Seed priming to alleviate salinity stress in germinating seeds.

Salinity is one of the major abiotic stresses that affect crop production in arid and semiarid areas. Seed germination and seedling growth are the stages most sensitive to salinity. Salt stress causes adverse physiological and biochemical changes in germinating seeds. It can affect the seed germination and stand establishment through osmotic stress, ion-specific effects and oxidative stress. The salinity delays or prevents the seed germination through various factors, such as a reduction in water availability, changes in the mobilization of stored reserves and affecting the structural organization of proteins. Various techniques can improve emergence and stand establishment under salt conditions. One of the most frequently utilized is seed priming. The process of seed priming involves prior exposure to an abiotic stress, making a seed more resistant to future exposure. Seed priming stimulates the pre-germination metabolic processes and makes the seed ready for radicle protrusion. It increases the antioxidant system activity and the repair of membranes. These changes promote seed vigor during germination and emergence under salinity stress. The aim of this paper is to review the recent literature on the response of plants to seed priming under salinity stress. The mechanism of the effect of salinity on seed germination is discussed and the seed priming process is summarized. Physiological, biochemical and molecular changes induced by priming that lead to seed enhancement are covered. Plants' responses to some priming agents under salinity stress are reported based on the best available data. For a great number of crops, little information exists and further research is needed.


Signal quality measures for pulse oximetry through waveform morphology analysis.

Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.


Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems

Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. To help address questions that can't easily be answered by reading others' reviews, some review websites also allow users to pose questions to the community via a question-answering (QA) system. As one would expect, just as opinions diverge among different reviewers, answers to such questions may also be subjective, opinionated, and divergent. This means that answering such questions automatically is quite different from traditional QA tasks, where it is assumed that a single 'correct' answer is available. While recent work introduced the idea of question-answering using product reviews, it did not account for two aspects that we consider in this paper: (1) Questions have multiple, often divergent, answers, and this full spectrum of answers should somehow be used to train the system, and (2) What makes a 'good' answer depends on the asker and the answerer, and these factors should be incorporated in order for the system to be more personalized. Here we build a new QA dataset with 800 thousand questions—and over 3.1 million answers—and show that explicitly accounting for personalization and ambiguity leads both to quantitatively better answers, but also a more nuanced view of the range of supporting, but subjective, opinions.


Dual wideband CPW-fed split-ring monopole antenna for WLAN applications

This paper presents a dual wideband CPW-fed monopole antenna based on split-ring elements for WLAN (2.4/5 GHz) applications. The proposed split-ring monopole antenna (SRMA) has a compact novel design which provides 13.8% and 55.5% impedance bandwidth at operational frequency bands. Moreover, the antenna exhibits good radiation pattern with predicted gains of 4–6 dBi in the related bands. The analysis and design of the proposed microstrip antenna have been carried out by means of Ansoft HFSS and an antenna prototype was then constructed and tested. Numerical analyses using the simulator and measurement results are also presented in this paper.


Machine learning in the Internet of Things: A semantic-enhanced approach

Novel Internet of Things (IoT) applications and services rely on an intelligent understanding of the environment leveraging data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking machine-understandable representation and explanation of taxonomies. This paper proposes a framework for semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection. The typical classification problem of ML is treated as a resource discovery by exploiting semantic matchmaking. Outputs of classification are endowed with computer-processable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis has allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.


Architecture and Implementation of a Java Package for Multiple Input Devices ( MID )

A major difficulty in writing Single Display Groupware (co-present collaborative) applications is getting input from multiple devices. We introduce MID, a Java package that addresses this problem and offers an architecture to access advanced events through Java. In this paper, we describe the features, architecture and limitations of MID. We also briefly describe an application that uses MID to get input from multiple mice: KidPad.


Series WP-1207 A Source of Bias in Public Opinion Stability

A long acknowledged but seldom addressed problem with political communication experiments concerns the use of captive participants. Study participants rarely have the opportunity to choose information themselves, instead receiving whatever information the experimenter provides. Druckman and his co-authors relax this assumption in the context of an over-time framing experiment focused on opinions about health care policy. Their results dramatically deviate from extant understandings of over-time communication effects. Allowing individuals to choose information for themselves—a common situation on many political issues—leads to the preeminence of early frames and the rejection of later frames. Instead of opinion decay, they find dogmatic adherence to opinions formed in response to the first frame to which participants were exposed (i.e., staunch opinion stability). The effects match those that occur when early frames are repeated multiple times. The results suggest that opinion stability may often reflect biased information seeking. Moreover, the findings have implications for a range of topics including the micro-macro disconnect in studies of public opinion, political polarization, normative evaluations of public opinion, the role of inequality considerations in the debate about health care, and perhaps most importantly, the design of experimental studies of public opinion. *The authors thank Cengiz Erisen, Larry Jacobs, Jon Krosnick, Heather Madonia, Leslie McCall, Spencer Piston, David Sterrett, and seminar participants in Northwestern’s Applied Quantitative Methods Workshop for helpful comments and advice.


