Optimization of photovoltaic farm under partial shading effects using artificial intelligent based matrix switch controller. Partial shading on photovoltaic array due to weather conditions reduces the energy generated and worsening the entire photovoltaic farm efficiency. The purpose of this research was to electric physically reconfigurate PV string through switch matrix by control of smart controller. It is great to find current and voltage at TCT point approaching work of range from MPPT, despite being awarded shading effect. The research method was reflected on the algorithms design. The aim of algorithms was to clustering in two steps. The first step was clustering one or more photovoltaic string based on parallelly current. Result of first clustering continued of second step clustering, clustering the first step result in series. The algorithm work smartly by current input from current sensors. Input processing from sensor by smart controller was signal control which for switch on/off the switches function in switch matrix circuit. Switch matrix output will be forwarded to MPPT circuit. This mechanism being novelty of this research. Based on the testing, it's mechanism able to protect the current at 18A level for all cases with each case voltage consecutively 3060V, 1360V, 2040V and 1020 V. By this mechanism, can be optimality power system increased up to 9.S % .similarly be affected or not partial shading effect.

Question: How to optimize photovoltaic farm under partial shading effects using artificial intelligent based matrix switch controller?


Characterizing the life cycle of online news stories using social media reactions. This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.

Question: How can we characterize the life cycle of online news stories using social media reactions?


Bringing contextual information to google speech recognition. In automatic speech recognition on mobile devices, very often what a user says strongly depends on the particular context he or she is in. The n-grams relevant to the context are often not known in advance. The context can depend on, for example, particular dialog state, options presented to the user, conversation topic, location, etc. Speech recognition of sentences that include these n-grams can be challenging, as they are often not well represented in a language model (LM) or even include out-of-vocabulary (OOV) words. In this paper, we propose a solution for using contextual information to improve speech recognition accuracy. We utilize an on-the-fly rescoring mechanism to adjust the LM weights of a small set of n-grams relevant to the particular context during speech decoding. Our solution handles out of vocabulary words. It also addresses efficient combination of multiple sources of context and it even allows biasing class based language models. We show significant speech recognition accuracy improvements on several datasets, using various types of contexts, without negatively impacting the overall system. The improvements are obtained in both offline and live experiments.

Question: How to improve speech recognition accuracy?


Adaptive Comparator Bias-Current Control of 0.6 V Input Boost Converter for ReRAM Program Voltages in Low Power Embedded Applications. A novel method adaptively controls the comparator bias current in a boost converter for sensor-data storage on Resistive Random Access Memory (ReRAM). ReRAM is ideal for low-power sensors because it is faster and lower voltage than NAND flash. However, ReRAM's voltage design needs to consider program current variation due to data pattern dependency and cell reliability is sensitive to small voltage fluctuations. The proposed control method of the boost converter architecture has three proposals. First, low program voltage ripple (VRIPPLE) and high program energy efficiency is obtained with an adaptive 2 to 8 μA comparator bias-current (ICMP) control method. ICMP is adjusted depending on the number of program bits (NBITS) in the SET data. In 2 cases of NBITS = 1 and 16, and compared to a conventional scheme (ICMP = 8 μA and 2 μA), respectively, VRIPPLE decreases by 41.2% and energy efficiency improves by 6.6%, respectively. Secondly, as NBITS decrease during SET verify, ICMP also decreases, which further reduces SET energy by 8.9 pJ. Thirdly, although the conventional boost converter fails in the `SS' process condition, an additional cross-coupled charge-pump boosts the buffer circuit, which provides full functional operation over the input range of 0.6 to 1.0 V.

Question: How to control the comparator bias current in a boost converter for sensor-data storage on Resistive Random Access Memory (ReRAM)?


Oil content, tocopherol composition and fatty acid patterns of the seeds of 51 Cannabis sativa L. genotypes. The oil content, the tocopherol composition, the plastochromanol-8 (P-8) content and the fatty acid composition (19 fatty acids) of the seed of 51 hemp (Cannabis sativa L.) genotypes were studied in the 2000 and 2001 seasons. The oil content of the hemp seed ranged from 26.25% (w/w) to 37.50%. Analysis of variance revealed significant effects of genotype, year and of the interaction (genotype × year) on the oil content. The oil contents of the 51 genotypes in 2000 and 2001 were correlated (r = 0.37**) and averaged 33.19 ± 1.45% in 2000 and 31.21 ± 0.96% in 2001. The γ-tocopherol, α-tocopherol, δ-tocopherol, P-8- and β-tocopherol contents of the 51 genotypes averaged 21.68 ± 3.19, 1.82 ± 0.49, 1.20 ± 0.40, 0.18 ± 0.07 and 0.16 ± 0.04 mg 100g−1 of seeds, respectively (2000 and 2001 data pooled). Hierarchical clustering of the fatty acid data did not group the hemp genotypes according to their geographic origin. The γ-linolenic acid yield of hemp (3–30 kg ha−1) was similar to the γ-linolenic acid yield of plant species that are currently used as sources of γ-linolenic acid (borage (19–30 kg ha−1), evening primrose (7–30 kg ha−1)). The linoleic acid yield of hemp (129–326 kg ha−1) was similar to flax (102–250 kg ha−1), but less than in sunflower (868–1320 kg ha−1). Significant positive correlations were detected between some fatty acids and some tocopherols. Even though the average content of P-8 in hemp seeds was only 1/120th of the average γ-tocopherol content, P-8 content was more closely correlated with the unsaturated fatty acid content than γ-tocopherol or any other tocopherol fraction. The average broad-sense heritabilities of the oil content, the antioxidants (tocopherols and P-8) and the fatty acids were 0.53, 0.14 and 0.23, respectively. The genotypes Fibrimon 56, P57, Juso 31, GB29, Beniko, P60, FxT, Félina 34, Ramo and GB18 were capable of producing t

Question: What is the average oil content of hemp seeds?


Local connectome phenotypes predict social, health, and cognitive factors. The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.Author SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how

Question: What is the local connectome?


Robust method for road sign detection and recognition. This paper describes a method for detecting and recognizing road signs in gray-level and color images acquired by a single camera mounted on a moving vehicle. The method works in three stages. First, the search for the road sign is reduced to a suitable region of the image by using some a priori knowledge on the scene or color clues (when available). Secondly, a geometrical analysis of the edges extracted from the image is carried out, which generates candidates to be circular and triangular signs. Thirdly, a recognition stage tests by cross-correlation techniques each candidate which, if validated, is classi ed according to the data-base of signs. An extensive experimentation has shown that the method is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways. A further improvement on the detection and recognition scheme has been obtained by means of temporal integration based on Kalman ltering methods of the extracted information. The proposed approach can be very helpful for the development of a system for driving assistance.

Question: What is a robust method for road sign detection and recognition?


Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females.. This article reports a comparison on childhood risk factors of males and females exhibiting childhood-onset and adolescent-onset antisocial behavior, using data from the Dunedin longitudinal study. Childhood-onset delinquents had childhoods of inadequate parenting, neurocognitive problems, and temperament and behavior problems, whereas adolescent-onset delinquents did not have these pathological backgrounds. Sex comparisons showed a male-to-female ratio of 10:1 for childhood-onset delinquency but a sex ratio of only 1.5:1 for adolescence-onset delinquency. Showing the same pattern as males, childhood-onset females had high-risk backgrounds but adolescent-onset females did not. These findings are consistent with core predictions from the taxonomic theory of life-course persistent and adolescence-limited antisocial behavior.

Question: What are the childhood predictors of life-course persistent and adolescence-limited antisocial pathways?


