Deep Model-Based 6D Pose Refinement in RGB. We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum. de/Main/FabianManhardt to ensure reproducibility.

Question: How do you train a deep neural network?


DBpedia: A Nucleus for a Web of Open Data. DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for humanand machineconsumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

Question: What is the DBpedia dataset?


IoT-based continuous glucose monitoring system: A feasibility study. Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device. c © 2017 The Authors. Published by E sevier B.V. i ilit f t f re ce r ra hairs.

Question: What is the feasibility of IoT-based continuous glucose monitoring system?


Towards Query Efficient Black-box Attacks: An Input-free Perspective. Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called input-free attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations on it. Following this approach, we propose two techniques to significantly reduce the query complexity. First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model. Then we shrink the dimension of the attack space by perturbing a small region and tiling it to cover the input image. To make our algorithm more effective, we stabilize a projected gradient ascent algorithm with momentum, and also propose a heuristic approach for region size selection. Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called input-free attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations

Question: What is the difference between input-free and black-box attacks?


ATR-Vis: Visual and Interactive Information Retrieval for Parliamentary Discussions in Twitter. The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public's feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers' actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This article presents Active Tweet Retrieval Visualization (ATR-Vis), a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the e

Question: What is the difference between the Canadian and Brazilian federal parliaments?


DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380,000 associations between >16,000 genes and 13,000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/

Question: What is the largest database of human gene-disease associations?


The effect of music background on the emotional appraisal of film sequences. In this study the effects of musical background on the emotional appraisal of film sequences was investigated. Four pairs of polar emotions defined in Plutchik's model were used as basic emotional qualities: joy-sadness, anticipation-surprise, fear-anger, and trustdisgust. In the preliminary study eight film sequences and eight music themes were selected as the best representatives of all eight Plutchik's emotions. In the main experiment the participant judged the emotional qualities of film-music combinations on eight seven-point scales. Half of the combinations were congruent (e.g. joyful film joyful music), and half were incongruent (e.g. joyful film sad music). Results have shown that visual information (film) had greater effects on the emotion appraisal than auditory information (music). The modulation effects of music background depend on emotional qualities. In some incongruent combinations (joysadness) the modulations in the expected directions were obtained (e.g. joyful music reduces the sadness of a sad film), in some cases (anger-fear) no modulation effects were obtained, and in some cases (trust-disgust, anticipation-surprise) the modulation effects were in an unexpected direction (e.g. trustful music increased the appraisal of disgust of a disgusting film). These results suggest that the appraisals of conjoint effects of emotions depend on the medium (film masks the music) and emotional quality (three types of modulation effects).

Question: What is the effect of music background on the emotional appraisal of film sequences?


SPARK: A New VANET-Based Smart Parking Scheme for Large Parking Lots. Searching for a vacant parking space in a congested area or a large parking lot and preventing auto theft are major concerns to our daily lives. In this paper, we propose a new smart parking scheme for large parking lots through vehicular communication. The proposed scheme can provide the drivers with real-time parking navigation service, intelligent antitheft protection, and friendly parking information dissemination. Performance analysis via extensive simulations demonstrates its efficiency and practicality. Keywords— Vehicular communications; smart parking; navigation; anti-theft; information dissemination; security & privacy

Question: What is a large parking lot?


Webshell detection techniques in web applications. With widely adoption of online services, malicious web sites have become a malignant tumor of the Internet. Through system vulnerabilities, attackers can upload malicious files (which are also called webshells) to web server to create a backdoor for hackers' further attacks. Therefore, finding and detecting webshell inside web application source code are crucial to secure websites. In this paper, we propose a novel method based on the optimal threshold values to identify files that contain malicious codes from web applications. Our detection system will scan and look for malicious codes inside each file of the web application, and automatically give a list of suspicious files and a detail log analysis table of each suspicious file for administrators to check further. The Experimental results show that our approach is applicable to identify webshell more efficient than some other approaches.

Question: How to detect webshells in web applications?


Classification of copy move forgery and normal images by ORB features and SVM classifier. Today, the characterization of the technological age is done by the digital images spread. They are the most common form of conveying information whether through internet, newspapers, magazines, or scientific journals. They are used as a strong proof of various crimes and as evidence used for various purposes. The modification, capturing or creating of the image has become easier and available with the emergence of means of image editing and processing tools. One of the most important and popular types of image forgery is a copy-move forgery in which an image part is copied and then pasted into the same image that has the intention of hiding something important or showing a false scene. Because the important properties of the copied parts come from the same image, such as brightness, noise, and texture which will be compatible with the entire image that makes more difficult for experts for the detection and distinguishing the alteration. Usually, the detecting copy move forgery conventional techniques suffer severely from the time-consuming problem. The evaluation of the improved method had been done using (150) images that were selected from two different datasets, "CoMoFoD" and "MICC-F2000". Experimental results show that the improved method can accurately and quickly reveal the doubled regions of a tampered image. In addition, greatly reducing the processing time in comparison to the Khan algorithm, and the accuracy is kept at the same level. Owing to the availability and technological advancement of the image editing sophisticated tools, there is an increase in the loss of authentication in digital images. Thus, this led us to the proposal of different detection techniques that checks whether the digital images are forged or authentic. The specific type of forgery technique is copy move forgery in which widely used research topic is detection under digital image forensics.

Question: What is the difference between copy move forgery and normal images?


Upvoting hurricane Sandy: event-based news production processes on a social news site. This paper uses the case of Hurricane Sandy and reddit's topical community (subreddit) /r/sandy to examine the production and curation of news content around events on a social news site. Through qualitative analysis, we provide a coded topology of produced content and describe how types of networked gatekeeping impact the framing of a crisis situation. This study also examines, through quantitative modeling, what kind of information becomes negotiated and voted as relevant. We suggest that highly scored content shared in a social news setting focused more on human-interest media and perspective-based citizen journalism than professional news reports. We conclude by discussing how the mechanisms of social news sites conflict with the social norms and culture of reddit to produce differing expectations around news.

Question: What is the difference between a hurricane and a tornado?


iHadoop: Asynchronous Iterations for MapReduce. MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important class of applications. The architecture of MapReduce, most critically its dataflow techniques and task scheduling, is completely unaware of the nature of iterative applications, tasks are scheduled according to a policy that optimizes the execution for a single iteration which wastes bandwidth, I/O, and CPU cycles when compared with an optimal execution for a consecutive set of iterations. This work presents iHadoop, a modified MapReduce model, and an associated implementation, optimized for iterative computations. The iHadoop model schedules iterations asynchronously. It connects the output of one iteration to the next, allowing both to process their data concurrently. iHadoop's task scheduler exploits inter-iteration data locality by scheduling tasks that exhibit a producer/consumer relation on the same physical machine allowing a fast local data transfer. For those iterative applications that require satisfying certain criteria before termination, iHadoop runs the check concurrently during the execution of the subsequent iteration to further reduce the application's latency. This paper also describes our implementation of the iHadoop model, and evaluates its performance against Hadoop, the widely used open source implementation of MapReduce. Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Furthermore, integrating iHadoop with HaLoop, a vari

Question: What is the difference between a mapreduce and a hadoop?