Choice of intra-articular injection in treatment of knee osteoarthritis: platelet-rich plasma, hyaluronic acid or ozone options

This study was performed to compare the efficacy of treatment in three groups of patients with knee osteoarthritis (OA) given an intra-articular injection of platelet-rich plasma (PRP), hyaluronic acid (HA) or ozone gas. A total of 102 patients with mild–moderate and moderate knee OA who presented at the polyclinic with at least a 1-year history of knee pain and VAS score ≥4 were randomly separated into three groups. Group 1 (PRP group) received intra-articular injection of PRP × 2 doses, Group 2 (HA group) received a single dose of HA, and Group 3 (Ozone group) received ozone × four doses. Weight-bearing anteroposterior–lateral and Merchant’s radiographs of both knees were evaluated. WOMAC and VAS scores were applied to all patients on first presentation and at 1, 3, 6 and 12 months. At the end of the 1st month after injection, significant improvements were seen in all groups. In the 3rd month, the improvements in WOMAC and VAS scores were similar in Groups 1 and 2, while those in Group 3 were lower (p < 0.001). At the 6th month, while the clinical efficacies of PRP and HA were similar and continued, the clinical effect of ozone had disappeared (p < 0.001). At the end of the 12th month, PRP was determined to be both statistically and clinically superior to HA (p < 0.001). In the treatment of mild–moderate knee OA, PRP was more successful than HA and ozone injections, as the application alone was sufficient to provide at least 12 months of pain-free daily living activities. Therapeutic study, Level I.


Polyphonic Music Modelling with LSTM-RTRBM

Recent interest in music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of the recent advancements in neural networks. The challenges of developing effective browsing, searching and organization techniques for the growing bodies of music collections call for more powerful statistical models. In this paper, we present LSTM-RTRBM, a new neural network model for the problem of creating accurate yet flexible models of polyphonic music. Our model integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our approach greatly improves the performance of polyphonic music sequence modelling, achieving the state-of-the-art results on multiple datasets.


Improving the effectiveness of peer feedback for learning

The present study examined the effectiveness of (a) peer feedback for learning, more specifically of certain characteristics of the content and style of the provided feedback, and (b) a particular instructional intervention to support the use of the feedback. A quasi-experimental repeated measures design was adopted. Writing assignments of 43 students of Grade 7 in secondary education showed that receiving ‘justified’ comments in feedback improves performance, but this effect diminishes for students with better pretest performance. Justification was superior to the accuracy of comments. The instructional intervention of asking assessees to reflect upon feedback after peer assessment did not increase learning gains significantly. 2009 Published by Elsevier Ltd.


Reference-Aware Language Models

We propose a general class of language models that treat reference as explicit stochastic latent variables. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or discourse context, even when the targets of the reference may be rare words. Experiments on three representative applications show our model variants outperform models based on deterministic attention.


Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin Index, and then classifying customer product loyalty in under B2B concept. The developed methodology has been implemented for SAPCO Co. in Iran. The result shows a tremendous capability to the firm to assess his customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran. 2009 Elsevier Ltd. All rights reserved.


Rationale for the RBAC96 family of access control models

A family of role-based access control (RBAC) models, referred to here as the RBAC96 models, was recently published by the author and his colleagues. This paper gives our rationale for the major decisions in developing these models and discusses alternatives that were considered.


Document dewarping via text-line based optimization

This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, linespacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg–Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. & 2015 Elsevier Ltd. All rights reserved.


Discipline and practices of TDD: (test driven development)

This panel brings together practitioners with experience in Agile and XP methodologies to discuss the approaches and benefits of applying Test Driven Development (TDD). The goal of TDD is clean code that works. The mantra of TDD is: write a test; make it run; and make it right. Open questions to be addressed by the panel include: - How are TDD approaches to be applied to databases, GUIs, and distributed systems? What are the quantitative benchmarks that can demonstrate the value of TDD, and what are the best approaches to solve the ubiquitous issue of scalability.


Ontology-based decision making on uncontrolled intersections and narrow roads

Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.


Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles

The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.


