Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning. We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes extensions to structural correspondence learning needed to make it appropriate for the task. For example, we propose a median-based classification instead of the standard binary classification used in previous work. Our results show that punctuation-based character n-grams form excellent pivot features. We also show how singular value decomposition plays a critical role in achieving domain adaptation, and that replacing (instead of concatenating) non-pivot features with correspondence features yields better performance.

Question: How to use SVD for domain adaptation?


An affective computational model for machine consciousness. In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations. Moreover, affective computing attributes that involve emotions, behavior and personality have not been the focus of models of consciousness as they lacked motivation for deployment in software applications and robots. The affective attributes are important factors for the future of machine consciousness with the rise of technologies that can assist humans. Personality and affection hence can give an additional flavor for the computational model of consciousness in humanoid robotics. Recent advances in areas of machine learning with a focus on deep learning can further help in developing aspects of machine consciousness in areas that can better replicate human sensory perceptions such as speech recognition and vision. With such advancements, one encounters further challenges in developing models that can synchronize different aspects of affective computing. In this paper, we review some existing models of consciousnesses and present an affective computational model that would enable the human touch and feel for robotic systems.

Question: What is the affective computational model for machine consciousness?


Service-Oriented Architecture for Big Data Analytics in Smart Cities. A smart city has recently become an aspiration for many cities around the world. These cities are looking to apply the smart city concept to improve sustainability, quality of life for residents, and economic development. The smart city concept depends on employing a wide range of advanced technologies to improve the performance of various services and activities such as transportation, energy, healthcare, and education, while at the same time improve the city's resources utilization and initiate new business opportunities. One of the promising technologies to support such efforts is the big data technology. Effective and intelligent use of big data accumulated over time in various sectors can offer many advantages to enhance decision making in smart cities. In this paper we identify the different types of decision making processes involved in smart cities. Then we propose a service-oriented architecture to support big data analytics for decision making in smart cities. This architecture allows for integrating different technologies such as fog and cloud computing to support different types of analytics and decision-making operations needed to effectively utilize available big data. It provides different functions and capabilities to use big data and provide smart capabilities as services that the architecture supports. As a result, different big data applications will be able to access and use these services for varying proposes within the smart city.

Question: What is a smart city?


Health literacy and public health: A systematic review and integration of definitions and models. BACKGROUND Health literacy concerns the knowledge and competences of persons to meet the complex demands of health in modern society. Although its importance is increasingly recognised, there is no consensus about the definition of health literacy or about its conceptual dimensions, which limits the possibilities for measurement and comparison. The aim of the study is to review definitions and models on health literacy to develop an integrated definition and conceptual model capturing the most comprehensive evidence-based dimensions of health literacy.   METHODS A systematic literature review was performed to identify definitions and conceptual frameworks of health literacy. A content analysis of the definitions and conceptual frameworks was carried out to identify the central dimensions of health literacy and develop an integrated model.   RESULTS The review resulted in 17 definitions of health literacy and 12 conceptual models. Based on the content analysis, an integrative conceptual model was developed containing 12 dimensions referring to the knowledge, motivation and competencies of accessing, understanding, appraising and applying health-related information within the healthcare, disease prevention and health promotion setting, respectively.   CONCLUSIONS Based upon this review, a model is proposed integrating medical and public health views of health literacy. The model can serve as a basis for developing health literacy enhancing interventions and provide a conceptual basis for the development and validation of measurement tools, capturing the different dimensions of health literacy within the healthcare, disease prevention and health promotion settings.

Question: What is the difference between health literacy and literacy?


Systems of Systems Engineering: Basic Concepts, Model-Based Techniques, and Research Directions. The term "System of Systems" (SoS) has been used since the 1950s to describe systems that are composed of independent constituent systems, which act jointly towards a common goal through the synergism between them. Examples of SoS arise in areas such as power grid technology, transport, production, and military enterprises. SoS engineering is challenged by the independence, heterogeneity, evolution, and emergence properties found in SoS. This article focuses on the role of model-based techniques within the SoS engineering field. A review of existing attempts to define and classify SoS is used to identify several dimensions that characterise SoS applications. The SoS field is exemplified by a series of representative systems selected from the literature on SoS applications. Within the area of model-based techniques the survey specifically reviews the state of the art for SoS modelling, architectural description, simulation, verification, and testing. Finally, the identified dimensions of SoS characteristics are used to identify research challenges and future research areas of model-based SoS engineering.

Question: What is the difference between a system of systems and a system of systems engineering?


Anomaly Based Web Phishing Page Detection. Many anti-phishing schemes have recently been proposed in literature. Despite all those efforts, the threat of phishing attacks is not mitigated. One of the main reasons is that phishing attackers have the adaptability to change their tactics with little cost. In this paper, we propose a novel approach, which is independent of any specific phishing implementation. Our idea is to examine the anomalies in Web pages, in particular, the discrepancy between a Web site's identity and its structural features and HTTP transactions. It demands neither user expertise nor prior knowledge of the Web site. The evasion of our phishing detection entails high cost to the adversary. As shown by the experiments, our phishing detector functions with low miss rate and low false-positive rate

Question: What is anomaly based web phishing page detection?


Internet Privacy Concerns Confirm the Case for Intervention. yberspace is invading private space. Controversies about spam, cookies, and the clickstream are merely the tip of an iceberg. Behind them loom real-time person location technologies includ-It's small wonder that lack of public confidence is a serious impediment to the take-up rate of consumer e-commerce. The concerns are not merely about security of value, but about something much more significant: trust in the information society. Conventional thinking has been the Internet renders laws less relevant. On the contrary, this article argues that the current debates about privacy and the Internet are the harbingers of a substantial shift. Because the U.S. has held off general privacy protections for so long, it will undergo much more significant adjustments than European countries. Privacy is often thought of as a moral right or a legal right. But it's often more useful to perceive privacy as the interest that individuals have in sustaining a personal space, free from interference by other people and organizations. Personal space has multiple dimensions, in particular , privacy of the person (concerned with the integrity of the individual's body), privacy of personal behavior, privacy of personal communications , and privacy of personal data. Information privacy refers to the claims of individuals that data about themselves should generally not be available to other individuals and organizations, and that, where data is possessed by another party, the individual must be able to exercise a substantial degree of control over that data and its use. (Definitional issues are examined in [6].) Information privacy has been under increasing threat as a result of the rapid replacement of expensive physical surveillance by what I referred to in Communications over a decade ago as " dataveillance: " the systematic use of personal data systems in the investigation or monitoring of people's actions or communications [2].

Question: What is the difference between information privacy and privacy?


The Many Faces of Formative Assessment.. In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically "what is done" was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.

Question: What is formative assessment?


