Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning. A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task. In this work, we argue that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation but also helps semantic attribute prediction. A novel dictionary learning model is proposed which decomposes the dictionary space into three parts corresponding to semantic, latent discriminative and latent background attributes respectively. Such a joint attribute learning model is then extended by following a multi-task transfer learning framework to address a more challenging unsupervised domain adaptation problem, where annotations are only available on an auxiliary dataset and the target dataset is completely unlabelled. Extensive experiments show that the proposed models, though being linear and thus extremely efficient to compute, produce state-of-the-art results on both zero-shot learning and person re-identification.

Question: What is the difference between a dictionary learning model and a multi-task learning model?


Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting. We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.

Question: How to predict the next value of a time series?


Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions. Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today's automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.

Question: What is the expected value of perfect information?


Adaptive Computation Offloading from Mobile Devices into the Cloud. The inherently limited processing power and battery lifetime of mobile phones hinder the possible execution of computationally intensive applications like content-based video analysis or 3D modeling. Offloading of computationally intensive application parts from the mobile platform into a remote cloud infrastructure or nearby idle computers addresses this problem. This paper presents our Mobile Augmentation Cloud Services (MACS) middleware which enables adaptive extension of Android application execution from a mobile client into the cloud. Applications are developed by using the standard Android development pattern. The middleware does the heavy lifting of adaptive application partitioning, resource monitoring and computation offloading. These elastic mobile applications can run as usual mobile application, but they can also reach transparently remote computing resources. Two prototype applications using the MACS middleware demonstrate the benefits of the approach. The evaluation shows that applications, which involve complicated algorithms and large computations, can benefit from offloading with around 95% energy savings and significant performance gains compared to local execution only.

Question: What is the name of the android application?


A low cost 3D scanner based on structured light. Automatic 3D acquisition devices (often called 3D scanners) allow to build highly accurate models of real 3D objects in a costand time-effective manner. We have experimented this technology in a particular application context: the acquisition of Cultural Heritage artefacts. Specific needs of this domain are: medium-high accuracy, easy of use, affordable cost of the scanning device, self-registered acquisition of shape and color data, and finally operational safety for both the operator and the scanned artefacts. According to these requirements, we designed a low-cost 3D scanner based on structured light which adopts a new, versatile colored stripe pattern approach. We present the scanner architecture, the software technologies adopted, and the first results of its use in a project regarding the 3D acquisition of an archeological statue.

Question: What is a low cost 3D scanner based on structured light?


WHY SUMMARIES OF RESEARCH ON PSYCHOLOGICAL THEORIES ARE OFTEN UNINTERPRETABLE. —Null hypothesis testing of correlational predictions from weak substantive theories in soft psychology is subject to the influence of ten obfuscating factors whose effects are usually (1) sizeable, (2) opposed, (3) variable, and (4) unknown The net epistemic effect of these ten obfuscating influences is that the usual research literature review is well nigh uninterpretable Major changes in graduate education, conduct of research, and editorial policy are proposed

Question: What is a null hypothesis?


A multi-method simulation of a high-frequency bus line. A methodology to model high-frequency bus lines is proposed and a realistic event-simulation model for such a line in the Netherlands is presented. This simulation model helps policy makers to predict changes that have to be made to bus routes and planned travel times before problems occur. With this model, different passenger growth scenarios can be easily evaluated. The model is validated using different key performance indicators, showing that under some model assumptions, it can realistically simulate real-life scenarios. The simulations workings are illustrated by a case study of passenger growth.

Question: How can a simulation model help policy makers?


Cascade Mask Generation Framework for Fast Small Object Detection. Detecting small objects is a challenging task. Existing CNN-based objection detection pipeline faces such a dilemma: using a high-resolution image as input incurs high computational cost, but using a low-resolution image as input loses the feature representation of small objects and therefore leads to low accuracy. In this work, we propose a cascade mask generation framework to tackle this issue. The proposed framework takes in multi-scale images as input and processes them in ascending order of the scale. Each processing stage outputs object proposals as well as a region-of-interest (RoI) mask for the next stage. With RoI convolution, the masked regions can be excluded from the computation in the next stage. The procedure continues until the largest scale image is processed. Finally, the object proposals generated from multiple scales are classified by a post classifier. Extensive experiments on Tsinghua-Tencent 100K traffic sign benchmark demonstrate that our approach achieves state-of-the-art small object detection performance at a significantly improved speed-accuracy tradeoff compared with previous methods.

Question: How to detect small objects?


10 kV/120 A SiC DMOSFET half H-bridge power modules for 1 MVA solid state power substation. In this paper, the extension of SiC power technology to higher voltage 10 kV/10 A SiC DMOSFETs and SiC JBS diodes is discussed. A new 10 kV/120 A SiC power module using these 10 kV SiC devices is also described which enables a compact 13.8 kV to 465/√3 solid state power substation (SSPS) rated at 1 MVA.

Question: What is a solid state power substation?


NameClarifier: A Visual Analytics System for Author Name Disambiguation. In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.

Question: How does NameClarifier work?


Life beyond Distributed Transactions: an Apostate's Opinion. Many decades of work have been invested in the area of distributed transactions including protocols such as 2PC, Paxos, and various approaches to quorum. These protocols provide the application programmer a façade of global serializability. Personally, I have invested a nontrivial portion of my career as a strong advocate for the implementation and use of platforms providing guarantees of global serializability. My experience over the last decade has led me to liken these platforms to the Maginot Line. In general, application developers simply do not implement large scalable applications assuming distributed transactions. When they attempt to use distributed transactions, the projects founder because the performance costs and fragility make them impractical. Natural selection kicks in... 1 The Maginot Line was a huge fortress that ran the length of the Franco-German border and was constructed at great expense between World War I and World War II. It successfully kept the German army from directly crossing the border between France and Germany. It was quickly bypassed by the Germans in 1940 who invaded through Belgium. This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/). You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but you must attribute the work to the author and CIDR 2007. 3 Biennial Conference on Innovative DataSystems Research (CIDR) January 7-10, Asilomar, California USA. Instead, applications are built using different techniques which do not provide the same transactional guarantees but still meet the needs of their businesses. This paper explores and names some of the practical approaches used in the implementations of large-scale mission-critical applications in a world which rejects distributed transactions. We discuss the management of fine-grained pieces of applica

Question: What is the Maginot Line?


Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet. Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Question: What is the difference between the ventral and dorsal visual streams?


