A theory of formal synthesis via inductive learning. Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.

Question: What is the difference between machine learning and formal inductive synthesis?


Generalised spatial modulation with multiple active transmit antennas. We propose a new generalised spatial modulation (GSM) technique, which can be considered as a generalisation of the recently proposed spatial modulation (SM) technique. SM can be seen as a special case of GSM with only one active transmit antenna. In contrast to SM, GSM uses the indices of multiple transmit antennas to map information bits, and is thus able to achieve substantially increased spectral efficiency. Furthermore, selecting multiple active transmit antennas enables GSM to harvest significant transmit diversity gains in comparison to SM, because all the active antennas transmit the same information. On the other hand, inter-channel interference (ICI) is completely avoided by transmitting the same symbols through these active antennas. We present theoretical analysis using order statistics for the symbol error rate (SER) performance of GSM. The analytical results are in close agreement with our simulation results. The bit error rate performance of GSM and SM is simulated and compared, which demonstrates the superiority of GSM. Moreover, GSM systems with configurations of different transmit and receive antennas are studied. Our results suggest that using a less number of transmit antennas with a higher modulation order will lead to better BER performance.

Question: What is the difference between GSM and SM?


Relating Newcomer Personality to Survival and Activity in Recommender Systems. In this work, we explore the degree to which personality information can be used to model newcomer retention, investment, intensity of engagement, and distribution of activity in a recommender community. Prior work shows that Big-Five Personality traits can explain variation in user behavior in other contexts. Building on this, we carry out and report on an analysis of 1008 MovieLens users with identified personality profiles. We find that Introverts and low Agreeableness users are more likely to survive into the second and subsequent sessions compared to their respective counterparts; Introverts and low Conscientiousness users are a significantly more active population compared to their respective counterparts; High Openness and High Neuroticism users contribute (tag) significantly more compared to their counterparts, but their counterparts consume (browse and bookmark) more; and low Agreeableness users are more likely to rate whereas high Agreeableness users are more likely to tag. These results show how modeling newcomer behavior from user personality can be useful for recommender systems designers as they customize the system to guide people towards tasks that need to be done or tasks the users will find rewarding and also decide which users to invest retention efforts in.

Question: How can personality information be used to model newcomer behavior in recommender systems?


Snapshots in a flash with ioSnap. Snapshots are a common and heavily relied upon feature in storage systems. The high performance of flash-based storage systems brings new, more stringent, requirements for this classic capability. We present ioSnap, a flash optimized snapshot system. Through careful design exploiting common snapshot usage patterns and flash oriented optimizations, including leveraging native characteristics of Flash Translation Layers, ioSnap delivers low-overhead snapshots with minimal disruption to foreground traffic. Through our evaluation, we show that ioSnap incurs negligible performance overhead during normal operation, and that common-case operations such as snapshot creation and deletion incur little cost. We also demonstrate techniques to mitigate the performance impact on foreground I/O during intensive snapshot operations such as activation. Overall, ioSnap represents a case study of how to integrate snapshots into a modern, well-engineered flash-based storage system.

Question: What is a flash-based storage system?


Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read. Spin Transfer Torque Magnetoresistive RAM (STT-MRAM) has been recently deemed as one promising main memory alternative for high-end mobile processors. With process technology scaling, the amplitude of write current approaches that of read current in deep sub-micrometer STT-MRAM arrays. As a result, read disturbance errors (RDEs) emerge. Both high current restore required (HCRR) reads and low current long latency (LCLL) reads can guarantee read reliability and utterly remove RDEs. However, both of them degrade system performance, because of extra restores or a longer read latency. And neither of them always achieves the better performance when running a wide variety of applications. In this paper, we present two architectural techniques to boost read performance for STT-MRAM based main memories in the presence of RDEs. We first propose Smash Read (S-RD) to shorten the latency of HCRR reads by injecting a larger read current. We further introduce Flexible Read (F-RD) to dynamically adopt different types of read schemes, S-RD and LCLL, to maximize main memory system performance. On average, our techniques improve system performance by 9~13% and reduces total energy by 4~8% over all existing read schemes including HCRR and LCLL.

Question: How to improve read performance of STT-MRAM based main memories?


A data mining approach for diagnosis of coronary artery disease. Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.

Question: What is the accuracy of the data mining approach for diagnosis of coronary artery disease?


Older adults' attitudes towards and perceptions of "smart home" technologies: a pilot study.. PRIMARY OBJECTIVE The study aim is to explore the perceptions and expectations of seniors in regard to "smart home" technology installed and operated in their homes with the purpose of improving their quality of life and/or monitoring their health status.   RESEARCH DESIGN AND METHODS Three focus group sessions were conducted within this pilot study to assess older adults' perceptions of the technology and ways they believe technology can improve their daily lives. Themes discussed in these groups included participants' perceptions of the usefulness of devices and sensors in health-related issues such as preventing or detecting falls, assisting with visual or hearing impairments, improving mobility, reducing isolation, managing medications, and monitoring of physiological parameters. The audiotapes were transcribed and a content analysis was performed.   RESULTS A total of 15 older adults participated in three focus group sessions. Areas where advanced technologies would benefit older adult residents included emergency help, prevention and detection of falls, monitoring of physiological parameters, etc. Concerns were expressed about the user-friendliness of the devices, lack of human response and the need for training tailored to older learners.   CONCLUSIONS All participants had an overall positive attitude towards devices and sensors that can be installed in their homes in order to enhance their lives.

Question: What are the benefits of "smart home" technology?


Exploiting Similarities among Languages for Machine Translation. Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs.

Question: What is the most common language in the world?


VisDB: database exploration using multidimensional visualization. Discusses how the VisDB system supports the query specification process by representing the result visually. The main idea behind the system stems from the view of relational database tables as sets of multidimensional data where the number of attributes corresponds to the number of dimensions. In such a view, it is often unclear. In this system, each display pixel represents one database item. Pixels are arranged and colored to indicate the item's relevance to a user query and to give a visual impression of the resulting data set.<<ETX>>

Question: What is the ETX?


Realistic and Fast Cloud Rendering. Clouds are an important aspect of rendering outdoor scenes. Thi paper describes a cloud system that extends texture splatting on par ticles to model a dozen cloud types (e.g., stratus, cumulus congestus, cumulonimb us), an improvement over earlier systems that modeled only one type of cumulus. W e also achieve fast real-time rendering, even for scenes of dense overcast cove rage, which was a limitation for previous systems. We present a new shading model that uses artist-driven contr ols rather than a programmatic approach to approximate lighting. This is sui table when fine-grained control over the look-and-feel is necessary, and artistic r esources are available. We also introduce a way to simulate cloud formation and dissipa tion using texture

Question: What is a realistic and fast cloud rendering?


