange (AKE) protocols. We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model. Some of these additional attacks have already been considered by Krawczyk [12]. In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary. Our contribution is a more compact, integrated, and comprehensive formulation of the security model. We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger

Question: What is the difference between the Canetti-Krawczyk model and the extended Canetti-Krawczyk model?


Structuring Space with Image Schemata: Wayfinding in Airports as a Case Study. Wayfinding is a basic activity people do throughout their entire lives as they navigate from one place to another. In order to create different spaces in such a way that they facilit ate people's wayfinding it is necessary to integrate principles of human spatial cognition into the design process. This paper presents a methodology to structure space based on experiental patterns, called image schemata. It integrates cognitive and engineering aspects in three steps: (1) interviewing people about their spatial experiences as they perform a wayfinding task in the application space, (2) extracting the image schemata from these interviews and formulating a sequence of subtasks, and (3) structuring the application space (i.e., the wayfinding task) with the extracted image schemata. We use wayfinding in airports as a case study to demonstrate the methodology. Our observations show that most often image schemata are correlated with other image schemata in the form of image-schematic blocks and rarely occur in isolation. Such image-schematic blocks serve as a knowledge-representation scheme for wayfinding tasks.

Question: How do we structure space?


Human Face Detection by using Skin Color Segmentation, Face Features and Regions Properties. The goal of face detection is to locate all regions that contain a face. This paper has a simple face detection procedure which has two major steps, first to segment skin region from an image, and second, to decide these regions contain human face or not. Our procedure is based on skin color segmentation and human face features (knowledge-based approach). In this paper, we used RGB, YCbCr, CEILAB (L*a*b) and HSV color models for skin color segmentation. These color models with thresholds, help to remove non skin like pixels from an image. We tested each skin region, that skin region is actually represents a human face or not, by using human face features based on knowledge of geometrical properties of human face. The experiment result shows that, the algorithm gives hopeful results. At last, we concluded this paper and proposed future work.

Question: How to detect human face in an image?


The Weighted Majority Algorithm. We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials with a prediction to be made in each and the goal of the learner is to make few mistakes We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well but the learner does not know which one A simple and e ective method based on weighted voting is introduced for constructing a compound algorithm in such a circumstance We call this method the Weighted Majority Algorithm We show that this algorithm is robust in the presence of errors in the data We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool For example given a sequence of trials if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c log jAj m mistakes on that sequence where c is xed constant

Question: What is the weighted majority algorithm?


Understanding IEEE 1451-Networked smart transducer interface standard - What is a smart transducer?. This article introduces the IEEE 1451 standard for networked smart transducers. It discusses the concepts of smart transducers, IEEE 1451 smart transducers, the architecture of the IEEE 1451 family of standards, application of IEEE 1451, and example implementations of the IEEE 1451 standards. In conclusion, the IEEE 1451 suite of standards provides a set of standard interfaces for networked smart transducers, helping to achieve sensor plug and play and interoperability for industry and government.

Question: What is a smart transducer?


Working Memory Deficits can be Overcome : Impacts of Training and Medication on Working Memory in Children with ADHD. This study evaluated the impact of two interventions—a training program and stimulant medication—on working memory (WM) function in children with attention deficit hyperactivity disorder (ADHD). Twenty-five children aged between 8 and 11 years participated in training that taxed WM skills to the limit for a minimum of 20 days, and completed other assessments of WM and IQ before and after training, and with and without prescribed drug treatment. While medication significantly improved visuo-spatial memory performance, training led to substantial gains in all components of WM across untrained tasks. Training gains associated with the central executive persisted over a 6month period. IQ scores were unaffected by either intervention. These findings indicate that the WM impairments in children with ADHD can be differentially ameliorated by training and by stimulant medication. Copyright # 2009 John Wiley & Sons, Ltd.

Question: What is the impact of training on working memory in children with ADHD?


Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling. Link Prediction using Knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities. Among various embedding models, there is a series of translation-based models such as TransE [1], TransH [2], and TransR[3]. This paper proposes modifications in the TransR model to address the issue of skewed data which is common in real-world knowledge graphs. The enhancements enable the model to smartly generate corrupted triplets during negative sampling, which significantly improves the training time and performance of TransR. The proposed approach can be applied to other translationbased models.

Question: What is the difference between TransE and TransR?


Teaching Machines to Read and Comprehend. Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.

Question: What is the new methodology?


Elliptic Curve Cryptosystems. The application of elliptic curves to the field of cryptography has been relatively recent. It has opened up a wealth of possibilities in terms of security, encryption, and real-world applications. In particular, we are interested in public-key cryptosystems that use the elliptic curve discrete logarithm problem to establish security. The objective of this thesis is to assemble the most important facts and findings into a broad, unified overview of this field. To illustrate certain points, we also discuss a sample implementation of the elliptic curve analogue of the El

Question: What is the elliptic curve discrete logarithm problem?


Privacy preservation and public auditing for cloud data using ASS in Multi-cloud. Cloud computing is the new business paradigm introduced in the IT market in the current decade providing the customers with the option to pay for the amount of service utilized. The use of cloud computing has increased rapidly in many organizations. Ensuring security of data in a cloud is a major factor in the cloud computing environment, as users often store fragile information in cloud storage. Public auditing on shared data stored in the cloud is done by exploiting Aggregate Signature Scheme. With this mechanism, the identity of the signer of various blocks of shared data is kept private from public verifiers, who are able to verify efficiently the integrity of shared data without retrieving the entire file. However traceability is a major issue. A Multi-clouds Database Model (MCDB) which is based on Multi-clouds service providers instead of using single cloud service provider such as in Amazon cloud service is used to implement traceability. Also, dealing with single cloud providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards multi-clouds is highly efficient and this system would also check for the data's which are attacked by malicious users, with short time and cost requirement.

