Health-related quality of life in breast cancer patients: A bibliographic review of the literature from 1974 to 2007. BACKGROUND Quality of life in patients with breast cancer is an important outcome. This paper presents an extensive overview on the topic ranging from descriptive findings to clinical trials.   METHODS This was a bibliographic review of the literature covering all full publications that appeared in English language biomedical journals between 1974 and 2007. The search strategy included a combination of key words 'quality of life' and 'breast cancer' or 'breast carcinoma' in titles. A total of 971 citations were identified and after exclusion of duplicates, the abstracts of 606 citations were reviewed. Of these, meetings abstracts, editorials, brief commentaries, letters, errata and dissertation abstracts and papers that appeared online and were indexed ahead of publication were also excluded. The remaining 477 papers were examined. The major findings are summarized and presented under several headings: instruments used, validation studies, measurement issues, surgical treatment, systemic therapies, quality of life as predictor of survival, psychological distress, supportive care, symptoms and sexual functioning.   RESULTS Instruments-Several valid instruments were used to measure quality of life in breast cancer patients. The European Organization for Research and Treatment of Cancer Core Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and its breast cancer specific complementary measure (EORTC QLQ-BR23) and the Functional Assessment Chronic Illness Therapy General questionnaire (FACIT-G) and its breast cancer module (FACIT-B) were found to be the most common and well developed instruments to measure quality of life in breast cancer patients. Surgery-different surgical procedures led to relatively similar results in terms of quality of life assessments, although mastectomy patients compared to conserving surgery patients usually reported a lower

Question: What is the difference between breast cancer and breast carcinoma?


Universal Planning Networks. A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via imagebased goals. Visit https://sites.google. com/view/upn-public/home for video highlights.

Question: What is the universal planning network?


Pattern Recognition: Possible Research Areas and Issues. Pattern recognition is a tough problem for computers, although humans are wired for it. Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval. This paper reviews possible application areas of Pattern recognition. Author covers various sub-disciplines of pattern recognition based on learning methods, such as supervised, unsupervised, semi-supervised learning and key research areas such as grammar induction. Novel solutions to these possible problems could be well deployed for character recognition, speech analysis, man and machine diagnostics, person identification, industrial inspection and so on. The paper concludes with brief discussion on open issues that need to be addressed by future researchers.

Question: What is the difference between supervised, unsupervised, semi-supervised and unsupervised learning?


The Ontogenesis of Social Dominance : A Strategy-Based Evolutionary Perspective. Social dominance results when members of a social group vary in their ability to acquire resources in the presence of others (i.e., compete). Traditional approaches to social dominance often emphasize coercive behavior, but nonetheless suggest that dominant individuals are socially central (e.g., watched, attractive social partners). These patterns, however, apply to humans only up to a certain age. This apparent discontinuity may give the false impression that social dominance is less relevant to human social organization than it is to animal social organization. This paper reintroduces the ethological concept of social dominance, but reinterprets it from a strategy-based perspective. That is, if social dominance is defined as differential ability to control resources—without reference to how this is done—then children evidently employ different strategies to compete with peers (e.g., coercive and prosocial). Furthermore, the type of strategy children employ and peers' responses to it depend largely on the ages of the children. By adopting a strategy-based approach to social dominance and explicitly incorporating developmental processes and uniquely human capacities, human social dominance patterns appear to be more similar to primate patterns than commonly believed. Implications for social competence, peer relationships, and the development of the self are discussed.  1999

Question: What is the ontogenesis of social dominance?


Smart home garden irrigation system using Raspberry Pi. Irrigation system is a method of allowing water to drip slowly to the roots of plants, either onto the soil surface or directly onto the root zone, through solenoid valve. However, it is found that the market price of the system is expensive for small area coverage. Thus, this paper proposes a design for smart home garden irrigation system that implements ready-to-use, energy-efficient, and cost effective devices. Raspberry Pi, which is implemented in this system is integrated with multi-sensors such as soil moisture sensors, ultrasonic sensors, and light sensors. This proposed system managed to reduce cost, minimize waste water, and reduce physical human interface. In this paper, the relay is utilized to control the switching of solenoid valve. The system also managed to measure moisture of the soil and control the solenoid valve according to human's requirements. It is conducted with Graphical User Interface (GUI) using Android application to activate watering activity. Email notification is also sent to the home user for alert purposes either for normal or critical operations. An experimental setup has been tested and it is proven that the system can intelligently control and monitor the soil moisture levels in the experiment field.

Question: What is the purpose of the raspberry pi in the smart home garden irrigation system?


House Price Prediction : Hedonic Price Model vs . Artificial Neural Network. The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.

Question: What is the hedonic model?


An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

Question: What is the difference between a multiobjective optimization algorithm and a single objective optimization algorithm?


The emergence of psychopathy: Implications for the neuropsychological approach to developmental disorders. In this paper, I am going to examine the disorder of psychopathy and consider how genetic anomalies could give rise to the relatively specific neuro-cognitive impairments seen in individuals with this disorder. I will argue that genetic anomalies in psychopathy reduce the salience of punishment information (perhaps as a function of noradrenergic disturbance). I will argue that the ability of the amygdala to form the stimulus-punishment associations necessary for successful socialization is disrupted and that because of this, individuals with psychopathy do not learn to avoid actions that will harm others. It is noted that this model follows the neuropsychological approach to the study of developmental disorders, an approach that has been recently criticized. I will argue that these criticisms are less applicable to psychopathy. Indeed, animal work on the development of the neural systems necessary for emotion, does not support a constructivist approach with respect to affect. Importantly, such work indicates that while environmental effects can alter the responsiveness of the basic neural architecture mediating emotion, environmental effects do not construct this architecture. However, caveats to the neuropsychological approach with reference to this disorder are noted.

Question: What is the neuropsychological approach to developmental disorders?


Performance improvement of synchronous reluctance motors: A review. Synchronous Reluctance motors can replace the more commonly used, induction, switched reluctance and permanent magnet motors. They have been shown to outperform similarly dimensioned induction motors in efficiency, torque and power density. They are simple and cheap to construct and require simple control. Such a motor would be ideal for wide spread use in Africa, which currently faces a power deficit, considering that motors account for about 43% of global electric power consumption. The use of synchronous reluctance motors in Electric Vehicles may contribute to the production of more efficient, heavier load and longer range vehicles. This would accelerate adoption of Electric Vehicles and it would help wean the world off fossil fuels. The synchronous reluctance motor has disadvantages of high torque ripple and poor power factor. A radical redesign of the rotor geometry and simultaneous optimisation of the rotor and the stator may significantly improve its performance.

Question: What is the performance of synchronous reluctance motors?