Forma Track: Tracking People based on Body Shape. Knowledge of a person's whereabouts in the home is key to context-aware applications, but many people do not want to carry or wear a tag or mobile device in the home. Therefore, many tracking systems are now using so-called weak biometrics such as height, weight, and width. In this paper, we propose to use body shape as a weak biometric, differentiating people based on features such as head size, shoulder size, or torso size. The basic idea is to scan the body with a radar sensor and to compute the reflection profile: the amount of energy that reflects back from each part of the body. Many people have different body shapes even if they have the same height, weight, or width, which makes body shape a stronger biometric. We built a proof-of-concept system called FormaTrack to test this approach, and evaluate using eight participants of varying height and weight. We collected over 2800 observations while capturing a wide range of factors such as clothing, hats, shoes, and backpacks. Results show that FormaTrack can achieve a precision, recall, direction and identity accuracy (over all possible groups of 2 people) of 100%, 99.86%, 99.7% and 95.3% respectively. Results indicate that FormaTrack can achieve over 99% tracking accuracy with 2 people in a home with 5 or more rooms.

Question: What is the accuracy of the system?


Agglomerative Information Bottleneck. We introduce a novel distributional clustering algorithm that explicitly maximizes the mutual information per cluster between the data and given categories. This algorithm can be considered as a bottom up hard version of the recently introduced "Information Bottleneck Method". We relate the mutual information between clusters and categories to the Bayesian classification error, which provides another motivation for using the obtained clusters as features. The algorithm is compared with the top-down soft version of the information bottleneck method and a relationship between the hard and soft results is established. We demonstrate the algorithm on the 20 Newsgroups data set. For a subset of two news-groups we achieve compression by 3 orders of magnitudes loosing only 10% of the original mutual information.

Question: What is the Bayesian classification error?


Experiments with a New Boosting Algorithm. In an earlier paper [9], we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a "pseudo-loss" which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's [1] "bagging" method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.

Question: What is a nearest-neighbor classifier?


Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions. Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.

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


Analysis and Defensive Tools for Social-Engineering Attacks on Computer Systems. The weakest link in an information-security chain is often the user because people can be manipulated. Attacking computer systems with information gained from social interactions is one form of social engineering (K. Mitnick, et al. 2002). It can be much easier to do than targeting the complex technological protections of systems (J. McDermott, Social engineering - the weakest link in information security). In an effort to formalize social engineering for cyberspace, we are building models of trust and attack. Models help in understanding the bewildering number of different tactics that can be employed. Social engineering attacks can be complex with multiple ploys and targets; our models function as subroutines that are called multiple times to accomplish attack goals in a coordinated plan. Models enable us to infer good countermeasures to social engineering

Question: What is social engineering?


Modelling of Parasitic Capacitances for Single-Gate, Double-Gate and Independent Double-Gate MOSFET. This paper discusses the type of capacitances for Single Gate MOSFET and Double Gate MOSFET including their quantity. The effect of parasitic capacitance makes double gate MOSFET more suitable component for the designing of digital logic switches than single gate MOSFET. Here, we introducing Independent double gate MOSFET operation based on VeSFET concept. Then introducing with the total capacitance model of Independent double gate MOSFET and compared its performance parameters with double gate MOSFET and the single gate MOSFET. Double gate transistor circuit is the first choice for reduction of short channel effect in application of MOSFET. The basic advantage of double gate MOSFET is its area required. But design CMOS double gate transistor for AND functionality suffering from high leakage current; while using Independent double gate MOSFET based on VeSFET concept reduction of that leakage current is possible. So, we can easily implement logic circuits while using CMOS design based on Independent double gate MOSFET which gives high performance.

Question: What is the difference between single gate and double gate MOSFET?


Sparsely sampled Fourier ptychography.. Fourier ptychography (FP) is an imaging technique that applies angular diversity functions for high-resolution complex image recovery. The FP recovery routine switches between two working domains: the spectral and spatial domains. In this paper, we investigate the spectral-spatial data redundancy requirement of the FP recovery process. We report a sparsely sampled FP scheme by exploring the sampling interplay between these two domains. We demonstrate the use of the reported scheme for bypassing the high-dynamic-range combination step in the original FP recovery routine. As such, it is able to shorten the acquisition time of the FP platform by ~50%. As a special case of the sparsely sample FP, we also discuss a sub-sampled scheme and demonstrate its application in solving the pixel aliasing problem plagued in the original FP algorithm. We validate the reported schemes with both simulations and experiments. This paper provides insights for the development of the FP approach.

Question: What is the difference between the original Fourier ptychography and the sparsely sampled Fourier ptychography?


Sparse Recovery of Hyperspectral Signal from Natural RGB Images. Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.

Question: How to recover a hyperspectral image from a RGB image?


Large-scale cost function learning for path planning using deep inverse reinforcement learning.

Question: How large is the canadian military?


Security in Cloud Computing Using Cryptographic Algorithms Miss .. Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. To secure the Cloud means secure the treatments (calculations) and storage (databases hosted by the Cloud provider). In this paper we analyses different security issues to cloud and different cryptographic algorithms adoptable to better security for the cloud. Keywords— Cloud Computing, Cryptographic Algorithm, Internet, Security Algorithms, Security Attacks, Security Issue

Question: What is the difference between cloud computing and cloud storage?


Spatio-temporal proximity and social distance: a confirmation framework for social reporting. Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.

Question: What is the quality of the reports?


Building event-centric knowledge graphs from news. Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations – e.g. a celebrity's birth date, occupation and family members – obtained from large repositories such as Freebase or Wikipedia. In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs. We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user's understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts. © 2016 Elsevier B.V. All rights reserved.

Question: What is the size of the canadian military?


16-Way Radial Divider/Combiner for Solid State Power Amplifiers in the K Band. This paper presents the design and measurement results of a 16-way radial power divider/combiner for solid state power amplifiers in the K band using an original assembly technique of the combined ways. The design is based on a radial non-resonant cavity connected to 8 peripheral rectangular waveguides with two waveguide-to-microstrip transitions inserted in each peripheral rectangular waveguide. Each combined way includes on the same tray the input and output waveguide-to-microstrip transitions and the MMIC amplifier location. The trays can be individually tested before mounting in the 16-way divider/combiner. The low insertion loss of the 16-way power divider/combiner leads to an estimated combining efficiency better than 86.5% from 16.5 GHz to 21 GHz with a maximum value about 88% from 17.2 to 19.2 GHz.

Question: How to design a 16-way radial divider/combiner for solid state power amplifiers in the K band?


