Sampling cube: a framework for statistical olap over sampling data. Sampling is a popular method of data collection when it is impossible or too costly to reach the entire population. For example, television show ratings in the United States are gathered from a sample of roughly 5,000 households. To use the results effectively, the samples are further partitioned in a multidimensional space based on multiple attribute values. This naturally leads to the desirability of OLAP (Online Analytical Processing) over sampling data. However, unlike traditional data, sampling data is inherently uncertain, i.e., not representing the full data in the population. Thus, it is desirable to return not only query results but also the confidence intervals indicating the reliability of the results. Moreover, a certain segment in a multidimensional space may contain none or too few samples. This requires some additional analysis to return trustable results.  In this paper we propose a Sampling Cube framework, which efficiently calculates confidence intervals for any multidimensional query and uses the OLAP structure to group similar segments to increase sampling size when needed. Further, to handle high dimensional data, a Sampling Cube Shell method is proposed to effectively reduce the storage requirement while still preserving query result quality.

Question: What is the sampling cube framework?


Proposed system for estimating intrinsic value of stock using Monte Carlo simulation. Stock market prediction has been the bane and goal for investors, and one of the biggest challenges for artificial intelligence (AI) community. National economies have a great impact by the behaviour of their stock markets. Markets have become a more accessible investment tool. Attribute that all stock markets have in common is uncertainty. This uncertainty associated with stock value in short and long term future investments are undesirable. Stock market prediction is instrumental in process of investment. Drawbacks of existing system faces technical difficulties such as estimating share of dividends, state vector while implementing stochastic modelling for risk analysis. To overcome drawbacks of existing system, proposed system makes an effort of generating a combination of more than one lakh seventy thousand scenarios to find intrinsic value of company, displaying results in graphical visualization. A large scenario generation of distinct intrinsic stock value done by the system will provide intrinsic stock value for each scenario. A large set of values for all the input parameters for example high growth value, declining growth value, terminal growth value, return on equity needs to be created, so that possible intrinsic value can be generated by system. The system will calculate statistical indicators like mean, median, mode, skewness and kurtosis of large data set consisting intrinsic value of company. Comparing statistically calculated intrinsic value and current market price, system will be able to add a robust statistical reasoning for investment decision. This reasoning will have no human or emotional biases as there will be no human intervention involved for arriving to the final intrinsic value of stock. Monte Carlo simulation is best suited solution for generating random scenarios that fall in line with Brownian walk motion of stock prices.

Question: What is Monte Carlo simulation?


Open-Domain Non-factoid Question Answering. We present an end-to-end system for open-domain non-factoid question answering. We leverage the information on the ever-growing World Wide Web, and the capabilities of modern search engines to find the relevant information. Our QA system is composed of three components: (i) query formulation module (QFM) (ii) candidate answer generation module (CAGM) and (iii) answer selection module (ASM). A thorough empirical evaluation using two datasets demonstrates that the proposed approach is highly competitive.

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


A Flexible Architecture for Ray Tracing Terrain Heightfields. High-quality interactive rendering of terrain surfaces is a challenging task, which requires compromises between rendering quality, rendering time and available resources. However, current solutions typically provide optimized strategies tailored to particular constraints. In this paper we propose a more scalable approach based on functional programming and introduce a flexible ray tracer for rendering terrain heightfields. This permits the dynamic composition of complex and recursive shaders. In order to exploit the concurrency of the GPU for a large number of dynamically created tasks with inter-dependencies, the functional model is represented as a token stream and is iteratively rewritten via pattern matching on multiple shader cores in parallel. A first prototype demonstrates the feasibility of our approach.

Question: What is a ray tracer?


Analogy just looks like high level perception: why a domain-general approach to analogical mapping is right. Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue speci® cally against SM E, our model of analogical matching, on the grounds that it is modular, and oŒer instead programs such as Mitchell and Hofstadter' s Copycat as examples of the high level perception approach. In this paper we argue against this position on two grounds. First, we demonstrate that most of their speci® c arguments involving SM E and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on ® ve issues : (1) how perception relates to analogy, (2) how  ̄ exibility arises in analogical processing, (3) whether analogy is a domain-general process, (4) how micro-worlds should be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. W e illustrate our discussion with examples taken from computer models embodying both views.

Question: What is the difference between perception and analogy?


IO Performance Interference among Consolidated n-Tier Applications: Sharing Is Better Than Isolation for Disks. The performance unpredictability associated with migrating applications into cloud computing infrastructures has impeded this migration. For example, CPU contention between co-located applications has been shown to exhibit counter-intuitive behavior. In this paper, we investigate IO performance interference through the experimental study of consolidated n-tier applications leveraging the same disk. Surprisingly, we found that specifying a specific disk allocation, e.g., limiting the number of Input/Output Operations Per Second (IOPs) per VM, results in significantly lower performance than fully sharing disk across VMs. Moreover, we observe severe performance interference among VMs can not be totally eliminated even with a sharing strategy (e.g., response times for constant workloads still increase over 1,100%). By using a micro-benchmark (Filebench) and an n-tier application benchmark systems (RUBBoS), we demonstrate the existence of disk contention in consolidated environments, and how performance loss occurs when co-located database systems in order to maintain database consistency flush their logs from memory to disk. Potential solutions to these isolation issues are (1) to increase the log buffer size to amortize the disk IO cost (2) to decrease the number of write threads to alleviate disk contention. We validate these methods experimentally and find a 64% and 57% reduction in response time (or more generally, a reduction in performance interference) for constant and increasing workloads respectively.

Question: How to reduce performance interference among co-located applications?


From a Set of Shapes to Object Discovery. This paper presents an approach to object discovery in a give n unlabeled image set, based on mining repetitive spatial config urations of image contours. Contours that similarly deform from one image to a n ther are viewed as collaborating, or, otherwise, conflicting. This is captu red by a graph over all pairs of matching contours, whose maximum a posteriori mult icoloring assignment is taken to represent the shapes of discovered objects. Multicoloring is conducted by our new Coordinate Ascent Swendsen-Wang cut (CASW ). CASW uses the Metropolis-Hastings (MH) reversible jumps to probabil istically sample graph edges, and color nodes. CASW extends SW cut by introducing a r e ularization in the posterior of multicoloring assignments that prevent s the MH jumps to arrive at trivial solutions. Also, CASW seeks to learn paramet ers of the posterior via maximizing a lower bound of the MH acceptance rate. This s peeds up multicoloring iterations, and facilitates MH jumps from local mi nima. On benchmark datasets, we outperform all existing approaches to unsuper vised object discovery.

Question: What is the approach to object discovery in a given image set?


A General Empirical Solution to the Macro Software Sizing and Estimating Problem. Application software development has been an area of organizational effort that has not been amenable to the normal managerial and cost controls. Instances of actual costs of several times the initial budgeted cost, and a time to initial operational capability sometimes twice as long as planned are more often the case than not.

Question: What is the cost of software development?


Introduction to Algorithms, 3rd Edition.

Question: What is the name of the book?


A Survey of Inter-Vehicle Communication ∗. As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry of, notably, US, EU, and Japan. The most important feature of IVC is its ability to extend the horizon of drivers and on-board devices (e.g., radar or sensors) and, thus, to improve road traffic safety and efficiency. This paper surveys IVC with respect to key enabling technologies ranging from physical radio frequency to group communication primitives and security issues. The mobility models used to evaluate the feasibility of these technologies are also briefly described. We focus on the discussion of various MAC protocols that seem to be indispensable components in the network protocol stack of IVC. By analyzing the application requirements and the protocols built upon the MAC layer to meet these requirements, we also advocate our perspective that ad hoc routing protocols and group communication primitives migrated from wired networks might not be an efficient way to support the envisioned applications, and that new coordination algorithms directly based on MAC could be designed for this purpose.