Question: What is the difference between cloud computing and multi-clouds?


mCloud: A Context-Aware Offloading Framework for Heterogeneous Mobile Cloud. Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices' proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.

Question: What is the context-aware offloading decision algorithm?


Semi-supervised Deep Learning with Memory. We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date "network-in-training" to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semisupervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100.

Question: What is semi-supervised deep learning?


Stereo-Matching Network for Structured Light. Recently, deep learning has been widely applied in binocular stereo matching for depth acquisition, which has led to an immense increase of accuracy. However, little attention has been paid to the structured light field. In this letter, a network for structured light is proposed to extract effective matching features for depth acquisition. The proposed network promotes the Siamese network by considering receptive fields of different scales and assigning proper weights to the corresponding features, which is achieved by combining pyramid-pooling structure with the squeeze-and-excitation network into the Siamese network for feature extraction and weight calculations, respectively. For network training and testing, a structured-light dataset with amended ground truths is generated by projecting a random pattern into the existing binocular stereo dataset. Experiments demonstrate that the proposed network is capable of real-time depth acquisition, and it provides superior depth maps using structured light.

Question: What is a structured light?


Visual discrimination learning requires sleep after training. Performance on a visual discrimination task showed maximal improvement 48–96 hours after initial training, even without intervening practice. When subjects were deprived of sleep for 30 hours after training and then tested after two full nights of recovery sleep, they showed no significant improvement, despite normal levels of alertness. Together with previous findings that subjects show no improvement when retested the same day as training, this demonstrates that sleep within 30 hours of training is absolutely required for improved performance.

Question: What is the effect of sleep deprivation on visual discrimination learning?


Unrealistic optimism about susceptibility to health problems. In this study, 100 college students compared their own chances of experiencing 45 different health- and life-threatening problems with the chances of their peers. They showed a significant optimistic bias for 34 of these hazards, consistently considering their own chances to be below average. Attempts to account for the amount of bias evoked by different hazards identified perceived controllability, lack of previous experience, and the belief that the problem appears during childhood as factors that tend to increase unrealistic optimism. The investigation also examined the importance of beliefs and emotions as determinants of self-reported interest in adopting precautions to reduce one's risk. It found that: (a) beliefs about risk likelihood, beliefs about risk severity, and worry about the risk all made independent contributions to interest in risk reduction; (b) unrealistic optimism undermined interest in risk reduction indirectly by decreasing worry; and (c) beliefs about risk likelihood and severity were not sufficient to explain the amount of worry expressed about different hazards.

Question: What is the difference between unrealistic optimism and unrealistic pessimism?


Robust Face Recognition Using Symmetric Shape-from-Shading. Sensitivity to variations in illumination is a fundamental and challenging problem in face recognition. In this paper, we describe a new method based on symmetric shape-from-shading (SFS) to develop a face recognition system that is robust to changes in illumination. The basic idea of this approach is to use the symmetric SFS algorithm as a tool to obtain a prototype image which is illumination-normalized. Applying traditional SFS algorithms to real images of complex objects (in terms of their shape and albedo variations) such as faces is very challenging. It is shown that the symmetric SFS algorithm has a unique point-wise solution. In practice, given a single real face image with complex shape and varying albedo, even the symmetric SFS algorithm cannot guarantee the recovery of accurate and complete shape information. For the particular problem of face recognition, we utilize the fact that all faces share a similar shape making the direct computation of the prototype image from a given face image feasible. The symmetry property has been used to develop a new model-based source-from-shading algorithm which is more accurate than existing algorithms. Using the symmetric property, we are also able to gain insight into how changes in illumination a ect eigen-subspace based face recognition systems. Finally, to demonstrate the e cacy of our method, we have applied it to several publicly available face databases. We rst compare a new illumination-invariant measure to the measure proposed in (Jacobs et al., 1998), and then demonstrate signi cant performance improvement over existing face recognition systems using PCA and/or LDA for images acquired under variable lighting conditions.

Question: How large is the canadian military?


Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling. A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

Question: What is a fuzzy rule-based decision support system?


A Survey of the ATP-Binding Cassette (ABC) Gene Superfamily in the Salmon Louse (Lepeophtheirus salmonis). Salmon lice, Lepeophtheirus salmonis (Krøyer, 1837), are fish ectoparasites causing significant economic damage in the mariculture of Atlantic salmon, Salmo salar Linnaeus, 1758. The control of L. salmonis at fish farms relies to a large extent on treatment with anti-parasitic drugs. A problem related to chemical control is the potential for development of resistance, which in L. salmonis is documented for a number of drug classes including organophosphates, pyrethroids and avermectins. The ATP-binding cassette (ABC) gene superfamily is found in all biota and includes a range of drug efflux transporters that can confer drug resistance to cancers and pathogens. Furthermore, some ABC transporters are recognised to be involved in conferral of insecticide resistance. While a number of studies have investigated ABC transporters in L. salmonis, no systematic analysis of the ABC gene family exists for this species. This study presents a genome-wide survey of ABC genes in L. salmonis for which, ABC superfamily members were identified through homology searching of the L. salmonis genome. In addition, ABC proteins were identified in a reference transcriptome of the parasite generated by high-throughput RNA sequencing (RNA-seq) of a multi-stage RNA library. Searches of both genome and transcriptome allowed the identification of a total of 33 genes / transcripts coding for ABC proteins, of which 3 were represented only in the genome and 4 only in the transcriptome. Eighteen sequences were assigned to ABC subfamilies known to contain drug transporters, i.e. subfamilies B (4 sequences), C (11) and G (2). The results suggest that the ABC gene family of L. salmonis possesses fewer members than recorded for other arthropods. The present survey of the L. salmonis ABC gene superfamily will provide the basis for further research into potential roles of ABC transporters in the toxi

Question: What is the difference between a salmon louse and a salmon?


