A Deep Learning Approach to Contract Element Extraction. We explore how deep learning methods can be used for contract element extraction. We show that a BILSTM operating on word, POS tag, and tokenshape embeddings outperforms the linear sliding-window classifiers of our previous work, without any manually written rules. Further improvements are observed by stacking an additional LSTM on top of the BILSTM, or by adding a CRF layer on top of the BILSTM. The stacked BILSTM-LSTM misclassifies fewer tokens, but the BILSTM-CRF combination performs better when methods are evaluated for their ability to extract entire, possibly multi-token contract elements.

Question: What is a deep learning approach to contract element extraction?


Conspicuous Consumption and Household Indebtedness. Using a novel, large dataset of consumer transactions in Singapore, we study how conspicuous consumption affects household indebtedness. The coexistence of private housing (condominiums) and subsidized public housing (HDB) allows us to identify conspicuous consumers. Conditional on the same income and other socioeconomic characteristics, those who choose to reside in condominiums—considered a status good—are likely to be more conspicuous than their counterparts living in HDB units. We consistently find that condominium residents spend considerably more (by up to 44%) on conspicuous goods but not differently on inconspicuous goods. Compared with their matched HDB counterparts, more conspicuous consumers have 13% more credit card debt and 151% more delinquent credit card debt. Furthermore, the association between conspicuous consumption and credit card debt is concentrated among younger, male, single individuals. These results suggest that status-seeking-induced conspicuous consumption is an important determinant of household indebtedness.

Question: How does conspicuous consumption affect household indebtedness?


An implementation of cloud-based platform with R packages for spatiotemporal analysis of air pollution. Recently, the R package has become a popular tool for big data analysis due to its several matured software packages for the data analysis and visualization, including the analysis of air pollution. The air pollution problem is of increasing global concern as it has greatly impacts on the environment and human health. With the rapid development of IoT and the increase in the accuracy of geographical information collected by sensors, a huge amount of air pollution data were generated. Thus, it is difficult to analyze the air pollution data in a single machine environment effectively and reliably due to its inherent characteristic of memory design. In this work, we construct a distributed computing environment based on both the softwares of RHadoop and SparkR for performing the analysis and visualization of air pollution with the R more reliably and effectively. In the work, we firstly use the sensors, called EdiGreen AirBox to collect the air pollution data in Taichung, Taiwan. Then, we adopt the Inverse Distance Weighting method to transform the sensors' data into the density map. Finally, the experimental results show the accuracy of the short-term prediction results of PM2.5 by using the ARIMA model. In addition, the verification with respect to the prediction accuracy with the MAPE method is also presented in the experimental results.

Question: How to predict the air pollution data?


Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

Question: What is the average accuracy of the algorithm?


FiberMesh: designing freeform surfaces with 3D curves. This paper presents a system for designing freeform surfaces with a collection of 3D curves. The user first creates a rough 3D model by using a sketching interface. Unlike previous sketching systems, the user-drawn strokes stay on the model surface and serve as handles for controlling the geometry. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. The curves can have arbitrary topology; they need not be connected to each other. For a given set of curves, the system automatically constructs a smooth surface embedding by applying functional optimization. Our system provides real-time algorithms for both control curve deformation and the subsequent surface optimization. We show that one can create sophisticated models using this system, which have not yet been seen in previous sketching or functional optimization systems.

Question: How can I design a freeform surface with 3D curves?


Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.. We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with o

Question: What is the area under the receiver operating characteristic curve?


Thickness Reduction and Performance Enhancement of Steerable Square Loop Antenna Using Hybrid High Impedance Surface. In order to reduce the thickness of a steerable beam square loop antenna, the effects of combining it with various periodic high impedance surfaces (HISs) are investigated. When a via-less HIS is used, the radiation pattern has high side lobes, which are shown to be due to surface waves propagating in the HIS lattice. Using a HIS with vias between the plates and ground removes the surface waves, but the beam is distorted due to strong coupling between the HIS's vias and the antenna element. Consequently a hybrid HIS is designed. This uses a via-less lattice beneath the loop, with vias at the edge of the HIS to suppress surface wave propagation. Consequently, a square loop antenna with four feeds on a hybrid HIS substrate is proposed for beam steering applications. This antenna has a low-profile with a total thickness of 4.69 mm for a test frequency band of 4.3 GHz to 5.0 GHz. It exhibits a gain of 8.65 dB at the test frequency (4.7 GHz). Compared to the earlier reported steerable square loop antenna, the new antenna achieves a 61% reduction in substrate thickness, a bandwidth enhancement by 150 MHz and an increase in gain by 1.95 dB.

Question: How thick is a square loop antenna?


Low Power High-Efficiency Shift Register Using Implicit Pulse-Triggered Flip-Flop in 130 nm CMOS Process for a Cryptographic RFID Tag. The shift register is a type of sequential logic circuit which is mostly used for storing digital data or the transferring of data in the form of binary numbers in radio frequency identification (RFID) applications to improve the security of the system. A power-efficient shift register utilizing a new flip-flop with an implicit pulse-triggered structure is presented in this article. The proposed flip-flop has features of high performance and low power. It is composed of a sampling circuit implemented by five transistors, a C-element for rise and fall paths, and a keeper stage. The speed is enhanced by executing four clocked transistors together with a transition condition technique. The simulation result confirms that the proposed topology consumes the lowest amounts of power of 30.1997 and 22.7071 nW for parallel in –parallel out (PIPO) and serial in –serial out (SISO) shift register respectively covering 22 μm2 chip area. The overall design consist of only 16 transistors and is simulated in 130 nm complementary-metal-oxide-semiconductor (CMOS) technology with a 1.2 V power supply.

Question: What is a shift register?


Fine-grained metadata journaling on NVM. Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.

Question: What is the overhead of journaling?


Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges. In barter-exchange markets, agents seek to swap their items with one another, in order to improve their own utilities. These swaps consist of cycles of agents, with each agent receiving the item of the next agent in the cycle. We focus mainly on the upcoming national kidney-exchange market, where patients with kidney disease can obtain compatible donors by swapping their own willing but incompatible donors. With over 70,000 patients already waiting for a cadaver kidney in the US, this market is seen as the only ethical way to significantly reduce the 4,000 deaths per year attributed to kidney diseas.  The clearing problem involves finding a social welfare maximizing exchange when the maximum length of a cycle is fixed. Long cycles are forbidden, since, for incentive reasons, all transplants in a cycle must be performed simultaneously. Also, in barter-exchanges generally, more agents are affected if one drops out of a longer cycle. We prove that the clearing problem with this cycle-length constraint is NP-hard. Solving it exactly is one of the main challenges in establishing a national kidney exchange.  We present the first algorithm capable of clearing these markets on a nationwide scale. The key is incremental problem formulation. We adapt two paradigms for the task: constraint generation and column generation. For each, we develop techniques that dramatically improve both runtime and memory usage. We conclude that column generation scales drastically better than constraint generation. Our algorithm also supports several generalizations, as demanded by real-world kidney exchanges.  Our algorithm replaced CPLEX as the clearing algorithm of the Alliance for Paired Donation, one of the leading kidney exchanges. The match runs are conducted every two weeks and transplants based on our optimizations have already been conducted.