Clinicopathological Findings of Chronic Rejection in a Face Grafted Patient.. BACKGROUND Skin chronic rejection (CR) in vascularized composite allotransplantation has not been included in the Banff classification yet. We report a face-transplant patient who developed cutaneous clinicopathologic changes suggestive of CR.   METHODS The recipient was a 27-year-old man with severe disfigurement of the lower face due to a pyrotechnic explosion. He received a facial allograft, including mandible, cheeks, lips, and chin, in November 2009. Immunosuppression included antithymocyte globulins and bone-marrow infusion then steroids, tacrolimus, and mycophenolate mofetil.   RESULTS During the first posttransplant year the acute rejection episodes were characterized by reversible oedema and erythema of the graft. Subsequently, the patient developed primary asymptomatic Epstein-Barr virus (EBV) infection, followed by EBV+ B-cell lymphoma and hepatic EBV-associated posttransplant smooth muscle tumors; therefore, the immunosuppressive treatment was greatly reduced. Since the second posttransplant year, the allografted facial skin became progressively sclerotic and presented pigmented macules on a background of hypopigmentation and teleangiectasias, resulting in a poikilodermatous aspect. Skin biopsies showed epidermal atrophy, basal cell vacuolization, and diffuse dermal sclerosis in the absence of significant dermal cell infiltration. The dermal capillaries showed thickened walls and narrowed lumina, whereas the large vessels did not show significant alterations. Neither donor-specific antibodies nor vascular Cd4 deposits were detected.A dysfunction of the graft functions occurred. It was evidenced by a decrease in mouth opening and modification of some phonemes although lip closure was still possible allowing food intake.   CONCLUSIONS This is the first report suggestive of CR in a face allotransplantation after immunosuppression minimization.

Question: What is the function of the face?


Categorical equational systems : algebraic models and equational reasoning. Declaration This dissertation is the result of my own work done under the guidance of my supervisor, and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation is not substantially the same as any that I have submitted or will be submitting for a degree or diploma or other qualification at this or any other University. This dissertation does not exceed the regulation length of 60,000 words, including tables and footnotes. 5 Summary We introduce two abstract notions of equational algebraic system, called Equational System (ES) and Term Equational System (TES), in order to achieve sufficient expressivity as needed in modern applications in computer science. These generalize the classical concept of (enriched) algebraic theory of Kelly and Power [1993]. We also develop a theory for constructing free algebras for ESs and a theory of equational reasoning for TESs. In Part I, we introduce the general abstract, yet practical, concept of equational system and develop finitary and transfinitary conditions under which we give an explicit construction of free algebras for ESs. This free construction extends the well-known construction of free algebras for ω-cocontinuous endofunctors to an equational setting, capturing the intuition that free algebras consist of freely constructed terms quotiented by given equations and congruence rules. We further show the monadicity and cocom-pleteness of categories of algebras for ESs under the finitary and transfinitary conditions. To illustrate the expressivity of equational systems, we exhibit various examples including two modern applications, the Σ-monoids of Fiore et al. [1999] and the π-algebras of Stark [2005]. In Part II, we introduce the more concrete notion of term equational system, which is obtained by specializing the concept of equational system, but remains more general th

Question: What is the difference between an algebraic theory and an equational system?


Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study.. Qualitative content analysis and thematic analysis are two commonly used approaches in data analysis of nursing research, but boundaries between the two have not been clearly specified. In other words, they are being used interchangeably and it seems difficult for the researcher to choose between them. In this respect, this paper describes and discusses the boundaries between qualitative content analysis and thematic analysis and presents implications to improve the consistency between the purpose of related studies and the method of data analyses. This is a discussion paper, comprising an analytical overview and discussion of the definitions, aims, philosophical background, data gathering, and analysis of content analysis and thematic analysis, and addressing their methodological subtleties. It is concluded that in spite of many similarities between the approaches, including cutting across data and searching for patterns and themes, their main difference lies in the opportunity for quantification of data. It means that measuring the frequency of different categories and themes is possible in content analysis with caution as a proxy for significance.

Question: What is the difference between content analysis and thematic analysis?


A handheld mirror simulation. We present the design and construction of a handheld mirror simulation device. The perception of the world reflected through a mirror depends on the viewer's position with respect to the mirror and the 3-D geometry of the world. In order to simulate a real mirror on a computer screen, images of the observed world, consistent with the viewer's position, must be synthesized and displayed in realtime. Our system is build around a LCD screen manipulated by the user, a single camera fixed on the screen, and a tracking device. The continuous input video stream and tracker data is used to synthesize, in real-time, a continuous video stream displayed on the LCD screen. The synthesized video stream is a close approximation of what the user would see on the screen surface if it were a real mirror. Our system provides a generic interface for applications involving rich, first-person interaction, such as the Virtual Daguerreotype.

Question: How do you simulate a mirror on a computer screen?


The structure of images. In practice the relevant details of images exist only over a restricted range of scale. Hence it is important to study the dependence of image structure on the level of resolution. It seems clear enough that visual perception treats images on several levels of resolution simultaneously and that this fact must be important for the study of perception. However, no applicable mathematically formulated theory to deal with such problems appers to exist. In this paper it is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied. The structure of this family is governed by the well known diffusion equation (a parabolic, linear, partial differential equation of the second order). As such the structure fits into existing theories that treat the front end of the visual system as a continuous tack of homogeneous layer, characterized by iterated local processing schemes. When resolution is decreased the images becomes less articulated because the extrem ("light and dark blobs") disappear one after the other. This erosion of structure is a simple process that is similar in every case. As a result any image can be described as a juxtaposed and nested set of light and dark blobs, wherein each blod has a limited range of resolution in which it manifests itself. The structure of the family of derived images permits a derivation of the sampling density required to sample the image at multiple scales of resolution. The natural scale along the resolution axis (leading to an informationally uniform sampling density) is logarithmic, thus the structure is apt for the description of size invariances.

Question: What is the structure of images?


Atlas-based rib-bone detection in chest X-rays. This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91±1.8% for Montgomery and 85.48±3.3% for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.

Question: How to detect rib-bones in chest X-rays?


Reflect , and React : A Culturally Responsive Model for Pre-service Secondary Social Studies Teachers. The purpose of this qualitative study was to design and implement a model of cultural-responsiveness within a social studies teacher education program. Specifically, we sought to understand how pre-service grades 6-12 social studies practitioners construct culturally responsive teaching (CRT) in their lesson planning. In addition, we examined the professional barriers that prevented teacher-candidates from actualizing culturally responsive pedagogy. Incorporating a conceptual model of Review, Reflect, and React, 20 teacher candidates in a social studies methods course engaged CRT theory and practice. Thematic analysis of lesson plans and clinical reflections indicated successful proponents of CRT critically analyzed their curriculum, explored the diverse needs of their students, and engaged learners in culturally appropriate social studies pedagogy. Findings also showed that unsuccessful CRT was characterized by a lack of content knowledge, resistance from the cooperating teacher, and a reliance on the textbook materials.

Question: How do pre-service teachers construct culturally responsive teaching?


An unsupervised long short-term memory neural network for event detection in cell videos. We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Current methods to detect cellular events are based on supervised machine learning and rely on tedious manual annotation from investigators with specific expertise. So that our LSTM network could be trained in an unsupervised manner, we designed it with a branched structure where one branch learns the frequent, regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. We tested our network on a publicly available dataset of densely packed stem cell phase-contrast microscopy images undergoing cell division. This dataset is considered to be more challenging that a dataset with sparse cells. We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method. We used an F1-score, which is a balanced measure for both precision and recall. Our results show that our unsupervised method has a higher or similar F1-score when compared to two fully supervised methods that are based on Hidden Conditional Random Fields (HCRF), and has comparable accuracy with the current best supervised HCRF-based method. Our method was generalizable as after being trained on one video it could be applied to videos where the cells were in different conditions. The accuracy of our unsupervised method approached that of its supervised counterpart.