The Internet of Things: a security point of view. ed as devices to provide services at low-levels as network discovery services, metadata exchange services, and asynchronous publish and subscribe event in (Kranenburg et al. 2011; Sundmaeker et al. 2010); In (Peris-Lopez et al. 2006), a representational state transfer (REST) is defined to increase interoperability between Page 14 of 32 http://mc.manuscriptcentral.com/intr Internet Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 or Peer Rview loosely coupled services and distributed applications. In (Hernandez-Castro et al. 2013), the services layer introduced a service provisioning process (SPP) that can provide the interaction between applications and services. It is important to design an effective security strategy to protect services against attacks in the service layer. The security requirements in the service layer include: • Authorization, service authentication, group authentication, privacy protection, integrity, integrity, security of keys, non-repudiation, anti-replay, availability,

Question: What is the security point of view?


Kinematics and Jacobian analysis of a 6UPS Stewart-Gough platform. In this paper a complete kinematics analysis of a 6UPS Stewart-Gough platform is performed. A new methodology to deal with the forward kinematics problem based on a multibody formulation and a numerical method is presented, resulting in a highly efficient formulation approach. The idea of a multibody formulation is basically to built for each joint that links the body of the robot, a set of equations that define the constraints on their movement so that the joint variables are included in these equations. Once the constraint function is defined, we may use a numerical method to find the kinematic solution. In order to avoid the problem reported with the orientation (Euler's Angles) of the moving platform, a generalized coordinates vector using quaternions is applied. Then, the Jacobian analysis using reciprocal screw systems is described and the workspace is determined. Finally, a design of a simulator using MATLAB as programming tool is presented.

Question: How to deal with the forward kinematics problem of a 6UPS Stewart-Gough platform?


Signal quality measures for pulse oximetry through waveform morphology analysis.. Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate deriv

Question: What is the difference between a pulse oximeter and a pulse oximeter?


Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings. Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis. For example, master, seasoned and familiar point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semisupervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman's rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.

Question: How to use sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics?


Depth-disparity calibration for augmented reality on binocular optical see-through displays. We present a study of depth-disparity calibration for augmented reality applications using binocular optical see-through displays. Two techniques were proposed and compared. The "paired-eyes" technique leverages the Panum's fusional area to help viewer find alignment between the virtual and physical objects. The "separate-eyes" technique eliminates the need of binocular fusion and involves using both eyes sequentially to check the virtual-physical object alignment on retinal images. We conducted a user study to measure the calibration results and assess the subjective experience of users with the proposed techniques.

Question: How do you calibrate a binocular optical see-through display?


The Computable News project: Research in the Newsroom. We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.

Question: What is the computable news project?


Technical Committees Switched-Capacitor Power Electronics Circuits By. One of the main orientations in power electronics in the last decade has been the development of switching-mode converters without inductors and transformers. Light weight, small size and high power density are the result of using only switches and capacitors in the power stage of these converters. Thus, they serve as ideal power supplies for mobile electronic systems (e.g. cellular phones, personal digital assistants, and so forth). Switched-capacitor (SC) converters, with their large voltage conversion ratio, promise to be a response to such challenges of the 21st century as high-efficiency converters with low EMI emissions and the ability to realize steep step-down of the voltage (to 3V or even a smaller supply voltage for integrated circuits) or steep step-up of the voltage for automotive industry or internet services in the telecom industry. This paper is a tutorial of the main results in SC-converter research and design. Switched-Capacitor Converters— A Typical Power Electronics Contribution of the CAS Society From all of the research subjects in the Power Systems and Power Electronics Circuits Technical Committee—the stability of power systems, analysis of power electronics circuits (which are time-variable circuits with internally-controlled switches), chaos, modeling and simulation of converters, hardswitching and soft-switching converters, and so forth—the SCconverter is probably the most pertinent contribution of the CAS Society to power electronics. In fact, most of the researchers in this field belong to our Society's technical committee and most of the breakthrough contributions on this subject have appeared in CAS publications. The reason for this is clear. Pursuing small size filters in the 1950's, the circuit theory community found a solution in the elimination of bulky inductors from the structure of a passive filter. Active and then switched-capacitor filters showed the possibi

Question: What is the difference between a switched capacitor and a switched inductor?


Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. BACKGROUND Depressive disorders were a leading cause of burden in the Global Burden of Disease (GBD) 1990 and 2000 studies. Here, we analyze the burden of depressive disorders in GBD 2010 and present severity proportions, burden by country, region, age, sex, and year, as well as burden of depressive disorders as a risk factor for suicide and ischemic heart disease.   METHODS AND FINDINGS Burden was calculated for major depressive disorder (MDD) and dysthymia. A systematic review of epidemiological data was conducted. The data were pooled using a Bayesian meta-regression. Disability weights from population survey data quantified the severity of health loss from depressive disorders. These weights were used to calculate years lived with disability (YLDs) and disability adjusted life years (DALYs). Separate DALYs were estimated for suicide and ischemic heart disease attributable to depressive disorders. Depressive disorders were the second leading cause of YLDs in 2010. MDD accounted for 8.2% (5.9%-10.8%) of global YLDs and dysthymia for 1.4% (0.9%-2.0%). Depressive disorders were a leading cause of DALYs even though no mortality was attributed to them as the underlying cause. MDD accounted for 2.5% (1.9%-3.2%) of global DALYs and dysthymia for 0.5% (0.3%-0.6%). There was more regional variation in burden for MDD than for dysthymia; with higher estimates in females, and adults of working age. Whilst burden increased by 37.5% between 1990 and 2010, this was due to population growth and ageing. MDD explained 16 million suicide DALYs and almost 4 million ischemic heart disease DALYs. This attributable burden would increase the overall burden of depressive disorders from 3.0% (2.2%-3.8%) to 3.8% (3.0%-4.7%) of global DALYs.   CONCLUSIONS GBD 2010 identified depressive disorders as a leading cause of burden. MDD was also a contributor of burden allocated to

Question: What is the burden of depressive disorders in canada?


Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM. Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

Question: How to extract sentiment-aware embeddings of emojis?


Toward Characterizing the Productivity Benefits of Very Large Displays. Larger display surfaces are becoming increasingly available due to multi-monitor capability built into many systems, in addition to the rapid decrease in their costs. However, little is known about the performance benefits of using these larger surfaces compared to traditional single-monitor displays. In addition, it is not clear that current software designs and interaction techniques have been properly tuned for these larger surfaces. A preliminary user study was carried out to provide some initial evidence about the benefits of large versus small display surfaces for complex, multi-application office work. Significant benefits were observed in the use of a prototype, larger display, in addition to significant positive user preference and satisfaction with its use over a small display. In addition, design guidelines for enhancing user interaction across large display surfaces were identified. User productivity could be significantly enhanced in future graphical user interface designs if developed with these findings in mind.

Question: What is the size of the display?