Question: What is the largest kidney exchange?


Genetic analysis of host resistance: Toll-like receptor signaling and immunity at large.. Classical genetic methods, driven by phenotype rather than hypotheses, generally permit the identification of all proteins that serve nonredundant functions in a defined biological process. Long before this goal is achieved, and sometimes at the very outset, genetics may cut to the heart of a biological puzzle. So it was in the field of mammalian innate immunity. The positional cloning of a spontaneous mutation that caused lipopolysaccharide resistance and susceptibility to Gram-negative infection led directly to the understanding that Toll-like receptors (TLRs) are essential sensors of microbial infection. Other mutations, induced by the random germ line mutagen ENU (N-ethyl-N-nitrosourea), have disclosed key molecules in the TLR signaling pathways and helped us to construct a reasonably sophisticated portrait of the afferent innate immune response. A still broader genetic screen--one that detects all mutations that compromise survival during infection--is permitting fresh insight into the number and types of proteins that mammals use to defend themselves against microbes.

Question: What is the difference between a random germ line mutagen and a chemical mutagen?


Transition-based Dependency DAG Parsing Using Dynamic Oracles. In most of the dependency parsing studies, dependency relations within a sentence are often presented as a tree structure. Whilst the tree structure is sufficient to represent the surface relations, deep dependencies which may result to multi-headed relations require more general dependency structures, namely Directed Acyclic Graphs (DAGs). This study proposes a new dependency DAG parsing approach which uses a dynamic oracle within a shift-reduce transitionbased parsing framework. Although there is still room for improvement on performance with more feature engineering, we already obtain competitive performances compared to static oracles as a result of our initial experiments conducted on the ITU-METU-Sabancı Turkish Treebank (IMST).

Question: What is the name of the dependency parsing approach?


A deadlock prevention method for railway networks using monitors for colored Petri nets. The real-time traffic control of railway networks authorizes movements of the trains and imposes safety constraints. The paper deals with the real time traffic control focusing on deadlock prevention problem. Colored Petri nets are used to model the dynamics of the railway network system: places represent tracks and stations, tokens are trains. The prevention policy is expressed by a set of linear inequality constraints, called colored Generalized Mutual Exclusion Constraints that are enforced by adding appropriate monitor places. Using digraph tools, deadlock situations are characterized and a strategy is established to define off-line a set of Generalized Mutual Exclusion Constraints that prevent deadlock. An example shows in detail the design of the proposed control logic.

Question: What is a colored Petri net?


Social network analysis: a powerful strategy, also for the information sciences. Social network analysis (SNA) is not a formal theory in sociology but rather a strategy for investigating social structures. As it is an idea that can be applied in many fields, we study, in particular, its influence in the information sciences. Information scientists study publication, citation and co-citation networks, collaboration structures and other forms of social interaction networks. Moreover, the Internet represents a social network of an unprecedented scale. We are convinced that in all these studies social network analysis can successfully be applied. SNA is further related to recent theories concerning the free market economy, geography and transport networks. The growth of SNA is documented and a co-author network of SNA is drawn. Centrality measures of this network are calculated. * To whom all correspondence should be addressed.

Question: What is the size of the co-author network of social network analysis?


Weakly Supervised Cyberbullying Detection Using Co-Trained Ensembles of Embedding Models. Social media has become an inevitable part of individuals personal and business lives. Its benefits come with various negative consequences. One major concern is the prevalence of detrimental online behavior on social media, such as online harassment and cyberbullying. In this study, we aim to address the computational challenges associated with harassment detection in social media by developing a machine learning framework with three distinguishing characteristics. (1) It uses minimal supervision in the form of expert-provided key phrases that are indicative of bullying or non-bullying. (2) It detects harassment with an ensemble of two learners that co-train one another; one learner examines the language content in the message, and the other learner considers the social structure. (3) It incorporates distributed word and graph-node representations by training nonlinear deep models. The model is trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. We evaluate the effectiveness of our approach using post-hoc, crowdsourced annotation of Twitter, Ask.fm, and Instagram data, finding that our deep ensembles outperform previous non-deep methods for weakly supervised harassment detection.

Question: What is the difference between weakly supervised and supervised learning?


Question: What is visual sensitivity analysis?


Multiple Text Document Summarization System using hybrid Summarization technique. Text Summarization plays an important role in the area of text mining and natural language processing. As the information resources are increasing tremendously, readers are overloaded with loads of information. Finding out the relevant data and manually summarizing it in short time is much more difficult, challenging and tedious task for a human being. Text Summarization aims to compress the source text into a shorter and concise form with preserving its information content and overall meaning. Summarization can be classified into two main categories i.e. extractive summarization and abstractive summarization. This paper presents a novel approach to generate abstractive summary from extractive summary using WordNet ontology. An experimental result shows the generated summary in well-compressed, grammatically correct and human readable format.

Question: What is the difference between extractive and abstractive summarization?


Steerable interfaces for pervasive computing spaces. This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.

Question: What is a steerable interface?


"I spy with my little eye!": breadth of attention, inattentional blindness, and tactical decision making in team sports.. Failures of awareness are common when attention is otherwise engaged. Such failures are prevalent in attention-demanding team sports, but surprisingly no studies have explored the inattentional blindness paradigm in complex sport game-related situations. The purpose of this paper is to explore the link between breadth of attention, inattentional blindness, and tactical decision-making in team ball sports. A series of studies revealed that inattentional blindness exists in the area of team ball sports (Experiment 1). More tactical instructions can lead to a narrower breadth of attention, which increases inattentional blindness, whereas fewer tactical instructions widen the breadth of attention in the area of team ball sports (Experiment 2). Further meaningful exogenous stimuli reduce inattentional blindness (Experiment 3). The results of all experiments are discussed in connection with consciousness and attention theories as well as creativity and training in team sports.

Question: What is inattentional blindness?


A user-centric evaluation framework for recommender systems. This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.

Question: What is a unifying evaluation framework for recommender systems?


Applying persuasive design in a diabetes mellitus application. This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.

Question: How to design persuasive design?


Application of Machine Learning Algorithms to an online Recruitment System. In this work, we present a novel approach for evaluating job applicants in online recruitment systems, leveraging machine learning algorithms to solve the candidate ranking problem. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants' LinkedIn profile, and infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters; thus, it can be trusted for the automation of applicant ranking and

Question: What is a machine learning algorithm?