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


Unsupervised soccer video abstraction based on pitch, dominant color and camera motion analysis. We present a soccer video abstraction method based on the analysis of the audio and video streams. This method could be applied to other sports as rugby or american football. The main contribution of this paper is the design of an unsupervised summarization method, and more specifically, the introduction of an efficient detector of excited speech segments. An excited commentary is supposed to correspond to an interesting moment of the game. It is simultaneously characterized by an increase of the pitch (or fundamental frequency) within the voiced segments and an increase of the energy supported by the harmonics of the pitch. The pitch is estimated from the autocorrelation function and its local increases are detected from a multiresolution technique. We introduce a specific energy measure for the voiced segments. A statistical analysis of the energy measures is performed to detect the most excited parts of the speech. A deterministic combination of excited speech detection, dominant color identification and camera motion analysis is then performed in order to discriminate between excited speech sequences of the game and excited speech sequences in commercials or in studio shots included in the processed TV programs.  The method presented here does not need any learning stage. It has been tested on seven soccer videos for a total duration of almost 20 hours.

Question: What is the difference between a soccer video abstraction and a soccer video summarization?


Doubly Fed Induction Generator Systems For Variable Speed Wind Turbine. This paper presents results of a study concerning the dynamic behaviour of a wind energy system powered by a doubly fed induction generator with the rotor connected to the electric network through an AC-AC converter. A tendency to put up more and more wind turbines can be observed all over the world. Also, there is awareness in taking into account the requirements of a clean environment, due to the need to preserve our habitat. Renewable energy sources not contributing to the enhanced greenhouse effect, especially wind power, are becoming an important component of the total generation. Hence, research concerning the dynamic behaviour of wind energy systems is important to achieve a better knowledge.

Question: What is the dynamic behaviour of a wind energy system?


3D video fragments: dynamic point samples for real-time free-viewpoint video. We present 3D video fragments, a dynamic point sample framework for real-time 3D video. By generalizing 2D video pixels towards 3D irregular point samples we combine the simplicity of conventional 2D video processing with the power of more complex polygonal representations for 3D video. We propose a differential update scheme exploiting the spatio-temporal coherence of the video streams of multiple cameras. Updates are issued by operators such as inserts and deletes accounting for changes in the input video images. The operators from multiple cameras are processed, merged into a 3D video stream and transmitted to a remote site. We also introduce a novel concept for camera control which dynamically selects the set of relevant cameras for reconstruction. Moreover, it adapts to the processing load and rendering platform. Our framework is generic in the sense that it works with any realtime 3D reconstruction method which extracts depth from images. The video renderer displays 3D videos using an efficient point based splatting scheme and makes use of state-of-the-art vertex and pixel processing hardware for real-time visual processing.

Question: What is a dynamic point sample?


The efficiency frontier approach to economic evaluation of health-care interventions.. BACKGROUND IQWiG commissioned an international panel of experts to develop methods for the assessment of the relation of benefits to costs in the German statutory health-care system.   PROPOSED METHODS The panel recommended that IQWiG inform German decision makers of the net costs and value of additional benefits of an intervention in the context of relevant other interventions in that indication. To facilitate guidance regarding maximum reimbursement, this information is presented in an efficiency plot with costs on the horizontal axis and value of benefits on the vertical. The efficiency frontier links the interventions that are not dominated and provides guidance. A technology that places on the frontier or to the left is reasonably efficient, while one falling to the right requires further justification for reimbursement at that price. This information does not automatically give the maximum reimbursement, as other considerations may be relevant. Given that the estimates are for a specific indication, they do not address priority setting across the health-care system.   CONCLUSION This approach informs decision makers about efficiency of interventions, conforms to the mandate and is consistent with basic economic principles. Empirical testing of its feasibility and usefulness is required.

Question: What is the efficiency frontier approach to economic evaluation of health-care interventions?


Corroborating Answers from Multiple Web Sources. The Internet has changed the way people look for information. Users now expect the answers to their questions to be available through a simple web search. Web search engines are increasingly efficient at identifying the best sources for any given keyword query, and are often able to identify the answer within the sources. Unfortunately, many web sources are not trustworthy, because of erroneous, misleading, biased, or outdated information. In many cases, users are not satisfied with —or do not trust— the results from any single source and prefer checking several sources for corroborating evidence. In this paper, we propose methods to aggregate query results from different sources in order to save users the hassle of individually checking query-related web sites to corroborate answers. To return the best aggregated answers to the users, our techniques consider the number, importance, and similarity of the web sources reporting each answer, as well as the importance of the answer within the source. We present an experimental evaluation of our technique on real web queries, comparing the corroborated answers returned with real user clicks.

Question: What is the best way to look for information on the internet?


Understanding Types of Cyberbullying in an Anonymous Messaging Application. The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application—Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages (∼ 20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.

Question: What is the difference between a cyberbullying and a cyberstalking?


Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition. Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet. PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called VSAD, by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).

Question: What is a weakly supervised patch network?


BotGuard: Lightweight real-time botnet detection in software defined networks. The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network (SDN) to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph (CLI-graph) to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental results prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.

Question: What is a botnet?


Overdose Fatalities Patterns of Abuse Among Unintentional Pharmaceutical. Supplementary material http://jama.ama-assn.org/cgi/content/full/300/22/2613/DC1 Report Video JAMA Correction Contact me if this article is corrected. Citations Contact me when this article is cited. This article has been cited 1 time. Topic collections Contact me when new articles are published in these topic areas. Drug Therapy, Other Pain; Public Health; Substance Abuse/ Alcoholism; Drug Therapy; Adverse Effects;

Question: What is the most common cause of death from drug overdose?


Social ties and mental health. It is generally agreed that social ties play a beneficial role in the maintenance of psychological well-being. In this targeted review, we highlight four sets of insights that emerge from the literature on social ties and mental health outcomes (defined as stress reactions, psychological well-being, and psychological distress, including depressive symptoms and anxiety). First, the pathways by which social networks and social supports influence mental health can be described by two alternative (although not mutually exclusive) causal models—the main effect model and the stress-buffering model. Second, the protective effects of social ties on mental health are not uniform across groups in society. Gender differences in support derived from social network participation may partly account for the higher prevalence of psychological distress among women compared to men. Social connections may paradoxically increase levels of mental illness symptoms among women with low resources, especially if such connections entail role strain associated with obligations to provide social support to others. Third, egocentric networks are nested within a broader structure of social relationships. The notion of social capital embraces the embeddedness of individual social ties within the broader social structure. Fourth, despite some successes reported in social support interventions to enhance mental health, further work is needed to deepen our understanding of the design, timing, and dose of interventions that work, as well as the characteristics of individuals who benefit the most.

Question: What is the difference between social ties and social support?


Flexible Learning on the Sphere Via Adaptive Needlet Shrinkage and Selection. This paper introduces an approach for flexible, robust Bayesian modeling of structure in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding for selection, and the horseshoe prior for shrinkage. We study the performance of the proposed methodology both on simulated data and on a real data set involving the cosmic microwave background radiation. Horseshoe shrinkage of needlet coefficients is shown to yield the best overall performance against some common benchmarks.

Question: What is the cosmic microwave background radiation?


MOS charge pumps for low-voltage operation. New MOS charge pumps utilizing the charge transfer switches (CTS's) to direct charge flow and generate boosted output voltage are described. Using the internal boosted voltage to backward control the CTS of a previous stage yields charge pumps that are suitable for low-voltage operation. Applying dynamic control to the CTS's can eliminate the reverse charge sharing phenomenon and further improve the voltage pumping gain. The limitation imposed by the diode-configured output stage can be mitigated by pumping it with a clock of enhanced voltage amplitude. Using the new circuit techniques, a 1.2-V-to-3.5-V charge pump and a 2-V-to-16-V charge pump are demonstrated.

Question: What is a charge pump?