Analysis and design of a high-efficiency multistage Doherty power amplifier for wireless communications. A comprehensive analysis of a multistage Doherty amplifier, which can be used to achieve higher efficiency at a lower output power level compared to the classical Doherty amplifier, is presented. Generalized design equations that explain the operation of a three-stage Doherty amplifier, which can be easily extended to an N-stage Doherty amplifier, are derived. In addition, the optimum device periphery, which minimizes AM-AM distortion for perfect Doherty amplifier operation, is analyzed. For the first time, a multistage Doherty power amplifier that meets wide-band code-division multiple-access (WCDMA) requirements is demonstrated. The designed power amplifier exhibits a power-added efficiency (PAE) of 42% at 6-dB output power backoff and 27% at 12-dB output power backoff. These PAEs are more than 2/spl times/ and 7/spl times/ better, respectively, than that of a single-stage linear power amplifier at the same output power backoff levels. The power amplifier is capable of delivering up to 33 dBm of output power, and has a maximum adjacent channel power leakage ratio of -35 and -47 dBc at 5- and 10-MHz offset, respectively. To the best of the authors' knowledge, these represent the best reported results of a Doherty amplifier for WCDMA application in the 1.95-GHz band to date.

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


Inf2vec: Latent Representation Model for Social Influence Embedding. As a fundamental problem in social influence propagation analysis, learning influence parameters has been extensively investigated. Most of the existing methods are proposed to estimate the propagation probability for each edge in social networks. However, they cannot effectively learn propagation parameters of all edges due to data sparsity, especially for the edges without sufficient observed propagation. Different from the conventional methods, we introduce a novel social influence embedding problem, which is to learn parameters for nodes rather than edges. Nodes are represented as vectors in a low-dimensional space, and thus social influence information can be reflected by these vectors. We develop a new model Inf2vec, which combines both the local influence neighborhood and global user similarity to learn the representations. We conduct extensive experiments on two real-world datasets, and the results indicate that Inf2vec significantly outperforms state-of-the-art baseline algorithms.

Question: How to learn the representations of nodes?


Stronger Baselines for Trustable Results in Neural Machine Translation. Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over "vanilla" NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-theart production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.

Question: How to choose a strong baseline for neural machine translation?


Direct and Indirect Discrimination Prevention Methods. Along with privacy, discrimination is a very import ant issue when considering the legal and ethical aspects of data mini ng. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those att ribu es are used for making decisions about them like giving them a job, loan, insu rance, etc. Discovering such potential biases and eliminating them from the traini ng data without harming their decision-making utility is therefore highly desirab le. For this reason, antidiscrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prev ention consists of inducing patterns that do not lead to discriminatory decisio n even if the original training datasets are inherently biased. In this chapter, by focusing on the discrimination prevention, we present a taxonomy for classifying a d examining discrimination prevention methods. Then, we introduce a group of p re-processing discrimination prevention methods and specify the different featur es of each approach and how these approaches deal with direct or indirect discr imination. A presentation of metrics used to evaluate the performance of those app ro ches is also given. Finally, we conclude our study by enumerating interesting fu ture directions in this research body.

Question: What is discrimination prevention?


"Constant, constant, multi-tasking craziness": managing multiple working spheres. Most current designs of information technology are based on the notion of supporting distinct tasks such as document production, email usage, and voice communication. In this paper we present empirical results that suggest that people organize their work in terms of much larger and thematically connected units of work. We present results of fieldwork observation of information workers in three different roles: analysts, software developers, and managers. We discovered that all of these types of workers experience a high level of discontinuity in the execution of their activities. People average about three minutes on a task and somewhat more than two minutes using any electronic tool or paper document before switching tasks. We introduce the concept of working spheres to explain the inherent way in which individuals conceptualize and organize their basic units of work. People worked in an average of ten different working spheres. Working spheres are also fragmented; people spend about 12 minutes in a working sphere before they switch to another. We argue that design of information technology needs to support people's continual switching between working spheres.

Question: How do people organize their work?


Marketing malpractice: the cause and the cure.. Ted Levitt used to tell his Harvard Business School students, "People don't want a quarter-inch drill--they want a quarter-inch hole." But 35 years later, marketers are still thinking in terms of products and ever-finer demographic segments. The structure of a market, as seen from customers' point of view, is very simple. When people need to get a job done, they hire a product or service to do it for them. The marketer's task is to understand what jobs periodically arise in customers' lives for which they might hire products the company could make. One job, the "I-need-to-send-this-from-here-to-there-with-perfect-certainty-as-fast-as-possible"job, has existed practically forever. Federal Express designed a service to do precisely that--and do it wonderfully again and again. The FedEx brand began popping into people's minds whenever they needed to get that job done. Most of today's great brands--Crest, Starbucks, Kleenex, eBay, and Kodak, to name a few-started out as just this kind of purpose brand. When a purpose brand is extended to products that target different jobs, it becomes an endorser brand. But, over time, the power of an endorser brand will surely erode unless the company creates a new purpose brand for each new job, even as it leverages the endorser brand as an overall marker of quality. Different jobs demand different purpose brands. New growth markets are created when an innovating company designs a product and then positions its brand on a job for which no optimal product yet exists. In fact, companies that historically have segmented and measured markets by product categories generally find that when they instead segment by job, their market is much larger (and their current share much smaller) than they had thought. This is great news for smart companies hungry for growth.

Question: What is the difference between a purpose brand and an endorser brand?


Architectural Mismatch or Why it's hard to build systems out of existing parts. Many would argue that future breakthroughs in software productivity will depend on our ability to combine existing pieces of software to produce new applications. An important step towards this goal is the development of new techniques to detect and cope with mismatches in the assembled parts. Some problems of composition are due to low-level issues of interoperability, such as mismatches in programming languages or database schemas. However, in this paper we highlight a different, and in many ways more pervasive, class of problem: architectural mismatch. Specifically, we use our experience in building a family of software design environments from existing parts to illustrate a variety of types of mismatch that center around the assumptions a reusable part makes about the structure of the application in which is to appear. Based on this experience we show how an architectural view of the mismatch problem exposes some fundamental, thorny problems for software composition and suggests possible research avenues needed to solve them.

Question: What is the difference between low-level and high-level issues of interoperability?


TUTORIAL ON AGENT-BASED MODELING AND SIMULATION. Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agentbased applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.

Question: What is agent-based modeling and simulation?


Subpixel rendering without color distortions for diamond-shaped PenTile displays. A novel subpixel rendering algorithm for diamond-shaped PenTile displays, which improves the apparent display resolution and reduces color fringing artifacts, is proposed in this work. We develop two types of filters: main filters and nonnegative filters. First, the main filters are derived to minimize the error between an original input image and a perceived image. Second, the nonnegative filters are modified from the main filters. While the main filters are optimized for improving the resolution, the nonnegative filters suppress color distortions effectively. We analyze local characteristics of an image and estimate the amount of color distortions in each region. Based on the color distortion analysis, we render the image by applying adaptive combinations of the main filters and the nonnegative filters. Experimental results demonstrate that the proposed algorithm outperforms conventional algorithms, by not only improving the apparent resolution but also suppressing color distortions effectively.

Question: How to improve the apparent resolution of a diamond-shaped PenTile display?


A Model of Data Warehousing Process Maturity. Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, "single-version of truth" data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.