All learning is Local: Multi-agent Learning in Global Reward Games. In large multiagent games, partial observability , coordination, and credit assignmentpersistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world fr om a single agent's limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings.A sequenceof increasinglycomplex empirical tests verifies the efficacy of this technique.

Question: What is a Kalman filter?


Android permissions: a perspective combining risks and benefits. The phenomenal growth of the Android platform in the past few years has made it a lucrative target of malicious application (app) developers. There are numerous instances of malware apps that send premium rate SMS messages, track users' private data, or apps that, even if not characterized as malware, conduct questionable actions affecting the user's privacy or costing them money. In this paper, we investigate the feasibility of using both the permissions an app requests, the category of the app, and what permissions are requested by other apps in the same category to better inform users whether the risks of installing an app is commensurate with its expected benefit. Existing approaches consider only the risks of the permissions requested by an app and ignore both the benefits and what permissions are requested by other apps, thus having a limited effect. We propose several risk signals that and evaluate them using two datasets, one consists of 158,062 Android apps from the Android Market, and another consists of 121 malicious apps. We demonstrate the effectiveness of our proposal through extensive data analysis.

Question: What permissions are requested by other apps in the same category?


Design Challenges/Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research. Crisis informatics investigates how society's pervasive access to technology is transforming how it responds to mass emergency events. To study this transformation, researchers require access to large sets of data that because of their volume and heterogeneous nature are difficult to collect and analyze. To address this concern, we have designed and implemented an environment - EPIC Analyze - that supports researchers with the collection and analysis of social media data. Our research has identified the types of components - such as NoSQL, MapReduce, caching, and search - needed to ensure that these services are reliable, scalable, extensible, and efficient. We describe the design challenges encountered - such as data modeling, time vs. Space tradeoffs, and the need for a useful and usable system - when building EPIC Analyze and discuss its scalability, performance, and functionality.

Question: How do you design a system that supports the analysis of social media data in crisis informatics research?


Optimizing Regular Path Expressions Using Graph Schemas. Several languages, such as LOREL and UnQL, support querying of semi-structured data. Others, such as WebSQL and WebLog, query Web sites. All these languages model data as labeled graphs and use regular path expressions to express queries that traverse arbitrary paths in graphs. Naive execution of path expressions is ineecient, however, because it often requires exhaustive graph search. We describe two optimization techniques for queries with regular path expressions, which we call regular queries. Both rely on graph schemas, which specify partial knowledge of a graph's structure. Query pruning restricts search to a fragment of the graph; we give an eecient algorithm for rewriting any regular query into a pruned one. Query rewriting using state extents can entirely eliminate or substantially reduce graph traversal; it is reminiscent of optimizing relational queries using indices. There may be several ways to optimize a query using state extents ; we give an exponential-time algorithm that nds all such optimizations. On special forms of regular queries, the algorithm is provably eecient. We also give an eecient approximation algorithm that works on all regular queries. Our target databases are \intranet" sites, i.e., Web sites associated with a company or organization. Intranet sites are good targets for our techniques, because they often reeect the structure of their organization, hence their structure is partially known. Preliminary results show that regular queries applied to an intranet site produce more precise results than keyword-only queries and that our optimizations substantially reduce the cost of evaluating regular queries, indicating regular queries could be evaluated eeciently by Web-site search engines.

Question: What is the structure of a company?


Classification of rice grain varieties using two artificial neural networks (MLP and neuro-fuzzy).. Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively.

Question: What is the classification of rice grain varieties using two artificial neural networks (MLP and neuro-fuzzy)


Examining the factors that influence early adopters' smartphone adoption: The case of college students. The influence of early adopters on potential adopters' decisions of whether or not to adopt a product is known to be critical. In this paper, we examine the factors that influence the adoption behavior of smartphone early adopters by looking at smartphone adoption behavior of college students, because a large portion of the early adopters of smartphones are college students. Our focus is on the effect of normative peer influence on a college student's smartphone adoption. We also examine the influence of other factors such as selfinnovativeness, self-efficacy, the decision maker's attitudes towards a product, financial burden of using the product, familial influence, and other demographic factors (e.g., age and gender). College students' adoption behavior is studied using logit and probit choice models developed based on random utility theory. The discrete choice models are empirically estimated using survey data. We find important influence of friends, financial burden, and other family members on the smartphone adoption of college students who adopted smartphones earlier than other students. 2013 Elsevier Ltd. All rights reserved.

Question: What factors influence early adopters' smartphone adoption?


Mobile ad hoc networking: imperatives and challenges. Mobile ad hoc networks (MANETs) represent complex distributed systems that comprise wireless mobile nodes that can freely and dynamically self-organize into arbitrary and temporary, ''ad-hoc'' network topologies, allowing people and devices to seamlessly internetwork in areas with no pre-existing communication infrastructure, e.g., disaster recovery environments. Ad hoc networking concept is not a new one, having been around in various forms for over 20 years. Traditionally, tactical networks have been the only communication networking application that followed the ad hoc paradigm. Recently, the introduction of new technologies such as the Bluetooth, IEEE 802.11 and Hyperlan are helping enable eventual commercial MANET deployments outside the military domain. These recent evolutions have been generating a renewed and growing interest in the research and development of MANET. This paper attempts to provide a comprehensive overview of this dynamic field. It first explains the important role that mobile ad hoc networks play in the evolution of future wireless technologies. Then, it reviews the latest research activities in these areas, including a summary of MANET s characteristics, capabilities, applications, and design constraints. The paper concludes by presenting a set of challenges and problems requiring further research in the future. 2003 Elsevier B.V. All rights reserved.

