WebGT: An Interactive Web-Based System for Historical Document Ground Truth Generation. We present WebGT, the first web-based system to help users produce ground truth data for document images. This user-friendly software system helps historians and computer scientists collectively annotate historical documents. It supports real time collaboration among remote sites independent of the local operating system and also provides several novel semi-automatic tools that have proven effective for annotating degraded documents.

Question: What is the first web-based system to help users produce ground truth data for document images?


Voice quality evaluation of recent open source codecs. • Averaged frequency responses at different 16, and 24 kHz. External sampling rate does not tell the internal sampling rate. • Supported signal bandwidth depends on bitrate, but no documentation exists bandwidths were found out expirementally • We tested 32kHz sampling with 16ms frame length. There is also 8 ms lookahead. • The results show that bitrates below 32 kbit/s are not useable for voice applications.The voice quality is much worse than with SILK or bitrates shown in steady state

Question: What is the difference between the internal and external sampling rate?


Interactive Spatial Augmented Reality in Collaborative Robot Programming: User Experience Evaluation. This paper presents a novel approach to interaction between human workers and industrial collaborative robots. The proposed approach addresses problems introduced by existing solutions for robot programming. It aims to reduce the mental demands and attention switches by centering all interaction in a shared workspace, combining various modalities and enabling interaction with the system without any external devices. The concept allows simple programming in the form of setting program parameters using spatial augmented reality for visualization and a touch-enabled table and robotic arms as input devices. We evaluated the concept utilizing a user experience study with six participants (shop-floor workers). All participants were able to program the robot and to collaborate with it using the program they parametrized. The final goal is to create a distraction-free, usable and low-effort interface for effective human-robot collaboration, enabling any ordinary skilled worker to customize the robot's program to changes in production or to personal (e.g. ergonomic) needs.

Question: What is the goal of the paper?


A rating approach based on sentiment analysis. With the advent to social media the number of reviews for any particular product is in millions, as there exist thousand of websites where that particular product exists. As the numbers of reviews are very high the user ends up spending a lot of time for searching best product based on the experiences shared by review writers. This paper presents a sentiment based rating approach for food recipes which sorts food recipes present on various website on the basis of sentiments of review writers. The results are shown with the help of a mobile application: Foodoholic. The output of the application is an ordered list of recipes with user input as their core ingredient.

Question: How to sort food recipes based on sentiments of review writers?


Continuous noninvasive glucose monitoring technology based on "occlusion spectroscopy".. BACKGROUND A truly noninvasive glucose-sensing device could revolutionalize diabetes treatment by leading to improved compliance with recommended glucose levels, thus reducing the long-term complications and cost of diabetes. Herein, we present the technology and evaluate the efficacy of a truly noninvasive device for continuous blood glucose monitoring, the NBM (OrSense Ltd.).   METHODS In vitro analysis was used to validate the technology and algorithms. A clinical study was performed to quantify the in vivo performance of the NBM device. A total of 23 patients with type 1 (n = 12) and type 2 (n = 11) diabetes were enrolled in the clinical study and participated in 111 sessions. Accuracy was assessed by comparing NBM data with paired self-monitoring of blood glucose meter readings.   RESULTS In vitro experiments showed a strong correlation between calculated and actual glucose concentrations. The clinical trial produced a total of 1690 paired glucose values (NBM vs reference). In the paired data set, the reference glucose range was 40-496 mg/dl. No systematic bias was found at any of the glucose levels examined (70, 100, 150, and 200 mg/dl). The mean relative absolute difference was 17.2%, and a Clarke error grid analysis showed that 95.5% of the measurements fall within the clinically acceptable A&B regions (zone A, 69.7%; and zone B, 25.7%).   CONCLUSIONS This study indicates the potential use of OrSense's NBM device as a noninvasive sensor for continuous blood glucose evaluation. The device was safe and well tolerated.

Question: What is the difference between a noninvasive and invasive glucose monitor?


Teenager ' s Addiction to Smart Phones and Its Integrated Therapy Method. The fundamental cause of teenagers' addiction to smart phone is due to stress arising from family and school. Smartphone can cause many problems including musculoskeletal symptom which can develop due to staying in one position for a long time. Furthermore, starring at a small screen for an extended time can cause decreased eye sight, along with amblyopia. Smart phone addiction causes depression and anxiety which will lead to severe mental problems. This will lead to problems associated to one's social life and human network. Fundamental treatment of cognitive and emotional elements needed to become harmonized as a whole in order to increase the effectiveness of the treatment results. The therapy has been verified to cure the behavioral and emotional aspect related to depression, sense of control, and assimilation in school. From these integrated treatment approaches, this report compared the treatment's upsides and downsides and also, assessed the possibility the integrated treatment process. We can presume that this report provided the opportunity and support for the creation of a fundamental treatment method that can help teenagers live a healthy life by overcoming addiction to smart phones.

Question: What is the fundamental cause of teenagers' addiction to smart phone?


Taking spiritual history in clinical practice: a systematic review of instruments.. BACKGROUND To facilitate the addressing of spirituality in clinical practice, several authors have created instruments for obtaining a spiritual history. However, in only a few studies have authors compared these instruments. The aim of this study was to compare the most commonly used instruments for taking a spiritual history in a clinical setting.   METHODS A systematic review of spiritual history assessment was conducted in five stages: identification of instruments used in the literature (databases searching); relevant articles from title and initial abstract review; exclusion and Inclusion criteria; full text retrieval and final analysis of each instrument.   RESULTS A total of 2,641 articles were retrieved and after the analysis, 25 instruments were included. The authors independently evaluated each instrument on 16 different aspects. The instruments with the greatest scores in the final analysis were FICA, SPIRITual History, FAITH, HOPE, and the Royal College of Psychiatrists. Concerning all 25 instruments, 20 of 25 inquire about the influence of spirituality on a person's life and 17 address religious coping. Nevertheless, only four inquire about medical practices not allowed, six deal with terminal events, nine have mnemonics to facilitate their use, and five were validated.   CONCLUSIONS FICA, SPIRITual History, FAITH, HOPE, and Royal College of Psychiatrists scored higher in our analysis. The use of each instrument must be individualized, according to the professional reality, time available, patient profile, and settings.

Question: What is the difference between the instruments?


Distributed Representations for Compositional Semantics. The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches—meaning distributed representations that exploit co-occurrence statistics of large corpora—have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP. Part I focuses on distributed representations and their application. In particular, in Chapter 3 we explore the semantic usefulness of distributed representations by evaluating their use in the task of semantic frame identification. Part II describes the transition from semantic representations for words to compositional semantics. Chapter 4 covers the relevant literature in this field. Following this, Chapter 5 investigates the role of syntax in semantic composition. For

Question: What is the role of syntax in semantic composition?


