Getting Physical with the Digital Investigation Process. In this paper, a process model for digital investigations is defined using the theories and techniques from the physical investigation world. While digital investigations have recently become more common, physical investigations have existed for thousands of years and the experience from them can be applied to the digital world. This paper introduces the notion of a digital crime scene with its own witnesses, evidence, and events that can be investigated using the same model as a physical crime scene. The proposed model integrates the physical crime scene investigation with the digital crime scene investigation to identify a person who is responsible for the digital activity. The proposed model applies to both law enforcement and corporate investigations.

Question: How can digital investigations be integrated with physical investigations?


Avoiding monotony: improving the diversity of recommendation lists. The primary premise upon which top-N recommender systems operate is that similar users are likely to have similar tastes with regard to their product choices. For this reason, recommender algorithms depend deeply on similarity metrics to build the recommendation lists for end-users.  However, it has been noted that the products offered on recommendation lists are often too similar to each other and attention has been paid towards the goal of improving diversity to avoid monotonous recommendations.  Noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem. We explore a solution strategy to this optimization problem by relaxing it to a trust-region problem.This leads to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution. We apply this approach to the top-N prediction problem, evaluate the system performance on the Movielens dataset and compare it with a standard item-based top-N algorithm. A new evaluation metric ItemNovelty is proposed in this work. Improvements on both diversity and accuracy are obtained compared to the benchmark algorithm.

Question: How to improve the diversity of recommendation lists?


Relations Among Loneliness, Social Anxiety, and Problematic Internet Use. The model of problematic Internet use advanced and tested in the current study proposes that individuals' psychosocial well-being, along with their beliefs about interpersonal communication (both face-to-face and online) are important cognitive predictors of negative outcomes arising from Internet use. The study examined the extent to which social anxiety explains results previously attributed to loneliness as a predictor of preference for online social interaction and problematic Internet use. The results support the hypothesis that the relationship between loneliness and preference for online social interaction is spurious, and that social anxiety is the confounding variable.

Question: What is the relationship between loneliness and social anxiety?


Proofs of Work for Blockchain Protocols. One of the most impactful applications of proofs of work (POW) currently is in the design of blockchain protocols such as Bitcoin. Yet, despite the wide recognition of POWs as the fundamental cryptographic tool in this context, there is no known cryptographic formulation that implies the security of the Bitcoin blockchain protocol. Indeed, all previous works formally arguing the security of the Bitcoin protocol relied on direct proofs in the random oracle model, thus circumventing the di culty of isolating the required properties of the core POW primitive. In this work we ll this gap by providing a formulation of the POW primitive that implies the security of the Bitcoin blockchain protocol in the standard model. Our primitive entails a number of properties that parallel an e cient non-interactive proof system: completeness and fast veri cation, security against malicious provers (termed hardness against tampering and chosen message attacks ) and security for honest provers (termed uniquely successful under chosen key and message attacks ). Interestingly, our formulation is incomparable with previous formulations of POWs that applied the primitive to contexts other than the blockchain. Our result paves the way for proving the security of blockchain protocols in the standard model assuming our primitive can be realized from computational assumptions.

Question: What is the proof of work for blockchain protocols?


Neither Quick Nor Proper - Evaluation of QuickProp for Learning Deep Neural Networks. Neural networks and especially convolutional neural networks are of great interest in current computer vision research. However, many techniques, extensions, and modifications have been published in the past, which are not yet used by current approaches. In this paper, we study the application of a method called QuickProp for training of deep neural networks. In particular, we apply QuickProp during learning and testing of fully convolutional networks for the task of semantic segmentation. We compare QuickProp empirically with gradient descent, which is the current standard method. Experiments suggest that QuickProp can not compete with standard gradient descent techniques for complex computer vision tasks like semantic segmentation.

Question: What is the current standard method for training deep neural networks?


Social Interactions and Social Preferences in Social Networks. We extend the utility specification in Ballester et al. (2006) to study social interactions when individuals hold altruistic preferences in social networks. We show that more enriched network features can be captured in the best response function derived from the maximization of the extended utility which incorporates altruism, providing microfoundation for studies which investigate how network features mediate peer effects or other important features in social interactions. We demonstrate that often ignored altruism is another serious confounding factor of peer effects. Our results show that the estimates of peer affects are about 36% smaller under altruistic preferences. Furthermore, we are able to separate the two different types of effects generated by peers: the (usually positive) spillover effects from peers and the (negative or positive) externality effects directly generated from peers' outcomes, which is not possible in conventional social interactions model based on self-interest hypothesis. JEL Classification: I10 I20 C31

Question: How to separate the two different types of effects generated by peers?


Comparative assessment of methods for aligning multiple genome sequences. Multiple sequence alignment is a difficult computational problem. There have been compelling pleas for methods to assess whole-genome multiple sequence alignments and compare the alignments produced by different tools. We assess the four ENCODE alignments, each of which aligns 28 vertebrates on 554 Mbp of total input sequence. We measure the level of agreement among the alignments and compare their coverage and accuracy. We find a disturbing lack of agreement among the alignments not only in species distant from human, but even in mouse, a well-studied model organism. Overall, the assessment shows that Pecan produces the most accurate or nearly most accurate alignment in all species and genomic location categories, while still providing coverage comparable to or better than that of the other alignments in the placental mammals. Our assessment reveals that constructing accurate whole-genome multiple sequence alignments remains a significant challenge, particularly for noncoding regions and distantly related species.

Question: How do we align multiple genomes?


Maximum Margin Clustering. We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our approach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simultaneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.

Question: What is maximum margin clustering?


A VLC media player plugin enabling dynamic adaptive streaming over HTTP. This paper describes the implementation of a VLC media player plugin enabling Dynamic Adaptive Streaming over HTTP (DASH). DASH is an emerging ISO/IEC MPEG and 3GPP standard for HTTP streaming. It aims to standardize formats enabling segmented progressive download by exploiting existing Internet infrastructure as such. Our implementation of these formats as described in this paper is based on the well-known VLC. Hence, it is fully integrated into the VLC structure and has been also submitted to the VLC development team for consideration in future releases of VLC. Therefore, it is licensed under the GNU Lesser General Public License (LGPL). The plugin provides a very flexible structure that could be easily extended with respect to different adaptation logics or profiles of the DASH standard. As a consequence, the plugin enables the integration of a variety of adaptation logics and comparison thereof, making it attractive for the research community.

Question: What is a VLC media player plugin?


How to evaluate technologies for health behavior change in HCI research. New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.

Question: How to evaluate technologies for health behavior change in HCI research.


A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures. Scientific problems that depend on processing largeamounts of data require overcoming challenges in multiple areas:managing large-scale data distribution, co-placement andscheduling of data with compute resources, and storing and transferringlarge volumes of data. We analyze the ecosystems of thetwo prominent paradigms for data-intensive applications, hereafterreferred to as the high-performance computing and theApache-Hadoop paradigm. We propose a basis, common terminologyand functional factors upon which to analyze the two approachesof both paradigms. We discuss the concept of "Big DataOgres" and their facets as means of understanding and characterizingthe most common application workloads found acrossthe two paradigms. We then discuss the salient features of thetwo paradigms, and compare and contrast the two approaches.Specifically, we examine common implementation/approaches ofthese paradigms, shed light upon the reasons for their current"architecture" and discuss some typical workloads that utilizethem. In spite of the significant software distinctions, we believethere is architectural similarity. We discuss the potential integrationof different implementations, across the different levelsand components. Our comparison progresses from a fully qualitativeexamination of the two paradigms, to a semi-quantitativemethodology. We use a simple and broadly used Ogre (K-meansclustering), characterize its performance on a range of representativeplatforms, covering several implementations from bothparadigms. Our experiments provide an insight into the relativestrengths of the two paradigms. We propose that the set of Ogreswill serve as a benchmark to evaluate the two paradigms alongdifferent dimensions.

