Efficient OFDMA for LiFi Downlink. Light-fidelity (LiFi) relies on visible light communication to provide multiuser access in a small cell. For higher throughput, this paper studies the resource allocation problem for an orthogonal frequency-division multiplexing access scheme for LiFi downlink transmissions and presents a joint design of the bias level, power and subcarrier allocation. Decomposing the original optimization into subproblems, we first develop an efficient algorithm for the bias optimization, along with its performance analysis in terms of convergence properties and the global optimality. We then propose several power and subcarrier allocation strategies, including an optimal algorithm and a near-optimal simplified method, to satisfy different complexity and accuracy requirements. Finally, we present two algorithms to jointly optimize the bias, power, and subcarrier, and analyze the convergence properties in terms of both the objective and the solution. Comprehensive simulation results under practical LiFi system setups illustrate the effectiveness of the proposed algorithms.

Question: What is the bias level for LiFi?


Rules and Ontologies for the Semantic Web. Rules and ontologies play a key role in the layered architecture of the Semantic Web, as they are used to ascribe meaning to, and to reason about, data on the Web. While the Ontology Layer of the Semantic Web is quite developed, and the Web Ontology Language (OWL) is a W3C recommendation since a couple of years already, the rules layer is far less developed and an active area of research; a number of initiatives and proposals have been made so far, but no standard as been released yet. Many implementations of rule engines are around which deal with Semantic Web data in one or another way. This article gives a comprehensive, although not exhaustive, overview of such systems, describes their supported languages, and sets them in relation with theoretical approaches for combining rules and ontologies as foreseen in the Semantic Web architecture. In the course of this, we identify desired properties and common features of rule languages and evaluate existing systems against their support. Furthermore, we review technical problems underlying the integration of rules and ontologies, and classify representative proposals for theoretical integration approaches into different categories.

Question: What is the ontology layer of the semantic web?


Modeling Propagation Dynamics and Developing Optimized Countermeasures for Rumor Spreading in Online Social Networks. The spread of rumors in Online Social Networks (OSNs) poses great challenges to the social peace and public order. It is imperative to model propagation dynamics of rumors and develop corresponding countermeasures. Most of the existing works either overlook the heterogeneity of social networks or do not consider the cost of countermeasures. Motivated by these issues, this paper proposes a heterogeneous network based epidemic model that incorporates both the network heterogeneity and various countermeasures. Through analyzing the existence and stability of equilibrium solutions of the proposed ODE (Ordinary Differential Equation) system, the critical conditions that determine whether a rumor continuously propagates or becomes extinct are derived. Moreover, we concern about the cost of the main two types of countermeasures, i.e., Blocking rumors at influential users and spreading truth to clarify rumors. Employing the Pontryagin's maximum principle, we obtain the optimized countermeasures that ensures a rumor can become extinct at the end of an expected time period with lowest cost. Both the critical conditions and the optimized countermeasures provide a real-time decision reference to restrain the rumor spreading. Experiments based on Digg2009 dataset are conducted to evaluate the effectiveness of the proposed dynamic model and the efficiency of the optimized countermeasures.

Question: How to model rumor spreading in online social networks?


A German Twitter Snapshot. We present a new corpus of German tweets. Due to the relatively small number of German messages on Twitter, it is possible to collect a virtually complete snapshot of German twitter messages over a period of time. In this paper, we present our collection method which produced a 24 million tweet corpus, representing a large majority of all German tweets sent in April, 2013. Further, we analyze this representative data set and characterize the German twitterverse. While German Twitter data is similar to other Twitter data in terms of its temporal distribution, German Twitter users are much more reluctant to share geolocation information with their tweets. Finally, the corpus collection method allows for a study of discourse phenomena in the Twitter data, structured into discussion threads.

Question: What is the German twitterverse?


Multi-granularity Generator for Temporal Action Proposal. Temporal action proposal generation is an important task, aiming to localize the video segments containing human actions in an untrimmed video. In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information. First, we propose to use a bilinear matching model to exploit the rich local information within the video sequence. Afterwards, two components, namely segment proposal generator (SPG) and frame actionness generator (FAG), are combined to perform the task of temporal action proposal at two distinct granularities. SPG considers the whole video in the form of feature pyramid and generates segment proposals from one coarse perspective, while FAG carries out a finer actionness evaluation for each video frame. Our proposed MGG can be trained in an end-to-end fashion. Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets. Moreover, we employ existing action classifiers to perform the classification of the proposals generated by MGG, leading to significant improvements compared against the competing methods for the video detection task.

Question: What is the multi-granularity generator for temporal action proposal?


Overconfidence as a cause of diagnostic error in medicine.. The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this tendency to overconfidence is related to both intrinsic and systemically reinforced factors. We present a comprehensive review of the available literature and current thinking related to these issues. The review covers the incidence and impact of diagnostic error, data on physician overconfidence as a contributing cause of errors, strategies to improve the accuracy of diagnostic decision making, and recommendations for future research.

Question: What is the incidence of diagnostic error in medicine?


5.10 A 1.4V 10.5MHz swing-boosted differential relaxation oscillator with 162.1dBc/Hz FOM and 9.86psrms period jitter in 0.18µm CMOS. Relaxation oscillators have a profound scope as on-chip reference clock sources or sensor front-ends in comparison to ring oscillators due to their superior frequency stability, control linearity, and wide tuning range. However, despite a better fundamental limit predicted in theory, the phase noise performance of relaxation oscillators trails behind that of ring oscillators. Furthermore, there is a huge gap between the maximum achievable 1/f2 phase noise FOM (169dBc/Hz) [1] and those achieved by recently proposed low-power relaxation oscillator implementations [1-4].

Question: What is the difference between a relaxation oscillator and a ring oscillator?


Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.

Question: What is a convolutional neural network?


Data-Efficient Hierarchical Reinforcement Learning. Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higherand lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higherand lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations,1 learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques.2

Question: What is the difference between a robot and a human?


#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter. Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-theart face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.

Question: What is the difference between #yankees and #greysanatomy?