Text detection and recognition using enhanced MSER detection and a novel OCR technique. Detection and recognition of text from any natural scene image is challenging but essential extensively for extracting information from the image. In this paper, we propose an accurate and effective algorithm for detecting enhanced Maximally Stable Extremal Regions (MSERs) as main character candidates and these character candidates are filtered by stroke width variation for removing regions where the stroke width exhibits too much variation. For the detection of text regions, firstly some preprocessing is applied to the natural image and then after detecting MSERs, an intersection of canny edge and MSER region is produced to locate regions that are even more likely to belong to text. Finally, the selected text region is taken as an input of a novel Optical Character Recognition (OCR) technique to make the text editable and usable. The evaluation results substantiates 77.47% of the f-measure on the ICDAR 2011 dataset which is better than the previous performance 76.22%.

Question: How to detect text from any natural scene image?


Loughborough University Institutional Repository Automated sizing of coarse-grained sediments : image-processing procedures. This is the first in a pair of papers in which we present image-processing-based procedures for the measurement of fluvial gravels. The spatial and temporal resolution of surface grain-size characterization is constrained by the time-consuming and costly nature of traditional measurement techniques. Several groups have developed image-processing-based procedures, but none have demonstrated the transferability of these techniques between sites with different lithological, clast form and textural characteristics. Here we focus on image-processing procedures for identifying and measuring image objects (i.e. grains); the second paper examines the application of such procedures to the measurement of fluvially deposited gravels. Four image-segmentation procedures are developed, each having several internal parameters, giving a total of 416 permutations. These are executed on 39 images from three field sites at which the clasts have contrasting physical properties. The performance of each procedure is evaluated against a sample of manually digitized grains in the same images, by comparing three derived statistics. The results demonstrate that it is relatively straightforward to develop procedures that satisfactorily identify objects in any single image or a set of images with similar sedimentary characteristics. However, the optimal procedure is that which gives consistently good results across sites with dissimilar sedimentary characteristics. We show that neighborhood-based operations are the most powerful, and a morphological bottom-hat transform with a double threshold is optimal. It is demonstrated that its performance approaches that of the procedures giving the best results for individual sites. Overall, it out-performs previously published, or improvements to previously published, methods.

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


Development of an Airborne Ground Penetrating Radar System: Antenna Design, Laboratory Experiment, and Numerical Simulation. A stepped-frequency continuous wave (SFCW) airborne ground penetrating radar (GPR) system was built using a vector network analyzer (VNA), an optical-to-electric ( \mmb O/\mmb E) converter, an electric-to-optical ( \mmb E/\mmb O) converter, and the resistively loaded planer dipole antennas. The resistively loaded antennas are based on the Altshuler discrete resistive loading approach. The distance from the loading point to the feeding point and the resistance has been optimized; in addition, a balun at the feeding point was introduced to convert the unbalanced signal from the coax to balanced signal. Test results show that the manufactured antennas have a wide bandwidth frequency range from 55 to 275 MHz. A laboratory experiment using the developed system was conducted in a building with high ceilings. The results registered all basic features including the ground, sub-ground surface, and the lamp hanging from the ceiling. Numerical modeling by finite-difference time-domain (FDTD) method using two-dimensional (2-D) transverse electric (TE) verifies the presence of these registered features in the measured airborne data. A bedrock outcrop in ice sheet model is reconstructed based on a piece of real airborne GPR data, the modeling profile results show excellent consistent with the observed data. The capability of airborne GPR detection of subsurface features is clearly validated.

Question: How does the resistively loaded planer dipole antenna work?


Do Multi-Sense Embeddings Improve Natural Language Understanding?. Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while 'multi-sense' methods have been proposed and tested on artificial wordsimilarity tasks, we don't know if they improve real natural language understanding tasks. In this paper we introduce a multisense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language un-

Question: What is the difference between a multi-sense embedding and a multi-sense model?


A Survey on Clustering Algorithms for Wireless Sensor Networks. A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.

Question: What is clustering?


Punching shear capacity of continuous slabs. Provisions for punching shear design of reinforced concrete slabs are usually calibrated on the basis of results from tests on isolated specimens that simulate the slab zone within the points of contraflexure around a column. However, the punching behavior of interior slab-column connections in actual continuous slabs without transverse reinforcement may be influenced by the effects of moment redistribution and compressive membrane action, which can lead to higher punching strengths and lower deformation capacities compared to those in isolated specimens. This paper discusses these behavioral differences on the basis of experiments performed on symmetric edge-restrained slabs and investigates available test data by means of a numerical model. A simplified calculation method (based on the Critical Shear Crack Theory) that accounts for these effects is also proposed. The calculation model shows consistent agreement with the results of the numerical evaluation and is sufficiently simple to be used in design and assessment.

Question: What is the punching shear capacity of continuous slabs?


A Hybrid Gripper With Soft Material and Rigid Structures. Various robotic grippers have been developed over the past several decades for robotic manipulators. Especially, the soft grippers based on the soft pneumatic actuator (SPA) have been studied actively, since it offers more pliable bending motion, inherent compliance, and a simple morphological structure. However, few studies have focused on simultaneously improving the fingertip force and actuation speed within the specified design parameters. In this study, we developed a hybrid gripper that incorporates both soft and rigid components to improve the fingertip force and actuation speed simultaneously based on three design principles: first, the degree of bending is proportional to the ratio of the rigid structure; second, a concave chamber design is preferred for large longitudinal strain; and third, a round shape between soft and rigid materials increases the fingertip force. The suggested principles were verified using the finite element methods. The improved performance of the hybrid gripper was verified experimentally and compared with the performance of a conventional SPAs. The ability of the hybrid gripper to grasp different objects was evaluated and was applied in a teleoperated system.

Question: What is the difference between a soft gripper and a rigid gripper?


Digital Forensic Model Based On Malaysian Investigation Process. Faculty Of Science & Technology Islamic Science University Of Malaysia Summary With the proliferation of the digital crime around the world, numerous digital forensic investigation models already being develop .In fact many of the digital forensic investigation model focus on technical implementation of the investigation process as most of it develop by traditional forensic expert and technologist. As an outcome of this problem most of the digital forensic practitioners focus on technical aspect and forget the core concept of digital forensic investigation model .In this paper we are introducing a new digital forensic model which will be capture a full scope of an investigation process based on Malaysia Cyber Law .The proposed model is also compared with the existing model which currently available and being apply in the investigation process.

Question: What is the difference between a digital forensic model and a digital forensic investigation model?


Learning with kernels. All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

Question: What is the publisher?


A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.

Question: What is a collaborative filtering framework based on fuzzy association rules and multiple-level similarity?


Active learning: theory and applications to automatic speech recognition. We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. In this paper we describe how to estimate the confidence score for each utterance through an on-line algorithm using the lattice output of a speech recognizer. The utterance scores are filtered through the informativeness function and an optimal subset of training samples is selected. The active learning algorithm has been applied to both batch and on-line learning scheme and we have experimented with different selective sampling algorithms. Our experiments show that by using active learning the amount of labeled data needed for a given word accuracy can be reduced by more than 60% with respect to random sampling.

Question: What is the difference between batch and on-line learning?