Industrial IoT lifecycle via digital twins. Currently, the IoT discussion is focused primarily on the operational phase. This includes how a IoT device behaves, operates, communicates, and interacts with other IoT devices during operation. However, IoT devices and systems have other lifecycle phases before and after operation. This extended abstract provides an overview of how other IoT lifecycle phases (e.g., design and service) can be improved with information feedback and feedforward flows between them. Digital Twins are a new mechanism to manage IoT devices and IoT systems-of-systems throughout their lifecycle. We present our vision on the industrial IoT lifecycle managed and optimized at scale via Digital Twins.

Question: What is the industrial IoT lifecycle?


Biology of language-The epistemology of reality. 2. What processes take place in a linguistic interaction that permit an organism (us) to describe and to predict events that it may experience? This is my way of honoring the memory of Eric H. Lenneberg, if one honors the memory of another scientist by speaking about one's own work Whatever the case, I wish to honor his memory not only because of his great accomplishments, but also because he was capable of inspiring his students, as the symposium on which this book is based revealed. The only way I can do this is to accept the honor of presenting my views about biology, language, and reality. I shall, accordingly, speak about language as a biologist. In doing so, I shall use language, notwithstanding that this use of language to speak about language is within the core of the problem I wish to consider.

Question: What is the epistemology of reality?


Diversifying search results. We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.

Question: What is the difference between a taxonomy and a taxonomy?


A polynomial-time approximation scheme for maximizing the minimum machine completion time.

Question: What is a polynomial-time approximation scheme?


A location-based smart shopping system with IoT technology. Localization is one important part of Internet of Things(IoT) where the Location of Everything (LoE) system plays a important role to improve most services in IoT area. On the other hand, data mining techniques are essential analyses when we have big data from IoT platforms. Indeed, integration of location-based methods and data mining analysis process can make a smart system service for IoT scenarios and applications. For this purpose, we design a smart shopping platform including four components, location of everything component, data collection component, data filtering/analysing component and data mining component. Then a novel accurate localization scheme named "location orbital" is developed that estimates the current location of mobile objects (users or everything) based on both current and the previous locations. Finally, an implementation of the experiment in a shopping mall is conducted to practically examine performance evaluation of the location-based scheme. The experimental results show that the proposed scheme could achieve significant higher precision than other localization techniques.

Question: What is the location orbital?


Knowledge Discovery in Databases (KDD) with Images: A Novel Approach toward Image Mining and Processing. We are in an age often referred to as the information age. In this information age, because we believe that information leads to power and success, from the technologies such as computers, satellites, etc., we have been collecting tremendous amounts of information. Our ability to analyze and understand massive datasets lags far behind our ability to gather and store data. Image and video data contains abundant, rich information for data miners to explore. On one hand, the rich literature on image and video data analysis will naturally provide many advanced methods that may help mining other kinds of data. On the other hand, recent research on data mining will also provide some new, interesting methods that may benefit image and video data retrieval and analysis. Today, a lot of data is available everywhere but the ability to understand and make use of that data is very less. Whether the context is business, medicine, science or government, the datasets themselves are of little value. What is of value is the knowledge that can be inferred from the data and put to use. We need systems which would analyze the data for us. This paper basically aims to find out important pixels of an image using one of the classification technique named as decision tree (ID-3). Our aim is to separate the important and unimportant pixels of an image using simple rules. Further one of the compression techniques named as Huffman algorithm is applied for image compression. Finally, resultant image is stored with lesser space complexity .

Question: What is the difference between image and video data?


Attributing physical and biological impacts to anthropogenic climate change. Significant changes in physical and biological systems are occurring on all continents and in most oceans, with a concentration of available data in Europe and North America. Most of these changes are in the direction expected with warming temperature. Here we show that these changes in natural systems since at least 1970 are occurring in regions of observed temperature increases, and that these temperature increases at continental scales cannot be explained by natural climate variations alone. Given the conclusions from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report that most of the observed increase in global average temperatures since the mid-twentieth century is very likely to be due to the observed increase in anthropogenic greenhouse gas concentrations, and furthermore that it is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica, we conclude that anthropogenic climate change is having a significant impact on physical and biological systems globally and in some continents.

Question: What is the impact of anthropogenic climate change on physical and biological systems?


Is Verbal Irony Special?. The way we speak can reveal much about what we intend to communicate, but the words we use often only indirectly relate to the meanings we wish to convey. Verbal irony is a commonly studied form of indirect speech in which a speaker produces an explicit evaluative utterance that implicates an unstated, opposing evaluation. Producing and understanding ironic language, as well as many other types of indirect speech, requires the ability to recognize mental states in others, sometimes described as a capacity for metarepresentation. This article aims to connect common elements between the major theoretical approaches to verbal irony to recent psycholinguistic, developmental, and neuropsychological research demonstrating the necessity for metarepresentation in the effective use of verbal irony in social interaction. Here I will argue that verbal irony is one emergent, strategic possibility given the interface between people's ability to infer mental states, and use language. Rather than think of ironic communication as a specialized cognitive ability, I will claim that it arises from the same set of abilities that underlie a wide range of inferential communicative behaviors. Language interaction involves a complex interplay of many cognitive abilities. Theorists across many disciplines struggle with questions of how to carve up these abilities, and whether they can be carved up in the first place (e.g., Christiansen and Chater 2008). Among the most difficult questions to address are those that involve how people recognize intentions in others' behavior, and how that is achieved through language in the context of many other sources of information. The way we speak can reveal much about what we intend to communicate, but the words we use are often quite different from the meanings we wish to convey. People often speak indirectly for a variety of strategic reasons, and these strategies rely intrinsically on social cognition. One well-researched exa

Question: What is the difference between irony and sarcasm?


Effects of elastic therapeutic taping on motor function in children with motor impairments: a systematic review.. BACKGROUND The elastic therapeutic taping has been considered a promising resource for disabled children.   OBJECTIVE To systematically review the evidence of the effects of elastic therapeutic taping on motor function in children with motor impairments.   METHOD Three independent evaluators conducted searches in electronic databases (MEDLINE/PubMed, Scopus, LILACS, BIREME/BVS, Science Direct, SciELO, and PEDro). Clinical studies design, published until 2016, involving elastic therapeutic taping and children aged 0-12 years with motor impairments were included. The variables considered were the methodological aspects (study design, participants, outcome measurements, and experimental conditions); results presented in the studies, and also the methodological quality of studies.   RESULTS Final selection was composed by 12 manuscripts (five randomized controlled trials), published in the last 10 years. Among them, cerebral palsy (CP) was the most recurrent disorder (n = 7), followed by congenital muscular torticollis (n = 2) and brachial plexus palsy (n = 2). Positive results were associated with taping application: improvement in the upper limb function, gross motor skills, postural control, muscular balance, and performance in the dynamics functional and daily activities.   LIMITATIONS Lower quality of the studies, clinical and population heterogeneity existed across studies.   CONCLUSIONS The elastic therapeutic taping has been shown to be a promising adjunct resource to the conventional rehabilitation in children with motor impairments. However, high methodological studies about its efficacy in this population are already scarce. Implications for Rehabilitation Elastic therapeutic taping has been shown to be a promising adjunct resource to the conventional rehabilitation in disabled children. Clinical trials have indicated improvement in the postural c

Question: What is the Canadian Armed Forces?


Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids. Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.

Question: What is a microgrid?


A neurocognitive approach to understanding the neurobiology of addiction. Recent concepts of addiction to drugs (e.g. cocaine) and non-drugs (e.g. gambling) have proposed that these behaviors are the product of an imbalance between three separate, but interacting, neural systems: an impulsive, largely amygdala-striatum dependent, neural system that promotes automatic, habitual and salient behaviors; a reflective, mainly prefrontal cortex dependent, neural system for decision-making, forecasting the future consequences of a behavior, and inhibitory control; and the insula that integrates interoception states into conscious feelings and into decision-making processes that are involved in uncertain risk and reward. These systems account for poor decision-making (i.e. prioritizing short-term consequences of a decisional option) leading to more elevated addiction risk and relapse. This article provides neural evidence for this three-systems neural model of addiction.

Question: What is the neural basis of addiction?


Mining Sequential Patterns. We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction.

Question: What is the minimum number of customers that must support a sequential pattern?


An updated survey of GA-based multiobjective optimization techniques. After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.

Question: What is the purpose of this paper?


Design 13.56MHz 10 kW resonant inverter using GaN HEMT for wireless power transfer systems. This paper presents the design process of a 13.56 MHz 10 kW and high efficiency inverter for wireless power transfer systems. The new 650V E-mode GaN is used in multiphase resonant topology to obtain high output power. The optimized efficiency design and the driver design are presented in detail. The circuit simulation results show that the inverter achieves 10 kW output power with the efficiency of over 98%. The experiment system is tested at 4 kW output power with the efficiency of 96.5%.

Question: How to design a resonant inverter?


IOT Based Air And Sound Pollution Monitoring System. The growing air and sound pollution is one of the serious issues these days. This large amount of increasing pollution has made human life prone to large number of diseases. Therefore, it has now become necessary to control the pollution to ensure healthy livelihood and better future. The Air and Sound Pollution Monitoring device can be accessed by the authorities and the common people belonging to the area. The device will be installed through a mobile application which will show the live updates of the pollution level of the area. This device is also capable of detecting the fire in its area and notify the same to the fire brigade authorities so that they could take necessary actions accordingly, and also the mobile applications will be installed in the fire brigades itself so that if a fire is taking place nearby, it could be controlled in time to reduce loss of people and property. This system works on the methods of IOT which is a rising technology based on the fusion of electronics and computer science. The embedded sensors in the system help to detect major air polluting gases such as CO2, SO2 and CO and level of sound pollution. The concept of IOT helps to access data from remote locations and save it in database so that we don't need to actually be present in that area.

Question: What is IOT?


A positive level shifter for high speed symmetric switching in flash memories. In this paper, a novel positive level shifter is designed for high speed interfacing between the digital circuits operating at 1.2V and the memory core circuits operating at high voltages in flash memories. The proposed design is also validated for a wide range of output voltage levels and has been optimized to perform efficiently across the different process corners and temperatures. For the targeted design of 1.2V and 33 MHz input signal to 4V output signal, the level shifter has a symmetric switching speed with a rise time and fall time of 2ns and 1.89ns respectively while the average power consumption of 0.056mW is obtained in the typical condition. Simulation results are compared with various other conventional positive level shifters at different process corners and temperatures.

Question: What is a positive level shifter?


HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation. Matrix factorization is one of the most powerful techniques in collaborative filtering, which models the (user, item) interactions behind historical explicit or implicit feedbacks. However, plain matrix factorization may not be able to uncover the structure correlations among users and items well such as communities and taxonomies. As a response, we design a novel algorithm, i.e., hierarchical group matrix factorization (HGMF), in order to explore and model the structure correlations among users and items in a principled way. Specifically, we first define four types of correlations, including (user, item), (user, item group), (user group, item) and (user group, item group); we then extend plain matrix factorization with a hierarchical group structure; finally, we design a novel clustering algorithm to mine the hidden structure correlations. In the experiments, we study the effectiveness of our HGMF for both rating prediction and item recommendation, and find that it is better than some state-of-the-art methods on several real-world data sets.

Question: What is the difference between matrix factorization and hierarchical group matrix factorization?


Single-Image Noise Level Estimation for Blind Denoising. Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.

Question: How to estimate the noise level of an image?


Pupillary Responses to Robotic and Human Emotions: The Uncanny Valley and Media Equation Confirmed. Physiological responses during human-robots interaction are useful alternatives to subjective measures of uncanny feelings for nearly humanlike robots (uncanny valley) and comparable emotional responses between humans and robots (media equation). However, no studies have employed the easily accessible measure of pupillometry to confirm the uncanny valley and media equation hypotheses, evidence in favor of the existence of these hypotheses in interaction with emotional robots is scarce, and previous studies have not controlled for low level image statistics across robot appearances. We therefore recorded pupil size of 40 participants that viewed and rated pictures of robotic and human faces that expressed a variety of basic emotions. The robotic faces varied along the dimension of human likeness from cartoonish to humanlike. We strictly controlled for confounding factors by removing backgrounds, hair, and color, and by equalizing low level image statistics. After the presentation phase, participants indicated to what extent the robots appeared uncanny and humanlike, and whether they could imagine social interaction with the robots in real life situations. The results show that robots rated as nearly humanlike scored higher on uncanniness, scored lower on imagined social interaction, evoked weaker pupil dilations, and their emotional expressions were more difficult to recognize. Pupils dilated most strongly to negative expressions and the pattern of pupil responses across emotions was highly similar between robot and human stimuli. These results highlight the usefulness of pupillometry in emotion studies and robot design by confirming the uncanny valley and media equation hypotheses.

Question: What is the uncanny valley?