The Semantic Web Revisited

The article included many scenarios in which intelligent agents and bots undertook tasks on behalf of their human or corporate owners. Of course, shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks: they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years


Pellet: A practical OWL-DL reasoner

In this report, we present Pellet: a complete and capable OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is written in Java and is open source under a very liberal license. It is used in a number of projects, from pure research to industrial settings. Pellet is the first sound and complete OWL-DL reasoner with extensive support for rea-soning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. It has proven to be a reliable tool for working with OWL-DL ontologies and experimenting with OWL extensions. In this paper we describe Pellet’s features, architecture and special capabilities, along with an empirical comparison of its performance against other leading OWL-DL reasoners.


RoadGraph: High level sensor data fusion between objects and street network

The RoadGraph is a graph based environmental model for driver assistance systems. It integrates information from different sources like digital maps, onboard sensors and V2X communication into one single model about the vehicle's environment. At the moment of information aggregation some function independent situation analysis is done. In this paper we look at techniques for lane-precise map-matching even with moderate GPS reception using distinct information sources. We also analyze the concepts of aggregating objects from different sources with a-priori knowledge of the street-layout. Results of this novelty approach are shown.


Defeasible Logic Programming: An Argumentative Approach

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, i. e. the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent’s knowledge and for providing an argumentation based reasoning mechanism to agents.


How Positive Informational Social Influence Affects Consumers’ Decision of Internet Shopping?

Given the amount of perceived risk involved in Internet shopping, many potential Internet shoppers tend to wait and observe the experiences of others who have tried it before considering adopting it. This study explores how positive informational social influence affects consumers’ decision of Internet shopping using a laboratory experiment. The research results reveal that positive informational social influence reinforces the relationships between perceived ease of use and consumer attitude toward Internet shopping, as well as attitude and their intention to shop. Implications for the current investigation and future research directions are provided.


Prisoner's dilemma game model for e-commerce

This study investigates how the user’s exchanges affect people’s trust in electronic commerce and empower them to interact with foreign customers. Online exchanges often occur between strangers who cannot rely on past behavior or the prospect of future interactions to establish mutual trust. Game theorists have formalized this problem as a prisoner’s dilemma and predict mutual noncooperation. In this paper, we introduce a social network based model to promote the cooperation in the prisoner’s dilemma game. For this study, we implemented an agent-based simulation framework, which models different types of behaviors in online auctions. Agents who represent buyers or sellers are located in the nodes of a small world graph. Each link weight between the agent and its neighbor symbolizes how much it trusts this neighbor. A link with trust inferior to some cut-link threshold will be removed from the graph. Our results show that the evolved structure of the graph induces considerable variation in the level of cooperation and the profit in the e-commerce system. This shows that the outcome of our co-evolutionary game, in terms of cooperative behavior, strongly depends on topology but also on the update rule used in the trust between users. Co-evolution of game dynamics in interconnected networks is also studied, where two networks are interconnected, and players have interactions not only with others in the same network, but also with players in the other network. We will demonstrate that the interdependence between networks promotes the cooperation in both networks. But, the degree of promotion changes as a function of interdependence


Flexible design of a wearable lower limb exoskeleton robot

Wearable exoskeleton robot is a kind of humanoid service robot to help the elderly and the patients with walking dysfunction, it is also an effective medical rehabilitation method to help patients who have walking disorders due to central neural system damage. This paper focuses on the flexible mechanism design of wearable lower limb exoskeleton robot. A wearable exoskeleton robot prototype was developed which can assist human walking. This paper analyzes the role of the major joints of walking human by experimental studies based on bionic design methods from human anatomy and bone surgery. We first analyze parameters of joint movements in a gait cycle, then we design a preliminary bionic model, finally we proposed a control strategy including a pair of electric crutches for the exoskeleton robot.


The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity

Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as "black teenagers" are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.


A monad for deterministic parallelism

We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.


A Lightweight Semantic Web-based Approach for Data Annotation on IoT Gateways

Internet of Things (IoT) applications rely on networks composed of set of heterogeneous sensors and smart devices, which have the capability to constantly, observe the surroundings and gather data. This heterogeneity is reflected on raw data gathered by such type of systems. Consequently, the task of high-level IoT applications to interpret such data and detect events in the real world is more complex. Moreover, data heterogeneity leads to the lack of interoperability between IoT applications. Semantic Web (SW) technologies have been widely adopted to model and integrate data from different sources on the web; extending them to the IoT domain can be used to mitigate the aforementioned challenges. Semantically annotating IoT data is a fundamental step toward developing smarter and interoperable IoT applications. However, this type of process requires a large amount of computing resources, especially in scenarios where a large number of sensors is expected to be involved such as smart city. To address these challenges, we propose a lightweight semantic annotation approach that can be implemented on resource-constrained IoT gateways connected to a limited number of sensors. To evaluate the feasibility of the proposed approach, we have carried out a set of experimentations using a middleware prototype implementation. Several benchmarks are considered such as: Data size, response time, and resource utilization. c © 2017 The Authors. Published by Elsevier B.V. .


MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification

A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.


Data Gloves for Sign Language Recognition System

Communication between deaf-dumb and a normal person have always been a challenging task . About 9 billion people in the world come into this category which is quite large number to be ignored. As deaf-dumb people use sign language for their communication which is difficult to understand by the normal people. This paper aims at eradicating the communication barrier between them by developing an embedded system which will translate the hand gestures into


A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.


Benchmarking Web API Quality

Web APIs are increasingly becoming an integral part of web or mobile applications. As a consequence, performance characteristics and availability of the APIs used directly impact the user experience of end users. Still, quality of web APIs is largely ignored and simply assumed to be sufficiently good and stable. Especially considering geo-mobility of today’s client devices, this can lead to negative surprises at runtime. In this work, we present an approach and toolkit for benchmarking the quality of web APIs considering geo-mobility of clients. Using our benchmarking tool, we then present the surprising results of a geo-distributed 3-month benchmark run for 15 web APIs and discuss how application developers can deal with volatile quality both from an architectural and engineering point of view.


Iterative localization refinement in convolutional neural networks for improved object detection

Accurate region proposals are of importance to facilitate object localization in the existing convolutional neural network (CNN)-based object detection methods. This paper presents a novel iterative localization refinement (ILR) method which, undertaken at a mid-layer of a CNN architecture, iteratively refines region proposals in order to match as much ground-truth as possible. The search for the desired bounding box in each iteration is first formulated as a statistical hypothesis testing problem and then solved by a divide-and-conquer paradigm. The proposed ILR is not only data-driven, free of learning, but also compatible with a variety of CNNs. Furthermore, to reduce complexity, an approximate variant based on a refined sampling strategy using linear interpolation is addressed. Simulations show that the proposed method improves the main state-of-the-art works on the PASCAL VOC 2007 dataset.


A Multimodal Analysis of Floor Control in Meetings

The participant in a human-to-human communication who controls the floor bears the burden of moving the communication process along. Change in control of the floor can happen through a number of mechanisms, including interruptions, delegation of the floor, and so on. This paper investigates floor control in multiparty meetings that are both audio and video taped; hence, we analyze patterns of speech (e.g., the use of discourse markers) and visual cues (e.g, eye gaze exchanges) that are often involved in floor control changes. Identifying who has control of the floor provides an important focus for information retrieval and summarization of meetings. Additionally, without understanding who has control of the floor, it is impossible to identify important events such as challenges for the floor. In this paper, we analyze multimodal cues related to floor control in two different meetings involving five participants each.


Transaction-Cost Economics: The Governance of Contractual Relations Author(s): Oliver E. Williamson Reviewed work(s): Source: Journal of Law and Economics, Vol. 22, No. 2 (Oct., 1979), pp. 233-261 Published by: The University of Chicago Press for The Booth School of Business of the University of Chicago and The University of Chicago Law School Stable URL: http://www.jstor.org/stable/725118 . Accessed: 07/01/2013 16:49


Product Classification in E-Commerce using Distributional Semantics

Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.


Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering

Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions. This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST). Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.


Analysing and improving the diagnosis of ischaemic heart disease with machine learning

Ischaemic heart disease is one of the world's most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy, and finally coronary angiography (which is considered to be the reference method). Machine learning methods may enable objective interpretation of all available results for the same patient and in this way may increase the diagnostic accuracy of each step. We conducted many experiments with various learning algorithms and achieved the performance level comparable to that of clinicians. We also extended the algorithms to deal with non-uniform misclassification costs in order to perform ROC analysis and control the trade-off between sensitivity and specificity. The ROC analysis shows significant improvements of sensitivity and specificity compared to the performance of the clinicians. We further compare the predictive power of standard tests with that of machine learning techniques and show that it can be significantly improved in this way.


IUI workshop on interactive machine learning

Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans.
 However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.


NLAST: A natural language assistant for students

This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.


Using Ranking-CNN for Age Estimation

Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel Convolutional Neural Network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a series of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. We theoretically obtain a much tighter error bound for ranking-based age estimation. Moreover, we rigorously prove that ranking-CNN is more likely to get smaller estimation errors when compared with multi-class classification approaches. Through extensive experiments, we show that statistically, ranking-CNN significantly outperforms other state-of-the-art age estimation models on benchmark datasets.