Automatic programming assessment and test data generation a review on its approaches. Automatic programming assessment has recently become an important method in assisting lecturers and instructors of programming courses to automatically mark and grade students' programming exercises as well as to provide useful feedbacks on students' programming solutions. As part of the method, test data generation process plays as an integral part to perform a dynamic testing on students' programs. To date, various automated methods for test data generation particularly in software testing field are available. Unfortunately, they are seldom used in the context of automatic programming assessment research area. Nevertheless, there have been limited studies taking a stab to integrate both of them due to more useful features and to include a better quality program testing coverage. Thus, this paper provides a review on approaches that have been implemented in various studies with regard to automatic programming assessment, test data generation and integration of both of them. This review is aimed at gathering different techniques that have been employed to forward an interested reader the starting points for finding further information regarding its trends. In addition, the result of the review reveals the main gap that exists within the context of the considered areas which contributes to our main research topic of interest.

Question: What is the main gap that exists within the context of the considered areas?


Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Do people really care about their privacy? Surveys show that privacy is a primary concern for citizens in the digital age. On the other hand, individuals reveal personal information for relatively small rewards, often just for drawing the attention of peers in an online social network. This inconsistency of privacy attitudes and privacy behavior is often referred to as the "privacy paradox". In this paper, we present the results of a review of research literature on the privacy paradox. We analyze studies that provide evidence of a paradoxical dichotomy between attitudes and behavior and studies that challenge the existence of such a phenomenon. The diverse research results are explained by the diversity in research methods, the different contexts and the different conceptualizations of the privacy paradox. We also present several interpretations of the privacy paradox, stemming from social theory, psychology, behavioral economics and, in one case, from quantum theory. We conclude that current research has improved our understanding of the privacy paradox phenomenon. It is, however, a complex phenomenon that requires extensive further research. Thus, we call for synthetic studies to be based on comprehensive theoretical models that take into account the diversity of personal information and the diversity of privacy concerns. We suggest that future studies should use evidence of actual behavior rather than self-reported behavior.

Question: What is the privacy paradox?


Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

Question: What is the largest thesaurus in the biomedical domain?


Computational Decomposition of Style for Controllable and Enhanced Style Transfer. Neural style transfer has been demonstrated to be powerful in creating artistic image with help of Convolutional Neural Networks (CNN). However, there is still lack of computational analysis of perceptual components of the artistic style. Different from some early attempts which studied the style by some pre-processing or post-processing techniques, we investigate the characteristics of the style systematically based on feature map produced by CNN. First, we computationally decompose the style into basic elements using not only spectrum based methods including Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) but also latent variable models such Principal Component Analysis (PCA), Independent Component Analysis (ICA). Then, the decomposition of style induces various ways of controlling the style elements which could be embedded as modules in state-of-the-art style transfer algorithms. Such decomposition of style brings several advantages. It enables the computational coding of different artistic styles by our style basis with similar styles clustering together, and thus it facilitates the mixing or intervention of styles based on the style basis from more than one styles so that compound style or new style could be generated to produce styled images. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also sketch style transfer which indicates possible applications on picture-to-sketch problems.

Question: What is computational decomposition of style?


Word Epoch Disambiguation: Finding How Words Change Over Time. In this paper we introduce the novel task of "word epoch disambiguation," defined as the problem of identifying changes in word usage over time. Through experiments run using word usage examples collected from three major periods of time (1800, 1900, 2000), we show that the task is feasible, and significant differences can be observed between occurrences of words in different periods of time.

Question: How do we find the changes in word usage over time?


Debugging the Internet of Things: The Case of Wireless Sensor Networks. The Internet of Things (IoT) has the strong potential to support a human society interacting more symbiotically with its physical environment. Indeed, the emergence of tiny devices that sense environmental cues and trigger actuators after consulting logic and human preferences promises a more environmentally aware and less wasteful society. However, the IoT inherently challenges software development processes, particularly techniques for ensuring software reliability. Researchers have developed debugging tools for wireless sensor networks (WSNs), which can be viewed as the enablers of perception in the IoT. These tools gather run-time information on individual sensor node executions and node interactions and then compress that information.

Question: What is the Internet of Things?


Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior. An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in practice, indirect encodings do not yield effective multimodal controllers. Thus, this paper introduces novel multimodal extensions to HyperNEAT, a popular indirect encoding. A previous multimodal HyperNEAT approach called situational policy geometry assumes that multiple brains benefit from being embedded within an explicit geometric space. However, experiments here illustrate that this assumption unnecessarily constrains evolution, resulting in lower performance. Specifically, this paper introduces HyperNEAT extensions for evolving many brains without assuming geometric relationships between them. The resulting MultiBrain HyperNEAT can exploit human-specified task divisions to decide when each brain controls the agent, or can automatically discover when brains should be used, by means of preference neurons. A further extension called module mutation allows evolution to discover the number of brains, enabling multimodal behavior with even less expert knowledge. Experiments in several multimodal domains highlight that multi-brain approaches are more effective than HyperNEAT without multimodal extensions, and show that brains without a geometric relation to each other outperform situational policy geometry. The conclusion is that Multi-Brain HyperNEAT provides several promising techniques for evolving complex multimodal behavior.

Question: How many brains are needed to control a robot?


3DMatch: Learning the Matching of Local 3D Geometry in Range Scans. Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch1, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of realworld scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-theart approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction. 1All of our code, datasets, and trained models are publicly available: http://3dmatch.cs.princeton.edu/

Question: What is the difference between a histogram and a local feature learner?


Cognitive effort drives workspace configuration of human brain functional networks.. Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16-32 Hz) and γ-band (32-63 Hz) networks, compared with both lower (α-band; 8-16 Hz) and higher (high γ-band; 63-125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.

Question: How does cognitive effort drive workspace configuration of human brain functional networks?


Realtime multi-aircraft tracking in aerial scene with deep orientation network. Tracking the aircrafts from an aerial view is very challenging due to large appearance, perspective angle, and orientation variations. The deep-patch orientation network (DON) method was proposed for the multi-ground target tracking system, which is general and can learn the target's orientation based on the structure information in the training samples. Such approach leverages the performance of tracking-by-detection framework into two aspects: one is to improve the detectability of the targets by using the patch-based model for the target localization in the detection component and the other is to enhance motion characteristics of the individual tracks by incorporating the orientation information as an association metric in the tracking component. Based on the DON structure, you only look once (YOLO) and faster region convolutional neural network (FrRCNN) detection frameworks with simple online and realtime tracking (SORT) tracker are utilized as a case study. The Comparative experiments demonstrate that the overall detection accuracy is improved at the same processing speed of both detection frameworks. Furthermore, the number of Identity switches (IDsw) has reduced about 67% without affecting the computational complexity of the tracking component. Consequently, the presented method is efficient for realtime ground target-tracking scenarios.

Question: How to track the aircrafts from an aerial view?