Investigating the Implications of 3D Printing in Special Education. Consumer-grade digital fabrication such as 3D printing is on the rise, and we believe it can be leveraged to great benefit in special education. Although 3D printing is infiltrating mainstream education, little research has explored 3D printing in the context of students with special support needs. We describe our studies on this topic and the resulting contributions. We initially conducted a formative study exploring the use of 3D printing at three locations serving populations with varying ability, including individuals with cognitive, motor, and visual impairments. We found that 3D design and printing perform three functions in special education: (1) STEM engagement, (2) creation of educational aids for accessible curriculum content, and (3) making custom adaptive devices. As part of our formative work, we also discussed a case study in the codesign of an assistive hand grip created with occupational therapists at one of our investigation sites. This work inspired further studies on the creation of adaptive devices using 3D printers. We identified the needs and constraints of these therapists and found implications for a specialized 3D modeling tool to support their use of 3D printers. We developed GripFab, 3D modeling software based on feedback from therapists, and used it to explore the feasibility of in-house 3D object designs in support of accessibility. Our contributions include case studies at three special education sites and discussion of obstacles to efficient 3D printing in this context. We have extended these contributions with a more in-depth look at the stakeholders and findings from GripFab studies. We have expanded our discussion to include suggestions for researchers in this space, in addition to refined suggestions from our earlier work for technologists creating 3D modeling and printing tools, therapists seeking to leverage 3D printers, and educators and administrators looking t

Question: What is the difference between a 3D printer and a 3D scanner?


Decision-Theoretic User Interface Generation. For decades, researchers have debated the pros and cons of adaptive user interfaces with enthusiastic AI practitioners often confronting skeptical HCI experts (Shneiderman & Maes, 1997). This paper summarizes the SUPPLE project's six years of work analyzing the characteristics of successful adaptive interfaces and developing efficient algorithms for their automatic generation (Gajos & Weld, 2004; Gajos et al., 2005; Gajos & Weld, 2005; Gajos et al., 2006; Gajos, Wobbrock, & Weld, 2007; Gajos et al., 2008; Gajos, Wobbrock, & Weld, 2008). We conclude that personalized user interfaces, which are adapted to a person's devices, tasks, preferences and abilities, can improve user satisfaction and performance. Further, we demonstrate that automatic generation of these interfaces is computationally feasible. We developed two systems which autonomously construct a broad range of personalized adaptive interfaces: 1) SUPPLE/ARNAULD uses decision-theoretic optimization to automatically generate user interfaces adapted to target device, usage patterns, and a learned model of user preferences, and 2) SUPPLE++ first performs a one-time assessment of a person's motor abilities and then automatically generates user interfaces adapted to that user's abilities. Our experiments show that these automatically generated interfaces significantly improve speed, accuracy and satisfaction of users with motor impairments compared to manufacturers' defaults. We also provide the first characterization of the design space of adaptive interfaces and demonstrate how such interfaces can significantly improve the quality and efficiency of daily interactions for typical users.

Question: How can we automatically generate user interfaces?


Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks. Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.

Question: What is the relation extraction?


A Survey of Surface-Based Illustrative Rendering for Visualization. In this paper, we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub-area of visualization. For the remainder of the survey, we then focus on surface-based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods' implementations. We then provide an overview of low-level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.

Question: What is the state of the art in illustrative rendering?


Private local automation clouds built by CPS: Potential and challenges for distributed reasoning.

Question: What is the potential and challenges for distributed reasoning?


DCU-UVT: Word-Level Language Classification with Code-Mixed Data. This paper describes the DCU-UVT team's participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Wordlevel classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments, we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.

Question: What is the DCU-UVT team?


Lifetime-Aware Scheduling and Power Control for M2M Communications in LTE Networks. In this paper the scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in LTE networks. To enable efficient scheduling for a massive number of machine-type subscribers, a novel distributed scheme is proposed to let machine nodes form local clusters and communicate with the base-station through the cluster-heads. Then, uplink scheduling and power control in LTE networks are introduced and lifetime-aware solutions are investigated to be used for the communication between cluster-heads and the base-station. Beside the exact solutions, low-complexity suboptimal solutions are presented in this work which can achieve near optimal performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended using the proposed protocols.

Question: What is the lifetime of a battery?


PID control system analysis, design, and technology. Designing and tuning a proportional-integral-derivative (PID) controller appears to be conceptually intuitive, but can be hard in practice, if multiple (and often conflicting) objectives such as short transient and high stability are to be achieved. Usually, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This stimulates the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating envelope. This development has further led to the incorporation of some advanced tuning algorithms into PID hardware modules. Corresponding to these developments, this paper presents a modern overview of functionalities and tuning methods in patents, software packages and commercial hardware modules. It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging. The inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree. This should also assist future development of "plug-and-play" PID controllers that are widely applicable and can be set up easily and operate optimally for enhanced productivity, improved quality and reduced maintenance requirements.

Question: What is PID control?


Review of performance metrics for green data centers: a taxonomy study. Data centers now play an important role in modern IT infrastructures. Although much research effort has been made in the field of green data center computing, performance metrics for green data centers have been left ignored. This paper is devoted to categorization of green computing performance metrics in data centers, such as basic metrics like power metrics, thermal metrics and extended performance metrics i.e. multiple data center indicators. Based on a taxonomy of performance metrics, this paper summarizes features of currently available metrics and presents insights for the study on green data center computing.

Question: What is a taxonomy of performance metrics?


Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements. Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity.

Question: What is the difference between the proposed method and the existing methods?


Collection flow. Computing optical flow between any pair of Internet face photos is challenging for most current state of the art flow estimation methods due to differences in illumination, pose, and geometry. We show that flow estimation can be dramatically improved by leveraging a large photo collection of the same (or similar) object. In particular, consider the case of photos of a celebrity from Google Image Search. Any two such photos may have different facial expression, lighting and face orientation. The key idea is that instead of computing flow directly between the input pair (I, J), we compute versions of the images (I', J') in which facial expressions and pose are normalized while lighting is preserved. This is achieved by iteratively projecting each photo onto an appearance subspace formed from the full photo collection. The desired flow is obtained through concatenation of flows (I → I') o (J' → J). Our approach can be used with any two-frame optical flow algorithm, and significantly boosts the performance of the algorithm by providing invariance to lighting and shape changes.