Question: What is the difference between the two paradigms?


Semi-Dense Depth Interpolation using Deep Convolutional Neural Networks. With advances of recent technologies, augmented reality systems and autonomous vehicles gained a lot of interest from academics and industry. Both these areas rely on scene geometry understanding, which usually requires depth map estimation. However, in case of systems with limited computational resources, such as smartphones or autonomous robots, high resolution dense depth map estimation may be challenging. In this paper, we study the problem of semi-dense depth map interpolation along with low resolution depth map upsampling. We present an end-to-end learnable residual convolutional neural network architecture that achieves fast interpolation of semi-dense depth maps with different sparse depth distributions: uniform, sparse grid and along intensity image gradient. We also propose a loss function combining classical mean squared error with perceptual loss widely used in intensity image super-resolution and style transfer tasks. We show that with some modifications, this architecture can be used for depth map super-resolution. Finally, we evaluate our results on both synthetic and real data, and consider applications for autonomous vehicles and creating AR/MR video games.

Question: Semi-dense depth map interpolation using deep convolutional neural networks


A 27-mW 3.6-gb/s I/O transceiver. This paper describes a 3.6-Gb/s 27-mW transceiver for chip-to-chip applications. A voltage-mode transmitter is proposed that equalizes the channel while maintaining impedance matching. A comparator is proposed that achieves sampling bandwidth control and offset compensation. A novel timing recovery circuit controls the phase by mismatching the current in the charge pump. The architecture maintains high signal integrity while each port consumes only 7.5 mW/Gb/s. The entire design occupies 0.2 mm/sup 2/ in a 0.18-/spl mu/m 1.8-V CMOS technology.

Question: What is a 27-mW 3.6-gb/s I/O transceiver?


Analytics using R for predicting credit defaulters. Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims to extract useful knowledge from tremendous amount of complex data sets. This work aims to develop a model and construct a prototype for the same using a data set available in the UCI repository. The model is a decision tree based classification model that uses the functions available in the R Package. Prior to building the model, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The final model is used for prediction with the test dataset and the experimental results prove the efficiency of the built model.

Question: What is a decision tree based classification model?


Continual Learning in Generative Adversarial Nets. Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.

Question: What is a generative adversarial network?


Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor. Deployment of convolutional neural networks (ConvNets) in always-on Internet of Everything (IoE) edge devices is severely constrained by the high memory energy consumption of hardware ConvNet implementations. Leveraging the error resilience of ConvNets by accepting bit errors at reduced voltages presents a viable option for energy savings, but few implementations utilize this due to the limited quantitative understanding of how bit errors affect performance. This paper demonstrates the efficacy of SRAM voltage scaling in a 9-layer CIFAR-10 binarized ConvNet processor, achieving memory energy savings of 3.12× with minimal accuracy degradation (∼99% of nominal). Additionally, we quantify the effect of bit error accumulation in a multi-layer network and show that further energy savings are possible by splitting weight and activation voltages. Finally, we compare the measured error rates for the CIFAR-10 binarized ConvNet against MNIST networks to demonstrate the difference in bit error requirements across varying complexity in network topologies and classification tasks.

Question: How does bit error tolerance affect performance?


Design Patterns: Elements of Reusable Object-Oriented Software. Design Patterns is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design. It describes patterns for managing object creation, composing objects into larger structures, and coordinating control flow between objects. The book provides numerous examples where using composition rather than inheritance can improve the reusability and flexibility of code. Note, though, that its not a tutorial but a catalog that you can use to find an object-oriented design pattern thats appropriate for the needs of your particular application--a selection for virtuoso programmers who appreciate (or require) consistent, well-engineered object-oriented designs.

Question: What is a design pattern?


Apollonian Circle Packings: Geometry and Group Theory II. Super-Apollonian Group and Integral Packings. Apollonian circle packings arise by repeatedly filling the interstices between four mutually tangent circles with further tangent circles. Such packings can be described in terms of the Descartes configurations they contain, where a Descartes configuration is a set of four mutually tangent circles in the Riemann sphere, having disjoint interiors. Part I showed there exists a discrete group, the Apollonian group, acting on a parameter space of (ordered, oriented) Descartes configurations, such that the Descartes configurations in a packing formed an orbit under the action of this group. It is observed there exist infinitely many types of integral Apollonian packings in which all circles had integer curvatures, with the integral structure being related to the integral nature of the Apollonian group. Here we consider the action of a larger discrete group, the super-Apollonian group, also having an integral structure, whose orbits describe the Descartes quadruples of a geometric object we call a super-packing. The circles in a super-packing never cross each other but are nested to an arbitrary depth. Certain Apollonian packings and super-packings are strongly integral in the sense that the curvatures of all circles are integral and the curvature×centers of all circles are integral. We show that (up to scale) there are exactly 8 different (geometric) strongly integral super-packings, and that

Question: What is the super-Apollonian group?


Affective Computing and Sentiment Analysis. Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.

Question: What is affective computing?


Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction. Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather employs existing knowledge bases to heuristically label a training corpus. However, previous work has failed to address the problem of false negative training examples mislabeled due to the incompleteness of knowledge bases. To tackle this problem, we propose a simple yet novel framework that combines a passage retrieval model using coarse features into a state-of-the-art relation extractor using multi-instance learning with fine features. We adapt the information retrieval technique of pseudorelevance feedback to expand knowledge bases, assuming entity pairs in top-ranked passages are more likely to express a relation. Our proposed technique significantly improves the quality of distantly supervised relation extraction, boosting recall from 47.7% to 61.2% with a consistently high level of precision of around 93% in the experiments.

Question: How to improve the quality of distantly supervised relation extraction?


A Review of Technical Approaches to Realizing Near-Field Communication Mobile Payments. This article describes and compares four approaches to storing payment keys and executing payment applications on mobile phones via near-field communication at the point of sale. Even though the comparison hinges on security--specifically, how well the keys and payment application are protected against misuse--other criteria such as hardware requirements, availability, management complexity, and performance are also identified and discussed.

Question: What is the difference between the four approaches?


Syntax and semantics in the acquisition of locative verbs.. Children between the ages of three and seven occasionally make errors with locative verbs like pour and fill, such as *I filled water into the glass and *I poured the glass with water (Bowerman, 1982). To account for this pattern of errors, and for how they are eventually unlearned, we propose that children use a universal linking rule called object affectedness: the direct object corresponds to the argument that is specified as 'affected' in some particular way in the semantic representation of a verb. However, children must learn which verbs specify which of their arguments as being affected; specifically, whether it is the argument whose referent is undergoing a change of location, such as the content argument of pour, or the argument whose referent is undergoing a change of state, such as the container argument of fill. This predicts that syntactic errors should be associated with specific kinds of misinterpretations of verb meaning. Two experiments were performed on the ability of children and adults to understand and produce locative verbs. The results confirm that children tend to make syntactic errors with sentences containing fill and empty, encoding the content argument as direct object (e.g. fill the water). As predicted, children also misinterpreted the meanings of fill and empty as requiring not only that the container be brought into a full or empty state, but also that the content move in some specific manner (by pouring, or by dumping). Furthermore, children who misinterpreted the verbs' meanings were more likely to make syntactic errors with them. These findings support the hypothesis that verb meaning and syntax are linked in precise ways in the lexicons of language learners.