On The Architecture Scheduling Problem Of Industry 4.0. The recently emerged Fourth Industrial Revolution (Industry 4.0) is characterised by the introduction of the new Cyber-Physical System (CPS) concepts and the Internet of Things (IoT) paradigm. These new collaborating computational entities offer a broad range of opportunity to consider with a different perspective. One of the perennial problems of the manufacturing operation is the scheduling problem of typical job-shop manufacturing systems. Starting from a comparison with the typical architecture of an operating systems scheduler module, we introduce a new manufacturing scheduling architecture. Overcoming the typical Full-Hierarchical configuration defined in the ANSI/ISA 95 in favour of a SemiHeterarchical one, the introduced scheduling architecture leads to a mixture of proactive and reactive approach to the Job-shop Scheduling Problem (JSP), taking advantage from both the common decentralised and the centralised methodology. Keywords— Industry 4.0; Cyber-Physical System (CPS); JobShop Scheduling Problem; Manufacturing System; System of

Question: What is the architecture of an operating systems scheduler module?


Graph based E-Government web service composition. Nowadays, e-government has emerged as a government policy to improve the quality and efficiency of public administrations. By exploiting the potential of new information and communication technologies, government agencies are providing a wide spectrum of online services. These services are composed of several web services that comply with well defined processes. One of the big challenges is the need to optimize the composition of the elementary web services. In this paper, we present a solution for optimizing the computation effort in web service composition. Our method is based on Graph Theory. We model the semantic relationship between the involved web services through a directed graph. Then, we compute all shortest paths using for the first time, an extended version of the FloydWarshall algorithm.

Question: What is the composition of a web service?


Toward Non-Intrusive Load Monitoring via Multi-Label Classification. Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers' energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home's power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RAkEL), and a bespoke multi-label classification algorithm (MLkNN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

Question: What is the difference between a time-domain and a wavelet-domain feature set?


A Framework for Clustering Uncertain Data Streams. In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.

Question: How to cluster uncertain data streams?


Music Education and Its Impact on Students with Special Needs. Part of the Curriculum and Instruction Commons, Curriculum and Social Inquiry Commons, Disability and Equity in Education Commons, Educational Methods Commons, Elementary Education and Teaching Commons, Higher Education and Teaching Commons, Junior High, Intermediate, Middle School Education and Teaching Commons, Other Educational Administration and Supervision Commons, Other Teacher Education and Professional Development Commons, Pre-Elementary, Early Childhood, Kindergarten Teacher Education Commons, Scholarship of Teaching and Learning Commons, and the Special Education Administration Commons Survey: Let us know how this paper benefits you.

Question: What is the impact of music education on students with special needs?


Imagination-Based Decision Making with Physical Models in Deep Neural Networks. Decision-making is challenging in continuous settings where complex sequences of events determine rewards, even when these event sequences are largely observable. In particular, traditional trial-and-error learning strategies may have a hard time associating continuous actions with their reward because of the size of the state space and the complexity of the reward function. Given a model of the world, a different strategy is to use imagination to exploit the knowledge embedded in that model. In this regime, the system directly optimizes the decision for each episode based on predictions from the model. We extend deep learning methods that have been previously used for model-free learning and apply them towards a model-based approach in which an expert is consulted multiple times in the agents' imagination before it takes an action in the world. We show preliminary results on a difficult physical reasoning task where our model-based approach outperforms a model-free baseline, even when using an inaccurate expert.

Question: How do deep neural networks work?


Matchmaking in multi-player on-line games: studying user traces to improve the user experience. Designing and implementing a quality matchmaking service for Multiplayer Online Games requires an extensive knowledge of the habits, behaviors and expectations of the players. Gathering and analyzing traces of real games offers insight on these matters, but game server providers are very protective of such data in order to deter possible reuse by the competition and to prevent cheating. We circumvented this issue by gathering public data from a League of Legends server (information over more than 28 million game sessions). In this paper, we present our database which is freely available online, and we detail the analysis and conclusions we draw from this data regarding the expected requirements for the matchmaking service.

Question: What is the league of legends database?


Learning a Deep Convolutional Network for Image Super-Resolution. We propose a deep learning method for single image superresolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the lowresolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.

Question: What is a deep convolutional neural network?


Affordances in Grounded Language Learning. We present a novel methodology involving mappings between different modes of semantic representations. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner's direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.

Question: What is a cognitively inspired model of grounded language learning?


The emulation theory of representation: motor control, imagery, and perception.. The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

Question: What is the emulation theory of representation?


Implementation of a Cognitive Radio Front-End Using Rotatable Controlled Reconfigurable Antennas. This communication presents a new antenna system designed for cognitive radio applications. The antenna structure consists of a UWB antenna and a frequency reconfigurable antenna system. The UWB antenna scans the channel to discover "white space" frequency bands while tuning the reconfigurable section to communicate within these bands. The frequency agility is achieved via a rotational motion of the antenna patch. The rotation is controlled by a stepper motor mounted on the back of the antenna structure. The motor's rotational motion is controlled by LABVIEW on a computer connected to the motor through its parallel port. The computer's parallel port is connected to a NPN Darlington array that is used to drive the stepper motor. The antenna has been simulated with the driving motor being taken into consideration. A good agreement is found between the simulated and the measured antenna radiation properties.

Question: How to implement a rotatable controlled reconfigurable antenna?


A Rule-Based Framework of Metadata Extraction from Scientific Papers. Most scientific documents on the web are unstructured or semi-structured, and the automatic document metadata extraction process becomes an important task. This paper describes a framework for automatic metadata extraction from scientific papers. Based on a spatial and visual knowledge principle, our system can extract title, authors and abstract from scientific papers. We utilize format information such as font size and position to guide the metadata extraction process. The experiment results show that our system achieves a high accuracy in header metadata extraction which can effectively assist the automatic index creation for digital libraries.

Question: What is a rule-based framework of metadata extraction from scientific papers?