Question: How to compute optical flow between two images?


Effects of a Dynamic Warm-Up, Static Stretching or Static Stretching with Tendon Vibration on Vertical Jump Performance and EMG Responses. The purpose of this study was to investigate the short-term effects of static stretching, with vibration given directly over Achilles tendon, on electro-myographic (EMG) responses and vertical jump (VJ) performances. Fifteen male, college athletes voluntarily participated in this study (n=15; age: 22±4 years old; body height: 181±10 cm; body mass: 74±11 kg). All stages were completed within 90 minutes for each participant. Tendon vibration bouts lasted 30 seconds at 50 Hz for each volunteer. EMG analysis for peripheral silent period, H-reflex, H-reflex threshold, T-reflex and H/M ratio were completed for each experimental phases. EMG data were obtained from the soleus muscle in response to electro stimulation on the popliteal post tibial nerve. As expected, the dynamic warm-up (DW) increased VJ performances (p=0.004). Increased VJ performances after the DW were not statistically substantiated by the EMG findings. In addition, EMG results did not indicate that either static stretching (SS) or tendon vibration combined with static stretching (TVSS) had any detrimental or facilitation effect on vertical jump performances. In conclusion, using TVSS does not seem to facilitate warm-up effects before explosive performance.

Question: What is the purpose of this study?


Low-Cost Phased-Array Antenna Using Compact Tunable Phase Shifters Based on Ferroelectric Ceramics. A low-cost phased-array antenna at 10 GHz is presented for a scan angle of ±50°. The array employs continuously tunable phase shifters based on a screen printed barium-strontium-titanate thick-film ceramic. Due to the use of artificial transmission line topology, the proposed phase-shifter design has a very compact size (3 mm × 2.8 mm) for 342° total phase shift. In the frequency range from 8 to 10 GHz, it exhibits a figure of merit >;52°/dB, which is among the best of phase shifters based on ferroelectric thick films. In a prototyped phased array, the RF circuit consists of a feeding network, phase shifters, and antenna elements, which are integrated into one planar metallization layer. Furthermore, a simple way for routing bias lines for phase shifters is demonstrated using high resistive electrodes. Using screen printed thick films and applying a simplified fabrication process for the RF and bias circuitry can reduce the total expense of phased arrays considerably.

Question: What is the figure of merit?


The Relevance of Data Warehousing and Data Mining in the Field of Evidence-based Medicine to Support Healthcare Decision Making. Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient's health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine. Keywords—data mining, data warehousing, decision-support systems, evidence-based medicine.

Question: What is the relevance of data warehousing and data mining in the field of evidence-based medicine?


Discriminant validity of well-being measures.. The convergent and discriminant validities of well-being concepts were examined using multitrait-multimethod matrix analyses (D. T. Campbell & D. W. Fiske, 1959) on 3 sets of data. In Study 1, participants completed measures of life satisfaction, positive affect, negative affect, self-esteem, and optimism on 2 occasions 4 weeks apart and also obtained 3 informant ratings. In Study 2, participants completed each of the 5 measures on 2 occasions 2 years apart and collected informant reports at Time 2. In Study 3, participants completed 2 different scales for each of the 5 constructs. Analyses showed that (a) life satisfaction is discriminable from positive and negative affect, (b) positive affect is discriminable from negative affect, (c) life satisfaction is discriminable from optimism and self-esteem, and (d) optimism is separable from trait measures of negative affect.

Question: How do we know that well-being is a valid construct?


The Experience of Nature A Psychological Perspective.

Question: What is the experience of nature?


Characterizing and visualizing physical world accessibility at scale using crowdsourcing, computer vision, and machine learning. Poorly maintained sidewalks and street intersections pose considerable accessibility challenges for people with mobility-impairments [13,14]. According to the most recent U.S. Census (2010), roughly 30.6 million adults have physical disabilities that affect their ambulatory activities [22]. Of these, nearly half report using an assistive aid such as a wheelchair (3.6 million) or a cane, crutches, or walker (11.6 million) [22]. Despite comprehensive civil rights legislation for Americans with Disabilities (e.g., [25,26]), many city streets, sidewalks, and businesses in the U.S. remain inaccessible. The problem is not just that street-level accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. Indeed, in a recent report, the National Council on Disability noted that they could not find comprehensive information on the "degree to which sidewalks are accessible" across the US [15]. This lack of information can have a significant negative impact on the independence and mobility of citizens [13,16] For example, in our own initial formative interviews with wheelchair users, we uncovered a prevailing view about navigating to new areas of a city: "I usually don't go where I don't know [about accessible routes]" (Interviewee 3, congenital polyneuropathy). Our overarching research vision is to transform the way in which street-level accessibility information is collected and used to support new types of assistive map-based tools.

Question: How can we improve the accessibility of the streets?


SDN-based solutions for Moving Target Defense network protection. Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.

Question: What is the overhead of implementing moving target defense using software defined networking?


Toward Automated Dynamic Malware Analysis Using CWSandbox. Malware is notoriously difficult to combat because it appears and spreads so quickly. In this article, we describe the design and implementation of CWSandbox, a malware analysis tool that fulfills our three design criteria of automation, effectiveness, and correctness for the Win32 family of operating systems

Question: What is malware?


Unsupervised Learning of Sequence Representations by Autoencoders. Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation. The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style). One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.

Question: What is the unsupervised learning of sequence representations by autoencoders?


Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions. We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This approach allows to forgo resource-intensive, domain-specific, error-prone feature engineering, which may drastically increase the applicability of machine learning to mobile phone sensor data.

Question: What is the practical approach for processing mobile sensor data for continual deep learning predictions?


Accelerating Differential Evolution Using an Adaptive Local Search. We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.

Question: How to accelerate differential evolution using an adaptive local search?


Participation in School-Based Extracurricular Activities and Adolescent Adjustment. This paper examines the association between participation in school-based extracurricular activities (ECAs) and adolescent adjustment (drinking, marijuana use, grades, academic attitudes and academic aspirations) among students from six high schools. Three major issues were addressed: the potential confounding of selective EGA participation by better adjusted students and measures of adjustment, variability in the strength of the association between ECA participation and adjustment as a function of adolescent demographic characteristics and activity type, and the role of peers as mediators of the association between ECA participation and adjustment. Adolescents who participated in ECAs reported higher grades, more positive attitudes toward schools, and higher acac demic aspirations once demographic characteristics and prior adjustment were controlled. Alcohol and marijuana use were not independently associated with ECA participation. The EGA-adjustment association did not vary by demographic characteristics and did not appear to be mediated by peer characteristics. Those who participated in non-sport ECAs reported consistendy better adjustment than those who did not participate in ECAs and those who participate in sports.