Question: What is the role of mobile ad hoc networks in the evolution of future wireless technologies?


Volumetric Object Recognition Using 3-D CNNs on Depth Data. Recognizing 3-D objects has a wide range of application areas from autonomous robots to self-driving vehicles. The popularity of low-cost RGB-D sensors has enabled a rapid progress in 3-D object recognition in the recent years. Most of the existing studies use depth data as an additional channel to the RGB channels. Instead of this approach, we propose two volumetric representations to reveal rich 3-D structural information hidden in depth images. We present a 3-D convolutional neural network (CNN)-based object recognition approach, which utilizes these volumetric representations and single and multi-rotational depth images. The 3-D CNN architecture trained to recognize single depth images produces competitive results with the state-of-the-art methods on two publicly available datasets. However, recognition accuracy increases further when the multiple rotations of objects are brought together. Our multirotational 3-D CNN combines information from multiple views of objects to provide rotational invariance and improves the accuracy significantly comparing with the single-rotational approach. The results show that utilizing multiple views of objects can be highly informative for the 3-D CNN-based object recognition.

Question: What is the difference between a single and multi-rotational 3-D CNN?


Brain development during childhood and adolescence: a longitudinal MRI study. Pediatric neuroimaging studies, up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.

Question: What is the age of the brain?


Characterization of defense mechanisms against distributed denial of service attacks. We propose a characterization of distributed denial-of-s ervice (DDOS) defenses where reaction points are network-based and attack responses are active. The purpose is to provide a framework for comparing the performance and deployment of DDOS defenses. We identify the characteristics in attack detection algorithms and a ttack responses by reviewing defenses that have appeared in the literature. We expect that this char a terization will provide practitioners and academia insights into deploying DDOS defense as network services.

Question: What is a distributed denial of service attack?


Efficiency optimization of half-bridge series resonant inverter with asymmetrical duty cycle control for domestic induction heating. In this paper, a method to improve efficiency in half-bridge series resonant inverter applied to domestic induction heating is presented. Low and medium output powers required for this application implies the use of higher switching frequencies, which leads to an efficiency decrease. Asymmetrical duty cycle (ADC) modulation scheme is proposed to improve efficiency due to its switching frequency reduction and absence of additional hardware requirements. Study methodology comprises, in a first step, a theoretical analysis of power balance as a function of control parameters: duty cycle and switching frequency. In addition, restrictions due to snubber and dead time, and variability of the induction loads have been considered. Afterwards, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine global importance of each one for different switching devices. ADC modulation efficiency improvement is achieved by means of a switching frequency reduction, mainly at low-medium power range and low quality factor (Q) loads. The analytical results obtained with this study have been validated through an induction heating test-bench. A discrete 3-kW RL load has been designed to emulate a typical induction heating load. Then, a commercial induction heating inverter is used to evaluate ADC modulation scheme.

Question: How to improve the efficiency of a half-bridge series resonant inverter?


The high-conductance state of neocortical neurons in vivo. Intracellular recordings in vivo have shown that neocortical neurons are subjected to an intense synaptic bombardment in intact networks and are in a 'high-conductance' state. In vitro studies have shed light on the complex interplay between the active properties of dendrites and how they convey discrete synaptic inputs to the soma. Computational models have attempted to tie these results together and predicted that high-conductance states profoundly alter the integrative properties of cortical neurons, providing them with a number of computational advantages. Here, we summarize results from these different approaches, with the aim of understanding the integrative properties of neocortical neurons in the intact brain.

Question: What is the integrative properties of neocortical neurons?


Learning to Generate Corrective Patches using Neural Machine Translation. Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc. Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixes to propose fixes of current/future bugs. Specifically, we propose Ratchet, a corrective patch generation system using neural machine translation. By learning corresponding pre-correction and post-correction code in past fixes with a neural sequence-to-sequence model, Ratchet is able to generate a fix code for a given bug-prone code query. We perform an empirical study with five open source projects, namely Ambari, Camel, Hadoop, Jetty and Wicket, to evaluate the effectiveness of Ratchet. Our findings show that Ratchet can generate syntactically valid statements 98.7% of the time, and achieve an F1-measure between 0.41-0.83 with respect to the actual fixes adopted in the code base. In addition, we perform a qualitative validation using 20 participants to see whether the generated statements can be helpful in correcting bugs. Our survey showed that Ratchet's output was considered to be helpful in fixing the bugs on many occasions, even if fix was not 100%

Question: What is a neural machine translation?


Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization. The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. This paper proposes a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm. The main contribution of our work is to balance the entire system load while trying to minimizing the make span of a given tasks set. The new scheduling strategy was simulated using the CloudSim toolkit package. Experiments results showed the proposed LBACO algorithm outperformed FCFS (First Come First Serve) and the basic ACO (Ant Colony Optimization).

Question: What is the cloud computing?


A survey of smoothing techniques for ME models. In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in ME smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing -gram language models. Because of the mature body of research in -gram model smoothing and the close connection between ME and conventional -gram models, this domain is well-suited to gauge the performance of ME smoothing methods. Over a large number of data sets, we find that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration. We contrast this method with previous -gram smoothing methods to explain its superior performance.