Optimal Parallel Algorithms for Computing the Sum, the Prefix-Sums, and the Summed Area Table on the Memory Machine Models. The main contribution of this paper is to show optimal parallel algorithms to compute the sum, the prefix-sums, and the summed area table on two memory machine models, the Discrete Memory Machine (DMM) and the Unified Memory Machine (UMM). The DMM and the UMM are theoretical parallel computing models that capture the essence of the shared memory and the global memory of GPUs. These models have three parameters, the number p of threads, and the width w of the memory, and the memory access latency l. We first show that the sum of n numbers can be computed in O(n w + nl p + l log n) time units on the DMM and the UMM. We then go on to show that Ω(n w + nl p + l log n) time units are necessary to compute the sum. We also present a parallel algorithm that computes the prefix-sums of n numbers in O( n w + nl p + l log n) time units on the DMM and the UMM. Finally, we show that the summed area table of size √ n × √n can be computed in O( n w + nl p + l log n) time units on the DMM and the UMM. Since the computation of the prefix-sums and the summed area table is at least as hard as the sum computation, these parallel algorithms are also optimal. key words: Memory machine models, prefix-sums computation, parallel algorithm, GPU, CUDA

Question: How many threads are there in a GPU?


Self-Similarity Driven Color Demosaicking. Demosaicking is the process by which from a matrix of colored pixels measuring only one color component per pixel, red, green, or blue, one can infer a whole color information at each pixel. This inference requires a deep understanding of the interaction between colors, and the involvement of image local geometry. Although quite successful in making such inferences with very small relative error, state-of-the-art demosaicking methods fail when the local geometry cannot be inferred from the neighboring pixels. In such a case, which occurs when thin structures or fine periodic patterns were present in the original, state-of-the-art methods can create disturbing artifacts, known as zipper effect, blur, and color spots. The aim of this paper is to show that these artifacts can be avoided by involving the image self-similarity to infer missing colors. Detailed experiments show that a satisfactory solution can be found, even for the most critical cases. Extensive comparisons with state-of-the-art algorithms will be performed on two different classic image databases.

Question: What is the self-similarity of an image?


Visual and Infrared Sensor Data-Based Obstacle Detection for the Visually Impaired Using the Google Project Tango Tablet Development Kit and the Unity Engine. A novel visual and infrared sensor data-based system to assist visually impaired users in detecting obstacles in their path while independently navigating indoors is presented. The system has been developed for the recently introduced Google Project Tango Tablet Development Kit equipped with a powerful graphics processor and several sensors which allow it to track its motion and orientation in 3-D space in real-time. It exploits the inbuilt functionalities of the Unity engine in the Tango SDK to create a 3-D reconstruction of the surrounding environment, then associates a Unity collider component with the user and utilizes it to determine his interaction with the reconstructed mesh in order to detect obstacles. The user is warned about any detected obstacles via audio alerts. An extensive empirical evaluation of the obstacle detection component has yielded favorable results, thus, confirming the potential of this system for future development work.

Question: How does the system detect obstacles?


BEST PRACTICE GUIDELINE FOR THE CFD SIMULATION OF FLOWS IN THE URBAN ENVIRONMENT. Legal notice by the COST Office Neither the COST Office nor any person acting on its behalf is responsible for the use which might be made of the information contained in the present publication. The COST Office is not responsible for the external web sites referred to in the present publication. No permission to reproduce or utilize the contents of this book by any means is necessary, other than in the case of images, diagrams or other material from other copyright holders. In such cases permission of the copyright holders is required. This book may be cited as: Title of the book and Action Number

Question: What is the best practice guideline for the CFD simulation of flows in the urban environment?


Automated Lip Reading Technique for Password Authentication. As technology is starting to conquer every strata of the society, the war for protecting confidential data from being intercepted is growing intense by the hour. Biometricsecurity stands out as the most secure form of authentication in high security zones such as defense, space missions and research head-quarters. Today, forms of password-protection range from facerecognition to retina -scan. Here, we develop a system for recognizing and converting lip movement of an individual into a recognized pattern which is set as a password for the system using image-processing. This system is also a break-through for providing people with motor-disabilities a robust and easy way of protecting their data. By capturing and tracing the successive movement of lips during speech, the corresponding word can be detected. The captured images are represented as points on a two-dimensional flat manifold that enables us to efficiently define the pronunciation of each word and thereby analyze or synthesize the motion of the lips. The motion of lips helps us track the word syllable-by-syllable. With multiple levels of image processing, it becomes possible to set the matching parameters to a very close value, hence not allowing any bruteforce or other infamous hacking techniques to break into the user's system. This lip reading technique also serves applications in areas where communication via direct speech is not possible.

Question: How to authenticate password using lip movement?


Euler Number and Connectivity Indexes of a Three Dimensional Digital Picture. Fundamental properties of topological structure of a 3D digitized picture are presented including the concept of neighborhood and connectivity among volume cells (voxels) of 3D digitized binary pictures defined on a cubic grid, the concept of simplicial decomposition of a 3D digitized object, and two algorithms for calculating the Euler number (genus). First we define four types of connectivity. Second we present two algorithms to calculate the Euler number of a 3D figure. Thirdly we introduce new local features called the connectivity number (CN) and the connectivity index to study topological properties of a 3D object in a 3D digitized picture. The CN at a 1-voxel (=a voxel with the value 1) x is defined as the change in the Euler number of the object caused by changing the value of x into zero, that is, caused by deleting x. Finally, by using them, we prove a necessary and sufficient condition that a 1-voxel is deletable. A 1-voxel x is said to be deletable if deletion of x causes no decrease and no increase in the numbers of connected components, holes and cavities in a given 3D picture.

Question: What is the euler number of a 3D digitized picture?


Embedded 3D printing of strain sensors within highly stretchable elastomers.. A new method, embedded-3D printing (e-3DP), is reported for fabricating strain sensors within highly conformal and extensible elastomeric matrices. e-3DP allows soft sensors to be created in nearly arbitrary planar and 3D motifs in a highly programmable and seamless manner. Several embodiments are demonstrated and sensor performance is characterized.

Question: How does embedded 3D printing work?


An overlap invariant entropy measure of 3D medical image alignment. This paper is concerned with the development of entropy-based registration criteria for automated 3D multi-modality medical image alignment. In this application where misalignment can be large with respect to the imaged field of view, invariance to overlap statistics is an important consideration. Current entropy measures are reviewed and a normalised measure is proposed which is simply the ratio of the sum of the marginal entropies and the joint entropy. The effect of changing overlap on current entropy measures and this normalised measure are compared using a simple image model and experiments on clinical image data. Results indicate that the normalised entropy measure provides significantly improved behaviour over a range of imaged fields of view. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Question: What is the normalised entropy measure?


JackIn: integrating first-person view with out-of-body vision generation for human-human augmentation. JackIn is a new human-human communication framework for connecting two or more people. With first-person view video streaming from a person (called Body) wearing a transparent head-mounted display and a head-mounted camera, the other person (called Ghost) participates in the shared first-person view. With JackIn, people's activities can be shared and assistance or guidance can be given through other peoples expertise. This can be applied to daily activities such as cooking lessons, shopping navigation, education in craft-work or electrical work, and sharing experiences of sporting and live events. For a better viewing experience with frist-person view, we developed the out-of-body view in which first-person images are integrated to construct a scene around a Body, and a Ghost can virtually control the viewpoint to look around the space surrounding the Body. We also developed a tele-pointing gesture interface. We conducted an experiment to evaluate how effective this framework is and found that Ghosts can understand the spatial situation of the Body.

Question: How does JackIn work?