Question: What is the association between participation in school-based extracurricular activities and adolescent adjustment?


Fake News Detection on Social Media using Geometric Deep Learning. Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access, and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current natural language processing algorithms are still missing. Recent studies have empirically shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper, we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical convolutional neural networks to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or c

Question: What is the difference between fake and real news?


Distributed Edge Cloud R-CNN for Real Time Object Detection. Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high-volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks, these performance improvements only focus on a single, contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN pipeline. By splitting the object detection pipeline into components and dynamically distributing these components in the cloud, we can achieve optimal performance to enable real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform including cloud servers and edge-embedded devices for real-time object detection on live video streams.

Question: What is the edge cloud r-cnn?


Working Memory: A View from Neuroimaging. We have used neuroimaging techniques, mainly positron emission tomography (PET), to study cognitively driven issues about working memory. Two kinds of experiments are described. In the first kind, we employ standard subtraction logic to uncover the basic components of working memory. These studies indicate that: (a) there are different working-memory systems for spatial, object, and verbal information (with the spatial system localized more in the right hemisphere, and the verbal system more in the left hemisphere); (b) within at least the spatial and verbal systems, separable components seem to be responsible for the passive storage of information and the active maintenance of information (with the storage component being localized more in the back of the brain, and the maintenance component in the front); and (c) there may be separate components responsible for processing the contents of working memory (localized in prefrontal cortex). In our second kind of experiment we have focused on verbal working memory and incrementally varied one task parameter-memory load-in an effort to obtain a more fine-grained analysis of the system's operations. The results indicate that all relevant components of the system show some increase in activity with increasing memory load (e.g., the frontal regions responsible for verbal rehearsal show incremental increases in activation with increasing memory load). In contrast, brain regions that are not part of the working-memory system show no effect of memory load. Furthermore, the time courses of activation may differ for regions that are sensitive to load versus those that are not. Taken together, our results provide support for certain cognitive models of working memory (e.g., Baddeley, 1992) and also suggest some distinctions that these models have not emphasized. And more fundamentally, the results provide a neural base for cognitive models of working memory.

Question: What is the difference between the spatial and verbal working memory systems?


Computational Thinking and Expository Writing in the Middle School. To broaden participation in computing we need to look beyond traditional domains of inquiry and expertise. We present results from a demonstration project in which interactive journalism was used to infuse computational thinking into the standard curriculum and regular classroom experience at a middle school with a diverse population. Outcomes indicate that we were able to develop positive attitudes about computational thinking and programming among students and teachers who did not necessarily view themselves as "math types." By partnering with language arts, technology and math teachers at Fisher Middle School, Ewing New Jersey, we introduced the isomorphism between the journalistic process and computational thinking to 7th and 8th graders. An intense summer institute, first with the teachers and then with students recruited from the school, immersed them in the "newsroom of the future" where they researched and wrote news stories, shot and edited video, and developed procedural animations in Scratch to support their storylines. An afterschool club sustained the experience. The teachers adapted interactive journalism and Scratch programming to enrich standard language arts curriculum and are infusing computational thinking in classroom experiences throughout the school.

Question: How did we infuse computational thinking into the standard curriculum and regular classroom experience?


Crawling AJAX by Inferring User Interface State Changes. AJAX is a very promising approach for improving rich interactivity and responsiveness of web applications. At the same time, AJAX techniques shatter the metaphor of a web "page" upon which general search crawlers are based. This paper describes a novel technique for crawling AJAX applications through dynamic analysis and reconstruction of user interface state changes. Our method dynamically infers a state-flow graph modeling the various navigation paths and states within an AJAX application. This reconstructed model can be used to generate linked static pages. These pages could be used to expose AJAX sites to general search engines. Moreover, we believe that the crawling techniques that are part of our solution have other applications, such as within general search engines, accessibility improvements, or in automatically exercising all user interface elements and conducting state-based testing of AJAX applications. We present our open source tool called CRAWLJAX which implements the concepts discussed in this paper. Additionally, we report a case study in which we apply our approach to a number of representative AJAX applications and elaborate on the obtained results.

Question: How does crawling AJAX work?


Antecedents and consequences of customer-company identification: expanding the role of relationship marketing.. This article presents an empirical test of organizational identification in the context of customer-company (C-C) relationships. It investigates whether customers identify with companies and what the antecedents and consequences of such identification are. The model posits that perceived company characteristics, construed external image, and the perception of the company's boundary-spanning agent lead to C-C identification. In turn, such identification is expected to impact both in-role behavior (i.e., product utilization) as well as extra-role behavior (i.e., citizenship). The model was tested in a consultative selling context of pharmaceutical sales reps calling on physicians. Results from the empirical test indicated that customers do indeed identify with organizations and that C-C identification positively impacts both product utilization behavior and extra-role behavior even when the effect of brand perception is accounted for. Second, the study found that the organization's characteristics as well as the salesperson's characteristics contributed to the development of C-C identification.

Question: What is the relationship between customer-company identification and customer-company relationship?


Founding Cryptography on Oblivious Transfer - Efficiently. We present a simple and efficient compiler for transforming secure multi-party computation (MPC) protocols that enjoy security only with an honest majority into MPC protocols that guarantee security with no honest majority, in the oblivious-transfer (OT) hybrid model. Our technique works by combining a secure protocol in the honest majority setting with a protocol achieving only security against semi-honest parties in the setting of no honest majority. Applying our compiler to variants of protocols from the literature, we get several applications for secure two-party computation and for MPC with no honest majority. These include: • Constant-rate two-party computation in the OT-hybrid model. We obtain a statistically UC-secure two-party protocol in the OT-hybrid model that can evaluate a general circuit C of size s and depth d with a total communication complexity of O(s)+ poly(k, d, log s) and O(d) rounds. The above result generalizes to a constant number of parties. • Extending OTs in the malicious model. We obtain a computationally efficient protocol for generating many string OTs from few string OTs with only a constant amortized communication overhead compared to the total length of the string OTs. • Black-box constructions for constant-round MPC with no honest majority. We obtain general computationally UC-secure MPC protocols in the OT-hybrid model that use only a constant number of rounds, and only make a black-box access to a pseudorandom generator. This gives the first constant-round protocols for three or more parties that only make a black-box use of cryptographic primitives (and avoid expensive zero-knowledge proofs).

Question: What is the difference between a constant-round protocol and a constant-round protocol that uses a black-box?