Question: What is the difference between the content and container arguments of a locative verb?


Dynamics co-simulation of a type of spot welding robot by RecurDyn and Simulink. A co-simulation on virtual prototype of a type of spot welding robot has been accomplished by combining MATLAB/Simulink with RecurDyn. The control algorithm is executed under MATLAB to generate command forces, which are then exported to RecurDyn environment and applied to actuators of the virtual prototype at each cycle of time. Dynamic control on joint angles was accomplished through the co-simulation. Torques of each joint and the velocity of the clamp end were also acquired by co-simulation, which can give useful information for optimizing robot design.

Question: How to simulate a robot?


Power Optimization of Battery Charging System Using FPGA Based Neural Network Controller. This paper involves designing a small scale battery charging system which is powered via a photovoltaic panel. This work aims at the usage of solar energy for charging the battery and optimizing the power of the system. Implementation is done using Artificial Neural Network (ANN) on FPGA. To develop this system an Artificial Neural Network is trained and its result is further used for the PWM technique. PWM pulse generation has been done using Papilio board which is based on XILINX Spartan 3E FPGA. The ANN with PWM technique is ported on FPGA which is programmed using VHDL. This able to automatically control the whole charging system operation without requirement of external sensory unit. The simulation results are achieved by using MATLAB and XILINX. These results allowed demonstrating the charging of the battery using proposed ANN and PWM technique. KeywordsPhotovoltaic battery charger, PWM, ANN, FPGA

Question: How to optimize the power of a battery charging system using FPGA?


Completion and Reconstruction with Primitive Shapes. We consider the problem of reconstruction from incomplete point-clouds. To find a closed mesh the reconstruction is guided by a set of primitive shapes which has been detected on the input point-cloud (e.g. planes, cylinders etc.). With this guidance we not only continue the surrounding structure into the holes but also synthesize plausible edges and corners from the primitives' intersections. To this end we give a surface energy functional that incorporates the primitive shapes in a guiding vector field. The discretized functional can be minimized with an efficient graphcut algorithm. A novel greedy optimization strategy is proposed to minimize the functional under the constraint that surface parts corresponding to a given primitive must be connected. From the primitive shapes our method can also reconstruct an idealized model that is suitable for use in a CAD system.

Question: What is a primitive shape?


A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems. Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keywordbased queries. We propose a reinforcementlearning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback as a reward in the learning process. Experiments are carried out on two TREC datasets. We outline the effectiveness of our approach.

Question: What is a reinforcement learning-driven translation model?


Advanced analytics: opportunities and challenges. Purpose – Advanced analytics-driven data analyses allow enterprises to have a complete or "360 degrees" view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics. Design/methodology/approach – A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice. Findings – The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses. Originality/value – The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Question: What is the difference between BI and predictive analytics?


Strip-type Grid Array Antenna with a Two-Layer Rear-Space Structure. A strip-type grid array antenna is analyzed using the finite-difference time-domain method. The space between the grid array and the ground plane, designated as the rear space, is filled with a dielectric layer and an air layer (two-layer rear-space structure). A VSWR bandwidth of approximately 13% is obtained with a maximum directivity of approximately 18 dB. The grid array exhibits a narrow beam with low side lobes. The cross-polarization component is small (-25 dB), as desired.

Question: What is a strip-type grid array antenna?


Business ecosystem as the new approach to complex adaptive business environments. This paper discusses the concept of business ecosystem. Business ecosystem is a relatively new concept in the field of business research, and there is still a lot of work to be done to establish it. First the subject is approached by examining a biological ecosystem, especially how biological ecosystems are defined, how they evolve and how they are classified and structured. Second, different analogies of biological ecosystem are reviewed, including industrial ecosystem, economy as an ecosystem, digital business ecosystem and social ecosystem. Third, business ecosystem concept is outlined by discussing views of main contributors and then bringing authors' own definition out. Fourth, the emerging research field of complexity in social sciences is brought out due to authors' attitude to consider ecosystems and business ecosystems as complex, adaptive systems. The focal complexity aspects appearing in business ecosystems are presented; they are self-organization, emergence, co-evolution and adaptation. By connecting business ecosystem concept to complexity research, it is possible to bring new insights to changing business environments.

Question: What is a business ecosystem?


Arbuscular mycorrhizal fungi in alleviation of salt stress: a review.. BACKGROUND Salt stress has become a major threat to plant growth and productivity. Arbuscular mycorrhizal fungi colonize plant root systems and modulate plant growth in various ways.   SCOPE This review addresses the significance of arbuscular mycorrhiza in alleviation of salt stress and their beneficial effects on plant growth and productivity. It also focuses on recent progress in unravelling biochemical, physiological and molecular mechanisms in mycorrhizal plants to alleviate salt stress.   CONCLUSIONS The role of arbuscular mycorrhizal fungi in alleviating salt stress is well documented. This paper reviews the mechanisms arbuscular mycorrhizal fungi employ to enhance the salt tolerance of host plants such as enhanced nutrient acquisition (P, N, Mg and Ca), maintenance of the K(+) : Na(+) ratio, biochemical changes (accumulation of proline, betaines, polyamines, carbohydrates and antioxidants), physiological changes (photosynthetic efficiency, relative permeability, water status, abscissic acid accumulation, nodulation and nitrogen fixation), molecular changes (the expression of genes: PIP, Na(+)/H(+) antiporters, Lsnced, Lslea and LsP5CS) and ultra-structural changes. Theis review identifies certain lesser explored areas such as molecular and ultra-structural changes where further research is needed for better understanding of symbiosis with reference to salt stress for optimum usage of this technology in the field on a large scale. This review paper gives useful benchmark information for the development and prioritization of future research programmes.

Question: What is the role of arbuscular mycorrhizal fungi in alleviation of salt stress?


Help seeking behavior and the Internet: A national survey. Health-related websites have the potential to powerfully influence the attitudes and behavior of consumers. Access to reliable disease information online has been linked to reduced anxiety, increased feelings of self-efficacy, and decreases in utilization of ambulatory care. Studies report that Internet health information seekers are more likely to have health concerns; adult seekers are more likely to rate themselves as having poor health status and adolescent seekers are more likely to demonstrate clinical impairment or depressive symptomatology compared to non-seekers. Although more and more Americans are using the Internet for healthcare information, little is known about how this information affects their health behaviors. The current study extends the literature by examining characteristics associated with help seeking, either from a healthcare provider or from peers, as a direct result of health information found online. Medical care seekers appear to be using the Internet to enhance their medical care; they report using the information online to diagnose a problem and feel more comfortable about their health provider's advice given the information found on the Internet. Support seekers tend to be of slightly lower income compared to non-support seekers. They are also significantly more likely to have searched for information about a loved one's medical or health condition, signaling that many of these consumers may be caretakers.

Question: What is the relationship between help seeking and the Internet?