Question: What is the difference between maximum entropy and maximum likelihood?


FLATM: A fuzzy logic approach topic model for medical documents. One of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.

Question: How to evaluate a topic model?


Feature selection in text classification. In recent years, text classification have been widely used. Dimension of text data has increased more and more. Working of almost all classification algorithms is directly related to dimension. In high dimension data set, working of classification algorithms both takes time and occurs over fitting problem. So feature selection is crucial for machine learning techniques. In this study, frequently used feature selection metrics Chi Square (CHI), Information Gain (IG) and Odds Ratio (OR) have been applied. At the same time the method Relevancy Frequency (RF) proposed as term weighting method has been used as feature selection method in this study. It is used for tf.idf term as weighting method, Sequential Minimal Optimization (SMO) and Naive Bayes (NB) in the classification algorithm. Experimental results show that RF gives successful results.

Question: What is the difference between feature selection and feature extraction?


Robotic Arm Based 3D Reconstruction Test Automation. The 3-D reconstruction involves the construction of a 3-D model from a set of images. The 3-D reconstruction has varied uses that include 3-D printing, the generation of 3-D models that can be shared through social media, and more. The 3-D reconstruction involves complex computations in mobile phones that must determine the pose estimation. The pose estimation involves the process of transforming a 2-D object into 3-D space. Once the pose estimation is done, then the mesh generation is performed using the graphics processing unit. This helps render the 3-D object. The competitive advantages of using hardware processors are to accelerate the intensive computation using graphics processors and digital signal processors. The stated problem that this technical paper addresses is the need for a reliable automated test for the 3-D reconstruction feature. The solution to this problem involved the design and development of an automated test system using a programmable robotic arm and rotor for precisely testing the quality of 3-D reconstruction features. The 3-D reconstruction testing involves using a robotic arm lab to accurately test the algorithmic integrity and end-to-end validation of the generated 3-D models. The robotic arm can move the hardware at different panning speeds, specific angles, fixed distances from the object, and more. The ability to reproduce the scanning at a fixed distance and the same panning speed helps to generate test results that can be benchmarked by different software builds. The 3-D reconstruction also requires a depth sensor to be mounted onto the device under examination. We use this robotic arm lab for functional, high performance, and stable validation of the 3-D reconstruction feature. This paper addresses the computer vision use case testing for 3-D reconstruction features and how we have used the robotic arm lab for automating these use cases.

Question: What is the difference between a robot and a robot arm?


Ultra-low on-resistance LDMOS implementation in 0.13µm CD and BiCD process technologies for analog power IC's. Toshiba's 5th generation BiCD/CD-0.13 is a new process platform for analog power applications based on 0.13µm CMOS technology. The process platform has six varieties of rated voltage, 5V, 6V, 18V, 25V, 40V, and 60V. 5 to 18V CD-0.13 process use P-type silicon substrate. 25 to 60V BiCD-0.13 process use N-Epi wafer with N+/P+ buried layer on P type silicon substrate. Each LDMOS recode ultra-low on-resistance compared with that of previous papers, and we will realize the highest performance analog power IC's using this technology.

Question: What is the highest voltage LDMOS?


A Hybrid Feature Selection Algorithm For Classification Unbalanced Data Processsing. The performance and accuracy of classifier are affected by the result of feature selection directly. Based on the one-class F-Score feature selection and the improved F-Score feature selection and genetic algorithm, combined with machine learning methods like the K nearest neighbor, support vector machine, random forest, naive Bayes, a hybrid feature selection algorithm is proposed to process the two classification unbalanced data problem and multi classification problem. Compared with the traditional machine learning algorithm, it can search in wider feature space and promote classifier to deal with the characteristics of unbalanced data sets according to heuristic rules, which can handle the problem of unbalanced classification better. The experiment results show that the area under receiver operating characteristic curve for two classifications and the accuracy rate for multi classification problem have been improved compared with other models

Question: What is the area under the receiver operating characteristic curve?


From goods to service ( s ) : Divergences and convergences of logics. There are two logics or mindsets from which to consider and motivate a transition from goods to service(s). The first, "goods-dominant (G-D) logic", views services in terms of a type of (e.g., intangible) good and implies that goods production and distribution practices should be modified to deal with the differences between tangible goods and services. The second logic, "service-dominant (S-D) logic", considers service – a process of using ones resources for the benefit of and in conjunction with another party – as the fundamental purpose of economic exchange and implies the need for a revised, service-driven framework for all of marketing. This transition to a service-centered logic is consistent with and partially derived from a similar transition found in the business-marketing literature — for example, its shift to understanding exchange in terms value rather than products and networks rather than dyads. It also parallels transitions in other sub-disciplines, such as service marketing. These parallels and the implications for marketing theory and practice of a full transition to a service-logic are explored. © 2008 Elsevier Inc. All rights reserved. Over the last several decades, leading-edge firms, as well as many business scholars and consultants, have advocated the need for refocusing substantial firm activity or transforming the entire firm orientation from producing output, primarily manufactured goods, to a concern with service(s) (see, e.g., Davies, Brady, & Hobday, 2007; Gebauer& Fleisch, 2007). These initiatives can be found in both business-to-business (e.g., IBM, GE) and businessto-consumer enterprises (e.g. Lowe's, Kodak, Apple) and in some cases entire industries (e.g., software-as-a-service). The common justification is that these initiatives are analogous with the shift from a manufacturing to a service economy in developed countries, if not globally. That is, it is based on th

Question: What is the difference between goods and services?