SME e-readiness in Malaysia : Implications for Planning and Implementation. This study hoped to answer 2 main objectives. The first objective was to assess the level of e-readiness of SMEs in Northern Malaysia. The second objective was to investigate the factors contributing to the e-readiness of SMEs in Northern Malaysia. Questionnaires were distributed using a simple random sampling method to 300 SMEs in Penang, Kedah and Perlis. The findings of this study show that SMEs in Northern Malaysia are ready to go for e-business, e-commerce and Internet in general. The findings also showed that in general top management commitment and infrastructure and technology have significant impact on SMEs' e-readiness. However, human capital, resistance to change, and information security do not have significant impact or contribution on e-readiness in SMEs.

Question: What is the level of e-readiness of SMEs in Northern Malaysia?


The world of emotions is not two-dimensional.. For more than half a century, emotion researchers have attempted to establish the dimensional space that most economically accounts for similarities and differences in emotional experience. Today, many researchers focus exclusively on two-dimensional models involving valence and arousal. Adopting a theoretically based approach, we show for three languages that four dimensions are needed to satisfactorily represent similarities and differences in the meaning of emotion words. In order of importance, these dimensions are evaluation-pleasantness, potency-control, activation-arousal, and unpredictability. They were identified on the basis of the applicability of 144 features representing the six components of emotions: (a) appraisals of events, (b) psychophysiological changes, (c) motor expressions, (d) action tendencies, (e) subjective experiences, and (f) emotion regulation.

Question: What is the dimensional space that most economically accounts for similarities and differences in emotional experience?


Data Science , Predictive Analytics , and Big Data : A Revolution That Will Transform Supply Chain Design and Management. W e illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.

Question: What is the difference between data science and predictive analytics?


Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz. The Yiddish language is over 1,000 years old and incorporates German, Slavic, and Hebrew elements. The prevalent view claims Yiddish has a German origin, whereas the opposing view posits a Slavic origin with strong Iranian and weak Turkic substrata. One of the major difficulties in deciding between these hypotheses is the unknown geographical origin of Yiddish speaking Ashkenazic Jews (AJs). An analysis of 393 Ashkenazic, Iranian, and mountain Jews and over 600 non-Jewish genomes demonstrated that Greeks, Romans, Iranians, and Turks exhibit the highest genetic similarity with AJs. The Geographic Population Structure analysis localized most AJs along major primeval trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from "Ashkenaz." Iranian and mountain Jews were localized along trade routes on the Turkey's eastern border. Loss of maternal haplogroups was evident in non-Yiddish speaking AJs. Our results suggest that AJs originated from a Slavo-Iranian confederation, which the Jews call "Ashkenazic" (i.e., "Scythian"), though these Jews probably spoke Persian and/or Ossete. This is compatible with linguistic evidence suggesting that Yiddish is a Slavic language created by Irano-Turko-Slavic Jewish merchants along the Silk Roads as a cryptic trade language, spoken only by its originators to gain an advantage in trade. Later, in the 9th century, Yiddish underwent relexification by adopting a new vocabulary that consists of a minority of German and Hebrew and a majority of newly coined Germanoid and Hebroid elements that replaced most of the original Eastern Slavic and Sorbian vocabularies, while keeping the original grammars intact.

Question: What is the origin of the Yiddish language?


MonoPerfCap : Human Performance Capture from Monocular Video WEIPENGXU , AVISHEKCHATTERJEE , MICHAELZOLLHÖFER , HELGERHODIN , DUSHYANTMEHTA. We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.

Question: How many people are in the canadian military?


On Cognitive Computing. Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing. [Article copies are available for purchase from InfoSci-on-Demand.com]

Question: What is cognitive computing?


Show and tell: A neural image caption generator. Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.

Question: What is the current state-of-the-art on the COCO dataset?


Adaptive median filters: new algorithms and results. Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L(p) filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters.

Question: What is the adaptive median filter?


HPI Question Answering System in BioASQ 2016. Question answering (QA) systems are crucial when searching for exact answers for natural language questions in the biomedical domain. Answers to many of such questions can be extracted from the 26 millions biomedical publications currently included in MEDLINE when relying on appropriate natural language processing (NLP) tools. In this work we describe our participation in the task 4b of the BioASQ challenge using two QA systems that we developed for biomedicine. Preliminary results show that our systems achieved first and second positions in the snippet retrieval sub-task and for the generation of ideal answers.

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


A Wide-Range Model for Metal-Oxide Surge Arrester. This paper presents an electric model of a metal-oxide surge arrester (MOSA). The proposed electric model accurately represents the MOSA in a wide range of frequencies and amplitudes. The model was developed and validated based on MOSA electrical behavior in each one of the three operating regions of the zinc-oxide (ZnO) surge arresters, and in a database composed of voltage and current waveforms measured from tests performed in 12 ZnO varistors having different physical dimensions and electrical characteristics—from five different manufacturers. These varistors were subjected to different voltage levels in the low current region, and multilevel amplitude of switching current impulses (30/60 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), lightning current impulses (8/20 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), high current impulses (4/10 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), and fast-front current impulses (1.5/26 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s and 3/6 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s) encompass the three regions of operation and a wide range of frequencies and amplitudes. The results provided by the MOSA wide-range (MWR) model were compared with those obtained in the laboratory. The MWR model has shown good agreement in terms of waveform, peak value, and absorbed energy for the evaluated cases.

Question: What is the wide-range model?


Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks. We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-world video frames. We also show that our model can be applied to visual analogy-making, and present an analysis of the learned network representations.

Question: What is the difference between a deterministic and a probabilistic model?


Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC. High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

Question: What is residual highway convolutional neural network?


Work Teams Applications and Effectiveness. " This article uses an ecological approach to analyze factors in the effectiveness of work teams--small groups of interdependent individuals who share responsibility for outcomes for their organizations. Applications include advice and involvement, as in quality control circles and committees; production and service, as in assembly groups and sales teams; projects and development, as in engineering and research groups; and action and negotiation, as in sports teams and combat units. An analytic framework depicts team effectiveness as interdependent with organizational context, boundaries, and team development. Key context factors include (a) organizational culture, (b) technology and task design, (c) mission clarity, (d) autonomy, (e) rewards, ( f ) performance feedback, (g) training/consultation, and (h) physical environment. Team boundaries may mediate the impact of organizational context on team development. Current research leaves unanswered questions but suggests that effectiveness depends on organizational context and boundaries as much as on internal processes. Issues are raised for research and practice. The terms work team and work group appear often in today's discussions of organizations. Some experts claim that to be effective modern firms need to use small teams for an increasing variety of jobs. For instance, in an article subtitled "The Team as Hero," Reich (1987) wrote, If we are to compete in today's world, we must begin to celebrate collective entrepreneurship, endeavors in which the whole of the effort is greater than the sum of individual contributions. We need to honor our teams more, our aggressive leaders and maverick geniuses less. (p. 78) Work teams occupy a pivotal role in what has been described as a management transformation (Walton, 1985), paradigm shift (Ketehum, 1984), and corporate renaissance (Kanter, 1983). In this management revolution, Peters (1988) advised that organizations use "multi-f

Question: What is the difference between work teams and work groups?