Semantic Parsing with Syntax- and Table-Aware SQL Generation. We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query wordby-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest questionSQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.

Question: What is the largest dataset for semantic parsing?


On image classification: city images vs. landscapes. Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, eeective indices can be built for an image database. In this paper, we show how a speciic high-level classiication problem (city images vs. landscapes) can be solved from relatively simple low-level features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classiication problem based on the plot of the intra-class and inter-class distance distributions. We use this approach to determine the discriminative power of the following features: color histogram, color coherence vector, DCT coeecient, edge direction histogram, and edge direction coherence vector. We determine that the edge direction-based features have the most discriminative power for the classiication problem of interest here. A weighted k-NN classiier is used for the classiication which results in an accuracy of 93:9% when evaluated on an image database of 2; 716 images using the leave-one-out method. This approach has been extended to further classify 528 landscape images into forests, mountains, and sunset/sunrise classes. First, the input images are classiied as sunset/sunrise images vs. forest & mountain images (94:5% accuracy) and then the forest & mountain images are classiied as forest images or mountain images (91:7% accuracy). We are currently identifying further semantic classes to assign to images as well as extracting low level features which are salient for these classes. Our nal goal is to combine multiple 2-class classiiers into a single hierarchical classiier.

Question: How to classify images into different semantic classes?


The effects of morphology and fitness on catastrophic interference. Catastrophic interference occurs when an agent improves in one training instance but becomes worse in other instances. Many methods intended to combat interference have been reported in the literature that modulate properties of a neural controller, such as synaptic plasticity or modularity. Here, we demonstrate that adjustments to the body of the agent, or the way its performance is measured, can also reduce catastrophic interference without requiring changes to the controller. Additionally, we introduce new metrics to quantify catastrophic interference. We do not show that our approach outperforms others on benchmark tests. Instead, by more precisely measuring interactions between morphology, fitness, and interference, we demonstrate that embodiment is an important aspect of this problem. Furthermore, considerations into morphology and fitness can combine with, rather than compete with, existing methods for combating catastrophic interference.

Question: What is catastrophic interference?


Why experts make errors. Expert latent f ingerprint examiners were presented with fingerprints taken from real criminal cases. Half of the prints had been previously judged as individualizations and the other half as exclusions. We re-presented the same prints to the same experts who had judged them previously, but provided biasing contextual information in both the individualizations and exclusions. A control set of individualizations and exclusions was also re-presented as part of the study. The control set had no biasing contextual information associated with it. Each expert examined a total of eight past decisions. Two-thirds of the experts made inconsistent decisions. The f indings are discussed in terms of psychological and cognitive vulnerabilities.

Question: Why experts make errors.


Warpage control of silicon interposer for 2.5D package application. In order to achieve high speed transmission and large volume data processing, large size silicon-interposer has been required. Warpage caused by the CTE mismatch between a large silicon-interposer and an organic substrate is the most significant problem. In this study, we investigated several warpage control techniques for 2.5D package assembly process. First was assembly process sequence. One is called "chip first process" that is, chips are mounted on Si-interposer at first. The other is called "chip last process" that is, silicon-interposer is mounted on organic substrate at first and chips are mounted on at last. The chip first process successfully processed using conventional mass reflow. By using the chip first process, apparent CTE of a large silicon-interposer become close to that of an organic substrate. Second was the warpage control using underfill resin. We focused on the selection of underfill materials for 0 level assembly. And third was the warpage control technique with Sn-57Bi solder using conventional reflow process. We observed warpage change during simulated reflow process using three-dimensional digital image correlation system (3D-DIC). Sn-57Bi solder joining has been noted as a low temperature bonding methods. It is possible to lower peak temperature 45-90 degree C during reflow compared with using Sn3.0wt%Ag0.5wt%Cu (SAC305). By using Sn-57Bi solder, the warpage after reflow was reduced to 75% of that using SAC305. The full assembly was successfully processed using conventional assembly equipment and processes. The full assembly packages were evaluated by some reliability tests. All samples passed each reliability test.

Question: What is the warpage control technique using underfill resin?


Paper-Based Synthetic Gene Networks. Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

Question: What is a paper-based synthetic gene network?


A Mixed Filtering Approach for Track Condition Monitoring Using Accelerometers on the Axle Box and Bogie. This paper describes a method of estimating irregularities in railway tracks using acceleration data measured from high-speed trains. Track irregularities are the main causes of the vibration of high-speed trains and thus should be carefully monitored to maintain the stability and ride quality of the trains. A mixed filtering approach is proposed for stable displacement estimation and waveband classification of the irregularities in the measured acceleration. Accelerometers are mounted on the axle box and the bogie of a high-speed train to measure the acceleration in the lateral and vertical directions. The estimated results are compared with those of a commercial track geometry measurement system. Finally, the performance of the proposed approach and the relationship between the mounted location of the accelerometers and the estimated track irregularities are discussed.

Question: How to estimate track irregularities using accelerometers?


Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems. Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained. The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Using the estimated channel, this paper proposes a new hybrid analog/digital precoding algorithm that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.

Question: How many antennas are in a mmWave system?


Mobile robot navigation based on lidar. A wheeled mobile robot based on Arduino was described in this article. Hardware selection, Simultaneous Localization and Mapping (SLAM), Path Planning and the principle of the algorithm applied in this process are introduced in detail. The algorithm used in ROS is further verified by experiments. In order to verify the feasibility of two different 2D SLAM algorithms, we use the wheeled robot built in the lab to do the experiment in the same indoor environment and compare their results.

Question: What is the difference between the two algorithms?


Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012. To provide an update to the "Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock," last published in 2008. A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict of interest policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independent of any industry funding. A stand-alone meeting was held for all subgroup heads, co- and vice-chairs, and selected individuals. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide assessment of quality of evidence from high (A) to very low (D) and to determine the strength of recommendations as strong (1) or weak (2). The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasized. Recommendations were classified into three groups: (1) those directly targeting severe sepsis; (2) those targeting general care of the critically ill patient and considered high priority in severe sepsis; and (3) pediatric considerations. Key recommendations and suggestions, listed by category, include: early quantitative resuscitation of the septic patient during the first 6 h after recognition (1C); blood cultures before antibiotic therapy (1C); imaging studies performed promptly to confirm a potential source of infection (UG); administration of broad-spectrum antimicrobials therapy within 1 h of the recognition of septic shock (1B) and severe sepsis without septic shock (1C) as the goa

Question: What is the difference between a recommendation and a suggestion?


The Effects of Feedback Interventions on Performance : A Historical Review , a Meta-Analysis , and a Preliminary Feedback Intervention Theory. the total number of papers may exceed 10,000. Nevertheless, cost consideration forced us to consider mostly published papers and technical reports in English. 4 Formula 4 in Seifert (1991) is in error—a multiplier of n, of cell size, is missing in the numerator. 5 Unfortunately, the technique of meta-analysis cannot be applied, at present time, to such effects because the distribution of dis based on a sampling of people, whereas the statistics of techniques such as ARIMA are based on the distribution of a sampling of observations in the time domain regardless of the size of the people sample involved (i.e., there is no way to compare a sample of 100 points in time with a sample of 100 people). That is, a sample of 100 points in time has the same degrees of freedom if it were based on an observation of 1 person or of 1,000 people. 258 KLUGER AND DENISI From the papers we reviewed, only 131 (5%) met the criteria for inclusion. We were concerned that, given the small percentage of usable papers, our conclusions might not fairly represent the larger body of relevant literature. Therefore, we analyzed all the major reasons to reject a paper from the meta-analysis, even though the decision to exclude a paper came at the first identification of a missing inclusion criterion. This analysis showed the presence of review articles, interventions of natural feedback removal, and papers that merely discuss feedback, which in turn suggests that the included studies represent 1015% of the empirical FI literature. However, this analysis also showed that approximately 37% of the papers we considered manipulated feedback without a control group and that 16% reported confounded treatments, that is, roughly two thirds of the empirical FI literature cannot shed light on the question of FI effects on performance—a fact that requires attention fro

Question: What is the total number of papers?