Accelerating MapReduce on a coupled CPU-GPU architecture. The work presented here is driven by two observations. First, heterogeneous architectures that integrate a CPU and a GPU on the same chip are emerging, and hold much promise for supporting power-efficient and scalable high performance computing. Second, MapReduce has emerged as a suitable framework for simplified parallel application development for many classes of applications, including data mining and machine learning applications that benefit from accelerators.  This paper focuses on the challenge of scaling a MapReduce application using the CPU and GPU together in an integrated architecture. We develop different methods for dividing the work, which are the map-dividing scheme, where map tasks are divided between both devices, and the pipelining scheme, which pipelines the map and the reduce stages on different devices. We develop dynamic work distribution schemes for both the approaches. To achieve high load balance while keeping scheduling costs low, we use a runtime tuning method to adjust task block sizes for the map-dividing scheme. Our implementation of MapReduce is based on a continuous reduction method, which avoids the memory overheads of storing key-value pairs.  We have evaluated the different design decisions using 5 popular MapReduce applications. For 4 of the applications, our system achieves 1.21 to 2.1 speedup over the better of the CPU-only and GPU-only versions. The speedups over a single CPU core execution range from 3.25 to 28.68. The runtime tuning method we have developed achieves very low load imbalance, while keeping scheduling overheads low. Though our current work is specific to MapReduce, many underlying ideas are also applicable towards intra-node acceleration of other applications on integrated CPU-GPU nodes.

Question: How to accelerate mapreduce on a coupled CPU-GPU architecture


Obsessive-compulsive disorder and gut microbiota dysregulation.. Obsessive-compulsive disorder (OCD) is a debilitating disorder for which the cause is not known and treatment options are modestly beneficial. A hypothesis is presented wherein the root cause of OCD is proposed to be a dysfunction of the gut microbiome constituency resulting in a susceptibility to obsessional thinking. Both stress and antibiotics are proposed as mechanisms by which gut microbiota are altered preceding the onset of OCD symptomology. In this light, pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) leading to episodic OCD is explained not by group A beta-hemolytic streptococcal infections, but rather by prophylactic antibiotics that are administered as treatment. Further, stressful life events known to trigger OCD, such as pregnancy, are recast to show the possibility of altering gut microbiota prior to onset of OCD symptoms. Suggested treatment for OCD would be the directed, specie-specific (re)introduction of beneficial bacteria modifying the gut microbiome, thereby ameliorating OCD symptoms. Special considerations should be contemplated when considering efficacy of treatment, particularly the unhealthy coping strategies often observed in patients with chronic OCD that may need addressing in conjunction with microbiome remediation.

Question: What is the cause of obsessive compulsive disorder?


Glaucoma Detection Using Dwt Based Energy Features and Ann Classifier. Glaucoma is a disease in which fluid pressure in the eye increases continously, and damage the optic nerve and leads to vision loss.It is the second leading cause of blindness. For identification of disease in human eyes we are using clinical decision support system which is based on retinal image analysis technique,that used to extract structure,contextual or texture features.Texture features within images which gives accurate and efficicent glaucoma classification.For finding this texture features we use energy distribution over wavelet subband.In this paper we focus on fourteen features which is obtained from daubecies(db3),symlets(sym3),and biorthogonal(bio3.3,bio3.5,and bio3.7).We propose a novel technique to extract this energy signatures using 2D wavelet transform and passed these signatures to different feature ranking and feature selection strategies.The energy obtained from detailed coefficent are used to classify normal and glaucomatous image with high accuracy. This will be classified using support vector machines, sequential minimal optimization, random forest, naive Bayes and artificial neural network.We observed an accuracy of 94% using the ANN classifier.Performance graph is shown for all classifiers.Finally the defected region founded by segmentation and this will be post processed by morphological processing technique for smoothing operation.

Question: What is glaucoma?


Cultural capital or relative risk aversion? Two mechanisms for educational inequality compared.. In this paper we empirically examined two explanatory mechanisms for educational inequality: cultural reproduction and relative risk aversion, using survey data taken from secondary school pupils in Amsterdam. Cultural reproduction theory seeks to explain class variations in schooling by cultural differences between social classes. Relative risk aversion theory argues that educational inequalities can be understood by between-class variation in the necessity of pursuing education at branching points in order to avoid downward mobility. We showed that class variations in early demonstrated ability are for a substantial part cultural: cultural capital - measured by parental involvement in highbrow culture - affected school performance at the primary and secondary level. However, relative risk aversion - operationalized by being concerned with downward mobility - strongly affects schooling ambitions, whereas cultural capital had no effect. Thus, we conclude that 'primary effects' of social origin on schooling outcomes are manifested through cultural capital and not through relative risk aversion (in addition to other potential sources of class variations such as genetics). Relative risk aversion, and not cultural capital, affects schooling ambitions, which is relevant for our understanding of secondary effects.

Question: What is the difference between cultural capital and relative risk aversion?


A Compact Wideband Dual-Polarized Slot Antenna With Five Resonances. This letter presents the design and analysis of a compact dual-polarized slot antenna using a triple-mode stepped slot-line resonator and a dual-mode cross-shaped monopole. The orthogonal polarization is achieved by feeding the two ports symmetrically at the opposite positions of the slot. All the five resonance frequencies can be flexibly adjusted by tuning different dimension parameters, so by increasing the frequency difference between the five resonance modes, a very wide common operating band (1.63–2.93 GHz) is obtained and also with a high port isolation (< $-$32 dB). Finally, by loading two grounded lumped capacitors on the cusp sections of the monopole, its second resonance frequency can be independently reconfigured.

Question: What is a slot antenna?


Full 3-D Printed Microwave Termination: A Simple and Low-Cost Solution. This paper describes the realization and characterization of microwave 3-D printed loads in rectangular waveguide technology. Several commercial materials were characterized at X-band (8-12 GHz). Their dielectric properties were extracted through the use of a cavity-perturbation method and a transmission/reflection rectangular waveguide method. A lossy carbon-loaded Acrylonitrile Butadiene Styrene (ABS) polymer was selected to realize a matched load between 8 and 12 GHz. Two different types of terminations were realized by fused deposition modeling: a hybrid 3-D printed termination (metallic waveguide + pyramidal polymer absorber + metallic short circuit) and a full 3-D printed termination (self-consistent matched load). Voltage standing wave ratio of less than 1.075 and 1.025 were measured over X-band for the hybrid and full 3-D printed terminations, respectively. Power behavior of the full 3-D printed termination was investigated. A very linear evolution of reflected power as a function of incident power amplitude was observed at 10 GHz up to 11.5 W. These 3-D printed devices appear as a very low cost solution for the realization of microwave matched loads in rectangular waveguide technology.

Question: What is the microwave termination?


Mutations in EZH2 cause Weaver syndrome.. We used trio-based whole-exome sequencing to analyze two families affected by Weaver syndrome, including one of the original families reported in 1974. Filtering of rare variants in the affected probands against the parental variants identified two different de novo mutations in the enhancer of zeste homolog 2 (EZH2). Sanger sequencing of EZH2 in a third classically-affected proband identified a third de novo mutation in this gene. These data show that mutations in EZH2 cause Weaver syndrome.

Question: What is the difference between Weaver syndrome and Weaver syndrome?