Friction modeling in linear chemical-mechanical planarization. In this article, we develop an analytical model of the relationship between the wafer/pad friction and process configuration. We also provide experimental validation of this model for in situ process monitoring. CMP thus demonstrates that the knowledge and methodologies developed for friction modeling and control can be used to advance the understanding, monitoring, and control of semiconductor manufacturing processes. Meanwhile, relevant issues and challenges in real-time monitoring of CMP are presented as sources of future development.

Question: What is the relationship between the wafer/pad friction and process configuration?


Effects of cognitive-behavioral therapy on brain activation in specific phobia. Little is known about the effects of successful psychotherapy on brain function in subjects with anxiety disorders. The present study aimed to identify changes in brain activation following cognitive-behavioral therapy (CBT) in subjects suffering from specific phobia. Using functional magnetic resonance imaging (fMRI), brain activation to spider videos was measured in 28 spider phobic and 14 healthy control subjects. Phobics were randomly assigned to a therapy-group (TG) and a waiting-list control group (WG). Both groups of phobics were scanned twice. Between scanning sessions, CBT was given to the TG. Before therapy, brain activation did not differ between both groups of phobics. As compared to control subjects, phobics showed greater responses to spider vs. control videos in the insula and anterior cingulate cortex (ACC). CBT strongly reduced phobic symptoms in the TG while the WG remained behaviorally unchanged. In the second scanning session, a significant reduction of hyperactivity in the insula and ACC was found in the TG compared to the WG. These results propose that increased activation in the insula and ACC is associated with specific phobia, whereas an attenuation of these brain responses correlates with successful therapeutic intervention.

Question: What is the difference between a phobia and a fear?


Human-comfortable navigation for an autonomous robotic wheelchair. Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of "comfortable map" and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

Question: How to make a robot wheelchair more comfortable?


Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching. Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.

Question: How does the cascade residual learning work?


What campus-based students think about the quality and benefits of e-learning. There is a trend in Irish universities to utilise the benefits of the e-learning as a mechanism to improve learning performance of campus-based students. Whilst traditional methods, such as face-to-face lectures, tutorials, and mentoring, remain dominant in the educational sector, universities are investing heavily in learning technologies, to facilitate improvements with respect to the quality of learning. The technology to support reuse and sharing of educational resources, or learning objects, is becoming more stable, with interoperability standards maturing. However, debate has raged about what constitutes effective use of learning technology. This research expands upon a study carried out in 2003 examining students' perceptions of e-learning in a large undergraduate accounting class environment. As a result, improvements were made to the instructional design of the course, to enable students to engage interactively with content. The subsequent study, reported in this paper, adopted a broad range of techniques to understand students' learning experience in depth. The findings of this research provide an insight into how these students really work and learn using technologies, if at all. It is hoped that our findings will improve the experience for both students and lecturers who engage in teaching and learning through this medium. 502 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. Introduction A recent trend in higher education is to create and provide online access to course materials. Over the past two decades academics and institutes of higher education have been diversifying their delivery of instruction through new Internet media such as learning management systems, asynchronous distance learning, and online classrooms amongst a myriad of other burgeoning educational technologies. This combination of tr

Question: What is the quality of learning in an online environment?


Effective Feature Integration for Automated Short Answer Scoring. A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

Question: How can we integrate scoring guidelines and labeled responses?


Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data. Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Question: How can we predict the success of a movie?


Fiber-Reinforced Polymer Composites for Construction — State-ofthe-Art Review. A concise state-of-the-art survey of fiber-reinforced polymer ~also known as fiber-reinforced plastic ! composites for construc tion applications in civil engineering is presented. The paper is organized into separate sections on structural shapes, bridge dec reinforcements, externally bonded reinforcements, and standards and codes. Each section includes a historical review, the curr the art, and future challenges. DOI: 10.1061/ ~ASCE!1090-0268~2002!6:2~73! CE Database keywords: Composite materials; Fiber-reinforced materials; Polymers; Bridge decks; State-of-the-art reviews. rials sive uilt , th To So-

Question: What is the state-of-the-art review?


Freebase: a collaboratively created graph database for structuring human knowledge. Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.

Question: What is the largest database?


Collaborative computer-aided design - research and development status. In order to facilitate product design and realization processes, presently, research is actively carried out for developing methodologies and technologies of collaborative computer-aided design systems to support design teams geographically dispersed based on the quickly evolving information technologies. In this paper, the developed collaborative systems, methodologies and technologies, which are organized as a horizontal or a hierarchical manner, are reviewed. Meanwhile, a 3D streaming technology, which can effectively transmit visualization information across networks for Web applications, is highlighted and the algorithms behind it are disclosed.

Question: What is the status of collaborative computer-aided design?


DeepMind Lab. DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.

Question: What is a first-person 3D game platform?


Diffusion Variational Autoencoders. A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstructions, we introduce Diffusion Variational Autoencoders with arbitrary manifolds as a latent space. A Diffusion Variational Autoencoder uses transition kernels of Brownian motion on the manifold. In particular, it uses properties of the Brownian motion to implement the reparametrization trick and fast approximations to the KL divergence. We show that the Diffusion Variational Autoencoder is capable of capturing topological properties of synthetic datasets. Additionally, we train MNIST on spheres, tori, projective spaces, SO(3), and a torus embedded in R. Although a natural dataset like MNIST does not have latent variables with a clear-cut topological structure, training it on a manifold can still highlight topological and geometrical properties.

Question: What is a diffusion variational autoencoder?


Deep Learning-Based Multimedia Analytics: A Review. The multimedia community has witnessed the rise of deep learning–based techniques in analyzing multimedia content more effectively. In the past decade, the convergence of deep-learning and multimedia analytics has boosted the performance of several traditional tasks, such as classification, detection, and regression, and has also fundamentally changed the landscape of several relatively new areas, such as semantic segmentation, captioning, and content generation. This article aims to review the development path of major tasks in multimedia analytics and take a look into future directions. We start by summarizing the fundamental deep techniques related to multimedia analytics, especially in the visual domain, and then review representative high-level tasks powered by recent advances. Moreover, the performance review of popular benchmarks gives a pathway to technology advancement and helps identify both milestone works and future directions.

Question: What is deep learning?