Question: What is the difference between the IEEE 802.11 and IEEE 802.15.4 wireless communication standards?


Space and space-time modeling using process convolutions. A continuous spatial model can be constructed by convolving a very simple, perhaps independent, process with a kernel or point spread function. This approach for constructing a spatial process offers a number of advantages over specification through a spatial covariogram. In particular, this process convolution specification leads to compuational simplifications and easily extends beyond simple stationary models. This paper uses process convolution models to build space and space-time models that are flexible and able to accomodate large amounts of data. Data from environmental monitoring is considered.

Question: What is a process convolution?


Design, modeling and control of a 5-DoF light-weight robot arm for aerial manipulation. The design, modeling and control of a 5 degrees-of-freedom light-weight robot manipulator is presented in this paper. The proposed robot arm, named Prisma Ultra-Lightweight 5 ARm (PUL5AR), is employed to execute manipulation tasks equipped on board of a vertical take-off and landing unmanned aerial vehicle. The arm is compact and light-weight. Its mechanics is designed such that it can fold on itself during landing manoeuvres. Moreover, the design is conceived to constrain the center of gravity of the arm as close as possible to vehicle base, thus reducing the total inertia and static unbalancing of the system. Experimental tests have been carried out in order to validate the dynamic model, the communication library, the developed electronics, and the control schemes implemented for the designed robot arm.

Question: What is the center of gravity of a robot arm?


A Powerful Generative Model Using Random Weights for the Deep Image Representation. To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.

Question: What is the difference between a generative model and a discriminative model?


Neural Machine Translation of Rare Words with Subword Units. Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU, respectively.

Question: What is the neural machine translation of rare words?


Notification and awareness: synchronizing task-oriented collaborative activity. People working collaboratively must establish and maintain awareness of one another's intentions, actions and results. Notification systems typically support awareness of the presence, tasks and actions of collaborators, but they do not adequately support awareness of persistent and complex activities. We analysed awareness breakdowns in use of our Virtual School system—stemming from problems related to the collaborative situation, group, task and tool support—to motivate the concept of activity awareness. Activity awareness builds on prior conceptions of social and action awareness, but emphasizes the importance of activity context factors like planning and coordination. This work suggests design strategies for notification systems to better support collaborative activity. r 2003 Elsevier Science Ltd. All rights reserved.

Question: What is the importance of activity awareness?


Network game traffic modelling. A significant share of today's Internet traffic is generated by network gaming. This kind of traffic is interesting in regard to it's market potential as well as to it's real time requirements on the network. For the consideration of game traffic in network dimensioning, traffic models are required that allow to generate a characteristic load for analytical or simulative performance evaluation of networks. In this paper we evaluate the fast action multiplayer game ,,Counter Strike" from a 36 hour LAN party measurement and present traffic models for client and server. The paper concludes with remarks on the use of game traffic models in simulations and on QoS metrics for an adequate evaluation of simulation results.

Question: What is the market potential of network gaming?


Design of an experimental setup for evaluation of inductive transcutaneous energy transfer systems. Work objective — is to develop an experimental setup for evaluation characteristics of inductive transcutaneous energy transfer systems, such as comparative studies of various topological solutions; studies of systems' thermal safety; influence of geometry of a coils couple on the parameters of energy transfer. The system consists of planar spiral coils, device for positioning, receiving and transmitting circuit boards, frequency generator, and National Instruments USB-6361 DAQ. Mathematical apparatus has been developed that makes it possible to obtain self-inductance and the internal equivalent resistance of the coils from the measured voltage drop in the transmitting circuit, as well as the dependence of the mutual inductance on displacements. LabVIEW virtual instrument for automation of measurements is designed. The values of the self-inductances of planar spiral coils and the practical dependences of the mutual inductance of spiral coils on displacements without and with using automation tools were experimentally found. The results corresponds theoretical calculations.

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


Misinformation and Its Correction: Continued Influence and Successful Debiasing.. The widespread prevalence and persistence of misinformation in contemporary societies, such as the false belief that there is a link between childhood vaccinations and autism, is a matter of public concern. For example, the myths surrounding vaccinations, which prompted some parents to withhold immunization from their children, have led to a marked increase in vaccine-preventable disease, as well as unnecessary public expenditure on research and public-information campaigns aimed at rectifying the situation. We first examine the mechanisms by which such misinformation is disseminated in society, both inadvertently and purposely. Misinformation can originate from rumors but also from works of fiction, governments and politicians, and vested interests. Moreover, changes in the media landscape, including the arrival of the Internet, have fundamentally influenced the ways in which information is communicated and misinformation is spread. We next move to misinformation at the level of the individual, and review the cognitive factors that often render misinformation resistant to correction. We consider how people assess the truth of statements and what makes people believe certain things but not others. We look at people's memory for misinformation and answer the questions of why retractions of misinformation are so ineffective in memory updating and why efforts to retract misinformation can even backfire and, ironically, increase misbelief. Though ideology and personal worldviews can be major obstacles for debiasing, there nonetheless are a number of effective techniques for reducing the impact of misinformation, and we pay special attention to these factors that aid in debiasing. We conclude by providing specific recommendations for the debunking of misinformation. These recommendations pertain to the ways in which corrections should be designed, structured, and applied in order to maximiz

Question: What is the difference between misinformation and disinformation?


Cross-Point Resistive RAM Based on Field-Assisted Superlinear Threshold Selector. We report a 3-D-stackable 1S1R passive cross-point resistive random access memory (RRAM). The sneak (leakage) current challenge in the cross-point RRAM integration has been overcome utilizing a field-assisted superlinear threshold selector. The selector offers high selectivity of >107, sharp switching slope of <;5 mV/decade, ability to tune the threshold voltage, stable operation at 125°C, and endurance of >1011. Furthermore, we demonstrate 1S1R integration in which the selector-subthreshold current is <;10 pA while offering >102 memory ON/OFF ratio and >106 selectivity during cycling. Combined with self-current-controlled RRAM, the 1S1R enables high-density and high-performance memory applications.

Question: What is the sneak current in a cross-point RRAM?


PubChem Substance and Compound databases. PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.

Question: What is the PubChem database?


Deep and Broad Learning on Content-Aware POI Recommendation. POI recommendation has attracted lots of research attentions recently. There are several key factors that need to be modeled towards effective POI recommendation - POI properties, user preference and sequential momentum of check- ins. The challenge lies in how to synergistically learn multi-source heterogeneous data. Previous work tries to model multi-source information in a flat manner, using either embedding based methods or sequential prediction models in a cross-related space, which cannot generate mutually reinforce results. In this paper, a deep and broad learning approach based on a Deep Context- aware POI Recommendation (DCPR) model was proposed to structurally learn POI and user characteristics. The proposed DCPR model includes three collaborative layers, a CNN layer for POI feature mining, an RNN layer for sequential dependency and user preference modeling, and an interactive layer based on matrix factorization to jointly optimize the overall model. Experiments over three data sets demonstrate that DCPR model achieves significant improvement over state-of-the-art POI recommendation algorithms and other deep recommendation models.

Question: How to model user preference?


Diversified Visual Attention Networks for Fine-Grained Object Classification. Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.

Question: What is the difference between attention and attention canvases?