Discriminative n-gram language modeling. This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. We describe a method based on regularized likelihood that makes use of the feature set given by the perceptron algorithm, and initialization with the perceptron's weights; this method gives an additional 0.5% reduction in word error rate (WER) over training with the perceptron alone. The final system achieves a 1.8% absolute reduction in WER for a baseline first-pass recognition system (from 39.2% to 37.4%), and a 0.9% absolute reduction in WER for a multi-pass recognition system (from 28.9% to 28.0%). 2006 Elsevier Ltd. All rights reserved.

Question: What is the difference between discriminative and generative language modeling?


Design of fuzzy controller for induction heating using DSP. This paper presents the development of fuzzy controller for induction heating temperature control. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) digital signal processor (DSP)-based inverter. DSP is used to store the required commands for generating the necessary waveforms to control the frequency of the inverter through proper design of switching pulses. The SPWM technique produces pure sinusoidal wave of voltage and current output. Fuzzy controller software has been developed in the C++ language. VisSim software is used to experiment with a DSP. The inverter is current-fed and SPWM is used for DC to AC conversion. SPWM normally cuts off high-order harmonic by low-pass filter to produce sine wave power. The LC low-pass filter with cutoff frequency at 300Hz is used. Since low frequency produces ripple to cause transformer and inductance magnetic saturation, the range of frequency is between 100 Hz and 300 Hz. The results of the experiment are presented and show the effectiveness and superiority of this temperature control system.

Question: How does fuzzy control work?


A Self-Cloning Agents Based Model for High-Performance Mobile-Cloud Computing. The rise of the mobile-cloud computing paradigm in recent years has enabled mobile devices with processing power and battery life limitations to achieve complex tasks in real-time. While mobile-cloud computing is promising to overcome the limitations of mobile devices for real-time computing, the lack of frameworks compatible with standard technologies and techniques for dynamic performance estimation and program component relocation makes it harder to adopt mobile-cloud computing at large. Most of the available frameworks rely on strong assumptions such as the availability of a full clone of the application code and negligible execution time in the cloud. In this paper, we present a dynamic computation offloading model for mobile-cloud computing, based on autonomous agents. Our approach does not impose any requirements on the cloud platform other than providing isolated execution containers, and it alleviates the management burden of offloaded code by the mobile platform using stateful, autonomous application partitions. We also investigate the effects of different cloud runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic make span estimation model integrated into autonomous agents to enhance them with self-performance evaluation in addition to self-cloning capabilities. The proposed performance profiling model is used in conjunction with a cloud resource optimization scheme to ensure optimal performance. Experiments with two mobile applications demonstrate the effectiveness of the proposed approach for high-performance mobile-cloud computing.

Question: What is the difference between a self-cloning agent and a self-cloning agent based model for high-performance mobile-cloud computing?


Hunting for Spammers: Detecting Evolved Spammers on Twitter. Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.

Question: How many spammers are there on twitter?


Deep joint demosaicking and denoising. Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moiré or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.

Question: How to train a deep neural network?


Transfer metric learning for action similarity using high-level semantics. The goal of transfer learning is to exploit previous experiences and knowledge in order to improve learning in a novel domain. This is especially beneficial for the challenging task of learning classifiers that generalize well when only few training examples are available. In such a case, knowledge transfer methods can help to compensate for the lack of data. The performance and robustness against negative transfer of these approaches is influenced by the interdependence between knowledge representation and transfer type. However, this important point is usually neglected in the literature; instead the focus lies on either of the two aspects. In contrast, we study in this work the effect of various high-level semantic knowledge representations on different transfer types in a novel generic transfer metric learning framework. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the semantic attribute space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach compared to state-of-the-art. c © 2015 Elsevier Ltd. All rights reserved.

Question: What is the goal of transfer learning?


RAMSYS: Resource-Aware Asynchronous Data Transfer with Multicore SYStems. High-speed data transfer is vital to data-intensive computing that often requires moving large data volumes efficiently within a local data center and among geographically dispersed facilities. Effective utilization of the abundant resources in modern multicore environments for data transfer remains a persistent challenge, particularly, for Non-Uniform Memory Access (NUMA) systems wherein the locality of data accessing is an important factor. This requires rethinking how to exploit parallel access to data and to optimize the storage and network I/Os. We address this challenge and present a novel design of asynchronous processing and resource-aware task scheduling in the context of high-throughput data replication. Our software allocates multiple sets of threads to different stages of the processing pipeline, including storage I/O and network communication, based on their capacities. Threads belonging to each stage follow an asynchronous model, and attain high performance via multiple locality-aware and peer-aware mechanisms, such as task grouping, buffer sharing, affinity control and communication protocols. Our design also integrates high performance features to enhance the scalability of data transfer in several scenarios, e.g., file-level sorting, block-level asynchrony, and thread-level pipelining. Our experiments confirm the advantages of our software under different types of workloads and dynamic environments with contention for shared resources, including a 28-160 percent increase in bandwidth for transferring large files, 1.7-66 times speed-up for small files, and up to 108 percent larger throughput for mixed workloads compared with three state of the art alternatives, GridFTP , BBCP and Aspera.

Question: What is RAMSYS?


New acquisition method based on firmware update protocols for Android smartphones. Android remains the dominant OS in the smartphone market even though the iOS share of the market increased during the iPhone 6 release period. As various types of Android smartphones are being launched in the market, forensic studies are being conducted to test data acquisition and analysis. However, since the application of new Android security technologies, it has become more difficult to acquire data using existing forensic methods. In order to address this problem, we propose a new acquisition method based on analyzing the firmware update protocols of Android smartphones. A physical acquisition of Android smartphones can be achieved using the flash memory read command by reverse engineering the firmware update protocol in the bootloader. Our experimental results demonstrate that the proposed method is superior to existing forensic methods in terms of the integrity guarantee, acquisition speed, and physical dump with screen-locked smartphones (USB debugging disabled). © 2015 The Authors. Published by Elsevier Ltd on behalf of DFRWS. This is an open access articleunder theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Question: How to acquire data from a locked Android smartphone?


Watch What You Wear: Preliminary Forensic Analysis of Smart Watches. This work presents preliminary forensic analysis of two popular smart watches, the Samsung Gear 2 Neo and LG G. These wearable computing devices have the form factor of watches and sync with smart phones to display notifications, track footsteps and record voice messages. We posit that as smart watches are adopted by more users, the potential for them becoming a haven for digital evidence will increase thus providing utility for this preliminary work. In our work, we examined the forensic artifacts that are left on a Samsung Galaxy S4 Active phone that was used to sync with the Samsung Gear 2 Neo watch and the LG G watch. We further outline a methodology for physically acquiring data from the watches after gaining root access to them. Our results show that we can recover a swath of digital evidence directly form the watches when compared to the data on the phone that is synced with the watches. Furthermore, to root the LG G watch, the watch has to be reset to its factory settings which is alarming because the process may delete data of forensic relevance. Although this method is forensically intrusive, it may be used for acquiring data from already rooted LG watches. It is our observation that the data at the core of the functionality of at least the two tested smart watches, messages, health and fitness data, e-mails, contacts, events and notifications are accessible directly from the acquired images of the watches, which affirms our claim that the forensic value of evidence from smart watches is worthy of further study and should be investigated both at a high level and with greater specificity and granularity.