Efficient FIR Filter Design Using Modified Carry Select Adder & Wallace Tree Multiplier. An area-power-delay efficient design of FIR filter is described in this paper. In proposed multiplier unit high speed is achieved using XOR-XNOR column by column reduction compressors instead of compressors using full adder. The carry propagation delay and area of carry select adder is reduced by splitting carry select adder into equal bit groups. The proposed carry select adder unit consumes less power than conventional carry select adder unit. With proposed multiplier unit and carry select adder unit, the designed FIR Filter consumes 55% power less than the conventional filter without significant increase in area. The power & delay comparison is performed for both existing and proposed method of FIR filter. The design is implemented using 0.18μm technology. Index Terms FIR filter design, FDA Tool, Low Power VLSI, Wallace tree multiplication.

Question: What is the area of a carry select adder?


Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model selection attempts to find the most likely (MAP) model, and uses its structure to answer these questions. However, when the amount of available data is modest, there might be many models that have non-negligible posterior. Thus, we want compute the Bayesian posterior of a feature, i.e., the total posterior probability of all models that contain it. In this paper, we propose a new approach for this task. We first show how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables. This allows us to compute, for a given order, both the marginal probability of the data and the posterior of a feature. We then use this result as the basis for an algorithm that approximates the Bayesian posterior of a feature. Our approach uses a Markov Chain Monte Carlo (MCMC) method, but over orders rather than over network structures. The space of orders is smaller and more regular than the space of structures, and has much a smoother posterior "landscape". We present empirical results on synthetic and real-life datasets that compare our approach to full model averaging (when possible), to MCMC over network structures, and to a non-Bayesian bootstrap approach.

Question: What is the Bayesian posterior of a feature?


Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks. Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justi es some level of quantization in order to achieve faster feature extraction. Our approach consists of approximating regions of the signal with low degree polynomials, and then di erentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite e ectively. The true convolution can be recovered by integrating the result of the convolution. This method yields substantial speed up in feature extraction and is applicable to convolutional neural networks.

Question: What is a convolutional neural network?


Spermatocele presenting as acute scrotum.. Corresponding Author: Atsuya Hikosaka, MD Department of Urology, Toyota Kosei Hospital, 500-1, Ibobara, Josui-cho, Toyota, Aichi 470-0396, Japan Tel: +81 565 43 5000 Fax: +81 565 43 5100 E-mail: uropatho@ybb.ne.jp INTRODUCTION Spermatocele, a retention cyst of the scrotum which is or has been in communication with the semen-carrying system,(1) is a relatively common clinical entity. It presents typically as an intrascrotal paratesticular mass, but it usually has few subjective symptoms. Here, we report a case of spermatocele manifested with acute scrotum due to its unique feature.

Question: What is a spermatocele?


Information Theoretic-Learning auto-encoder. We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a paritition function. This paper also formalizes, generative moment matching networks under the ITL framework.

Question: What is the ITL framework?


A Unifying View of Sparse Approximate Gaussian Process Regression. We provide a new unifying view, including all existing prope r probabilistic sparse approximations for Gaussian process regression. Our approach relies on exp ressing theeffective priorwhich the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically j ustified ranking of the closeness of the known approximations to the corresponding full GPs. Finall y we point directly to designs of new better sparse approximations, combining the best of the exi sting strategies, within attractive computational constraints.

Question: What is a unifying view?


SiRiUS: Securing Remote Untrusted Storage. This paper presents SiRiUS, a secure file system designed to be layered over insecure network and P2P file systems such as NFS, CIFS, OceanStore, and Yahoo! Briefcase. SiRiUS assumes the network storage is untrusted and provides its own read-write cryptographic access control for file level sharing. Key management and revocation is simple with minimal out-of-band communication. File system freshness guarantees are supported by SiRiUS using hash tree constructions. SiRiUS contains a novel method of performing file random access in a cryptographic file system without the use of a block server. Extensions to SiRiUS include large scale group sharing using the NNL key revocation construction. Our implementation of SiRiUS performs well relative to the underlying file system despite using cryptographic operations.

Question: What is the size of the canadian military?


Good Word Attacks on Statistical Spam Filters. Unsolicited commercial email is a significant problem for users and providers of email services. While statistical spam filters have proven useful, senders of spam are learning to bypass these filters by systematically modifying their email messages. In a good word attack, one of the most common techniques, a spammer modifies a spam message by inserting or appending words indicative of legitimate email. In this paper, we describe and evaluate the effectiveness of active and passive good word attacks against two types of statistical spam filters: naive Bayes and maximum entropy filters. We find that in passive attacks without any filter feedback, an attacker can get 50% of currently blocked spam past either filter by adding 150 words or fewer. In active attacks allowing test queries to the target filter, 30 words will get half of blocked spam past either filter.

Question: How many words are needed to bypass a spam filter?


Background Removal in Image Indexing and Retrieval. This paper presents our research in image content based indexing and retrieval, a key technology in digital image libraries. In most of the existing image content-based techniques, image features used for indexing and retrieval are global, features are computed over the entire image. The major problem with the global image feature based retrieval methods is that background features can be easily mistakenly taken as object features. When a user attempts to retrieve images using color features, he/she usually means the color feature of an object or objects of interests contained in the image. The approach we describe in this paper utilizes color clusters for image background analysis. Once the background regions are identified, they are removed from the image indexing procedure; therefore, no longer interfering with the meaningful image content during the retrieval process. The algorithm consists of three major steps of computation, fuzzy clustering, color image segmentation, and background analysis.

Question: What is the background removal in image indexing and retrieval?


Optimal Execution with Nonlinear Impact Functions and Trading-Enhanced Risk. We determine optimal trading strategies for liquidation of a large single-asset portfolio to minimize a combination of volatility risk and market impact costs. We take the market impact cost per share to be a power law function of the trading rate, with an arbitrary positive exponent. This includes, for example, the square-root law that has been proposed based on market microstructure theory. In analogy to the linear model, we define a "characteristic time" for optimal trading, which now depends on the initial portfolio size and decreases as execution proceeds. We also consider a model in which uncertainty of the realized price is increased by demanding rapid execution; we show that optimal trajectories are described by a "critical portfolio size" above which this effect is dominant and below which it may be neglected.

Question: What is the optimal execution strategy for a large portfolio?


An Efficient Privacy-Preserving Ranked Keyword Search Method. Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.

Question: What is the size of the canadian military?


Thirteen Ways to Look at the Correlation Coefficient. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://dv1litvip.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How to use JSTOR?