Learning Automata as a Basis for Multi Agent Reinforcement Learning. Learning Automata (LA) are adaptive decision making devices suited for operation in unknown environments [12]. Originally they were developed in the area of mathematical psychology and used for modeling observed behavior. In its current form, LA are closely related to Reinforcement Learning (RL) approaches and most popular in the area of engineering. LA combine fast and accurate convergence with low computational complexity, and have been applied to a broad range of modeling and control problems. However, the intuitive, yet analytically tractable concept of learning automata makes them also very suitable as a theoretical framework for Multi agent Reinforcement Learning (MARL). Reinforcement Learning (RL) is already an established and profound theoretical framework for learning in stand-alone or single-agent systems. Yet, extending RL to multi-agent systems (MAS) does not guarantee the same theoretical grounding. As long as the environment an agent is experiencing is Markov, and the agent can experiment sufficiently, RL guarantees convergence to the optimal strategy. In a MAS however, the reinforcement an agent receives, may depend on the actions taken by the other agents acting in the same environment. Hence, the Markov property no longer holds. And as such, guarantees of convergence are lost. In the light of the above problem it is important to fully understand the dynamics of multi-agent reinforcement learning. Although, they are not fully recognized as such, LA are valuable tools for current MARL research. LA are updated strictly on the basis of the response of the environment, and not on the basis of any knowledge regarding other automata, i.e. nor their strategies, nor their feedback. As such LA agents are very simple. Moreover, LA can be treated analytically. Convergence proofs do exist for a variety of settings ranging from a single automaton model acting in a simple stationary random enviro

Question: What is the difference between a reinforcement learning agent and a learning automata agent?


Monadic Parsing in Haskell. This paper is a tutorial on defining recursive descent parsers in Haskell. In the spirit of one-stop shopping , the paper combines material from three areas into a single source. The three areas are functional parsers (Burge, 1975; Wadler, 1985; Hutton, 1992; Fokker, 1995), the use of monads to structure functional programs (Wadler, 1990, 1992a, 1992b), and the use of special syntax for monadic programs in Haskell (Jones, 1995; Peterson et al., 1996). More specifically, the paper shows how to define monadic parsers using do notation in Haskell. Of course, recursive descent parsers defined by hand lack the efficiency of bottomup parsers generated by machine (Aho et al., 1986; Mogensen, 1993; Gill and Marlow, 1995). However, for many research applications, a simple recursive descent parser is perfectly sufficient. Moreover, while parser generators typically offer a fixed set of combinators for describing grammars, the method described here is completely extensible: parsers are first-class values, and we have the full power of Haskell available to define new combinators for special applications. The method is also an excellent illustration of the elegance of functional programming. The paper is targeted at the level of a good undergraduate student who is familiar with Haskell, and has completed a grammars and parsing course. Some knowledge of functional parsers would be useful, but no experience with monads is assumed. A Haskell library derived from the paper is available on the web from:

Question: What is the monadic parsing in haskell?


Anterior prefrontal function and the limits of human decision-making.. The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly limited, which suggests that the FPC is efficient for protecting the execution of long-term mental plans from immediate environmental demands and for generating new, possibly more rewarding, behavioral or cognitive sequences, rather than for complex decision-making and reasoning.

Question: What is the anterior prefrontal cortex?


Beyond Wise et al . : Neuroleptic-induced " anhedonia " in rats : Pimozide blocks reward quality of food. O all the neurotransmitters in the brain the one that is best known to the lay public, by far, is dopamine (DA). To the public, and much of the media, DA is the brain's "pleasure transmitter." By this popular view, it is a burst of DA that produces the pleasure associated with the receipt or anticipation of natural rewards – a chocolate cake, sex, even social rewards – as well as the euphoria produced by addictive drugs, such as amphetamine, cocaine or heroin. In the popular media it is often suggested that it is pursuit of this DA "rush" that leads to a variety of impulse control disorders, such as over-eating, gambling and addiction. Of all the ideas, in all of neuroscience, this is one of the most widely known, and its origin can be traced to the 1978 paper by Roy Wise, Joan Spindler, Harriet deWit and Gary Gerber, that is the topic of this chapter. This paper is an excellent example of how the interpretation of a seemingly simple experimental finding can profoundly influence the thinking and trajectory of an entire field, and the public imagination, for decades – even if it is wrong. But before getting to that conclusion, we should step back and consider the time leading up to the publication of this extremely influential paper. It was only a little over a decade earlier that DA was first recognized as a neurotransmitter in its own right (rather than just being a precursor of norepinephrine). This came about from a series of important studies by researchers in Sweden who developed a histofluorescence method to identify and map monoamine neurotransmitters in the brain (including DA). They thus effectively created the field of chemical neuroanatomy, as well as vividly mapping the location of dopamine in the brain. They also produced useful methods to selectively lesion monoamine-containing cells with neurotoxins, such as 6-hydroxydopamine (6-OHDA), which would

Question: What is the origin of the idea that dopamine is the "pleasure transmitter"?


Mobile robot localization by tracking geometric beacons.

Question: How to localize a robot?


Dietary phenolics: chemistry, bioavailability and effects on health.. There is much epidemiological evidence that diets rich in fruit and vegetables can reduce the incidence of non-communicable diseases such as cardiovascular diseases, diabetes, cancer and stroke. These protective effects are attributed, in part, to phenolic secondary metabolites. This review summarizes the chemistry, biosynthesis and occurrence of the compounds involved, namely the C6-C3-C6 flavonoids-anthocyanins, dihydrochalcones, flavan-3-ols, flavanones, flavones, flavonols and isoflavones. It also includes tannins, phenolic acids, hydroxycinnamates and stilbenes and the transformation of plant phenols associated with food processing (for example, production of black tea, roasted coffee and matured wines), these latter often being the major dietary sources. Events that occur following ingestion are discussed, in particular, the deglycosylation, glucuronidation, sulfation and methylation steps that occur at various points during passage through the wall of the small intestine into the circulatory system and subsequent transport to the liver in the portal vein.We also summarise the fate of compounds that are not absorbed in the small intestine, but which pass into the large intestine where they are degraded by the colonic microflora to phenolic acids, which can be absorbed into the circulatory system and subjected to phase II metabolism prior to excretion. Initially, the protective effect of dietary phenolics was thought to be due to their antioxidant properties which resulted in a lowering of the levels of free radicals within the body.However, there is now emerging evidence that themetabolites of dietary phenolics,which appear in the circulatory systemin nmol/L to low mmol/L concentrations, exertmodulatory effects in cells through selective actions on different components of the intracellular signalling cascades vital for cellular functions such as growth, proliferation and apoptosis. In additi

Question: What is the difference between a flavonoid and a phenolic?


Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems. Millimeter-wave (mm-wave) signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramér-Rao bound (CRB) on position and rotation angle estimation uncertainty from mm-wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of-sight, non-line-of-sight, or obstructed-line-of-sight conditions.

Question: How accurate is the position and orientation estimation of a 5G network?


Understanding Online Consumer Stickiness in E-commerce Environment : A Relationship Formation Model. Consumers with online shopping experience often stick to some special websites. Does it mean that long-term relationships between consumers and the websites form? To solve the problem, this paper analyzed the belief and attitude factors influencing online consumer stickiness intention. Based on the relationships between belief-attitude-intention implied in TPB (Theory of Planned Behavior), according to Expectation-Confirmation theory and Commitment-trust theory, we developed a concept model on online consumer stickiness. Six research hypotheses derived from this model were empirically validated using a survey of online shoppers. SEM (Structure Equation Modeling) was as a data analysis method. The results suggest that online consumer stickiness intention is influenced by consumers' commitment to the website (e-vendors) and their overall satisfaction to the transaction process, and commitment's effect is stronger; in turn, both consumers' commitment and overall satisfaction are influenced by consumers' ongoing trust significantly; confirmation has a significant effect on overall satisfaction but has not the same effect on commitment. The findings show that the long-term relationships between consumers and special websites exactly exist during online repeat consumption.