From Single to Dual Robotic Therapy: A Review of the Development Process of iPAM. Stroke is a common condition in the UK with over 30,000 people per annum are left with significant disability. When severe arm paresis is occurs, functional recovery of the affected arm is often poor. This can have a major impact on physical independence. A contributing factor to the poor recovery relates to lack of intensity of appropriate arm exercise practice. Through the use of robotic technology it may be possible to augment the physical therapist's efforts and maximize the impact of the limited physical therapy resource. At Leeds University a prototype single robot system has been developed that is capable of moving a patient's arm through a set of movements prescribed by a physiotherapist. This system is robust to changes in the biomechanical and physiological properties through a large range of patients. Further development of the system has led to a dual robot system that provides a greater level of control over upper-arm joint co-ordination while offering additional support to the shoulder complex. In order to test the systems safely, two models of the human arm have been produced: 1) a 6 degree of freedom computational model simulating how the robot system could provide therapy to a patient. 2) A mechanical model (Derryck), representing the biomechanical properties of the shoulder and elbow. The prototype single robot system is shown to be capable of administering trajectory following (for Derryck) for a large range of arm parameters

Question: What is the difference between a single and dual robotic therapy?


Near-Duplicates Detection and Elimination Based on Web Provenance for Effective Web Search. Users of World Wide Web utilize search engines for information retrieval in web as search engines play a vital role in finding information on the web. However, the performance of a web search is greatly affected by flooding of search results with information that is redundant in nature i.e., existence of nearduplicates. Such near-duplicates holdup the other promising results to the users. Many of these near-duplicates are from distrusted websites and/or authors who host information on web. Such nearduplicates may be eliminated by means of Provenance. Thus, this paper proposes a novel approach to identify such near-duplicates based on provenance. In this approach a provenance model has been built using web pages which are the search results returned by existing search engine. The proposed model combines both content based and trust based factors for classifying the results as original or near-duplicates. Keywords— Web search, Near-duplicates, Provenance, Semantics,

Question: How to detect near-duplicates in web search?


Interfacing industrial robots using Realtime Primitives. Today, most industrial robots are interfaced using text-based programming languages. These languages offer the possibility to declare robotic-specific data types, to specify simple motions, and to interact with tools and sensors via I/O operations. While tailored to the underlying robot controller, they usually only offer a fixed and controller-specific set of possible instructions. The specification of complex motions, the synchronization of cooperating robots and the advanced use of sensors is often very difficult or not even feasible. To overcome these limitations, this paper presents a generic and extensible interface for industrial robots, the Realtime Primitives Interface, as part of a larger software architecture. It allows a flexible specification of complex control instructions and can facilitate the development of sustainable robot controllers. The advantages of this approach are illustrated with several examples.

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


Speech recognition of Malayalam numbers. Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.

Question: How to recognize malayalam numbers?


Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer's interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.

Question: What is a book recommendation system?


Role and users' approach to social networking sites (SNSs): a study of universities of North India. Purpose – The purpose of this paper is to evaluate and assess the awareness and extent of the use of social networking sites (SNSs) by the students and research scholars of universities of North India. Design/methodology/approach – The study is a questionnaire-based survey on the usage of SNSs among the students and research scholars of the universities of North India. The data of the study were collected through questionnaires, which were personally distributed to the identified population, i.e. undergraduate students, postgraduate students and research scholars, by the authors. The survey was based on a sample of 610 questionnaires; of which, 486 questionnaires were received, having a response rate of 79.67 per cent. Findings – The study showed that all the respondents were found to be aware of and making use of such applications in their academic affairs. It was revealed from the study that Facebook is the most popular SNS by all categories of respondents. To determine the purpose of SNSs, it emerged that SNSs are mostly used for entertainment and communication. The study also found that the majority of respondents were aware about the security aspects of SNSs. It signifies that excessive time consumption and fear of misusing personal information were the major hurdles in the way of accessing SNSs. Research limitations/implications – The study covers the students and research scholars of select universities of North India. It also signifies the use of SNSs in their research and academic environment. Originality/value – The paper provides reliable and authentic data. The study is worth, justifiable and enlightens the salient findings on the topic, which will be very useful for researchers in this area.

Question: What is the purpose of social networking sites?


Quantifying Trading Behavior in Financial Markets Using Google Trends. Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as "early warning signs" of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.

Question: What is the potential of combining extensive behavioral data sets?


Consumer Brand Engagement in Social Media : Conceptualization , Scale Development & Validation. Consumer Brand Engagement in Social Media: Conceptualization, Scale Development & Validation Abstract In the last three decades an influential research stream has emerged, which highlights the dynamics of focal consumer/brand relationships. Specifically, more recently the 'consumer brand engagement' (CBE) concept has been postulated to more comprehensively reflect the nature of consumers' particular interactive brand relationships, relative to traditional concepts, including 'involvement.' However, despite the growing scholarly interest regarding the undertaking of marketing research addressing 'engagement,' studies have been predominantly exploratory in nature; thus generating a lack of empirical research in this area to date. By developing and validating a CBE scale in specific social media settings, we address this identified literature gap. Specifically, we conceptualize CBE as a consumer's positively valenced brand-related cognitive, emotional and behavioral activity during or related to focal consumer/brand interactions. We derive three CBE dimensions, including cognitive processing, affection, and activation. Within three different social media contexts, we employ exploratory and confirmatory factor analyses to develop a reliable, 10-item CBE scale, which we proceed to validate within a nomological net of conceptual relationships and a rival model. The findings suggest that while consumer brand 'involvement' acts as a CBE antecedent, consumer 'self-brand connection' and 'brand usage intent' represent key CBE consequences; thus providing a platform for further research in this emerging area. We conclude with an overview of key managerial and scholarly implications arising from this research.

Question: What is the difference between consumer brand engagement and brand involvement?


Punctuation Prediction with Transition-based Parsing. Punctuations are not available in automatic speech recognition outputs, which could create barriers to many subsequent text processing tasks. This paper proposes a novel method to predict punctuation symbols for the stream of words in transcribed speech texts. Our method jointly performs parsing and punctuation prediction by integrating a rich set of syntactic features when processing words from left to right. It can exploit a global view to capture long-range dependencies for punctuation prediction with linear complexity. The experimental results on the test data sets of IWSLT and TDT4 show that our method can achieve high-level performance in punctuation prediction over the stream of words in transcribed speech text.

Question: Punctuation prediction with transition-based parsing


Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance. In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems. To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account. To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features. Experiments show that our method is effective and outperforms several competitive baseline methods.

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


Subcarrier-index modulation OFDM. A new transmission approach, referred to as subcarrier-index modulation (SIM) is proposed to be integrated with the orthogonal frequency division multiplexing (OFDM) systems. More specifically, it relates to adding an additional dimension to the conventional two-dimensional (2-D) amplitude/phase modulation (APM) techniques, i.e. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). The key idea of SIM is to employ the subcarrier-index to convey information to the receiver. Furthermore, a closed-form analytical bit error ratio (BER) of SIM OFDM in Rayleigh channel is derived. Analytical and simulation results show error probability performance gain of 4 dB over 4-QAM OFDM systems for both coded and uncoded data without power saving policy. Alternatively, power saving policy retains an average gain of 1 dB while using 3 dB less transmit power per OFDM symbol.

Question: What is the closed-form analytical bit error ratio (BER) of SIM OFDM in Rayleigh channel?