A Systematic Review of Virtual Reality in Education.. Virtual reality has existed in the realm of education for over half a century. However, its widespread adoption is still yet to occur. This is a result of a myriad of limitations to both the technologies themselves, and the costs and logistics required to deploy them. In order to gain a better understanding of what these issues are, and what it is that educators hope to gain by using these technologies in the first place, we have performed both a systematic review of the use of virtual reality in education, as well as two distinct thematic analyses. The first analysis investigated the applications and reported motivations provided by educators in academic literature for developing virtual reality educational systems, while the second investigated the reported problems associated with doing so. These analyses indicate that the majority of researchers use virtual reality to increase the intrinsic motivation of students, and refer to a narrow range of factors such as constructivist pedagogy, collaboration, and gamification in the design of their experiences. Similarly, a small number of educational areas account for the vast majority of educational virtual reality implementations identified in our analyses. Next, we introduced and compared a multitude of recent virtual reality technologies, discussing their potential to overcome several of the problems identified in our analyses, including cost, user experience and interactivity. However, these technologies are not without their own issues, thus we conclude this paper by providing several novel techniques to potentially address them, as well as potential directions for future researchers wishing to apply these emerging technologies to education.

Question: What are the limitations of virtual reality in education?


3D Printing for the Rapid Prototyping of Structural Electronics. In new product development, time to market (TTM) is critical for the success and profitability of next generation products. When these products include sophisticated electronics encased in 3D packaging with complex geometries and intricate detail, TTM can be compromised - resulting in lost opportunity. The use of advanced 3D printing technology enhanced with component placement and electrical interconnect deposition can provide electronic prototypes that now can be rapidly fabricated in comparable time frames as traditional 2D bread-boarded prototypes; however, these 3D prototypes include the advantage of being embedded within more appropriate shapes in order to authentically prototype products earlier in the development cycle. The fabrication freedom offered by 3D printing techniques, such as stereolithography and fused deposition modeling have recently been explored in the context of 3D electronics integration - referred to as 3D structural electronics or 3D printed electronics. Enhanced 3D printing may eventually be employed to manufacture end-use parts and thus offer unit-level customization with local manufacturing; however, until the materials and dimensional accuracies improve (an eventuality), 3D printing technologies can be employed to reduce development times by providing advanced geometrically appropriate electronic prototypes. This paper describes the development process used to design a novelty six-sided gaming die. The die includes a microprocessor and accelerometer, which together detect motion and upon halting, identify the top surface through gravity and illuminate light-emitting diodes for a striking effect. By applying 3D printing of structural electronics to expedite prototyping, the development cycle was reduced from weeks to hours.

Question: What is the development process used to design a novelty six-sided gaming die?


A decision support based on data mining in e-banking. The use of data mining techniques in banking domain is suitable due to the nature and sensitivity of bank data and due to the real time complex decision processes. The main concern for a bank's manager is to take good decisions in order to minimize the risks level associated to bank's activities. It is very important for a bank to have knowledge of causes which generate the financial crises or imbalances. Lending is one of the most risky activities in banking area and adequate methods to support the decision making process are necessary. In this paper the authors present a prototype decision support system based on data mining techniques used in lending process. The proposed system was designed to assist a customer who applies for a credit and it may represent an extension for e-banking activities.

Question: What is a decision support system?


A multi-view intelligent editor for digital video libraries. Silver is an authoring tool that aims to allow novice users to edit di gital video. The goal is to make editing of digital video as easy as text editing. Silver provides multiple coordinated views, including project, source, outline, subject, storyboard, textual transcript and timeline views. Selections and edits in any view are synchronized with all other views. A variety of recognition algorithms are applied to the video and audio content and then are used to aid in the editing tasks. The Informedia Digital Library supplies the recognition algorithms and metadata used to support intelligent editing, and Informedia also provides search and a repository. The metadata includes shot boundaries and a time-synchronized transcript, which are used to support intelligent selection and intelligent cut/copy/paste.

Question: What is a multi-view intelligent editor for digital video libraries?


Obtaining accurate trajectories from repeated coarse and inaccurate location logs. Context awareness is a key property for enabling context aware services. For a mobile device, the user's location or trajectory is one of the crucial contexts. One common challenge for detecting location or trajectory by mobile devices is to manage the tradeoff between accuracy and power consumption. Typical approaches are (1) controlling the frequency of usage of sensors and (2) sensor fusion technique. The algorithm proposed in this paper takes a different approach to improve the accuracy by merging repeatedly measured coarse and inaccurate location data from cell tower. The experimental result shows that the mean error distance between the detected trajectory and the ground truth is improved from 44m to 10.9m by merging data from 41 days' measurement.

Question: How to improve the accuracy of location detection?


An ultra-low power (ULP) zero-current-detector (ZCD) circuit for switching inductor converter applied in energy harvesting system. In this paper, we present an ultra-low power (ULP) zero-current-detector (ZCD) circuit by using sub-threshold design method and discontinued operating time to decrease power consumption, which can be widely used in switching inductor converters such as boost, buck or buck-boost converters. The designed ZCD circuit consumes only 40 nA quiescent current to realize nearly 70 dB DC gain and 650 kHz gain-bandwidth (GBW) when it operates. The main comparator of the ZCD circuit has high sensitivity and very low delay time to turn-off the synchronous switches of the DC-DC converter on time. The average quiescent current consumption is less than 1.6 nA because the operating time is only 40 μs under the minimum switching period of 1 ms. Finally, the designed ZCD circuit is used in a boost converter for vibrational energy harvesting applications.

Question: What is the purpose of the circuit?


Novel Six-Slot Four-Pole Axial Flux-Switching Permanent Magnet Machine for Electric Vehicle. This paper proposes several novel 6-slot/4-pole axial flux-switching machine topologies for electric and hybrid electric vehicles. The proposed topologies include stator shifting, rotor shifting, and a combination of shifting both the stator and the rotor. The proposed topologies eliminate the even harmonics of the flux linkage that create unbalanced flux linkage in the traditional 6-slot/4-pole machine. Fundamental frequency will be reduced by 60% compared with a 12-slot/10-pole axial flux-switching machine without unbalanced flux linkage. An analytical method is used to find the offset angle between stator a-axis and the rotor pole axis to realize the elimination of even harmonics of the flux linkage. The analytical method is verified by finite-element analysis and experiments. The results confirm that the proposed novel topologies efficiently eliminate the even harmonics of the flux linkage in the 6-slot/4-pole machine.

Question: What is the fundamental frequency of a 6-slot/4-pole axial flux-switching machine?


Nonlinear Dynamics of Spring Softening and Hardening in Folded-MEMS Comb Drive Resonators. This paper studies analytically and numerically the spring softening and hardening phenomena that occur in electrostatically actuated microelectromechanical systems comb drive resonators utilizing folded suspension beams. An analytical expression for the electrostatic force generated between the combs of the rotor and the stator is derived and takes into account both the transverse and longitudinal capacitances present. After formulating the problem, the resulting stiff differential equations are solved analytically using the method of multiple scales, and a closed-form solution is obtained. Furthermore, the nonlinear boundary value problem that describes the dynamics of inextensional spring beams is solved using straightforward perturbation to obtain the linear and nonlinear spring constants of the beam. The analytical solution is verified numerically using a Matlab/Simulink environment, and the results from both analyses exhibit excellent agreement. Stability analysis based on phase plane trajectory is also presented and fully explains previously reported empirical results that lacked sufficient theoretical description. Finally, the proposed solutions are, once again, verified with previously published measurement results. The closed-form solutions provided are easy to apply and enable predicting the actual behavior of resonators and gyroscopes with similar structures.