An ear-worn continuous ballistocardiogram (BCG) sensor for cardiovascular monitoring. Traditionally, ballistocardiogram (BCG) has been measured using large and stationary devices. In this work, we demonstrate a portable and continuous BCG monitor that is wearable at the ear. The device has the form factor of a hearing aid and is wirelessly connected to a PC for data recording and analysis. With the ear as an anchoring point, the device uses a MEMS tri-axial accelerometer to measure BCG at the head. Morphological differences exist between head BCG and traditional BCG, but the principal peaks (J waves) and their vectors are preserved. The frequency of J waves corresponds to heart rate, and when used in conjunction with an electrocardiogram's (ECG) R wave, the timing of J waves yields the RJ interval. Results from our clinical study show linear correlation between the RJ interval and the heart's pre-ejection period during hemodynamic maneuvers, thus revealing important information about cardiac contractility and its regulation.

Question: What is a ballistocardiogram?


Learning Attractor Dynamics for Generative Memory. A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively clean up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we avoid the vanishing gradient problem by training a generative distributed memory without simulating the attractor dynamics. Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model.

Question: How does a generative memory work?


Visceral pain: the ins and outs, the ups and downs.. PURPOSE OF REVIEW Visceral pain represents a major clinical problem, yet far less is known about its mechanisms compared with somatic pains, for example, from cutaneous and muscular structures.   RECENT FINDINGS In this review, we describe the neuroanatomical bases of visceral pain signalling in the peripheral and central nervous system, comparing to somatic pains and also the channels and receptors involved in these events. We include an overview of potential new targets in the context of mechanisms of visceral pain and hypersensitivity.   SUMMARY This review should inform on the recognition of what occurs in patients with visceral pain, why comorbidities are common and how analgesic treatments work.

Question: What is the difference between visceral and somatic pain?


Fast and Robust High Dynamic Range Image Generation with Camera and Object Movement. High dynamic range (HDR) images play an important role in todays computer graphics: Typical applications are the improved display of a photograph with a tone mapper and the relighting of a virtual object in a natural environment using an HDR environment map. Generating an HDR image usually requires a sequence of photographs with different exposure times which are combined to a single image. In practice two problems occur when taking an image sequence: The camera is moving or objects are moving during the sequence. This results in a blurry HDR image due to the camera movement and moving objects appear multiple times as ghost images in the combined image. In this paper, we present a simple and fast algorithm for a robust HDR generation that removes these artifacts without prior knowledge of the camera response function.

Question: How to generate an HDR image?


Causal Generative Domain Adaptation Networks. An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments.

Question: What is the difference between causal generative domain adaptation and generative domain adaptation?


MapReduce : Distributed Computing for Machine Learning. We use Hadoop, an open-source implementation of Google's distributed file system and the MapReduce framework for distributed data processing, on modestly-sized compute clusters to evaluate its efficacy for standard machine learning tasks. We show benchmark performance on searching and sorting tasks to investigate the effects of various system configurations. We also distinguish classes of machine-learning problems that are reasonable to address within the MapReduce framework, and offer improvements to the Hadoop implementation. We conclude that MapReduce is a good choice for basic operations on large datasets, although there are complications to be addressed for more complex machine learning tasks.

Question: What is the mapreduce framework?


A localization and tracking scheme for target gangs based on big data of Wi-Fi locations. The modeling and analysis of target gangs' usual haunts plays a very important role in law enforcement and supervision. Existing localization and tracking schemes usually need to deploy a large number of monitoring devices or continue to move with the target, which lead to high cost. In this paper, a localization and tracking scheme based on big data of Wi-Fi locations is proposed. Firstly, the characteristic of the smart mobile device that continuously broadcasts probe request frames is used to obtain its MAC address and Wi-Fi connection history. Secondly, the service set identifier (SSID) in the Wi-Fi connection history of smart mobile devices held by the target gangs are queried from the Wi-Fi location database, and the target gangs' usual haunts are gained by statistical analysis. Lastly, monitoring devices are deployed in these places, and most of the target gangs' activity pattern are known with only a small number of monitoring devices. The results of the related experimental tests demonstrate the feasibility of the proposed scheme.

Question: What is the localization and tracking scheme based on big data of Wi-Fi locations?


A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education. 0950-7051/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.knosys.2010.03.010 * Corresponding author. Address: Software Quality L and Technology, Hellenic Open University, 12–15 Tsa 26222, Greece. Tel.: +3

Question: What is a combinational incremental ensemble of classifiers?


A review on software development security engineering using Dynamic System Method (DSDM). Agile methodology such as Scrum, Extreme Programming (XP), Feature Driven Development (FDD) and the Dynamic System Development Method (DSDM) have gained enough recognition as efficient development process by delivering software fast even under the time constrains. However, like other agile methods DSDM has been criticized because of unavailability of security element in its four phases. In order to have a deeper look into the matter and discover more about the reality, we conducted a literature review. Our findings highlight that, in its current form, the DSDM does not support developing secure software. Although, there are a few researches on this topic about Scrum, XP and FDD but, based on our findings, there is no research on developing secure software using DSDM. Thus, in our future work we intend to propose enhanced DSDM that will cater the security aspects in software development.

Question: What is the difference between DSDM and Scrum?


Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of ConVisIT. Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user's current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users' feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user's information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.

Question: How to explore long conversations?