Measuring and predicting software productivity: A systematic map and review. 0950-5849/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.infsof.2010.12.001 ⇑ Address: School of Computing, Blekinge Institute 372 25, Sweden. E-mail addresses: kai.petersen@bth.se, kai.petersen URLs: http://www.bth.se/besq, http://www.ericsso Context: Software productivity measurement is essential in order to control and improve the performance of software development. For example, by identifying role models (e.g. projects, individuals, tasks) when comparing productivity data. The prediction is of relevance to determine whether corrective actions are needed, and to discover which alternative improvement action would yield the best results. Objective: In this study we identify studies for software productivity prediction and measurement. Based on the identified studies we first create a classification scheme and map the studies into the scheme (systematic map). Thereafter, a detailed analysis and synthesis of the studies is conducted. Method: As a research method for systematically identifying and aggregating the evidence of productivity measurement and prediction approaches systematic mapping and systematic review have been used. Results: In total 38 studies have been identified, resulting in a classification scheme for empirical research on software productivity. The mapping allowed to identify the rigor of the evidence with respect to the different productivity approaches. In the detailed analysis the results were tabulated and synthesized to provide recommendations to practitioners. Conclusion: Risks with simple ratio-based measurement approaches were shown. In response to the problems data envelopment analysis seems to be a strong approach to capture multivariate productivity measures, and allows to identify reference projects to which inefficient projects should be compared. Regarding simulation no general prediction model can be identified. Simulation and statistical process c

Question: How to measure software productivity?


Input Sentence Splitting and Translating. We propose a method to split and translate input sentences for speech translation in order to overcome the long sentence problem. This approach is based on three criteria used to judge the goodness of translation results. The criteria utilize the output of an MT system only and assumes neither a particular language nor a particular MT approach. In an experiment with an EBMT system, in which prior methods cannot work or work badly, the proposed split-and-translate method achieves much better results in translation quality.

Question: How to split and translate input sentences for speech translation?


Neural Programmer-Interpreters. We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-tosequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.

Question: What is a neural programmer-interpreter?


Distributed computing in practice: the Condor experience. Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational Grid. In this paper, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems. Copyright c © 2005 John Wiley & Sons, Ltd.

Question: What is distributed computing?


Design and Implementation of a TEM Stripline for EMC Testing. In this paper, a stripline for radiated field immunity testing of audio/video products is designed, constructed, calibrated, and verified. Since the canonical one is only suitable for testing equipments with height less than 70 cm, there is a need of a new device which is also in compliance with EN 55020 standard for testing new large sets, like 47'' thin-film transistor liquid crystal display (TFT LCD) television sets, in the frequency range of 150 kHz-150 MHz. Increasing the height and width of testing area causes important problems in terms of field uniformity regarding the higher order modes, characteristic impedance, and reflections in addition to the room resonances and field interference sources like corners at the edges of stripline. Comprehensive numerical study is performed to overcome these problems and obtain the optimum design before the construction. Measured data show that the new stripline is in very good agreement with the specifications determined by the standards and therefore it can be used for the electromagnetic compatibility (EMC) testing.

Question: What is the difference between a stripline and a coaxial cable?


NANOFIBRILLATED CELLULOSE AS PAPER ADDITIVE IN EUCALYPTUS PULPS. In this work, the physical and mechanical properties of bleached Eucalyptus pulp reinforced with nanofibrillated cellulose (NFC) are compared with those of traditional beaten pulp used in the making of writing/printing and offset printing papers. For this purpose, three different types of hardwood slurries were prepared: beaten pulps, unbeaten pulps reinforced with NFC, and slightly beaten pulps also reinforced with NFC. Physical and mechanical tests were performed on handsheets from these different slurries. The results showed that adding NFC to unbeaten pulps results in physical and mechanical properties similar to those in pulps used for printing/writing papers. Nevertheless, the best results were obtained in slurries previously beaten at slight conditions and subsequently reinforced with NFC. These results demonstrate that the addition of NFC allows a reduction in beating intensity without decreasing the desired mechanical properties for this specific purpose.

Question: What is the difference between beating and beating at slight conditions?


AP16-OL7: A multilingual database for oriental languages and a language recognition baseline. We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research.

Question: What is the oriental language recognition challenge?


Black-Box Test Data Generation for GUI Testing. Effective system testing of applications with a Graphical User Interface (GUI) front-end demands careful generation of event sequences as well as providing relevant test data for parameterized widgets, i.e., widgets that accept input values such as textboxes and textareas. Current GUI testing techniques either manipulate the source code of the application under test (AUT) to generate the test data, or blindly use a set of random string values. In this paper, we propose a third novel way to generate relevant test data for GUI testing. We exploit the information provided in the GUI structure to extract a set of key identifiers for each parameterized widget. These identifiers are used to compose appropriate search phrases and collect relevant test data from the Internet. The results of an empirical study on five GUI-based applications show that the proposed approach is applicable and can get some hard-to-cover branches in the subject programs to execute. The proposed technique works from the black-box perspective and is entirely independent from GUI modeling and event sequence generation, thus it does not need access to the source code of AUT and provides an opportunity to be integrated with the existing GUI testing frameworks.

Question: How to generate test data for GUI testing?


Visual Transfer between Atari Games using Competitive Reinforcement Learning. This paper explores the use of deep reinforcement learning agents to transfer knowledge from one environment to another. More specifically, the method takes advantage of asynchronous advantage actor critic (A3C) architecture to generalize a target game using an agent trained on a source game in Atari. Instead of fine-tuning a pre-trained model for the target game, we propose a learning approach to update the model using multiple agents trained in parallel with different representations of the target game. Visual mapping between video sequences of transfer pairs is used to derive new representations of the target game; training on these visual representations of the target game improves model updates in terms of performance, data efficiency and stability. In order to demonstrate the functionality of the architecture, Atari games Pong-v0 and Breakout-v0 are being used from the OpenAI gym environment; as the source and target environment.

Question: How to transfer knowledge from one environment to another?


Introduction to WordNet: An On-line Lexical Database. Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.

Question: What is the difference between a lexical database and a dictionary?


5G cellular network integration with SDN: Challenges, issues and beyond. The tremendous growth in communication technology is shaping a hyper-connected network where billions or connected devices are producing a huge volume of data. Cellular and mobile network is a major contributor towards this technology shift and require new architectural paradigm to provide low latency, high performance in a resource constrained environment. 5G technology deployment with fully IP-based connectivity is anticipated by 2020. However, there is no standard established for 5G technology and many efforts are being made to establish a unified 5G stander. In this context, variant technology such as Software Defined Network (SDN) and Network Function virtualization (NFV) are the best candidate. SDN dissociate control plane from data plane and network management is done on the centralized control plane. In this paper, a survey on state of the art on the 5G integration with the SDN is presented. A comprehensive review is presented for the different integrated architectures of 5G wireless network and the generalized solutions over the period 2010–2016. This comparative analysis of the existing solutions of SDN-based cellular network (5G) implementations provides an easy and concise view of the emerging trends by 2020.