Question: What is the difference between a comb drive and a gyroscope?


Cogeneration of mechanical, electrical, and software designs for printable robots from structural specifications. Designing and fabricating new robotic systems is typically limited to experts, requiring engineering background, expensive tools, and considerable time. In contrast, to facilitate everyday users developing custom robots for personal use, this work presents a new system to easily create printable foldable robots from high-level structural specifications. A user merely needs to select electromechanical components from a library of basic building blocks and pre-designed mechanisms, then connect them to define custom robot assemblies. The system then generates complete mechanical drawings suitable for fabrication, instructions for the assembly of electronics, and software to control and drive the final robot. Several robots designed in this manner demonstrate the ability and versatility of this process.

Question: How to design a robot?


A Universal-Input Single-Stage High-Power-Factor Power Supply for HB-LEDs Based on Integrated Buck-Flyback Converter. Due to the high rise in luminous efficiency that HB-LEDs have experienced in the last recent years, many new applications have been researched. In this paper, a streetlight LED application will be covered, using the Integrated Buck-Flyback Converter developed in previous works, which performs power factor correction (PFC) from a universal ac source, as well as a control loop using the LM3524 IC, which allows PWM dimming operation mode. Firstly, the LED load will be linearized and modeled in order to calculate the IBFC topology properly. Afterwards, the converter will be calculated, presenting the one built in the lab. Secondly, the converter will be modeled in order to build the closed loop system, modeling the current sensor as well in order to develop an adequate controller. Finally, experimental results obtained from the lab tests will be presented.

Question: How to model a LED load?


Organizational Use of Big Data and Competitive Advantage - Exploration of Antecedents. The use of Big Data can hold considerable benefits for organizations, but there is as yet little research on the impacts of Big Data use on key variables such as competitive advantage. The antecedents to Big Data use aiming at competitive advantage are also poorly understood. Drawing from prior research on the ICT-competitiveness link, this research examined the relationship of organizational use of Big Data and competitive advantage – as well as certain hypothesized antecedents to this relationship. Data was collected from a nationwide survey in Japan which was addressed to Big Data users in private companies. The result shows that the competitiveness is influenced by the systematic and extensive use of Big Data in the organization, and top management understanding for Big Data use. Antecedents of systematic and extensive Big Data use were found to be the cross-functional activities of analysts, proactive attitude of users, secured data management, and human resources. Future research should strive to verify these respondent opinions using longitudinal approaches based on objective financial data.

Question: What is the relationship between big data and competitive advantage?


Design of a scalable InfiniBand topology service to enable network-topology-aware placement of processes. Over the last decade, InfiniBand has become an increasingly popular interconnect for deploying modern super-computing systems. However, there exists no detection service that can discover the underlying network topology in a scalable manner and expose this information to runtime libraries and users of the high performance computing systems in a convenient way. In this paper, we design a novel and scalable method to detect the InfiniBand network topology by using Neighbor-Joining techniques (NJ). To the best of our knowledge, this is the first instance where the neighbor joining algorithm has been applied to solve the problem of detecting InfiniBand network topology. We also design a network-topology-aware MPI library that takes advantage of the network topology service. The library places processes taking part in the MPI job in a network-topology-aware manner with the dual aim of increasing intra-node communication and reducing the long distance inter-node communication across the InfiniBand fabric.

Question: How scalable is the infiniband topology service?


Daylight Design of Office Buildings : Optimisation of External Solar Shadings by Using Combined Simulation Methods. Integrating daylight and energy performance with optimization into the design process has always been a challenge for designers. Most of the building environmental performance simulation tools require a considerable amount of time and iterations for achieving accurate results. Moreover the combination of daylight and energy performances has always been an issue, as different software packages are needed to perform detailed calculations. A simplified method to overcome both issues using recent advances in software integration is explored here. As a case study; the optimization of external shadings in a typical office space in Australia is presented. Results are compared against common solutions adopted as industry standard practices. Visual comfort and energy efficiency are analysed in an integrated approach. The DIVA (Design, Iterate, Validate and Adapt) plug-in for Rhinoceros/Grasshopper software is used as the main tool, given its ability to effectively calculate daylight metrics (using the Radiance/Daysim engine) and energy consumption (using the EnergyPlus engine). The optimization process is carried out parametrically controlling the shadings' geometries. Genetic Algorithms (GA) embedded in the evolutionary solver Galapagos are adopted in order to achieve close to optimum results by controlling iteration parameters. The optimized result, in comparison with conventional design techniques, reveals significant enhancement of comfort levels and energy efficiency. Benefits and drawbacks of the proposed strategy are then discussed. OPEN ACCESS Buildings 2015, 5 561

Question: How to optimize the shading of an office building?


Text Document Clustering based on Phrase Similarity using Affinity Propagation. Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper novel text document clustering algorithm has been developed based on vector space model, phrases and affinity propagation clustering algorithm. Proposed algorithm can be called Phrase affinity clustering (PAC). PAC first finds the phrase by ukkonen suffix tree construction algorithm, second finds the vector space model using tf-idf weighting scheme of phrase. Third calculate the similarity matrix form VSD using cosine similarity . In Last affinity propagation algorithm generate the clusters . F-Measure ,Purity and Entropy of Proposed algorithm is better than GAHC ,ST-GAHC and ST-KNN on OHSUMED ,RCV1 and News group data sets.

Question: What is the difference between the vector space model and the phrase?


Structural modeling and analysis of dengue-mediated inhibition of interferon signaling pathway.. Dengue virus (DENV) belongs to the family Flaviviridae and can cause major health problems worldwide, including dengue fever and dengue shock syndrome. DENV replicon in human cells inhibits interferon α and β with the help of its non-structural proteins. Non-structural protein 5 (NS5) of DENV is responsible for the proteasome-mediated degradation of signal transducer and activator of transcription (STAT) 2 protein, which has been implicated in the development of resistance against interferon-mediated antiviral effect. This degradation of STAT2 primarily occurs with the help of E3 ubiquitin ligases. Seven in absentia homologue (SIAH) 2 is a host protein that can mediate the ubiquitination of proteins and is known for its interaction with NS5. In this study, comprehensive computational analysis was performed to characterize the protein-protein interactions between NS5, SIAH2, and STAT2 to gain insight into the residues and sites of interaction between these proteins. The objective of the study was to structurally characterize the NS5-STAT2, SIAH2-STAT2, and NS5-SIAH2 interactions along with the determination of the possible reaction pattern for the degradation of STAT2. Docking and physicochemical studies indicated that DENV NS5 may first interact with the host SIAH2, which can then proceed towards binding with STAT2 from the side of SIAH2. These implications are reported for the first time and require validation by wet-lab studies.

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


Avoiding, finding and fixing spreadsheet errors - A survey of automated approaches for spreadsheet QA. Spreadsheet programs can be found everywhere in organizations and they are used for a variety of purposes, including financial calculations, planning, data aggregation and decision making tasks. A number of research surveys have however shown that such programs are particularly prone to errors. Some reasons for the error-proneness of spreadsheets are that spreadsheets are developed by end users and that standard software quality assurance processes are mostly not applied. Correspondingly, during the last two decades, researchers have proposed a number of techniques and automated tools aimed at supporting the end user in the development of error-free spreadsheets. In this paper, we provide a review of the research literature and develop a classification of automated spreadsheet quality assurance (QA) approaches, which range from spreadsheet visualization, static analysis and quality reports, over testing and support to model-based spreadsheet development. Based on this review, we outline possible opportunities for future work in the area of automated spreadsheet QA.