Data Structures for Traveling Salesmen. The choice of data structure for tour representation plays a critical role in the efficiency of local improvement heuristics for the Traveling Salesman Problem. The tour data structure must permit queries about the relative order of cities in the current tour and must allow sections of the tour to be reversed. The traditional array-based representation of a tour permits the relative order of cities to be determined in small constant time, but requires worst-case Ω(N) time (where N is the number of cities) to implement a reversal, which renders it impractical for large instances. This paper considers alternative tour data structures, examining them from both a theoretical and experimental point of view. The first alternative we consider is a data structure based on splay trees, where all queries and updates take amortized time O( logN). We show that this is close to the best possible, because in the cell probe model of computation any data structure must take worst-case amortized time Ω( logN /log logN) per operation. Empirically (for random Euclidean instances), splay trees overcome their large constant-factor overhead and catch up to arrays by N = 10 , 000, pulling ahead by a factor of 4-10 (depending on machine) when N = 100 , 000. Two alternative tree-based data structures do even better in this range, however. Although both are asymptotically inferior to the splay tree representation, the latter does not appear to pull even with them until N ∼ 1 , 000 , 000. ________________ 1 Rutgers University, New Brunswick, NJ 08903, and University of California at San Diego, La Jolla, CA 92093 2 Room 2D-150, AT&T Bell Laboratories, Murray Hill, NJ 07974 3 Department of Mathematics and Computer Science, Amherst College, Amherst, MA 01002 4 Department of Mathematics, Rutgers University, New Brunswick, NJ 08903 * A preliminary version of this paper appeared under the same title in Proceedings 4th Ann. ACM-SIAM Symp. on Discrete Algorithm

Question: What is the best data structure for traveling salesman problem?


Risk-based adaptive security for smart IoT in eHealth.

Question: What is risk-based adaptive security?


Nutritional Ecology and Human Health.. In contrast to the spectacular advances in the first half of the twentieth century with micronutrient-related diseases, human nutrition science has failed to stem the more recent rise of obesity and associated cardiometabolic disease (OACD). This failure has triggered debate on the problems and limitations of the field and what change is needed to address these. We briefly review the two broad historical phases of human nutrition science and then provide an overview of the main problems that have been implicated in the poor progress of the field with solving OACD. We next introduce the field of nutritional ecology and show how its ecological-evolutionary foundations can enrich human nutrition science by providing the theory to help address its limitations. We end by introducing a modeling approach from nutritional ecology, termed nutritional geometry, and demonstrate how it can help to implement ecological and evolutionary theory in human nutrition to provide new direction and to better understand and manage OACD.

Question: What is the difference between micronutrient-related diseases and obesity?


Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR. This is the supplementary material for the paper "Discriminative Correlation Filter with Channel and Spatial Reliability" submitted to the CVPR 2017. Due to spatial constraints, parts not crucial for understanding the DCF-CSR tracker formulation, but helpful for gaining insights, were moved here. 1. Derivation of the augmented Lagrangian minimizer This section provides the complete derivation of the closed-form solutions for the relations (9,10) in the submitted paper [3]. The augmented Lagrangian from Equation (5) in [3] is L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖ + (1) [̂l(ĥc − ĥm) + l̂(ĥc − ĥm)] + μ‖ĥc − ĥm‖, with hm = (m h). For the purposes of derivation we will rewrite (1) into a fully vectorized form L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖+ (2) [̂ l(ĥc − √ DFMh) + l̂(ĥc − √ DFMh) ] + μ‖ĥc − √ DFMh‖, where F denotes D × D orthonormal matrix of Fourier coefficients, such that the Fourier transform is defined as x̂ = F(x) = √ DFx and M = diag(m). For clearer representation we denote the four terms in the summation (2) as L(ĥc,h, l̂) = L1 + L2 + L3 + L4, (3) where L1 = ( ĥc diag(f̂)− ĝ )( ĥc diag(f̂)− ĝ )T , (4)

Question: What is the Fourier transform?


Series No . 16 Profiting from Innovation in the Digital Economy : Standards , Complementary Assets , and Business Models In the Wireless World.

Question: What is the series number of this document?


Depressive realism: a meta-analytic review.. The current investigation represents the first meta-analysis of the depressive realism literature. A search of this literature revealed 75 relevant studies representing 7305 participants from across the US and Canada, as well as from England, Spain, and Israel. Results generally indicated a small overall depressive realism effect (Cohen's d=-.07). Overall, however, both dysphoric/depressed individuals (d=.14) and nondysphoric/nondepressed individuals evidenced a substantial positive bias (d=.29), with this bias being larger in nondysphoric/nondepressed individuals. Examination of potential moderator variables indicated that studies lacking an objective standard of reality (d=-.15 versus -.03, for studies possessing such a standard) and that utilize self-report measures to measure symptoms of depression (d=.16 versus -.04, for studies which utilize structured interviews) were more likely to find depressive realism effects. Methodological paradigm was also found to influence whether results consistent with depressive realism were found (d's ranged from -.09 to .14).

Question: What is the first meta-analysis of the depressive realism literature?


Playing online games against computer- vs. human-controlled opponents: Effects on presence, flow, and enjoyment.

Question: What is the difference between a computer-controlled and a human-controlled opponent?


Cascading Bandits for Large-Scale Recommendation Problems. Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items from a large set of L candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items L. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.

Question: What is a recommender system?


LEARNING ENGLISH IS FUN VIA KAHOOT : STUDENTS ' ATTITUDE , MOTIVATION AND PERCEPTIONS. This study is aimed to investigate primary level students' attitude and motivation and perceptions in learning English using Kahoot game. According to current development in Malaysian education field, 21st century learning style is more focused and given importance. Thus, Kahoot, game-based learning platform has been used to stimulate students' from a rural area in southern Malaysia to learn English as their second language more effectively, actively and interestingly. The research design for this study is action research. Nine students were chosen as the target group for this study. The Kahoot game was conducted for three different topics in English after they completed the lesson each day. The data was collected using Attitude and Motivation Test Battery questionnaires consisting of 10 items, interview session with 5 semi-structured questions for three selected students and results of each Kahoot game played by the students. The data was analysed using descriptive analysis. The results of the questionnaires, interview session and results of the game were shown in figures and tables. Findings of the study show that all the nine students were able to engage actively in the game and they were able to master the target language effectively. They enjoy learning English using games and wish to have more games in future.

Question: What is the Kahoot game?


Self-Supervised Generalisation with Meta Auxiliary Learning. Learning with auxiliary tasks has been shown to improve the generalisation of a primary task. However, this comes at the cost of manuallylabelling additional tasks which may, or may not, be useful for the primary task. We propose a new method which automatically learns labels for an auxiliary task, such that any supervised learning task can be improved without requiring access to additional data. The approach is to train two neural networks: a label-generation network to predict the auxiliary labels, and a multi-task network to train the primary task alongside the auxiliary task. The loss for the label-generation network incorporates the multi-task network's performance, and so this interaction between the two networks can be seen as a form of meta learning. We show that our proposed method, Meta AuXiliary Learning (MAXL), outperforms single-task learning on 7 image datasets by a significant margin, without requiring additional auxiliary labels. We also show that MAXL outperforms several other baselines for generating auxiliary labels, and is even competitive when compared with human-defined auxiliary labels. The self-supervised nature of our method leads to a promising new direction towards automated generalisation. The source code is available at https://github.com/lorenmt/maxl.

Question: How does self-supervised learning work?