Question: What is the difference between SDN and NFV?


Energy characterization of a tiled architecture processor with on-chip networks. Tiled architectures provide a paradigm for designers to turn silicon resources into processors with burgeoning quantities of programmable functional units and memories. The architecture has a dual responsibility: first, it must expose these resources in a way that is programmable. Second, it needs to manage the power associated with such resources.We present the power management facilities of the 16-tile Raw microprocessor. This design selectively turns on and off 48 SRAM macros, 96 functional unit clusters, 32 fetch units, and over 250 unique processor pipeline stages, all according to the needs of the computation and environment at hand.

Question: What is a tiled architecture?


Machine Learning for Sequential Data: A Review. Statistical learning problems in many fields involve sequential data. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. The paper also discusses some open research issues.

Question: What is a sliding window?


Prometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications. Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and manages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control policies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturallyformed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under real-time constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead.

Question: What is a peer-to-peer service?


Detection of buried objects in FLIR imaging using mathematical morphology and SVM. In this paper we describe a method for detecting buried objects of interest using a forward looking infrared camera (FLIR) installed on a moving vehicle. Infrared (IR) detection of buried targets is based on the thermal gradient between the object and the surrounding soil. The processing of FILR images consists in a spot-finding procedure that includes edge detection, opening and closing. Each spot is then described using texture features such as histogram of gradients (HOG) and local binary patterns (LBP) and assigned a target confidence using a support vector machine (SVM) classifier. Next, each spot together with its confidence is projected and summed in the UTM space. To validate our approach, we present results obtained on 6 one mile long runs recorded with a long wave IR (LWIR) camera installed on a moving vehicle.

Question: How to detect buried objects in FLIR images using mathematical morphology and SVM?


STOCK MARKET PREDICTION USING BIO-INSPIRED COMPUTING : A SURVEY. Bio-inspired evolutionary algorithms are probabilistic search methods that mimic natural biological evolution. They show the behavior of the biological entities interacting locally with one another or with their environment to solve complex problems. This paper aims to analyze the most predominantly used bio-inspired optimization techniques that have been used for stock market prediction and hence present a comparative study between them.

Question: What is a bio-inspired evolutionary algorithm?


Distance Transforms of Sampled Functions. We describe linear-time algorithms for solving a class of problems that involve transforming a cost function on a grid using spatial information. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a grid. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A consequence of our techniques is a simple and fast method for computing the Euclidean distance transform of a binary image. Our algorithms are also applicable to Viterbi decoding, belief propagation, and optimal control. ACM Classification: F.2.1, I.4 AMS Classification: 68T45, 68W40

Question: What is the distance transform of a binary image?


Gravity compensation in robotics. The actuator power required to resist joint torque caused by the weight of robot links can be a significant problem. Gravity compensation is a well-known technique in robot design to achieve equilibrium throughout the range of motion and as a result to reduce the loads on the actuator. Therefore, it is desirable and commonly implemented in many situations. Various design concepts for gravity compensation are available in the literature. This paper proposes an overview of gravity compensation methods applied in robotics. The examined properties of the gravity compensation are disclosed and illustrated via kinematic schemes. In order to classify the considered balancing schemes three principal groups are distinguished due to the nature of the compensation force: counterweight, spring or active force developed by an auxiliary actuator. Then, each group is reviewed through sub-groups organized via structural features of balancing schemes. The author believes that such an arrangement of gravity compensation methods allows one to carry out a systematized analysis and provides a comprehensive view on the problem.

Question: What is gravity compensation?


III-nitride micro-emitter arrays : development and applications. III-nitride micro-emitter array technology was developed in the authors' laboratory around 1999. Since its inception, much progress has been made by several groups and the technology has led to the invention of several novel devices. This paper provides an overview on recent progress in single-chip ac-micro-size light emitting diodes (μLEDs) that can be plugged directly into standard high ac voltage power outlets, self-emissive microdisplays and interconnected μLEDs for boosting light emitting diodes's wall-plug efficiency, all of which were evolved from III-nitride micro-emitter array technology. Finally, potential applications of III-nitride visible micro-emitter arrays as a light source for DNA microarrays and future prospects of III-nitride deep ultraviolet micro-emitter arrays for label-free protein analysis in microarray format by taking advantage of the direct excitation of intrinsic protein fluorescence are discussed. (Some figures in this article are in colour only in the electronic version)

Question: What is the difference between a micro-emitter array and a micro-emitter?


Dual-Band Filter Design With Flexible Passband Frequency and Bandwidth Selections. In this paper, improved dual-band filter design is studied. The dual-band resonators are composed of shunt open- and short-circuited stubs. In order to fulfil the requirements of dual-band inverters, a structure of stepped-impedance asymmetric coupled lines is proposed and its equivalent circuit is also derived. The dual-band filter is then designed based on this equivalent circuit. This type of filter can achieve relatively large practical passband center frequency ratios (in theory infinite), and it has more freedom of bandwidth ratio. The circuit size is also reduced. Detailed design procedure is presented and, finally, a filter example is given to validate the theoretical study

Question: What is the dual-band filter?


Sneaking around concatMap: efficient combinators for dynamic programming. We present a framework of dynamic programming combinators that provides a high-level environment to describe the recursions typical of dynamic programming over sequence data in a style very similar to algebraic dynamic programming (ADP). Using a combination of type-level programming and stream fusion leads to a substantial increase in performance, without sacrificing much of the convenience and theoretical underpinnings of ADP.  We draw examples from the field of computational biology, more specifically RNA secondary structure prediction, to demonstrate how to use these combinators and what differences exist between this library, ADP, and other approaches.  The final version of the combinator library allows writing algorithms with performance close to hand-optimized C code.

Question: What is the difference between ADP and this library?


Convolutional CRFs for Semantic Segmentation. For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. To facilitating further CRF research we make our implementation publicly available. Please visit: https://github.com/MarvinTeichmann/ConvCRF

Question: How to train a convolutional CRF?


An OpenCCG-Based Approach to Question Generation from Concepts. Dialogue systems are often regarded as being tedious and inflexible. We believe that one reason is rigid and inadaptable system utterances. A good dialogue system should automatically choose a formulation that reflects the user's expectations. However, current dialogue system development environments only allow the definition of questions with unchangeable formulations. In this paper we present a new approach to the generation of system questions by only defining basic concepts. This is the basis for realising adaptive, user-tailored, and human-like system questions in dialogue systems.

Question: What is an openccg based approach to question generation from concepts?