Advances in Digital Forensics XIII. In digital forensics, examinations are carried out to explain events and demonstrate the root cause from a number of plausible causes. Yin's approach to case study research offers a systematic process for investigating occurrences in their real-world contexts. The approach is well suited to examining isolated events and also addresses questions about causality and the reliability of findings. The techniques that make Yin's approach suitable for research also apply to digital forensic examinations. The merits of case study research are highlighted in previous work that established the suitability of the case study research method for conducting digital forensic examinations. This research extends the previous work by demonstrating the practicality of Yin's case study method in examining digital events. The research examines the relationship between digital evidence – the effect – and its plausible causes, and how patterns can be identified and applied to explain the events. Establishing these patterns supports the findings of a forensic examination. Analytic strategies and techniques inherent in Yin's case study method are applied to identify and analyze patterns in order to establish the findings of a digital forensic examination.

Question: What is the relationship between digital evidence and its plausible causes?


Automatic text summarization of Wikipedia articles. The main objective of a text summarization system is to identify the most important information from the given text and present it to the end users. In this paper, Wikipedia articles are given as input to system and extractive text summarization is presented by identifying text features and scoring the sentences accordingly. The text is first pre-processed to tokenize the sentences and perform stemming operations. We then score the sentences using the different text features. Two novel approaches implemented are using the citations present in the text and identifying synonyms. These features along with the traditional methods are used to score the sentences. The scores are used to classify the sentence to be in the summary text or not with the help of a neural network. The user can provide what percentage of the original text should be in the summary. It is found that scoring the sentences based on citations gives the best results.

Question: What is the best way to summarize a wikipedia article?


Very-low-profile, stand-alone, tri-band WLAN antenna design for laptop-tablet computer with complete metal-backed cover. A very-low-profile, tri-band, planar inverted-F antenna (PIFA) with a height of 3.7 mm only for wireless local area network (WlAN) applications in the 2.4 GHz (2400∼2484 MHz), 5.2 GHz (5150∼5350 MHz), and 5.8 GHz (5725∼5825 MHz) bands for tablet-laptop computer with a metal back cover is presented. The antenna was made of a flat plate of size 18.7 mm × 32.5 mm and bent into a compact structure with the dimensions 3.7 mm × 7 mm × 32.5 mm. The PIFA comprised a radiating top patch, a feeding/shorting portion, and an antenna ground plane. The top patch was placed horizontally above the ground with the feeding/shorting portion connecting perpendicularly therebetween. The antenna was designed in a way that the radiating patch consisted of three branches, in which each branch controlled the corresponding resonant mode in the 2.4/5.2/5.8 GHz bands.

Question: What is a tri-band antenna?


Stakeholders in Explainable AI. There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general consensus over what is meant by 'explainable' and 'interpretable'. In this paper, we argue that this lack of consensus is due to there being several distinct stakeholder communities. We note that, while the concerns of the individual communities are broadly compatible, they are not identical, which gives rise to different intents and requirements for explainability/interpretability. We use the software engineering distinction between validation and verification, and the epistemological distinctions between knowns/unknowns, to tease apart the concerns of the stakeholder communities and highlight the areas where their foci overlap or diverge. It is not the purpose of the authors of this paper to 'take sides' — we count ourselves as members, to varying degrees, of multiple communities — but rather to help disambiguate what stakeholders mean when they ask 'Why?' of an AI.

Question: What is the difference between explainable and interpretable?


Artificial Neural Networks architectures for stock price prediction: comparisons and applications. We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief résumé of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We focus on the importance of choosing the correct input features, along with their preprocessing, for the specific learning algorithm one wants to use. Eventually, we consider the S&P500 historical time series, predicting trend on the basis of data from the past days, and proposing a novel approach based on combination of wavelets and CNN, which outperforms the basic neural networks ones. We show, that neural networks are able to predict financial time series movements even trained only on plain time series data and propose more ways to improve results. Key–Words: Artificial neural networks, Multi-layer neural network, Convolutional neural network, Long shortterm memory, Recurrent neural network, Deep Learning, Stock markets analysis, Time series analysis, financial forecasting

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


Smarter City : Smart Energy Grid based on Blockchain Technology. The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger. Keywords— Information Technology, Smart City, Digital Revolution, Digital Innovation, Blockchain, Smart Energy Grid, Machine Learning.

Question: What is the difference between blockchain and smart city?


Constrained Arc-Eager Dependency Parsing. Arc-eager dependency parsers process sentences in a single left-to-right pass over the input and have linear time complexity with greedy decoding or beam search. We show how such parsers can be constrained to respect two different types of conditions on the output dependency graph: span constraints, which require certain spans to correspond to subtrees of the graph, and arc constraints, which require certain arcs to be present in the graph. The constraints are incorporated into the arc-eager transition system as a set of preconditions for each transition and preserve the linear time complexity of the parser.

Question: What is the linear time complexity of arc-eager dependency parsing?


Predicting SMEs' adoption of enterprise systems. Purpose – The purpose of this paper is to develop a model that can be used to predict which small to medium-sized enterprises (SMEs) are more likely to become adopters of enterprise systems (ERP, CRM, SCM and e-procurement). Design/methodology/approach – Direct interviews were used to collect data from a random sample of SMEs located in the Northwest of England. Using logistic regression, 102 responses were analysed. Findings – The results reveal that the factors influencing SMEs' adoption of enterprise systems are different from the factors influencing SMEs' adoption of other previously studied information systems (IS) innovations. SMEs were found to be more influenced by technological and organisational factors than environmental factors. Moreover, the results indicate that firms with a greater perceived relative advantage, a greater ability to experiment with these systems before adoption, greater top management support, greater organisational readiness and a larger size are predicted to become adopters of enterprise systems. Research limitations/implications – Although this study focused on the factors that influence SMEs' adoption of a set of enterprise systems (i.e. ERP, CRM, SCM and e-procurement), it fails to differentiate between factors that influence each of these systems. Practical implications – The model can be used to assist software vendors not only to develop marketing strategies that can target potential adopters, but also to develop strategies to increase the adoption of ES among SMEs. Originality/value – The paper contributes to the continuing research in IS innovations adoption/diffusion in the small business context.

Question: What is the difference between enterprise systems and other information systems?


Semantic web-mining and deep vision for lifelong object discovery. Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object hypotheses generation, and employ a semantic web-mining framework along with deep-learning-based object detectors. This allows us to make use of both visual and semantic features in combined hypotheses generation. Experiments on data from mobile robots in real world application deployments show that this combination improves performance over the use of either method in isolation.

Question: What is the difference between semantic web-mining and deep vision for lifelong object discovery?


Pl@ntNet mobile app. Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

Question: What is the pl@ntNet app?


Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social. The traditional humanism of the twentieth century, inspired by the culture of the book, systematically distanced itself from the new society of digital information; the Internet and tools of information processing revolutionized the world, society during this period developed certain adaptive characteristics based on coexistence (Human Machine), this transformation sets based on the impact of three technology segments: devices, applications and infrastructure of social communication, which are involved in various physical, behavioural and cognitive changes of the human being; and the emergence of new models of influence and social control through the new ubiquitous communication; however in this new process of conviviality new models like the "collaborative thinking" and "InfoSharing" develop; managing social information under three Human ontological dimensions (h) Information (i) Machine (m), which is the basis of a new physical-cyber ecosystem, where they coexist and develop new social units called "virtual communities ". This new communication infrastructure and social management of information given discovered areas of vulnerability "social perspective of risk", impacting all social units through massive impact vector (i); The virtual environment "H + i + M"; and its components, as well as the life cycle management of social information allows us to understand the path of integration "Techno Social" and setting a new contribution to cybernetics, within the convergence of technology with society and the new challenges of coexistence, aimed at a new holistic and not pragmatic vision, as the human component (h) in the virtual environment is the precursor of the future and needs to be studied not as an application, but as the hub of a new society.

