Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.. Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel co

Question: What is the canadian military?


SPDT Switch Using Both nMOS and pMOS Transistors for Improving Power Handling. An SPDT switch consisting of both nMOS and pMOS transistors is presented. Compared with conventional SPDT switches using only nMOS transistors under the same bias condition, the proposed switch exhibits better power-handling capability (PHC). The mechanism for the PHC improvement is explained. A prototype is implemented using a 0.18-um CMOS process. Measurement results show that, at 2.4 GHz, the insertion loss is 0.62 dB when the nMOS transistors are on and 0.91 dB when the pMOS transistors are on. For both modes, the measured return loss and isolation are better than 10 dB and 19 dB, respectively, up to 6 GHz. Under 1.8-V operation, the switch is able to handle a 26.1-dBm input power when the nMOS transistors are on and a 24.0-dBm input power when the pMOS transistors are on.

Question: How does the proposed switch work?


Enable IoT interoperability in ambient assisted living: Active and healthy aging scenarios. Ambient Assisted Living (AAL) represents one of the most promising Internet of Things (IoT) applications due to its influence on the quality of life and health of the elderly people. However, the interoperability is one of the major issues that needs to be addressed to promote the adoption of AAL solutions in real environments. In this paper, an IoT prototype system for AAL called AAL-IoTSys is proposed, which includes a Smart IoT Gateway as a key component to enable the interoperability from several heterogeneous devices over different communication protocols and technologies (e.g., WiFi, ZigBee, Bluetooth, IEEE 802.15.4, 6LowPAN). This system aims to monitor environmental (temperature, humidity, C02), health (heart rate) and location (GPS) parameters in the elderly people's houses in order to facilitate events early detection and automatic sending notifications to elderly care stakeholders in active and healthy aging scenarios (included health professionals, caregivers, family members and emergency services).

Question: What is the purpose of the IoT system?


Multiagent Systems Engineering. This paper describes the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE also provides a way to specify architecture-independent detailed definition of the internal agent design. An example of applying the MaSE methodology is also presented.

Question: What is a multiagent system?


Learning Theory and Instructional Design. Designing effective instruction goes beyond systematically executing various steps within an instructional design model. Among a host of considerations, effective instructional design should take into consideration the theoretical bases in which it is grounded. This is not to say that learning theory offers instructional designers answers to design problems but instead, offers clarity, direction and focus throughout the instructional design process. Merrill (2001, p. 294) explains that a "theoretical tool, in and of itself, is not an instructional design theory but defines instructional components that can be used to define instructional prescriptions more precisely." Likewise, Merriam and Caffarella (1999, p. 250) make the point that "[learning] theories do not give us solutions, but they do direct our attention to those variables that are crucial in finding solutions." Thus, understanding theoretical frameworks and properly incorporating them within the scope of instructional design is important for designers to effectively prepare and present instruction as well as for organizational entities to more precisely and efficiently address training-appropriate issues.

Question: What is the difference between learning theory and instructional design?


Advanced control strategies for balancing LED usage of AC LED driver. This paper presents two enhanced methods to balance LEDs energy usage in the AC LED Driver. While conventional AC LED driver has energy consumption imbalance, the proposed control methods provide equally distributed energy usage. First method is to apply the appropriate firing voltage that is the threshold voltage for equalizing energy consumption of LEDs. This method improves balancing energy consumption within a rectified line cycle. The other method is to change the order of LEDs turning on sequence by every two rectified line cycle. These two approaches are compared with mathematical analysis in terms of the energy consumption of each LED. A prototype driver with three 12 W-LED strings is built. The proposed method 1 and method 2 achieve 0.0030 J and 0.0033 J standard deviation of energy consumption, respectively, compared to the 0.0070 J deviation for the conventional approach.

Question: How to balance energy consumption of LEDs?


Overview of the TREC 2017 Real-Time Summarization Track. Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com

Question: What is the TREC 2017 Real-Time Summarization Track?


Continuous Auctions and Insider Trading. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/econosoc.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.

Question: How to use JSTOR archive?


Building a HighLevel Dataflow System on top of MapReduce: The Pig Experience. Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of MapReduce leads to much low-level hacking to deal with the many-step, branching dataflows that arise in practice. Moreover, users must repeatedly code standard operations such as join by hand. These practices waste time, introduce bugs, harm readability, and impede optimizations. Pig is a high-level dataflow system that aims at a sweet spot between SQL and Map-Reduce. Pig offers SQL-style high-level data manipulation constructs, which can be assembled in an explicit dataflow and interleaved with custom Mapand Reduce-style functions or executables. Pig programs are compiled into sequences of Map-Reduce jobs, and executed in the Hadoop Map-Reduce environment. Both Pig and Hadoop are open-source projects administered by the Apache Software Foundation. This paper describes the challenges we faced in developing Pig, and reports performance comparisons between Pig execution and raw Map-Reduce execution.

Question: What is a high-level dataflow system?


A Pipelined CRC Calculation Using Lookup Tables. We present a fast cyclic redundancy check (CRC) algorithm that performs CRC computation for any length of message in parallel. Traditional CRC implementations have feedbacks, which make pipelining problematic. In the proposed approach, we eliminate feedbacks and implement a pipelined calculation of 32-bit CRC in the SMIC 0.13 μm CMOS technology. For a given message, the algorithm first chunks the message into blocks, each of which has a fixed size equal to the degree of the generator polynomial. Then it computes CRC for the chunked blocks in parallel using lookup tables, and the results are combined together by performing XOR operations. The simulation results show that our proposed pipelined CRC is more efficient than existing CRC implementations.

Question: What is a pipelined CRC?


Classificationof Mammographic BreastDensityUsinga CombinedClassifierParadigm Keir Bovis andSameerSingh. In thispaperweinvestigateanew approach to theclassificationof mammographic imagesaccording to breasttype. The classificationof breastdensityin this studyis motivatedby its useasprior knowledge in theimageprocessingpipeline.By utilising thisknowledgeat differentstagesincludingenhanceme nt, segmentation and featureextraction, its applicationaims to increasethe sensiti vity of detectingbreastcancer . Our implementeddiscriminationof breastdensityis basedon the underlyingtexture containedwithin the breast tissueapparent on a digital mammogramandrealisedby utilising four approachesto quantifyingthe texture. Following featureextraction,we adopta variationon bootstrapaggregation('bagging') to meetthe assumptionsof independencein datarepresentationof theinputdataset,necessaryfor classifiercombination. Multiple classifierscomprisingfeed-forward Artifi cial NeuralNetwork (ANN) aresubseque ntly trainedwith the differentperturbedinput dataspacesusing10-fold cross-validation. Thesetof classifieroutputs,expressedin a probabilistic framework, aresubsequently combinedusingsix differentclassifiercombinationrulesandtheresultscompared.In this studywe examinetwo differentclassificationtasks;a four-classclassificationproblem differentiatingbetweenfatty, partly fatty, denseandextremelydensebreasttypesanda two-classproblems, differentiatingbetweendenseandfatty breasttypes. The datasetusedin this study is the Digital Database of ScreeningMammograms(DDSM) containingMedio-LateralOblique(MLO) views for eachbreastfor 377 patients. For both tasksthe bestcombinationstrategy was found using the productrule giving an average recognition rateon testof 71.4%for thefour-classproblemand96.7%for thetwo-classproblem.