11.5 A time-correlated single-photon-counting sensor with 14GS/S histogramming time-to-digital converter. Time-correlated single photon counting (TCSPC) is a photon-efficient technique to record ultra-fast optical waveforms found in numerous applications such as time-of-flight (ToF) range measurement (LIDAR) [1], ToF 3D imaging [2], scanning optical microscopy [3], diffuse optical tomography (DOT) and Raman sensing [4]. Typical instrumentation consists of a pulsed laser source, a discrete detector such as an avalanche photodiode (APD) or photomultiplier tube (PMT), time-to-digital converter (TDC) card and a FPGA or PC to assemble and compute histograms of photon time stamps. Cost and size restrict the number of channels of TCSPC hardware. Having few detection and conversion channels, the technique is limited to processing optical waveforms with low intensity, with less than one returned photon per laser pulse, to avoid pile-up distortion [4]. However, many ultra-fast optical waveforms exhibit high dynamic range in the number of photons emitted per laser pulse. Examples are signals observed at close range in ToF with multiple reflections, diffuse reflected photons in DOT or local variations in fluorescent dye concentration in microscopy. This paper provides a single integrated chip that reduces conventional TCSPC pile-up mechanisms by an order of magnitude through ultra-parallel realizations of both photon detection and time-resolving hardware. A TDC architecture is presented which combines the two step iterated TCSPC process of time-code generation, followed by memory lookup, increment and write, into one parallel direct-to-histogram conversion. The sensor achieves 71.4ps resolution, over 18.85ns dynamic range, with 14GS/s throughput. The sensor can process 1.7Gphoton/s and generate 21k histograms/s (with 4.6μs readout time), each capturing a total of 1.7kphotons in a 1μs exposure.

Question: What is a time-correlated single photon counting sensor?


Position-based impedance control of an industrial hydraulic manipulator. This paper addresses the problem of impedance control in hydraulic robots. Whereas most impedance and hybrid force/position control formulations have focussed on electrically-driven robots with controllable actuator torques, torque control of hydraulic actuators is a difficult task. Motivated by the previous works [2,9, lo], a position-based impedance controller is proposed and demonstrated on an existing industrial hydraulic robot (a Unimate MKII-2000). A nonlinear proportional-integral (NPI) controller is first developed to meet the accurate positioning requirements of this impedance control formulation. The NPI controller is shown to make the manipulator match a range of second-order target impedances. Various experiments in free space and in environmental contact, including a simple impedance modulation experiment, demonstrate the feasibility and the promise of the technique.

Question: What is a hydraulic manipulator?


Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining. A common feature of many online review sites is the use of an overall rating that summarizes the opinions expressed in a review. Unfortunately, these document-level ratings do not provide any information about the opinions contained in the review that concern a specific aspect (e.g., cleanliness) of the product being reviewed (e.g., a hotel). In this paper we study the finer-grained problem of aspect-oriented opinion mining at the sentence level, which consists of predicting, for all sentences in the review, whether the sentence expresses a positive, neutral, or negative opinion (or no opinion at all) about a specific aspect of the product. For this task we propose a set of increasingly powerful models based on conditional random fields (CRFs), including a hierarchical multi-label CRFs scheme that jointly models the overall opinion expressed in the review and the set of aspect-specific opinions expressed in each of its sentences. We evaluate the proposed models against a dataset of hotel reviews (which we here make publicly available) in which the set of aspects and the opinions expressed concerning them are manually annotated at the sentence level. We find that both hierarchical and multi-label factors lead to improved predictions of aspect-oriented opinions.

Question: What is the set of aspects and the set of opinions expressed concerning them?


Compact CPW-Fed microstrip octagonal patch antenna with H slot for WLAN and WIMAX applications. In this paper, a Coplanar Wave Guide (CPW)-Fed microstrip octagonal patch antenna for WLAN and WIMAX Applications is proposed. The studied structure is suitable for 2.3/2.5/3.3/3.5/5/5.5GHz WiMAX and for 3.6/2.4–2.5/4.9–5.9GHz WLAN applications. The octagonal shape is obtained by making a cut in the four angles of the rectangular microstrip patch antenna; in addition the using of CPW-Fed allows obtaining UWB characteristics. The miniaturization in the antenna size for lower band is achieved by introducing an H slot in the radiating element. The proposed antenna is designed on a single and a small substrate board of dimensions 46×40×1.6 mm3. Moreover the miniaturized antenna has a good impedance matching and a reasonable gain. All the simulations were performed in CADFEKO, a Method of Moment (MoM) based solver.

Question: What is the size of the antenna?


Compressed Inverted Indexes for In-Memory Search Engines. We present the algorithmic core of a full text data base that allows fast Boolean queries, phrase queries, and document reporting using less space than the input text. The system uses a carefully choreographed combination of classical data compression techniques and inverted index based search data structures. It outperforms suffix array based techniques for all the above operations for real world (natural language) texts.

Question: What is the core of a full text data base?


The combinatorial assignment problem: approximate competitive equilibrium from equal incomes. Impossibility theorems suggest that the only efficient and strategyproof mechanisms for the problem of combinatorial assignment - e.g., assigning schedules of courses to students - are dictatorships. Dictatorships are mostly rejected as unfair: for any two agents, one chooses all their objects before the other chooses any. Any solution will involve compromise amongst efficiency, incentive and fairness considerations.  This paper proposes a solution to the combinatorial assignment problem. It is developed in four steps. First, I propose two new criteria of outcome fairness, the maximin share guarantee and envy bounded by a single good, which weaken well-known criteria to accommodate indivisibilities; the criteria formalize why dictatorships are unfair. Second, I prove existence of an approximation to Competitive Equilibrium from Equal Incomes in which (i) incomes are unequal but arbitrarily close together; (ii) the market clears with error, which approaches zero in the limit and is small for realistic problems. Third, I show that this Approximate CEEI satisfies the fairness criteria. Last, I define a mechanism based on Approximate CEEI that is strategyproof for the zero-measure agents economists traditionally regard as price takers. The proposed mechanism is calibrated on real data and is compared to alternatives from theory and practice: all other known mechanisms are either manipulable by zero-measure agents or unfair ex-post, and most are both manipulable and unfair.

Question: What is the combinatorial assignment problem?


Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt. This chapter examines the state of modern intrusion detection, with a particular emphasis on the emerging approach of data mining. The discussion paralleIs two important aspects of intrusion detection: general detection strategy (misuse detection versus anomaly detection) and data source (individual hosts versus network trafik). Misuse detection attempts to match known patterns of intrusion , while anomaly detection searches for deviations from normal behavior . Between the two approaches, only anomaly detection has the ability to detect unknown attacks. A particularly promising approach to anomaly detection combines association mining with other forms of machine learning such as classification. Moreover, the data source that an intrusion detection system employs significantly impacts the types of attacks it can detect. There is a tradeoff in the level of detailed information available verD. Barbará et al. (ed .), Applications of Data Mining in Computer Security © Kluwer Academic Publishers 2002 s

Question: What is the difference between misuse and anomaly detection?


Additive manufacturing of polymer-derived ceramics. The extremely high melting point of many ceramics adds challenges to additive manufacturing as compared with metals and polymers. Because ceramics cannot be cast or machined easily, three-dimensional (3D) printing enables a big leap in geometrical flexibility. We report preceramic monomers that are cured with ultraviolet light in a stereolithography 3D printer or through a patterned mask, forming 3D polymer structures that can have complex shape and cellular architecture. These polymer structures can be pyrolyzed to a ceramic with uniform shrinkage and virtually no porosity. Silicon oxycarbide microlattice and honeycomb cellular materials fabricated with this approach exhibit higher strength than ceramic foams of similar density. Additive manufacturing of such materials is of interest for propulsion components, thermal protection systems, porous burners, microelectromechanical systems, and electronic device packaging.

Question: What is additive manufacturing?


