Importance filtering for image retargeting. Content-aware image retargeting has attracted a lot of interests recently. The key and most challenging issue for this task is how to balance the tradeoff between preserving the important contents and minimizing the visual distortions on the consistency of the image structure. In this paper we present a novel filtering-based technique to tackle this issue, called "importance filtering". Specifically, we first filter the image saliency guided by the image itself to achieve a structure-consistent importance map. We then use the pixel importance as the key constraint to compute the gradient map of pixel shifts from the original resolution to the target. Finally, we integrate the shift gradient across the image using a weighted filter to construct a smooth shift map and render the target image. The weight is again controlled by the pixel importance. The two filtering processes enforce to maintain the structural consistency and yet preserve the important contents in the target image. Furthermore, the simple nature of filter operations allows highly efficient implementation for real-time applications and easy extension to video retargeting, as the structural constraints from the original image naturally convey the temporal coherence between frames. The effectiveness and efficiency of our importance filtering algorithm are confirmed in extensive experiments.

Question: How to filter an image?


Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture. In physical Human-Robot Interaction, the basic problem of fast detection and safe robot reaction to unexpected collisions has been addressed successfully on advanced research robots that are torque controlled, possibly equipped with joint torque sensors, and for which an accurate dynamic model is available. In this paper, an end-user approach to collision detection and reaction is presented for an industrial manipulator having a closed control architecture and no additional sensors. The proposed detection and reaction schemes have minimal requirements: only the outer joint velocity reference to the robot manufacturer's controller is used, together with the available measurements of motor currents and joint positions. No a priori information on the robot dynamic model and existing low-level joint controllers is strictly needed. A suitable on-line processing of the motor currents allows to distinguish between accidental collisions and intended human-robot contacts, so as to switch the robot to a collaboration mode when needed. Two examples of reaction schemes for collaboration are presented, with the user pushing/pulling the robot at any point of its structure (e.g., for manual guidance) or with a compliant-like robot behavior in response to forces applied by the human. The actual performance of the methods is illustrated through experiments on a KUKA KR5 manipulator.

Question: What is a closed control architecture?


Boosting for transfer learning. Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.

Question: What is transfer learning?


A Focus on Recent Developments and Trends in Underwater Imaging. ■ Compact, efficient and easy to program digital signal processors execute algorithms once too computationally expensive for real-time applications. ■ Modeling and simulation programs more accurately predict the effects that physical ocean parameters have on the performance of imaging systems under different geometric configurations. ■ Image processing algorithms that handle data from multiple synchronous sources and that can extract and match feature points from each such source derive accurate 3-D scene information. ■ Digital compression schemes provide high-quality standardizations for increased data transfer rates (i.e. streaming video) and reduced storage requirements. This paper reports developments over the past three years in the following topics: ■ Image formation and image processing methods; ■ Extended range imaging techniques;

Question: What is the difference between image formation and image processing?


A gesture-driven computer interface using Kinect. Automatic recognition of human actions from video has been studied for many years. Although still very difficult in uncontrolled scenarios, it has been successful in more restricted settings (e.g., fixed viewpoint, no occlusions) with recognition rates approaching 100%. However, the best-performing methods are complex and computationally-demanding and thus not well-suited for real-time deployments. This paper proposes to leverage the Kinect camera for close-range gesture recognition using two methods. Both methods use feature vectors that are derived from the skeleton model provided by the Kinect SDK in real-time. Although both methods perform nearest-neighbor classification, one method does this in the space of features using the Euclidean distance metric, while the other method does this in the space of feature covariances using a log-Euclidean metric. Both methods recognize 8 hand gestures in real time achieving correct-classification rates of over 99% on a dataset of 20 subjects but the method based on Euclidean distance requires feature-vector collections to be of the same size, is sensitive to temporal misalignment, and has higher computation and storage requirements.

Question: What is gesture-driven computer interface?


Detecting Semantic Parts on Partially Occluded Objects. In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. WANG ET.AL.: DETECTING SEMANTIC PARTS ON PARTIALLY OCCLUDED OBJECTS 1 Detecting Semantic Parts on Partially Occluded Objects Jianyu Wang*1 wjyouch@gmail.com Cihang Xie*2 cihangxie306@gmail.com Zhishuai Zhang*2 zhshuai.zhang@gmail.com Jun Zhu1 zhujun.sjtu@gmail.com Lingxi Xie( )2 198808xc@gmail.com Alan L. Yuille2 alan.l.yuille@gmail.com 1 Baidu Research (USA), Sunnyvale, CA 94089 USA 2 Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA . . . . * This work was done when the first author was a Ph.D. student at UCLA. The first three authors contributed equally to this work.

Question: How to detect semantic parts on partially occluded objects?


Soft-Switching Solid-State Transformer (S4T). This paper presents a new topology for a fully bidirectional soft-switching solid-state transformer (S4T). The minimal topology, featuring 12 main devices and a high-frequency transformer, does not use an intermediate dc voltage link, and provides sinusoidal input and output voltages. The S4T can be configured to interface with two- or multiterminal dc, single- or multiphase ac systems. An auxiliary resonant circuit creates zero-voltage-switching conditions for main devices from no-load to full-load, and helps manage interactions with circuit parasitic elements. The modularized structure allows series and/or parallel stacking of converter cells for high-voltage and high-power applications.

Question: What is a soft-switching solid-state transformer?


Orestes: A scalable Database-as-a-Service architecture for low latency. Today, the applicability of database systems in cloud environments is considerably restricted because of three major problems: I) high network latencies for remote/mobile clients, II) lack of elastic horizontal scalability mechanisms, and III) missing abstraction of storage and data models. In this paper, we propose an architecture, a REST/HTTP protocol and a set of algorithms to solve these problems through a Database-as-a-Service middleware called Orestes (Objects RESTfully Encapsulated in Standard Formats). Orestes exposes cloud-hosted NoSQL database systems through a scalable tier of REST servers. These provide database-independent, object-oriented schema design, a client-independent REST-API for database operations, globally distributed caching, cache consistency mechanisms and optimistic ACID transactions. By comparative evaluations we offer empirical evidence that the proposed Database-as-a-Service architecture indeed solves common latency, scalability and abstraction problems encountered in modern cloud-based applications.

Question: What is a scalable database?


Low-Quality Product Review Detection in Opinion Summarization. Product reviews posted at online shopping sites vary greatly in quality. This paper addresses the problem of detecting lowquality product reviews. Three types of biases in the existing evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of specifications for judging the quality of reviews is first defined. A classificationbased approach is proposed to detect the low-quality reviews. We apply the proposed approach to enhance opinion summarization in a two-stage framework. Experimental results show that the proposed approach effectively (1) discriminates lowquality reviews from high-quality ones and (2) enhances the task of opinion summarization by detecting and filtering lowquality reviews.

Question: How to detect low-quality product reviews?


CO2 bio-mitigation using microalgae. Microalgae are a group of unicellular or simple multicellular photosynthetic microorganisms that can fix CO2 efficiently from different sources, including the atmosphere, industrial exhaust gases, and soluble carbonate salts. Combination of CO2 fixation, biofuel production, and wastewater treatment may provide a very promising alternative to current CO2 mitigation strategies.