Question: What is the future of the human being?


A DYNAMIC FUZZY-COGNITIVE-MAP APPROACH BASED ON RANDOM NEURAL NETWORKS. A fuzzy cognitive map is a graphical means of representing arbitrarily complex models of interrelations between concepts. The purpose of this paper is to describe a dynamic fuzzy cognitive map based on the random neural network model. Previously, we have developed a random fuzzy cognitive map and illustrated its application in the modeling of processes. The adaptive fuzzy cognitive map changes its fuzzy causal web as causal patterns change and as experts update their causal knowledge. Our model carries out inferences via numerical calculation instead of symbolic deduction. We show how the dynamic random fuzzy cognitive map can reveal implications of models composed of dynamic processes. Copyright c ©2003 Yang's Scientific Research Institute, LLC. All rights reserved.

Question: What is a dynamic fuzzy cognitive map?


Towards Further Automation in AutoML. Even though recent AutoML systems have been successful in various applications, they introduce new hyper-hyperparameters of their own, including the choice of the evaluation strategy used in the loss function, time budgets to use and the optimization strategy with its hyper-hyperparameters. We study whether it is possible to make these choices in a data-driven way for a dataset at hand. Using 437 datasets from OpenML, we demonstrate the possibility of automating these choices, that this improves over picking a fixed strategy and that for different time horizons different strategies are necessary.

Question: What is the difference between a hyperparameter and a hyper-hyperparameter?


Neighboring gray level dependence matrix for texture classification. A new approach, neighboring gray level dependence matrix (NGLDM), for texture classification is presented. The major properties of this approach are as follows: (a) texture features can be easily computed; (b) they are essentially invariant under spatial rotation; (c) they are invariant under linear gray level transformation and can be made insensitive to monotonic gray level transformation. These properties have enhanced the practical applications of the texture features. The accuracies of the classification are comparable with those found in the literature.

Question: What is the difference between the gray level dependence matrix and the neighboring gray level dependence matrix?


Efficacy of Topical Combination of 0.25% Finasteride and 3% Minoxidil Versus 3% Minoxidil Solution in Female Pattern Hair Loss: A Randomized, Double-Blind, Controlled Study.. BACKGROUND The relationship between female pattern hair loss (FPHL) and androgenic hormones is not well established, but some evidence indicates oral finasteride may be efficacious in FPHL. Use of a topical formulation has been proposed to minimize unwanted effects.   OBJECTIVES Our objective was to compare the efficacy and safety of topical 0.25% finasteride combined with 3% minoxidil solution and 3% minoxidil solution as monotherapy in the treatment of FPHL.   METHODS This was a prospective, randomized, double-blind study in 30 postmenopausal women with FPHL. Each participant was randomized to receive either topical 0.25% finasteride combined with topical 3% minoxidil or topical 3% minoxidil solution as monotherapy for 24 weeks. To determine efficacy, the hair density and diameter was measured and global photographic assessment was conducted at baseline and 8, 16, and 24 weeks. Side effects and serum dihydrotestosterone levels were also evaluated.   RESULTS By 24 weeks, hair density and diameter had increased in both groups, and finasteride/minoxidil was significantly superior to minoxidil solution in terms of hair diameter (p = 0.039). No systemic side effects were reported. However, serum dihydrotestosterone levels in the finasteride/minoxidil group significantly decreased from baseline (p = 0.016).   CONCLUSION A topical combination of 0.25% finasteride and 3% minoxidil may be a promising option in the treatment of FPHL with an additional benefit of increasing hair diameter. Nevertheless, as it may be absorbed percutaneously, it should be reserved for postmenopausal women.   TRIAL REGISTRATION clinicaltrials.in.th; identifier TCTR20160912002.

Question: What is the difference between finasteride and minoxidil?


Constructivism and Computer-Mediated Communication in Distance Education. The fields of learning theory and instructional design are in the midst of a scientific revolution in which their objectivist philosophical foundations are being replaced by a constructivist epistemology. This article describes the assumptions of a constructivist epistemology, contrasts them with objectivist assumptions, and then describes instructional systems that can support constructive learning at a distance Limitations of Distance Learning Technologies In an effort to supplement or replace live face-to-face instruction, technologicany mediated distance learning has more often than not merely replicated the ineffective methods that limit learning in face-to-face classrooms (Turoff 1995). Too often, potentially interactive technologies are used to present one-way lectures to students in remote locations. However, we believe that the most valuable activity in a classroom of any kind is the opportunity for students to work and interact together and to build and become part of a community of scholars and practitioners (Selfe and Eilola 1989; Bates 1990; Seaton 1993; Nalley 1995). A good learning experience is one in which a student can "master new knowledge and skills, criticany examine assumptions and beliefs, and engage in an invigorating, collaborative quest for wisdom and personal, holistic development" (Eastmond and Ziegahn 1995, 59). Technology used in distance education should facilitate these "good learning experiences" in an "extended classroom model" rather than broadcast teacher-centered lectures and demonstrations (Burge and Roberts 1993). A significant impediment to this goal is the fact that many teachers and instructional designers come to distance education from traditional backgrounds, bringing with them assumptions about teaching and learning that are not theory-based and do not translate well to technologicany mediated instruction (Schieman, Taere, and McLaren 1992).

Question: What is the difference between constructivism and objectivism?


CHAPTER TWO Self-Distancing : Theory , Research , and Current Directions. When people experience negative events, they often try to understand their feelings to improve the way they feel. Although engaging in this meaning-making process leads people to feel better at times, it frequently breaks down leading people to ruminate and feel worse. This raises the question: What factors determine whether people's attempts to "work-through" their negative feelings succeed or fail? In this article, we describe an integrative program of research that has addressed this issue by focusing on the role that self-distancing plays in facilitating adaptive self-reflection. We begin by describing the "self-reflection puzzle" that initially motivated this line of work. Next, we introduce the concept of self-distancing and describe the conceptual framework we developed to explain how this process should facilitate adaptive self-reflection. After describing the early studies that evaluated this framework, we discuss how these findings have been extended to broaden and deepen our understanding of the role that this process plays in self-regulation. We conclude by offering several parting thoughts that integrate the ideas discussed in this chapter. 1. THE SELF-REFLECTION PUZZLE Many people try to understand their feelings when they are upset, under the assumption that doing so will lead them to feel better. Indeed, it would seem that many of us reflexively heed Socrates' advice to "know thyself " when we experience emotional pain. But are people's attempts to work-through their feelings productive? Do they actually lead people to feel better? A great deal of research has addressed these questions over the past 40 years, and the results reveal a puzzle. On the one hand, several studies suggest that it is indeed helpful for people to reflect on their emotions when they experience distress 82 E. Kross and O. Ayduk

Question: How do you feel about the self-reflection puzzle?


Design of a Wheelchair with Legs for People with Motor Disabilities. Low cost. For example, golf carts, outdoor chairs and special purpose sand buggies. wheelchair. AbstractA proof-of-concept prototype wheelchair with legs for people with motor disabilities is proposed, with the objective of demonstrating the feasibility of a completely new approach to mobility. Our prototype system consists of a chair equipped with wheels and legs, and is capable of traversing uneven terrain and circumventing obstacles. The important design considerations, the system design and analysis, and an experimental prototype of a chair are discussed. The results from the analysis and experimentation show the feasibility of the proposed concept and its advantages.