Question: What is the forensic value of smart watches?


Successful failure: what Foucault can teach us about privacy self-management in a world of Facebook and big data. The "privacy paradox" refers to the discrepancy between the concern individuals express for their privacy and the apparently low value they actually assign to it when they readily trade personal information for low-value goods online. In this paper, I argue that the privacy paradox masks a more important paradox: the self-management model of privacy embedded in notice-and-consent pages on websites and other, analogous practices can be readily shown to underprotect privacy, even in the economic terms favored by its advocates. The real question, then, is why privacy self-management occupies such a prominent position in privacy law and regulation. Borrowing from Foucault's late writings, I argue that this failure to protect privacy is also a success in ethical subject formation, as it actively pushes privacy norms and practices in a neoliberal direction. In other words, privacy self-management isn't about protecting people's privacy; it's about inculcating the idea that privacy is an individual, commodified good that can be traded for other market goods. Along the way, the self-management regime forces privacy into the market, obstructs the functioning of other, more social, understandings of privacy, and occludes the various ways that individuals attempt to resist adopting the market-based view of themselves and their privacy. Throughout, I use the analytics practices of Facebook and social networking sites as a sustained case study of the point.

Question: What is the difference between a privacy paradox and a privacy paradox?


Dynamic MRI reconstruction using low rank plus sparse tensor decomposition. In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the state-of-the-art reconstruction methods.

Question: What is the difference between the state-of-the-art and our proposed method?


Constructing attack scenarios through correlation of intrusion alerts. Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive intrusions, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. This paper presents a practical technique to address this issue. The proposed approach constructs attack scenarios by correlating alerts on the basis of  prerequisites and consequences of intrusions. Intuitively, the prerequisite of an intrusion is the necessary condition for the intrusion to be successful, while the consequence of an intrusion is the possible outcome of the intrusion. Based on the prerequisites and consequences of different types of attacks, the proposed approach correlates alerts by (partially) matching the consequence of some previous alerts and the prerequisite of some later ones. The contribution of this paper includes a formal framework for alert correlation, the implementation of an off-line alert correlator based on the framework, and the evaluation of our method with the 2000 DARPA intrusion detection scenario specific datasets. Our experience and experimental results have demonstrated the potential of the proposed method and its advantage over alternative methods.

Question: How to correlate alerts?


A Survey of Personality Computing. Personality is a psychological construct aimed at explaining the wide variety of human behaviors in terms of a few, stable and measurable individual characteristics. In this respect, any technology involving understanding, prediction and synthesis of human behavior is likely to benefit from Personality Computing approaches, i.e. from technologies capable of dealing with human personality. This paper is a survey of such technologies and it aims at providing not only a solid knowledge base about the state-of-the-art, but also a conceptual model underlying the three main problems addressed in the literature, namely Automatic Personality Recognition (inference of the true personality of an individual from behavioral evidence), Automatic Personality Perception (inference of personality others attribute to an individual based on her observable behavior) and Automatic Personality Synthesis (generation of artificial personalities via embodied agents). Furthermore, the article highlights the issues still open in the field and identifies potential application areas.

Question: What is personality computing?


Semi-supervised time series classification. The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other domain specialists. As in many other domains, there are often copious amounts of unlabeled data available. For example, the PhysioBank archive contains gigabytes of ECG data. In this work we propose a semi-supervised technique for building time series classifiers. While such algorithms are well known in text domains, we will show that special considerations must be made to make them both efficient and effective for the time series domain. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, handwritten documents, and video datasets. The experimental results demonstrate that our approach requires only a handful of labeled examples to construct accurate classifiers.

Question: What is semi-supervised time series classification?


Automatic License Plate Recognition: a Review. In recent years, many researches on Intelligent Transportation Systems (ITS) have been reported. Automatic License Plate Recognition (ALPR) is one form of ITS technology that not only recognizes and counts vehicles, but distinguishes each as unique by recognizing and recording the license plate's characters. This paper discusses the main techniques of ALPR. Several open problems are proposed at the end of the paper for future research.

Question: What is the main technique of ALPR?


Wavelet Feature Based Confusion Character Sets for Gujarati Script. Indic script recognition is a difficult task due to the large number of symbols that result from concatenation of vowel modifiers to basic consonants and the conjunction of consonants with modifiers etc. Recognition of Gujarati script is a less studied area and no attempt is made so far to constitute confusion sets of Gujarati glyphs. In this paper, we present confusion sets of glyphs in printed Gujarati. Feature vector made up of Daubechies D4 wavelet coefficients were subjected to two different classifiers, giving more than 96% accuracy for a larger set of symbols. Novel application of GR neural-net architecture allows for fast building of a classifier for the large character data set. The combined approach of wavelet feature extraction and GRNN classification has given the highest recognition accuracy reported on this script.

Question: What is the largest character set in Gujarati?


The Semantic Web - ISWC 2003. The Semantic Network, a component of the Unified Medical Language System® (UMLS), describes core biomedical knowledge consisting of semantic types and relationships. It is a well established, semi-formal ontology in widespread use for over a decade. We expected to "publish" this ontology on the Semantic Web, using OWL, with relatively little effort. However, we ran into a number of problems concerning alternative interpretations of the SN notation and the inability to express some of the interpretations in OWL. We detail these problems, as a cautionary tale to others planning to publish pre-existing ontologies on the Semantic Web, as a list of issues to consider when describing formally concepts in any ontology, and as a collection of criteria for evaluating alternative representations, which could form part of a methodology of ontology development.

Question: What is the semantic network?


CRF-CNN: Modeling Structured Information in Human Pose Estimation. Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model structural information in both output and hidden feature layers in a probabilistic way, and it is applied to human pose estimation. A message passing scheme is proposed, so that in various layers each body joint receives messages from all the others in an efficient way. Such message passing can be implemented with convolution between features maps in the same layer, and it is also integrated with feedforward propagation in neural networks. Finally, a neural network implementation of endto-end learning CRF-CNN is provided. Its effectiveness is demonstrated through experiments on two benchmark datasets.

Question: How to integrate convolution and feedforward propagation in neural networks?