Gamification Solutions to Enhance Software User Engagement - A Systematic Review. Gamification is the use of video-game mechanics and elements in nongame contexts to enhance user engagement and performance. The purpose of this study is to conduct a systematic review to have an in-depth investigation into the existing gamification solutions targeted at solving user engagement problems in different categories of software. We carried out this systematic review by proposing a framework of gamifying process, which is the basis for comparison of existing gamification solutions. In order to report the review, the primary studies are categorized according to the following: a) gamified software and their platforms, b) elements of the gamifying process, c) gamification solutions in each software type, d) gamification solutions for software user engagement problems, e) gamification solutions in general, and f) effects of gamification on software user engagement and performance. Based on the search procedure and criteria, a total of 78 primary studies were extracted. Most of the studies focused on educational and social software, which were developed for web or mobile platforms. We concluded that the number of studies on motivating users to use software content, solving problems in learning software, and using real identity is very limited. Furthermore, few studies have been carried out on gamifying the following software categories: productivity software, cloud storage, utility software, entertainment software, search engine software, tool software, fitness software, software engineering, information worker software, and health-care software. In addition, a large number of gamification solutions are relatively simple and require improvement. Thus, for future studies, researchers can work on the items discovered in this review; they can improve the quality of the current gamified systems by using a wide variety of game mechanics and interface elements, utilizing a combination o

Question: What is gamification?


Finding and Optimizing Solvable Priority Schemes for Decoupled Path Planning Techniques for Teams of Mobile Robots. Coordinating the motion of multiple mobile robots is one of the fundamental problems in robotics. The predominant algorithms for coordinating teams of robots are decoupled and prioritized, thereby avoiding combinatorially hard planning problems typically faced by centralized approaches. While these methods are very efficient, they have two major drawbacks. First, they are incomplete, i.e. they sometimes fail to find a solution even if one exists, and second, the resulting solutions are often not optimal. In this paper we present a method for finding and optimizing priority schemes for such prioritized and decoupled planning techniques. Existing approaches apply a single priority scheme which makes them overly prone to failure in cases where valid solutions exist. By searching in the space of priorization schemes, our approach overcomes this limitation. It performs a randomized search with hill-climbing to find solutions and to minimize the overall path length. To focus the search, our algorithm is guided by constraints generated from the task specification. To illustrate the appropriateness of this approach, this paper discusses experimental results obtained with real robots and through systematic robot simulation. The experimental results illustrate the superior performance of our approach, both in terms of efficiency of robot motion and in the ability to find valid plans.

Question: What is the difference between a decoupled and a prioritized planning technique?


A DIRT-T A PPROACH TO U NSUPERVISED D OMAIN. Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two domains is via domain adversarial training (Ganin & Lempitsky, 2015), which attempts to induce a feature extractor that matches the source and target feature distributions in some feature space. However, domain adversarial training faces two critical limitations: 1) if the feature extraction function has high-capacity, then feature distribution matching is a weak constraint, 2) in non-conservative domain adaptation (where no single classifier can perform well in both the source and target domains), training the model to do well on the source domain hurts performance on the target domain. In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions. We propose two novel and related models: 1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes violation of the cluster assumption; 2) the Decision-boundary Iterative Refinement Training with a Teacher (DIRT-T)1 model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation. Extensive empirical results demonstrate that the combination of these two models significantly improve the state-of-the-art performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation benchmarks.

Question: What is the cluster assumption?


Forward Error Correction for DNA Data Storage. We report on a strong capacity boost in storing digital data in synthetic DNA. In principle, synthetic DNA is an ideal media to archive digital data for very long times because the achievable data density and longevity outperforms today's digital data storage media by far. On the other hand, neither the synthesis, nor the amplification and the sequencing of DNA strands can be performed error-free today and in the foreseeable future. In order to make synthetic DNA available as digital data storage media, specifically tailored forward error correction schemes have to be applied. For the purpose of realizing a DNA data storage, we have developed an efficient and robust forwarderror-correcting scheme adapted to the DNA channel. We based the design of the needed DNA channel model on data from a proof-of-concept conducted 2012 by a team from the Harvard Medical School [1]. Our forward error correction scheme is able to cope with all error types of today's DNA synthesis, amplification and sequencing processes, e.g. insertion, deletion, and swap errors. In a successful experiment, we were able to store and retrieve error-free 22 MByte of digital data in synthetic DNA recently. The found residual error probability is already in the same order as it is in hard disk drives and can be easily improved further. This proves the feasibility to use synthetic DNA as longterm digital data storage media.

Question: How much data can be stored in synthetic DNA?


Sentiment Analysis and Subjectivity. Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people's sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people's positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others' opinions. This is not only true for individuals but also true for organizations.

Question: What is the difference between facts and opinions?


Preservation of nostril morphology in nasal base reduction. Asian patients often desire reduction of the base and alar lobules of the Asian mesorrhine nose. Sill excision is commonly used, but may result in an angular or notched nostril rim. We developed an internal method of alar base reduction involving triangle flaps for sill resection. This method avoids alar rim notching and teardrop deformity. Cinching sutures and double-layer closure avoid tension on the wound. We categorized the results in 50 patients (4 men, 46 women) who underwent surgery between November 2012 and August 2015 and who could be followed up for more than 3 months. The mean age of the subjects was 26.3 years and the mean follow-up period was 8.9 months. Forty patients underwent base reduction with the internal method, while ten with alar flare were treated with additional external resection. The mean reduction of the nostril sill width was 4.8 mm for both methods. In the subjects receiving flare resection, the mean reduction of the lateral alar width was 4.4 mm. There was no notching at the suture site. Complications included a short scar running obliquely under the sill in 13 patients and a trap door deformity in one patient. Nasal base reduction is widely performed, but subject to outcomes with abnormal nostril contour. We used triangle flaps to narrow the sill, and cinching sutures to prevent tension on the wound. Our methods prevent nostril notching and/or teardrop deformity. Scarring can occur, but can be reduced using cinching sutures for wound relaxation. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Question: How to prevent nostril notching and teardrop deformity?


A Novel UWB Monopole Antenna With Tunable Notched Behavior Using Varactor Diode. This letter presents a novel ultrawideband (UWB) antenna with tunable notched band. The antenna is assembled on an FR4 substrate with thickness of 0.8 mm and εr = 4.4. By inserting a π-shaped slot on the radiating patch, band-notch function is achieved. By loading the slot with lumped varactor, tunability of the created notch would be possible. Based on this technique, an electronically controlled notched-band antenna is designed and fabricated including a single varactor diode with a varying capacitance value of 0.63-2.67 pF. Using the cited varactor, notched band tunability of 2.7-7.2 GHz is obtained.

Question: What is a varactor diode?


Food, livestock production, energy, climate change, and health. Food provides energy and nutrients, but its acquisition requires energy expenditure. In post-hunter-gatherer societies, extra-somatic energy has greatly expanded and intensified the catching, gathering, and production of food. Modern relations between energy, food, and health are very complex, raising serious, high-level policy challenges. Together with persistent widespread under-nutrition, over-nutrition (and sedentarism) is causing obesity and associated serious health consequences. Worldwide, agricultural activity, especially livestock production, accounts for about a fifth of total greenhouse-gas emissions, thus contributing to climate change and its adverse health consequences, including the threat to food yields in many regions. Particular policy attention should be paid to the health risks posed by the rapid worldwide growth in meat consumption, both by exacerbating climate change and by directly contributing to certain diseases. To prevent increased greenhouse-gas emissions from this production sector, both the average worldwide consumption level of animal products and the intensity of emissions from livestock production must be reduced. An international contraction and convergence strategy offers a feasible route to such a goal. The current global average meat consumption is 100 g per person per day, with about a ten-fold variation between high-consuming and low-consuming populations. 90 g per day is proposed as a working global target, shared more evenly, with not more than 50 g per day coming from red meat from ruminants (ie, cattle, sheep, goats, and other digastric grazers).

Question: What is the average meat consumption per person per day?


Fire flame detection in video sequences using multi-stage pattern recognition techniques. In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempospatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate. & 2012 Elsevier Ltd. All rights reserved.

Question: How to detect fire in video sequences using multi-stage pattern recognition techniques?