Question: What is a wheelchair?


Social Persuasion in Online and Physical Networks. Social persuasion to influence the actions, beliefs, and behaviors of individuals, embedded in a social network, has been widely studied. It has been applied to marketing, healthcare, sustainability, political campaigns, and public policy. Traditionally, there has been a separation between physical (offline) and cyber (online) worlds. While persuasion methods in the physical world focused on strong interpersonal trust and design principles, persuasion methods in the online world were rich on data-driven analysis and algorithms. Recent trends including Internet of Things, "big data," and smartphone adoption point to the blurring divide between the cyber world and the physical world in the following ways. Fine grained data about each individual's location, situation, social ties, and actions are collected and merged from different sources. The messages for persuasion can be transmitted through both worlds at suitable times and places. The impact of persuasion on each individual is measurable. Hence, we posit that the social persuasion will soon be able to span seamlessly across these worlds and will be able to employ computationally and empirically rigorous methods to understand and intervene in both cyber and physical worlds. Several early examples indicate that this will impact the fundamental facets of persuasion including who, how, where, and when, and pave way for multiple opportunities as well as research challenges.

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


CAPTCHAs: An Artificial Intelligence Application to Web Security. Nowadays, it is hard to find a popular Web site with a registration form that is not protected by an automated human proof test which displays a sequence of characters in an image, and requests the user to enter the sequence into an input field. This security mechanism is based on the Turing Test—one of the oldest concepts in Artificial Intelligence—and it is most often called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). This kind of test has been conceived to prevent the automated access to an important Web resource, for example, a Web mail service or a Social Network. There are currently hundreds of these tests, which are served millions of times a day, thus involving a huge amount of human work. On the other side, a number of these tests have been broken, that is, automated programs designed by researchers, hackers, and spammers have been able to automatically serve the correct answer. In this chapter, we present the history and the concept of CAPTCHAs, along with their applications and a wide review of their instantiations. We also discuss their evaluation, both from the user and the security perspectives, including usability, attacks, and countermeasures. We expect this chapter provides to the reader a good overview of this interesting field. CES IN COMPUTERS, VOL. 83 109 Copyright © 2011 Elsevier Inc. 65-2458/DOI: 10.1016/B978-0-12-385510-7.00003-5 All rights reserved. 110 J.M. GOMEZ HIDALGO AND G. ALVAREZ MARAÑON 1. I ntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 1 .1. T he Turing Test and the Origin of CAPTCHAs . . . . . . . . . . . . . . . 111 2. M otivation and Applications . . . . . . . . . . . . . . . . . . . . . . . . 116 2 .1. G eneral Description of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . 116 2 .2. D esirable Properties of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . 117 2 .3. I m plementation

Question: What is the origin of CAPTCHAs?


Time series forecasting using a deep belief network with restricted Boltzmann machines. Multi-layer perceptron (MLP) and other artificial neural networks (ANNs) have been widely applied to time series forecasting since 1980s. However, for some problems such as initialization and local optima existing in applications, the improvement of ANNs is, and still will be the most interesting study for not only time series forecasting but also other intelligent computing fields. In this study, we propose a method for time series prediction using Hinton & Salakhutdinov's deep belief nets (DBN) which are probabilistic generative neural network composed by multiple layers of restricted Boltzmann machine (RBM). We use a 3-layer deep network of RBMs to capture the feature of input space of time series data, and after pretraining of RBMs using their energy functions, gradient descent training, i.e., back-propagation learning algorithm is used for fine-tuning connection weights between "visible layers" and "hidden layers" of RBMs. To decide the sizes of neural networks and the learning rates, Kennedy & Eberhart's particle swarm optimization (PSO) is adopted during the training processes. Furthermore, "trend removal", a preprocessing to the original data, is also approached in the forecasting experiment using CATS benchmark data. Additionally, approximating and short-term prediction of chaotic time series such as Lorenz chaos and logistic map were also applied by the proposed method.

Question: What is the difference between a deep belief network and a restricted Boltzmann machine?


A Strategic Empty Container Logistics Optimization in a Major Shipping Company. In this paper, we present a system that Compañía Sud Americana de Vapores (CSAV), one of the world's largest shipping companies, developed to support its decisions for repositioning and stocking empty containers. CSAV's main business is shipping cargo in containers to clients worldwide. It uses a fleet of about 700,000 TEU containers of different types, which are carried by both CSAV-owned and third-party ships. Managing the container fleet is complex; CSAV must make thousands of decisions each day. In particular, imbalances exist among the regions. For example, China often has a deficit of empty containers and is a net importer; Saudi Arabia often has a surplus and is a net exporter. CSAV and researchers from the University of Chile developed the Empty Container Logistics Optimization System (ECO) to manage this imbalance. ECO's multicommodity, multiperiod model manages the repositioning problem, whereas an inventory model determines the safety stock required at each location. CSAV uses safety stock to ensure high service levels despite uncertainties, particularly in the demand for containers. A hybrid forecasting system supports both the inventory and the multicommodity network flow model. Major improvements in data gathering, real-time communications, and automation of data handling were needed as input to the models. A collaborative Web-based optimization framework allows agents from different zones to interact in decision making. The use of ECO led to direct savings of $81 million for CSAV, a reduction in inventory stock of 50 percent, and an increase in container turnover of 60 percent. Moreover, the system helped CSAV to become more efficient and to overcome the 2008 economic crisis.

Question: How large is the canadian military?


Security Challenges in the IP-based Internet of Things. A direct interpretation of the term Internet of Things refers to the use of standard Internet protocols for the human-to-thing or thingto-thing communication in embedded networks. Although the security needs are well-recognized in this domain, it is still not fully understood how existing IP security protocols and architectures can be deployed. In this paper, we discuss the applicability and limitations of existing Internet protocols and security architectures in the context of the Internet of Things. First, we give an overview of the deployment model and general security needs. We then present challenges and requirements for IP-based security solutions and highlight specific technical limitations of standard IP security protocols.

Question: What is the security challenges in the internet of things?


Application of information technology: MedEx: a medication information extraction system for clinical narratives. Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.

Question: What is the name of the medication information extraction system?


Conceptual model of enterprise resource planning and business intelligence systems usage. Businesses have invested considerable resources in the usage of enterprise resource planning (ERP) and business intelligence (BI) systems. These systems were heavily studied in developed countries while there are a few and narrowly focused studies in developing ones. However, studies on the integration of ERP and BI have not been given enough attention (hereafter ERPBI). There are many challenges facing the ERPBI usage in term of the steadily increasing speed with which new technologies are evolving. In addition, there are a number of factors that affecting this usage. Based on the finding of the literature, a model from a critical success factors (CSFs) perspective that examine the relationship between ERPBI usage and organisational performance is proposed. The conceptual model provides a foundation for more research in the future. The expected results of the study will improve the business outcome and help design strategies based on an investigation between ERPBI usage and organisational performance.

Question: What is the relationship between ERPBI usage and organisational performance?