Understanding network failures in data centers: measurement, analysis, and implications. We present the first large-scale analysis of failures in a data center network. Through our analysis, we seek to answer several fundamental questions: which devices/links are most unreliable, what causes failures, how do failures impact network traffic and how effective is network redundancy? We answer these questions using multiple data sources commonly collected by network operators. The key findings of our study are that (1) data center networks show high reliability, (2) commodity switches such as ToRs and AggS are highly reliable, (3) load balancers dominate in terms of failure occurrences with many short-lived software related faults,(4) failures have potential to cause loss of many small packets such as keep alive messages and ACKs, and (5) network redundancy is only 40% effective in reducing the median impact of failure.

Question: What is the median impact of failure?


Invertible Conditional GANs for image editing. Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.

Question: How to design a conditional generative adversarial network?


Risk management applied to software development projects in incubated technology-based companies : literature review , classification , and analysis Gestão.

Question: What is the risk management applied to software development projects in incubated technology-based companies?


Multi-view 3D Object Detection Network for Autonomous Driving. This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the birds eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 14.9% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.

Question: What is the multi-view 3D object detection network for autonomous driving?


Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.

Question: What is the difference between a fault-tolerant abstraction and a fault-tolerant implementation?


Multi-factor Authentication Security Framework in Cloud Computing. Data Security is the most critical issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Traditional password authentication does not provide enough security for information in cloud computing environment to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. In this paper the features of various access control mechanisms are discussed and a novel framework of access control is proposed for cloud computing, which provides a multi -step and multifactor authentication of a user. The model proposed is well-organized and provably secure solution of access control for externally hosted applications.

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


Deep Cosine Metric Learning for Person Re-identification. Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.

Question: What is deep cosine metric learning?


GraphBuilder: scalable graph ETL framework. Graph abstraction is essential for many applications from finding a shortest path to executing complex machine learning (ML) algorithms like collaborative filtering. Graph construction from raw data for various applications is becoming challenging, due to exponential growth in data, as well as the need for large scale graph processing. Since graph construction is a data-parallel problem, MapReduce is well-suited for this task. We developed GraphBuilder, a scalable framework for graph Extract-Transform-Load (ETL), to offload many of the complexities of graph construction, including graph formation, tabulation, transformation, partitioning, output formatting, and serialization. GraphBuilder is written in Java, for ease of programming, and it scales using the MapReduce model. In this paper, we describe the motivation for GraphBuilder, its architecture, MapReduce algorithms, and performance evaluation of the framework. Since large graphs should be partitioned over a cluster for storing and processing and partitioning methods have significant performance impacts, we develop several graph partitioning methods and evaluate their performance. We also open source the framework at https://01.org/graphbuilder/.

Question: What is the motivation for GraphBuilder?


Missing data correction in still images and image sequences. The ability to replace missing data in images and video is of key importance to many application fields. The general-purpose algorithm presented here is inspired by texture synthesis techniques but is suited to any complex natural scene and not restricted to stationary patterns. It has the property to be adapted to both still images and image sequences and to incorporate temporal information when available while preserving the simplicity of the algorithm. This method gives very good results in various situations without user intervention. The resulting computational cost is relatively low and corrections are usually produced within seconds.

Question: How to correct missing data in still images and image sequences?


It's only a computer: Virtual humans increase willingness to disclose. Research has begun to explore the use of virtual humans (VHs) in clinical interviews (Bickmore, Gruber, & Picard, 2005). When designed as supportive and ''safe'' interaction partners, VHs may improve such screenings by increasing willingness to disclose information (Gratch, Wang, Gerten, & Fast, 2007). In health and mental health contexts, patients are often reluctant to respond honestly. In the context of health-screening interviews, we report a study in which participants interacted with a VH interviewer and were led to believe that the VH was controlled by either humans or automation. As predicted, compared to those who believed they were interacting with a human operator, participants who believed they were interacting with a computer reported lower fear of self-disclosure, lower impression management, displayed their sadness more intensely, and were rated by observers as more willing to disclose. These results suggest that automated VHs can help overcome a significant barrier to obtaining truthful patient information. 2014 Elsevier Ltd. All rights reserved.

Question: How does a virtual human increase willingness to disclose?


Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration. We present a novel CNN-based image editing method that allows the user to change the semantic information of an image over a user-specified region. Our method makes this possible by combining the idea of manifold projection with spatial conditional batch normalization (sCBN), a version of conditional batch normalization with userspecifiable spatial weight maps. With sCBN and manifold projection, our method lets the user perform (1) spatial class translation that changes the class of an object over an arbitrary region of user's choice, and (2) semantic transplantation that transplants semantic information contained in an arbitrary region of the reference image to an arbitrary region in the target image. These two transformations can be used simultaneously, and can realize a complex composite image-editing task like "change the nose of a beagle to that of a bulldog, and open her mouth". The user can also use our method with intuitive copy-paste-style manipulations. We demonstrate the power of our method on various images. Code will be available at https://github. com/pfnet-research/neural-collage.

Question: How does this method work?


Privacy-preserving personal health record using multi-authority attribute-based encryption with revocation. Personal health record (PHR) service is an emerging model for health information exchange. In PHR systems, patient's health records and information are maintained by the patient himself through the Web. In reality, PHRs are often outsourced to be stored at the third parties like cloud service providers. However, there have been serious privacy concerns about cloud service as it may expose user's sensitive data like PHRs to those cloud service providers or unauthorized users. Using attribute-based encryption (ABE) to encrypt patient's PHRs in cloud environment, secure and flexible access control can be achieved. Yet, problems like scalability in key management, fine-grained access control, and efficient user revocation remain to be addressed. In this paper, we propose a privacy-preserving PHR, which supports fine-grained access control and efficient revocation. To be specific, our scheme achieves the goals (1) scalable and fine-grained access control for PHRs by using multi-authority ABE scheme, and (2) efficient on-demand user/attribute revocation and dynamic policy update. In our scheme, we consider the situation that multiple data owners exist, and patient's PHRs are encrypted and stored in semi-trust servers. The access structure in our scheme is expressive access tree structure, and the security of our scheme can be reduced to the standard decisional bilinear Diffie–Hellman assumption.

Question: What is the privacy-preserving PHR?


Bandwidth Improvement of Microstrip Patch Antenna Using H- Shaped Patch. Despite the many advantages of microstrip patch antennas, they do have some considerable drawbacks. One of the main limitations with patch antennas is their inherently narrowband performance due to its resonant nature. With bandwidth as low as a few percent; broadband applications using conventional patch designs are limited. So for the antenna miniaturization and bandwidth improvement H-shaped microstrip patch antenna used. In this paper, authors cover two aspects of microstrip antenna designs. The first is the analysis of single element narrowband rectangular microstrip antenna which operates at the central frequency of 3.3 GHz. The second aspect is the analysis and design of slot cut H-shaped microstrip antenna. The simulation process has been done through high frequency structure simulator (HFSS). The properties of antenna such as bandwidth, S parameter, VSWR has been investigated and compared between a single element rectangular and H-shaped microstrip antenna.