Detection and prevention system against cyber attacks and botnet malware for information systems and Internet of Things. The explosion of interconnected devices and the Internet of Things has triggered new important challenges in the area of internet security, due to the various device vulnerabilities and increased potential for cyber-attacks. This paper touches on the areas of Cybersecurity, intrusion detection, prevention systems and artificial intelligence. Our aim is to create a system capable of understanding, detecting and preventing malicious connections using applied concepts of machine learning. We emphasize the importance of selecting and extracting features that can lead to an accurate decision of classification for malware and intrusion attacks. We propose a solution that combines features that extract correlations from the packet history for the same and different services and hosts, based on the rate of REJ, SYN and ACK flags and connection states, with HTTP features extracted from URI and RESTful methods. Our proposed solution is able to detect network intrusions and botnet communications with a precision of 98.4% on the binary classification problem.

Question: What is the detection and prevention system against cyber attacks and botnet malware for information systems and Internet of Things?


An Evaluation of Fine and Gross Motor Skills in Adolescents with Down Syndromes. Down syndrome (DS) is the most common genetic cause of intellectual disability. Motor development of children with DS is delayed. The aim of the present study was to evaluate of fine and gross motor skills of adolescents with DS. The study sample of a total of 34 participants aged between 14 to 20 years comprised 16 adolescents with DS and a normally developing group of 18 adolescents without DS. Fine and gross motor skills of the participants were assessed by Bruininks-Oseretsky Test of Motor Proficiency, second edition short form (BOT-2 SF). The highest score of the test is 88 and higher score shows higher performance. The average age of adolescents with DS and without DS who participated in the study were 17.06±2.79 and 16.56±1.09, respectively. All partcipants were male. It was found a significant differences for all BOT-2 SF subtests and total scores when compared between adolescents with DS and adolescents without DS (p<0.05). Adolescents without DS has higher scores than adolescents with DS. In conclusion, both fine and gross motor skill performance of adolescents with DS is lower than normally developing adolescents. This study stresses the importance of interventions facilitating motor skills.

Question: What is the difference between fine and gross motor skills?


Off-Line Arabic Character Recognition – A Review. Off-line recognition requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text. Other complexities that an off-line recognition system has to deal with are the lower resolution of the document and the poor binarisation, which can contribute to readability when essential features of the characters are deleted or obscured. Recognising Arabic script presents two additional challenges: orthography is cursive and letter shape is context sensitive. Certain character combinations form new ligature shapes, which are often font-dependent. Some ligatures involve vertical stacking of characters. Since not all letters connect, word boundary location becomes an interesting problem, as spacing may separate not only words, but also certain characters within a word. Various techniques have been implemented to achieve high recognition rates. These techniques have tackled different aspects of the recognition system. This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.

Question: What is the best OCR software for Arabic?


Analyze your Scratch projects with Dr . Scratch and assess your Computational Thinking skills. In this paper we present the feature of Dr. Scratch that allows to automatically assessing the Computational Thinking skills of Scratch projects. The paper reviews similar initiatives, like Hairball, and investigates the literature with proposals for assessment of Scratch projects that we have studied and remixed in order to develop the Computational Thinking analysis. Then it introduces the various aspects that Dr. Scratch takes into consideration to compute a Computational Thinking score for a Scratch project and presents some preliminary findings of the analysis of over 100 investigated Scratch projects. Finally, future directions and limitations are presented and discussed.

Question: What is the computational thinking of a scratch project?


Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network. Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.

Question: How does a wireless sensor network work?


Fast and accurate analysis of scanning slotted waveguide arrays. An iterative procedure is presented for a fast and accurate analysis of scanning slotted waveguide arrays including the effects of mutual couplings and their dependence on the scanning angle. The analysis takes also into account the input transitions from the feeding network; each slotted waveguide being excited either at one end or at the centre. The procedure is applied to the simple case of a linear array of four slots, for which a full-wave analysis could be carried out for comparison. The proposed method is shown to provide excellent agreement with the full-wave computation still requiring extremely short computation times.

Question: What is a waveguide array?


A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset. Intrusion detection systems aim to detect malicious viruses from computer and network traffic, which is not possible using common firewall. Most intrusion detection systems are developed based on machine learning techniques. Since datasets which used in intrusion detection are imbalanced, in the previous methods, the accuracy of detecting two attack classes, R2L and U2R, is lower than that of the normal and other attack classes. In order to overcome this issue, this study employs a hybrid approach. This hybrid approach is a combination of synthetic minority oversampling technique (SMOTE) and cluster center and nearest neighbor (CANN). Important features are selected using leave one out method (LOO). Moreover, this study employs NSL KDD dataset. Results indicate that the proposed method improves the accuracy of detecting U2R and R2L attacks in comparison to the baseline paper by 94% and 50%, respectively. Keywords—intrusion detection system; feature selection; imbalanced dataset; SMOTE; NSL KDD

Question: What is the difference between a hybrid approach and a baseline paper?


Experience with SCRAM, a SCenario Requirements Analysis Method. A method of scenario based requirements engineering is described that uses a combination of early prototypes, scenario scripts and design rationale to elicit and validate user requirements. Experience in using the method on an EU project, Multimedia Broker, is reported. Quantitative data on requirements sessions is analysed to assess user participation and quality of requirements captured. The method worked well but there were problems in the use of design rationale and control of turn taking in RE sessions. Lessons learned in using the method are summarised and future improvements for the method are discussed.

Question: What is a scenario based requirements engineering method?


A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and in Vivo Bioavailability. A biopharmaceutics drug classification scheme for correlating in vitro drug product dissolution and in vivo bioavailability is proposed based on recognizing that drug dissolution and gastrointestinal permeability are the fundamental parameters controlling rate and extent of drug absorption. This analysis uses a transport model and human permeability results for estimating in vivo drug absorption to illustrate the primary importance of solubility and permeability on drug absorption. The fundamental parameters which define oral drug absorption in humans resulting from this analysis are discussed and used as a basis for this classification scheme. These Biopharmaceutic Drug Classes are defined as: Case 1. High solubility-high permeability drugs, Case 2. Low solubility-high permeability drugs, Case 3. High solubility-low permeability drugs, and Case 4. Low solubility-low permeability drugs. Based on this classification scheme, suggestions are made for setting standards for in vitro drug dissolution testing methodology which will correlate with the in vivo process. This methodology must be based on the physiological and physical chemical properties controlling drug absorption. This analysis points out conditions under which no in vitro-in vivo correlation may be expected e.g. rapidly dissolving low permeability drugs. Furthermore, it is suggested for example that for very rapidly dissolving high solubility drugs, e.g. 85% dissolution in less than 15 minutes, a simple one point dissolution test, is all that may be needed to insure bioavailability. For slowly dissolving drugs a dissolution profile is required with multiple time points in systems which would include low pH, physiological pH, and surfactants and the in vitro conditions should mimic the in vivo processes. This classification scheme provides a basis for establishing i

Question: What is the correlation between in vitro dissolution and in vivo bioavailability?


Differentially Private Online Learning. In this paper, we consider the problem of preserving privacy in the context of online learning. Online learning involves learning from data in real-time, due to which the learned model as well as its predictions are continuously changing. This makes preserving privacy of each data point significantly more challenging as its effect on the learned model can be easily tracked by observing changes in the subsequent predictions. Furthermore, with more and more online systems (e.g. search engines like Bing, Google etc.) trying to learn their customers' behavior by leveraging their access to sensitive customer data (through cookies etc.), the problem of privacy preserving online learning has become critical. We study the problem in the framework of online convex programming (OCP)—a popular online learning setting with several theoretical and practical implications—while using differential privacy as the formal measure of privacy. For this problem, we provide a generic framework that can be used to convert any given OCP algorithm into a private OCP algorithm with provable privacy as well as regret guarantees (utility), provided that the given OCP algorithm satisfies the following two criteria: 1) linearly decreasing sensitivity, i.e., the effect of the new data points on the learned model decreases linearly, 2) sub-linear regret. We then illustrate our approach by converting two popular OCP algorithms into corresponding differentially private algorithms while guaranteeing Õ( √ T ) regret for strongly convex functions. Next, we consider the practically important class of online linear regression problems, for which we generalize the approach by Dwork et al. (2010a) to provide a differentially private algorithm with just poly-log regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for the offline learning problem as well. For the offline learning problem, our approach guar

Question: What is the difference between online and offline learning?