Question: What is the data warehouse maturity model?


Improving search engines by query clustering. search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

Question: What is the framework based on?


A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data. Recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. Thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. This model is built as micro-clusters using semi-supervised clustering technique and classification is performed with kappa-nearest neighbor algorithm. An ensemble of these models is used to classify the unlabeled data. Empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training, outperforms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.

Question: How to classify evolving data streams?


Chemoinformatics and Drug Discovery. This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.

Question: What is the main data mining approach used in cheminformatics?


Vegan diets: practical advice for athletes and exercisers. With the growth of social media as a platform to share information, veganism is becoming more visible, and could be becoming more accepted in sports and in the health and fitness industry. However, to date, there appears to be a lack of literature that discusses how to manage vegan diets for athletic purposes. This article attempted to review literature in order to provide recommendations for how to construct a vegan diet for athletes and exercisers. While little data could be found in the sports nutrition literature specifically, it was revealed elsewhere that veganism creates challenges that need to be accounted for when designing a nutritious diet. This included the sufficiency of energy and protein; the adequacy of vitamin B12, iron, zinc, calcium, iodine and vitamin D; and the lack of the long-chain n-3 fatty acids EPA and DHA in most plant-based sources. However, via the strategic management of food and appropriate supplementation, it is the contention of this article that a nutritive vegan diet can be designed to achieve the dietary needs of most athletes satisfactorily. Further, it was suggested here that creatine and β-alanine supplementation might be of particular use to vegan athletes, owing to vegetarian diets promoting lower muscle creatine and lower muscle carnosine levels in consumers. Empirical research is needed to examine the effects of vegan diets in athletic populations however, especially if this movement grows in popularity, to ensure that the health and performance of athletic vegans is optimised in accordance with developments in sports nutrition knowledge.

Question: What is the difference between a vegan diet and a vegetarian diet?


Persistent Homology: An Introduction and a New Text Representation for Natural Language Processing. Persistent homology is a mathematical tool from topological data analysis. It performs multi-scale analysis on a set of points and identifies clusters, holes, and voids therein. These latter topological structures complement standard feature representations, making persistent homology an attractive feature extractor for artificial intelligence. Research on persistent homology for AI is in its infancy, and is currently hindered by two issues: the lack of an accessible introduction to AI researchers, and the paucity of applications. In response, the first part of this paper presents a tutorial on persistent homology specifically aimed at a broader audience without sacrificing mathematical rigor. The second part contains one of the first applications of persistent homology to natural language processing. Specifically, our Similarity Filtration with Time Skeleton (SIFTS) algorithm identifies holes that can be interpreted as semantic "tie-backs" in a text document, providing a new document structure representation. We illustrate our algorithm on documents ranging from nursery rhymes to novels, and on a corpus with child and adolescent writings.

Question: What is a tutorial on persistent homology?


Algorithms for continuous queries: A geometric approach. Algorithms for Continuous Queries: A Geometric

Question: What is a continuous query?


Domain Adaptation for Statistical Machine Translation with Domain Dictionary and Monolingual Corpora. tra Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to perform domain adaptation for statistical machine translation, where in-domain bilingual corpora do not exist. This method first uses out-of-domain corpora to train a baseline system and then uses in-domain translation dictionaries and in-domain monolingual corpora to improve the indomain performance. We propose an algorithm to combine these different resources in a unified framework. Experimental results indicate that our method achieves absolute improvements of 8.16 and 3.36 BLEU scores on Chinese to English translation and English to French translation respectively, as compared with the baselines using only out-ofdomain corpora.

Question: How to combine different resources in a unified framework?


Pattern-based synonym and antonym extraction. Many research studies adopt manually selected patterns for semantic relation extraction. However, manually identifying and discovering patterns is time consuming and it is difficult to discover all potential candidates. Instead, we propose an automatic pattern construction approach to extract verb synonyms and antonyms from English newspapers. Instead of relying on a single pattern, we combine results indicated by multiple patterns to maximize the recall.

Question: What is an automatic pattern construction approach?


Aiding in the Treatment of Low Back Pain by a Fuzzy Linguistic Web System. Low back pain affects a large proportion of the adult population at some point in their lives and has a major economic and social impact. To soften this impact, one possible solution is to make use of recommender systems, which have already been introduced in several health fields. In this paper, we present TPLUFIB-WEB, a novel fuzzy linguistic Web system that uses a recommender system to provide personalized exercises to patients with low back pain problems and to offer recommendations for their prevention. This system may be useful to reduce the economic impact of low back pain, help professionals to assist patients, and inform users on low back pain prevention measures. A strong part of TPLUFIB-WEB is that it satisfies the Web quality standards proposed by the Health On the Net Foundation (HON), Official College of Physicians of Barcelona, and Health Quality Agency of the Andalusian Regional Government, endorsing the health information provided and warranting the trust of users.

Question: What is a fuzzy linguistic Web system?


Benefiting from Social Capital in Online Support Groups: An Empirical Study of Cancer Patients. With measures specific to the online cancer environment and data from an online survey of cancer patients, the current study finds support for the following model: asynchronous online communication --> social interaction --> social support --> positive health outcomes in terms of stress, depression, and coping. The findings suggest that the Internet can be a positive cyber venue for cancer patients as they confront illness, undergo treatment, and seek out support.

Question: What is the benefit of social capital in online support groups?


Learning Character-level Representations for Part-of-Speech Tagging. Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Information about word morphology and shape is normally ignored when learning word representations. However, for tasks like part-of-speech tagging, intra-word information is extremely useful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level representation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce stateof-the-art POS taggers for two languages: English, with 97.32% accuracy on the Penn Treebank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2% on the best previous known result.

Question: What is a neural network?


Using Screen Brightness to Improve Security in Mobile Social Network Access. In the today's mobile communications scenario, smartphones offer new capabilities to develop sophisticated applications that seem to make daily life easier and more convenient for users. Such applications, which may involve mobile ticketing, identification, access control operations, etc., are often accessible through social network aggregators, that assume a fundamental role in the federated identity management space. While this makes modern smartphones very powerful devices, it also makes them very attractive targets for spyware injection. This kind of malware is able to bypass classic authentication measures and steal user credentials even when a secure element is used, and can, therefore, perform unauthorized mobile access to social network services without the user's consent. Such an event allows stealing sensitive information or even a full identity theft. In this work, we address this issue by introducing BrightPass, a novel authentication mechanism based on screen brightness. BrightPass allows users to authenticate safely with a PIN-based confirmation in the presence of specific operations on sensitive data. We compare BrightPass with existing schemes, in order to show its usability and security within the social network arena. Furthermore, we empirically assess the security of BrightPass through experimentation. Our tests indicate that BrightPass protects the PIN code against automatic submissions carried out by malware while granting fast authentication phases and reduced error rates.

Question: How to protect the PIN code from being stolen by malware?