A classification of transaction processing systems. The problems in building a transaction processing system are discussed, and it is shown that the difficulties are a function of specific attributes of the underlying database system. A model of a transaction processing system is presented, and five system dimensions important in classifying transaction processing systems-the process, machine, heterogeneity, data, and site components-are introduced. The specific problems posed by various combinations of system characteristics are analyzed. The evolution of transaction processing systems are described in terms of the framework.<<ETX>>

Question: What is the ETX?


Game-Theoretic Analysis of DDoS Attacks Against Bitcoin Mining Pools. One of the unique features of the digital currency Bitcoin is that new cash is introduced by so-called miners carrying out resourceintensive proof-of-work operations. To increase their chances of obtaining freshly minted bitcoins, miners typically join pools to collaborate on the computations. However, intense competition among mining pools has recently manifested in two ways. Miners may invest in additional computing resources to increase the likelihood of winning the next mining race. But, at times, a more sinister tactic is also employed: a mining pool may trigger a costly distributed denial-of-service (DDoS) attack to lower the expected success outlook of a competing mining pool. We explore the trade-off between these strategies with a series of game-theoretical models of competition between two pools of varying sizes. We consider differences in costs of investment and attack, as well as uncertainty over whether a DDoS attack will succeed. By characterizing the game's equilibria, we can draw a number of conclusions. In particular, we find that pools have a greater incentive to attack large pools than small ones. We also observe that larger mining pools have a greater incentive to attack than smaller ones.

Question: What is a DDoS attack?


Transformer-Less Converter Concept for a Grid-Connection of Thin-Film Photovoltaic Modules. A transformer-less converter concept for grid- connected photovoltaic systems is proposed that combines a DC/DC converter front-end with a DC/AC inverter. The converter system has an earth-connected DC input, as required from many thin-film photovoltaic modules. The DC/DC converter increases the positive photovoltaic DC-bus voltage by its negative DC output voltage to supply a grid-connected 3-phase inverter. This architecture extends today's power electronic converter topologies for thin-film photovoltaic modules considering their special requirements with the ambition to realize higher power conversion efficiencies at lower cost.

Question: What is the difference between a transformer and a converter?


Xbase: implementing domain-specific languages for Java. Xtext is an open-source framework for implementing external, textual domain-specific languages (DSLs). So far, most DSLs implemented with Xtext and similar tools focus on structural aspects such as service specifications and entities. Because behavioral aspects are significantly more complicated to implement, they are often delegated to general-purpose programming languages. This approach introduces complex integration patterns and the DSL's high level of abstraction is compromised.  We present Xbase as part of Xtext, an expression language that can be reused via language inheritance in any DSL implementation based on Xtext. Xbase expressions provide both control structures and program expressions in a uniform way. Xbase is statically typed and tightly integrated with the Java type system. Languages extending Xbase inherit the syntax of a Java-like expression language as well as language infrastructure components, including a parser, an unparser, a linker, a compiler and an interpreter. Furthermore, the framework provides integration into the Eclipse IDE including debug and refactoring support.  The application of Xbase is presented by means of a domain model language which serves as a tutorial example and by the implementation of the programming language Xtend. Xtend is a functional and object-oriented general purpose language for the Java Virtual Machine (JVM). It is built on top of Xbase which is the reusable expression language that is the foundation of Xtend.

Question: What is the difference between Xbase and Xtext?


Security, privacy and safety evaluation of dynamic and static fleets of drones. Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life — often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.

Question: What is swarm intelligence?


Strategies and struggles with privacy in an online social networking community. Online social networking communities such as Facebook and MySpace are extremely popular. These sites have changed how many people develop and maintain relationships through posting and sharing personal information. The amount and depth of these personal disclosures have raised concerns regarding online privacy. We expand upon previous research on users' under-utilization of available privacy options by examining users' current strategies for maintaining their privacy, and where those strategies fail, on the online social network site Facebook. Our results demonstrate the need for mechanisms that provide awareness of the privacy impact of users' daily interactions.

Question: How do people maintain their privacy on Facebook?


New Frontiers in Formal Methods: Learning, Cyber-Physical Systems, Education, and Beyond. We survey promising directions for research in the area of formal methods and its applications, including fundamental questions about the combination of formal methods and machine learning, and applications to cyberphysical systems and education.

Question: What is the area of formal methods?


Electricity Smart Meters Interfacing the Households. The recent worldwide measures for energy savings call for a larger awareness of the household energy consumption, given the relevant contribution of domestic load to the national energy balance. On the other hand, electricity smart meters together with gas, heat, and water meters can be interconnected in a large network offering a potential value to implement energy savings and other energy-related services, as long as an efficient interface with the final user is implemented. Unfortunately, so far, the interface of such devices is mostly designed and addressed at the utilities supervising the system, giving them relevant advantages, while the communication with the household is often underestimated. This paper addresses this topic by proposing the definition of a local interface for smart meters, by looking at the actual European Union and international regulations, at the technological solutions available on the market, and at those implemented in different countries, and, finally, by proposing specific architectures for a proper consumer-oriented implementation of a smart meter network.

Question: What is the definition of a local interface for smart meters?


Collaborative Quantization for Cross-Modal Similarity Search. Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.

Question: What is the state-of-the-art approach for cross-modal similarity search?


NONCONVEX MODEL FOR FACTORING NONNEGATIVE MATRICES. We study the Nonnegative Matrix Factorization problem which approximates a nonnegative matrix by a low-rank factorization. This problem is particularly important in Machine Learning, and finds itself in a large number of applications. Unfortunately, the original formulation is ill-posed and NPhard. In this paper, we propose a row sparse model based on Row Entropy Minimization to solve the NMF problem under separable assumption which states that each data point is a convex combination of a few distinct data columns. We utilize the concentration of the entropy function and the `∞ norm to concentrate the energy on the least number of latent variables. We prove that under the separability assumption, our proposed model robustly recovers data columns that generate the dataset, even when the data is corrupted by noise. We empirically justify the robustness of the proposed model and show that it is significantly more robust than the state-ofthe-art separable NMF algorithms.

Question: How to solve the nonnegative matrix factorization problem?


Audit and Analysis of Impostors: An experimental approach to detect fake profile in online social network. In the present generation, the social life of every person has become associated with online social networks (OSN). These sites have made drastic changes in the way we socialize. Making friends and keeping in contact with them as well as being updated of their activities, has become easier. But with their rapid growth, problems like fake profiles, online impersonation have also increased. The risk lies in the fact that anybody can create a profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with a targeted person purely through online interactions with the friends of victim.  In present work, we have proposed experimental framework with which detection of fake profile is feasible within the friend list, however this framework is restricted to a specific online social networking site namely Facebook. This framework extracts data from the friend list and uses it to classify them as real or fake by using unsupervised and supervised machine learning.

Question: How to detect fake profiles in online social networks?


A Low-delay Protocol for Multihop Wireless Body Area Networks. Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.

Question: What is a low-delay protocol for multihop wireless body area networks?