Question: What is CO2 bio-mitigation using microalgae?


Dynamic Traffic Diversion in SDN: testbed vs Mininet. In this paper, we first propose a simple Dynamic Traffic Diversion (DTD) algorithm for Software Defined Networks (SDN). After implementing the algorithm inside the controller, we then compare the results obtained under two different test environments: 1) a testbed using real Cisco equipment and 2) a network emulation using Mininet. From the results, we get two key messages. First, we can clearly see that dynamically diverting important traffic on a backup path will prevent packet loss and reduce jitter. Finally, the two test environments provide relatively similar results. The small differences could be explained by the early field trial image that was used on the Cisco equipment and by the many setting parameters that are available in both environments.

Question: What is the difference between a testbed and a mininet?


Organoids as an in vitro model of human development and disease. The in vitro organoid model is a major technological breakthrough that has already been established as an essential tool in many basic biology and clinical applications. This near-physiological 3D model facilitates an accurate study of a range of in vivo biological processes including tissue renewal, stem cell/niche functions and tissue responses to drugs, mutation or damage. In this Review, we discuss the current achievements, challenges and potential applications of this technique.

Question: What is an in vitro model of human development and disease?


Towards Controlled Transformation of Sentiment in Sentences. An obstacle to the development of many natural language processing products is the vast amount of training examples necessary to get satisfactory results. The generation of these examples is often a tedious and timeconsuming task. This paper this paper proposes a method to transform the sentiment of sentences in order to limit the work necessary to generate more training data. This means that one sentence can be transformed to an opposite sentiment sentence and should reduce by half the work required in the generation of text. The proposed pipeline consists of a sentiment classifier with an attention mechanism to highlight the short phrases that determine the sentiment of a sentence. Then, these phrases are changed to phrases of the opposite sentiment using a baseline model and an autoencoder approach. Experiments are run on both the separate parts of the pipeline as well as on the end-to-end model. The sentiment classifier is tested on its accuracy and is found to perform adequately. The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible. We use human evaluation to judge the performance of the full (end-to-end) pipeline and that reveals that a model using word vectors outperforms the encoder model. Numerical evaluation shows that a success rate of 54.7% is achieved on the sentiment change.

Question: How to change the sentiment of a sentence?


Weakly supervised localization of novel objects using appearance transfer. We consider the problem of localizing unseen objects in weakly labeled image collections. Given a set of images annotated at the image level, our goal is to localize the object in each image. The novelty of our proposed work is that, in addition to building object appearance model from the weakly labeled data, we also make use of existing detectors of some other object classes (which we call "familiar objects"). We propose a method for transferring the appearance models of the familiar objects to the unseen object. Our experimental results on both image and video datasets demonstrate the effectiveness of our approach.

Question: What is the novelty of our proposed work?


An investigation of current models of second language speech perception: the case of Japanese adults' perception of English consonants.. This study reports the results of two experiments with native speakers of Japanese. In experiment 1, near-monolingual Japanese listeners participated in a cross-language mapping experiment in which they identified English and Japanese consonants in terms of a Japanese category, then rated the identifications for goodness-of-fit to that Japanese category. Experiment 2 used the same set of stimuli in a categorial discrimination test. Three groups of Japanese speakers varying in English-language experience, and one group of native English speakers participated. Contrast pairs composed of two English consonants, two Japanese consonants, and one English and one Japanese consonant were tested. The results indicated that the perceived phonetic distance of second language (L2) consonants from the closest first language (L1) consonant predicted the discrimination of L2 sounds. In addition, this study investigated the role of experience in learning sounds in a second language. Some of the consonant contrasts tested showed evidence of learning (i.e., significantly higher scores for the experienced than the relatively inexperienced Japanese groups). The perceived phonetic distance of L1 and L2 sounds was found to predict learning effects in discrimination of L1 and L2 sounds in some cases. The results are discussed in terms of models of cross-language speech perception and L2 phonetic learning.

Question: What is the difference between the Japanese and English languages?


Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy. The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS andWDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem. 2013 Elsevier Ltd. All rights reserved.

Question: What is the difference between a cuckoo search algorithm and a wind driven optimization algorithm?


Divertible Protocols and Atomic Proxy Cryptography. First, we introduce the notion of divertibility as a protocol property as opposed to the existing notion as a language property (see Okamoto, Ohta [OO90]). We give a definition of protocol divertibility that applies to arbitrary 2-party protocols and is compatible with Okamoto and Ohta's definition in the case of interactive zero-knowledge proofs. Other important examples falling under the new definition are blind signature protocols. We propose a sufficiency criterion for divertibility that is satisfied by many existing protocols and which, surprisingly, generalizes to cover several protocols not normally associated with divertibility (e.g., Diffie-Hellman key exchange). Next, we introduce atomic proxy cryptography, in which an atomic proxy function, in conjunction with a public proxy key, converts ciphertexts (messages or signatures) for one key into ciphertexts for another. Proxy keys, once generated, may be made public and proxy functions applied in untrusted environments. We present atomic proxy functions for discrete-log-based encryption, identification, and signature schemes. It is not clear whether atomic proxy functions exist in general for all public-key cryptosystems. Finally, we discuss the relationship between divertibility and proxy cryptography.

Question: What is atomic proxy cryptography?


Unikernel-based approach for software-defined security in cloud infrastructures. The heterogeneity of cloud resources implies sub-stantial overhead to deploy and configure adequate security mechanisms. In that context, we propose a software-defined security strategy based on unikernels to support the protection of cloud infrastructures. This approach permits to address management issues by uncoupling security policy from their enforcement through programmable security interfaces. It also takes benefits from unikernel virtualization properties to support this enforcement and provide resources with low attack surface. These resources correspond to highly constrained configurations with the strict minimum for a given period. We describe the management framework supporting this software-defined security strategy, formalizing the generation of unikernel images that are dynamically built to comply with security requirements over time. Through an implementation based on MirageOS, and extensive experiments, we show that the cost induced by our security integration mechanisms is small while the gains in limiting the security exposure are high.

Question: How to implement software-defined security in cloud infrastructures?


Union simulation on lower limbs rehabilitation robot based on MATLAB and ADAMS. For people who suffers the lower limbs motor dysfunction, using lower limbs exoskeleton rehabilitation devices is a very effective method to restore the motor ability of lower limbs. However, it works only if the device has a suitable mechanical structure and appropriate design parameters for the specific individual. To get this work completed, we carry out the modeling and simulation of a new proposed lower limbs rehabilitation robot with joint help of two kinds of software, i.e. the MATLAB and the ADAMS. Firstly, the three-dimensional model of the robot is created with the SolidWorks software. After importing the model into ADAMS, some essential kinematic pairs are added into the movable joints. Meanwhile, the control system model is established using the MATLAB/SIMULINK software. Then, the union simulation of the lower limbs rehabilitation robot is carried out by using the interface modular ADAMS/CONTROL. The results show that the proposed rehabilitation robot has a good kinematic performance, which means that the device can be used in the actual rehabilitation process. Besides, the results also indicate that the control method is of good trajectory tracking ability. These simulated results will be significant for the development of physical prototype of lower limbs rehabilitation robot.