In-Cell Self-Capacitive-Type Mobile Touch System and Embedded Readout Circuit in Display Driver IC. A mobile display system, which includes a self- capacitive type in-cell touch screen panel and a display driver IC, in which the readout circuit is embedded, is developed for reduced cost, improved display performance, and superior touch performance. A touch electrode is implemented with a segmented common electrode of an LCD display. The display system of the in-cell touch panel is 5.7-in a-Si HD (720 × 1280), the number of touch electrodes is 576 (18 × 32), and the sensor pitch is approximately 4 mm. Compared to the display-only system, display performance degradation does not occur in the proposed system since in-cell technology is adopted. Despite using a self-capacitance sensing method in the panel, unlimited multitouch is available by using an all-point sensing scheme; this system shows a touch accuracy of less than 0.2 mm and signal-to-noise ratio is measured to be more than 48 dB in the entire panel without any digital filter. To enhance the touch performance, we proposed noise reduction techniques that are strongly related to the display operation, such as base line compensation, delta adding correlated double sampling, and the use of a high-voltage charge compensator. The power consumption including micro controller unit (MCU) is less than 30 mW.

Question: What is the in-cell self-capacitive-type mobile touch system?


Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities. Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes. For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.

Question: How does deep learning work?


Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. Traditionally, the vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. Recent advances in communications, controls and embedded systems have changed this model, paving the way to the Intelligent Vehicle Grid. The car is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable to make its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g., the smart building), the Internet of Vehicles will have communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the Vehicular Cloud, the equivalent of Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from Intelligent Vehicle Grid to Autonomous, Internet-connected Vehicles, and Vehicular Cloud.

Question: What is the Internet of Vehicles?


Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms. The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

Question: What is a neural network?


The effectiveness of yoga in modifying risk factors for cardiovascular disease and metabolic syndrome: A systematic review and meta-analysis of randomized controlled trials.. BACKGROUND Yoga, a popular mind-body practice, may produce changes in cardiovascular disease (CVD) and metabolic syndrome risk factors.   DESIGN This was a systematic review and random-effects meta-analysis of randomized controlled trials (RCTs).   METHODS Electronic searches of MEDLINE, EMBASE, CINAHL, PsycINFO, and The Cochrane Central Register of Controlled Trials were performed for systematic reviews and RCTs through December 2013. Studies were included if they were English, peer-reviewed, focused on asana-based yoga in adults, and reported relevant outcomes. Two reviewers independently selected articles and assessed quality using Cochrane's Risk of Bias tool.   RESULTS Out of 1404 records, 37 RCTs were included in the systematic review and 32 in the meta-analysis. Compared to non-exercise controls, yoga showed significant improvement for body mass index (-0.77 kg/m(2) (95% confidence interval -1.09 to -0.44)), systolic blood pressure (-5.21 mmHg (-8.01 to -2.42)), low-density lipoprotein cholesterol (-12.14 mg/dl (-21.80 to -2.48)), and high-density lipoprotein cholesterol (3.20 mg/dl (1.86 to 4.54)). Significant changes were seen in body weight (-2.32 kg (-4.33 to -0.37)), diastolic blood pressure (-4.98 mmHg (-7.17 to -2.80)), total cholesterol (-18.48 mg/dl (-29.16 to -7.80)), triglycerides (-25.89 mg/dl (-36.19 to -15.60), and heart rate (-5.27 beats/min (-9.55 to -1.00)), but not fasting blood glucose (-5.91 mg/dl (-16.32 to 4.50)) nor glycosylated hemoglobin (-0.06% Hb (-0.24 to 0.11)). No significant difference was found between yoga and exercise. One study found an impact on smoking abstinence.   CONCLUSIONS There is promising evidence of yoga on improving cardio-metabolic health. Findings are limited by small trial sample sizes, heterogeneity, and moderate quality of RCTs.

Question: What is the difference between yoga and exercise?


Evaluating a tool for improving accessibility to charts and graphs. We discuss factors in the design and evaluation of natural language-driven assistive technologies that generate descriptions of, and allow interaction with, graphical representations of numerical data. In particular, we provide data in favor of 1) screen-reading technologies as a usable, useful, and cost-effective means of interacting with graphs. The data also show that by carrying out evaluation of Assistive Technologies on populations other than the target communities, certain subtleties of navigation and interaction may be lost or distorted.

Question: How do you evaluate a tool for improving accessibility to charts and graphs?


Analyzing the transitional region in low power wireless links. The wireless sensor networks community, has now an increased understanding of the need for realistic link layer models. Recent experimental studies have shown that real deployments have a "transitional region" with highly unreliable links, and that therefore the idealized perfect-reception-within-range models used in common network simulation tools can be very misleading. In this paper, we use mathematical techniques from communication theory to model and analyze the low power wireless links. The primary contribution of this work is the identification of the causes of the transitional region, and a quantification of their influence. Specifically, we derive expressions for the packet reception rate as a function of distance, and for the width of the transitional region. These expressions incorporate important channel and radio parameters such as the path loss exponent and shadowing variance of the channel; and the modulation and encoding of the radio. A key finding is that for radios using narrow-band modulation, the transitional region is not an artifact of the radio non-ideality, as it would exist even with perfect-threshold receivers because of multi-path fading. However, we hypothesize that radios with mechanisms to combat multi-path effects, such as spread-spectrum and diversity techniques, can reduce the transitional region.

Question: What is the transitional region?


Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments. Autonomous micro aerial vehicles (MAVs) will soon play a major role in tasks such as search and rescue, environment monitoring, surveillance, and inspection. They allow us to easily access environments to which no humans or other vehicles can get access. This reduces the risk for both the people and the environment. For the above applications, it is, however, a requirement that the vehicle is able to navigate without using GPS, or without relying on a preexisting map, or without specific assumptions about the environment. This will allow operations in unstructured, unknown, and GPS-denied environments. We present a novel solution for the task of autonomous navigation of a micro helicopter through a completely unknown environment by using solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift in the xy plane or from the dependency on a clean GPS signal. The novelty in the here-presented approach is to use a monocular simultaneous localization and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that allows us to perform all basic maneuvers such as hovering, set point and trajectory following, vertical takeoff, and landing. All calculations including SLAM and controller are running in real time and online while the helicopter is flying. No offline processing or preprocessing is done. We show real experiments that demonstrate that the vehicle can fly autonomously in an unknown and unstructured environment. To the best of our knowledge, the here-presented work describes the first aerial vehicle that uses onboard monocular vision as a main sensor to navigate through an unknown GPS-denied environment and independently of any external artif

Question: How to navigate a micro helicopter in a GPS-denied environment?


The Quadtree and Related Hierarchical Data Structures. A tutorial survey is presented of the quadtree and related hierarchical data structures. They are based on the principle of recursive decomposition. The emphasis is on the representation of data used in applications in image processing, computer graphics, geographic information systems, and robotics. There is a greater emphasis on region data (i.e., two-dimensional shapes) and to a lesser extent on point, curvilinear, and threedimensional data. A number of operations in which such data structures find use are examined in greater detail.