Are we dependent upon coffee and caffeine? A review on human and animal data. Caffeine is the most widely used psychoactive substance and has been considered occasionally as a drug of abuse. The present paper reviews available data on caffeine dependence, tolerance, reinforcement and withdrawal. After sudden caffeine cessation, withdrawal symptoms develop in a small portion of the population but are moderate and transient. Tolerance to caffeine-induced stimulation of locomotor activity has been shown in animals. In humans, tolerance to some subjective effects of caffeine seems to occur, but most of the time complete tolerance to many effects of caffeine on the central nervous system does not occur. In animals, caffeine can act as a reinforcer, but only in a more limited range of conditions than with classical drugs of dependence. In humans, the reinforcing stimuli functions of caffeine are limited to low or rather moderate doses while high doses are usually avoided. The classical drugs of abuse lead to quite specific increases in cerebral functional activity and dopamine release in the shell of the nucleus accumbens, the key structure for reward, motivation and addiction. However, caffeine doses that reflect the daily human consumption, do not induce a release of dopamine in the shell of the nucleus accumbens but lead to a release of dopamine in the prefrontal cortex, which is consistent with caffeine reinforcing properties. Moreover, caffeine increases glucose utilization in the shell of the nucleus accumbens only at rather high doses that stimulate most brain structures, non-specifically, and likely reflect the side effects linked to high caffeine ingestion. That dose is also 5-10-fold higher than the one necessary to stimulate the caudate nucleus, which mediates motor activity and the structures regulating the sleep-wake cycle, the two functions the most sensitive to caffeine. In conclusion, it appears that although caffeine fulfils some of the criteria for drug

Question: What is the difference between a drug of abuse and a drug of dependence?


Evaluating mobile apps for breathing training: The effectiveness of visualization. Deep and slow breathing exercises can be an effective adjunct in the treatment of stress, anxiety, post-traumatic stress disorder, chronic pain and depression. Breathing techniques are traditionally learned in courses with trainers and/or with materials such as audio CDs for home practice. Recently, mobile apps have been proposed as novel breathing training tools, but to the best of our knowledge no research has focused on their evaluation so far. In this paper, we study three different designs for breathing training apps. The first employs audio instructions as in traditional training based on audio CDs, while the other two include visualizations of the breathing process, representative of those employed in current breathing training apps. We carry out a thorough analysis, focusing on users' physiological parameters as well as subjective perception. One visualization produces better results both objectively (measured deepness of breath) and subjectively (users' preferences and perceived effectiveness) than the more traditional audio-only design. This indicates that a visualization can contribute to the effectiveness of breathing training apps. We discuss which features could have allowed one visualization (but not the other) to obtain better results than traditional audio-only instructions.

Question: How effective are breathing training apps?


A Low Voltage Delta-Sigma Fractional Frequency Divider for Multi-band WSN Frequency Synthesizers. A 1 V low voltage delta-sigma fractional-N frequency divider for multi-band (780/868/915 MHz and 2.4 GHz) WSN frequency synthesizers is presented. The frequency divider consists of a dual-modulus prescaler, a pulse-swallow counter and a delta-sigma modulator. The high-speed and low-voltage phase-switching dualmodulus prescaler is used in the frequency divider. Low threshold voltage transistors are applied to overcome low voltage supply and forward phase-switching technique is adopted to prevent glitches. The modified deltasigma modulator with long output sequence length and less spurs is adopted to minimize the fractional spurs. The frequency divider is designed in 0.18 μm TSMC RF CMOS technology under 1 V supply instead of the standard 1.8 V supply. The total chip area is 1190 μm × 485 μm including I/O pads. The post simulation results show the frequency divider operates normally over a wide range of 1.3-5.0 GHz and the core circuit (without test buffers) consumes 2.3 mW. Copyright © 2013 IFSA.

Question: What is a delta-sigma modulator?


Streaming Live Media over a Peer-to-Peer Network. The high bandwidth required by live streaming video greatly limits the number of clients that can be served by a source. In this work, we discuss and evaluate an architecture, calledSpreadIt, for streaming live media over a network of clients, using the resources of the clients themselves. Using SpreadIt, we can distribute bandwidth requirements over the network. The key challenge is to allow an application level multicast tree to be easily maintained over a network of transient peers, while ensuring that quality of service does not degrade. We propose a basic peering infrastructure layer for streaming applications, which uses a redirect primitive to meet the challenge successfully. Through empirical and simulation studies, we show that SpreadIt provides a good quality of service, which degrades gracefully with increasing number of clients. Perhaps more significantly, existing applications can be made to work with SpreadIt, without any change to their code base. Keywords— peer-to-peer networks, live streaming media, peering layer, redirect primitive, transient nodes

Question: How to stream live media over a peer-to-peer network?


Permission Re-Delegation: Attacks and Defenses. Modern browsers and smartphone operating systems treat applications as mutually untrusting, potentially malicious principals. Applications are (1) isolated except for explicit IPC or inter-application communication channels and (2) unprivileged by default, requiring user permission for additional privileges. Although inter-application communication supports useful collaboration, it also introduces the risk of permission redelegation. Permission re-delegation occurs when an application with permissions performs a privileged task for an application without permissions. This undermines the requirement that the user approve each application's access to privileged devices and data. We discuss permission re-delegation and demonstrate its risk by launching real-world attacks on Android system applications; several of the vulnerabilities have been confirmed as bugs. We discuss possible ways to address permission redelegation and present IPC Inspection, a new OS mechanism for defending against permission re-delegation. IPC Inspection prevents opportunities for permission redelegation by reducing an application's permissions after it receives communication from a less privileged application. We have implemented IPC Inspection for a browser and Android, and we show that it prevents the attacks we found in the Android system applications.

Question: How to prevent permission re-delegation?


Data integration driven ontology design, case study smart city. Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology.

Question: What is the difference between ontology design and ontology engineering?


Arabic Tokenization, Part-of-Speech Tagging and Morphological Disambiguation in One Fell Swoop. We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including partof-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the high nineties.

Question: How does one morphologically tag Arabic words?


Managing and Mining Graph Data. Will reading habit influence your life? Many say yes. Reading managing and mining graph data is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.

Question: What is the best way to manage and mine graph data?


Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine. Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v) spacing between letters and (vi) spacing between words in a document to identify the personality of the writer. Segmentation is used to calculate the features from digital handwriting and is trained to SVM which outputs the behavior of the writer. For this experiment 100 different writers were used for different handwriting data samples. The proposed method gives about 94% of accuracy rate with RBF kernel. In this paper an automatic method has been proposed to predict the psychological personality of the writer. The system performance is measured under two different conditions with the same sample.

Question: What is the psychological personality of the writer?


A Markov Model for Headway/Spacing Distribution of Road Traffic. In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.

Question: What is a Markov model?


A mixed-signal X-band SiGe multi-function control MMIC for phased array radar applications. In this paper is reported the design, fabrication and test of a mixed-signal SiGe X-band multi-function control MMIC for phased array radar applications. Said MMIC, fabricated, with the ST-Microelectronics BiCMOS7RF SiGe technology, comprises a 5-bit phase shifter, 5-bit attenuator, SPDT switches, several gain amplifiers and a digital serial to parallel converter to reduce the number of MMIC I/O control lines. The gain amplifiers are implemented using SiGe HBTs, while phase shifter, attenuator and SPDT switches are based on CMOS transistors. The measurement results show a return loss better than 15 dB and a gain of 17 dB, equal in both RX and TX state. In RX mode the obtained noise figure is lower than 10 dB, while in TX mode the output P1dB is higher than 12 dBm. The achieved RF performance, the low power consumption and associated low cost, make this SiGe control-chip an attractive solution for high performance/low cost Tx/Rx components for phased array radar applications.

Question: What is a phased array radar?


Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning. This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.

Question: What is the difference between CCA and RMEN-CCA?


Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles. Precise trajectory tracking is a crucial property for Micro Air Vehicles (MAVs) to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for MAV trajectory tracking. A classical Linear Model Predictive Controller (LMPC) is presented and compared against a more advanced Nonlinear Model Predictive Controller (NMPC) that considers the full system model. In a careful analysis we show the advantages and disadvantages of the two implementations in terms of speed and tracking performance. This is achieved by evaluating hovering performance, step response, and aggressive trajectory tracking under nominal conditions and under external wind disturbances.