C V ] 2 1 N ov 2 01 7 Emotion Classification with Data Augmentation Using Generative Adversarial Networks. It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework with a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN's performance. Empirical results show that we can obtain 5%∼10% increase in the classification accuracy after employing the GAN-based data augmentation techniques.

Question: How to classify images with multiple class labels using only a small number of labeled examples?


Computed tomographic analysis of tooth-bearing alveolar bone for orthodontic miniscrew placement.. INTRODUCTION When monocortical orthodontic miniscrews are placed in interdental alveolar bone, the safe position of the miniscrew tip should be ensured. This study was designed to quantify the periradicular space in the tooth-bearing area to provide practical guidelines for miniscrew placement.   METHODS Computerized tomographs of 30 maxillae and mandibles were taken from nonorthodontic adults with normal occlusion. Both mesiodistal interradicular distance and bone thickness over the narrowest interradicular space (safety depth) were measured at 2, 4, 6, and 8 mm from the cementoenamel junction.   RESULTS Mesiodistal space greater than 3 mm was available at the 8-mm level in the maxillary anterior region, between the premolars, and between the second premolar and the first molar at 4 mm. In the mandible, sufficient mesiodistal space was found between the premolars, between the molars, and between the second premolar and the first molar at the 4-mm level. Safety depth greater than 4 mm was found in the maxillary and mandibular intermolar regions, and between the second premolar and the first molar in both arches.   CONCLUSIONS Subapical placement is advocated in the anterior segment. Premolar areas appear reliable in both arches. Angulated placement in the intermolar area is suggested to use the sufficient safety depth in this area.

Question: How to place orthodontic miniscrews?


Observations of achievement and motivation in using cloud computing driven CAD: Comparison of college students with high school and vocational high school backgrounds. 0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.08.001 ⇑ Corresponding author. Tel.: +886 02 7734 3347; f E-mail address: joum@ntnu.edu.tw (M. Jou). Cloud computing technology has matured as it has been integrated with every kind of digitalization processes. It offers numerous advantages for data and software sharing, and thus making the management of complex IT systems much simpler. For education in engineering, cloud computing even provides students with versatile and ubiquitous access to software commonly used in the field without having to step into an actual computer lab. Our study analyzed learning attitudes and academic performances induced by the utilization of resources driven by cloud computing technologies. Comparisons were made between college students with high school and vocational high school backgrounds. One hundred and thirtytwo students who took the computer-aided designing (CAD) course participated in the study. Technology Acceptance Model (TAM) was used as the fundamental framework. Open-ended sets of questionnaires were designed to measure academic performance and causal attributions; the results indicated no significant differences in the cognitive domain between the two groups of students, though it is not so in both the psychomotor and the affective domains. College students with vocational high school background appeared to possess higher learning motivation in CAD applications. 2012 Elsevier Ltd. All rights reserved.

Question: What is the difference between college students with high school and vocational high school backgrounds?


Beyond traditional DTN routing: social networks for opportunistic communication. This article examines the evolution of routing protocols for intermittently connected ad hoc networks and discusses the trend toward socialbased routing protocols. A survey of current routing solutions is presented, where routing protocols for opportunistic networks are classified based on the network graph employed. The need to capture performance trade-offs from a multi-objective perspective is highlighted.

Question: What is the trend towards social based routing protocols?


A framework for thermal building parameter identification and simulation. Studies related to Smart Energy technologies have been highlighted in the last years, specially by significantly contributing to the saving of energy consumption, to the reduction of CO2 emissions and to the diminishing of the paid values in electricity bills. The electrical-thermal analogy can be used in order to simulate heating behavior in real situations and it also may be considered a contributor to Smart Energy technologies. This is the reason why several simulation tools are used. Taking this into account, this paper presents a framework using NGSpice simulator to identify both, the thermal capacitance and the thermal resistance parameters from a hypothetical monitored room which in a Smart Building context. The results from the proposed framework are compared to OpenModelica software and to the contributions from literature.

Question: What is a framework for thermal building parameter identification and simulation?


Hybrid electric vehicle propulsion system architectures of the e-CVT type. There is now significant interest in hybrid electric vehicle (HEV) propulsion systems globally. Economics play a major role as evidenced by oil prices in North America pressing upwards of $100/Bbl coupled with a customer preference for full size crossover and sport utility vehicles. The situation in Oceania is milder, but emerging markets such as China are experiencing automotive sector growth rates of 37%/year. Europe remains least affected by hybrids since nearly 47% of all new vehicles sold are diesel fueled and have economy ratings on par with that of gasoline-electric hybrids. In the global economy there are presently some 57 Mil new vehicles manufactured each year. Toyota and Honda have projected that HEVs will be 10 % to 15 % of the U.S. market by 2009, with Toyota raising the bar further by stating they will produce 1 Mil hybrids a year in the 2012 time frame. Hybrid propulsion system types are only vaguely comprehended by the buying public, and to a large measure, even by technical professionals. This paper addresses this latter issue by presenting a summary of the globally accepted standard in hybrid power trains-the power split architecture, or more generically and in common usage, the electronic-continuously variable transmission

Question: What is the power split architecture?


Automatic Building of Synthetic Voices from Audio Books. Current state-of-the-art text-to-speech systems produce intelligible speech but lack the prosody of natural utterances. Building better models of prosody involves development of prosodically rich speech databases. However, development of such speech databases requires a large amount of effort and time. An alternative is to exploit story style monologues (long speech files) in audio books. These monologues already encapsulate rich prosody including varied intonation contours, pitch accents and phrasing patterns. Thus, audio books act as excellent candidates for building prosodic models and natural sounding synthetic voices. The processing of such audio books poses several challenges including segmentation of long speech files, detection of mispronunciations, extraction and evaluation of representations of prosody. In this thesis, we address the issues of segmentation of long speech files, capturing prosodic phrasing patterns of a speaker, and conversion of speaker characteristics. Techniques developed to address these issues include – text-driven and speech-driven methods for segmentation of long speech files; an unsupervised algorithm for learning speaker-specific phrasing patterns and a voice conversion method by modeling target speaker characteristics. The major conclusions of this thesis are – • Audio books can be used for building synthetic voices. Segmentation of such long speech files can be accomplished without the need for a speech recognition system. • The prosodic phrasing patterns are specific to a speaker. These can be learnt and incorporated to improve the quality of synthetic voices. • Conversion of speaker characteristics can be achieved by modeling speaker-specific features of a target speaker. Finally, the techniques developed in this thesis enable prosody research by leveraging a large number of audio books available in the public domain.

Question: How can we build a synthetic voice from an audio book?


Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures. This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.