Question: How to simulate the lower limbs rehabilitation robot?


Capturing the mood: facebook and face-to-face encounters in the workplace. What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.

Question: What is the difference between online and offline social interactions?


Supporting public health priorities: recommendations for physical education and physical activity promotion in schools.. Physical activity (PA) provides numerous physiological and psychosocial benefits. However, lifestyle changes, including reduced PA opportunities in multiple settings, have resulted in an escalation of overweight and obesity and related health problems. Poor physical and mental health, including metabolic and cardiovascular problems is seen in progressively younger ages, and the systematic decline in school PA has contributed to this trend. Of note, the crowded school curriculum with an intense focus on academic achievement, lack of school leadership support, funding and resources, plus poor quality teaching are barriers to PA promotion in schools. The school setting and physical educators in particular, must embrace their role in public health by adopting a comprehensive school PA program. We provide an overview of key issues and challenges in the area plus best bets and recommendations for physical education and PA promotion in the school system moving forward.

Question: What is the role of physical education in public health?


300mm low k wafer dicing saw study. With the farther shrink of the IC dimension, the low k material has been widely used to replace the traditional SiO/sub 2/ ILD in order to reduce the interconnect delay. The introduction of low k material into silicon imposed challenges on dicing saw process, ILD and metal layers peeling and its penetration into the sealing ring of the die during dicing saw are the most common defects. In this paper, the low k material structure and its impact on wafer dicing were elaborated. A practical dicing quality inspection matrix was developed to assess the cutting process variation. A 300mm CMOS 90nm dual damascene low k wafer was chosen as a test vehicle to develop robust low k dicing saw process. The critical factors (dicing blade, index speed, spindle speed, cut in depth, test pattern in the saw street...) affecting cutting quality were studied and optimized. The selected C90 dual damascene low k device passed package reliability test with the optimized low k dicing saw recipe and process. The further improvement and solutions in eliminating the low k dicing saw peeling from both wafer fab and packaging assembly were also explored.

Question: What is the impact of low k material on dicing saw?


A power primer.. One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

Question: How to calculate sample size for a power analysis?


Object Recognition from Local Scale-Invariant Features. Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

Question: What is the nearest-neighbor indexing method?


Evolutionary Computation and Convergence to a Pareto Front. Research into solving multiobjective optimization problems (MOP) has as one of its an overall goals that of developing and defining foundations of an Evolutionary Computation (EC)-based MOP theory. In this paper, we introduce relevant MOP concepts, and the notion of Pareto optimality, in particular. Specific notation is defined and theorems are presented ensuring Paretobased Evolutionary Algorithm (EA) implementations are clearly understood. Then, a specific experiment investigating the convergence of an arbitrary EA to a Pareto front is presented. This experiment gives a basis for a theorem showing a specific multiobjective EA statistically converges to the Pareto front. We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods.

Question: What is the pareto front?


A density-based cluster validity approach using multi-representatives. Although the goal of clustering is intuitively compelling and its notion arises in many fields, it is difficult to define a unified approach to address the clustering problem and thus diverse clustering algorithms abound in the research community. These algorithms, under different clustering assumptions, often lead to qualitatively different results. As a consequence the results of clustering algorithms (i.e. data set partitionings) need to be evaluated as regards their validity based on widely accepted criteria. In this paper a cluster validity index, CDbw, is proposed which assesses the compactness and separation of clusters defined by a clustering algorithm. The cluster validity index, given a data set and a set of clustering algorithms, enables: i) the selection of the input parameter values that lead an algorithm to the best possible partitioning of the data set, and ii) the selection of the algorithm that provides the best partitioning of the data set. CDbw handles efficiently arbitrarily shaped clusters by representing each cluster with a number of points rather than by a single representative point. A full implementation and experimental results confirm the reliability of the validity index showing also that its performance compares favourably to that of several others.

Question: What is a cluster validity index?


Hard Mixtures of Experts for Large Scale Weakly Supervised Vision. Training convolutional networks (CNNs) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large networks that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new [7, 3], but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.

Question: How large is the canadian military?


Metamorphic Testing: A Review of Challenges and Opportunities. Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.

Question: What is metamorphic testing?


Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions. The author compares the multi-attribute and subjective expected utility (SEU) models popular in research on consumer behavior to the approach offered by the theory of planed behavior (TPB). Unlike the multi-attribute and SEU models, the TPB relies not on revealed preferences to infer the underlying decision process but instead on direct assessment of its theoretical constructs. According to the theory, the consumer's behavior is a function of intention to perform the behavior in question; the intention is based on attitude, subjective norm, and perceived behavioral control with respect to the behavior; and these factors are determined, respectively, by behavioral, normative, and control beliefs. The theory allows us to predict intentions and behavior with respect to the purchase or use of a single brand or product as well as in relation to choice among different brands or products.

Question: What is the theory of planned behavior?


Character-Aware Neural Language Models. We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long shortterm memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-ofthe-art despite having 60% fewer parameters. On languages with rich morphology (Czech, German, French, Spanish, Russian), the model consistently outperforms a Kneser-Ney baseline and word-level/morpheme-level LSTM baselines, again with far fewer parameters. Our results suggest that on many languages, character inputs are sufficient for language modeling.

Question: What is a character-level neural language model?


Bloom Filter Performance on Graphics Engines. Bloom filters are a probabilistic technique for large-scale set membership tests. They exhibit no false negative test results but are susceptible to false positive results. They are well-suited to both large sets and large numbers of membership tests. We implement the Bloom filters present in an accelerated version of BLAST, a genome biosequence alignment application, on NVIDIA GPUs and develop an analytic performance model that helps potential users of Bloom filters to quantify the inherent tradeoffs between throughput and false positive rates.

Question: What is a bloom filter?


The infrared camera-based system to evaluate the human sleepiness. The eye's blinking is a significant indicator of the sleepiness. The existing systems of blink detection and sleepiness analysis require usually to fix camera on spectacle frame or a special helmet that is not convenient and can affect the obtained result. In this paper, the infrared camera-based contact-less system is proposed to evaluate the human's sleepiness. The infrared light switching is used to detect the pupil in each frame and, as a result, the blink event. The active pan-tilt unit is used to make possible free head movements. The algorithm is pointed out to process the camera frames in order to distinguish the involuntary blinks from voluntary ones. Preliminary experimental tests are shown with the intent to validate the proposed hardware and software system pointed out.

Question: What is the infrared camera-based system to evaluate the human sleepiness?


Transductive Face Sketch-Photo Synthesis. Face sketch-photo synthesis plays a critical role in many applications, such as law enforcement and digital entertainment. Recently, many face sketch-photo synthesis methods have been proposed under the framework of inductive learning, and these have obtained promising performance. However, these inductive learning-based face sketch-photo synthesis methods may result in high losses for test samples, because inductive learning minimizes the empirical loss for training samples. This paper presents a novel transductive face sketch-photo synthesis method that incorporates the given test samples into the learning process and optimizes the performance on these test samples. In particular, it defines a probabilistic model to optimize both the reconstruction fidelity of the input photo (sketch) and the synthesis fidelity of the target output sketch (photo), and efficiently optimizes this probabilistic model by alternating optimization. The proposed transductive method significantly reduces the expected high loss and improves the synthesis performance for test samples. Experimental results on the Chinese University of Hong Kong face sketch data set demonstrate the effectiveness of the proposed method by comparing it with representative inductive learning-based face sketch-photo synthesis methods.