Question: What is the relationship between online consumer stickiness and online repeat consumption?


The Helmholtz Machine. Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.

Question: How to find the structure of a set of patterns?


Code coverage for suite evaluation by developers. One of the key challenges of developers testing code is determining a test suite's quality -- its ability to find faults. The most common approach is to use code coverage as a measure for test suite quality, and diminishing returns in coverage or high absolute coverage as a stopping rule. In testing research, suite quality is often evaluated by a suite's ability to kill mutants (artificially seeded potential faults). Determining which criteria best predict mutation kills is critical to practical estimation of test suite quality. Previous work has only used small sets of programs, and usually compares multiple suites for a single program. Practitioners, however, seldom compare suites --- they evaluate one suite. Using suites (both manual and automatically generated) from a large set of real-world open-source projects shows that evaluation results differ from those for suite-comparison: statement (not block, branch, or path) coverage predicts mutation kills best.

Question: What is the best way to evaluate test suite quality?


SharesSkew: An algorithm to handle skew for joins in MapReduce. In this paper, we investigate the problem of computing a multiway join in one round of MapReduce when the data may be skewed. We optimize on communication cost, i.e., the amount of data that is transferred from the mappers to the reducers. We identify join attributes values that appear very frequently, Heavy Hitters (HH). We distribute HH valued records to reducers avoiding skew by using an adaptation of the Shares [3] algorithm to achieve minimum communication cost. Our algorithm is implemented for experimentation and is offered as open source software. Furthermore, we investigate a class of multiway joins for which a simpler variant of the algorithm can handle skew. We offer closed forms for computing the parameters of the algorithm for chain and symmetric joins.

Question: How to handle skew in joins in mapreduce?


Background modelling based on generative unet. Background Modelling is a crucial step in background/foreground detection which could be used in video analysis, such as surveillance, people counting, face detection and pose estimation. Most methods need to choose the hyper parameters manually or use ground truth background masks (GT). In this work, we present an unsupervised deep background (BG) modelling method called BM-Unet which is based on a generative architecture that given a certain frame as input it generates as output the corresponding background image — to be more precise, a probabilistic heat map of the colour values. Our method learns parameters automatically and an augmented version of it that utilises colour, intensity differences and optical flow between a reference and a target frame is robust to rapid illumination changes and camera jitter. Besides, it can be used on a new video sequence without the need of ground truth background/foreground masks for training. Experiment evaluations on challenging sequences in SBMnet data set demonstrate promising results over state-of-the-art methods.

Question: What is background modelling based on generative unet?


CRCTOL: A semantic-based domain ontology learning system. Keywords: ontologies < controlled vocabularies < index languages < language types < (language), knowledge acquisition < knowledge engineering < artificial intelligence < computer applications < computer operations < (activities and operations)

Question: What is a semantic-based domain ontology learning system?


The City Browser : Utilizing Massive Call Data to Infer City Mobility Dynamics. This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city's mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.

Question: How does the city browser work?


SWAM: Stuxnet Worm Analysis in Metasploit. Nowadays cyber security is becoming a great challenge. Attacker's community is progressing towards making smart and intelligent malwares (viruses, worms and Root kits). They stealth their existence and also use administrator rights without knowing legal user. Stuxnet worm is an example of a recent malware first detected in July 2010. Its variants were also detected earlier. It is the first type of worm that affects the normal functionality of industrial control systems (ICS) having programmable logic controllers (PLC) through PLC Root kit. Its main goal is to modify ICS behavior by changing the code of PLC and make it to behave in a way that attacker wants. It is a complex piece of malware having different operations and functionalities which are achieved by exploiting zero day vulnerabilities. Stuxnet exploits various vulnerable services in Microsoft Windows. In this paper we will show real time simulation of first three vulnerabilities of these through Metasploit Framework 3.2 and analyze results. A real time scenario is established based on some assumptions. We assumed Proteus design (pressure sensor) as PLC and showed after exploitation that the pressure value drops to an unacceptable level by changing Keil code of this design.

Question: What is a PLC?


Emotion Recognition in Speech Using Neural Networks. Emotion recognition in speech is a topic on which little research has been done to-date. In this paper, we discuss why emotion recognition in speech is a significant and applicable research topic, and present a system for emotion recognition using oneclass-in-one neural networks. By using a large database of phoneme balanced words, our system is speakerand context-independent. We achieve a recognition rate of approximately 50% when testing eight emotions.

Question: What is the canadian military?


Vision-based automated parking system. This paper describes an approach to overcome a situation of monitoring and managing a parking area using a vision based automated parking system. With the rapid increase of cars the need to find available parking space in the most efficient manner, to avoid traffic congestion in a parking area, is becoming a necessity in car park management. Current car park management is dependent on either human personnel keeping track of the available car park spaces or a sensor based system that monitors the availability of each car park space or the overall number of available car park spaces. In both situations, the information available was only the total number of car park spaces available and not the actual location available. In addition, the installation and maintenance cost of a sensor based system is dependent on the number of sensors used in a car park. This paper shows a vision based system that is able to detect and indicate the available parking spaces in a car park. The methods utilized to detect available car park spaces were based on coordinates to indicate the regions of interest and a car classifier. This paper shows that the initial work done here has an accuracy that ranges from 90% to 100% for a 4 space car park. The work done indicated that the application of a vision based car park management system would be able to detect and indicate the available car park spaces

Question: What is a vision based automated parking system?


Trust-aware crowdsourcing with domain knowledge. The rise of social network and crowdsourcing platforms makes it convenient to take advantage of the collective intelligence to estimate true labels of questions of interest. However, input from workers is often noisy and even malicious. Trust is used to model workers in order to better estimate true labels of questions. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. Finally, we demonstrate that our model is superior to state-of-the-art by testing it on multiple real-world datasets.

Question: What is the canadian military?


Improved ID3 algorithm. as the classical algorithm of the decision tree classification algorithm, ID3 is famous for the merits of high classifying speed easy, strong learning ability and easy construction. But when use it to classify, there does exist the problem of inclining to chose attributions which has many values, which affects its practicality. This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. The experimental analysis of the data show that the improved ID3 algorithm can get more reasonable and more effective rules.

Question: What is the improved ID3 algorithm?


Improving the Reliability of Series Resonant Inverters for Induction Heating Applications. This paper analyzes a high-power (100-kW) high-frequency (50-kHz) voltage-fed inverter with a series resonant load circuit for industrial induction heating applications which is characterized by a full-bridge inverter composed of isolated-gate bipolar transistors and a new power control based on phase-shift (PS) control. This power control circuit incorporates a load-adaptive variable-frequency controller and automated blanking time management in order to allow the inverter to work in zero-voltage switching for all output power levels and load conditions. An important improvement of the inverter reliability is achieved by choosing an appropriate and novel switching sequence for the PS inverter. The results are verified experimentally using a prototype for induction hardening applications. A comparative study between the proposed and standard PS power control will be made.

Question: How to improve the reliability of a series resonant inverter for induction heating applications?