Harnessing Context Sensing to Develop a Mobile Intervention for Depression. BACKGROUND Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.   OBJECTIVE The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.   METHODS We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.   RESULTS Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet crit

Question: What is the difference between a mobile phone and a mobile phone application?


AGA: Attribute-Guided Augmentation. We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.

Question: What is the difference between data augmentation and attribute-guided augmentation?


Aligning 3D models to RGB-D images of cluttered scenes. The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [34], while being an order of magnitude faster.

Question: How do you align 3D models to RGB-D images of cluttered scenes?


Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data. Models for predicting the risk of cardiovascular (CV) events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restrict the predictive power and generalizability of these risk models to other populations. Electronic health data (EHD) from large health care systems provide access to data on large, heterogeneous, and contemporaneous patient populations. The unique features and challenges of EHD, including missing risk factor information, non-linear relationships between risk factors and CV event outcomes, and differing effects from different patient subgroups, demand novel machine learning approaches to risk model development. In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a CV event within 5 years. In such data, event status may be unknown for some individuals, as the event time is right-censored due to disenrollment and incomplete follow-up. Since many traditional data mining methods are not well-suited for such data, we describe how to modify both modeling and assessment techniques to account for censored observation times. We show that our approach can lead to better predictive performance than the Cox proportional hazards model (i.e., a regression-based approach commonly used for censored, time-to-event data) or a Bayesian network with ad hoc approaches to right-censoring. Our techniques are motivated by and illustrated on data from a large US Midwestern health care system.

Question: What is the difference between a Bayesian network and a regression model?


MAPS: A Multi Aspect Personalized POI Recommender System. The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user play a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recommender System) which will be the first in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance aspects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets.

Question: What is the first large-scale Canadian peacekeeping mission?


The role played by the concept of presence in validating the efficacy of a cybertherapy treatment: a literature review. The present paper considers the existing research in cybertherapy, which is a psychological therapy carried out with the use of a mediated environment, and examines the way in which the users' sense of presence in the mediated environment can be of relevance for the validation of the intervention. With this purpose, a collection of 41 papers reporting the measurement of presence in the context of a cybertherapy treatment has been identified and examined. The general relevance of presence in cybertherapy and the measurement techniques adopted in the studies collected here are described and discussed. The way in which presence corresponds to establishing internal validity, convergent or predictive validity and external validity of a treatment is examined. In conclusion, a checklist to apply when planning a validation study is proposed, to improve the way in which presence is used.

Question: How can presence be used to validate a cybertherapy treatment?


Large scale predictive process mining and analytics of university degree course data. For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years.

Question: What is the degree pathway tool?


A k-mean clustering algorithm for mixed numeric and categorical data. Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach. 2007 Elsevier B.V. All rights reserved.

Question: What is a k-mean clustering algorithm?


Effects of growth mode and pyruvate carboxylase on succinic acid production by metabolically engineered strains of Escherichia coli.. Escherichia coli NZN111, which lacks activities for pyruvate-formate lyase and lactate dehydrogenase, and AFP111, a derivative which contains an additional mutation in ptsG (a gene encoding an enzyme of the glucose phophotransferase system), accumulate significant levels of succinic acid (succinate) under anaerobic conditions. Plasmid pTrc99A-pyc, which expresses the Rhizobium etli pyruvate carboxylase enzyme, was introduced into both strains. We compared growth, substrate consumption, product formation, and activities of seven key enzymes (acetate kinase, fumarate reductase, glucokinase, isocitrate dehydrogenase, isocitrate lyase, phosphoenolpyruvate carboxylase, and pyruvate carboxylase) from glucose for NZN111, NZN111/pTrc99A-pyc, AFP111, and AFP111/pTrc99A-pyc under both exclusively anaerobic and dual-phase conditions (an aerobic growth phase followed by an anaerobic production phase). The highest succinate mass yield was attained with AFP111/pTrc99A-pyc under dual-phase conditions with low pyruvate carboxylase activity. Dual-phase conditions led to significant isocitrate lyase activity in both NZN111 and AFP111, while under exclusively anaerobic conditions, an absence of isocitrate lyase activity resulted in significant pyruvate accumulation. Enzyme assays indicated that under dual-phase conditions, carbon flows not only through the reductive arm of the tricarboxylic acid cycle for succinate generation but also through the glyoxylate shunt and thus provides the cells with metabolic flexibility in the formation of succinate. Significant glucokinase activity in AFP111 compared to NZN111 similarly permits increased metabolic flexibility of AFP111. The differences between the strains and the benefit of pyruvate carboxylase under both exclusively anaerobic and dual-phase conditions are discussed in light of the cellular constraint for

Question: What is the difference between the tricarboxylic acid cycle and the glyoxylate shunt?


Partially observable Markov decision processes for spoken dialog systems. In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed on three largely disparate techniques: parallel dialog state hypotheses, local use of confidence scores, and automated planning. While in isolation each of these approaches can improve action selection, taken together they currently lack a unified statistical framework that admits global optimization. In this paper we cast a spoken dialog system as a partially observable Markov decision process (POMDP). We show how this formulation unifies and extends existing techniques to form a single principled framework. A number of illustrations are used to show qualitatively the potential benefits of POMDPs compared to existing techniques, and empirical results from dialog simulations are presented which demonstrate significant quantitative gains. Finally, some of the key challenges to advancing this method – in particular scalability – are briefly outlined. 2006 Elsevier Ltd. All rights reserved.

Question: What is a partially observable Markov decision process?