Question: What is the best classification algorithm for breast density classification?


OpenFst: A General and Efficient Weighted Finite-State Transducer Library. We describe OpenFst, an open-source library for weighted finite-state transducers (WFSTs). OpenFst consists of a C++ template library with efficient WFST representations and over twenty-five operations for constructing, combining, optimizing, and searching them. At the shell-command level, there are corresponding transducer file representations and programs that operate on them. OpenFst is designed to be both very efficient in time and space and to scale to very large problems. This library has key applications speech, image, and natural language processing, pattern and string matching, and machine learning. We give an overview of the library, examples of its use, details of its design that allow customizing the labels, states, and weights and the lazy evaluation of many of its operations. Further information and a download of the OpenFst library can be obtained from http://www.openfst.org.

Question: What is a WFST?


Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition. While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact in pattern recognition tasks has received relatively little attention to date. In this paper, we explicitly take feature measurement uncertainty into account and show how multimodal classification and learning rules should be adjusted to compensate for its effects. Our approach is particularly fruitful in multimodal fusion scenarios, such as audiovisual speech recognition, where multiple streams of complementary time-evolving features are integrated. For such applications, provided that the measurement noise uncertainty for each feature stream can be estimated, the proposed framework leads to highly adaptive multimodal fusion rules which are easy and efficient to implement. Our technique is widely applicable and can be transparently integrated with either synchronous or asynchronous multimodal sequence integration architectures. We further show that multimodal fusion methods relying on stream weights can naturally emerge from our scheme under certain assumptions; this connection provides valuable insights into the adaptivity properties of our multimodal uncertainty compensation approach. We show how these ideas can be practically applied for audiovisual speech recognition. In this context, we propose improved techniques for person-independent visual feature extraction and uncertainty estimation with active appearance models, and also discuss how enhanced audio features along with their uncertainty estimates can be effectively computed. We demonstrate the efficacy of our approach in audiovisual speech recognition experiments on the CUAVE database using either synchronous or asynchronous multimodal integration models.

Question: Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition.


A Computational Model of Plan-Based Narrative Conflict at the Fabula Level. Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the "who?" "why?" and "when?" questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.

Question: What is a computational model of plan-based narrative conflict?


Interpretation and trust: designing model-driven visualizations for text analysis. Statistical topic models can help analysts discover patterns in large text corpora by identifying recurring sets of words and enabling exploration by topical concepts. However, understanding and validating the output of these models can itself be a challenging analysis task. In this paper, we offer two design considerations - interpretation and trust - for designing visualizations based on data-driven models. Interpretation refers to the facility with which an analyst makes inferences about the data through the lens of a model abstraction. Trust refers to the actual and perceived accuracy of an analyst's inferences. These considerations derive from our experiences developing the Stanford Dissertation Browser, a tool for exploring over 9,000 Ph.D. theses by topical similarity, and a subsequent review of existing literature. We contribute a novel similarity measure for text collections based on a notion of "word-borrowing" that arose from an iterative design process. Based on our experiences and a literature review, we distill a set of design recommendations and describe how they promote interpretable and trustworthy visual analysis tools.

Question: What is the difference between interpretation and trust?


Comparison of Sparse Representation and Fourier Discriminant Methods : Damage Location Classification in Indirect Lab-scale Bridge Structural Health Monitoring. This paper presents a novel method for interpreting data to improve the indirect structural health monitoring (SHM) of bridges. The research presented in the study is part of an ongoing study aimed at developing a novel SHM paradigm for the health assessment of bridges. In this paradigm, we envision the use of an instrumented vehicle that assesses a bridge's dynamic characteristics while traveling across the bridge. These characteristics are then correlated to the health of the structure by means of advanced signal processing and pattern recognition approaches. In this paper, we present and compare two classification algorithms that locate the presence of damages at well-defined locations on the structure: sparse representation and the Fourier discriminant methods, and find that the sparse representation method provides superior classification accuracy.

Question: What is the difference between the sparse representation and the Fourier discriminant methods?


Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services. The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.

Question: What is the semantic data model for IoT data?


Protein function in precision medicine: deep understanding with machine learning.. Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.

Question: What is the difference between precision medicine and personalized health?


Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies.. Polyphenols are abundant micronutrients in our diet, and evidence for their role in the prevention of degenerative diseases is emerging. Bioavailability differs greatly from one polyphenol to another, so that the most abundant polyphenols in our diet are not necessarily those leading to the highest concentrations of active metabolites in target tissues. Mean values for the maximal plasma concentration, the time to reach the maximal plasma concentration, the area under the plasma concentration-time curve, the elimination half-life, and the relative urinary excretion were calculated for 18 major polyphenols. We used data from 97 studies that investigated the kinetics and extent of polyphenol absorption among adults, after ingestion of a single dose of polyphenol provided as pure compound, plant extract, or whole food/beverage. The metabolites present in blood, resulting from digestive and hepatic activity, usually differ from the native compounds. The nature of the known metabolites is described when data are available. The plasma concentrations of total metabolites ranged from 0 to 4 mumol/L with an intake of 50 mg aglycone equivalents, and the relative urinary excretion ranged from 0.3% to 43% of the ingested dose, depending on the polyphenol. Gallic acid and isoflavones are the most well-absorbed polyphenols, followed by catechins, flavanones, and quercetin glucosides, but with different kinetics. The least well-absorbed polyphenols are the proanthocyanidins, the galloylated tea catechins, and the anthocyanins. Data are still too limited for assessment of hydroxycinnamic acids and other polyphenols. These data may be useful for the design and interpretation of intervention studies investigating the health effects of polyphenols.

Question: What is the bioavailability of polyphenols?


Cache-aware Roofline model: Upgrading the loft. The Roofline model graphically represents the attainable upper bound performance of a computer architecture. This paper analyzes the original Roofline model and proposes a novel approach to provide a more insightful performance modeling of modern architectures by introducing cache-awareness, thus significantly improving the guidelines for application optimization. The proposed model was experimentally verified for different architectures by taking advantage of built-in hardware counters with a curve fitness above 90%.

Question: What is the roofline model?


EXPLORING THE CONSUMERS ' ACCEPTANCE OF ELECTRONIC RETAILING USING TECHNOLOGY ACCEPTANCE MODEL. Following the approach of Pavlou (2003), we investigate key drivers for consumers̀ electronic retailing (e-tailing) acceptance that are integrated into TAM (technology acceptance model). TAM is one of the better known models for explaining the intention to use technology and it integrates two perceptions: usefulness and ease of use. Th e main purpose of this paper is to explore the relationship between perceived ease of use and perceived usefulness in accepting e-tailing among Croatian consumers. In particular, the focus of the paper is on the areas which infl uenced customer commitment and loyalty in e-tailing. Th e results of the quantitative study among Croatian consumers show that electronic retailing, e.g. purchasing via the Internet, would enhance consumers̀ eff ectiveness in getting product information and their eff ectiveness in purchasing products.