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


Field test of autonomous loading operation by wheel loader. The authors have been conducting research on an autonomous system for loading operation by wheel loader. Experimental results at a field test site using full-size model (length: 6.1m) will be described in this paper. Basic structure of system consists of three sub systems: measuring and modeling of environment, task planning and motion control. The experimental operation includes four cycles of scooping and loading to dump truck. The experimental results prove that the developed system performed the autonomous operation smoothly and completed the mission.

Question: What is the purpose of the field test?


VEGF-A stimulates lymphangiogenesis and hemangiogenesis in inflammatory neovascularization via macrophage recruitment.. Lymphangiogenesis, an important initial step in tumor metastasis and transplant sensitization, is mediated by the action of VEGF-C and -D on VEGFR3. In contrast, VEGF-A binds VEGFR1 and VEGFR2 and is an essential hemangiogenic factor. We re-evaluated the potential role of VEGF-A in lymphangiogenesis using a novel model in which both lymphangiogenesis and hemangiogenesis are induced in the normally avascular cornea. Administration of VEGF Trap, a receptor-based fusion protein that binds and neutralizes VEGF-A but not VEGF-C or -D, completely inhibited both hemangiogenesis and the outgrowth of LYVE-1(+) lymphatic vessels following injury. Furthermore, both lymphangiogenesis and hemangiogenesis were significantly reduced in mice transgenic for VEGF-A(164/164) or VEGF-A(188/188) (each of which expresses only one of the three principle VEGF-A isoforms). Because VEGF-A is chemotactic for macrophages and we demonstrate here that macrophages in inflamed corneas release lymphangiogenic VEGF-C/VEGF-D, we evaluated the possibility that macrophage recruitment plays a role in VEGF-A-mediated lymphangiogenesis. Either systemic depletion of all bone marrow-derived cells (by irradiation) or local depletion of macrophages in the cornea (using clodronate liposomes) prior to injury significantly inhibited both hemangiogenesis and lymphangiogenesis. We conclude that VEGF-A recruitment of monocytes/macrophages plays a crucial role in inducing inflammatory neovascularization by supplying/amplifying signals essential for pathological hemangiogenesis and lymphangiogenesis.

Question: What is the role of macrophages in lymphangiogenesis?


Biomedical question answering: A survey. OBJECTIVES In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics.   MATERIALS AND METHODS We devised a conceptual framework within which to categorize current QA approaches. In particular, we used "semantic knowledge-based QA" as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches.   RESULTS Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference.   CONCLUSION Corresponding to the growth of biomedical information, there

Question: What is the size of the canadian military?


Microformats: a pragmatic path to the semantic web. Microformats are a clever adaptation of semantic XHTML that makes it easier to publish, index, and extract semi-structured information such as tags, calendar entries, contact information, and reviews on the Web. This makes it a pragmatic path towards achieving the vision set forth for the Semantic Web.Even though it sidesteps the existing "technology stack" of RDF, ontologies, and Artificial Intelligence-inspired processing tools, various microformats have emerged that parallel the goals of several well-known Semantic Web projects. This poster compares their prospects to the Semantic Web according to Rogers' Diffusion of Innovation model.

Question: What is the diffussion of innovation model?


Factors contributing to attrition behavior in diabetes self-management programs: A mixed method approach. BACKGROUND Diabetes self-management education is a critical component in diabetes care. Despite worldwide efforts to develop efficacious DSME programs, high attrition rates are often reported in clinical practice. The objective of this study was to examine factors that may contribute to attrition behavior in diabetes self-management programs.   METHODS We conducted telephone interviews with individuals who had Type 2 diabetes (n = 267) and attended a diabetes education centre. Multivariable logistic regression was performed to identify factors associated with attrition behavior. Forty-four percent of participants (n = 118) withdrew prematurely from the program and were asked an open-ended question regarding their discontinuation of services. We used content analysis to code and generate themes, which were then organized under the Behavioral Model of Health Service Utilization.   RESULTS Working full and part-time, being over 65 years of age, having a regular primary care physician or fewer diabetes symptoms were contributing factors to attrition behaviour in our multivariable logistic regression. The most common reasons given by participants for attrition from the program were conflict between their work schedules and the centre's hours of operation, patients' confidence in their own knowledge and ability when managing their diabetes, apathy towards diabetes education, distance to the centre, forgetfulness, regular physician consultation, low perceived seriousness of diabetes, and lack of familiarity with the centre and its services. There was considerable overlap between our quantitative and qualitative results.   CONCLUSION Reducing attrition behaviour requires a range of strategies targeted towards delivering convenient and accessible services, familiarizing individuals with these services, increasing communication between centres and their patients, and crea

Question: What are the factors contributing to attrition behavior in diabetes self-management programs?


Human behavior and the principle of least effort : An introduction to human ecology. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.

Question: What is JSTOR?


Automatic Game Design via Mechanic Generation. Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.

Question: What is a game mechanic?


Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition. Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AVASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7% relative improvement in WER is reported at -3 SNR dB1.

Question: How does multi-task learning work?


CSIFT based locality-constrained linear coding for image classification. In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image-classification systems depend on the gray-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image-classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. locality-constrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image-classification system for the evaluation. Moreover, we propose a simple $$\ell _2$$ ℓ 2 -norm regularized local distance to improve the traditional LLC method. The real experiments are carried out on several benchmarks. With the enhancements to color SIFT and $$\ell _2$$ ℓ 2 -norm regularization, the proposed image-classification system obtains approximately $$2\,\%$$ 2 % improvement of classification accuracy on the Caltech-101 dataset and approximately $$5\,\%$$ 5 % improvement of classification accuracy on the Caltech-256 dataset.

Question: What is the difference between gray-based SIFT and color-based SIFT?


TangiPaint: A Tangible Digital Painting System. TangiPaint is a digital painting application that provides the experience of working with real materials such as canvas and oil paint. Using fingers on the touchscreen of an iPad or iPhone, users can lay down strokes of thick, three-dimensional paint on a simulated canvas. Then using the Tangible Display technology introduced by Darling and Ferwerda [1], users can tilt the display screen to see the gloss and relief or "impasto" of the simulated surface, and modify it until they get the appearance they desire. Scene lighting can also be controlled through direct gesture-based interaction. A variety of "paints" with different color and gloss properties and substrates with different textures are available and new ones can be created or imported. The tangiPaint system represents a first step toward developing digital art media that look and behave like real materials. Introduction The development of computer-based digital art tools has had a huge impact on a wide range of creative fields. In commercial art, advertisements incorporating images, text, and graphic elements can be laid out and easily modified using digital illustration applications. In cinema, background matte elements can be digitally drawn, painted, and seamlessly integrated with live footage. In fine art, painters, printers, and engravers have also been embracing the new creative possibilities of computer-based art tools. The recent introduction of mobile, tablet-based computers with high-resolution displays, graphics processing units (GPUs) and multi-touch capabilities is also creating new possibilities for direct interaction in digital painting. However a significant limitation of most digital painting tools is that the final product is just a digital image (typically an array of RGB color values). All the colors, textures and lighting effects that we see when we look at the digital painting are "baked in" to the image by the painter. In contrast, when a

Question: What is the difference between a digital image and a digital painting?