Primary gonadal failure and precocious adrenarche in a boy with Prader-Labhart-Willi syndrome. A 7-year-old boy with Prader-Labhart-Willi syndrome who had precocious adrenarche was found to have primary gonadal failure, as evidenced by appropriate laboratory investigations: elevated basal levels of plasma FSH and LH with exaggerated responses to LH-RH stimulation and unresponsiveness of plasma testosterone to repeated hCG stimulations. The elevated values of plasma DHEA which were found indicate an early activation of the adrenal gland. This patient demonstrates the varibility of pubertal development in the Prader-Labhart-Willi syndrome, with the unusual association of primary gonadal failure and precocious adrenarche.

Question: What is precocious adrenarche?


Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions. An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.

Question: What is the primary object in a video?


Wealth and happiness across the world: material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling.. The Gallup World Poll, the first representative sample of planet Earth, was used to explore the reasons why happiness is associated with higher income, including the meeting of basic needs, fulfillment of psychological needs, increasing satisfaction with one's standard of living, and public goods. Across the globe, the association of log income with subjective well-being was linear but convex with raw income, indicating the declining marginal effects of income on subjective well-being. Income was a moderately strong predictor of life evaluation but a much weaker predictor of positive and negative feelings. Possessing luxury conveniences and satisfaction with standard of living were also strong predictors of life evaluation. Although the meeting of basic and psychological needs mediated the effects of income on life evaluation to some degree, the strongest mediation was provided by standard of living and ownership of conveniences. In contrast, feelings were most associated with the fulfillment of psychological needs: learning, autonomy, using one's skills, respect, and the ability to count on others in an emergency. Thus, two separate types of prosperity-economic and social psychological-best predict different types of well-being.

Question: What is the relationship between income and happiness?


Ricci Flow for 3D Shape Analysis. Ricci flow is a powerful curvature flow method, which is invariant to rigid motion, scaling, isometric, and conformal deformations. We present the first application of surface Ricci flow in computer vision. Previous methods based on conformal geometry, which only handle 3D shapes with simple topology, are subsumed by the Ricci flow-based method, which handles surfaces with arbitrary topology. We present a general framework for the computation of Ricci flow, which can design any Riemannian metric by user-defined curvature. The solution to Ricci flow is unique and robust to noise. We provide implementation details for Ricci flow on discrete surfaces of either Euclidean or hyperbolic background geometry. Our Ricci flow-based method can convert all 3D problems into 2D domains and offers a general framework for 3D shape analysis. We demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration, and shape indexing. Surfaces with large nonrigid anisotropic deformations can be registered using Ricci flow with constraints of feature points and curves. We show how conformal equivalence can be used to index shapes in a 3D surface shape space with the use of Teichmuller space coordinates. Experimental results are shown on 3D face data sets with large expression deformations and on dynamic heart data.

Question: What is the first large-scale Canadian peacekeeping mission?


A confirmatory factor analysis of IS employee motivation and retention. It is widely recognized that the relationships between organizations and their IS departments are changing. This trend threatens to undermine the retention of IS workers and the productivity of IS operations. In the study reported here, we examine IS employees' motivation and intent to remain using structural equation modeling. A survey was conducted among existing IS employees and analyzed with LISREL VIII. Results showed that latent motivation has an impact on latent retention, with job satisfaction and perceptions of management on career development as indicator variables for the former, and burnout, loyalty, and turnover intent as indicator variables for the latter. Implications for management in developing strategies for the retention of IS employees are provided. # 2001 Elsevier Science B.V. All rights reserved.

Question: What is the impact of motivation on retention?


GNU Radio 802 . 15 . 4 En-and Decoding. The IEEE wireless standard 802.15.4 gets widespread attention because of its adoption in sensor networks, home automation, and other networked systems. The goal of the project is to implement an enand decoding block for the IEEE 802.15.4 protocol in GNU Radio, an open source solution for software defined radios. This report will give an insight into the working of GNU Radio and some of its hardware components. Additionally, it gives details about the implementation of the enand decoding blocks. At the end, we will verify the implementation by sending and receiving messages to and from an actual IEEE 802.15.4 radio chip, the CC2420 from ChipCon, and give a small bandwidth comparison of the two solutions.

Question: What is the IEEE 802.15.4 wireless standard?


Crowdsourcing of Pollution Data using Smartphones. In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.

Question: What is the noise pollution in your city?


Exploration and apprenticeship learning in reinforcement learning. We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near-optimal policies by using "exploration policies" to drive the system towards poorly modeled states, so as to encourage exploration. But this makes these algorithms impractical for many systems; for example, on an autonomous helicopter, overly aggressive exploration may well result in a crash. In this paper, we consider the apprenticeship learning setting in which a teacher demonstration of the task is available. We show that, given the initial demonstration, no explicit exploration is necessary, and we can attain near-optimal performance (compared to the teacher) simply by repeatedly executing "exploitation policies" that try to maximize rewards. In finite-state MDPs, our algorithm scales polynomially in the number of states; in continuous-state linear dynamical systems, it scales polynomially in the dimension of the state. These results are proved using a martingale construction over relative losses.

Question: What is reinforcement learning?