Question: What is the relationship between perceived ease of use and perceived usefulness in accepting e-tailing among Croatian consumers?


Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network. Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal information into CNNs are still being actively explored in the recent literature. Motivated by the popular recurrent attention models in the research area of natural language processing, we propose the Attention-aware Temporal Weighted CNN (ATW CNN) for action recognition in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is simply implemented as temporal weighting yet it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Our experimental results on the UCF-101 and HMDB-51 datasets showed that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.

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


PicToSeek: combining color and shape invariant features for image retrieval. We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object's pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.

Question: What is the name of the image retrieval scheme?


Automatic Tempo-based Classification of Argentine Tango Music. Tempo-based classification is a popular technique to classify music, with applications in media-art projects, systematic study of music etc. The success of this technique using dance music genres has typically been found to be over 70%, yet it was mostly evaluated using standardized music genres, such as ballroom music. Argentine tango is a music genre, originating in Buenos Aires at the end of the 19th century, which has gained a lot of popularity since its creation and is currently being danced in social settings all over the world (see side Photo taken in San Francisco, courtesy of Ref. [10]). There are three music types which define the Argentine tango played in social settings; although these types have different meters from one another, their tempi feature variations influenced by the trends of the era of composition and the composers' personal style. Therefore, Argentine tango seemingly makes a good, yet interesting candidate to explore for tempo-based classification schemes. In this report, we construct and evaluate an automatic, bayes tempo classifier with application to Argentine tango music, using a library with more than 2,000 pieces. We find that the accuracy of our classifier stands at 79%, which is well within the bound set by similar schemes applied to ballroom music, proving our original point that tempo is a promising feature in the classification of Argentine tango. We also come up with interesting insights about how to improve classification schemes for this particular musical genre.

Question: What is the accuracy of a tempo classifier for Argentine tango music?


A Design and Assessment of a Direction Finding Proximity Fuze Sensor. This paper presents the implementation and assessment of a direction finding proximity fuze sensor for anti-aircrafts or anti-air missiles. A higher rejection of clutter signals is achieved by employing a binary phase shift keying modulation using Legendre sequence. The direction finding is implemented by comparing received powers from six receiving antennas equally spaced by an angle of 60° around a cylindrical surface. In addition, target detection algorithms have been developed for a robust detection of the target, taking the wide variation of target related parameters into considerations. The performances of the developed fuze sensor are experimentally verified by constructing the fuze-specific encounter simulation test apparatus, which collects and analyzes reflected signals from a standard target. The developed fuze sensor can successfully detect the -10 dBsm target over a 10 m range as well as the direction with an out-of-range rejection of about 40 dB. Furthermore, the developed fuze sensor can clearly detect the target with mesh clutter environment. To assess realistic operation, the fuze sensor is tested using 155 mm gun firing test setup. Through the gun firing test, the successful fuzing range and direction finding performances are demonstrated.

Question: What is a proximity fuze sensor?


Studying Online Social Networks. What is Social Network Analysis? The Social Network Approach Units of Analysis Relations Ties Multiplexity Composition Beyond the Tie Social Networks Two Views: Ego-Centered and Whole Networks Network Characteristics Partitioning Networks Groups STUDYING ONLINE SOCIAL NETWORKS, by Laura Garton... http://jcmc.indiana.edu/vol3/issue1/garton.html 1 of 32 9/21/11 11:24 AM Positional Analysis Networks of Networks Placing CMC in Context Collecting Network Data for CMC Studies Selecting a Sample Collecting Data How are Network Data Analyzed? Ego-Centered Analysis Whole Network Analysis Conclusions References About the Authors

Question: What is social network analysis?


A novel redis security extension for NoSQL database using authentication and encryption. Redis is a new generation NoSQL database. Redis in its simplest form is a key value pair based data system. It supports all the data structures like variables, Linked list, arrays, strings, and queues. However unlike the conventional databases, Redis does not provide enough security for the data. Anyone can get the value if the key is known because the data is stored in the form of key value pair. Therefore such a database is unsuitable for enterprise and most practical application data. In this paper, the work is carried out to add immense security to a Redis system using following: a) Authentication Service b) Encryption Services c) Security to persistent data d) Security to blob data (multimedia data for images). The Encryption algorithm plays a very important role in the field of Database Management System. Here we make use of AES algorithm because the AES algorithm consumes least Encryption and Decryption time in comparison with RSA and DES [12]. The principle of the work is that a separate Key is created in the database whose value is an encrypted data, encrypted by symmetric key cryptography using AES. This data contains all other key values being concatenated and encrypted. Once a query is generated, first the extraction of independent data entities are made, followed by decryption of data using symmetric key. We also design a UI system to demonstrate the capabilities of the system with and without the security implementation. The result of our work shows that adding the security extension does not increase the overhead by much in terms of system resources and latency. We also extend the key-value based system to be able to store binary image data which is also stored in the encrypted pattern.

Question: What is the difference between AES and DES?


Margin based feature selection - theory and algorithms. Feature selection is the task of choosing a small set out of a given set of features that capture the relevant properties of the data. In the context of supervised classification problems the relevance is determined by the given labels on the training data. A good choice of features is a key for building compact and accurate classifiers. In this paper we introduce a margin based feature selection criterion and apply it to measure the quality of sets of features. Using margins we devise novel selection algorithms for multi-class classification problems and provide theoretical generalization bound. We also study the well known Relief algorithm and show that it resembles a gradient ascent over our margin criterion. We apply our new algorithm to various datasets and show that our new Simba algorithm, which directly optimizes the margin, outperforms Relief.

Question: What is a margin based feature selection criterion?


Benchmarking open government: An open data perspective. Available online 3 April 2014

Question: What is the benchmarking open government?


Functorial Boxes in String Diagrams. String diagrams were introduced by Roger Penrose as a handy notation to manipulate morphisms in a monoidal category. In principle, this graphical notation should encompass the various pictorial systems introduced in proof-theory (like Jean-Yves Girard's proof-nets) and in concurrency theory (like Robin Milner's bigraphs). This is not the case however, at least because string diagrams do not accomodate boxes — a key ingredient in these pictorial systems. In this short tutorial, based on our accidental rediscovery of an idea by Robin Cockett and Robert Seely, we explain how string diagrams may be extended with a notion of functorial box to depict a functor separating an inside world (its source category) from an outside world (its target category). We expose two elementary applications of the notation: first, we characterize graphically when a faithful balanced monoidal functor F : C −→ D transports a trace operator from the category D to the category C, and we then exploit this to construct well-behaved fixpoint operators in cartesian closed categories generated by models of linear logic; second, we explain how the categorical semantics of linear logic induces that the exponential box of proof-nets decomposes as two enshrined functorial boxes. ∗Invited paper at the conference Computer Science Logic 2006 in Szeged, Hungary. To appear in the proceedings of the conference. c © Springer Verlag. This research was partially supported by the ANR Project INVAL "Invariants algébriques des systèmes informatiques".

Question: What is a functorial box?


Analysis of Human Electrocardiogram for Biometric Recognition. Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.