Indoor climate, psychosocial work environment and symptoms in open-plan offices.. To study the indoor climate, the psychosocial work environment and occupants' symptoms in offices a cross-sectional questionnaire survey was made in 11 naturally and 11 mechanically ventilated office buildings. Nine of the buildings had mainly cellular offices; five of the buildings had mainly open-plan offices, whereas eight buildings had a mixture of cellular, multi-person and open-plan offices. A total of 2301 occupants, corresponding to a response rate of 72%, completed a retrospective questionnaire. The questionnaire comprised questions concerning environmental perceptions, mucous membrane irritation, skin irritation, central nervous system (CNS) symptoms and psychosocial factors. Occupants in open-plan offices are more likely to perceive thermal discomfort, poor air quality and noise and they more frequently complain about CNS and mucous membrane symptoms than occupants in multi-person and cellular offices. The association between psychosocial factors and office size was weak. Open-plan offices may not be suited for all job types. PRACTICAL IMPLICATION: Open-plan offices may be a risk factor for adverse environmental perceptions and symptoms.

Question: What is the association between office size and indoor climate?


DALEX: explainers for complex predictive models. Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.

Question: What is a black box model?


X band septum polarizer as feed for parabolic antenna. Contemporary RHCP and LHCP wave occurs in several applications of microwave communication and measurement system. From this point of view the septum polarizer can be useful. The septum polarizer is a four-port waveguide device. The square waveguide at one end constitutes two ports because it can support two orthogonal modes. A slopping (or stepped) septum divides the square waveguide into two standard rectangular waveguides sharing a common broad-wall. The size of the septum as well as two versions of the waveguides excitation were analyzed and are described in this paper.

Question: What is a slopping septum?


The FuzzyLog: A Partially Ordered Shared Log. The FuzzyLog is a partially ordered shared log abstraction. Distributed applications can concurrently append to the partial order and play it back. FuzzyLog applications obtain the benefits of an underlying shared log – extracting strong consistency, durability, and failure atomicity in simple ways – without suffering from its drawbacks. By exposing a partial order, the FuzzyLog enables three key capabilities for applications: linear scaling for throughput and capacity (without sacrificing atomicity), weaker consistency guarantees, and tolerance to network partitions. We present Dapple, a distributed implementation of the FuzzyLog abstraction that stores the partial order compactly and supports efficient appends / playback via a new ordering protocol. We implement several data structures and applications over the FuzzyLog, including several map variants as well as a ZooKeeper implementation. Our evaluation shows that these applications are compact, fast, and flexible: they retain the simplicity (100s of lines of code) and strong semantics (durability and failure atomicity) of a shared log design while exploiting the partial order of the FuzzyLog for linear scalability, flexible consistency guarantees (e.g., causal+ consistency), and network partition tolerance. On a 6-node Dapple deployment, our FuzzyLogbased ZooKeeper supports 3M/sec single-key writes, and 150K/sec atomic cross-shard renames.

Question: What is a partially ordered shared log?


Kirner's deformity and clinodactyly in one family. Clinodactyly and Kirner's deformity are generally regarded as independent anomalies. We present the first family in which both deformities are present. This concurrence raises the question as to whether these deformities are variable expressions of the same genetic disorder. However, because a conclusive genetic test could not be performed, coincidence cannot be excluded.

Question: What is Kirner's deformity?


A probabilistic model for item-based recommender systems. Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.

Question: What is a probabilistic calculus?


Understanding Loneliness in Social Awareness Streams: Expressions and Responses. We studied the experience of loneliness as communicated by thousands of people on Twitter. Using a data set of public Twitter posts containing explicit expressions of loneliness, we qualitatively developed a categorization scheme for these expressions, showing how the context of loneliness expressed on Twitter relates to existing theories about loneliness. A quantitative analysis of the data exposed categories and patterns in communication practices around loneliness. For example, users expressing more severe, enduring loneliness are more likely to be female, and less likely to include requests for social interaction in their tweets. Further, we studied the responses to expressions of loneliness in Twitter's social settings. Deriving from the same dataset, we examined factors that correlate with the existence and type of response, showing, for example, that men were more likely to receive responses to lonely tweets, and expressions of enduring loneliness are critically less likely to receive responses.

Question: What is the difference between the expressions of loneliness in social awareness streams?


Developing an Air Hockey Game in LabVIEW. Air Hockey consists of two players volleying a puck, using mallets, across a low friction table surface. Points are scored when a player lands the puck in the opponent's goal. The game is won by the player with the higher score at the end of the match. Virtual versions of air hockey have been implemented using various game development software. This paper details the implementation of the air hockey game in LabVIEW. This version of the game uses a physics engine and an Artificial Intelligence (AI) player. This effort discusses the steps followed to design the game and the effectiveness of the AI player versus a human player.

Question: How to develop an air hockey game in LabVIEW?


Representations in Distributed Cognitive Tasks. In this paper we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasksÑtasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed. This research was supported by a grant to Donald Norman and Edwin Hutchins from the Ames Research Center of the National Aeronautics & Space Agency, Grant NCC 2-591 in the Aviation Safety/Automation Program, technical monitor, Everett Palmer. Additional support was provided by funds from the Apple Computer Company and the Digital Equipment Corporation to the Affiliates of Cognitive Science at

Question: What is the nature of external representations?


Research on metadata-driven data quality assessment architecture. So far there have never been systematic evaluation criteria and entire assessment and evaluation system in Data quality internationally. Based on the research on the relative content of both international and domestic data quality, this article analyzes the requests of data quality for the large enterprises. First of all, this paper raises and builds a complete data quality assessment system. Second, the definitions and specific algorithms of data quality assessment indicators and poses data quality analysis are built to evaluate the architecture and processes. A frame structure of data quality meta-model is presented in this paper. In addition, this paper also designs an evaluation system. This system includes the classification and definition of data quality and the algorithm in evaluation index and the system and process of data quality evaluation. This paper provides credibility basis for enterprises in evaluation of data quality.

Question: What is the definition of data quality?


Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech. Although isiZulu speakers code-switch with English as a matter of course, extremely little appropriate data is available for acoustic modelling. Recently, a small five-language corpus of code-switched South African soap opera speech was compiled. We used this corpus to evaluate the application of multilingual neural network acoustic modelling to English-isiZulu code-switched speech recognition. Our aim was to determine whether English-isiZulu speech recognition accuracy can be improved by incorporating three other language pairs in the corpus: English-isiXhosa, English-Setswana and English-Sesotho. Since isiXhosa, like isiZulu, belongs to the Nguni language family, while Setswana and Sesotho belong to the more distant Sotho family, we could also investigate the merits of additional data from within and across language groups. Our experiments using both fully connected DNN and TDNN-LSTM architectures show that English-isiZulu speech recognition accuracy as well as language identification after code-switching is improved more by the incorporation of English-isiXhosa data than by the incorporation of the other language pairs. However additional data from the more distant language group remained beneficial, and the best overall performance was always achieved with a multilingual neural network trained on all four language pairs.