RISC I: A Reduced Instruction Set VLSI Computer. The Reduced Instruction Set Computer (RISC) Project investigates an alternatrve to the general trend toward computers wrth increasingly complex instruction sets: With a proper set of instructions and a corresponding architectural design, a machine wrth a high effective throughput can be achieved. The simplicity of the instruction set and addressing modes allows most Instructions to execute in a single machine cycle, and the srmplicity of each instruction guarantees a short cycle time. In addition, such a machine should have a much shorter design trme. This paper presents the architecture of RISC I and its novel hardware support scheme for procedure call/return. Overlapprng sets of regrster banks that can pass parameters directly to subrouttnes are largely responsible for the excellent performance of RISC I. Static and dynamtc comparisons between this new architecture and more traditional machines are given. Although instructions are simpler, the average length of programs was found not to exceed programs for DEC VAX 11 by more than a factor of 2. Preliminary benchmarks demonstrate the performance advantages of RISC. It appears possible to build a single chip computer faster than VAX 11/780.

Question: What is a reduced instruction set computer?


A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics. As spatio-temporal data have become ubiquitous, an increasing challenge facing computer scientists is that of identifying discrete patterns in continuous spatio-temporal fields. In this paper, we introduce a parameter-free pattern mining application that is able to identify dynamic anomalies in ocean data, known as ocean eddies. Despite ocean eddy monitoring being an active field of research, we provide one of the first quantitative analyses of the performance of the most used monitoring algorithms. We present an incomplete information validation technique, that uses the performance of two methods to construct an imperfect ground truth to test the significance of patterns discovered as well as the relative performance of pattern mining algorithms. These methods, in addition to the validation schemes discussed provide researchers new directions in analyzing large unlabeled climate datasets.

Question: What is the performance of the most used monitoring algorithms?


A particle filter for monocular vision-aided odometry. We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.

Question: How does the particle filter compare to the extended Kalman filter?


Improving feature selection techniques for machine learning. As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance

Question: What is a wrapper method?


A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis. Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.

Question: What is the new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis?


A Topic-based Reviewer Assignment System. Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.

Question: What is a topic-based reviewer assignment system?


Feature Engineering for Knowledge Base Construction. Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student. Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user "think about features— not algorithms." We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.

Question: What is the difference between inference and learning?


Effect of different surface pre-treatments and luting materials on shear bond strength to PEEK.. OBJECTIVES To assess the bonding potential of a universal composite resin cement and an adhesive/composite system to differently pre-treated PEEK surfaces.   METHODS One hundred and fifty PEEK disks were embedded in epoxy resin, polished (P4000 grit) and treated as follows (n=30/group): (A) no treatment, (B) acid etching with sulfuric acid (98%) for 1 min, (C) sandblasting for 10s with 50 microm alumina, (D) sandblasting for 10s with 110 microm alumina and (E) silica coating using the Rocatec system (3M ESPE). Polished and sandblasted (50 microm alumina) cp titanium (grade 4) served as a control. Acrylic hollow cylinders were either luted with a universal composite resin cement (RelyX Unicem) or an unfilled resin (Heliobond) and a hybrid composite (Tetric) to the specimens. Bond strength was measured in a shear test and failure modes were assessed. Statistic analysis was performed with one-way ANOVA followed by a post hoc Scheffé test and unpaired t-tests.   RESULTS With the universal composite resin cement, no bond could be established on any PEEK surfaces, except specimens etched with sulfuric acid (19.0+/-3.4MPa). Shear bond strength to titanium was significantly lower (8.7+/-2.8MPa, p<0.05). Applying the adhesive/composite system, shear bond strength values on pre-treated PEEK ranged from 11.5+/-3.2MPa (silica coating) to 18.2+/-5.4MPa (acid etched) with no statistically significant differences (p>0.05). No bond was obtained on the polished surface.   SIGNIFICANCE Bonding to PEEK is possible when using a bonding system. No adherence can be achieved with the tested universal composite resin cement except on an etched surface. The results strongly encourage further research in PEEK application in dentistry.

Question: What is the difference between a universal and a hybrid composite?


Cross-Cultural Software Production and Use: A Structurational Analysis. This paper focuses on cross-cultural software production and use, which is increasingly common in today's more globalized world. A theoretical basis for analysis is developed, using concepts drawn from structuration theory. The theory is illustrated using two cross-cultural case studies. It is argued that structurational analysis provides a deeper examination of cross-cultural working and IS than is found in the current literature, which is dominated by Hofstede-type studies, in particular, the theoretical approach can be used to analyze cross-cultural conflict and contradiction, cultural heterogeneity, detailed work patterns, and the dynamic nature of culture. The paper contributes to the growing body of literature that emphasizes the essential role of cross-cultural understanding in contemporary society. 'Michael D. Myers was the accepting senior editor for this paper. introduction There has been much debate over the last decade about the major sociat transformations taking place in the world such as the increasing interconnectedness of different societies, the compression of time and space, and an intensification of consciousness of the world as a whole (Robertson 1992), Such changes are often tabeted with the term globatization, atthough the precise nature of this phenomenon is highly complex on closer examination. For example. Beck (2000) distinguishes between globality, the change in consciousness of the world as a single entity, and globaiism, the ideology of neoliberatism which argues that the world market eliminates or supplants the importance of locat potiticat action. Despite the complexity of the globalization phenomena, all commentators would agree that information and communication technologies (ICTs) are deeply implicated in the changes that are taking ptace through their abitity to enabte new modes of work, communication, and organization MiS Quarterly Vol. 26 No. 4. pp. 359-380/

Question: What is the difference between globality and globaiism?


Towards a holistic analysis of mobile payments: A multiple perspectives approach. As the mobile technologies and services are in constant evolution, many speculate on whether or not mobile payments will be a killer application for mobile commerce. To have a better understanding of the market, there is a need to analyze not only the technology but also the different actors that are involved. For this purpose, we propose to conduct two disruption analyses to draw the disruptiveness profile of mobile payment solutions compared to other payment instruments. Then, we try to discover what factors have hindered the technical and commercial development by using a DSS based on a multi-criteria decision making method called Electre I.

Question: What is a disruption analysis?


Evaluating STT-RAM as an energy-efficient main memory alternative. In this paper, we explore the possibility of using STT-RAM technology to completely replace DRAM in main memory. Our goal is to make STT-RAM performance comparable to DRAM while providing substantial power savings. Towards this goal, we first analyze the performance and energy of STT-RAM, and then identify key optimizations that can be employed to improve its characteristics. Specifically, using partial write and row buffer write bypass, we show that STT-RAM main memory performance and energy can be significantly improved. Our experiments indicate that an optimized, equal capacity STT-RAM main memory can provide performance comparable to DRAM main memory, with an average 60% reduction in main memory energy.

Question: How much energy does a DRAM chip use?