Two-Switch Voltage Equalizer Using an LLC Resonant Inverter and Voltage Multiplier for Partially Shaded Series-Connected Photovoltaic Modules. Various kinds of differential power processing converters and voltage equalizers have been proposed for series-connected photovoltaic (PV) modules to prevent negative influences of partial shading, such as significant reduction in power generation and the occurrence of multiple maximum power points (MPPs), including local and global MPPs, that hinders and confuses MPP tracking algorithms to operate properly. However, since conventional topologies are based on multiple individual dc-dc converters, the required switch count increases proportionally to the number of modules connected in series, increasing the complexity. A two-switch voltage equalizer using an LLC resonant inverter and voltage multiplier is proposed in this paper. The circuitry can be dramatically simplified compared with conventional topologies due to the two-switch configuration. Detailed operation analyses for the LLC resonant inverter and voltage multiplier are separately performed. Experimental equalization tests emulating partially shaded conditions were performed for four PV modules connected in series. With the proposed voltage equalizer, local MPPs successfully disappeared, and extractable maximum power increased compared with those without equalization, demonstrating the effectiveness and performance of the proposed voltage equalizer.

Question: How many switches are required to equalize a series of PV modules?


Opinion-aware Knowledge Graph for Political Ideology Detection. Identifying individual's political ideology from their speeches and written texts is important for analyzing political opinions and user behavior on social media. Traditional opinion mining methods rely on bag-of-words representations to classify texts into different ideology categories. Such methods are too coarse for understanding political ideologies. The key to identify different ideologies is to recognize different opinions expressed toward a specific topic. To model this insight, we classify ideologies based on the distribution of opinions expressed towards real-world entities or topics. Specifically, we propose a novel approach to political ideology detection that makes predictions based on an opinion-aware knowledge graph. We show how to construct such graph by integrating the opinions and targeted entities extracted from text into an existing structured knowledge base, and show how to perform ideology inference by information propagation on the graph. Experimental results demonstrate that our method achieves high accuracy in detecting ideologies compared to baselines including LR, SVM and RNN.

Question: What is the difference between a knowledge graph and a structured knowledge base?


Sistemas de reconocimiento basados en la imagen facial. ─ This article summarizes the most important research projects developed in thearea of recognitions y stemsusing facial images. It presents the description of the principal lines of study about people identification systems using face recognition. Furthermore, it contains a synthesis of recent mathematical techniques for pattern sextraction in these systems.

Question: What is the most important research project developed in the area of recognitions and stems using facial images?


Recovering punctuation marks for automatic speech recognition. This paper shows results of recovering punctuation over speech transcriptions for a Portuguese broadcast news corpus. The approach is based on maximum entropy models and uses word, part-of-speech, time and speaker information. The contribution of each type of feature is analyzed individually. Separate results for each focus condition are given, making it possible to analyze the differences of performance between planned and spontaneous speech.

Question: How to recover punctuation marks for automatic speech recognition?


Efficiency Degradation in Wideband Power Amplifiers. This paper addresses the efficiency degradation observed in wideband power amplifiers. It starts by presenting a detailed explanation that relates this observed performance degradation with the terminations at baseband. Then, a comparison between two implemented power amplifiers with the same fundamental and harmonic terminations, but with different baseband networks is presented, showing that an optimized bias network design can improve the observed efficiency degradation.

Question: What is the baseband network?


Simultaneous robot-world and hand-eye calibration. 1 Simultaneous Robot-World and Hand-Eye Calibration Fadi Dornaika and Radu Horaud, Member, IEEE Abstract|Recently, Zhuang, Roth, & Sudhakar [1] proposed a method that allows simultaneous computation of the rigid transformations from world frame to robot base frame and from hand frame to camera frame. Their method attempts to solve a homogeneous matrix equation of the form AX = ZB. They use quaternions to derive explicit linear solutions for X and Z. In this short paper, we present two new solutions that attempt to solve the homogeneous matrix equation mentioned above: (i) a closed-form method which uses quaternion algebra and a positive quadratic error function associated with this representation and (ii) a method based on non-linear constrained minimization and which simultaneously solves for rotations and translations. These results may be useful to other problems that can be formulated in the same mathematical form. We perform a sensitivity analysis for both our two methods and the linear method developed by Zhuang et al. [1]. This analysis allows the comparison of the three methods. In the light of this comparison the non-linear optimization method, which solves for rotations and translations simultaneously, seems to be the most stable one with respect to noise and to measurement errors. Keywords| hand/eye calibration, robot/world calibration, quaternion algebra.

Question: What is the difference between a robot and a hand?


A convenient nucleic acid test on the basis of the capillary convective PCR for the on-site detection of enterovirus 71.. The recent and continuing epidemic of enterovirus 71 in China has affected millions of children and resulted in thousands of deaths. Timely diagnosis and management is essential for disease control. Current enterovirus 71 molecular tests require resources that are unavailable for on-site testing. We have developed a simple-to-operate nucleic acid test, the convenient and integrated nucleic acid test, for local medical institutions. It uses a convective PCR for rapid amplification, a dipstick for visual detection of PCR products, and a simple commercial kit for nucleic acid extraction. By using a specially designed reagent and reaction tube containing a dipstick, the amplification and detection processes are well integrated and simplified. Moreover, cross contamination that may be caused by an open-tube detection system can be avoided. On the basis of the convenient and integrated nucleic acid test, an enterovirus 71 assay for on-site testing was developed. After evaluating known hand, foot, and mouth disease virus stocks of 17 strains of 11 different serotypes, this assay showed a favorable detection spectrum and no cross-reactivity. Its clinical performance was established by testing 141 clinical samples and comparing the results with a nested RT-PCR method. The assay showed a clinical sensitivity and specificity of 98.5% and 100%, respectively. Our results suggest that this convenient and integrated nucleic acid test enterovirus 71 assay may serve as an on-site diagnosis tool.

Question: What is the name of the nucleic acid test?


An efficient selection of HOG feature for SVM classification of vehicle. Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance.

Question: How to reduce the dimension of HOG feature?