Question: How much data is needed to improve the accuracy of a neural network?


Approximating dynamic global illumination in image space. Physically plausible illumination at real-time framerates is often achieved using approximations. One popular example is ambient occlusion (AO), for which very simple and efficient implementations are used extensively in production. Recent methods approximate AO between nearby geometry in screen space (SSAO). The key observation described in this paper is, that screen-space occlusion methods can be used to compute many more types of effects than just occlusion, such as directional shadows and indirect color bleeding. The proposed generalization has only a small overhead compared to classic SSAO, approximates direct and one-bounce light transport in screen space, can be combined with other methods that simulate transport for macro structures and is visually equivalent to SSAO in the worst case without introducing new artifacts. Since our method works in screen space, it does not depend on the geometric complexity. Plausible directional occlusion and indirect lighting effects can be displayed for large and fully dynamic scenes at real-time frame rates.

Question: What is the overhead of the method?


Doing good or doing well ? Image motivation and monetary incentives in behaving prosocially. Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially This paper experimentally examines image motivation – the desire to be liked and wellregarded by others – as a driver in prosocial behavior (doing good), and asks whether extrinsic monetary incentives (doing well) have a detrimental effect on prosocial behavior due to crowding out of image motivation. By definition, image depends on one's behavior being visible to other people. Using this unique property we show that image is indeed an important part of the motivation to behave prosocially. Moreover, we show that extrinsic incentives interact with image motivation and are therefore less effective in public than in private. Together, these results imply that image motivation is crowded out by monetary incentives; which in turn means that monetary incentives are more likely to be counterproductive for public prosocial activities than for private ones. JEL Classification: D64, C90, H41

Question: What is the difference between doing good and doing well?


Remembering can cause forgetting: retrieval dynamics in long-term memory.. Three studies show that the retrieval process itself causes long-lasting forgetting. Ss studied 8 categories (e.g., Fruit). Half the members of half the categories were then repeatedly practiced through retrieval tests (e.g., Fruit Or_____). Category-cued recall of unpracticed members of practiced categories was impaired on a delayed test. Experiments 2 and 3 identified 2 significant features of this retrieval-induced forgetting: The impairment remains when output interference is controlled, suggesting a retrieval-based suppression that endures for 20 min or more, and the impairment appears restricted to high-frequency members. Low-frequency members show little impairment, even in the presence of strong, practiced competitors that might be expected to block access to those items. These findings suggest a critical role for suppression in models of retrieval inhibition and implicate the retrieval process itself in everyday forgetting.

Question: What is the retrieval-induced forgetting?


Uplift Modeling with Multiple Treatments and General Response Types. Randomized experiments have been used to assist decisionmaking in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods. Accepted: 2017 SIAM International Conference on Data

Question: How to evaluate uplift models?


Face recognition using PCA and SVM. Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.

Question: What is the difference between linear and polynomial SVM?


Community Detection based on Distance Dynamics. In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots communities in a network by examining the changes of "distances" among nodes (i.e. distance dynamics). The fundamental idea is to envision the target network as an adaptive dynamical system, where each node interacts with its neighbors. The interaction will change the distances among nodes, while the distances will affect the interactions. Such interplay eventually leads to a steady distribution of distances, where the nodes sharing the same community move together and the nodes in different communities keep far away from each other. Building upon the distance dynamics, Attractor has several remarkable advantages: (a) It provides an intuitive way to analyze the community structure of a network, and more importantly, faithfully captures the natural communities (with high quality). (b) Attractor allows detecting communities on large-scale networks due to its low time complexity (O(|E|)). (c) Attractor is capable of discovering communities of arbitrary size, and thus small-size communities or anomalies, usually existing in real-world networks, can be well pinpointed. Extensive experiments show that our algorithm allows the effective and efficient community detection and has good performance compared to state-of-the-art algorithms.

Question: What is the difference between distance dynamics and distance-based community detection?


Forward Thinking: Building Deep Random Forests. The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation, called Forward Thinking that allows for other types of deep learning problems to be considered.

Question: What is a forward thinking deep random forest?


Prevalence of autism in a US metropolitan area.. CONTEXT Concern has been raised about possible increases in the prevalence of autism. However, few population-based studies have been conducted in the United States.   OBJECTIVES To determine the prevalence of autism among children in a major US metropolitan area and to describe characteristics of the study population.   DESIGN, SETTING, AND POPULATION Study of the prevalence of autism among children aged 3 to 10 years in the 5 counties of metropolitan Atlanta, Ga, in 1996. Cases were identified through screening and abstracting records at multiple medical and educational sources, with case status determined by expert review.   MAIN OUTCOME MEASURES Autism prevalence by demographic factors, levels of cognitive functioning, previous autism diagnoses, special education eligibility categories, and sources of identification.   RESULTS A total of 987 children displayed behaviors consistent with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for autistic disorder, pervasive developmental disorder-not otherwise specified, or Asperger disorder. The prevalence for autism was 3.4 per 1000 (95% confidence interval [CI], 3.2-3.6) (male-female ratio, 4:1). Overall, the prevalence was comparable for black and white children (black, 3.4 per 1000 [95% CI, 3.0-3.7] and white, 3.4 per 1000 [95% CI, 3.2-3.7]). Sixty-eight percent of children with IQ or developmental test results (N = 880) had cognitive impairment. As severity of cognitive impairment increased from mild to profound, the male-female ratio decreased from 4.4 to 1.3. Forty percent of children with autism were identified only at educational sources. Schools were the most important source for information on black children, children of younger mothers, and children of mothers with less than 12 years of education.   CONCLUSION The rate of autism found in this study was higher than the rates from studies conducted in the United States during the 1

Question: What is the prevalence of autism in a US metropolitan area?


Information Security Threats Classification Pyramid. Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.

Question: What is the information security threat classification pyramid?


Cloud-enabled wireless body area networks for pervasive healthcare. With the support of mobile cloud computing, wireless body area networks can be significantly enhanced for massive deployment of pervasive healthcare applications. However, several technical issues and challenges are associated with the integration of WBANs and MCC. In this article, we study a cloud-enabled WBAN architecture and its applications in pervasive healthcare systems. We highlight the methodologies for transmitting vital sign data to the cloud by using energy-efficient routing, cloud resource allocation, semantic interactions, and data security mechanisms.