Robust model predictive control for an uncertain smart thermal grid. The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.

Question: What is a robust model predictive control?


Metamodel for Service Design and Service Innovation: Integrating Service Activities, Service Systems, and Value Constellations. This paper presents a metamodel that addresses service system design and innovation by traversing and integrating three essential layers, service activities, service systems, and value constellations. The metamodel's approach to service systems as service-inoperation is an alternative to another currently used approach that views service systems as systems of economic exchange. The metamodel addresses service science topics including basic concepts of service science, design thinking for service systems, decomposition within service systems, and integration of IT service architecture with

Question: What is a metamodel?


Accelerometer's position free human activity recognition using a hierarchical recognition model. Monitoring of physical activities is a growing field with potential applications such as lifecare and healthcare. Accelerometry shows promise in providing an inexpensive but effective means of long-term activity monitoring of elderly patients. However, even for the same physical activity the output of any body-worn Triaxial Accelerometer (TA) varies at different positions of a subject's body, resulting in a high within-class variance. Thus almost all existing TA-based human activity recognition systems require firm attachment of TA to a specific body part, making them impractical for long-term activity monitoring during unsupervised free living. Therefore, we present a novel hierarchical recognition model that can recognize human activities independent of TA's position along a human body. The proposed model minimizes the high within-class variance significantly and allows subjects to carry TA freely in any pocket without attaching it firmly to a body-part. We validated our model using six daily physical activities: resting (sit/stand), walking, walk-upstairs, walk-downstairs, running, and cycling. Activity data is collected from four most probable body positions of TA: chest pocket, front trousers pocket, rear trousers pocket, and inner jacket pocket. The average accuracy of about 95% illustrates the effectiveness of the proposed method.

Question: How do you recognize human activities?


Autonomous Farming and Surveillance Agribot in Adjacent Boundary. This research strives to implement the agricultural farming robot which can move instinctively, involuntarily for ploughing in different depths depending upon soil, seeding and irrigation in adjacent field. This research also contributes to enhance functionality of agribot in the field for surveillance. The agribot is equipped with a camera and sends the data to pc through Wi-Fi network. The robot has servo motors for ploughing, distributing the seeds and irrigation into the field. It works with ultrasonic sensor and IR sensor. Ultrasonic sensor is for avoiding the obstacles in the field, IR sensor is for sensing the path and field of adjacent boundary. Arduino Uno controller acts as heart and brain of the system, it makes fast, accurate, autonomous movement. This Research features a agribot that uses Wi-Fi 802.11G along with TCP/IP protocol. IP address and Video is received in laptop for further processing. This research intends to reduce human efforts, increase profit and provide an intelligent aid to the farmers.

Question: What is the purpose of this research?


Development of personality in early and middle adulthood: set like plaster or persistent change?. Different theories make different predictions about how mean levels of personality traits change in adulthood. The biological view of the Five-factor theory proposes the plaster hypothesis: All personality traits stop changing by age 30. In contrast, contextualist perspectives propose that changes should be more varied and should persist throughout adulthood. This study compared these perspectives in a large (N = 132,515) sample of adults aged 21-60 who completed a Big Five personality measure on the Internet. Conscientiousness and Agreeableness increased throughout early and middle adulthood at varying rates; Neuroticism declined among women but did not change among men. The variety in patterns of change suggests that the Big Five traits are complex phenomena subject to a variety of developmental influences.

Question: What is the difference between the plaster and the contextualist theories?


Text Steganography in SMS. One of the services used in mobile phone is the short message service (SMS) which is widely used by the public in all parts of the world especially in Asia and Europe. This service enables people to write and exchange short messages via mobile phone. Due to the limited size of SMS, lack of a proper keyboard on the mobile phone and to improve the speed of typing, new abbreviations have been invented for different words and phrases which has lead to the invention of a new language called SMS-texting. One of the main issues in communication is information security and privacy. There are many methods for secret communication and many researchers are working on steganography. In steganography the data is hidden in a cover media such as picture or text. The present paper offers a new method for secret exchange of information through SMS by using and developing abbreviation text steganography with the use of the invented language of SMS-texting. This project has been implemented by J2ME (Java 2 Micro Edition) programming language and tested on a Nokia N71 mobile phone.

Question: What is the size of the canadian military?


Foundations of Cryptography: Basic Tools. If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, foundations of cryptography basic tools always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.

Question: What is the most wanted book?


Multi-threshold CMOS design for low power digital circuits. Multi-threshold CMOS (MTCMOS) power gating is a design technique in which a power gating transistor is connected between the logic transistors and either power or ground, thus creating a virtual supply rail or virtual ground rail, respectively. Power gating transistor sizing, transition (sleep mode to active mode) current, short circuit current and transition time are design issues for power gating design. The use of power gating design results in the delay overhead in the active mode. If both nMOS and pMOS sleep transistor are used in power gating, delay overhead will increase. This paper proposes the design methodology for reducing the delay of the logic circuits during active mode. This methodology limits the maximum value of transition current to a specified value and eliminates short circuit current. Experiment results show 16.83% reduction in the delay.

Question: How to reduce the delay of the logic circuits during active mode?


A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: Improved productivity and lactate metabolism.. A chemically defined nutrient feed (CDF) coupled with basal medium preloading was developed to replace a hydrolysate-containing feed (HCF) for a fed-batch NS0 process. The CDF not only enabled a completely chemically defined process but also increased recombinant monoclonal antibody titer by 115%. Subsequent tests of CDF in a CHO process indicated that it could also replace the hydrolysate-containing nutrient feed in this expression system as well as providing an 80% increase in product titer. In both CDF NS0 and CHO processes, the peak lactate concentrations were lower and, more interestingly, lactate metabolism shifted markedly from net production to net consumption when cells transitioned from exponential to stationary growth phase. Subsequent investigations of the lactate metabolic shift in the CHO CDF process were carried out to identify the cause(s) of the metabolic shift. These investigations revealed several metabolic features of the CHO cell line that we studied. First, glucose consumption and lactate consumption are strictly complementary to each other. The combined cell specific glucose and lactate consumption rate was a constant across exponential and stationary growth phases. Second, Lactate dehydrogenase (LDH) activity fluctuated during the fed-batch process. LDH activity was at the lowest when lactate concentration started to decrease. Third, a steep cross plasma membrane glucose gradient exists. Intracellular glucose concentration was more than two orders of magnitude lower than that in the medium. Fourth, a large quantity of citrate was diverted out of mitochondria to the medium, suggesting a partially truncated tricarboxylic acid (TCA) cycle in CHO cells. Finally, other intermediates in or linked to the glycolytic pathway and the TCA cycle, which include alanine, citrate, isocitrate, and succinate, demonstrated a m

Question: What is the difference between a chemically defined nutrient feed and a hydrolysate-containing feed?