A study of array antenna with phase compensated technique for 60 GHz communication. This paper presents phase compensated techniques for 60 GHz communication. A phase compensation method for antenna embedded in a mobile device and a planar lens for base station antenna are introduced. For the Line-of Sight (LoS) scenario, end-fire antenna arrays are adapted for mobile device. 8×1 end-fire patch antenna arrays are designed in Low temperature co-fired ceramic (LTCC) substrate. Simulated max gain of the antenna arrays is 13.03 dBi. When the antenna module is mounted, scattering problem is occurred and its max gain is severely degraded to 6.4 dBi. By using phase compensated technique, max gain of the antenna arrays are enhanced to 12.5 dBi. The beam steered gain of the arrays is also simulated and good coverage can be achieved. A planar lens antenna is also studied for high gain of the base station antenna.

Question: What is the max gain of the antenna arrays?


A One-Layer Recurrent Neural Network with a Discontinuous Activation Function for Linear Programming. A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.

Question: What is a recurrent neural network?


Video-Based Human Walking Estimation Using Joint Gait and Pose Manifolds. We study two fundamental issues about video-based human walking estimation, where the goal is to estimate 3D gait kinematics (i.e., joint positions) from 2D gait appearances (i.e., silhouettes). One is how to model the gait kinematics from different walking styles, and the other is how to represent the gait appearances captured under different views and from individuals of distinct walking styles and body shapes. Our research is conducted in three steps. First, we propose the idea of joint gait-pose manifold (JGPM), which represents gait kinematics by coupling two nonlinear variables, pose (a specific walking stage) and gait (a particular walking style) in a unified latent space. We extend the Gaussian process latent variable model (GPLVM) for JGPM learning, where two heuristic topological priors, a torus and a cylinder, are considered and several JGPMs of different degrees of freedom (DoFs) are introduced for comparative analysis. Second, we develop a validation technique and a series of benchmark tests to evaluate multiple JGPMs and recent GPLVMs in terms of their performance for gait motion modeling. It is shown that the toroidal prior is slightly better than the cylindrical one, and the JGPM of 4 DoFs that balances the toroidal prior with the intrinsic data structure achieves the best performance. Third, a JGPM-based visual gait generative model (JGPM-VGGM) is developed, where JGPM plays a central role to bridge the gap between the gait appearances and the gait kinematics. Our proposed JGPM-VGGM is learned from Carnegie Mellon University MoCap data and tested on the HumanEva-I and HumanEva-II data sets. Our experimental results demonstrate the effectiveness and competitiveness of our algorithms compared with existing algorithms.

Question: How to model human walking?


Bridging the divide in financial market forecasting: machine learners vs. financial economists. Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.

Question: What is the best forecasting method for financial markets?


Brain mechanisms of pain affect and pain modulation. Recent animal studies reveal ascending nociceptive and descending modulatory pathways that may contribute to the affective-motivational aspects of pain and play a critical role in the modulation of pain. In humans, a reliable pattern of cerebral activity occurs during the subjective experience of pain. Activity within the anterior cingulate cortex and possibly in other classical limbic structures, appears to be closely related to the subjective experience of pain unpleasantness and may reflect the regulation of endogenous mechanisms of pain modulation.

Question: What is the affective-motivational aspects of pain?


MaxMax: A Graph-Based Soft Clustering Algorithm Applied to Word Sense Induction. This paper introduces a linear time graph-based soft clustering algorithm. The algorithm applies a simple idea: given a graph, vertex pairs are assigned to the same cluster if either vertex has maximal affinity to the other. Clusters of varying size, shape, and density are found automatically making the algorithm suited to tasks such Word Sense Induction (WSI), where the number of classes is unknown and where class distributions may be skewed. The algorithm is applied to two WSI tasks, obtaining results comparable with those of systems adopting existing, state-of-the-art methods.

Question: What is a graph-based soft clustering algorithm?


Food image analysis: Segmentation, identification and weight estimation. We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, we describe the image analysis tools to determine the regions where a particular food is located (image segmentation), identify the food type (feature classification) and estimate the weight of the food item (weight estimation). An image segmentation and classification system is proposed to improve the food segmentation and identification accuracy. We then estimate the weight of food to extract the nutrient content from a single image using a shape template for foods with regular shapes and area-based weight estimation for foods with irregular shapes.

Question: How to estimate the weight of a food?


A printed antenna array fed by balanced Schiffman shifter used for UWB location system. A double-sided printed bow-tie antenna array fed by balanced Schiffman phase shifter, which used as a reader antenna in ultra-wideband (UWB) location system, is proposed in this paper. The presented antenna has a impedance bandwidth of 3.4-4.4 GHz and a high gain of more than 5dB within the whole bandwidth. Good omnidirectional radiation pattern is obtained in the plane of θ = 15°. Good simulation results are achieved by HFSS.

Question: What is a bow-tie antenna?


Automatic Chinese food identification and quantity estimation. Computer-aided food identification and quantity estimation have caught more attention in recent years because of the growing concern of our health. The identification problem is usually defined as an image categorization or classification problem and several researches have been proposed. In this paper, we address the issues of feature descriptors in the food identification problem and introduce a preliminary approach for the quantity estimation using depth information. Sparse coding is utilized in the SIFT and Local binary pattern feature descriptors, and these features combined with gabor and color features are used to represent food items. A multi-label SVM classifier is trained for each feature, and these classifiers are combined with multi-class Adaboost algorithm. For evaluation, 50 categories of worldwide food are used, and each category contains 100 photographs from different sources, such as manually taken or from Internet web albums. An overall accuracy of 68.3% is achieved, and success at top-N candidates achieved 80.6%, 84.8%, and 90.9% accuracy accordingly when N equals 2, 3, and 5, thus making mobile application practical. The experimental results show that the proposed methods greatly improve the performance of original SIFT and LBP feature descriptors. On the other hand, for quantity estimation using depth information, a straight forward method is proposed for certain food, while transparent food ingredients such as pure water and cooked rice are temporarily excluded.

Question: What is the food identification problem?


Churn Prediction : Does Technology Matter ?. The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. Keywords—Churn, Decision Trees, Neural Networks, Regression.

Question: What is churn?


Automated Assembly Using 3D and 2D Cameras. 2D and 3D computer vision systems are frequently being used in automated production to detect and determine the position of objects. Accuracy is important in the production industry, and computer vision systems require structured environments to function optimally. For 2D vision systems, a change in surfaces, lighting and viewpoint angles can reduce the accuracy of a method, maybe even to a degree that it will be erroneous, while for 3D vision systems, the accuracy mainly depends on the 3D laser sensors. Commercially available 3D cameras lack the precision found in high-grade 3D laser scanners, and are therefore not suited for accurate measurements in industrial use. In this paper, we show that it is possible to identify and locate objects using a combination of 2D and 3D cameras. A rough estimate of the object pose is first found using a commercially available 3D camera. Then, a robotic arm with an eye-in-hand 2D camera is used to determine the pose accurately. We show that this increases the accuracy to < 1 mm and < 1◦. This was demonstrated in a real industrial assembly task where high accuracy is required.