Question: What is the difference between inductive learning and transductive learning?


Improving Text Simplification Language Modeling Using Unsimplified Text Data. In this paper we examine language modeling for text simplification. Unlike some text-to-text translation tasks, text simplification is a monolingual translation task allowing for text in both the input and output domain to be used for training the language model. We explore the relationship between normal English and simplified English and compare language models trained on varying amounts of text from each. We evaluate the models intrinsically with perplexity and extrinsically on the lexical simplification task from SemEval 2012. We find that a combined model using both simplified and normal English data achieves a 23% improvement in perplexity and a 24% improvement on the lexical simplification task over a model trained only on simple data. Post-hoc analysis shows that the additional unsimplified data provides better coverage for unseen and rare n-grams.

Question: How to improve text simplification language model?


Comparison between SPM and IPM motor drives for EV application. Electric Vehicles make use of permanent magnet synchronous traction motors for their high torque density and efficiency. A comparison between interior permanent magnet (IPM) and surface mounted permanent magnet (SPM) motors is carried out, in terms of performance at given inverter ratings. A simplified analytical approach is proposed and its results are confirmed by actual motor design, validated through FEA. The main result of the proposed analysis is to put in evidence the different behavior of IPM and SPM motors in terms of overload capability: as expected, the overload current results in a power overload of the IPM motor, while in the SPM motor the maximum power is upper limited irrespectively of the current overload.

Question: What is the difference between IPM and SPM motors?


Defining Media Enjoyment as the Satisfaction of Intrinsic Needs. This article presents a model of enjoyment rooted in self-determination theory (Deci & Ryan, 1985) that includes the satisfaction of three needs related to psychological wellbeing: autonomy, competence, and relatedness. In an experiment designed to validate this conceptualization of enjoyment, we manipulate video game characteristics related to the satisfaction of these needs and examine their relative effects on enjoyment. The validated model explains 51% of the variance in enjoyment, even without including needs usually studied in relation to enjoyment such as pleasure seeking. Results indicate the utility of defining enjoyment as need satisfaction. These results are discussed in terms of a broader conceptualization of enjoyment represented as the satisfaction of a comprehensive set of functional needs.

Question: What is the difference between intrinsic and extrinsic needs?


A Component-Based Dynamic Link Support for Safety-Critical Embedded Systems. Safety-critical embedded systems have to undergo rigorous development process in order to ensure that their function will not compromise humans or environment where they operate. Therefore, they rely on simple and proven-in-use design. However, with growing software complexity, maintenance becomes very important aspect in safety domain. Recent approaches for managing maintenance allow to perform changes on software at design-time, which implies that the whole system has to be rebuilt when the application software changes. In this paper, we describe more flexible solution for updating the application software. We apply the component-based paradigm to construct the application software, i.e. we define a model of a software function that can be dynamically linked with the entire operating system (OS). In order to avoid the usage of the OS-provided support for dynamic linking, we design software functions as position-independent and relocation-free binaries with well-defined interfaces. With the help of component-based paradigm we show how to simplify the link support and make it suitable for safety domain.

Question: How to update the application software?


Parallel and distributed simulation. Research and development efforts in the parallel and distributed simulation field over the last 15 years has progressed, largely independently, in two separate camps: the largely academic high performance parallel and distributed (discrete event) simulation (PADS) community, and the DoD-centered distributed interactive simulation (DIS) community. This tutorial gives an overview and comparison of work in these two areas, emphasizing issues related to distributed execution where these fields have the most overlap. Differences in the fundamental assumptions routinely used within each community are contrasted, followed by overviews of work in each community.

Question: What is the difference between parallel and distributed simulation?


An Optimal Algorithm for Finding the Kernel of a Polygon. The kernel K(P) of a simple polygon P wah n verUces is the locus of the points internal to P from which all verUces of P are wslble Equwalently, K(P) is the mtersectmn of appropriate half-planes determined by the polygon's edges Although it is known that to find the intersection of n generic half-planes requires time O(n log n), we show that one can exploit the ordering of the half-planes corresponding to the sequence of the polygon's edges to obtain a kernel finding algorithm which runs m time O(n) and is therefore optimal

Question: What is the optimal algorithm for finding the kernel of a polygon?


Visibility estimation and joint inpainting of lidar depth maps. This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.

Question: How to estimate the depth of a point cloud from a color image?


Cerberus: a context-aware security scheme for smart spaces. Ubiquitous computing has fueled the idea of constructing sentient, information-rich "smart spaces" that extend the boundaries of traditional computing to encompass physical spaces, embedded devices, sensors, and other machinery. To achieve this, smart spaces need to capture situational information so that they can detect changes in context and adapt themselves accordingly. However, without considering basic security issues ubiquitous computing environments could be rife with vulnerabilities. Ubiquitous computing environments impose new requirements on security. Security services, like authentication and access control, have to be non-intrusive, intelligent, and able to adapt to the rapidly changing contexts of the spaces. We present a ubiquitous security mechanism that integrates context-awareness with automated reasoning to perform authentication and access control in ubiquitous computing environments.

Question: What is the context-aware security scheme for smart spaces?


Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. It's not surprisingly when entering this site to get the book. One of the popular books now is the genetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases. You may be confused because you can't find the book in the book store around your city. Commonly, the popular book will be sold quickly. And when you have found the store to buy the book, it will be so hurt when you run out of it. This is why, searching for this popular book in this website will give you benefit. You will not run out of this book.

Question: What is the book about?


Impact of Customer Relationship Management on Customer Loyalty , Customer Retention and Customer Profitability for Hotelier Sector. Since the entrance of strategies oriented to marketing relational in Hotelier Sector, the traditional way of travel agents and other representatives arranging hospitality services for hotel and travel reservations has changed. The strategies oriented to customer relationship management are a relatively new area of specialty loyalty marketing in the hotel and hotelier sector, with advancements being made constantly. The use of this type of strategy can allow hoteliers or companies to tailor special guest programs, services and promotions based on hotel guest preferences. The hotel can use the data collected in a program to identify the needs of particular customers across hotel chains to be able to use marketing that can be targeted at specific groups of people. It also gives hoteliers the opportunity to evaluate frequent guest programs, personalize their services and perform trend analysis. A program based in marketing relational is typically run by hotels and companies to collect guest information and transaction data for use and examining to allow hoteliers to see target groups that should be marketed too. Based on these transactions hotels are able to create and manage guest loyalty programs and reward schemes. This research approach is to appraise the impact of customer relationship management on customer profitability as mediated by customer loyalty and customer retention within the hotelier sector of Mexico, and specifically for those hoteliers classified like of three stars. A sample of 100 hotels three stars was interviewed as respondents in this study. The objective of the study was to find the impact relationship between effective customer relationship implementation, customer loyalty, and customer retention and customer profitability. The findings of the study add value to hotels three stars in Mexico, and provide some invalu

Question: What is the impact of customer relationship management on customer loyalty?