Question: How to recognize a person from his/her heartbeat?


Collaborative GAN Sampling. Generative adversarial networks (GANs) have shown great promise in generating complex data such as images. A standard practice in GANs is to discard the discriminator after training and use only the generator for sampling. However, this loses valuable information of real data distribution learned by the discriminator. In this work, we propose a collaborative sampling scheme between the generator and discriminator for improved data generation. Guided by the discriminator, our approach refines generated samples through gradient-based optimization, shifting the generator distribution closer to the real data distribution. Additionally, we present a practical discriminator shaping method that can further improve the sample refinement process. Orthogonal to existing GAN variants, our proposed method offers a new degree of freedom in GAN sampling. We demonstrate its efficacy through experiments on synthetic data and image generation tasks.

Question: What is the difference between a GAN and a GAN-based sampling method?


An Algebraic Fault Attack on the LED Block Cipher. In this paper we propose an attack on block ciphers where we combine techniques derived from algebraic and fault based cryptanalysis. The recently introduced block cipher LED serves us as a target for our attack. We show how to construct an algebraic representation of the encryption map and how to cast the side channel information gained from a fault injection into polynomial form. The resulting polynomial system is converted into a logical formula in conjunctive normal form and handed over to a SAT solver for reconstruction of the secret key. Following this approach we were able to mount a new, successful attack on the version of LED that uses a 64-bit secret key, requiring only a single fault injection.

Question: How to attack the LED block cipher?


Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images. Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

Question: What is the difference between the SIFT and the improved SIFT?


Extracting Data from NoSQL Databases. Businesses and organizations today generate increasing volumes of data. Being able to analyze and visualize this data to find trends that can be used as input when making business decisions is an important factor for competitive advantage. Spotfire is a software platform for doing this. Spotfire uses a tabular data model similar to the relational model used in relational database management systems (RDBMSs), which are commonly used by companies for storing data. Extraction and import of data from RDBMSs to Spotfire is generally a simple task. In recent years, because of changing application requirements, new types of databases under the general term NoSQL have become popular. NoSQL databases differ from RDBMSs mainly in that they use non-relational data models, lack explicit schemas and scale horizontally. Some of these features cause problems for applications like Spotfire when extracting and importing data. This thesis investigates how these problems can be solved, thus enabling support for NoSQL databases in Spotfire. The approach and conclusions are valid for any application that interacts with databases in a similar way as Spotfire. General solutions for supporting NoSQL databases are suggested. Also, two concrete tools for importing data from Cassandra and Neo4j that have been implemented in the Spotfire platform are described. The presented solutions comprise a data model mapping from the NoSQL system to Spotfire tables, sampling and possibly clustering for finding schemas, and an extraction mechanism tailored to the particular system's query interface. The suggested solutions are not claimed to be complete. Rather, the work in this thesis can serve as a starting point for more thorough investigations or as a basis for something that can be extended.

Question: How to extract data from NoSQL databases?


Interoperable Data Migration between NoSQL Columnar Databases. NoSQL databases have emerged as the solution to handle large quantities of user-generated contents still guaranteeing fault tolerance, availability and scalability. Each NoSQL database offers differentiated properties and characteristics as well as different data models and architectures. As a result, the development of applications exploiting such kind of technology is strictly dependent on the specific NoSQL solution being adopted, and the migration from a NoSQL to the other requires the development of ad-hoc code managing the transfer of data. In order to mitigate such issue, this paper proposes an interoperable migration system for columnar NoSQL databases. The proposed approach is based on an orginal Metamodel, capable of preserving both strong and weak consistency between data updates, secondary indexes and various data types. Moreover, the approach allows developers to easily add support for new databases.

Question: What is a columnar database?


A cryptosystem based on Vigenère cipher with varying key. — Privacy is one of the key issues information Security addresses. Through encryption one can prevent a third party from understanding raw data during signal transmission. The encryption methods for enhancing the security of digital contents has gained high significance in the current era of breach of security and misuse of the confidential information intercepted and misused by the unauthorized parties. This paper sets out to contribute to the general body of knowledge in the area of cryptography application and by developing a new way of implementing Vigenère cipher encryption algorithm by automatically changing the cipher key after each encryption step. The new method will use successive keys that will be dependent on the initial key value during the encryption process. The algorithm ultimately makes it possible for encryption and decryption of the text. The ciphertext will have different encryption key pattern and the Vigenère cryptosystem will be more difficult to decipher using frequency attack. The implementation will be done using java programming.

Question: How to implement Vigenère cipher?


QUO VADIS , DYNAMIC CAPABILITIES ? ACONTENT-ANALYTICREVIEWOFTHECURRENTSTATEOF KNOWLEDGE AND RECOMMENDATIONS FOR FUTURE RESEARCH. Although the dynamic capabilities perspective has become one of the most frequently used theoretical lenses in management research, critics have repeatedly voiced their frustration with this literature, particularly bemoaning the lack of empirical knowledge and the underspecification of the construct of dynamic capabilities. But research on dynamic capabilities has advanced considerably since its early years, in which most contributions to this literature were purely conceptual. A plethora of empirical studies as well as further theoretical elaborations have shed substantial light on a variety of specific, measurable factors connected to dynamic capabilities. Our article starts out by analyzing these studies to develop a meta-framework that specifies antecedents, dimensions, mechanisms, moderators, and outcomes of dynamic capabilities identified in the literature to date. This framework provides a comprehensive and systematic synthesis of the dynamic capabilities perspective that reflects the richness of the research while at the same time unifying it into a cohesive, overarching model. Such an analysis has not yet been undertaken; no comprehensive framework with this level of detail has previously been presented for dynamic capabilities. Our analysis shows where research has made the most progress and where gaps and unresolved tensions remain. Based on this analysis, we propose a forward-looking research agenda that outlines directions for future research.

Question: What is the canadian military?


Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning. Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

Question: What is the difference between the removal operation and the redistribution operation?


SUITOR: an attentive information system. Attentive systems pay attention to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad — ranging from human butlers to web sites that profile users — we have focused specifically on attentive information systems, which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe an implemented system, Simple User Interest Tracker (Suitor), that tracks computer users through multiple channels — gaze, web browsing, application focus — to determine their interests and to satisfy their information needs. By observing behavior and modeling users, Suitor finds and displays potentially relevant information that is both timely and non-disruptive to the users' ongoing activities.

Question: What is an attentive information system?


Identity-based encryption with efficient revocation. Identity-based encryption (IBE) is an exciting alternative to public-key encryption, as IBE eliminates the need for a Public Key Infrastructure (PKI). The senders using an IBE do not need to look up the public keys and the corresponding certificates of the receivers, the identities (e.g. emails or IP addresses) of the latter are sufficient to encrypt. Any setting, PKI- or identity-based, must provide a means to revoke users from the system. Efficient revocation is a well-studied problem in the traditional PKI setting. However in the setting of IBE, there has been little work on studying the revocation mechanisms. The most practical solution requires the senders to also use time periods when encrypting, and all the receivers (regardless of whether their keys have been compromised or not) to update their private keys regularly by contacting the trusted authority. We note that this solution does not scale well -- as the number of users increases, the work on key updates becomes a bottleneck. We propose an IBE scheme that significantly improves key-update efficiency on the side of the trusted party (from linear to logarithmic in the number of users), while staying efficient for the users. Our scheme builds on the ideas of the Fuzzy IBE primitive and binary tree data structure, and is provably secure.