Question: What is the accuracy of a 3D camera?


Broadband biquad UHF antenna array for DOA. In this contribution a broadband biquad antenna array for DOA applications is investigated. Simulation and measurement results of the single broadband biquad antenna element and results of antenna array configurations for vertical polarisation, horizontal polarisation or polarisation independant direction of arrival estimation using different algorithms are shown.

Question: What is a broadband biquad antenna array for DOA?


Pair-Wise Temporal Pooling Method for Rapid Training of the HTM Networks Used in Computer Vision Applications. In the paper, several modifications to the conventional learning algorithms of the Hierarchical Temporal Memory (HTM) – a biologically inspired largescale model of the neocortex by Numenta – have been proposed. Firstly, an alternative spatial pooling method has been introduced, which makes use of a random pattern generator exploiting the Metropolis-Hastings algorithm. The original inference algorithm by Numenta has been reformulated, in order to reduce a number of tunable parameters and to optimize its computational efficiency. The main contribution of the paper consists in the proposal of a novel temporal pooling method – the pair-wise explorer – which allows faster and more reliable training of the HTM networks using data without inherent temporal information (e.g., static images). While the conventional temporal pooler trains the HTM network on a finite segment of the smooth Brownian-like random walk across the training images, the 902 S. Štolc, I. Bajla, K. Valent́ın, R. Škoviera proposed method performs training by means of the pairs of patterns randomly sampled (in a special manner) from a virtually infinite smooth random walk. We have conducted a set of experiments with the single-layer HTM network applied to the position, scale, and rotation-invariant recognition of geometric objects. The obtained results provide a clear evidence that the pair-wise method yields significantly faster convergence to the theoretical maximum of the classification accuracy with respect to both the length of the training sequence (defined by the maximum allowed number of updates of the time adjacency matrix – TAM) and the number of training patterns. The advantage of the proposed explorer manifested itself mostly in the lower range of TAM updates where it caused up to 10% relative accuracy improvement over the conventional method. Therefore we suggest to use the pair-wise

Question: What is the difference between the conventional and the proposed temporal pooling methods?


Analysis and Design of Tag Antenna Based UHF RFID for Libraries. A kind of UHF RFID tag antenna for library management is designed. The series of design requirements are proposed through rich analytics. Refer to the management model of intelligent library based on UHF RFID. For the antenna, the size is mm mm 3 90  , adopting T-shaped matching which good for adjusting of antenna impedance matching, the center frequency by simulations is 918 MHz under application environment, Return Loss is less than -10dB in the frequency band of 860MHz~960MHz , the minimum value of return loss is -24.4dB . Simulation results show that the tag antenna has a good performance under application environment for library.

Question: What is the size of the tag antenna?


Machine Learning Based Missing Value Imputation Method for Clinical Datasets. Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.

Question: What is a machine learning based missing value imputation method for clinical datasets?


Mechanisms of decompensation and organ failure in cirrhosis: From peripheral arterial vasodilation to systemic inflammation hypothesis.. The peripheral arterial vasodilation hypothesis has been most influential in the field of cirrhosis and its complications. It has given rise to hundreds of pathophysiological studies in experimental and human cirrhosis and is the theoretical basis of life-saving treatments. It is undisputed that splanchnic arterial vasodilation contributes to portal hypertension and is the basis for manifestations such as ascites and hepatorenal syndrome, but the body of research generated by the hypothesis has revealed gaps in the original pathophysiological interpretation of these complications. The expansion of our knowledge on the mechanisms regulating vascular tone, inflammation and the host-microbiota interaction require a broader approach to advanced cirrhosis encompassing the whole spectrum of its manifestations. Indeed, multiorgan dysfunction and failure likely result from a complex interplay where the systemic spread of bacterial products represents the primary event. The consequent activation of the host innate immune response triggers endothelial molecular mechanisms responsible for arterial vasodilation, and also jeopardizes organ integrity with a storm of pro-inflammatory cytokines and reactive oxygen and nitrogen species. Thus, the picture of advanced cirrhosis could be seen as the result of an inflammatory syndrome in contradiction with a simple hemodynamic disturbance.

Question: What is the mechanism of decompensation and organ failure in cirrhosis?


A Machine Learning-based Forensic Discriminator of Pornographic and Bikini Images. The increased use of microcomputers and smart-phones has contributed to social progress, but it has also facilitated the exchange of illegal files, such as child pornography photographs, increasing the demand for digital forensic examination in these devices, which can store more than 300,000 images. Based on the large number of files to be analyzed, it is necessary to use capable algorithms to perform this detection, especially in the most challenging scenarios, such as the distinction between pornographic and bikini images. In this work, we present an approach that improves the ``Algorithm for detection of Nudity proposed by Ap-Apid. Our method improved the performance of this algorithm by using machine learning in extracted features from detected skin regions instead of using static rules to classify this kind of images. In addition, we used detected faces as features in order to mitigate false positives in portrait photos. For conducting the training, validation and testing phases, we used the AIIA-PID4 pornographic data set. Furthermore, we also present a statistical analysis by comparing our approach to two algorithms, one based on the "Algorithm for detection of Nudity" and another proposed by the AIIA-PID4 pornographic data set author. The experimental results showed that we achieved an accuracy of 96.96% and 94.94 in the F1-score metric, increasing the accuracy by 79.19% and 18.21% compared to the referred works, respectively.

Question: What is the difference between a bikini and a pornographic image?


MAUI: making smartphones last longer with code offload. This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

Question: How does MAUI work?


SDN for network security. Software Defined Networking, SDN, is the programmable separation of control and forwarding elements of networking that enables software control of network forwarding that can be logically and/or physically separated from physical switches and routers. The following important question is considered in this paper: to what extent can SDN-based networks address network security management problems? The new opportunities for enhancing network security brought by this separation are considered in the paper.

Question: What is SDN?


Structure Based User Identification across Social Networks. Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users' content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users ar

Question: What is the difference between a supervised and an unsupervised scheme?


PAST-TENSE GENERATION FROM FORM VERSUS MEANING: BEHAVIOURAL DATA AND SIMULATION EVIDENCE.. The standard task used to study inflectional processing of verbs involves presentation of the stem form from which the participant is asked to generate the past tense. This task reveals a processing disadvantage for irregular relative to regular English verbs, more pronounced for lower-frequency items. Dual- and single-mechanism theories of inflectional morphology are both able to account for this pattern; but the models diverge in their predictions concerning the magnitude of the regularity effect expected when the task involves past-tense generation from meaning. In this study, we asked normal speakers to generate the past tense from either form (verb stem) or meaning (action picture). The robust regularity effect observed in the standard form condition was no longer reliable when participants were required to generate the past tense from meaning. This outcome would appear problematic for dual-mechanism theories to the extent that they assume the process of inflection requires stem retrieval. By contrast, it supports single-mechanism models that consider stem retrieval to be task-dependent. We present a single-mechanism model of verb inflection incorporating distributed phonological and semantic representations that reproduces this task-dependent pattern.