The ecology of human performance: a framework for considering the effect of context.. In theory and in practice, context (as an area of concern to occupational therapists) has not received the same attention as performance components and performance areas. The Ecology of Human Performance serves as a framework for considering the effect of context. Context is described as a lens from which persons view their world. The interrelationship of person and context determines which tasks fall within the person's performance range. The Ecology of Human Performance framework provides guidelines for encompassing context in occupational therapy theory, practice, and research.

Question: What is the ecology of human performance?


A review of the production of ethanol from softwood. Ethanol produced from various lignocellulosic materials such as wood, agricultural and forest residues has the potential to be a valuable substitute for, or complement to, gasoline. One of the major resources in the Northern hemisphere is softwood. This paper reviews the current status of the technology for ethanol production from softwood, with focus on hemicellulose and cellulose hydrolysis, which is the major problem in the overall process. Other issues of importance, e.g. overall process configurations and process economics are also considered.

Question: What is the current status of ethanol production from softwood?


EW-SHOT L EARNING. A recent approach to few-shot classification called matching networks has demonstrated the benefits of coupling metric learning with a training procedure that mimics test. This approach relies on an attention scheme that forms a distribution over all points in the support set, scaling poorly with its size. We propose a more streamlined approach, prototypical networks, that learns a metric space in which few-shot classification can be performed by computing Euclidean distances to prototype representations of each class, rather than individual points. Our method is competitive with state-of-the-art few-shot classification approaches while being much simpler and more scalable with the size of the support set. We empirically demonstrate the performance of our approach on the Omniglot and miniImageNet datasets. We further demonstrate that a similar idea can be used for zero-shot learning, where each class is described by a set of attributes, and achieve state-ofthe-art results on the Caltech UCSD bird dataset.

Question: How does prototypical networks work?


Supply Chain Risk Management: Literature Review and Future Research. Supply Chain Risk Management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2006 will be analyzed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. The literature review will provide the basis for outlining future research opportunities in this field.

Question: What is a literature review?


A sneak peek into digital innovations and wearable sensors for cardiac monitoring. Many mobile phone or tablet applications have been designed to control cardiovascular risk factors (obesity, smoking, sedentary lifestyle, diabetes and hypertension) or to optimize treatment adherence. Some have been shown to be useful but the long-term benefits remain to be demonstrated. Digital stethoscopes make easier the interpretation of abnormal heart sounds, and the development of pocket-sized echo machines may quickly and significantly expand the use of ultrasounds. Daily home monitoring of pulmonary artery pressures with wireless implantable sensors has been shown to be associated with a significant decrease in hospital readmissions for heart failure. There are more and more non-invasive, wireless, and wearable sensors designed to monitor heart rate, heart rate variability, respiratory rate, arterial oxygen saturation, and thoracic fluid content. They have the potential to change the way we monitor and treat patients with cardiovascular diseases in the hospital and beyond. Some may have the ability to improve quality of care, decrease the number of medical visits and hospitalization, and ultimately health care costs. Validation and outcome studies are needed to clarify, among the growing number of digital innovations and wearable sensors, which tools have real clinical value.

Question: What are the benefits of digital innovations and wearable sensors for cardiac monitoring?


Linear feedback control : analysis and design with MATLAB.

Question: What is linear feedback control?


Guidelines for the process of cross-cultural adaptation of self-report measures.. With the increase in the number of multinational and multicultural research projects, the need to adapt health status measures for use in other than the source language has also grown rapidly. Most questionnaires were developed in English-speaking countries, but even within these countries, researchers must consider immigrant populations in studies of health, especially when their exclusion could lead to a systematic bias in studies of health care utilization or quality of life. The cross-cultural adaptation of a health status selfadministered questionnaire for use in a new country, culture, and/or language necessitates use of a unique method, to reach equivalence between the original source and target versions of the questionnaire. It is now recognized that if measures are to be used across cultures, the items must not only be translated well linguistically, but also must be adapted culturally to maintain the content validity of the instrument at a conceptual level across different cultures. Attention to this level of detail allows increased confidence that the impact of a disease or its treatment is described in a similar manner in multinational trials or outcome evaluations. The term "cross-cultural adaptation" is used to encompass a process that looks at both language (translation) and cultural adaptation issues in the process of preparing a questionnaire for use in another setting. Cross-cultural adaptations should be considered for several different scenarios. In some cases, this is more obvious than in others. Guillemin et al suggest five different examples of when attention should be paid to this adaptation by comparing the target (where it is going to be used) and source (where it was developed) language and culture. The first scenario is that it is to be used in the same language and culture in which it was developed. No adaptation is necessary. The last scenario is the oppos

Question: What is the difference between cross-cultural adaptation and translation?


Low kickback noise techniques for CMOS latched comparators. The latched comparator is utilized in virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. Such high voltage variations in the regeneration nodes are coupled to the input voltage - kickback noise. This paper reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations verify the effectiveness of our techniques.

Question: What is a CMOS latched comparator?


No double-dissociation between optic ataxia and visual agnosia: Multiple sub-streams for multiple visuo-manual integrations. The current dominant view of the visual system is marked by the functional and anatomical dissociation between a ventral stream specialised for perception and a dorsal stream specialised for action. The "double-dissociation" between visual agnosia (VA), a deficit of visual recognition, and optic ataxia (OA), a deficit of visuo-manual guidance, considered as consecutive to ventral and dorsal damage, respectively, has provided the main argument for this dichotomic view. In the first part of this paper, we show that the currently available empirical data do not suffice to support a double-dissociation between OA and VA. In the second part, we review evidence coming from human neuropsychology and monkey data, which cast further doubts on the validity of a simple double-dissociation between perception and action because they argue for a far more complex organisation with multiple parallel visual-to-motor connections: 1. A dorso-dorsal pathway (involving the most dorsal part of the parietal and pre-motor cortices): for immediate visuo-motor control--with OA as typical disturbance. The latest research about OA is reviewed, showing how these patients exhibit deficits restricted to the most direct and fast visuo-motor transformations. We also propose that mild mirror ataxia, consisting of misreaching errors when the controlesional hand is guided to a visual goal though a mirror, could correspond to OA with an isolated "hand effect". 2. A ventral stream-prefrontal pathway (connections from the ventral visual stream to pre-frontal areas, by-passing the parietal areas): for "mediate" control (involving spatial or temporal transpositions [Rossetti, Y., & Pisella, L. (2003). Mediate responses as direct evidence for intention: Neuropsychology of Not to-, Not now- and Not there-tasks. In S. Johnson (Ed.), Cognitive Neuroscience perspectives on the problem of

Question: What is the difference between optic ataxia and visual agnosia?