Question: What is the identity-based encryption scheme?


Targeting the right students using data mining. The education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore. The application involves many data mining tasks. This paper focuses only on one task, namely, selecting students for remedial classes. Traditionally, a cut-off mark for each subject is used to select the weak students. That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject. In this paper, we show that this traditional method requires too many students to take part in the remedial classes. This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics. With the help of data mining, we are able to select the targeted students much more precisely.

Question: What is the GEP?


Effective and efficient correlation analysis with application to Market Basket Analysis and Network Community Detection. Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into several communities for successful advertising campaigns? Which set of individuals on a social network should we target to convince in order to trigger a large cascade of further adoptions? When conducting correlation analysis, traditional methods have both effectiveness and efficiency problems, which will be addressed in this dissertation. Here, we explore the effectiveness problem in three ways. First, we expand the set of desirable properties and study the property satisfaction for different correlation measures. Second, we study different techniques to adjust original correlation measure, and propose two new correlation measures: the Simplified χ with Continuity Correction and the Simplified χ with Support. Third, we study the upper and lower bounds of different measures and categorize them by the bound differences. Combining with the above three directions, we provide guidelines for users to choose the proper measure according to their situations. With the proper correlation measure, we start to solve the efficiency problem for a large dataset. Here, we propose a fully-correlated itemset (FCI) framework to decouple the correlation measure from the need for efficient search. By wrapping the desired measure in our FCI framework, we take advantage of the desired measure's superiority in evaluating itemsets, eliminate itemsets with irrelevant items, and achieve good computational performance. In addition, we identify a 1-dimensional monotone property of the upper bound of any good correlation measure, and

Question: What is the upper bound of the correlation measure?


Can visual fixation patterns improve image fidelity assessment?. This paper presents the results of a computational experiment designed to investigate the extent to which metrics of image fidelity can be improved through knowledge of where humans tend to fixate in images. Five common metrics of image fidelity were augmented using two sets of fixation data, one set obtained under task-free viewing conditions and another set obtained when viewers were asked to judge image quality. The augmented metrics were then compared to subjective ratings of the images. The results show that most metrics can be improved using eye fixation data, but a greater improvement was found using fixations obtained in the task-free condition (task-free viewing).

Question: How can we improve image fidelity assessment?


K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality. The K-means algorithm is a commonly used technique in cluster analysis. In this paper, several questions about the algorithm are addressed. The clustering problem is first cast as a nonconvex mathematical program. Then, a rigorous proof of the finite convergence of the K-means-type algorithm is given for any metric. It is shown that under certain conditions the algorithm may fail to converge to a local minimum, and that it converges under differentiability conditions to a Kuhn-Tucker point. Finally, a method for obtaining a local-minimum solution is given.

Question: What is a k-means algorithm?


A 0.5ps 1.4mW 50MS/s Nyquist bandwidth time amplifier based two-step flash-ΔΣ time-to-digital converter. We propose a 50-MS/s two-step flash-ΔΣ time-to-digital converter (TDC) using stable time amplifiers (TAs). The TDC demonstrates low-levels of shaped quantization noise. The system is simulated in 40-nm CMOS and consumes 1.3 mA from a 1.1 V supply. The bandwidth is broadened to Nyquist rate. At frequencies below 25 MHz, the integrated TDC error is as low as 143 fsrms, which is equal to an equivalent TDC resolution of 0.5 ps.

Question: What is a time-to-digital converter?


REVIEW OF PROVEN TECHNOLOGIES AVAILABLE FOR THE REDUCTION OF RAW SUGAR COLOUR. Colour removal is the chief cost centre of sugar re fineries, hence, by reducing the colour of the raw sugar to be processed, the level of process ing and costs incurred by a refinery are also reduced. A lower colour raw sugar is thus highly ma rketable. There are a number of proven technologies that can be applied in raw sugar mills to control and reduce raw sugar colour. These mechanisms are r eviewed in order to identify effective and economically viable means of reducing the colou r f South African raw sugars to a reasonable level, ca 1 000 ICUMSA units. The technologies include: • syrup flotation clarification • juice and syrup sulphitation • alternative boiling schemes • double curing of C-massecuites • additional washing in centrifugals • the use of specialised chemicals and flocculants du ring clarification • hydrogen peroxide in wash water during centrifugati on.

Question: What is the colour of raw sugar?


Self-Supervised Generative Adversarial Networks. Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labelled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, to close the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to stateof-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 33 on unconditional IMAGENET generation.

Question: How to train a self-supervised generative adversarial network?


Predicting Students' Performance using Modified ID3 Algorithm. The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every decision making point. Several other algorithms like J48 and Naive Bayes classification algorithm are also applied on the dataset. The WEKA tool was used for the analysis of J48 and Naive Bayes algorithms. The results are compared and presented. The dataset used in our study is taken from the School of Computing Sciences and Engineering (SCSE), VIT University. Keyworddata mining, educational data mining (EDM), decision tree, gain ratio, weighted ID3

Question: What is the dataset used in this study?


Combining template tracking and laser peak detection for 3D reconstruction and grasping in underwater environments. Autonomous grasping of unknown objects by a robot is a highly challenging skill that is receiving increasing attention in the last years. This problem becomes still more challenging (and less explored) in underwater environments, with highly unstructured scenarios, limited availability of sensors and, in general, adverse conditions that affect in different degree the robot perception and control systems. This paper describes an approach for semi-autonomous grasping and recovery on underwater unknown objects from floating vehicles. A laser stripe emitter is attached to a robot forearm that performs a scan of a target of interest. This scan is captured by a camera that also estimates the motion of the floating vehicle while doing the scan. The scanned points are triangulated and transformed according to the motion estimation, thus recovering partial 3D structure of the scene with respect to a fixed frame. A user then indicates the part where to grab the object, and the final grasp is automatically planned on that area. The approach herein presented is tested and validated in water tank conditions.

Question: How to grasp an object in water?


Detecting Targeted Smartphone Malware with Behavior-Triggering Stochastic Models. Malware for current smartphone platforms is becoming increasingly sophisticated. The presence of advanced networking and sensing functions in the device is giving rise to a new generation of targeted malware characterized by a more situational awareness, in which decisions are made on the basis of factors such as the device location, the user profile, or the presence of other apps. This complicates behavioral detection, as the analyst must reproduce very specific activation conditions in order to trigger malicious payloads. In this paper, we propose a system that addresses this problem by relying on stochastic models of usage and context events derived from real user traces. By incorporating the behavioral particularities of a given user, our scheme provides a solution for detecting malware targeting such a specific user. Our results show that the properties of these models follow a power-law distribution: a fact that facilitates an efficient generation of automatic testing patterns tailored for individual users, when done in conjunction with a cloud infrastructure supporting device cloning and parallel testing. We report empirical results with various representative case studies, demonstrating the effectiveness of this approach to detect complex activation patterns.