Question: What is a quadtree?


The Exact Security of Digital Signatures - HOw to Sign with RSA and Rabin. We describe an RSA-based signing scheme which combines essentially optimal e ciency with attractive security properties. Signing takes one RSA decryption plus some hashing, veri cation takes one RSA encryption plus some hashing, and the size of the signature is the size of the modulus. Assuming the underlying hash functions are ideal, our schemes are not only provably secure, but are so in a tight way| an ability to forge signatures with a certain amount of computational resources implies the ability to invert RSA (on the same size modulus) with about the same computational e ort. Furthermore, we provide a second scheme which maintains all of the above features and in addition provides message recovery. These ideas extend to provide schemes for Rabin signatures with analogous properties; in particular their security can be tightly related to the hardness of factoring. Department of Computer Science and Engineering, Mail Code 0114, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. E-mail: mihir@cs.ucsd.edu ; Web page: http://www-cse.ucsd.edu/users/mihir y Department of Computer Science, University of California at Davis, Davis, CA 95616, USA. Email: rogaway@cs.ucdavis.edu ; Web page: http://wwwcsif.cs.ucdavis.edu/~rogaway/homepage.html

Question: How to sign with RSA and Rabin?


The Great Grevy ' s Rally : The Need , Methods , Findings , Implications and Next Steps.

Question: What is the great grevy's rally?


1 An End-to-End Framework for Evaluating Surface Reconstruction. We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation.We employ implicit surfaces for modeling shapes which are expressive enough to contain details of varying size, in addition to preserving sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data. We validate our synthetic sampling scheme by comparing against scan data produced via a commercial optical laser scanner, wherein we scan a 3D-printed version of the original implicit surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformly-distributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for the comparison of surface reconstruction algorithms, they lack a fine-grain analysis. Hence to complement this, the second set of experiments are designed to measure specific properties of surface reconstruction, both from a sampling and surface modeling viewpoint. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms. An End-to-End Framework for Evaluating Surface Reconstruction

Question: What is the difference between a surface and a surface reconstruction?


Out-of-order transmission enabled congestion and scheduling control for multipath TCP. With development of wireless communication technologies, mobile devices are commonly equipped with multiple network interfaces and ready to adopt emerging transport layer protocols such as multipath TCP (MPTCP). The protocol is specifically useful for Internet of Things streaming applications with critical latency and bandwidth demands. To achieve full potential of MPTCP, major challenges on congestion control, fairness, and path scheduling are identified and draw considerable research attention. In this paper, we propose a joint congestion control and scheduling algorithm allowing out-of-order transmission as an overall solution. It is achieved by adaptive window coupling, congestion discrimination, and delay-aware packet ordering. The algorithm is implemented in the Linux kernel for real-world experiments. Favorable results are obtained in both shared or distinct bottleneck scenarios.

Question: What is the out-of-order transmission?


DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks. Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.

Question: What is the difference between a location-based social network and a social network?


Deep Learning for Fixed Model Reuse. Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and videos. There are several studies have the sense of model reuse, by trying to reuse pre-trained deep networks architectures or deep model features to train a new deep model. They, however, neglect the fact that there are many other fixed models or features available. In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse). FMR utilizes the learning power of deep models to implicitly grab the useful discriminative information from fixed model/features that have been widely used in general tasks. We firstly arrange the convolution layers of a deep network and the provided fixed model/features in parallel, fully connecting to the output layer nodes. Then, the dependencies between the output layer nodes and the fixed model/features are knockdown such that only the raw feature inputs are needed when the model is being used for testing, though the helpful information in the fixed model/features have already been incorporated into the model. On one hand, by the FMR scheme, the required amount of training data can be significantly reduced because of the reuse of fixed model/features. On the other hand, the fixed model/features are not explicitly used in testing, and thus, the scheme can be quite useful in applications where the fixed model/features are protected by patents or commercial secrets. Experiments on five real-world datasets validate the effectiveness of FMR compared with state-of-the-art deep methods.

Question: What is deep learning for fixed model reuse?


A General Agnostic Active Learning Algorithm. We present a simple, agnostic active learning algorithm that works for any hypothesis class of bounded VC dimension, and any data distribution. Our algorithm extends a scheme of Cohn, Atlas, and Ladner [6] to the agnostic setting, by (1) reformulating it using a reduction to supervised learning and (2) showing how to apply generalization bounds even for the non-i.i.d. samples that result from selective sampling. We provide a general characterization of the label complexity of our algorithm. This quantity is never more than the usual PAC sample complexity of supervised learning, and is exponentially smaller for some hypothesis classes and distributions. We also demonstrate improvements experimentally.

Question: What is the label complexity of the agnostic active learning algorithm?


Timing Attack against Protected RSA-CRT Implementation Used in PolarSSL. In this paper, we present a timing attack against the RSACRT algorithm used in the current version 1.1.4 of PolarSSL, an opensource cryptographic library for embedded systems. This implementation uses a classical countermeasure to avoid two previous attacks of Schindler and another one due to Boneh and Brumley. However, a careful analysis reveals a bias in the implementation of Montgomery multiplication. We theoretically analyse the distribution of output values for Montgomery multiplication when the output is greater than the Montgomery constant, R. In this case, we show that an extra bit is set in the top most significant word of the output and a time variance can be observed. Then we present some proofs with reasonable assumptions to explain this bias due to an extra bit. Moreover, we show it can be used to mount an attack that reveals the factorisation. We also study another countermeasure and show its resistance against attacked library.

Question: What is the Montgomery constant?


High Performance Rogowski Current Transducers. A Rogowski current transducer is an invaluable tool for semiconductor and power electronic circuit development since it is non-intrusive and does not saturate at high currents. This paper reviews the operating principles, performance limitations and development of this measurement technology and outlines improvements to the integrator design that enables bandwidths of 10MHz to be achieved.

Question: What is a Rogowski current transducer?


Millimeter-Wave Microstrip Array Antenna with High Efficiency for Automotive Radar Systems. Automotive radar systems utilizing the millimeterwave band have been developed 2) since they can detect targets even in bad weather. In the automotive radar systems, linear polarization inclined 45 degrees is utilized in order to avoid mutual radio interference between them. The antenna for the automotive radar systems is required to have high efficiency, which means high gain against for an aperture area determined by the size of a radar sensor, in order to detect the targets at a long distance. In addition, low profile is required not to spoil the appearance of a car. Ease of manufacturing is also one of the significant factors in order to install the automotive radar systems in popular cars. As candidate antennas for the automotive radar systems, a slotted waveguide array antenna and a triplate-type array antenna have been studied. These antennas have low profile and high efficiency 7

Question: What is the difference between a slotted waveguide array antenna and a triplate-type array antenna?