Question: What is a spreadsheet?


Knowledge Collaboration in Online Communities. O communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community—resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing—induced by online communities—has implications for theorizing about the more general case of knowledge collaboration in organizations.

Question: What is knowledge collaboration?


An Ontology-based Context Model in Intelligent Environments. Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.

Question: What is a context-aware middleware?


A new primary power regulation method for contactless power transfer. This paper presents a new power regulation method for contactless power transfer systems that combines current magnitude control of a series tuned resonant converter with energy injection concepts suitable for constant load or single pick-up applications. In the latter, the control of the primary converter operates with asymmetrical control, to regulate the magnitude of the high frequency primary track current according to the load requirement of the secondary power pickup. The feedback control algorithm is developed by monitoring the output voltage of the secondary power pickup. Switching losses and EMI are reduced due to the ZCS (Zero Current Switching) operation of the converter. Simulation and experimental results show that the primary converter has a fast and smooth start-up transient response, and the magnitudes of the primary current and the output voltage of a secondary power pickup are both fully controllable against load variations.

Question: What is the new primary power regulation method for contactless power transfer?


Webcam-Based Accurate Eye-Central Localization. This paper contains experimental procedure of webcam-based eye-tracker specially for low power devices. This paper consists of five processes. First one is background suppression for reducing average processing requirement. Second one is Haar-cascade feature-based face detection algorithm. Third one is geometrically eye-position determination. The fourth part is to detect and track eye-ball center using mean of gradient vector. The fifth and last step is to detect where the user looking. We simply calculate percentage of movement of eye to detect either it looking left, right, up or down. This procedure is highly effective with satisfactory accuracy. It also requires less processing power.

Question: How to detect where the user looking?


Text Coherence Analysis Based on Deep Neural Network. In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.

Question: How to evaluate the coherence of a document?


Low Data Drug Discovery with One-Shot Learning. Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf.   MODEL 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

Question: How does one-shot learning work?


Rule extraction from autoencoder-based connectionist computational models. 1College of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China 2Developmental Neurocognition Lab, Department of Psychological Sciences, Birkbeck, University of London, London, UK 3College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China Correspondence Juan Yang, College of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China. Email: jkxy_yjuan@sicnu.edu.cn Funding information National Natural Science Foundation of China, Grant/Award Number: (61402309); UK Economic and Social Research Council, Grant/Award Number: RES-062-23-2721 Summary Mapping problems are typical research topics related to natural language learning, and they include not only classification mappings but also nonclassification mappings, such as verbs and their past tenses. Connectionist computational models are one of the most popular approaches for simulating those mapping problems; however, their lack of explanatory ability has prevented them from being further used to understand the language learning process. Therefore, the work of extracting rational rules from a connectionist model is as important as simulating the mapping behaviors. Unfortunately, there is no available technique that can be directly applied in those computational models to simulate nonclassification problems. In this paper, an autoencoder-based connectionist computational model is proposed to derive a rule extraction method that can construct "If-Then" rational relations with high fidelity for nonclassification mapping problems. To demonstrate its generalizability, this computational model is extended to a modified version to address a multi-label classification mapping problem related to cognitive style prediction. Experiments prove this computational model's simulation ability and its explanatory ability on nonclassification problems by comparing its fidelity pe

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


A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces. In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.

Question: What is a resource-driven approach for anchoring linguistic resources to conceptual spaces?


A mathematical theory of communication: Meaning, information, and topology.

Question: What is the meaning of communication?


A practical solution for scripting language compilers. Although scripting languages are becoming increasingly popular, even mature scripting language implementations remain interpreted. Several compilers and reimplementations have been attempted, generally focusing on performance.  Based on our survey of these reimplementations, we determine that there are three important features of scripting languages that are difficult to compile or reimplement. Since scripting languages are defined primarily through the semantics of their original implementations, they often change semantics between releases. They provide large standard libraries, which are difficult to re-use, and costly to reimplement. They provide C APIs, used both for foreign-function-interfaces and to write third-party extensions. These APIs typically have tight integration with the original implementation. Finally, they support run-time code generation. These features make the important goal of correctness difficult to achieve.  We present a technique to support these features in an ahead-of-time compiler for PHP. Our technique uses the original PHP implementation through the provided C API, both in our compiler, and an our generated code. We support all of these important scripting language features, particularly focusing on the correctness of compiled programs. Additionally, our approach allows us to automatically support limited future language changes. We present a discussion and performance evaluation of this technique, which has not previously been published.

Question: What is a scripting language?


Modular Construction of Time-Delay Neural Networks for Speech Recognition. Several strategies are described that overcome limitations of basic network models as steps towards the design of large connectionist speech recognition systems. The two major areas of concern are the problem of time and the problem of scaling. Speech signals continuously vary over time and encode and transmit enormous amounts of human knowledge. To decode these signals, neural networks must be able to use appropriate representations of time and it must be possible to extend these nets to almost arbitrary sizes and complexity within finite resources. The problem of time is addressed by the development of a Time-Delay Neural Network; the problem of scaling by Modularity and Incremental Design of large nets based on smaller subcomponent nets. It is shown that small networks trained to perform limited tasks develop time invariant, hidden abstractions that can subsequently be exploited to train larger, more complex nets efficiently. Using these techniques, phoneme recognition networks of increasing complexity can be constructed that all achieve superior recognition performance.

Question: How do neural networks work?


Retrograde amnesia and memory consolidation: a neurobiological perspective. The fact that information acquired before the onset of amnesia can be lost (retrograde amnesia) has fascinated psychologists, biologists, and clinicians for over 100 years. Studies of retrograde amnesia have led to the concept of memory consolidation, whereby medial temporal lobe structures direct the gradual establishment of memory representations in neocortex. Recent theoretical accounts have inspired a simple neural network model that produces behavior consistent with experimental data and makes these ideas about memory consolidation more concrete. Recent physiological and anatomical findings provide important information about how memory consolidation might actually occur.

Question: What is the concept of memory consolidation?


Designing the Future of Personal Fashion. Advances in computer vision and machine learning are changing the way people dress and buy clothes. Given the vast space of fashion problems, where can data-driven technologies provide the most value? To understand consumer pain points and opportunities for technological interventions, this paper presents the results from two independent need-finding studies that explore the gold-standard of personalized shopping: interacting with a personal stylist. Through interviews with five personal stylists, we study the range of problems they address and their in-person processes for working with clients. In a separate study, we investigate how styling experiences map to online settings by building and releasing a chatbot that connects users to one-on-one sessions with a stylist, acquiring more than 70 organic users in three weeks. These conversations reveal that in-person and online styling sessions share similar goals, but online sessions often involve smaller problems that can be resolved more quickly. Based on these explorations, we propose future highly personalized, online interactions that address consumer trust and uncertainty, and discuss opportunities for automation.

Question: How can we design the future of personal fashion?


Representation Learning for Grounded Spatial Reasoning. The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.

Question: How does reinforcement learning work?