Question: How to detect targeted malware?


Propulsion Drive Models for Full Electric Marine Propulsion Systems. Integrated full electric propulsion systems are being introduced across both civil and military marine sectors. Standard power systems analysis packages cover electrical and electromagnetic components, but have limited models of mechanical subsystems and their controllers. Hence electromechanical system interactions between the prime movers, power network and driven loads are poorly understood. This paper reviews available models of the propulsion drive system components: the power converter, motor, propeller and ship. Due to the wide range of time-constants in the system, reduced order models of the power converter are required. A new model using state-averaged models of the inverter and a hybrid model of the rectifier is developed to give an effective solution combining accuracy with speed of simulation and an appropriate interface to the electrical network model. Simulation results for a typical ship manoeuvre are presented.

Question: What is a hybrid model?


A Classification and Comparison Framework for Software Architecture Description Languages. ÐSoftware architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future. Index TermsÐSoftware architecture, architecture description language, component, connector, configuration, definition, classification, comparison.

Question: What is a classification and comparison framework for software architecture description languages?


A study into the believability of animated characters in the context of bullying intervention. The VICTEC (Virtual ICT with Empathic Characters) project provides an opportunity to explore the use of animated characters in a virtual environment for educational issues such as bullying behaviour. Our research aim was to evaluate whether an early prototype of the VICTEC demonstrator could provide useful information about character and story believability, physical aspects of the characters and story comprehensibility. Results from an evaluation with 76 participants revealed high levels of bullying story believability and character conversation was rated as convincing and interesting. In contrast, character movement was poorly rated. Overall the results imply that poor physical aspects of characters do not have detrimental effects on story believability and interest levels with the demonstrator. It is concluded that even at this early design phase, the demonstrator provides a suitable means to explore how virtual environments in terms of character and storyline believability may assist in the development of systems to deal with the educational issue of bullying.

Question: What is the believability of the characters in the virtual environment?


Multi-label Text Categorization with Model Combination based on F1-score Maximization. Text categorization is a fundamental task in natural language processing, and is generally defined as a multi-label categorization problem, where each text document is assigned to one or more categories. We focus on providing good statistical classifiers with a generalization ability for multi-label categorization and present a classifier design method based on model combination and F1-score maximization. In our formulation, we first design multiple models for binary classification per category. Then, we combine these models to maximize the F1-score of a training dataset. Our experimental results confirmed that our proposed method was useful especially for datasets where there were many combinations of category labels.

Question: How to design a classifier for multi-label categorization?


COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking. In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating. We employ structured output prediction to optimize directly for ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative filtering tasks.

Question: What is the maximum margin matrix factorization?


A Batteryless Sensor ASIC for Implantable Bio-Impedance Applications. The measurement of the biological tissue's electrical impedance is an active research field that has attracted a lot of attention during the last decades. Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications. Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin. This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz. The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm × 1.22 mm and consumes 165 μA from a 1.8 V power supply. The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling. The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs. The proposed ASIC is able to extract complex impedances with around 1 Ω resolution; therefore enabling accurate wireless tissue measurements.

Question: What is the size of the batteryless sensor ASIC?


Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices. To verify cluster separation in high-dimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visual encoding choices, we conducted an empirical data study in which two human coders manually inspected a broad set of 816 scatterplots derived from 75 datasets, 4 DR techniques, and the 3 previously mentioned scatterplot techniques. Each coder scored all color-coded classes in each scatterplot in terms of their separability from other classes. We analyze the resulting quantitative data with a heatmap approach, and qualitatively discuss interesting scatterplot examples. Our findings reveal that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique. If 2D is not good enough, the most promising approach is to use an alternative DR technique in 2D. Beyond that, SPLOM occasionally adds additional value, and interactive 3D rarely helps but often hurts in terms of poorer class separation and usability. We summarize these results as a workflow model and implications for design. Our results offer guidance to analysts during the DR exploration process.

Question: What is the difference between scatterplot and scatterplot matrix?


The Iowa Model of Evidence-Based Practice to Promote Quality Care.. The UIHC Department of Nursing is nationally known for its work on use of research to improve patient care. This reputation is attributable to staff members who continue to question "how can we improve practice?" or "what does the latest evidence tell us about this patient problem?" and to administrators who support, value, and reward EBP. The revisions made in the original Iowa Model are based on suggestions from staff at UIHC and other practitioners across the country who have implemented the model. We value their feedback and have set forth this revised model for evaluation and adoption by others.

Question: What is the Iowa model of evidence-based practice?


An overview of 3D object grasp synthesis algorithms. This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions [7] or robot hand design and their control [1]. Robot grasp synthesis algorithms have been reviewed in [63], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.

Question: What is a grasp synthesis algorithm?


Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models. Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network. Their output is combined using a decision based on majority voting or stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature. Our submission achieved the highest performance in the task during the 2017 challenge. Keywords—relation extraction; deep learning; chemical; protein

Question: What is the chemical-protein relation extraction?


Class-Conditional Superresolution with GANs. ● Super resolution enhances the resolution of a low-res image. ● Many deep learning models today work fairly well with an upscaling factor of 4x but use only the downscaled image as input. ● We demonstrate two class-conditional GAN models that outperform state-of-the-art GANs for superresolution: ○ Explicitly passes in a known class parameter as input ○ Uses a separately trained classifier to inform a ResNet about character attributes ● Both improve the quality of the super-resolved output.

Question: How do you improve the resolution of an image?


Technology adoption: A conjoint analysis of consumers' preference on future online banking services. The importance of service delivery technology and online service adoption and usage in the banking industry has received an increased discussion in the literature in recent years. Owing to the fact that Strong online banking services are important drivers for bank performance and customer service delivery; several studies have been carried out on online banking service adoption or acceptance where services are already deployed and on the factors that influence customers' adoption and use or intention to use those services. However, despite the increasing discussion in the literatures, no attempt has been made to look at consumers' preference in terms of future online banking service adoption. This study used conjoint analysis and stated preference methods with discrete choice model to analyze the technology adoption pattern regarding consumers' preference for potential future online banking services in the Nigerian banking industry. The result revealed that to increase efficiency and strengthen competitiveness, banks need to promote smart and practical branded services especially self-services at the same time promote a universal adoption of e-banking system services that add entertainment or extra convenience to customers such as ease of usage including digital wallet, real-time interaction (video banking), ATMs integrated with smart phones, website customization, biometric services, and digital currency. These services can contribute to an increasing adoption of online services. & 2015 Elsevier Ltd. All rights reserved.

Question: What is the importance of service delivery technology and online service adoption and usage in the banking industry?