A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer. The advance of deep learning has made huge changes in computer vision and produced various off-the-shelf trained models. Particularly, Convolutional Neural Network (CNN) has been widely used to build image classification model which allow researchers transfer the pre-trained learning model for other classifications. We propose a transfer learning method to detect breast cancer using histopathology images based on Google's Inception v3 model which were initially trained for the classification of non-medical images. The pilot study shows the feasibility of transfer learning in the detection of breast cancer with AUC of 0.93.

Question: How to classify medical images?


Behavior-driven visualization recommendation. We present a novel approach to visualization recommendation that monitors user behavior for implicit signals of user intent to provide more effective recommendation. This is in contrast to previous approaches which are either insensitive to user intent or require explicit, user specified task information. Our approach, called Behavior-Driven Visualization Recommendation (BDVR), consists of two distinct phases: (1) pattern detection, and (2) visualization recommendation. In the first phase, user behavior is analyzed dynamically to find semantically meaningful interaction patterns using a library of pattern definitions developed through observations of real-world visual analytic activity. In the second phase, our BDVR algorithm uses the detected patterns to infer a user's intended visual task. It then automatically suggests alternative visualizations that support the inferred visual task more directly than the user's current visualization. We present the details of BDVR and describe its implementation within our lab's prototype visual analysis system. We also present study results that demonstrate that our approach shortens task completion time and reduces error rates when compared to behavior-agnostic recommendation.

Question: What is the difference between a visualization recommendation and a visualization recommendation system?


Conserving Energy Through Neural Prediction of Sensed Data. The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors' data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors' idle periods and save over 65 percent of energy.

Question: How to conserve energy in wireless sensor networks?


Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness. Since Internet has become an excellent source of consumer reviews, the area of sentiment analysis (also called sentiment extraction, opinion mining, opinion extraction, and sentiment mining) has seen a large increase in academic interest over the last few years. Sentiment analysis mines opinions at word, sentence, and document levels, and gives sentiment polarities and strengths of articles. As known, the opinions of consumers are expressed in sentiment Chinese phrases. But due to the fuzziness of Chinese characters, traditional machine learning techniques can not represent the opinion of articles very well. In this paper, we propose a multi-strategy sentiment analysis method with semantic fuzziness to solve the problem. The results show that this hybrid sentiment analysis method can achieve a good level of effectiveness.

Question: What is the difference between sentiment analysis and sentiment mining?


Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier Hayder. Cervical cancer threatens the lives of many women in our world today. In 2014, the number of women infected with this disease in the United States was 12,578, of which 4,115 died, with a death rate of nearly 32%. Cancer data, including cervical cancer datasets, represent a significant challenge data mining techniques because absence of different costs for error cases. The proposed model present a cost sensitive classifiers that has three main stages; the first stage is prepressing the original data to prepare it for classification model which is build based on decision tree classifier with cost selectivity and finallyevaluation the proposed model based on many metrics in addition to apply a cross validation.The proposed model provides more accurate result in both binary class and multi class classification. It has a TP rate (0.429) comparing with (0.160) for typical decision tree in binary class task.

Question: What is the cost sensitive classifier?


Accidental scald burns in sinks.. Scald burns to the feet and lower extremities in children are described in the literature as often resulting from forced immersions. This report illustrates 3 cases of burns whose distribution and historical factors identify them as accidental. The location of these accidental burns is similar to those found in inflicted injury, but the patterns were indicative of flowing water burns, not forced immersions. Burns in these locations may be confused with abuse. Medical providers need to be aware of information that may enable them to distinguish the 2 causes. Effective caregiver education regarding the importance of lowering the temperature of water heaters and discouraging play in household sinks is critical to prevent additional tap water scald burn injuries.

Question: What is the difference between accidental and inflicted burns?


Food recognition using statistics of pairwise local features. Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft pixel-level segmentation of the image into eight ingredient types. We accumulate these statistics in a multi-dimensional histogram, which is then used as a feature vector for a discriminative classifier. Our experiments show that the proposed representation is significantly more accurate at identifying food than existing methods.

Question: How to recognize food?


Assessing The Impact Of Gender And Personality On Film Preferences. In this paper, the impact of gender and Big Five personality factors (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) on visual media preferences (for comedy, horror, action, romance and fantasy film genres) was examined. The analyses were carried out using data collected by Stillwell's (2007) myPersonality application on Facebook; and focused on a sub-sample of British residents aged 16-25 (n=29,197). It was confirmed that females prefer romantic films, whereas males favour action films. As predicted, there was also a marked interaction between gender and personality characteristics: females score higher on Neuroticism and Agreeableness than males. Individuals scoring high on the dimension of Openness were more likely to prefer comedy and fantasy films than romantic films. Conscientious individuals were more inclined to enjoy action and romance genres. Individuals scoring high on Neuroticism tended to prefer romantic films. Finally, significant interactions were noted between gender and Openness with regard to preferences for comedy, action and romance genres. Overall, it is certain that the combination of gender and Big Five personality factors contributes substantially to an understanding of people's film preferences. GENDER, PERSONALITY AND FILM PREFERENCES 3 Assessing the impact of Gender and Personality on Film Preferences Do comedies have a sexually differentiated public? Are males and females who enjoy horror films similar in terms of personality? Regardless of gender, do personality characteristics influence preference for the fantasy genre? In the present study, we have attempted to provide a meaningful answer to the aforementioned set of questions, based on the analysis of gender and personality interactions with regard to film preferences. In an attempt to complement previous research findings, we focused our analysis on the role of sex and personality in film p

Question: What is the impact of gender and personality on film preferences?


Toward open manufacturing: A cross-enterprises knowledge and services exchange framework based on blockchain and edge computing.