Question: What is the bandwidth of a microstrip antenna?


Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.

Question: How to design a dialogue policy for a spoken dialogue system?


Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs. Although popular and extremely well established in mainstream statistical data analysis, logistic regression is strangely absent in the field of data mining. There are two possible explanations of this phenomenon. First, there might be an assumption that any tool which can only produce linear classification boundaries is likely to be trumped by more modern nonlinear tools. Second, there is a legitimate fear that logistic regression cannot practically scale up to the massive dataset sizes to which modern data mining tools are This paper consists of an empirical examination of the first assumption, and surveys, implements and compares techniques by which logistic regression can be scaled to data with millions of attributes and records. Our results, on a large life sciences dataset, indicate that logistic regression can perform surprisingly well, both statistically and computationally, when compared with an array of more recent classification algorithms.

Question: What is the difference between logistic regression and other classification algorithms?


Light warping for enhanced surface depiction. Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance.  Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios.  Note: Third-Party Material Attribution  Third-party material used in ACM Transactions on Graphics 28(3), Article 25 - "Light Warping for Enhanced Surface Depiction," by Vergne, Pacanowski, Barla, Granier, and Schlick - was used without proper attribution.  The 3D model used in Figures 1, 3, and 5, as well as in the cover image of this volume of the journal, was downloaded from the Shape Repository of AIM@SHAPE Project (http://shapes.aimatshape.net) and is the property of CNR-IMATI.  We regret this oversight.

Question: How to light a 3D model?


A Benchmark for Open Link Prediction. Knowledge graphs (KG) and open information extraction systems (OIE) both represent information by a set of (subject, relation, object)-triples. In KGs, arguments and relations are taken from a closed vocabulary, i.e., entities are disambiguated and relations are assumed to be semantically distinct. In OIE, arguments and relations are extracted from natural language text. There is no predefined or closed vocabulary, many different mentions may refer to the same entity, and relations can be semantically close but not equal, e.g. "visited by" and "frequently visited by". The set of OIE triples can be viewed as an open knowledge graph (OKG). In this paper, we study the problem of open link prediction: Given a mention of an entity and an open relation (e.g., "A. Einstein" and "is a native of"), predict correct entity mentions (e.g., "Imperial Germany") using solely the OKG. Models that aim to solve the open link prediction task sucessfully need to be able to reason about different entity mentions, as well as the relations between them, in a fully automatic, unsupervised way. As a benchmark for this task we design an OKG dataset, an evaluation protocol and present and evaluate a number of baseline models. In particular, we compare a token-based co-occurrence model, a non token-based link prediction model, and several token-based link prediction models. Our results indicate that the latter combination is beneficial.

Question: What is the benchmark for open link prediction?


Software reuse research: status and future. This paper briefly summarizes software reuse research, discusses major research contributions and unsolved problems, provides pointers to key publications, and introduces four papers selected from The Eighth International Conference on Software Reuse (ICSR8).

Question: What is software reuse research?


CompNet: Neural networks growing via the compact network morphism. It is often the case that the performance of a neural network can be improved by adding layers. In real-world practices, we always train dozens of neural network architectures in parallel which is a wasteful process. We explored CompNet, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact. The work of the paper makes two contributions: a). The modified network can converge fast and keep the same functionality so that we do not need to train from scratch again; b). The layer size of the added layer in the neural network is controlled by removing the redundant parameters with sparse optimization. This differs from previous network morphism approaches which tend to add more neurons or channels beyond the actual requirements and result in redundance of the model. The method is illustrated using several neural network structures on different data sets including MNIST and CIFAR10.

Question: What is the compact network morphism?


Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management. Spyware is a rapidly spreading problem for PC users causing significant impact on system stability and privacy concerns. It attaches to extensibility points in the system to ensure the spyware will be instantiated when the system starts. Users may willingly install free versions of software containing spyware as an alternative to paying for it. Traditional anti-virus techniques are less effective in this scenario because they lack the context to decide if the spyware should be removed. In this paper, we introduce Auto-Start Extensibility Points (ASEPs) as the key concept for modeling the spyware problem. By monitoring and grouping ''hooking'' operations made to the ASEPs, our Gatekeeper solution complements the traditional signature-based approach and provides a comprehensive framework for spyware management. We present ASEP hooking statistics for 120 real-world spyware programs. We also describe several techniques for discovering new ASEPs to further enhance the effectiveness of our solution.

Question: What is the difference between a hook and an ASEP?


Development of an Assessment Model for Industry 4.0: Industry 4.0-MM. The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry.

Question: What is the difference between Industry 4.0 and Industry 4.0-MM?


Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming. This paper presents a color structured light technique for recovering object shape from one or more images. The technique works by projecting a pattern of stripes of alternating colors and matching the projected color transitions with observed edges in the image. The correspondence problem is solved using a novel, multi-pass dynamic programming algorithm that eliminates global smoothness assumptions and strict ordering constraints present in previous formulations. The resulting approach is suitable for generating both highspeed scans of moving objects when projecting a single stripe pattern and high-resolution scans of static scenes using a short sequence of time-shifted stripe patterns. In the latter case, spacetime analysis is used at each sensor pixel to obtain inter-frame depth localization. Results are demonstrated for a variety of complex scenes.

Question: What is a multi-pass dynamic programming algorithm?


On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis. In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario. More specifically, we define a use case in the context of media monitoring and media response analysis and claim that even using a simple query-based extractive approach can dramatically save the processing time of the employees without significantly reducing the quality of their work.

Question: What is the use case of media monitoring and media response analysis?


Accounting for the effects of accountability.. This article reviews the now extensive research literature addressing the impact of accountability on a wide range of social judgments and choices. It focuses on 4 issues: (a) What impact do various accountability ground rules have on thoughts, feelings, and action? (b) Under what conditions will accountability attenuate, have no effect on, or amplify cognitive biases? (c) Does accountability alter how people think or merely what people say they think? and (d) What goals do accountable decision makers seek to achieve? In addition, this review explores the broader implications of accountability research. It highlights the utility of treating thought as a process of internalized dialogue; the importance of documenting social and institutional boundary conditions on putative cognitive biases; and the potential to craft empirical answers to such applied problems as how to structure accountability relationships in organizations.

Question: What is the impact of accountability on a wide range of social judgments and choices?


Latent Semantic Indexing (LSI): TREC-3 Report. We used LSI for both TREC-3 routing and adhoc tasks. For the routing tasks an LSI space was constructed using the training documents. We compared profiles constructed using just the topic words (no training) with profiles constructed using the average of relevant documents (no use of the topic words). Not surprisingly, the centroid of the relevant documents was 30% better than the topic words. This simple feedback method was quite good compared to the routing performance of other systems. Various combinations of information from the topic words and relevant documents provide small additional improvements in performance. For the adhoc task we compared LSI to keyword vector matching (i.e. using no dimension reduction). Small advantages were obtained for LSI even with the long TREC topic statements.