A study to evaluate the safety, tolerability, and efficacy of brodalumab in subjects with rheumatoid arthritis and an inadequate response to methotrexate.. OBJECTIVE To evaluate the efficacy and safety of brodalumab, a human monoclonal antibody inhibitor of the interleukin 17 receptor, in subjects with rheumatoid arthritis (RA).   METHODS Patients (n = 252) with inadequate response to methotrexate (MTX) were randomized to receive subcutaneous injections of brodalumab (70 mg, 140 mg, or 210 mg) or placebo. The primary endpoint was the American College of Rheumatology 50% response (ACR50) at Week 12.   RESULTS Demographics and baseline characteristics were generally balanced among treatment groups. At Week 12, ACR50 occurred in 16% (70 mg), 16% (140 mg), 10% (210 mg), and 13% (placebo; all nonsignificant vs placebo) of subjects. No significant treatment effects were observed for the secondary endpoints, including ACR20, ACR70, and Disease Activity Score in 28 joints. Incidences of all adverse events (AE), including serious AE (SAE), were similar across treatment groups. A total of 7 subjects reported SAE during the study (2 in the placebo group and 5 in the brodalumab groups), none of which was treatment related. There was 1 death (cardiopulmonary failure) ∼1 week after the last dose in the 140 mg group.   CONCLUSION Our study failed to find evidence of meaningful clinical efficacy with brodalumab treatment in subjects with RA who had an inadequate response to MTX. These preliminary results do not support further evaluation of brodalumab as a treatment for RA. Clinicaltrials.gov number: NCT00950989.

Question: What is the difference between a study and a trial?


Implantable Antennas for Biomedical Applications: An Overview on Alternative Antenna Design Methods and Challenges. Implanted biomedical devices are witnessing great attention in finding solutions to complex medical conditions. Many challenges face the design of implantable biomedical devices including designing and implanting antennas within hostile environment due to the surrounding tissues of human body. Implanted antennas must be compact in size, efficient, safe, and can effectively work within adequate medical frequency bands. This paper presents an overview of the major aspects related to the design and challenges of in body implanted antennas. The review includes surveying the applications, design methods, challenges, simulation tools, and testing and manufacturing of implantable biomedical antennas.

Question: What is the size of the canadian military?


High-quality computational imaging through simple lenses. Modern imaging optics are highly complex systems consisting of up to two dozen individual optical elements. This complexity is required in order to compensate for the geometric and chromatic aberrations of a single lens, including geometric distortion, field curvature, wavelength-dependent blur, and color fringing.  In this article, we propose a set of computational photography techniques that remove these artifacts, and thus allow for postcapture correction of images captured through uncompensated, simple optics which are lighter and significantly less expensive. Specifically, we estimate per-channel, spatially varying point spread functions, and perform nonblind deconvolution with a novel cross-channel term that is designed to specifically eliminate color fringing.

Question: How to remove color fringing from images?


High sigma measurement of random threshold voltage variation in 14nm Logic FinFET technology. Random variation of threshold voltage (Vt) in MOSFETs plays a central role in determining the minimum operating voltage of products in a given process technology. Properly characterizing Vt variation requires a large volume of measurements of minimum size devices to understand the high sigma behavior. At the same time, a rapid measurement approach is required to keep the total measurement time practical. Here we describe a new test structure and measurement approach that enables practical characterization of Vt distributions to high sigma and its application to 14nm Logic FinFET technology. We show that both NMOS and PMOS single fin devices have very low random Vt variation of 19mV and 24mV respectively, normally distributed out to +/-5σ.

Question: What is the sigma of a normally distributed random variable?


Review of Semantic-Free Utterances in Social Human-Robot Interaction. Review of Semantic-Free Utterances in Social Human–Robot Interaction Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst To cite this article: Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst (2016) Review of Semantic-Free Utterances in Social Human–Robot Interaction, International Journal of Human–Computer Interaction, 32:1, 63-85, DOI: 10.1080/10447318.2015.1093856 To link to this article: http://dx.doi.org/10.1080/10447318.2015.1093856

Question: What is the name of the paper?


The medial patellofemoral ligament: location of femoral attachment and length change patterns resulting from anatomic and nonanatomic attachments.. BACKGROUND Incompetence of the medial patellofemoral ligament (MPFL) is an integral factor in patellofemoral instability. Reconstruction of this structure is gaining increasing popularity. However, the natural behavior of the ligament is still not fully understood, and crucially, the correct landmark for femoral attachment of the MPFL at surgery is poorly defined.   PURPOSE To determine the length change pattern of the native MPFL, investigate the effect of nonanatomic femoral and differing patellar attachment sites on length changes, and recommend a reproducible femoral attachment site for undertaking anatomic MPFL reconstruction.   STUDY DESIGN Descriptive laboratory study.   METHODS Eight cadaveric knees were dissected of skin and subcutaneous fat and mounted in a kinematics rig with the quadriceps tensioned. The MPFL length change patterns were measured for combinations of patellar and femoral attachments using a suture and displacement transducer. Three attachments were along the superomedial border of the patella, and 5 femoral attachments were at the MPFL center and 5 mm proximal, distal, anterior, and posterior to this point. Reproducibility of attachment sites was validated radiographically.   RESULTS The femoral attachment point, taking the anterior-posterior medial femoral condyle diameter to be 100%, was identified 40% from the posterior, 50% from the distal, and 60% from the anterior border of the medial femoral condyle. This point was most isometric, with a mean maximal length change to the central patellar attachment of 2.1 mm from 0° to 110° of knee flexion. The proximal femoral attachment resulted in up to 6.4 mm mean lengthening and the distal attachment up to 9.1 mm mean shortening through 0° to 110° of knee flexion, resulting in a significantly nonisometric graft (P < .05).   CONCLUSION We report the

Question: What is the medial patellofemoral ligament?


EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text. Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.

Question: What is a sense inventory?


Assembly algorithms for next-generation sequencing data.. The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly.

Question: What is the difference between the de Bruijn graph approach and the overlap/layout/consensus approach to assembly?


Mobile Food Recognition with an Extreme Deep Tree. Food recognition is an emerging topic in the field of computer vision. The recent interest of the research community in this area is justified by the rise in popularity of food diary applications, where the users take note of their food intake for self-monitoring or to provide useful statistics to dietitians. However, manually annotating food intake can be a tedious task, thus explaining the need of a system that automatically recognizes food, and possibly its amount, from pictures acquired by mobile devices. In this work we propose an approach to food recognition which combines the strengths of different state-of-the-art classifiers, namely Convolutional Neural Networks, Extreme Learning Machines and Neural Trees. We show that the proposed architecture can achieve good results even with low computational power, as in the case of mobile devices.

Question: What is the name of the food recognition system?


Real time contextual collective anomaly detection over multiple data streams. Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more di cult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and e ciency of our framework, we empirically validate it on two real world applications.

Question: What is a data stream?


Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store. Mobile app stores provide a unique platform for developers to rapidly deploy new updates of their apps. We studied the frequency of updates of 10,713 mobile apps (the top free 400 apps at the start of 2014 in each of the 30 categories in the Google Play store). We find that a small subset of these apps (98 apps representing ˜1 % of the studied apps) are updated at a very frequent rate — more than one update per week and 14 % of the studied apps are updated on a bi-weekly basis (or more frequently). We observed that 45 % of the frequently-updated apps do not provide the users with any information about the rationale for the new updates and updates exhibit a median growth in size of 6 %. This paper provides information regarding the update strategies employed by the top mobile apps. The results of our study show that 1) developers should not shy away from updating their apps very frequently, however the frequency varies across store categories. 2) Developers do not need to be too concerned about detailing the content of new updates. It appears that users are not too concerned about such information. 3) Users highly rank frequently-updated apps instead of being annoyed about the high update frequency.