MRJS : A JavaScript MapReduce Framework for Web Browsers. This paper describes MRJS, a JavaScript based MapReduce framework. MRJS runs on the open internet over HTTP rather than in a data center. Instead of using clusters of machines for processing power, it relies on volunteer clients in the form of JavaScript engines in web browsers. Users can specify, execute, and fetch data for arbitrary MapReduce jobs with HTTP requests. Our centralized job server acts as a coordinator. We have implemented MRJS and results show that it performs well for certain types of MapReduce computations.

Question: What is a JavaScript MapReduce Framework?


Automatic Word Sense Discrimination. This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words.

Question: How does the algorithm work?


A Stochastic Approximation Method. Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown tot he experiment, and it is desire to find the solution x=0 of the equation M(x) = a, where x is a given constant. we give a method for making successive experiments at levels x1, x2,... in such a way that x, will tend to 0 in probability.

Question: What is a stochastic approximation method?


Answering Schrödinger's question: A free-energy formulation. The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.

Question: What is the free-energy principle?


Linking Transformational Leadership and Knowledge Sharing: The Mediating Roles of Perceived Team Goal Commitment and Perceived Team Identification. It is widely assumed that transformational leadership can effectively facilitate the sharing of knowledge among followers, but most investigations of the underlying mechanisms were based on the social exchange perspective. Based on a sensegiving theory perspective, this article attempts to uncover the mechanisms by which transformational leadership has its impact on employee knowledge sharing behavior by proposing two team-directed mediating mechanisms: perceived team goal commitment and perceived team identification. Results of multi-source and time-lagged data from 186 leader-follower pairs supported the proposed mediating effects. Implications and limitations are discussed.

Question: How does transformational leadership affect knowledge sharing?


Computational intelligence and tower defence games. The aim of this paper is to introduce the use of Tower Defence (TD) games in Computational Intelligence (CI) research. We show how TD games can provide an important test-bed for the often under-represented casual games research area. Additionally, the use of CI in the TD games has the potential to create a more interesting, interactive and ongoing game experience for casual gamers. We present a definition of the current state and development of TD games, and include a classification of TD game components. We then describe some potential ways CI can be used to augment the TD experience. Finally, a prototype TD game based on experience-driven procedural content generation is presented.

Question: What is the current state of tower defence games?


Joint Embedding Models for Textual and Social Analysis. In online social networks, users openly interact, share content, and endorse each other. Although the data is interconnected, previous research has primarily focused on modeling the social network behavior separately from the textual content. Here we model the data in a holistic way, taking into account connections between social behavior and content. Specifically, we define multiple decision tasks over the relationships between users and the content generated by them. We show, on a real world dataset, that a learning a joint embedding (over user characteristics and language) and using joint prediction (based on intraand inter-task constraints) produces consistent gains over (1) learning specialized embeddings, and (2) predicting locally w.r.t. a single task, with or without constraints.

Question: How to model the data in a holistic way?


An Introduction to the Theory of Mechanism Design. 1 The authors will donate all payments that they receive from the publisher for this book to Amnesty International.

Question: What is the theory of mechanism design?


A honeycomb-shaped planar monopole antenna for broadband millimeter-wave applications. This paper investigates a planar monopole antenna for fifth generation (5G) wireless communication networks. The proposed antenna has an ultra-wide band impedance response in millimeter wave (mmW) spectrum, 25–39 GHz covering Ka band. The antenna has unique structural layout resembling hexagonal honeycomb and has low profile (8×7 mm2) on 0.254 mm thick Rogers substrate, enabling the design for incorporation into future mobile phones. This antenna provides peak gain of 4.15 dBi along with 90% efficiency in the working band. The design is also extended to an 8×1 element array presenting maximum gain of 12.7 dBi at central frequency of the antenna.

Question: What is the working band of the antenna?


Short-term meditation training improves attention and self-regulation.. Recent studies suggest that months to years of intensive and systematic meditation training can improve attention. However, the lengthy training required has made it difficult to use random assignment of participants to conditions to confirm these findings. This article shows that a group randomly assigned to 5 days of meditation practice with the integrative body-mind training method shows significantly better attention and control of stress than a similarly chosen control group given relaxation training. The training method comes from traditional Chinese medicine and incorporates aspects of other meditation and mindfulness training. Compared with the control group, the experimental group of 40 undergraduate Chinese students given 5 days of 20-min integrative training showed greater improvement in conflict scores on the Attention Network Test, lower anxiety, depression, anger, and fatigue, and higher vigor on the Profile of Mood States scale, a significant decrease in stress-related cortisol, and an increase in immunoreactivity. These results provide a convenient method for studying the influence of meditation training by using experimental and control methods similar to those used to test drugs or other interventions.

Question: What is the integrative body-mind training method?


You Are What You Post: What the Content of Instagram Pictures Tells About Users' Personality. Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at the relationship between the content of the uploaded Instagram pictures and the personality traits of users. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. Through the Google Vision API, we analyzed the pictures on their content and clustered the returned labels with the k-means clustering approach. With a total of 17 clusters, we analyzed the relationship with users' personality traits. Our findings suggest a relationship between personality traits and picture content. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems. Author

Question: What is the relationship between personality traits and picture content?


Tuning in to RF MEMS. RF MEMS technology was initially developed as a replacement for GaAs HEMT switches and p-i-n diodes for low-loss switching networks and X-band to mm-wave phase shifters. However, we have found that its very low loss properties (high device Q), its simple microwave circuit model and zero power consumption, its high power (voltage/current) handling capabilities, and its very low distortion properties, all make it the ideal tuning device for reconfigurable filters, antennas and impedance matching networks. In fact, reconfigurable networks are currently being funded at the same level-if not higher-than RF MEMS phase shifters, and in our opinion, are much more challenging for high-Q designs.

Question: What is the difference between RF MEMS and RF MEMS phase shifters?


Understanding the influence of dead-time on GaN based synchronous boost converter. Gallium Nitride (GaN) based power switching devices are known to be superior to conventional Si devices due to properties such as low loss and fast switching speed. However, as a new technology, some special characteristics of GaN-based power devices still remain unknown to the public. This paper tries to investigate the effect of dead-time on a GaN-based synchronous boost converter, as compared to its Si counterpart. It is found out that GaN-based converter is more sensitive to dead-time related loss as a result of fast switching transition and high reverse voltage drop. Improper selection of dead-time value can offset its advantage over Si converter. Analyses also show that GaN HEMTs have different switching characteristics compared to Si MOSFET due to low Cgd to Cds ratio and lack of reverse recovery. These critical findings will help power electronic engineers take better advantage of GaN technology in synchronous rectification and inverter applications.

Question: What is the effect of dead-time on a GaN based synchronous boost converter?


Agents on Stage: Advancing the State of the Art of AI. Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.

Question: What is the state of the art of artificial intelligence?