Two hearts in three-quarter time : How to waltz the social media / viral marketing dance. The concept of viral marketing has been discussed in the literature for over 15 years, since Jeffrey Rayport first introduced the term in 1996. However, the more widespread use of social media has recently pushed this idea to a whole new level. We provide insight into the relationship between social media and viral marketing, and illustrate the six steps executives should take in order to dance the social media/viral marketing waltz. We define viral marketing as electronic word-of-mouth whereby some form of marketing message related to a company, brand, or product is transmitted in an exponentially growing way—often through the use of social media applications. We consider the three conditions that need to be fulfilled to create a viral marketing epidemic (i.e., giving the right message to the right messengers in the right environment) and present four different groups of social media viral marketing campaigns (nightmares, strokes-of-luck, homemade issues, and triumphs). We conclude Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

Question: How to waltz the social media / viral marketing dance.


An efficient probabilistic occupancy map-based people localization approach. The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.

Question: How to improve the efficiency of the POM-based people localization?


Logic design with unipolar memristors. Memristors are novel devices that could naturally be designed as memory elements. Recently, several methods of designing memristors for logic operations have been proposed. These methods, mostly, make use of bipolar memristors. In this paper, we propose a method for performing logic with unipolar memristors based on OR and NOT logic gates. An integration of the basic building blocks into more complicated logic functions is described and demonstrated. Our results indicate that any logic function could be performed using an external controller. Thus, adding the capability of performing logic computations to numerous types of unipolar memristive materials in addition to their memory capability.

Question: What is the difference between a bipolar and unipolar memristor?


Face recognition using PCA and different distance classifiers. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. In this paper we focus on 3-D facial recognition system and biometric facial recognition system. We do critics on facial recognition system giving effectiveness and weaknesses. This paper also introduces scope of recognition system in India. Face recognition has received substantial attention from researchers in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing, Verification of Electoral identification and Card Security measure at ATM's. Principal Component Analysis (PCA) is a technique among the most common feature extraction techniques used in Face Recognition. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis with different distance classifiers is proposed. The test results in the ORL face database produces interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. Different classifiers were used to match the image of a person to a class (a subject) obtained from the training data. These classifiers are: the City-Block Distance Classifier, the Euclidian distance classifier, the Squared Euclidian Distance Classifier, and the Squared Chebyshev distance Classifier.

Question: What is the difference between face recognition and biometric facial recognition?


The Influence of IS Affordances on Work Practices in Health Care: A Relational Coordination Approach. High-reliability healthcare organizations, such as Intensive Care Units, operate in challenging high-velocity environments regarding decision-making, uncertainty, and time constraints. Further, these organizations are multidisciplinary, professionally driven, and characterized by a strong hierarchical structure. Given such conditions, effective cross-functional communication is critical for ensuring successful work practices. We propose a theoretical model of IS affordances and Relational Coordination in the presence of functional status differences. We establish a set of propositions regarding how use patterns and types of IS affordances enable or constrain potential effects on Relational Coordination. We are currently conducting a case study of an ICU in a major University hospital, with the goal to iteratively refine our theory and propositions. We hope that this research will offer an in-depth understanding of the types of opportunities related to information systems, and the role of individual-level interpretations and actions across functional groups for these opportunities.

Question: How can we improve the quality of healthcare?


Extraction of Multi-word Expressions from Small Parallel Corpora. M.Sc. in Computer Science Cum Laude, advised by Shuly Wintner. 2007-2010 • Research topics: semi-supervised multiword expression extraction from parallel and monolingual corpora. • Thesis: "Extraction of Multi-word Expressions from Small Parallel Corpora". • Awards: Gutwirth award for excellence (awarded top 50 graduate students in Israel); excellence scholarship in honor of Captain Uri Akavia.

Question: What is the Gutwirth award for excellence?


Autonomous indoor 3D exploration with a micro-aerial vehicle. In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a system of particles with Newtonian dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single and multi-floor indoor experiments.

Question: How to navigate in a 3D environment?


Near-Field Microwave Imaging Based on Aperture Raster Scanning With TEM Horn Antennas. The design, fabrication, and characterization of an ultrawideband (UWB) antenna for near-field microwave imaging of dielectric objects are presented together with the imaging setup. The focus is on an application in microwave breast tumor detection. The new antenna operates as a sensor with the following properties: 1) direct contact with the imaged body; 2) more than 90% of the microwave power is coupled directly into the tissue; 3) UWB performance; 4) excellent de-coupling from the outside environment; 5) small size; and 6) simple fabrication. The antenna characterization includes return loss, total efficiency, near-field directivity, fidelity, and group velocity. The near-field imaging setup employs planar aperture raster scanning. It consists of two antennas aligned along each other's boresight and moving together to scan two parallel apertures. The imaged object lies between the two apertures. With a blind de-convolution algorithm, the images are de-blurred. Simulation and experimental results confirm the satisfactory performance of the antenna as an UWB sensor for near-field imaging.

Question: What is the near-field microwave imaging?


Towards Integrating Plot and Character for Interactive Drama. Interactive drama concerns itself with building dramaticall y interesting virtual worlds inhabited by computer-controlled characters, within which the user (hereafter referred to as the player) experiences a story from a first person perspective (Bates 1992). Over the past decade there has been a fair amount of research into believable agents, that is, autonomous characters exhibiting rich personaliti es, emotions, and social interactions (Mateas 1997; Bates, Loyall and Reill y 1992; Blumberg 1996; Hayes-Roth, van Gent and Huber 1997; Lester and Stone 1997; Stern, Frank, and Resner 1998). There has been comparatively littl e work, however, exploring how the local, reactive behavior of believable agents can be integrated with the more global, deliberative nature of a story plot, so as to build interactive, dramatic worlds (Weyrauch 1997; Blumberg and Galyean 1995). The authors are currently engaged in a two to three year collaboration to build an interactive story world integrating believable agents and interactive plot. This paper provides a brief description of the project goals and design requirements, discusses the problem of autonomy in the context of story-based believable agents, and finall y describes an architecture that uses the dramatic beat as a structural principle to integrate plot and character.

Question: How can we integrate plot and character for interactive drama?