Question: What is the cloud-enabled wireless body area networks for pervasive healthcare?


Finding the face in the crowd: an anger superiority effect.. Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.

Question: How does the face pop out of a crowd?


Self-efficacy: toward a unifying theory of behavioral change.. The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.

Question: What is the difference between self-efficacy and self-esteem?


CASIA@V2: A MLN-based Question Answering System over Linked Data. We present a question answering system (CASIA@V2) over Linked Data (DBpedia), which translates natural language questions into structured queries automatically. Existing systems usually adopt a pipeline framework, which contains four major steps: 1) Decomposing the question and detecting candidate phrases; 2) mapping the detected phrases into semantic items of Linked Data; 3) grouping the mapped semantic items into semantic triples; and 4) generating the rightful SPARQL query. We present a jointly learning framework using Markov Logic Network(MLN) for phrase detection, phrases mapping to semantic items and semantic items grouping. We formulate the knowledge for resolving the ambiguities in three steps of QALD as first-order logic clauses in a MLN. We evaluate our approach on QALD-4 test dataset and achieve an F-measure score of 0.36, an average precision of 0.32 and an average recall of 0.40 over 50 questions.

Question: What is the name of the system?


What we know about leadership. Effectiveness and personality.. Although psychologists know a great deal about leadership, persons who make decisions about real leaders seem largely to ignore their accumulated wisdom. In an effort to make past research more accessible, interpretable, and relevant to decision makers, this article defines leadership and then answers nine questions that routinely come up when practical decisions are made about leadership (e.g., whom to appoint, how to evaluate them, when to terminate them.

Question: What is leadership?


Driver head pose estimation using efficient descriptor fusion. A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Modelbased approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster andmore adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearancebased discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor's features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing'04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.

Question: What is the driver attention level?


Winding Machine for Automated Production of an Innovative Air-Gap Winding for Lightweight Electric Machines. This paper presents a newly developed winding machine, which enables an automated production of stator-mounted air-gap windings with meander structure. This structure has very high accuracy requirements. Therefore, automation is realized by the interaction of 15 actuators and a compound construction with 13 degrees of freedom. The programming works with discrete open-loop motion control to generate the kinematics. Above all, a flexible prototype of the winding machine is developed, manufactured, and tested for a motor with external rotor. Finally, experimental results of the developed automation for air-gap windings with meander structure are presented.

Question: What is the windings for an electric machine?


基於對照表以及語言模型之簡繁字體轉換 (Chinese Characters Conversion System based on Lookup Table and Language Model) [In Chinese]. The character sets used in China and Taiwan are both Chinese, but they are divided into simplified and traditional Chinese characters. There are large amount of information exchange between China and Taiwan through books and Internet. To provide readers a convenient reading environment, the character conversion between simplified and traditional Chinese is necessary. The conversion between simplified and traditional Chinese characters has two problems: one-to-many ambiguity and term usage problems. Since there are many traditional Chinese characters that have only one corresponding simplified character, when converting simplified Chinese into traditional Chinese, the system will face the one-to-many ambiguity. Also, there are many terms that have different usages between the two Chinese societies. This paper focus on designing an extensible conversion system, that can take the advantage of community knowledge by accumulating lookup tables through Wikipedia to tackle the term usage problem and can integrate language model to disambiguate the one-to-many ambiguity. The system can reduce the cost of proofreading of character conversion for books, e-books, or online publications. The extensible architecture makes it easy to improve the system with new training data.

Question: How to convert simplified Chinese to traditional Chinese?


Cloaking Malware with the Trusted Platform Module. The Trusted Platform Module (TPM) is commonly thought of as hardware that can increase platform security. However, it can also be used for malicious purposes. The TPM, along with other hardware, can implement acloaked computation , whose memory state cannot be observed by any other software, including the operating system and hypervisor. We show that malware can use cloaked computations to hide essential secrets (like the target of an attack) from a malware analyst. We describe and implement a protocol that establishes an encryption key under control of the TPM that can only be used by a specific infection program. An infected host then proves the legitimacy of this key to a remote malware distribution platform, and receives and executes an encrypted payload in a way that prevents software visibility of the decrypted payload. We detail how malware can benefit from cloaked computations and discuss defenses against our protocol. Hardening legitimate uses of the TPM against attack improves the resilience of our malware, creating a Catch-22 for secure computing technology.

Question: How can malware hide secrets from a malware analyst?


Non linear hybrid watermarking for High Dynamic Range images. The present work explores the use of a specific method, called non linear hybrid watermarking to watermark High Dynamic Range (HDR) images. The non linear hybrid technique combines both additive and multiplicative watermark embedding and is based on a square root embedding equation. We evaluate the robustness and objective quality of HDR tone mapped watermarked images on two different HDR databases. The experimentations show that the watermark is imperceptible and successfully survives tone mapping attack.

Question: How to watermark HDR images?


Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation. Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning "imitation-from-observation," and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.

Question: How to learn a robot to do a task from a video of a human doing the task?


Space-time video completion. We present a method for space-time completion of large space-time "holes" in video sequences of complex dynamic scenes. The missing portions are filled-in by sampling spatio-temporal patches from the available parts of the video, while enforcing global spatio-temporal consistency between all patches in and around the hole. This is obtained by posing the task of video completion and synthesis as a global optimization problem with a well-defined objective function. The consistent completion of static scene parts simultaneously with dynamic behaviors leads to realistic looking video sequences. Space-time video completion is useful for a variety of tasks, including, but not limited to: (i) Sophisticated video removal (of undesired static or dynamic objects) by completing the appropriate static or dynamic background information, (ii) Correction of missing/corrupted video frames in old movies, and (iii) Synthesis of new video frames to add a visual story, modify it, or generate a new one. Some examples of these are shown in the paper.

Question: How to fill in a hole in a video?


Third-wave livestreaming: teens' long form selfie. Mobile livestreaming is now well into its third wave. From early systems such as Bambuser and Qik, to more popular apps Meerkat and Periscope, to today's integrated social streaming features in Facebook and Instagram, both technology and usage have changed dramatically. In this latest phase of livestreaming, cameras turn inward to focus on the streamer, instead of outwards on the surroundings. Teens are increasingly using these platforms to entertain friends, meet new people, and connect with others on shared interests. We studied teens' livestreaming behaviors and motivations on these new platforms through a survey completed by 2,247 American livestreamers and interviews with 20 teens, highlighting changing practices, teens' differences from the broader population, and implications for designing new livestreaming services.

Question: What is the difference between the first and third wave of livestreaming?