Question: What is the difference between form and meaning?


A Book Dewarping System by Boundary-Based 3D Surface Reconstruction. Non-contact imaging devices such as digital cameras and overhead scanners can convert hardcopy books to digital images without cutting them to individual pages. However, the captured images have distinct distortions. A book dewarping system is proposed to remove the perspective and geometric distortions automatically from single images. A book boundary model is extracted, and a 3D book surface is reconstructed. And then the horizontal and vertical metrics of each column are restored from it. Experimental results show the good dewarping and speed performance. Since no additional equipments and no restrictions to specific book layouts or contents are needed, the proposed system is very practical in real applications.

Question: How to dewarp a book?


Protecting Respondents' Identities in Microdata Release. ÐToday's globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of the respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal

Question: How to protect the anonymity of respondents in microdata release?


A 37.5 /spl mu/W Body Channel Communication Wake-Up Receiver With Injection-Locking Ring Oscillator for Wireless Body Area Network. An ultra-low power wake-up receiver for body channel communication (BCC) is implemented in 130 nm CMOS process. The proposed wake-up receiver uses the injection-locking ring oscillator (ILRO) to replace the RF amplifier with low power consumption. Through the ILRO, the frequency modulated input signal is amplified to the full swing rectangular signal directly demodulated by the following low power PLL-based FSK demodulator. In addition, the energy-efficient BCC link mitigates the sensitivity and selectivity requirements for the receiver, which significantly reduces the power consumption. Furthermore, the auto frequency calibrator (AFC) is adopted to compensate the free running frequency of the ring oscillator which is caused by temperature variation and leakage current. The AFC reuses the PLL-based demodulator to periodically set the free running frequency to the desired frequency without any area overhead. As a result, the proposed wake-up receiver achieves a sensitivity of -62.7 dBm at a data rate of 200 kbps while consuming only 37.5 μW from the 0.7 V supply.

Question: What is the difference between a wake-up receiver and a receiver?


Filling Holes in Complex Surfaces using Volumetric Diffusion. We address the problem of building watertight 3D models from surfaces that contain holes − for example, sets of range scans that observe most but not all of a surface. We specifically address situations in which the holes are too geometrically and topologically complex to fill using triangulation algorithms. Our solution begins by constructing a signed distance function, the zero set of which defines the surface. Initially, this function is defined only in the vicinity of observed surfaces. We then apply a diffusion process to extend this function through the volume until its zero set bridges whatever holes may be present. If additional information is available, such as known-empty regions of space inferred from the lines of sight to a 3D scanner, it can be incorporated into the diffusion process. Our algorithm is simple to implement, is guaranteed to produce manifold noninterpenetrating surfaces, and is efficient to run on large datasets because computation is limited to areas near holes.

Question: How to fill holes in complex surfaces?


Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources. The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation.   In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice.   By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation.

Question: What is the joint representation learning for top-n recommendation?


Putting value co-creation into practice: a case for advisory support. The concept of value co-creation and its notion of the customer as co-creator of value have gained much academic interest, notably in marketing and operations research. While several competing perspectives have been conceptually discussed in literature, research on the practical implications of value co-creation is scarce. Using the example of sales-oriented advisory, we show gaps between existing co-creation concepts and current practice in five problem areas. We develop four general solution perspectives on the advisor-client encounter as guidelines to overcome these gaps and discuss design requirements of their technological instantiations in advisory support systems. We present exemplary implementations of such systems in two domains: travel counseling and financial advisory. Revealing the practical implications of value co-creation on advisory encounters, these examples also demonstrate that the solution perspectives have to be implemented quite differently for individual domains.

Question: What is the difference between value co-creation and value co-creation?


Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences. With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like 'What outfit goes well with this pair of shoes?' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In this paper, we propose a novel learning framework to help answer these types of questions. The main idea of this framework is to learn a feature transformation from images of items into a latent space that expresses compatibility. For the feature transformation, we use a Siamese Convolutional Neural Network (CNN) architecture, where training examples are pairs of items that are either compatible or incompatible. We model compatibility based on co-occurrence in large-scale user behavior data, in particular co-purchase data from Amazon.com. To learn cross-category fit, we introduce a strategic method to sample training data, where pairs of items are heterogeneous dyads, i.e., the two elements of a pair belong to different high-level categories. While this approach is applicable to a wide variety of settings, we focus on the representative problem of learning compatible clothing style. Our results indicate that the proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together.

Question: What outfit goes well with this pair of shoes?


Visualizing Scholarly Publications and Citations to Enhance Author Profiles. With data on scholarly publications becoming more abundant and accessible, there exist new opportunities for using this information to provide rich author profiles to display and explore scholarly work. We present a pair of linked visualizations connected to the Microsoft Academic Graph that can be used to explore the publications and citations of individual authors. We provide an online application with which a user can manage collections of papers and generate these visualizations.

Question: How to visualize scholarly publications and citations?


Active torque control of electric power steering system using composite nonlinear feedback control. Electric power steering (EPS) system plays an important role in assisting the driver to achieve better handling, steering feel and response in securing vehicle stability. In EPS control system, controlling the torque of the EPS motor is crucial where tracking of the robust command of motor angle to generate desired assist torque is very challenging. In this paper, a reference model is adopted to generate an ideal motor angle that can guarantee the desired performance and composite nonlinear feedback (CNF) control is applied to achieve a faster transient response in tracking the robust reference motor angle. The H2 state optimization is used to obtain the linear control parameters whereas Hooke-Jeeves auto-tuning algorithm is used to fine-tune the nonlinear control parameters to achieve the best transient performance. The proposed control strategy is validated with CarSim Software by interfacing Simulink model with CarSim full nonlinear vehicle model under different test procedures and compared to other controllers based on ITAE performance criteria.

Question: How to control the torque of the EPS motor?


A Persona-Based Neural Conversation Model. We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speakeraddressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.

Question: What is a persona-based neural conversation model?


A System to Verify Network Behavior of Known Cryptographic Clients. Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. In this paper, we develop a system for verifying in near real-time that a cryptographic client's message sequence is consistent with its known implementation. Moreover, we accomplish this without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client's messages explores multiple candidate execution paths in the client concurrently, an innovation useful for aspects of certain cryptographic protocols such as message padding (which will be permitted in TLS 1.3). In addition, our toolchain includes a novel approach to symbolically executing the client software in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL and BoringSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet. On legitimate traffic our verification keeps pace with Gmail-shaped workloads, with a median lag of 0.85s.