Question: What is the difference between linear and nonlinear model predictive control?


An embedded, GSM based, multiparameter, realtime patient monitoring system and control — An implementation for ICU patients. Wireless, remote patient monitoring system and control using feedback and GSM technology is used to monitor the different parameters of an ICU patient remotely and also control over medicine dosage is provided. Measurement of vital parameters can be done remotely and under risk developing situation can be conveyed to the physician with alarm triggering systems in order to initiate the proper control actions. In the implemented system a reliable and efficient real time remote patient monitoring system that can play a vital role in providing better patient care is developed. This system enables expert doctors to monitor vital parameters viz body temperature, blood pressure and heart rate of patients in remote areas of hospital as well as he can monitor the patient when he is out of the premises. The system in addition also provides a feedback to control the dosage of medicine to the patient as guided by the doctor remotely, in response to the health condition message received by the doctor. Mobile phones transfer measured parameters via SMS to clinicians for further analysis or diagnosis. The timely manner of conveying the real time monitored parameter to the doctor and control action taken by him is given high priority which is very much needed and which is the uniqueness of the developed system. The system even facilitates the doctor to monitor the patient's previous history from the data in memory inbuilt in the monitoring device. Also data can be sent to several doctors incase a doctor fails to respond urgently.

Question: What is the uniqueness of the developed system?


Creating Ground Truth for Historical Manuscripts with Document Graphs and Scribbling Interaction. Ground truth is both - indispensable for training and evaluating document analysis methods, and yet very tedious to create manually. This especially holds true for complex historical manuscripts that exhibit challenging layouts with interfering and overlapping handwriting. In this paper, we propose a novel semi-automatic system to support layout annotations in such a scenario based on document graphs and a pen-based scribbling interaction. On the one hand, document graphs provide a sparse page representation that is already close to the desired ground truth and on the other hand, scribbling facilitates an efficient and convenient pen-based interaction with the graph. The performance of the system is demonstrated in the context of a newly introduced database of historical manuscripts with complex layouts.

Question: How to create ground truth for historical manuscripts?


Using Spoken Language Benchmarks to Characterize the Expressive Language Skills of Young Children With Autism Spectrum Disorders.. PURPOSE Spoken language benchmarks proposed by Tager-Flusberg et al. (2009) were used to characterize communication profiles of toddlers with autism spectrum disorders and to investigate if there were differences in variables hypothesized to influence language development at different benchmark levels.   METHOD The communication abilities of a large sample of toddlers with autism spectrum disorders (N = 105) were characterized in terms of spoken language benchmarks. The toddlers were grouped according to these benchmarks to investigate whether there were differences in selected variables across benchmark groups at a mean age of 2.5 years.   RESULTS The majority of children in the sample presented with uneven communication profiles with relative strengths in phonology and significant weaknesses in pragmatics. When children were grouped according to one expressive language domain, across-group differences were observed in response to joint attention and gestures but not cognition or restricted and repetitive behaviors.   CONCLUSION The spoken language benchmarks are useful for characterizing early communication profiles and investigating features that influence expressive language growth.

Question: What is the difference between the spoken language benchmarks and the communication profiles?


Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition. In this paper, we show that learning features with convolutional neural networks is better than using hand-crafted features for handwritten word recognition. We consider two kinds of systems: a grapheme based segmentation and a sliding window segmentation. In both cases, the combination of a convolutional neural network with a HMM outperform a state-of-the art HMM system based on explicit feature extraction. The experiments are conducted on the Rimes database. The systems obtained with the two kinds of segmentation are complementary: when they are combined, they outperform the systems in isolation. The system based on grapheme segmentation yields lower recognition rate but is very fast, which is suitable for specific applications such as document classification.

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


A Comparison over Focused Web Crawling Strategies. In this paper we review and compare focused crawling strategies, studied and published during the past decade. Despite giant leaps in communication, storage and computing power in recent years, crawlers have always struggled to keep up with Web content generation and modification. Focused crawlers attempt to i) accelerate the crawling process, ii) maximize the harvest of high quality pages, iii) assign appropriate credit to different documents along a crawling path, such that short-term gains are not pursued at the expense of less obvious paths that ultimately yield larger sets of valuable pages. Beyond the review and comparison of the focused crawling strategies, we additionally propose additions to the corresponding architectures for further research.

Question: What is a focused crawler?


Improved Neural Machine Translation with SMT Features. Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-of-the-art NMT system on Chinese-toEnglish translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.

Question: How to improve neural machine translation?


Stochastic Dynamics for Video Infilling.. In this paper, we introduce a stochastic generation framework (SDVI) to infill long intervals in video sequences. To enhance the temporal resolution, video interpolation aims to produce transitional frames for a short interval between every two frames. Video Infilling, however, aims to complete long intervals in a video sequence. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation to guarantee the spatial-temporal coherency among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear and varied sequences. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the referenced start frame to the end frame.

Question: How to generate realistic and varied sequences?


Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm. Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person riding on the bicycle is a non-rigid object. In this paper, we present a vision-based framework to detect and track bicycles that takes into account these issues. A mixture model of multiple viewpoints is defined and trained via a Support Vector Machine (SVM) to detect bicycles under a variety of circumstances. Each component of the model uses a part-based representation and known geometric context is used to improve overall detection efficiency. An extended Kalman filter (EKF) is used to estimate the position and velocity of the bicycle in vehicle coordinates. We demonstrate the effectiveness of this approach through a series of experiments run on video data of moving bicycles captured from a vehicle-mounted camera

Question: What is a part-based representation?


Characteristics of personal space during obstacle circumvention in physical and virtual environments.. It is not known how the flexible protective zone maintained around oneself during locomotion (personal space or PS; see [Gérin-Lajoie M, Richards CL, McFadyen BJ. The negotiation of stationary and moving obstructions during walking: anticipatory locomotor adaptations and preservation of personal space. Motor Control 2005;9:242-69]) is modulated with walking speed, whether both sides of the PS are symmetrical, and whether the circumvention of physical and virtual obstructions elicit the same use of such PS. Personal space was measured in ten adults as they circumvented a cylindrical obstacle that was stationary within their path. Both left and right passes were performed at natural self-selected, slow and fast walking speeds. The same circumvention task was also performed at natural speeds in an immersive virtual environment (VE) replicating the same obstruction scenario. The shape and size of PS were maintained across walking speeds, and a smaller PS was generally observed on the dominant side. The general shape and lateral bias of the PS were preserved in the VE while its size was slightly increased. The systematic behavior across walking speeds and types of environment and the lateral bias suggest that PS is used to control navigation. This study deepens our understanding of normal adaptive walking behavior and has implications for the development of better tools for the assessment and retraining of locomotor capacity in different populations, from people with walking deficits to elite athletes. Since the PS behavior was shown to be robust in the VE used for this study, the virtual reality technology is proposed as a promising platform for the development of such assessment and retraining applications.

Question: How does the shape and size of personal space change with walking speed?


A High Torque Density Vernier PM Machines for Hybrid Electric Vehicle Applications. This paper proposes a novel vernier permanent magnet (VPM) machine for hybrid electric vehicle (HEV) applications. The main features of the proposed machine are concentrated armature windings with short and non- overlapping end-winding, and the permanent magnets (PMs) are on both sides of rotor and stator. Unlike conventional motors, this machine operates with flux modulation principles, and the electromagnetic torque is produced by the interaction between the armature magneto motive force (MMF) and the exciting fields by stator and rotor PMs. In this paper, the electromagnetic performance is simulated by the finite element analysis (FEA). The results show that the torque density is about 33% higher than an existing interior permanent magnet (IPM) machines.