Question: What is the update frequency of the top mobile apps?


The substrate integrated circuits - a new concept for high-frequency electronics and optoelectronics. A new generation of high-frequency integrated circuits is presented, which is called substrate integrated circuits (SICs). Current state-of-the-art of circuit design and implementation platforms based on this new concept are reviewed and discussed in detail. Different possibilities and numerous advantages of the SICs are shown for microwave, millimeter-wave and optoelectronics applications. Practical examples are illustrated with theoretical and experimental results for substrate integrated waveguide (SIW), substrate integrated slab waveguide (SISW) and substrate integrated nonradiating dielectric (SINRD) guide circuits. Future research and development trends are also discussed with reference to low-cost innovative design of millimeter-wave and optoelectronic integrated circuits.

Question: What is the substrate integrated circuits?


Inferring 3D layout of building facades from a single image. In this paper, we propose a novel algorithm that infers the 3D layout of building facades from a single 2D image of an urban scene. Different from existing methods that only yield coarse orientation labels or qualitative block approximations, our algorithm quantitatively reconstructs building facades in 3D space using a set of planes mutually related by 3D geometric constraints. Each plane is characterized by a continuous orientation vector and a depth distribution. An optimal solution is reached through inter-planar interactions. Due to the quantitative and plane-based nature of our geometric reasoning, our model is more expressive and informative than existing approaches. Experiments show that our method compares competitively with the state of the art on both 2D and 3D measures, while yielding a richer interpretation of the 3D scene behind the image.

Question: How to infer 3D layout of building facades from a single image?


A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios. Collisions are a main cause of throughput degradation in wireless local area networks. The current contention mechanism used in the IEEE 802.11 networks is called carrier sense multiple access with collision avoidance CSMA/CA. It uses a binary exponential backoff technique to randomize each contender attempt of transmitting, effectively reducing the collision probability. Nevertheless, CSMA/CA relies on a random backoff that while effective and fully decentralized, in principle is unable to completely eliminate collisions, therefore degrading the network throughput as more contenders attempt to share the channel. To overcome these situations, carrier sense multiple access with enhanced collision avoidance CSMA/ECA is able to create a collision-free schedule in a fully decentralized manner using a deterministic backoff after successful transmissions. Hysteresis and fair share are two extensions of CSMA/ECA to support a large number of contenders in a collision-free schedule. CSMA/ECA offers better throughput than CSMA/CA and short-term throughput fairness. This paper describes CSMA/ECA and its extensions. In addition, it provides the first evaluation results of CSMA/ECA with non-saturated traffic, channel errors, and its performance when coexisting with CSMA/CA nodes. Furthermore, it describes the effects of imperfect clocks over CSMA/ECA and presents a mechanism to leverage the impact of channel errors and the addition/withdrawal of nodes over collision-free schedules. Finally, the experimental results on throughput and lost frames from a CSMA/ECA implementation using commercial hardware and open-source firmware are presented.

Question: What is the difference between CSMA/CA and CSMA/ECA?


PUBLIC DEBT AND GROWTH. This paper examines the impact of high public debt on long-run economic growth in a panel of advanced and emerging economies over four decades, while taking into account various estimation issues including reverse causality and endogeneity. Threshold effects, non-linearities, and differences between advanced and emerging market economies are also explored. High initial public debt is found to be significantly and consistently associated with slower subsequent growth, controlling for other determinants of growth. The adverse effect largely reflects a slowdown in labor productivity growth mainly due to reduced investment and slower growth of capital stock. Extensive robustness checks confirm the results.

Question: What is the impact of public debt on growth?


Car monitoring, alerting and tracking model: Enhancement with mobility and database facilities. Statistics show that the number of cars is increasing rapidly and so is the number of car theft attempts, locally and internationally. Although there are a lot of car security systems that had been produced lately, but the result is still disappointing as the number of car theft cases still increases. The thieves are inventing cleverer and stronger stealing techniques that need more powerful security systems. This project "Car Monitoring and Tracking System" is being proposed to solve the issue. It introduces the integration between monitoring and tracking system. Both elements are very crucial in order to have a powerful security system. The system can send SMS and MMS to the owner to have fast response especially if the car is nearby. This paper focuses on using MMS and database technology, the picture of the intruder will be sent via local GSM/GPRS service provider to user (and/or) police. The Database offers the required information about car and owner, which will help the police or security authorities in their job. Moreover, in the event of theft local police and user can easily track the car using GPS system that can be link to Google Earth and other mapping software. The implementation and testing results show the success of prototype in sending MMS to owner within 40 seconds and receiving acknowledgment to the database (police or security unit) within 4 minutes. The timing and results are suitable to owner and police to take suitable action against intruder.

Question: What is the difference between a car security system and a car tracking system?


Performance analysis of DCO-OFDM in VLC system. The performance of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The impact of nonlinearity of Light Emitting Diode (LED) and its beam angle on the VLC system performance is studied. We later analyze the effect of modulation order, number of subcarriers, signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major issue in OFDM based VLC system. Simulation results show that the bit error rate (BER) decreases as the degree of nonlinearity increases and a better BER can be achieved as the LED behavior approaches linear model. In addition, it is shown that reducing the QAM order or increasing the number of subcarriers may reduce the effect of the LED nonlinearity, thus improving the BER performance of the VLC system. Moreover, it is demonstrated that PAPR is higher for a large number of subcarriers and modulation order. Finally, the PAPR of the visible light OFDM system can significantly be reduced by employing the signal scaling combined with biasing operation.

Question: What is the effect of LED nonlinearity on the performance of VLC system?


Learning object detection from a small number of examples: the importance of good features. Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples.

Question: What is the importance of good features?


A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology. We consider the problem of identifying the patients who are diagnosed with highgrade prostate cancer using the histopathology of tumor in a prostate needle biopsy and are at a very high risk of lethal cancer progression. We hypothesize that the morphology of tumor cell nuclei in digital images from the biopsy can be used to predict tumor aggressiveness and posit the presence of metastasis as a surrogate for disease specific mortality. For this purpose, we apply a compositional multiinstance learning approach which encodes images of nuclei through a convolutional neural network, then predicts the presence of metastasis from sets of encoded nuclei. Through experiments on prostate needle biopsies (PNBX) from a patient cohort with known presence (M1 stage, n = 85) or absence (M0 stage, n = 86) of metastatic disease, we obtained an average area under the receiver operating characteristic curve of 0.71± 0.08 for predicting metastatic cases. These results support our hypothesis that information related to metastatic capacity of prostate cancer cells can be obtained through analysis of nuclei and establish a baseline for future research aimed at predicting the risk of future metastatic disease at a time when it might be preventable.

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


Topic Detection and Tracking Pilot Study Final Report. Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of "topic" than was used in the pilot study. The following individuals participated in the research reported.

Question: What is the topic detection and tracking project?