Confabulation based sentence completion for machine reading. Sentence completion and prediction refers to the capability of filling missing words in any incomplete sentences. It is one of the keys to reading comprehension, thus making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics the human information processing. The building of confabulation knowledge base uses an unsupervised machine learning algorithm that extracts the relations between objects at the symbolic level. In this work, we propose performance improved training and recall algorithms that apply the cogent confabulation model to solve the sentence completion problem. Our training algorithm adopts a two-level hash table, which significantly improves the training speed, so that a large knowledge base can be built at relatively low computation cost. The proposed recall function fills missing words based on the sentence context. Experimental results show that our software can complete trained sentences with 100% accuracy. It also gives semantically correct answers to more than two thirds of the testing sentences that have not been trained before.

Question: What is the difference between sentence completion and prediction?


(Leader/Randomization/Signature)-free Byzantine Consensus for Consortium Blockchains. This paper presents a new resilience optimal Byzantine consensus algorithm targeting consortium blockchains. To this end, it first revisits the consensus validity property by requiring that the decided value satisfies a predefined predicate, which does not systematically exclude a value proposed only by Byzantine processes, thereby generalizing the validity properties found in the literature. Then the paper presents a simple and modular Byzantine consensus algorithm that relies neither on a leader, nor on signatures, nor on randomization. It consists of a reduction of multivalued Byzantine consensus to binary Byzantine consensus satisfying this validity property. This reduction terminates after a constant-sized sequence of binary Byzantine consensus instances. The idea is to spawn concurrent instances of binary consensus but to decide only after a sequence of two of these instances. The binary consensus instances result in a bitmask that the reduction applies to a vector of multivalued proposals to filter out a valid proposed value that is decided. The paper then presents an underlying binary Byzantine consensus algorithm that assumes eventual synchrony to terminate.

Question: What is a consortium blockchain?


Design, Modeling, Control, and Evaluation of a Hybrid Hip Joint Miniature Climbing Robot. The subject of this paper is the design, control, and evaluation of a biped–climbing robot featuring a new hybrid hip joint. The hybrid hip provides both prismatic and revolute motion, discretely, to the robot, using a single actuator. This is intended to improve its adaptability in confined environments and its capability to maneuver over and around obstacles. Optimization of the hybrid hip joint relative to robot size, weight, and actuation limits is considered while maximizing range of motion. The mechanical structure of the robot is discussed, as well as forward and inverse kinematics for motion planning. Reachable robot workspace in both revolute and prismatic phases of the hybrid hip joint is analyzed. Different robot locomotion strides are developed and dynamic controller requirements are considered. Experimental evaluation of the robot walking and climbing on and between surfaces with different inclinations is conducted to evaluate performance and repeatability of the system. KEY WORDS—biped robot, climbing robot, robot design, kinematics, locomotion strides

Question: What is the hybrid hip joint?


Bayesian face recognition. We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA's 1996 "FERET" face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases. Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2002 201 Broadway, Cambridge, Massachusetts 02139 Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000.

Question: What is the difference between a Bayesian and Euclidean distance?


Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations. Knowledge graphs are typical large-scale multi-relational structures, which comprise a large amount of fact triplets. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is widely used to embed fact triplets into low-dimensional spaces. Many existing knowledge graph embedding models either focus on learning rich features from entities but fail to extract good features of relations, or employ sophisticated models that have rather high time and memory-space complexities. In this paper, we propose a novel knowledge graph embedding model, CombinE. It exploits entity features from two complementary perspectives via the plus and minus combinations. We start with the plus combination, where we use shared features of entity pairs participating in a relation to convey its relation features. To also allow differences of each pairs of entities participating in a relation, we also use the minus combination, where we concentrate on individual entity features, and regard relations as a channel to offset the divergence and preserve the prominence between head and tail entities. Compared with the state-of-the-art models, our experimental results demonstrate that CombinE outperforms existing ones and has low time and memory-space complexities.

Question: What is the difference between the plus and minus combinations?


Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks. Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU ) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.

Question: What is the next-generation airborne collision avoidance system for unmanned aircraft?


A taxonomy for user-healthcare robot interaction. This paper evaluates existing taxonomies aimed at characterizing the interaction between robots and their users and modifies them for health care applications. The modifications are based on existing robot technologies and user acceptance of robotics. Characterization of the user, or in this case the patient, is a primary focus of the paper, as they present a unique new role as robot users. While therapeutic and monitoring-related applications for robots are still relatively uncommon, we believe they will begin to grow and thus it is important that the spurring relationship between robot and patient is well understood.

Question: What is a taxonomy for user-healthcare robot interaction?


Graphical Models for Probabilistic and Causal Reasoning. This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network. Figure 1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X1), whether rain falls (X2) during the season, whether the sprinkler is on (X3) during that season, whether the pavement would get wet (X4), and whether the pavement would be slippery (X5). All variables in this figure are binary, taking a value of either true or false, except the root variable X1 which can take one of four values: Spring, Summer, Fall, or Winter. Here, the absence of a direct link between X1 and X5, for example, captures our understanding that the influence of seasonal variations on the slipperiness of the pavement is mediated by other conditions (e.g., the wetness of the pavement). As this example illustrates, a Bayesian network constitutes a model of the environment rather than, as in many other knowledge representation schemes (e.g., logic, rule-based systems and neural networks), a model of the reasoning process. It simulates, in fact, the causal mechanisms that operate in the environment, and thus allows the investigator to answer a variety of queries, including: associational queries, such as "Having observed A, what can we expect of B?"; abductive queries, such as "What is the most plausible explanation for a given set of observations?"; and control qu

Question: What is the most plausible explanation for a given set of observations?


On happiness and human potentials: a review of research on hedonic and eudaimonic well-being.. Well-being is a complex construct that concerns optimal experience and functioning. Current research on well-being has been derived from two general perspectives: the hedonic approach, which focuses on happiness and defines well-being in terms of pleasure attainment and pain avoidance; and the eudaimonic approach, which focuses on meaning and self-realization and defines well-being in terms of the degree to which a person is fully functioning. These two views have given rise to different research foci and a body of knowledge that is in some areas divergent and in others complementary. New methodological developments concerning multilevel modeling and construct comparisons are also allowing researchers to formulate new questions for the field. This review considers research from both perspectives concerning the nature of well-being, its antecedents, and its stability across time and culture.

Question: What is the difference between hedonic and eudaimonic well-being?


Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft. Abstract The blended-wing body is an aircraft configuration that has the potential to be more efficient than conventional large transport aircraft configurations with the same capability. However, the design of the blended-wing is challenging due to the tight coupling between aerodynamic performance, trim, and stability. Other design challenges include the nature and number of the design variables involved, and the transonic flow conditions. To address these issues, we perform a series of aerodynamic shape optimization studies using Reynolds-averaged Navier–Stokes computational fluid dynamics with a Spalart–Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. A total of 273 design variables—twist, airfoil shape, sweep, chord, and span—are considered. The drag coefficient at the cruise condition is minimized subject to lift, trim, static margin, and center plane bending moment constraints. The studies investigate the impact of the various constraints and design variables on optimized blended-wing-body configurations. The lowest drag among the trimmed and stable configurations is obtained by enforcing a 1% static margin constraint, resulting in a nearly elliptical spanwise lift distribution. Trim and static stability are investigated at both onand off-design flight conditions. The single-point designs are relatively robust to the flight conditions, but further robustness is achieved through a multi-point optimization.

Question: What is the lowest drag among the trimmed and stable configurations?


Efficient Evaluation of the Terminal Response of a Twisted-Wire Pair Excited by a Plane-Wave Electromagnetic Field. Twisted-wire pair (TWP) loops are used as one of the primary communication channels in digital subscriber line (DSL) networks. As part of the development of channel noise models, a transmission-line-based model for predicting the terminal response of a single TWP to an illuminating plane-wave electromagnetic field is presented. Closed-form analytic approximations of the terminal response valid for an arbitrary direction of incidence and polarization of the incoming plane-wave field are provided. A worst-case model with the very same purpose has been previously proposed in the DSL literature; however, a worst-case approach only provides partial information about the field coupling mechanism. The goal of the presented transmission-line model is to introduce a more robust yet efficient approach for characterizing radio noise interference in DSL systems. Computed results for two TWP configurations of interest are presented together with measured results for one of them.

Question: What is the worst case for a twisted wire pair?


Ontology based Semantic Search Engine. We live in a connected age when every entity is linked to each other through numerous relationships thus forming complex networks, information networks etc., In this meshy scenario, storing analyzing, managing extracting information is going to be highly challenging task. Information retrieval happened to be an activity that only a few people engaged in. due to the advent of search engines and communication facilities, millions of people are engaged in information retrieval and extraction process. The information retrieval deals with finding a set of documents relevant to the user query. Commercial search engines like Google deals with key word search which is based on Boolean logical queries. The major disadvantage of this kind of keyword search is that it returns a lot of irrelevant information to the users which results in low precision. Nowadays the field of information retrieval is moving towards semantic level from syntactic level. These semantic search engines are based on the concept of ontology which gives a Meta data representation of concepts. In this paper we focus on the retrieval mechanisms related to it. This includes various phases in which the ontology is designed first and then the indexing and retrieval phases work. Key wordsOntology, semantic web, Information retrieval, Query builder I.INTRODUCTION In this digital age each and every concept is linked to each other through various relationships forming the complex information web. By passing through the web we face some sort of challenges in storing and extracting the information from them. We therefore rely on search engines to overcome those difficulties. Any way these search engines facilitate only keyword search which does not returns accurate results to the external user. Aiming to solve the limitations of keyword based models, the idea of semantic search, understood as searching by meanings rather than by literals has been the focus of a wide body of re

Question: What is the difference between ontology and semantic web?


Integrated staffing and scheduling for an aircraft line maintenance problem. This paper presents a method for constructing the workforce schedules of an aircraft maintenance company. The method integrates both the staffing and the scheduling decision. We formulate the optimization problem using a mixed integer linear programming approach and solve it heuristically using a branch-and-bound enumeration framework. Each node of the tree represents a combination of teams and team sizes. An extensive computational experiment is added to illustrate the algorithmic performance using a set of test instances that are derived from a real-life setting. We conclude this paper by discussing some results and by addressing interesting future extensions to the model.

Question: How to solve a scheduling problem?


Multimodal Convolutional Neural Networks for Matching Image and Sentence. In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.

Question: What is the end-to-end framework for matching image and sentence?


Learned Optimizers that Scale and Generalize. Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal perparameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse optimization tasks capturing common properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on. We release an open source implementation of the meta-training algorithm.

Question: What is a convolutional neural network?


Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature. 2009 Elsevier Ltd. All rights reserved.

Question: What is the difference between particle swarm optimization and quantum-behaved particle swarm optimization?


Flexible sensor for Mckibben pneumatic actuator. A flexible electro-conductive rubber sensor for measuring the length of a McKibben pneumatic actuator has been developed. Mckibben actuators are flexible, lightweight, and widely used to actuate power assisting devices for which compliance is required for safety. The actuator's length needs to be measured to control the devices accurately. However, the flexibility and lightweight properties might be lost if rigid sensors such as linear potentionmeters or encoders are directly attached to the actuators. To keep the desirable properties of McKibben actuators, compact and flexible sensors are necessary. In this paper, a flexible sensor using electro-conductive flexible rubber is proposed to measure the length of McKibben actuators. Using this sensor, a higher accuracy can be obtained by measuring the circumference displacement than directly measuring the axial displacement. The estimation accuracy is evaluated and the usefulness of the proposed method is verified by adopting to a multiple-link robot arm driven by the McKibben actuators.

Question: What is a flexible sensor?


Applying graph-based anomaly detection approaches to the discovery of insider threats. The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. In this paper we present graph-based approaches to uncovering anomalies in applications containing information representing possible insider threat activity: e-mail, cell-phone calls, and order processing.

Question: What is graph-based anomaly detection?


Bootstrapping Biomedical Ontologies for Scientific Text using NELL. We describe an open information extraction system for biomedical text based on NELL (the NeverEnding Language Learner) [7], a system designed for extraction from Web text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of "seeds" for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL's bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL's original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.

Question: How does NELL bootstrap?


Safer smart contracts through type-driven development. We show how dependent and polymorphic types can make smart contract development safer. This is demonstrated by using the functional language Idris to describe smart contracts on the Ethereum platform. In particular, we show how one class of common errors can be captured at compile time using dependent types and algebraic side effects. We also bring type annotations to the realm of smart contracts, helping developers to circumvent another class of common errors. To demonstrate the feasibility of our solutions, we have extended the Idris compiler with a backend for the Ethereum Virtual Machine. While we find that the functional paradigm might not be the most suitable for the domain, our approach solves the identified problems and provides advantages over the languages in current use.

Question: What is the difference between dependent and polymorphic types?


Comparative analysis of big data transfer protocols in an international high-speed network. Large-scale scientific installations generate voluminous amounts of data (or big data) every day. These data often need to be transferred using high-speed links (typically with 10 Gb/s or more link capacity) to researchers located around the globe for storage and analysis. Efficiently transferring big data across countries or continents requires specialized big data transfer protocols. Several big data transfer protocols have been proposed in the literature, however, a comparative analysis of these protocols over a long distance international network is lacking in the literature. We present a comparative performance and fairness study of three open-source big data transfer protocols, namely, GridFTP, FDT, and UDT, using a 10 Gb/s high-speed link between New Zealand and Sweden. We find that there is limited performance difference between GridFTP and FDT. GridFTP is stable in terms of handling file system and TCP socket buffer. UDT has an implementation issue that limits its performance. FDT has issues with small buffer size limiting its performance, however, this problem is overcome by using multiple flows. Our work indicates that faster file systems and larger TCP socket buffers in both the operating system and application are useful in improving data transfer rates.

Question: What is the difference between GridFTP and FDT?