An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing. Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

Question: What is fog computing?


Improving Vector Space Word Representations Via Kernel Canonical Correlation Analysis. Cross-lingual word embeddings are representations for vocabularies of two or more languages in one common continuous vector space and are widely used in various natural language processing tasks. A state-of-the-art way to generate cross-lingual word embeddings is to learn a linear mapping, with an assumption that the vector representations of similar words in different languages are related by a linear relationship. However, this assumption does not always hold true, especially for substantially different languages. We therefore propose to use kernel canonical correlation analysis to capture a non-linear relationship between word embeddings of two languages. By extensively evaluating the learned word embeddings on three tasks (word similarity, cross-lingual dictionary induction, and cross-lingual document classification) across five language pairs, we demonstrate that our proposed approach achieves essentially better performances than previous linear methods on all of the three tasks, especially for language pairs with substantial typological difference.

Question: What is the difference between linear and non-linear methods?


ON STAGNATION OF THE DIFFERENTIAL EVOLUTION ALGORITHM. This article discusses the stagnation of an evolutionary optimization algorithm called Differential Evolution. Stagnation problem refers to a situation in which the optimum seeking process stagnates before finding a globally optimal solution. Typically, stagnation occurs virtually without any obvious reason. The stagnation differs from the premature convergence so that the population remains diverse and unconverged after stagnation, but the optimization process does not progress anymore. The reasons for this problem have remained unknown so far. This article uncovers this problem describing the basic nature of stagnation phenomena, a mechanism behind it and some reasons for stagnation. Advices for reducing the risk of stagnation are concluded on basis of the new findings.

Question: What is the difference between stagnation and premature convergence?


Fabrication of zero-speed sensor using weakly coupled NiFe/CoFe multilayer films. A magnetic sensor consisting of NiFe/Al/sub 2/O/sub 3//CoFe thin film was fabricated. Magnetization reversal of the NiFe soft layer was asymmetric in switching to parallel and antiparallel alignments to magnetization of CoFe because of magnetostatic coupling between the NiFe and CoFe layers. When the magnetization of the NiFe was paralleled to that of CoFe, a steep switching accompanied a large Barkhausen jump-induced pulse voltage in a pickup coil as sensor output. Amplitude of the pulse was not dependent on frequency of an external magnetic field. The pulse voltage output was obtained from the sensor element consisting of the optimized film structure excited by the magnetic field at almost zero frequency. These features are also achieved by twisted FeCoV and other wire materials. However, an integrated on-chip sensor with electronic circuits can be fabricated with film-based materials. The film-based sensor elements fabricated in this study can be utilized for speed sensor, rotation sensor, flow meter, and other applications.

Question: What is the difference between a sensor and a transducer?


Statics and Dynamics of Continuum Robots With General Tendon Routing and External Loading. Tendons are a widely used actuation strategy for continuum robots that enable forces and moments to be transmitted along the robot from base-mounted actuators. Most prior robots have used tendons routed in straight paths along the robot. However, routing tendons through general curved paths within the robot offers potential advantages in reshaping the workspace and enabling a single section of the robot to achieve a wider variety of desired shapes. In this paper, we provide a new model for the statics and dynamics of robots with general tendon routing paths that is derived by coupling the classical Cosserat-rod and Cosserat-string models. This model also accounts for general external loading conditions and includes traditional axially routed tendons as a special case. The advantage of the usage of this coupled model for straight-tendon robots is that it accounts for the distributed wrenches that tendons apply along the robot. We show that these are necessary to consider when the robot is subjected to out-of-plane external loads. Our experimental results demonstrate that the coupled model matches experimental tip positions with an error of 1.7% of the robot length, in a set of experiments that include both straight and nonstraight routing cases, with both point and distributed external loads.

Question: How do continuum robots work?


Intrinsically Motivated Learning of Hierarchical Collections of Skills. Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies.

Question: What is intrinsically motivated learning?


A framework for facial surgery simulation. The accurate prediction of the post-surgical facial shape is of paramount importance for surgical planning in facial surgery. In this paper we present a framework for facial surgery simulation which is based on volumetric finite element modeling. We contrast conventional procedures for surgical planning against our system by accompanying a patient during the entire process of planning, medical treatment and simulation. In various preprocessing steps a 3D physically based facial model is reconstructed from CT and laser range scans. All geometric and topological changes are modeled interactively using Alias.#8482; Applying fully 3D volumetric elasticity allows us to represent important volumetric effects such as incompressibility in a natural and physically accurate way. For computational efficiency, we devised a novel set of prismatic shape functions featuring a globally C1-continuous surface in combination with a C0 interior. Not only is it numerically accurate, but this construction enables us to compute smooth and visually appealing facial shapes.

Question: How to simulate facial surgery?


Performance indicators for an objective measure of public transport service quality. The measurement of transit performance represents a very useful tool for ensuring continuous increase of the quality of the delivered transit services, and for allocating resources among competing transit agencies. Transit service quality can be evaluated by subjective measures based on passengers' perceptions, and objective measures represented by disaggregate performance measures expressed as numerical values, which must be compared with fixed standards or past performances. The proposed research work deals with service quality evaluation based on objective measures; specifically, an extensive overview and an interpretative review of the objective indicators until investigated by researchers are proposed. The final aim of the work is to give a review as comprehensive as possible of the objective indicators, and to provide some suggestions for the selection of the most appropriate indicators for evaluating a transit service aspect.

Question: What is the objective of the research?


Computational Linguistics and Deep Learning. Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pundits are predicting that the final damage will be even worse. Accompanying ICML 2015 in Lille, France, there was another, almost as big, event: the 2015 Deep Learning Workshop. The workshop ended with a panel discussion, and at it, Neil Lawrence said, "NLP is kind of like a rabbit in the headlights of the Deep Learning machine, waiting to be flattened." Now that is a remark that the computational linguistics community has to take seriously! Is it the end of the road for us? Where are these predictions of steamrollering coming from? At the June 2015 opening of the Facebook AI Research Lab in Paris, its director Yann LeCun said: "The next big step for Deep Learning is natural language understanding, which aims to give machines the power to understand not just individual words but entire sentences and paragraphs."1 In a November 2014 Reddit AMA (Ask Me Anything), Geoff Hinton said, "I think that the most exciting areas over the next five years will be really understanding text and videos. I will be disappointed if in five years' time we do not have something that can watch a YouTube video and tell a story about what happened. In a few years time we will put [Deep Learning] on a chip that fits into someone's ear and have an English-decoding chip that's just like a real Babel fish."2 And Yoshua Bengio, the third giant of modern Deep Learning, has also increasingly oriented his group's research toward language, including recent exciting new developments in neural machine translation systems. It's not just Deep Learning researchers. When leading machine learning researcher Michael Jordan was asked at a September 2014 AMA, "If you got a billion dollars to spend on a huge research project that y

Question: What is the future of deep learning?


A Software Defined Networking architecture for the Internet-of-Things. The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the

Question: What is the difference between a software defined network and a software defined network controller?