Using Machine Learning to Refine Black-Box Test Specifications and Test Suites. In the context of open source development or software evolution, developers often face test suites which have been developed with no apparent rationale and which may need to be augmented or refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this process as the re-engineering of test suites. It is important to provide both methodological and tool support to help people understand the limitations of test suites and their possible redundancies, so as to be able to refine them in a cost effective manner. To address this problem in the case of black-box testing, we propose a methodology based on machine learning that has shown promising results on a case study.

Question: How to refine black-box test suites?


Artificial Life and Real Robots. The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behavior-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.

Question: What is the difference between artificial life and real robots?


Jigsaw: indoor floor plan reconstruction via mobile crowdsensing. The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity is 100% correct.

Question: How does jigsaw work?


A multi-DoF anthropomorphic transradial prosthetic arm. An anthropomorphic transradial prosthetic arm is proposed in this paper. In order to generate the wrist flexion/extension and ulna/radial deviation, a novel wrist mechanism is proposed based on the parallel prismatic manipulators. It is expected to realize high speed operation, higher positional accuracy and anthropomorphic features using the proposed mechanism. The prosthetic arm consists of an under-actuated hand as the terminal device. The hand mechanism is capable of providing the grasping adaptation. With the intention of verifying the effectiveness of the mechanisms in motion generation, motion simulation and kinematic analysis are carried out.

Question: What is a multi-DoF anthropomorphic transradial prosthetic arm?


Challenges in Shared-Environment Human-Robot Collaboration. We present and discuss four important yet underserved research questions critical to the future of sharedenvironment human-robot collaboration. We begin with a brief survey of research surrounding individual components required for a complete collaborative robot control system, discussing the current state of the art in Learning from Demonstration, active learning, adaptive planning systems, and intention recognition. We motivate the exploration of the presented research questions by relating them to existing work and representative use cases from the domains of construction and cooking.

Question: What are the challenges in shared-environment human-robot collaboration?


Autonomous human-robot proxemics: socially aware navigation based on interaction potential. To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control proxemics—the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and modelswere implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human–robot proxemics with respect to "interaction potential"—predicted automated speech and gesture recognition rates as the robot enters into and engages in faceto-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI. Electronic supplementary material The online version of this article (doi:10.1007/s10514-016-9572-2) contains supplementary material, which is available to authorized users. B Ross Mead rossmead@usc.edu 1 3710 McClintock Avenue, RTH 423, Los Angeles, CA 90089-0781, USA 2 3710 McClintock Avenue, RTH 407, Los Angeles, CA 90089-0781, USA

Question: What is the canadian military?


iCONCUR: informed consent for clinical data and bio-sample use for research. Background Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings.   Methods We developed a web-based tiered informed consent tool called informed consent for clinical data and bio-sample use for research (iCONCUR) that honors granular patient preferences for use of electronic health record data in research. We piloted this tool in 4 outpatient clinics of an academic medical center.   Results Of patients offered access to iCONCUR, 394 agreed to participate in this study, among whom 126 patients accessed the website to modify their records according to data category and data recipient. The majority consented to share most of their data and specimens with researchers. Willingness to share was greater among participants from an Human Immunodeficiency Virus (HIV) clinic than those from internal medicine clinics. The number of items declined was higher for for-profit institution recipients. Overall, participants were most willing to share demographics and body measurements and least willing to share family history and financial data. Participants indicated that having granular choices for data sharing was appropriate, and that they liked being informed about who was using their data for what purposes, as well as about outcomes of the research.   Conclusion This study suggests that a tiered electronic informed consent system is a workable solution that respects patient preferences, increases satisfaction, and does not significantly affect participation in research.

Question: What is the difference between a granular and a non-granular informed consent?


A general approach to control a Positive Buck-Boost converter to achieve robustness against input voltage fluctuations and load changes. A positive buck-boost converter is a known DC- DC converter which may be controlled to act as buck or boost converter with same polarity of the input voltage. This converter has four switching states which include all the switching states of the above mentioned DC-DC converters. In addition there is one switching state which provides a degree of freedom for the positive buck-boost converter in comparison to the buck, boost, and inverting buck-boost converters. In other words the positive buck- boost converter shows a higher level of flexibility for its inductor current control compared to the other DC-DC converters. In this paper this extra degree of freedom is utilised to increase the robustness against input voltage fluctuations and load changes. To address this capacity of the positive buck-boost converter, two different control strategies are proposed which control the inductor current and output voltage against any fluctuations in input voltage and load changes. Mathematical analysis for dynamic and steady state conditions are presented in this paper and simulation results verify the proposed method.

Question: What is a positive buck-boost converter?


EbbRT : Elastic Building Block Runtime-overview Schatzberg.

Question: What is the name of the project?


NANOPARTICLES – AN OVERVIEW. Recently particulate systems like nanoparticles have been used as a physical approach to alter and improve the pharmacokinetic and pharmacodynamic properties of various types of drug molecules. They have been used in vivo to protect the drug entity in the systemic circulation, restrict access of the drug to the chosen sites and to deliver the drug at a controlled and sustained rate to the site of action.Drug delivery research is clearly moving from the microto the nanosize scale. Nanotechnology is therefore emerging as a field in medicine that is expected to elicit significant therapeutic benefits. The development of effective nanodelivery systems capable of carrying a drug specifically and safely to a desired site of action is one of the most challenging tasks of pharmaceutical formulation investigators. They are attempting to reformulate and add new indications to the existing blockbuster drugs to maintain positive scientific outcomes and therapeutic breakthroughs. The nanodelivery systems mainly include nanoemulsions, lipid or polymeric Nanoparticles and liposomes.For the past few years, there has been a considerable research on the basis of Novel drug delivery system, using particulate vesicle systems as such drug carriers for small and large molecules. Nanoparticles have been improving the therapeutic effect of drugs and minimize the side effects. Basically, Nanoparticles have been prepared by using various techniques as such dispersion of preformed polymers, polymerization of monomers and ionic gelation or co-acervation of hydrophilic polymer.

Question: What is the difference between nanoparticles and nanodelivery systems?


Beyond information seeking: towards a general model of information behaviour. Introduction. The aim of the paper is to propose new models of information behaviour that extend the concept beyond simply information seeking to consider other modes of behaviour. The models chiefly explored are those of Wilson and Dervin. Argument A shortcoming of some models of information behaviour is that they present a sequence of stages where it is evident that actual behaviour is not always sequential. In addition, information behaviour models tend to confine themselves to depictions of information seeking. Development. A model of 'multi-directionality' is explored, to overcome the notion of sequential stages. Inspired by authors such as Chatman, Krikelas, and Savolainen, modes of information behaviour such as creating, destroying and avoiding information are included. Conclusion. New models of information behaviour are presented that replace the notion of 'barriers' with the concept of 'gap', as a means of integrating the views of Wilson and Dervin. The proposed models incorporate the notion of multi-directionality and identify ways in which an individual may navigate 'gap' using modes of information behaviour beyond information seeking.