Network medicine: a network-based approach to human disease. Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Question: What is network medicine?


Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis. Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear discriminant analysis (TR-LDA) to deal with high-dimensional non-Gaussian fault data for dimension reduction and fault classification. Motor bearing data with single-point faults and generalized-roughness faults are used to validate the effectiveness of the proposed method for fault diagnosis. Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.

Question: What is the difference between a bearing and a motor?


Overview on Additive Manufacturing Technologies. This paper provides an overview on the main additive manufacturing/3D printing technologies suitable for many satellite applications and, in particular, radio-frequency components. In fact, nowadays they have become capable of producing complex net-shaped or nearly net-shaped parts in materials that can be directly used as functional parts, including polymers, metals, ceramics, and composites. These technologies represent the solution for low-volume, high-value, and highly complex parts and products.

Question: What are the main additive manufacturing technologies suitable for satellite applications?


Factors Influencing Consumers ' E-Commerce Commodity Purchases. The 2002 North America Online Report published by eMarketer estimates that almost 24 million pre-college age students (ages 9-17) are already shopping online and gaining valuable e-commerce purchasing experience. Estimates of online shopping usage project steady growth, the number of young adults buying online will increase proportionally. This research seeks to develop a better understanding of the factors motivating young people to select e-commerce vendors for commodity purchases by exploring attitudes, demographic characteristics and purchase decision perceptions (i.e., the product, shopping experience, customer service, and consumer risk). Findings indicate that young adults with a history of e-commerce purchasing experience have a more positive attitude towards online buying than do young adults without e-commerce purchasing experience. In a related finding, a history of e-commerce purchasing experience serves as a good predictor of future e-commerce commodity purchases. Additionally, consumer risk and shopping experience perceptions were found to influence experienced e-commerce shoppers' commodity purchase decisions more than customer service or consumer risk.

Question: What are the factors influencing consumers' e-commerce commodity purchases?


Qualitative Methods in Research on Teaching. This chapter reviews basic issues of theory and method in approaches to research on teaching that are alternatively called ethnographic, qualitative, participant observational, case study, symbolic interactionist, phenomenological, constructivist, or interpretive. These approaches are all slightly different, but each bears strong family resemblance to the others. The set of related approaches is relatively new in the field of research on teaching. The approaches have emerged as significant in the decade of the 1960s in England and in the 1970s in the United States, Australia, New Zealand, and Germany. Because interest in these approaches is so recent, the previous editions of the Handbook of Research on Teaching do not contain a chapter devoted to participant observational research. Accordingly, this chapter attempts to describe r~s and their theoretical _nresup~s in considerable detail and does not attempt an exhaustive review of the rapidly growing literature in the field. Such a review will be appropriate for the next edition of this handbook. From this point on I will use the term interpretive to refer to the whole family of approaches to participant observational research. I adopt this term for three reasons: (a) It is more inclusive than many of the others (e.g., ethnography, case study); (b) W Blake

Question: What is the difference between qualitative and interpretive research?


Systematic Variation of Prosthetic Foot Spring Affects Center-of-Mass Mechanics and Metabolic Cost During Walking. Lower-limb amputees expend more energy to walk than non-amputees and have an elevated risk of secondary disabilities. Insufficient push-off by the prosthetic foot may be a contributing factor. We aimed to systematically study the effect of prosthetic foot mechanics on gait, to gain insight into fundamental prosthetic design principles. We varied a single parameter in isolation, the energy-storing spring in a prototype prosthetic foot, the controlled energy storage and return (CESR) foot, and observed the effect on gait. Subjects walked on the CESR foot with three different springs. We performed parallel studies on amputees and on non-amputees wearing prosthetic simulators. In both groups, spring characteristics similarly affected ankle and body center-of-mass (COM) mechanics and metabolic cost. Softer springs led to greater energy storage, energy return, and prosthetic limb COM push-off work. But metabolic energy expenditure was lowest with a spring of intermediate stiffness, suggesting biomechanical disadvantages to the softest spring despite its greater push-off. Disadvantages of the softest spring may include excessive heel displacements and COM collision losses. We also observed some differences in joint kinetics between amputees and non-amputees walking on the prototype foot. During prosthetic push-off, amputees exhibited reduced energy transfer from the prosthesis to the COM along with increased hip work, perhaps due to greater energy dissipation at the knee. Nevertheless, the results indicate that spring compliance can contribute to push-off, but with biomechanical trade-offs that limit the degree to which greater push-off might improve walking economy.

Question: What is the effect of prosthetic foot spring on gait?


Generation from Abstract Meaning Representation using Tree Transducers. Language generation from purely semantic representations is a challenging task. This paper addresses generating English from the Abstract Meaning Representation (AMR), consisting of re-entrant graphs whose nodes are concepts and edges are relations. The new method is trained statistically from AMRannotated English and consists of two major steps: (i) generating an appropriate spanning tree for the AMR, and (ii) applying tree-tostring transducers to generate English. The method relies on discriminative learning and an argument realization model to overcome data sparsity. Initial tests on held-out data show good promise despite the complexity of the task. The system is available open-source as part of JAMR at: http://github.com/jflanigan/jamr

Question: What is the abstract meaning representation?


A 330nA energy-harvesting charger with battery management for solar and thermoelectric energy harvesting. Harvesting energy from ambient energy sources such as solar and thermal gradients is one solution to address the dramatic increase in energy consumption of personal electronics. In this paper, an ultra low quiescent current charger and battery management IC that can efficiently extract energy from solar panels and thermoelectric generators to charge batteries and super capacitors is presented. While previous works on energy harvesting [1-5] report efficient DC-DC converters to extract the energy from the harvester, not all of them provide complete battery management functionality for various chemistries. Also, maximum power point tracking (MPPT) which is critical in energy harvesting applications is not provided in some of the published work. In this paper, a charger and battery management IC with 330nA quiescent current is presented. The IC can cold start from 330mV and 5μW of input power. The charger achieves efficiency greater than 80% at single cell solar voltages of 0.5V. A low quiescent current battery management architecture involving sampled circuits, sub regulated rails and clock gating is demonstrated.

Question: What is the quiescent current of a battery?


Greed is good: Approximating independent sets in sparse and bounded-degree graphs. Theminimum-degree greedy algorithm, or Greedy for short, is a simple and well-studied method for finding independent sets in graphs. We show that it achieves a performance ratio of (Δ+2)/3 for approximating independent sets in graphs with degree bounded by Δ. The analysis yields a precise characterization of the size of the independent sets found by the algorithm as a function of the independence number, as well as a generalization of Turán's bound. We also analyze the algorithm when run in combination with a known preprocessing technique, and obtain an improved $$(2\bar d + 3)/5$$ performance ratio on graphs with average degree $$\bar d$$ , improving on the previous best $$(\bar d + 1)/2$$ of Hochbaum. Finally, we present an efficient parallel and distributed algorithm attaining the performance guarantees of Greedy.

Question: What is the performance ratio of greedy?


A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data. Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

Question: What is the difference between a Markov Switching Vector Autoregressive model and a Vector Autoregressive model?