Piaget : Implications for Teaching. Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academic journals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community take advantage of advances in technology. For more information regarding JSTOR, please contact support@jstor.org.

Question: What is JSTOR?


Handling outliers in non-blind image deconvolution. Non-blind deconvolution is a key component in image deblurring systems. Previous deconvolution methods assume a linear blur model where the blurred image is generated by a linear convolution of the latent image and the blur kernel. This assumption often does not hold in practice due to various types of outliers in the imaging process. Without proper outlier handling, previous methods may generate results with severe ringing artifacts even when the kernel is estimated accurately. In this paper we analyze a few common types of outliers that cause previous methods to fail, such as pixel saturation and non-Gaussian noise. We propose a novel blur model that explicitly takes these outliers into account, and build a robust non-blind deconvolution method upon it, which can effectively reduce the visual artifacts caused by outliers. The effectiveness of our method is demonstrated by experimental results on both synthetic and real-world examples.

Question: How to handle outliers in non-blind image deconvolution?


DemNet: A Convolutional Neural Network for the detection of Alzheimer's Disease and Mild Cognitive Impairment. The early diagnosis of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the diagnosis of AD and MCI using structural Magnetic Resonance Imaging (MRI) scans. In this paper, we propose the use of a Convolutional Neural Network (CNN) in the detection of AD and MCI. In particular, we modified the 16-layered VGGNet for the 3-way classification of AD, MCI and Healthy Controls (HC) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset achieving an overall accuracy of 91.85% and outperforming several classifiers from other studies.

Question: What is the accuracy of the CNN?


A Cross-Type Thermal Wind Sensor With Self-Testing Function. A 2-D smart flow sensor with self-testing function was designed, fabricated and tested in this paper. It is composed of cross-structure heaters and four symmetrically located sensing parts. When the flow angle changes in the clockwise direction, the temperature differences among the four parts, namely Mode 1 given by T13(T24) and Model 2 given by T12(T14) will be Sine(Cosine) functions of wind direction. Further study shows that the magnitude of mode 1 is 1.414 times that of Mode 2, and the phase of Mode 2 leads Mode 1 by 45°. By using the fixed phase gap between the two modes, the self-test function can be realized without additional components. In order to achieve a high sensitivity and robust sensor, thermal insulated substrate was introduced to fabricate calorimetric flow sensor instead of silicon wafers. The proposed sensor was fabricated on the Pyrex7740 substrate using single liftoff process. Finally, a wind tunnel test was carried out in constant power (CP) mode, and the test results show reasonable agreement with the simulation curves.

Question: What is the self-test function?


Minimum Error Rate Training in Statistical Machine Translation. Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality. These training criteria make use of recently proposed automatic evaluation metrics. We describe a new algorithm for efficient training an unsmoothed error count. We show that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.

Question: What is the minimum error rate training in statistical machine translation?


Decomposing Adult Age Differences in Working Memory. Two studies, involving a total of 460 adults between 18 and 87 years of age, were conducted to determine which of several hypothesized processing components was most responsible for age-related declines in working memory functioning. Significant negative correlations between age and measures of working memory (i.e., from -.39 to -.52) were found in both studies, and these relations were substantially attenuated by partialing measures hypothesized to reflect storage capacity, processing efficiency, coordination effectiveness, and simple comparison speed. Because the greatest attenuation of the age relations occurred with measures of simple processing speed, it was suggested that many of the age differences in working memory may be mediated by age-related reductions in the speed of executing elementary operations.

Question: What is the age of the oldest person in the world?


Camera calibration with spheres: linear approaches. This paper addresses the problem of camera calibration from spheres. By studying the relationship between the dual images of spheres and that of the absolute conic, a linear solution has been derived from a recently proposed non-linear semi-definite approach. However, experiments show that this approach is quite sensitive to noise. In order to overcome this problem, a second approach has been proposed, where the orthogonal calibration relationship is obtained by regarding any two spheres as a surface of revolution. This allows a camera to be fully calibrated from an image of three spheres. Besides, a conic homography is derived from the imaged spheres, and from its eigenvectors the orthogonal invariants can be computed directly. Experiments on synthetic and real data show the practicality of such an approach.

Question: How to calibrate a camera from spheres?


Trainable videorealistic speech animation. We describe how to create with machine learning techniques a generative, speech animation module. A human subject is first recorded using a videocamera as he/she utters a predetermined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth uttering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is videorealistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned.The two key contributions of this paper are 1) a variant of the multidimensional morphable model (MMM) to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes; and 2) a trajectory synthesis technique based on regularization, which is automatically trained from the recorded video corpus, and which is capable of synthesizing trajectories in MMM space corresponding to any desired utterance.

Question: How to create a generative, speech animation module?


An MRF Model for Binarization of Natural Scene Text. Inspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by the value of the energy function. We minimize the energy function, i.e. find the optimal binarization, using an iterative graph cut scheme. Our model is robust to variations in foreground and background colours as we use a Gaussian Mixture Model in the energy function. In addition, our algorithm is efficient to compute, and adapts to a variety of document images. We show results on word images from the challenging ICDAR 2003 dataset, and compare our performance with previously reported methods. Our approach shows significant improvement in pixel level accuracy as well as OCR accuracy.

Question: What is the difference between a MRF and a CRF?


Phone Merging For Code-Switched Speech Recognition. Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of codeswitched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.

Question: How to merge phones in code-switched speech?


Gradient Flow in Recurrent Nets : the Difficulty of Learning Long-Term Dependencies.

Question: What is the difficulty of learning long-term dependencies?


A Lie Never Lives to be Old: The Effects of Fake Social Information on Consumer Decision-Making in Crowdfunding. The growing success of social media led to a strong presence of social information such as customer product reviews and product ratings in electronic markets. While this information helps consumers to better assess the quality of goods before purchase, its impact on consumer decision-making also incentivizes sellers to game the system by creating fake data in favor of specific goods in order to deliberately mislead consumers. As a consequence, consumers could make suboptimal choices or could choose to disregard social information altogether. In this exploratory study, we assess the effects nongenuine social information has on the consumer's decision-making in the context of reward-based crowdfunding. Specifically, we capture unnatural peaks in the number of Facebook Likes that a specific crowdfunding campaign receives on the platforms Kickstarter and Indiegogo and observe subsequent campaign performance. Our results show that fake Facebook Likes have a very short-term positive effect on the number of backers participating in the respective crowdfunding campaign. However, this peak in participation is then followed by a period in which participation is lower than prior to the existence of the non-genuine social information. We further discuss circumstances that foster this artificial manipulation of quality signals.

Question: What is the effect of fake social information on crowdfunding?


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