Question: What is the difference between information seeking and information behaviour?


Fast time series classification using numerosity reduction. Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many realtime applications. One way to mitigate this problem is to speed up the DTW calculations. Nonetheless, there is a limit to how much this can help. In this work, we propose an additional technique, numerosity reduction, to speed up one-nearest-neighbor DTW. While the idea of numerosity reduction for nearest-neighbor classifiers has a long history, we show here that we can leverage off an original observation about the relationship between dataset size and DTW constraints to produce an extremely compact dataset with little or no loss in accuracy. We test our ideas with a comprehensive set of experiments, and show that it can efficiently produce extremely fast accurate classifiers.

Question: How to speed up nearest neighbor classification?


Classification of Liver Cancer using Artificial Neural Network and Support Vector Machine. The purpose of this study is to compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for liver cancer classification. The performance of both models is compared and validated on BUPA Liver Disorder Dataset in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The comparative results show that the SVM classifier outperforms ANN classifier where SVM gives an accuracy of 63.11%, specificity of 100% and AUC of 68.34% where as ANN gives classification accuracy of 57.28%, specificity of 32.56 and AUC of 53.78. This result indicates that the classification capability of SVM is better than ANN and may potentially fill in a critical gap in the use of current or future classification algorithms for liver cancer.

Question: What is the purpose of this study?


The Skiplist-Based LSM Tree. Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is aŠractive for write-optimized data systems. Maintaining an ecient bu'er in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the SkiplistBased LSM Tree (sLSM), a novel system in which the memory bu'er of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad ƒexibility for excellent performance across a wide variety of workloads.

Question: What is the skiplist based lsm tree?


Large-scale concept ontology for multimedia. As increasingly powerful techniques emerge for machine tagging multimedia content, it becomes ever more important to standardize the underlying vocabularies. Doing so provides interoperability and lets the multimedia community focus ongoing research on a well-defined set of semantics. This paper describes a collaborative effort of multimedia researchers, library scientists, and end users to develop a large standardized taxonomy for describing broadcast news video. The large-scale concept ontology for multimedia (LSCOM) is the first of its kind designed to simultaneously optimize utility to facilitate end-user access, cover a large semantic space, make automated extraction feasible, and increase observability in diverse broadcast news video data sets

Question: What is the first large-scale concept ontology for multimedia?


Stanley: The robot that won the DARPA Grand Challenge. Sebastian Thrun1, Mike Montemerlo1, Hendrik Dahlkamp1, David Stavens1, Andrei Aron1, James Diebel1, Philip Fong1, John Gale1, Morgan Halpenny1, Gabriel Hoffmann1, Kenny Lau1, Celia Oakley1, Mark Palatucci1, Vaughan Pratt1, Pascal Stang1, Sven Strohband2, Cedric Dupont2, Lars-Erik Jendrossek2, Christian Koelen2, Charles Markey2, Carlo Rummel2, Joe van Niekerk2, Eric Jensen2, Philippe Alessandrini2, Gary Bradski3, Bob Davies3, Scott Ettinger3, Adrian Kaehler3, Ara Nefian3, and Pamela Mahoney4

Question: What is the name of the robot that won the DARPA Grand Challenge?


The active layer morphology of organic solar cells probed with grazing incidence scattering techniques.. Grazing incidence X-ray scattering (GIXS) provides unique insights into the morphology of active materials and thin film layers used in organic photovoltaic devices. With grazing incidence wide angle X-ray scattering (GIWAXS) the molecular arrangement of the material is probed. GIWAXS is sensitive to the crystalline parts and allows for the determination of the crystal structure and the orientation of the crystalline regions with respect to the electrodes. With grazing incidence small angle X-ray scattering (GISAXS) the nano-scale structure inside the films is probed. As GISAXS is sensitive to length scales from nanometers to several hundred nanometers, all relevant length scales of organic solar cells are detectable. After an introduction to GISAXS and GIWAXS, selected examples for application of both techniques to active layer materials are reviewed. The particular focus is on conjugated polymers, such as poly(3-hexylthiophene) (P3HT).

Question: What is the difference between GIWAXS and GISAXS?


Arthrodesis with Intramedular Fixation in Posttraumatic Arthrosis of the Midfoot: A Case Report. We present two middle-aged men with posttraumatic arthrosis of the midfoot. Both patients suffered from severe pain and their foot was unable to bear weight. Both were operated using new fusion bolt 6.5 mm and additional screws. In their case, arthrodesis was mandatory and effective intervention. After surgical treatment both patients were pain free and able to walk without crutches and return to daily work.

Question: What is the treatment for posttraumatic arthrosis of the midfoot?


Deep Texture Features for Robust Face Spoofing Detection. Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

Question: What is a deep texture feature?


Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric statespace models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once an approximation of the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.

Question: What is the difference between a Gaussian process and a Gaussian mixture model?


Prospective outcomes of young and middle-aged adults with medial compartment osteoarthritis treated with a proximal tibial opening wedge osteotomy.. PURPOSE The purpose of this study was to conduct a prospective outcome analysis of proximal tibial opening wedge osteotomies performed in young and middle-aged patients (aged <55 years) for the treatment of symptomatic medial compartment osteoarthritis of the knee.   METHODS A consecutive series of young and middle-aged adults who underwent proximal tibial opening wedge osteotomies for symptomatic medial compartment osteoarthritis and genu varus alignment were prospectively followed up. Patients were evaluated with preoperative and postoperative modified Cincinnati Knee Scores and International Knee Documentation Committee objective knee subscores for knee effusions and the single-leg hop. Calculations were made of the preoperative and postoperative long-leg radiographic mechanical weight-bearing axis, patellar height (Insall-Salvati index), and tibial slope. A separate cohort of asymptomatic patients was used to quantify tibial plateau anatomy to provide an objective description of the lower extremity mechanical axis.   RESULTS There were 47 patients, with a mean age of 40.5 years, with a minimum of 2 years' follow-up, who formed this patient cohort. Modified Cincinnati Knee Scores improved significantly from 42.9 preoperatively to 65.1 at a mean of 3.6 years of follow-up. Radiographic analysis of a separate cohort showed the medial tibial eminence to be located at the 41% point along the tibial plateau from medial (0%) to lateral (100%). There was a significant improvement in malalignment: the mean mechanical axis passed through the tibial plateau at 23% of the distance along the proximal tibia preoperatively versus 54% postoperatively. The Insall-Salvati index decreased from 1.03 to 0.95 (P < .05), and posterior tibial slope increased from 9.4° to 11.7° (P < .05). Of the osteotomies, 3 (6%) were considered failures,

Question: What is the purpose of this study?