Question: What is LSI?


Dual Encoding for Abstractive Text Summarization.. Recurrent neural network-based sequence-to-sequence attentional models have proven effective in abstractive text summarization. In this paper, we model abstractive text summarization using a dual encoding model. Different from the previous works only using a single encoder, the proposed method employs a dual encoder including the primary and the secondary encoders. Specifically, the primary encoder conducts coarse encoding in a regular way, while the secondary encoder models the importance of words and generates more fine encoding based on the input raw text and the previously generated output text summarization. The two level encodings are combined and fed into the decoder to generate more diverse summary that can decrease repetition phenomenon for long sequence generation. The experimental results on two challenging datasets (i.e., CNN/DailyMail and DUC 2004) demonstrate that our dual encoding model performs against existing methods.

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


Ethical and Social Aspects of Self-Driving Cars. As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.

Question: What is the difference between a self-driving car and a regular car?


Bricks, Blocks, Boxes, Cubes, and Dice: On the Role of Cubic Shapes for the Design of Tangible Interactive Devices. Cubic shapes play an astonishing role in the design of tangible interactive devices. Due to our curiosity for this widespread design preference lasting over thirty years, we constituted a literature survey of papers, books and products since the late 1970s. Out of a corpus of fourty-seven papers, books and products that propose cubic shapes for tangible interactive devices we trace the origins of cubicle tangibles and highlight the rationale for their application. Through a comparative study, we analyze the properties of this shape for tangible interaction design and classify these along the themes of: Manipulation as Input, Placement in Space as Input, Arrangement, Multifunctionality, Randomness, Togetherness & Variations, Physical Qualities, Container, and Pedestal for Output. We propose a taxonomy for cubic shaped tangible interactive devices based on the reviewed contributions, in order to support researchers and designers in their future work of designing cubic shaped tangible interactive devices.

Question: What is the role of cubic shapes for tangible interactive devices?


LIMSI$@$WMT'16: Machine Translation of News. This paper describes LIMSI's submissions to the shared WMT'16 task "Translation of News". We report results for Romanian-English in both directions, for English to Russian, as well as preliminary experiments on reordering to translate from English into German. Our submissions use mainly NCODE and MOSES along with continuous space models in a post-processing step. The main novelties of this year's participation are the following: for the translation into Russian and Romanian, we have attempted to extend the output of the decoder with morphological variations and to use a CRF model to rescore this new search space; as for the translation into German, we have been experimenting with source-side pre-ordering based on a dependency structure allowing permutations in order to reproduce the tar-

Question: What is the difference between the Romanian and the Russian language?


Cannabis - from cultivar to chemovar.. The medicinal use of Cannabis is increasing as countries worldwide are setting up official programs to provide patients with access to safe sources of medicinal-grade Cannabis. An important question that remains to be answered is which of the many varieties of Cannabis should be made available for medicinal use. Drug varieties of Cannabis are commonly distinguished through the use of popular names, with a major distinction being made between Indica and Sativa types. Although more than 700 different cultivars have already been described, it is unclear whether such classification reflects any relevant differences in chemical composition. Some attempts have been made to classify Cannabis varieties based on chemical composition, but they have mainly been useful for forensic applications, distinguishing drug varieties, with high THC content, from the non-drug hemp varieties. The biologically active terpenoids have not been included in these approaches. For a clearer understanding of the medicinal properties of the Cannabis plant, a better classification system, based on a range of potentially active constituents, is needed. The cannabinoids and terpenoids, present in high concentrations in Cannabis flowers, are the main candidates. In this study, we compared cultivars obtained from multiple sources. Based on the analysis of 28 major compounds present in these samples, followed by principal component analysis (PCA) of the quantitative data, we were able to identify the Cannabis constituents that defined the samples into distinct chemovar groups. The study indicates the usefulness of a PCA approach for chemotaxonomic classification of Cannabis varieties.

Question: What is the difference between chemovar and chemotype?


New compact OMT based on a septum solution. This paper presents a new compact orthomode transducer (OMT) able to extract or combine two orthogonal linear polarizations in a very compact manner with simple manufacturing process. The component looks similar to the well-known septum polarizer but contrary to this one, the new component is able to handle dual linear polarization.

Question: What is the difference between a septum polarizer and a compact OMT?


Car-like robot navigation at high speed. This paper investigates the use of dynamic window approach (DWA) to solve the high speed autonomous navigation problem for mobile robots. In this paper, our main interest is to adapt the DWA to a car-like robot Robucar. This latter can moves with high speed. It is characterized also by its bounded steering angle imposed by its mechanical structure which thereby implies that the robot always moves on circular trajectories whose radius is upper than a certain extent. This approach considers the car-like robot shape, kinematics and dynamics in the determination of translational velocity and steering angle in one step. The approach has been tested in simulation for different environments and demonstrates its practicability.

Question: How to navigate a car-like robot at high speed?


Reference-Aware Language Models. We propose a general class of language models that treat reference as explicit stochastic latent variables. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or discourse context, even when the targets of the reference may be rare words. Experiments on three representative applications show our model variants outperform models based on deterministic attention.

Question: How does reference-aware language models work?


Cluster-Based Scalable Network Services. We identify three fundamental requirements for scalable network services: incremental scalability and overflow growth provisioning, 24x7 availability through fault masking, and costeffectiveness. We argue that clusters of commodity workstations interconnected by a high-speed SAN are exceptionally well-suited to meeting these challenges for Internet-server workloads, provided the software infrastructure for managing partial failures and administering a large cluster does not have to be reinvented for each new service. To this end, we propose a general, layered architecture for building cluster-based scalable network services that encapsulates the above requirements for reuse, and a service-programming model based on composable workers that perform transformation, aggregation, caching, and customization (TACC) of Internet content. For both performance and implementation simplicity, the architecture and TACC programming model exploit BASE, a weaker-than-ACID data semantics that results from trading consistency for availability and relying on soft state for robustness in failure management. Our architecture can be used as an "off the shelf" infrastructural platform for creating new network services, allowing authors to focus on the "content" of the service (by composing TACC building blocks) rather than its implementation. We discuss two real implementations of services based on this architecture: TranSend, a Web distillation proxy deployed to the UC Berkeley dialup IP population, and HotBot, the commercial implementation of the Inktomi search engine. We present detailed measurements of TranSend's performance based on substantial client traces, as well as anecdotal evidence from the TranSend and HotBot experience, to support the claims made for the architecture.

Question: What is the difference between a cluster and a cluster-based scalable network service?


Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software. The use of automatic static analysis has been a software engineering best practice for decades. However, we still do not know a lot about its use in real-world software projects: How prevalent is the use of Automated Static Analysis Tools (ASATs) such as FindBugs and JSHint? How do developers use these tools, and how does their use evolve over time? We research these questions in two studies on nine different ASATs for Java, JavaScript, Ruby, and Python with a population of 122 and 168,214 open-source projects. To compare warnings across the ASATs, we introduce the General Defect Classification (GDC) and provide a grounded-theory-derived mapping of 1,825 ASAT-specific warnings to 16 top-level GDC classes. Our results show that ASAT use is widespread, but not ubiquitous, and that projects typically do not enforce a strict policy on ASAT use. Most ASAT configurations deviate slightly from the default, but hardly any introduce new custom analyses. Only a very small set of default ASAT analyses is widely changed. Finally, most ASAT configurations, once introduced, never change. If they do, the changes are small and have a tendency to occur within one day of the configuration's initial introduction.

Question: How prevalent is the use of Automated Static Analysis Tools (ASATs) such as FindBugs and JSHint?


Strokelets: A Learned Multi-scale Representation for Scene Text Recognition. Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels, (2) Robustness: insensitive to interference factors, (3) Generality: applicable to variant languages, and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.

Question: What is a strokelet?


CATCH: A detecting algorithm for coalition attacks of hit inflation in internet advertising. As the Internet flourishes, online advertising becomes essential for marketing campaigns for business applications. To perform a marketing campaign, advertisers provide their advertisements to Internet publishers and commissions are paid to the publishers of the advertisements based on the clicks made for the posted advertisements or the purchases of the products of which advertisements posted. Since the payment given to a publisher is proportional to the amount of clicks received for the advertisements posted by the publisher, dishonest publishers are motivated to inflate the number of clicks on the advertisements hosted on their web sites. Since the click frauds are critical for online advertising to be reliable, the online advertisers make the efforts to prevent them effectively. However, the methods used for click frauds are also becoming more complex and sophisticated. In this paper, we study the problem of detecting coalition attacks of click frauds. The coalition attacks of click fraud is one of the latest sophisticated techniques utilized for click frauds because the fraudsters can obtain not only more gain but also less probability of being detected by joining a coalition. We introduce new definitions for the coalition and propose the novel algorithm called CATCH to find such coalitions. Extensive experiments with synthetic and real-life data sets confirm that our notion of coalition allows us to detect coalitions much more effectively than that of

Question: What is the difference between a coalition and a coalition attack?


Fuzzy controlled fast charging system for lithium-ion batteries. A DSP is adopted to construct a fuzzy controlled lithium-ion battery charging system. By using this intelligent charging system, the data collection, calculation and peripheral circuit control are performed for the battery charging status. According to the lithium-ion battery charging specifications, two hours are required for battery charging. A fuzzy logic controller (FLC) is constructed by using the battery protection cell voltage and voltage difference among the batteries. They are used as the input variable to shorten the charging and equalizing time and assure that the battery will be operated within the safety voltage range.

Question: What is fuzzy logic?


An LED Driver With Pulse Current Driving Technique. A novel driving technique, called pulse current driving technique for LED driver, is proposed. A complete introduction and analysis of the proposed driving technique and the conventional driving technique will be presented in this paper. The LED driver supply maximum peak current is 220 mA and operating frequency is between 500 kHz and1 MHz. Simulation and experimental results are presented to verify the analytical results. The LED driver is fabricated with CMOS 0.35-μm technology and chip area with pads is 0.89 mm 2. The design of the proposed circuit structure is simple, without additional output capacitance compensation for stability and with small power consumption, and is implemented in integrated circuit. As a result of the above benefits, the proposed approach is believed to be suitable in portable application.

Question: What is the difference between the proposed circuit and the conventional circuit?


Socioeconomic disparities in health: pathways and policies.. Socioeconomic status (SES) underlies three major determinants of health: health care, environmental exposure, and health behavior. In addition, chronic stress associated with lower SES may also increase morbidity and mortality. Reducing SES disparities in health will require policy initiatives addressing the components of socioeconomic status (income, education, and occupation) as well as the pathways by which these affect health. Lessons for U.S. policy approaches are taken from the Acheson Commission in England, which was charged with reducing health disparities in that country.

Question: What is the Acheson Commission?


Fast Approximate Energy Minimization via Graph Cuts. In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function's smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed. The first move we consider is an α-βswap: for a pair of labels α, β, this move exchanges the labels between an arbitrary set of pixels labeled α and another arbitrary set labeled β. Our first algorithm generates a labeling such that there is no swap move that decreases the energy. The second move we consider is an α-expansion: for a label α, this move assigns an arbitrary set of pixels the label α. Our second algorithm, which requires the smoothness term to be a metric, generates a labeling such that there is no expansion move that decreases the energy. Moreover, this solution is within a known factor of the global minimum. We experimentally demonstrate the effectiveness of our approach on image restoration, stereo and motion. 1 Energy minimization in early vision Many early vision problems require estimating some spatially varying quantity (such as intensity or disparity) from noisy measurements. Such quantities tend to be piecewise smooth; they vary smoothly at most points, but change dramatically at object boundaries. Every pixel p ∈ P must be assigned a label in some set L; for motion or stereo, the labels are disparities, while for image restoration they represent intensities. The goal is to find a labeling f that assigns each pixel p ∈ P a label fp ∈ L, where f is both piecewise smooth and consistent with the observed data. These vision problems can be naturally formulated in terms of energy minimization. In this framework, one seeks the labeling f that minimizes the energy E(f) = Esmooth(f) + Edata(f). Here Esmooth measures the extent to which f is not piecewise smooth, while Edata me

Question: What is the energy of a labeling?


Counter-Forensics: Attacking Image Forensics. This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images. Research on counter-forensics is motivated by the need to assess and improve the reliability of forensic methods in situations where intelligent adversaries make efforts to induce a certain outcome of forensic analyses. Counter-forensics is first defined in a formal decision-theoretic framework. This framework is then interpreted and extended to encompass the requirements to forensic analyses in practice, including a discussion of the notion of authenticity in the presence of legitimate processing, and the role of image models with regard to the epistemic underpinning of the forensic decision problem. A terminology is developed that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks. This terminology is directly applied in a self-contained technical survey of counter-forensics against image forensics, notably techniques that suppress traces of image processing and techniques that synthesize traces of authenticity, including examples and brief evaluations. A discussion of relations to other domains of multimedia security and an overview of open research questions concludes the chapter. 1 Definition of Counter-Forensics This final chapter changes the perspective. It is devoted to digital image counterforensics, the art and science of impeding and misleading forensic analyses of digital images. Rainer Böhme Westfälische Wilhelms-Universität Münster, Dept. of Information Systems, Leonardo-Campus 3, 48149 Münster, Germany e-mail: rainer.boehme@wi.uni-muenster.de Matthias Kirchner International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704 e-mail: kirchner@icsi.berkeley.edu

Question: What is the definition of counter-forensics?