Question: What is the torque density of a vernier PM machine?


A 28GHz Bulk-CMOS dual-polarization phased-array transceiver with 24 channels for 5G user and basestation equipment. Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.

Question: What is the mm-wave band?


Predictive Analytics On Public Data - The Case Of Stock Markets. This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor's 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.

Question: What is the case of stock markets?


Automatic Image Annotation using Deep Learning Representations. We propose simple and effective models for the image annotation that make use of Convolutional Neural Network (CNN) features extracted from an image and word embedding vectors to represent their associated tags. Our first set of models is based on the Canonical Correlation Analysis (CCA) framework that helps in modeling both views - visual features (CNN feature) and textual features (word embedding vectors) of the data. Results on all three variants of the CCA models, namely linear CCA, kernel CCA and CCA with k-nearest neighbor (CCA-KNN) clustering, are reported. The best results are obtained using CCA-KNN which outperforms previous results on the Corel-5k and the ESP-Game datasets and achieves comparable results on the IAPRTC-12 dataset. In our experiments we evaluate CNN features in the existing models which bring out the advantages of it over dozens of handcrafted features. We also demonstrate that word embedding vectors perform better than binary vectors as a representation of the tags associated with an image. In addition we compare the CCA model to a simple CNN based linear regression model, which allows the CNN layers to be trained using back-propagation.

Question: What is the difference between a kernel CCA and a linear CCA?


Blind Signatures for Untraceable Payments.

Question: How to sign a document?


Social Brand Value and the Value Enhancing Role of Social Media Relationships for Brands. Due to the social media revolution and the emergence of communities, social networks, and user generated content portals, prevalent branding concepts need to catch up with this reality. Given the importance of social ties, social interactions and social identity in the new media environment, there is a need to account for a relationship measure in marketing and branding. Based on the concept of social capital we introduce the concept of social brand value, defined as the perceived value derived by exchange and interactions with other users of the brand within a `community. Within a qualitative study marketing experts were interviewed and highlighted the importance towards social media activities, but also indicated that they do not have a clear picture on how strategies should look like and how their success can be measured. A second quantitative study was conducted which demonstrates the influence the social brand value construct has for consumers brand evangelism and willingness to pay a price premium and hence the value contribution of the social brand value for consumers.

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


A Survey of Arabic Text Representation and Classification Methods. In this paper we have presented a brief current state of the Art for Arabic text representation and classification methods. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC.

Question: What is the current state of the art for Arabic text representation and classification methods?


Low-power high-speed level shifter design for block-level dynamic voltage scaling environment. Two novel level shifters that are suitable for block-level dynamic voltage scaling environment (namely, V/sub DD/-hopping) are proposed. In order to achieve reduction in power consumption and delay, the first proposed level shifter which is called contention mitigated level shifter (CMLS) uses a contention-reduction technique. The simulation results with 65-nm CMOS model show 24% reduction in power and 50% decrease in delay with 4% area increase compared with the conventional level shifter. The second proposed level shifter which is called bypassing enabled level shifter (BELS) implements a bypass function and it is fabricated using 0.35/spl mu/m CMOS technology. The measurement results show that the power and delay of the proposed BELS are reduced by 50% and 65%, respectively with 60% area overhead over the conventional level shifter.

Question: What is the area overhead of the proposed level shifter?


Automatic Labelling of Topic Models. We propose a method for automatically labelling topics learned via LDA topic models. We generate our label candidate set from the top-ranking topic terms, titles of Wikipedia articles containing the top-ranking topic terms, and sub-phrases extracted from the Wikipedia article titles. We rank the label candidates using a combination of association measures and lexical features, optionally fed into a supervised ranking model. Our method is shown to perform strongly over four independent sets of topics, significantly better than a benchmark method.

Question: How to automatically label topics learned via LDA topic models?


Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.. Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create "cultural bridges," or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.

Question: How do advocacy organizations stimulate conversation on social media?


UltraGesture: Fine-Grained Gesture Sensing and Recognition. With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound based human gesture recognition systems have been proposed. They mostly rely on low resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 97% for 12 gestures including finger click and rotation.

Question: What is the resolution of ultrasound?


Recognizing Textual Entailment Using a Subsequence Kernel Method. We present a novel approach to recognizing Textual Entailment. Structural features are constructed from abstract tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5% for text pairs in the Information Extraction and Question Answering task, 63.6% for the RTE-2 test data, and 66.9% for the RET-3 test data.

Question: What is the accuracy of the entailment relation recognition?


Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. This research tests and develops the Technology Acceptance Model (TAM), introduced by Davis (1986), which attempts to explain end users' attitudes to computing technologies. It introduces several new variables, including compatibility, user characteristics, system rating and the enduser computing satisfaction (EUCS) construct, a surrogate measure for IT success and acceptance. A questionnaire with over seventy items was completed by a large number of users and LISREL, a technique for modelling a system of structural equations, was used to analyse the responses. The output shows the model as a whole ® ts the data very well and indicates signi® cant relationships between variables in the model. These results con® rm that TAM is a valuable tool for predicting attitudes, satisfaction, and usage from beliefs and external variables. They also show that relative advantage of the system contributed most to attitudes and satisfaction. Compatibility (of the system to the task performed) contributed most to usage and was the most important antecedent of the belief variables, including relative advantage.

Question: What is the difference between the technology acceptance model and the technology acceptance model?


A Comparative Study of Feature Extraction Algorithms in Customer Reviews. The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the log likelihood ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms.

Question: What is the difference between POS patterns and association rule mining?


Evaluation of alternative glyph designs for time series data in a small multiple setting. We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.

Question: What is the difference between a line glyph and a radial glyph?


Three-Level Bidirectional Converter for Fuel-Cell/Battery Hybrid Power System. A novel three-level (3L) bidirectional converter (BDC) is proposed in this paper. Compared with the traditional BDC, the inductor of the 3L BDC can be reduced significantly so that the dynamic response is greatly improved. Hence, the proposed converter is very suitable for fuel-cell/battery hybrid power systems. In addition, the voltage stress on the switch of the proposed converter is only half of the voltage on the high-voltage side, so it is also suitable for high-voltage applications. The operation principle and the implementation of the control circuit are presented in detail. This paper also proposes a novel bidirectional soft-start control strategy for the BDC. A 1-kW prototype converter is built to verify the theoretical analysis.

Question: What is the voltage stress on the switch of the converter?


Lightweight Memory Tracing. Dynamic binary instrumentation is a technique that consists of translating the binary code of executables on-the-fly, including all the loaded libraries. The original binary code is instrumented in the process of translation. Applications of this technique range from sandboxes to debugging and profiling tools. In this thesis we describe lMem, a lightweight binary translation framework for the IA-32/x64 architectures. One of the challenges when writing instrumentation tools is that resources such as registers and memory have to be shared between the instrumented program and the instrumentation logic. A possible way to approach this problem is to translate a given machine code snippet in an intermediate representation (IR), add instrumentation code written in this IR and then recompile the snippet to machine code and run it. Tools such as Valgrind successfully make use of this technique. These IR-based methods however are heavyweight in the sense that a significant amount of work done by the compiler has to be replicated at runtime. IR-based recompilation increases the runtime overhead and the complexity of the binary translation framework itself. A second approach consists of saving and restoring some of the state before and after the inserted instrumentation code. This approach, which is used for example by the selDebug debugger, is simple but it suffers from overhead due to frequent saving and restoring of state. A third approach is to let the instrumentation code make use of hardware resources that are not used by the instrumented program. The minemu taint checking tool achieves good performance by exploiting the fact that some 32 bit machines lack SSE registers. This enables it to fool the application into thinking that it is running on such a machine while in reality these registers are used by the instrumentation code. lMem translates IA-32 programs to x64 and uses additional registers and memory for instrumentation. To gauge the per

Question: What is the difference between a sandbox and a taint checking tool?