Android Code Protection via Obfuscation Techniques: Past, Present and Future Directions. Mobile devices have become ubiquitous due to centralization of private user information, contacts, messages and multiple sensors. Google Android, an open-source mobile Operating System (OS), is currently the market leader. Android popularity has motivated the malware authors to employ set of cyber attacks leveraging code obfuscation techniques. Obfuscation is an action that modifies an application (app) code, preserving the original semantics and functionality to evade anti-malware. Code obfuscation is a contentious issue. Theoretical code analysis techniques indicate that, attaining a verifiable and secure obfuscation is impossible. However, obfuscation tools and techniques are popular both among malware developers (to evade anti-malware) and commercial software developers (protect intellectual rights). We conducted a survey to uncover answers to concrete and relevant questions concerning Android code obfuscation and protection techniques. The purpose of this paper is to review code obfuscation and code protection practices, and evaluate efficacy of existing code de-obfuscation tools. In particular, we discuss Android code obfuscation methods, custom app protection techniques, and various de-obfuscation methods. Furthermore, we review and analyze the obfuscation techniques used by malware authors to evade analysis efforts. We believe that, there is a need to investigate efficiency of the defense techniques used for code protection. This survey would be beneficial to the researchers and practitioners, to understand obfuscation and de-obfuscation techniques to propose novel solutions on Android.

Question: What is the purpose of this paper?


Hybrid speech recognition with Deep Bidirectional LSTM. Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.

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


Rectification of QR-code images using the parametric cylindrical surface model. In recent years, QR codes have found a wide range of applications in business and industry. Moreover, their popularity and function are still growing (fast). Therefore, the processing of the images of QR codes is an important task. In this paper, we try to solve a problem encountered in QR decoding when the QR code is printed near the binding edge inside a book or on the surface of a cylindrical container. When a picture is taken on such a QR code, the picture is no longer in a rectangular shape, which is supposedly the "right" shape that any QR code should take, due to warping from the image capturing mechanism by the camera. We refer to such warping as cylinder-to-screen (C2S) warping. This warping translates into serious distortion of the original QR-code image (also referred to as QR image, for short) and thus causes failure in its decoding. In this paper, a mathematical model called the parametric cylindrical surface model (PCSM) is proposed. Then, based on it, a scheme is proposed to rectify C2S-warped QR images. Experiments show that the proposed scheme is effective, in the sense that many failed QR decodings become successful after the rectification.

Question: What is a QR code?


A First-Estimates Jacobian EKF for Improving SLAM Consistency. In this work, we study the inconsistency of EKF-based SLAM from the perspective of observability. We analytically prove that when the Jacobians of the system and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has observable subspace of dimension higher than that of the actual, nonlinear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of the inconsistency. Furthermore, a new "First-Estimates Jacobian" (FEJ) EKF is proposed to improve the estimator's consistency during SLAM. The proposed algorithm performs better in terms of consistency, because when the filter Jacobians are calculated using the first-ever available estimates for each state variable, the error-state system model has an observable subspace of the same dimension as the underlying nonlinear SLAM system. The theoretical analysis is validated through both simulations and experiments.

Question: How to improve the consistency of the EKF?


Vectorization vs. compilation in query execution. Compiling database queries into executable (sub-) programs provides substantial benefits comparing to traditional interpreted execution. Many of these benefits, such as reduced interpretation overhead, better instruction code locality, and providing opportunities to use SIMD instructions, have previously been provided by redesigning query processors to use a vectorized execution model. In this paper, we try to shed light on the question of how state-of-the-art compilation strategies relate to vectorized execution for analytical database workloads on modern CPUs. For this purpose, we carefully investigate the behavior of vectorized and compiled strategies inside the Ingres VectorWise database system in three use cases: Project, Select and Hash Join. One of the findings is that compilation should always be combined with block-wise query execution. Another contribution is identifying three cases where "loop-compilation" strategies are inferior to vectorized execution. As such, a careful merging of these two strategies is proposed for optimal performance: either by incorporating vectorized execution principles into compiled query plans or using query compilation to create building blocks for vectorized processing.

Question: How does compilation compare to vectorization?


Automated Summarization Evaluation with Basic Elements. As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced 'ideal' summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.

Question: What is a basic element?


The Impact of Power Allocation on Cooperative Non-orthogonal Multiple Access Networks With SWIPT. In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.

Question: What is the impact of power allocation on cooperative non-orthogonal multiple access networks with SWIPT?


Learning Feature Pyramids for Human Pose Estimation. Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multibranch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.

Question: How to learn feature pyramids in deep convolutional neural networks?


Evolution of the mid range theory of comfort for outcomes research.. constructs. VOLUME 49 • NUMBER 2 NURSING OUTLOOK 88 Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Figure 2. MR theory of comfort. Reprinted with permission from Aspen Publishers, Inc. Copyright © 1992 Aspen Publishers, Inc. Adv Nurs Sci 1992;15:1. The work of psychologist Murray in 1938 met these criteria. Because his theory was about human needs, it was applicable to patients who experience multiple stimuli in stressful health care situations. This was the deductive stage of theory development: beginning with an abstract, general theoretic construction and substructing downward to more specific levels that included concepts for nursing practice. Each nursing concept then could be operationalized relative to a specific research setting. Murray's intent was to synthesize a grand theory for psychology from existing lesser psychologic theories of his time. His concepts are found in Figure 2, lines 1, 2, and 3. Because comfort was perceived by patients, it was logically substructed under Murray's concept of "perception." "Obstructing forces" were substructed for nursing as health care needs; "facilitating forces" were nursing interventions, and "interacting forces" were intervening variables (line 4). This was the first and altruistic part of the theory, which stated that nurses identified unmet comfort needs of their patients, designed interventions to address those needs, and sought to enhance their patients' comfort, the immediate desired outcome. The second and practical part of the theory addressed the question? "Why comfort?" For nursing, unitary trend was substructed to health thema, which was further substructed to HSBs. HSB was Schlotfeldt's concept and represented the broad category of subsequent desired outcomes. She stated that HSBs could be internal, external, or a peaceful death. Some examples of HSBs are decreased length of stay, improved functional status, b

Question: What is the mid range theory of comfort?


A transmission control scheme for media access in sensor networks. We study the problem of media access control in the novel regime of sensor networks, where unique application behavior and tight constraints in computation power, storage, energy resources, and radio technology have shaped this design space to be very different from that found in traditional mobile computing regime. Media access control in sensor networks must not only be energy efficient but should also allow fair bandwidth allocation to the infrastructure for all nodes in a multihop network. We propose an adaptive rate control mechanism aiming to support these two goals and find that such a scheme is most effective in achieving our fairness goal while being energy efficient for both low and high duty cycle of network traffic.

Question: What is a sensor network?


A novel ultra low power temperature sensor for UHF RFID tag chip. A novel ultra low power temperature sensor for UHF RFID tag chip is presented. The sensor consists of a constant pulse generator, a temperature related oscillator, a counter and a bias. Conversion of temperature to digital output is fulfilled by counting the number of the clocks of the temperature related oscillator in a constant pulse period. The sensor uses time domain comparing, where high power consumption bandgap voltage references and traditional ADCs arc not needed. The sensor is realized in a standard 0.18 mum CMOS process, and the area is only 0.2 mm2. The accuracy of the temperature sensor is plusmn1degC after calibration. The power consumption of the sensor is only 0.9 muW.

Question: What is the area of the sensor?


Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner. Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I¿s RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The discussion follows the data mining process. The focus will be on the data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction.

Question: What is the diabetes prediction model?