Question: What is the difference between blockchain and edge computing?


Multi-hop Communication in the Uplink for LPWANs. Low-Power Wide Area Networks (LPWANs) have arisen as a promising communication technology for supporting Internet of Things (IoT) services due to their low power operation, wide coverage range, low cost and scalability. However, most LPWAN solutions like SIGFOXTMor LoRaWANTMrely on star topology networks, where stations (STAs) transmit directly to the gateway (GW), which often leads to rapid battery depletion in STAs located far from it. In this work, we analyze the impact on LPWANs energy consumption of multi-hop communication in the uplink, allowing STAs to transmit data packets in lower power levels and higher data rates to closer parent STAs, reducing their energy consumption consequently. To that aim, we introduce the DistanceRing Exponential Stations Generator (DRESG) framework, designed to evaluate the performance of the so-called optimal-hop routing model, which establishes optimal routing connections in terms of energy efficiency, aiming to balance the consumption among all the STAs in the network. Results show that enabling such multi-hop connections entails higher network lifetimes, reducing significantly the bottleneck consumption in LPWANs with up to thousands of STAs. These results lead to foresee multi-hop communication in the uplink as a promising routing alternative for extending the lifetime of LPWAN deployments.

Question: How to extend the lifetime of LPWAN deployments?


SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports. We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

Question: What is the purpose of the project?


Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks. A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).1

Question: What is the state of the art in entity linking?


Opposite Effects of Δ-9-Tetrahydrocannabinol and Cannabidiol on Human Brain Function and Psychopathology. Δ-9-tetrahydrocannabinol (Δ-9-THC) and Cannabidiol (CBD), the two main ingredients of the Cannabis sativa plant have distinct symptomatic and behavioral effects. We used functional magnetic resonance imaging (fMRI) in healthy volunteers to examine whether Δ-9-THC and CBD had opposite effects on regional brain function. We then assessed whether pretreatment with CBD can prevent the acute psychotic symptoms induced by Δ-9-THC. Fifteen healthy men with minimal earlier exposure to cannabis were scanned while performing a verbal memory task, a response inhibition task, a sensory processing task, and when viewing fearful faces. Subjects were scanned on three occasions, each preceded by oral administration of Δ-9-THC, CBD, or placebo. BOLD responses were measured using fMRI. In a second experiment, six healthy volunteers were administered Δ-9-THC intravenously on two occasions, after placebo or CBD pretreatment to examine whether CBD could block the psychotic symptoms induced by Δ-9-THC. Δ-9-THC and CBD had opposite effects on activation relative to placebo in the striatum during verbal recall, in the hippocampus during the response inhibition task, in the amygdala when subjects viewed fearful faces, in the superior temporal cortex when subjects listened to speech, and in the occipital cortex during visual processing. In the second experiment, pretreatment with CBD prevented the acute induction of psychotic symptoms by Δ-9-tetrahydrocannabinol. Δ-9-THC and CBD can have opposite effects on regional brain function, which may underlie their different symptomatic and behavioral effects, and CBD's ability to block the psychotogenic effects of Δ-9-THC.

Question: What is the difference between cannabidiol and tetrahydrocannabinol?


CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information. Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using sidechannel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.

Question: How to reverse engineer a neural network using side-channel information?


A design of an ICN architecture within the framework of SDN. The core design principle of Information-Centric Networking (ICN) is in the name based routing that enables users to ask for a data object by its name and makes the network deliver it to users from a nearby cache if available. Software-Defined Networking (SDN) lowers the cost and complexity of network management by decoupling architecture from infrastructure, which promises the continuous evolution of the network architecture in a flexible manner. The synergy between ICN supporting efficient data dissemination as the norm and SDN providing flexible management framework enables the combination to be a fully controllable framework for efficient data dissemination. In this paper, we propose a design of an ICN architecture within the framework of SDN.

Question: What is the synergy between ICN and SDN?


IoT system for Human Activity Recognition using BioHarness 3 and Smartphone. This paper presents an Internet of Things (IoT) approach to Human Activity Recognition (HAR) using remote monitoring of vital signs in the context of a healthcare system for self-managed chronic heart patients. Our goal is to create a HAR-IoT system using learning algorithms to infer the activity done within 4 categories (lie, sit, walk and run) as well as the time consumed performing these activities and, finally giving feedback during and after the activity. Alike in this work, we provide a comprehensive insight on the cloud-based system implemented and the conclusions after implementing two different learning algorithms and the results of the overall system for larger implementations.

Question: What is the difference between a cloud-based system and a local system?


Approximation Theory of the Mlp Model in Neural Networks. In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models. Mathematically it is also one of the simpler models. Nonetheless the mathematics of this model is not well understood, and many of these problems are approximation-theoretic in character. Most of the research we will discuss is of very recent vintage. We will report on what has been done and on various unanswered questions. We will not be presenting practical (algorithmic) methods. We will, however, be exploring the capabilities and limitations of this model. In the rst two sections we present a brief introduction and overview of neural networks and the multilayer feedforward perceptron model. In Section 3 we discuss in great detail the question of density. When does this model have the theoretical ability to approximate any reasonable function arbritrarily well? In Section 4 we present conditions for simultaneously approximating a function and its derivatives. Section 5 considers the interpolation capability of this model. In Section 6 we study upper and lower bounds on the order of approximation of this model. The material presented in Sections 3{6 treats the single hidden layer MLP model. In Section 7 we discuss some of the diierences that arise when considering more than one hidden layer. The lengthy list of references includes many papers not cited in the text, but relevant to the subject matter of this survey.

Question: What is the approximation theory of the multilayer perceptron model in neural networks?


BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits. BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.

Question: What is Bayesian optimization?


Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization. We propose 'Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden. Our approach only needs to modify the input image and can work with any network designed for object localization. During testing, we do not need to hide any patches. Our Hide-and-Seek approach obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset. We also demonstrate that our framework can be easily extended to weakly-supervised action localization.

Question: How to localize objects in images?