Three dual polarized 2.4GHz microstrip patch antennas for active antenna and in-band full duplex applications. This paper presents the design, implementation and interport isolation performance evaluation of three dual port, dual polarized 2.4GHz microstrip patch antennas. Input matching and interport isolation (both DC and RF) performance of implemented antennas has been compared by measuring the input return losses (Sii, S22) and interport isolation (S12) respectively at 2.4GHz. Two implemented single layer antennas provide around 40dB RF interport isolation while the multilayer antenna has 60dB isolation between transmit and receive ports at centre frequency with DC isolated ports. The multilayer antenna provides more than 55dB interport isolation for antenna's 10 dB input impedance bandwidth of 50MHz.

Question: What is the difference between a dual polarized and a single polarized antenna?


An Intra-Panel Interface With Clock-Embedded Differential Signaling for TFT-LCD Systems. In this paper, an intra-panel interface with a clock embedded differential signaling for TFT-LCD systems is proposed. The proposed interface reduces the number of signal lines between the timing controller and the column drivers in a TFT-LCD panel by adopting the embedded clock scheme. The protocol of the proposed interface provides a delay-locked loop (DLL)-based clock recovery scheme for the receiver. The timing controller and the column driver integrated with the proposed interface are fabricated in 0.13- μm CMOS process technology and 0.18-μm high voltage CMOS process technology, respectively. The proposed interface is verified on a 47-inch Full High-Definition (FHD) (1920RGB×1080) TFT-LCD panel with 8-bit RGB and 120-Hz driving technology. The maximum data rate per differential pair was measured to be as high as 2.0 Gb/s in a wafer test.

Question: What is the maximum data rate per differential pair?


A non-isolated three-port converter for stand-alone renewable power system. A novel non-isolated three-port converter (NI-TPC) is proposed interfacing one PV port, one bidirectional battery port and one load port. Single stage power conversion between any two of the three ports is achieved. The topology is derived by decoupling the bidirectional power flow path of the conventional structure into two unidirectional ones. Two of the three ports can be tightly regulated to achieve maximum power harvesting for PV or charge control for battery, and maintain the load voltage constant at the same time, while the third port is left flexible to compensate the power imbalance of the converter. Operation states are analyzed. The multi-regulator competition control strategy is presented to achieve autonomous and smooth state switching when the PV input power fluctuates. The analysis is verified by the experimental results.

Question: What is a non-isolated three-port converter?


Platform-as-a-Service (PaaS): The Next Hype of Cloud Computing. Cloud Computing is expected to become the driving force of information technology to revolutionize the future. Presently number of companies is trying to adopt this new technology either as service providers, enablers or vendors. In this way the cloud market is estimated be likely to emerge at a remarkable rate. Under the whole cloud umbrella, PaaS seems to have a relatively small market share. However, it is expected to offer much more as it is compared with its counterparts SaaS and IaaS. This paper is aimed to assess and analyze the future of PaaS technology. Year 2015 named as "the year of PaaS". It means that PaaS technology has established strong roots and ready to hit the market with better technology services. This research will discuss future PaaS market trends, growth and business competitors. In the current dynamic era, several companies in the market are offering PaaS services. This research will also outline some of the top service providers (proprietary & open source) to discuss their current technology status and present a futuristic look into their services and business strategies. Analysis of the present and future PaaS technology infrastructure will also be a major discussion in this paper.

Question: What is the future of PaaS?


iMAP-CampUS: Developing an Intelligent Mobile Augmented Reality Program on Campus as a Ubiquitous System. Augmented Reality (AR) offers a combination of physical and virtual objects, drawing on the strengths of each. Therefore, it is different from virtual reality since it permits users to sight the real world enhanced with virtual objects. Thus, AR technology holds potential to support users with valuable information about the surrounding area. In this paper, we demonstrate the development of an AR app that provides the students with rich information about the buildings nearby. The application, which we name "iMAP-CampUS", is designed to assist students at Macquarie University in locating places of interest close to them by moving the camera of the device in all probable directions to overlay information of places around them. The iMAP-CampUS has been developed for both iOS and Android platforms and run on smartphones and tablets with different screen sizes.

Question: What is the difference between AR and VR?


Social Media: The Good, the Bad, and the Ugly. Companies when strategizing are looking for innovative ways to have a competitive advantage over their competitors. One way in which they compete is by the adoption of social media. Social media has evolved over the years and as a result, new concepts and applications are developed which promises to provide business value to a company. However, despite the usefulness of social media, many businesses fail to reap its full benefits. The current literature shows evidence of lack of strategically designed process for companies to successfully implement social media. The purpose of this study is to suggest a framework which provides the necessary alignment between social media goals with business objectives. From the literature review, a social media strategy framework was derived to offer an effective step by step approach to the development and imple‐ mentation of social media goals aligned with a company's business objectives. The contribution to this study is the development of a social media strategy framework that can be used by organisations for business value.

Question: How can social media be used to increase business value?


Humanoid locomotion on uneven terrain using the time-varying divergent component of motion. This paper presents a framework for dynamic humanoid locomotion on uneven terrain using a novel time-varying extension to the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic CoM height trajectories during stepping. The proposed planning algorithm computes admissible DCM reference trajectories given desired ZMP plans for single and double support. This is accomplished using reverse-time integration of the discretized DCM dynamics over a finite time horizon. To account for discontinuities during replanning, linear Model Predictive Control (MPC) is implemented over a short preview window. DCM tracking control is achieved using a time-varying proportional-integral controller based on the Virtual Repellent Point (VRP). The effectiveness of the combined approach is verified in simulation using a 30-DoF model of THOR, a compliant torque-controlled humanoid.

Question: How do we plan a walking gait?


Hashtag recommendation for hyperlinked tweets. Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.

Question: What is the purpose of the paper?


The base-rate fallacy and the difficulty of intrusion detection. Many different demands can be made of intrusion detection systems. An important requirement is that an intrusion detection system be effective; that is, it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This article demonstrates that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate P(Intrusion***Alarm), we have to achieve a (perhaps in some cases unattainably) low false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.

Question: What is the base-rate fallacy?


On unifying key-frame and voxel-based dense visual SLAM at large scales. This paper proposes an approach to real-time dense localisation and mapping that aims at unifying two different representations commonly used to define dense models. On one hand, much research has looked at 3D dense model representations using voxel grids in 3D. On the other hand, image-based key-frame representations for dense environment mapping have been developed. Both techniques have their relative advantages and disadvantages which will be analysed in this paper. In particular each representation's space-size requirements, their effective resolution, the computation efficiency, their accuracy and robustness will be compared. This paper then proposes a new model which unifies various concepts and exhibits the main advantages of each approach within a common framework. One of the main results of the proposed approach is its ability to perform large scale reconstruction accurately at the scale of mapping a building.

Question: What is the difference between key-frame and voxel-based dense visual SLAM?


Trust mechanisms for cloud computing. Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self assessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing trust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms based on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating various trust mechanisms together to reveal chains of trust in the cloud.

Question: What is the difference between trust and reputation?


Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection. We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.

Question: How to learn hand-eye coordination for robotic grasping?


A Soldier Health Monitoring System for Military Applications. With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.

Question: What is a body sensor network?