Wechsler Adult Intelligence Scale-IV Dyads for Estimating Global Intelligence.. All possible two-subtest combinations of the core Wechsler Adult Intelligence Scale-IV (WAIS-IV) subtests were evaluated as possible viable short forms for estimating full-scale IQ (FSIQ). Validity of the dyads was evaluated relative to FSIQ in a large clinical sample (N = 482) referred for neuropsychological assessment. Sample validity measures included correlations, mean discrepancies, and levels of agreement between dyad estimates and FSIQ scores. In addition, reliability and validity coefficients were derived from WAIS-IV standardization data. The Coding + Information dyad had the strongest combination of reliability and validity data. However, several other dyads yielded comparable psychometric performance, albeit with some variability in their particular strengths. We also observed heterogeneity between validity coefficients from the clinical and standardization-based estimates for several dyads. Thus, readers are encouraged to also consider the individual psychometric attributes, their clinical or research goals, and client or sample characteristics when selecting among the dyadic short forms.

Question: What is the best short form for estimating full-scale IQ?


Performance assessment of Multilateration Systems - a solution to nextgen surveillance. Multilateration (MLAT) surveillance is now being used in all types of airspace for air traffic management. MLAT can be used for airport surface movement surveillance as well as for terminal and en route surveillance, using Wide Area Multilateration (WAM). MLAT is a low-cost technology that not only has major advantages in operations and maintenance, but also provides excellent performance under all conditions, especially for countries with large geographic areas or mountainous terrain to cover. This paper focuses on performance metrics from operational system data to demonstrate that the performance of Era's MLAT systems meet surveillance requirements for all surveillance applications. This includes the key performance requirements for the various surveillance applications that are evaluated, including accuracy, coverage, update rate, integrity and availability.

Question: What is the performance of multilateration surveillance?


'Just What the Doctor Ordered': A Revised UTAUT for EMR System Adoption and Use by Doctors. Electronic medical record (EMR) systems can deliver many benefits to healthcare organizations and the patients they serve. However, one of the biggest stumbling blocks in garnering these benefits is the limited adoption and use by doctors. We employ the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation and adapt the theory to the context of EMR system adoption and use by doctors. Specifically, we suggest that age will be the only significant moderator, and gender, voluntariness and experience will not play significant moderating roles. We tested our model in a longitudinal study over a 7-month period in a hospital implementing a new EMR system. We collected 3 waves of survey data from 141 doctors and used system logs to measure use. While the original UTAUT only predicted about 20% of the variance in intention, the modified UTAUT predicted 44%. Both models were comparable in their prediction of use. In addition to contributing to healthcare IT and UTAUT research, we hope this work will serve as a foundation for future work that integrates UTAUT with other theoretical perspectives.

Question: What is the unified theory of acceptance and use of technology?


Thirty-Five Years of Research on Neuro-Linguistic Programming . NLP Research Data Base . State of the Art or Pseudoscientific Decoration ?. The huge popularity of Neuro-Linguistic Programming (NLP) therapies and training has not been accompanied by knowledge of the empirical underpinnings of the concept. The article presents the concept of NLP in the light of empirical research in the Neuro-Linguistic Programming Research Data Base. From among 315 articles the author selected 63 studies published in journals from the Master Journal List of ISI. Out of 33 studies, 18.2% show results supporting the tenets of NLP, 54.5% results non-supportive of the NLP tenets and 27.3% brings uncertain results. The qualitative analysis indicates the greater weight of the non-supportive studies and their greater methodological worth against the ones supporting the tenets. Results contradict the claim of an empirical basis of NLP.

Question: What is the empirical basis of NLP?


Conditional Generative Adversarial Networks for Commonsense Machine Comprehension. Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake sentence. We proposed a Conditional GANs (CGANs) in which the generator is conditioned by the context. Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods.

Question: How to generate fake sentence?


LLC series resonant converter based LED lamp driver with ZVS. This paper proposes an LLC resonant converter based LED (Light Emitting Diode) lamp driver with high power factor. The proposed circuit uses a boost converter for PFC (Power Factor Correction) which operates in continuous conduction mode (CCM) and a quasi half bridge resonant converter to drive the LED lamp load. The LLC converter is designed such that solid state switches of quasi half bridge are working under zero voltage switching (ZVS) to reduce switching losses. The analysis, design, modeling and simulation of 50 W LED driver are carried out using MATLAB/Simulink tool for universal AC mains. The power quality indices are calculated such as total harmonic distortion of AC mains current (THDi), power factor (PF) and crest factor (CF) to evaluate the performance of proposed LED lamp driver.

Question: How to design a LED lamp driver?


Automated extraction of product comparison matrices from informal product descriptions. Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users. Preprint submitted to JSS January 5, 2017

Question: What is a product comparison matrix?


Hamming Clustering: A New Approach to Rule Extraction. A new algorithm, called Hamming Clustering (HC), is proposed to extract a set of rules underlying a given classification problem. It is able to reconstruct the and-or expression associated with any Boolean function from a training set of

Question: What is a Boolean function?


Optimal Expectations and the Welfare Cost of Climate Variability. Uncertainty about the future is an important determinant of well-being, especially in developing countries where financial markets and other market failures result in ineffective insurance mechanisms. However, separating the effects of future uncertainty from realised events, and then measuring the impact of uncertainty on utility, presents a number of empirical challenges. This paper aims to address these issues and provides supporting evidence to show that increased climate variability (a proxy for future income uncertainty) reduces farmers' subjective well-being, consistent with the theory of optimal expectations (Brunnermeier & Parker, 2005 AER), using panel data from rural Ethiopia and a new data set containing daily atmospheric parameters. The magnitude of our result indicates that a one standard deviation (7%) increase in climate variability has an equivalent effect on life satisfaction to a two standard deviation (1-2%) decrease in consumption. This effect is one of the largest determinants of life satisfaction in rural Ethiopia. (JEL: C25, D60, I31.) ∗Alem – Department of Economics, University of Gothenburg, Sweden and Department of Agricultural and Resource Economics, UC, Berkeley, USA. E-mail: yonas.alem@economics.gu.se. Colmer – The Grantham Research Institute and Centre for Economic Performance, London School of Economics, UK. E-mail: j.m.colmer@lse.ac.uk. We are grateful to Allen Blackman, Jeffery Bookwalter, Gharad Bryan, Douglas Dalenberg, Paul Dolan, John Feddersen, Greer Gosnell, Cameron Hepburn, Derek Kellenberg, Peter Martinsson, Kyle Meng, Rob Metcalfe, Eric Neumayer, Jonathan Parker, Hendrik Wolff and seminar participants at the EfD 6th Annual Meeting, EAERE, the University of Oxford, London School of Economics, University of Montana, and Resources for the Future for helpful thoughts, comments and discussions. The first author would like to thank the Swedish International Develop

Question: What is the difference between climate variability and uncertainty?


Design of Compact Wide Stopband Microstrip Low-pass Filter using T-shaped Resonator. In this letter, a compact microstrip low-pass filter (LPF) using T-shaped resonator with wide stopband is presented. The proposed LPF has capability to remove the eighth harmonic and a low insertion loss of 0.12 dB. The bandstop structure using stepped impendence resonator and two open-circuit stubs are used to design a wide stopband with attenuation level better than −20 dB from 3.08 up to 22 GHz. The proposed filter with −3-dB cutoff frequency of 2.68 GHz has been designed, fabricated, and measured. The operating of the LPF is investigated based on equivalent circuit model. Simulation results are verified by measurement results and excellent agreement between them is observed.

Question: What is the design of compact wide stopband microstrip low-pass filter using T-shaped resonator?