Development of a three-dimensional ball rotation sensing system using optical mouse sensors. Robots using ball(s) as spherical wheels have the advantage of omnidirectional motion. Some of these robots use only a single ball as their wheel and dynamically balance on it. However, driving a ball wheel is not straightforward. These robots usually use one or more motor-driven rollers or wheels in frictional contact with the ball wheel. Some slippage can occur with these schemes, so it is important to measure the actual rotation of the ball wheel for good control. This paper proposes one method for measuring the three dimensional rotation of a sphere, which is applicable to the case of a ball wheel. The system measures surface speed by using two or more optical mouse sensors and transforms them into the angular velocity vector of the ball, followed by integration to give the rotational angle. Experiments showed the correctness of this method, yielding an error of approximately 1%, with a 10 ms response.

Question: How to measure the rotation of a ball?


7 Toward a Universal Underspecified Semantic Representation. We define Canonical Form Minimal Recursion Semantics (CF-MRS) and prove that all the well-formed MRS structures generated by the MRS semantic composition algorithm are in this form. We prove that the qeq relationships are equivalent to outscoping relations when MRS structures are in this form. This result fills the gap between some underspecification formalisms and motivates defining a Canonical Form Underspecified Representation (CF-UR) which brings those underspecification formalisms together.

Question: What is the difference between a well-formed MRS structure and a well-formed MRS structure?


Abstract Structures in Spatial Cognition. Structures in Spatial Cognition Christopher Habel and Carola Eschenbach University of Hamburg Abstract. The importance of studying spatial cognition in cognitive science is enforced by the fact that the applicability of spatial concepts and spatial expressions is not limited to the spatial domain. We claim that common structures underlying both concrete, physical space and other domains are the basis for using spatial expressions, e.g., prepositions like between, with respect to space as well as time or other domains. This claim opposes the thesis that the common use is based upon an analogy between concrete space and other domains. The development of geometry from Euclid's Elements to more differentiated systems of diverse geometries and topologies can be perceived of as an example of the transfer from modeling concrete space towards describing abstract spatial structures. The importance of studying spatial cognition in cognitive science is enforced by the fact that the applicability of spatial concepts and spatial expressions is not limited to the spatial domain. We claim that common structures underlying both concrete, physical space and other domains are the basis for using spatial expressions, e.g., prepositions like between, with respect to space as well as time or other domains. This claim opposes the thesis that the common use is based upon an analogy between concrete space and other domains. The development of geometry from Euclid's Elements to more differentiated systems of diverse geometries and topologies can be perceived of as an example of the transfer from modeling concrete space towards describing abstract spatial structures. 1 The Current Interest in Spatial Cognition: Spatial Representations and Spatial Concepts Human behavior is anchored in space and time. Spatial information, i.e., information about spatial properties of the entities in our environment, about spatial constellations in our surrounding, and

Question: What is the current interest in spatial cognition?


Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease.. An extensive research literature in the behavioral sciences and medicine suggests that psychological and social factors may play a direct role in organic coronary artery disease (CAD) pathology. However, many in the medical and scientific community regard this evidence with skepticism. This chapter critically examines research on the impact of psychological and psychosocial factors on the development and outcome of coronary heart disease, with particular emphasis on studies employing verifiable outcomes of CAD morbidity or mortality. Five key variables identified as possible psychosocial risk factors for CAD are addressed: acute and chronic stress, hostility, depression, social support, and socioeconomic status. Evidence regarding the efficacy of psychosocial interventions is also presented. It is suggested that, taken as a whole, evidence for a psychological and social impact on CAD morbidity and mortality is convincing. However, continued progress in this area requires multidisciplinary research integrating expertise in cardiology and the behavioral sciences, and more effective efforts to communicate research findings to a biomedical audience.

Question: What is the evidence for a psychological and social impact on coronary heart disease?


The Missing Link of Jewish European Ancestry: Contrasting the Rhineland and the Khazarian Hypotheses. The question of Jewish ancestry has been the subject of controversy for over two centuries and has yet to be resolved. The "Rhineland hypothesis" depicts Eastern European Jews as a "population isolate" that emerged from a small group of German Jews who migrated eastward and expanded rapidly. Alternatively, the "Khazarian hypothesis" suggests that Eastern European Jews descended from the Khazars, an amalgam of Turkic clans that settled the Caucasus in the early centuries CE and converted to Judaism in the 8th century. Mesopotamian and Greco-Roman Jews continuously reinforced the Judaized empire until the 13th century. Following the collapse of their empire, the Judeo-Khazars fled to Eastern Europe. The rise of European Jewry is therefore explained by the contribution of the Judeo-Khazars. Thus far, however, the Khazars' contribution has been estimated only empirically, as the absence of genome-wide data from Caucasus populations precluded testing the Khazarian hypothesis. Recent sequencing of modern Caucasus populations prompted us to revisit the Khazarian hypothesis and compare it with the Rhineland hypothesis. We applied a wide range of population genetic analyses to compare these two hypotheses. Our findings support the Khazarian hypothesis and portray the European Jewish genome as a mosaic of Near Eastern-Caucasus, European, and Semitic ancestries, thereby consolidating previous contradictory reports of Jewish ancestry. We further describe a major difference among Caucasus populations explained by the early presence of Judeans in the Southern and Central Caucasus. Our results have important implications for the demographic forces that shaped the genetic diversity in the Caucasus and for medical studies.

Question: What is the difference between the Khazarian and Rhineland hypotheses?


Vertical ridge augmentation in the esthetic zone.. The reconstruction of deficient alveolar ridges using vertical and/or horizontal guided bone regeneration techniques allows for ideal implant placement, which is crucial for function and also for esthetically successful outcomes. Unlike in the past, when meeting a patient's functional demands was sufficient, many patients now have greater expectations from their implant restoration. Hence, it is no longer enough simply to restore the edentulous space with a functioning tooth or teeth. It has been suggested that patients now measure their final restoration using the contralateral natural tooth as the gold standard. Both subjective and objective levels of patient information on dental implants have increased significantly in the last decade. As a result of this demand, implant literature has inherited and developed specific esthetic parameters and patient-centered outcomes from studies in the restorative field. Unfortunately, studies reporting on guided bone regeneration in the esthetic zone entirely lack such parameters and outcomes. Currently, there is a strong need for a consensus on objective and well-defined parameters to assess the esthetics in bone regeneration and subsequently on implant dentistry.

Question: What is the difference between vertical ridge augmentation and guided bone regeneration?


Double Refinement Network for Efficient Indoor Monocular Depth Estimation. Monocular Depth Estimation is an important problem of Computer Vision that may be solved with Neural Networks and Deep Learning nowadays. Though recent works in this area have shown significant improvement in accuracy, state-of-the-art methods require large memory and time resources. The main purpose of this paper is to improve performance of the latest solutions with no decrease in accuracy. To achieve this, we propose a Double Refinement Network architecture. We evaluate the results using the standard benchmark RGB-D dataset NYU Depth v2. The results are equal to the current state-of-the-art, while frames per second rate of our approach is significantly higher (up to 15 times speedup per image with batch size 1), RAM per image is significantly lower.

Question: How to improve the performance of monocular depth estimation?


On neural networks and learning systems for business computing.

Question: What is a neural network?