Cyberbullying perpetration and victimization among adolescents in Hong Kong. a r t i c l e i n f o Cyberbullying is a growing concern worldwide. Using a sample of 1917 secondary adolescents from seven schools, five psychometric measures (self-efficacy, empathy level, feelings regarding a harmonious school, sense of belonging to the school, and psychosocial wellbeing) and five scales regarding bullying experiences (cyber-and traditional bullying perpetration and victimization; reactions to cyberbullying victimization) were administered to explore the prevalence of cyberbullying in Hong Kong. Findings indicated that male adolescents were more likely than female adolescents to cyberbully others and to be cyber-victimized. Cyberbullying perpe-tration and victimization were found to be negatively associated with the adolescents' psychosocial health and sense of belonging to school. Cyber-and traditional bullying were positively correlated. Multivariate analyses indicated that being male, having a low sense of belonging to school, involvement in traditional bullying perpetra-tion, and experiencing cyber-victimization were associated with an increased propensity to cyberbully others. Technology is advancing rapidly, and peer harassment and aggression are no longer limited to traditional bullying through physical contact. Over the past decade, information and communication technology (ICT) has become increasingly important in the lives of adolescents. In a report by Lenhart, Madden, and Hitlin (2005), it is estimated that close to 90% of American adolescents aged 12 to 17 years surf the Inter-net, with 51% of them using it on a daily basis. Nearly half of the adolescents surveyed have personal mobile phones, and 33% have used a mobile phone to send a text message (Lenhart et al., 2005). Such heavy use of the Internet is not novel among adolescents in Hong Kong. Many empirical studies have been conducted on Hong Kong adolescents on their excessive and/or addictive use of the

Question: What is the prevalence of cyberbullying in hong kong?


An improved LLC resonant inverter for induction heating with asymmetrical control. This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction heating applications by using asymmetrical voltage cancellation control. The proposed control method is implemented in a full-bridge inverter topology. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load parameter variation. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed with a matching transformer in between the series inductor and paralleled LC resonant tank for short circuit protection capability of the matching transformer and the induction coil. The validity of the proposed method is verified through computer simulation and hardware experiment at the operating frequency of 108.7 to 110.6 kHz.

Question: What is the operating frequency of an induction heater?


Toward an affect-sensitive multimodal human-computer interaction. The ability to recognize affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligenceisafacetofhumanintelligencethathasbeenarguedtobe indispensable and perhaps the most important for successful interpersonal social interaction. This paper argues that next-generation human–computer interaction (HCI) designs need to include the essence of emotional intelligence—the ability to recognize a user's affective states—in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI—an automatic personalized analyzer of a user's nonverbal affective feedback.

Question: What is the first part of an intelligent multimodal HCI?


From Modeling to Implementation of Virtual Sensors in Body Sensor Networks. Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.

Question: How to implement a virtual sensor in a body sensor network?


Low-Cost 60-GHz Smart Antenna Receiver Subsystem Based on Substrate Integrated Waveguide Technology. In this paper, a low-cost integrated 60-GHz switched-beam smart antenna subsystem is studied and demonstrated experimentally for the first time based on almost all 60-GHz substrate integrated waveguide (SIW) components including a slot antenna, 4×4 Butler matrix network, bandpass filter, sub-harmonically pumped mixer, and local oscillator (LO) source. In this study, an antenna array, a Butler matrix, and a bandpass filter are integrated and fabricated into one single substrate. Instead of using a 60-GHz LO source, a 30-GHz LO source is developed to drive a low-cost 60-GHz sub-harmonically pumped mixer. This 30-GHz LO circuit consists of 10-GHz SIW voltage-controlled oscillator and frequency tripler. Following the frequency down-conversion of four 60-GHz signals coming from the 4×4 Butler matrix and a comparison of the four IF signals executed in the digital processor based on the maximum received power criterion, control signals will be feed-backed to drive the single-pole four-throw switch array and then the beam is tuned in order to point toward the main beam of the transmit antenna. In this way, the arriving 60-GHz RF signal can be tracked effectively. All designed components are verified experimentally. The proposed smart receiver subsystem that integrates all those front-end components is concluded with satisfactory measured results.

Question: What is the difference between a 60-GHz smart antenna and a 60-GHz smart antenna receiver?


Non-linear robust control for inverted-pendulum 2D walking. We present an approach to high-level control for bipedal walking exemplified with a 2D point-mass inextensible-legs inverted-pendulum model. Balance control authority here is only from step position and trailing-leg push-off, both of which are bounded to reflect actuator limits. The controller is defined implicitly as the solution of an optimization problem. The optimization robustly avoids falling for given bounded disturbances and errors and, given that, minimizes the number of steps to reach a given target speed. The optimization can be computed in advance and stored for interpolated real-time use online. The general form of the resulting optimized controller suggests a few simple principles for regulating walking speed: 1) The robot should take bigger steps when speeding up and should also take bigger steps when slowing down 2) push-off is useful for regulating small changes in speed, but it is fully saturated or inactive for larger changes in speed. While the numerically optimized model is simple, the approach should be applicable to, and we plan to use it for, control of bipedal robots in 3D with many degrees of freedom.

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


The wave kernel signature: A quantum mechanical approach to shape analysis. We introduce the Wave Kernel Signature (WKS) for characterizing points on non-rigid three-dimensional shapes. The WKS represents the average probability of measuring a quantum mechanical particle at a specific location. By letting vary the energy of the particle, the WKS encodes and separates information from various different Laplace eigenfrequencies. This clear scale separation makes the WKS well suited for a large variety of applications. Both theoretically and in quantitative experiments we demonstrate that the WKS is substantially more discriminative and therefore allows for better feature matching than the commonly used Heat Kernel Signature (HKS). As an application of the WKS in shape analysis we show results on shape matching.

Question: What is the wave kernel signature?


Differential effects of atomoxetine on executive functioning and lexical decision in attention-deficit/hyperactivity disorder and reading disorder.. OBJECTIVE The effects of a promising pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), atomoxetine, were studied on executive functions in both ADHD and reading disorder (RD) because earlier research demonstrated an overlap in executive functioning deficits in both disorders. In addition, the effects of atomoxetine were explored on lexical decision.   METHODS Sixteen children with ADHD, 20 children with ADHD + RD, 21 children with RD, and 26 normal controls were enrolled in a randomized placebo-controlled crossover study. Children were measured on visuospatial working memory, inhibition, and lexical decision on the day of randomization and following two 28-day medication periods.   RESULTS Children with ADHD + RD showed improved visuospatial working memory performance and, to a lesser extent, improved inhibition following atomoxetine treatment compared to placebo. No differential effects of atomoxetine were found for lexical decision in comparison to placebo. In addition, no effects of atomoxetine were demonstrated in the ADHD and RD groups.   CONCLUSION Atomoxetine improved visuospatial working memory and to a lesser degree inhibition in children with ADHD + RD, which suggests differential developmental pathways for co-morbid ADHD + RD as compared to ADHD and RD alone.   CLINICAL TRIAL REGISTRY B4Z-MC-LYCK, NCT00191906; http://clinicaltrials.gov/ct2/show/NCT00191906.

Question: What is the difference between ADHD and ADHD + RD?