Question: What is the difference between a client and a server?


Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments. We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus coldwater valve usage classification and a comparisonbetween two classification approaches: the template-based matching scheme used in Froehlich et al. (2009) [53] and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valveand fixturelevel water events with greater than 90% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward. © 2010 Elsevier B.V. All rights reserved.

Question: What is the difference between a single point sensor and a multi-point sensor?


Worthiness of Sentences in Scientific Reports. Does this sentence need citation? In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. The task is to detect whether a sentence in a scientific article needs to be cited or not. It can be incorporated into citation recommendation systems to help automate the citation process by marking sentences where needed. It may also be useful for publishers to regularize the citation process. We construct a dataset using the ACLAnthology Reference Corpus; consisting of over 1.1M "not_cite" and 85K "cite" sentences. We study the performance of a set of state-of-the-art sentence classifiers for the citation worthiness task and show the practical challenges. We also explore sectionwise difficulty of the task and analyze the performance of our best model on a published article. ACM Reference Format: Hamed Bonab, Hamed Zamani, Erik Learned-Miller, and James Allan. 2018. Citation Worthiness of Sentences in Scientific Reports. In SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8–12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3209978.3210162

Question: What is the citation worthiness of this sentence?


A comprehensive model of PMOS NBTI degradation. Negative bias temperature instability has become an important reliability concern for ultra-scaled Silicon IC technology with significant implications for both analog and digital circuit design. In this paper, we construct a comprehensive model for NBTI phenomena within the framework of the standard reaction–diffusion model. We demonstrate how to solve the reaction–diffusion equations in a way that emphasizes the physical aspects of the degradation process and allows easy generalization of the existing work. We also augment this basic reaction–diffusion model by including the temperature and field-dependence of the NBTI phenomena so that reliability projections can be made under arbitrary circuit operating conditions. 2004 Published by Elsevier Ltd.

Question: How to solve the reaction–diffusion equations in a way that emphasizes the physical aspects of the degradation process and allows easy generalization of the existing work?


Programming languages and dimensions. Scientists and engineers must ensure that the equations and formulae which they use are dimensionally consistent, but existing programming languages treat all numeric values as dimensionless. This thesis investigates the extension of programming languages to support the notion of physical dimension. A type system is presented similar to that of the programming language ML but extended with polymorphic dimension types. An algorithm which infers most general dimension types automatically is then described and proved correct. The semantics of the language is given by a translation into an explicitlytyped language in which dimensions are passed as arguments to functions. The operational semantics of this language is specified in the usual way by an evaluation relation defined by a set of rules. This is used to show that if a program is well-typed then no dimension errors can occur during its evaluation. More abstract properties of the language are investigated using a denotational semantics: these include a notion of invariance under changes in the units of measure used, analogous to parametricity in the polymorphic lambda calculus. Finally the dissertation is summarised and many possible directions for future research in dimension types and related type systems are described.

Question: What is the dimension of a programming language?


IGBT and Diode Behavior During Short-Circuit Type 3. A short circuit during inverter operation can result in the so-called short-circuit types 2 and 3. The short-circuit type 3 results in an interaction with several current commutations between the Insulate-Gate Bipolar Transistor (IGBT) and the antiparallel diode. The switched ON IGBT, conducting no current before the short circuit occurs, has no plasma inside and can block any voltage. It behaves like a voltage-controlled current source, which can stress the diode by commutating the complete short-circuit current into the diode. An avalanche arises inside the diode, which can lead to a destruction of the diode. The current through the diode determines dv/dt and makes the diode a dominating semiconductor during the short circuit. The current source behavior of the IGBT can be verified with semiconductor simulations and measurements on a high-power 3.3-kV IGBT. In the simulations, the IGBT is replaced by a current source, which gives similar results. For the measurements, the gate-emitter voltage is varied to change the IGBT current without influencing the collector-emitter voltage.

Question: What is the difference between a short circuit type 2 and a short circuit type 3?


Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text. This paper describes the shared task for the English Twitter geolocation prediction associated with WNUT 2016. We discuss details of the task settings, data preparation and participant systems. The derived dataset and performance figures from each system provide baselines for future research in this realm.

Question: What is the size of the twitter dataset?


Verification of Periodically Controlled Hybrid Systems: Application to an Autonomous Vehicle. This article introduces Periodically Controlled Hybrid Automata (PCHA) for modular specification of embedded control systems. In a PCHA, control actions that change the control input to the plant occur roughly periodically, while other actions that update the state of the controller may occur in the interim. Such actions could model, for example, sensor updates and information received from higher-level planning modules that change the set point of the controller. Based on periodicity and subtangential conditions, a new sufficient condition for verifying invariant properties of PCHAs is presented. For PCHAs with polynomial continuous vector fields, it is possible to check these conditions automatically using, for example, quantifier elimination or sum of squares decomposition. We examine the feasibility of this automatic approach on a small example. The proposed technique is also used to manually verify safety and progress properties of a fairly complex planner-controller subsystem of an autonomous ground vehicle. Geometric properties of planner-generated paths are derived which guarantee that such paths can be safely followed by the controller.

Question: What is a periodically controlled hybrid automata?


Artificial Neural Networks to Forecast Air Pollution 221 Artificial Neural Networks to Forecast Air Pollution. European laws concerning urban and suburban air pollution requires the analysis and implementation of automatic operating procedures in order to prevent the risk for the principal air pollutants to be above alarm thresholds (e.g. the Directive 2002/3/EC for ozone or the Directive 99/30/CE for the particulate matter with an aerodynamic diameter of up to 10 μm, called PM10). As an example of European initiative to support the investigation of air pollution forecast, the COST Action ES0602 (Towards a European Network on Chemical Weather Forecasting and Information Systems) provides a forum for standardizing and benchmarking approaches in data exchange and multi-model capabilities for air quality forecast and (near) real-time information systems in Europe, allowing information exchange between meteorological services, environmental agencies, and international initiatives. Similar efforts are also proposed by the National Oceanic and Atmospheric Administration (NOAA) in partnership with the United States Environmental Protection Agency (EPA), which are developing an operational, nationwide Air Quality Forecasting (AQF) system. Critical air pollution events frequently occur where the geographical and meteorological conditions do not permit an easy circulation of air and a large part of the population moves frequently between distant places of a city. These events require drastic measures such as the closing of the schools and factories and the restriction of vehicular traffic. Indeed, many epidemiological studies have consistently shown an association between particulate air pollution and cardiovascular (Brook et al., 2007) and respiratory (Pope et al., 1991) diseases. The forecasting of such phenomena with up to two days in advance would allow taking more efficient countermeasures to safeguard citizens' health. Air pollution is highly correlated with meteorologi

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