Compiler-Directed Lightweight Checkpointing for Fine-Grained Guaranteed Soft Error Recovery. This paper presents Bolt, a compiler-directed soft error recovery scheme, that provides fine-grained and guaranteed recovery without excessive performance and hardware overhead. To get rid of expensive hardware support, the compiler protects the architectural inputs during their entire liveness period by safely checkpointing the last updated value in idempotent regions. To minimize the performance overhead, Bolt leverages a novel compiler analysis that eliminates those checkpoints whose value can be reconstructed by other checkpointed values without compromising the recovery guarantee. As a result, Bolt incurs only 4.7% performance overhead on average which is 57% reduction compared to the state-of-the-art scheme that requires expensive hardware support for the same recovery guarantee as Bolt.

Question: What is compiler-directed soft error recovery?


Learning Latent Vector Spaces for Product Search. We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.

Question: What is a latent vector space model?


Snakeboard motion planning with viscous friction and skidding. The snakeboard is a well-studied example for mechanical systems analysis, largely because of its simultaneous richness in behavior and simplicity in design. However, few snakeboard models incorporate dissipative friction in the traveling direction and skidding as a violation of the rigid nonholonomic constraints. In this paper we investigate these effects on trajectory planning by evaluating a previously proposed friction model as well as a novel skidding model based on the addition of Rayleigh dissipation functions. We show how these additions change the usual behavior of gaits in the forward planning problem, and incorporate the changes into the solutions of the inverse planning problem by utilizing body coordinates along with a curvature parameterization for trajectories.

Question: How do you plan a snakeboard?


IT Consumerization: When Gadgets Turn Into Enterprise IT Tools.

Question: What is enterprise IT?


SIFT Features Tracking for Video Stabilization. This paper presents a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.

Question: How to track features in video?


The usability of everyday technology: emerging and fading opportunities. Current work in the field of usability tends to focus on snapshots of use as the basis for evaluating designs. However, giving due consideration to the fact that everyday use of technology involves a process of evolution, we set out to investigate how the design of the technology may be used to support this. Based on a long-term empirical study of television use in the homes of two families, we illustrate how use continuously develops in a complex interplay between the users' expectations---as they are formed and triggered by the design---and the needs and context of use per se. We analyze the empirical data from the perspective of activity theory. This framework serves to highlight how use develops, and it supports our analysis and discussion about how design, the users' backgrounds, previous experience, and needs, and the specific context of use supports or hinders the development of use. Moreover, we discuss how the characteristics of the home settings, in which the televisions studied were situated, represent a challenge to usability work. The concluding discussion leads to a set of hypotheses relevant to designers and researchers who wish to tackle some of the aspects of usability of particular importance to development in the use of home technology.

Question: How does the design of everyday technology support the development of use?


Causal Interventions for Fairness. Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative publicity, we believe it is often too little, too late. By the time the training data is collected, individuals in disadvantaged groups have already suffered from discrimination and lost opportunities due to factors out of their control. In the present work we focus instead on interventions such as a new public policy, and in particular, how to maximize their positive effects while improving the fairness of the overall system. We use causal methods to model the effects of interventions, allowing for potential interference–each individual's outcome may depend on who else receives the intervention. We demonstrate this with an example of allocating a budget of teaching resources using a dataset of schools in New York City.

Question: How to maximize the positive effects of interventions?


Quasi-Resonant (QR) Controller With Adaptive Switching Frequency Reduction Scheme for Flyback Converter. A quasi-resonant (QR) controller with an adaptive frequency reduction scheme has been developed for a flyback converter. While maintaining the valley switching, the QR controller reduces the switching frequency for lighter load by skipping some valleys to reduce the power loss and thereby achieve better light-load efficiency. If the QR controller cannot detect any valley due to the damped oscillation of switch voltage, then the valley switching is given up and the nonvalley switching is employed to keep reducing the switching frequency for lighter load. The proposed QR controller has been implemented in a 0.35-μm 700-V BCDMOS process and applied to a 40-W flyback converter. The power efficiency of the flyback converter is improved by up to 3.0% when the proposed QR controller is employed compared to the one employing the conventional QR controller.

Question: What is a quasi-resonant controller?


Label Embedding Trees for Large Multi-Class Tasks. Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a treestructure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.

Question: What is the label embedding tree?


Urban computing with taxicabs. Urban computing for city planning is one of the most significant applications in Ubiquitous computing. In this paper we detect flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas. The detected results consist of 1) pairs of regions with salient traffic problems and 2) the linking structure as well as correlation among them. These results can evaluate the effectiveness of the carried out planning, such as a newly built road and subway lines in a city, and remind city planners of a problem that has not been recognized when they conceive future plans. We conduct our method using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and evaluate our results with the real urban planning of Beijing.

Question: What is urban computing?


Classifying Data Streams with Skewed Class Distributions and Concept Drifts. Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task of learning in streams difficult. The authors aim to develop a new approach to classify skewed data streams that uses an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives.

Question: How to classify skewed data streams?


Unsupervised Cross-Domain Image Generation. We study the ecological use of analogies in AI. Specifically, we address the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given representation function f, which accepts inputs in either domains, would remain unchanged. Other than f, the training data is unsupervised and consist of a set of samples from each domain, without any mapping between them. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f preserving component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.

Question: How do you transfer a sample from one domain to another?


Efficient Restoration Method for Images Corrupted with Impulse Noise. This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in realtime digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.

Question: How to restore images corrupted with impulse noise?


Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. This paper proposes a method of modeling and simulation of photovoltaic arrays. The main objective is to find the parameters of the nonlinear I-V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit. Given these three points, which are provided by all commercial array data sheets, the method finds the best I-V equation for the single-diode photovoltaic (PV) model including the effect of the series and parallel resistances, and warranties that the maximum power of the model matches with the maximum power of the real array. With the parameters of the adjusted I-V equation, one can build a PV circuit model with any circuit simulator by using basic math blocks. The modeling method and the proposed circuit model are useful for power electronics designers who need a simple, fast, accurate, and easy-to-use modeling method for using in simulations of PV systems. In the first pages, the reader will find a tutorial on PV devices and will understand the parameters that compose the single-diode PV model. The modeling method is then introduced and presented in details. The model is validated with experimental data of commercial PV arrays.

Question: What is the difference between a photovoltaic array and a photovoltaic module?