The 500MHz band low power rectenna for DTV in the Tokyo area

In this paper, the 500MHz band low power rectenna with high sensitivity characteristic is described. The rectenna is consisting of the folded dipole antenna with a 1.6kΩ impedance and the Cockcroft-Walton type rectifier for improvement of the rectification efficiency and the output dc voltage. Furthermore an L-Type LPF is added to reduce the negative influence due to the junction capacitance of the SBD. The developed rectennas (stage number: 1, 2) achieve the rectification efficiency of 48.9 % (stage number: 1) and 43.1 % (stage number: 2) at -15dBm of the input power. In addition to above, the top level efficiency of 8.7 % (stage number:1) at the input power of -30dBm can be achieved. In the Tokyo are, the output dc voltage of 0.22 V can be measured at the 25 km point from the antenna tower.


Cochrane handbook for systematic reviews of interventions

Chapter 1: Introduction .......................................................................... 3 Key points .................................................................................. 3 1.1 Cochrane ................................................................................ 3 1.1.1 What is Cochrane? ......................................................................... 4 1.1.2 A brief history of Cochrane .................................................................... 4 1.1.3 Cochrane organization and structure .............................................................. 5 1.2 Systematic reviews .......................................................................... 5 1.2.1 The need for systematic reviews ................................................................. 5 1.2.2 What is a systematic review? ................................................................... 5 1.3 Cochrane Reviews .......................................................................... 6 1.3.1 Reviews of the effects of interventions ............................................................. 6 1.3.2 Reviews of diagnostic test accuracy .............................................................. 6 1.3.3 Overviews of Reviews ....................................................................... 6 1.3.4 Reviews of methodology ...................................................................... 7 1.4 Publication of Cochrane Reviews ................................................................. 7 1.4.1 The Cochrane Library ....................................................................... 7 1.5 Handbook structure .......................................................................... 8 1.6 Chapter information .......................................................................... 8 1.7 References ............................................................................... 8 Chapter 3: Reporting the review ..................................................................... 9 3.


Simplified markov random fields for efficient semantic labeling of 3D point clouds

In this paper, we focus on 3D point cloud classification by assigning semantic labels to each point in the scene. We propose to use simplified Markov networks to model the contextual relations between points, where the node potentials are calculated from point-wise classification results using off-the-shelf classifiers, such as Random Forest and Support Vector Machines, and the edge potentials are set by physical distance between points. Our experimental results show that this approach yields comparable if not better results with improved speed compared with state-of-the-art methods. We also propose a novel robust neighborhood filtering method to exclude outliers in the neighborhood of points, in order to reduce noise in local geometric statistics when extracting features and also to reduce number of false edges when constructing Markov networks. We show that applying robust neighborhood filtering improves the results when classifying point clouds with more object categories.


Contactless vital signs measurement for self-service healthcare kiosk in intelligent building

Contactless healthcare system is becoming a new trend to solve the existing healthcare system limitations. High flexibility and accessibility are the main advantages of contactless measurements. In this paper, we proposed a contactless healthcare kiosk for intelligent building application. The Remote Imaging Photoplethysmography (rIPPG) based framework to measure multiple human's vital signs, including pulse rate (PR), respiratory rate (RR), and systolic blood pressure (SBP) was developed to fulfill the performance requirement with a personal computer. Our experimental results show the Mean Absolute Error (MAE) of each measured parameters are 1.7 BPM, 0.41 BPM and 8.15 mmHg for PR, RR, and SBP respectively compared with clinical apparatus. These statistical numbers show the feasibility of using the contactless based system to measure numerous vital signs and also meet the requirements of standard healthcare device.


Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012

To provide an update to the “Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock,” last published in 2008. A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict of interest policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independent of any industry funding. A stand-alone meeting was held for all subgroup heads, co- and vice-chairs, and selected individuals. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide assessment of quality of evidence from high (A) to very low (D) and to determine the strength of recommendations as strong (1) or weak (2). The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasized. Recommendations were classified into three groups: (1) those directly targeting severe sepsis; (2) those targeting general care of the critically ill patient and considered high priority in severe sepsis; and (3) pediatric considerations. Key recommendations and suggestions, listed by category, include: early quantitative resuscitation of the septic patient during the first 6 h after recognition (1C); blood cultures before antibiotic therapy (1C); imaging studies performed promptly to confirm a potential source of infection (UG); administration of broad-spectrum antimicrobials therapy within 1 h of the recognition of septic shock (1B) and severe sepsis without septic shock (1C) as the goal of therapy; reassessment of antimicrobial therapy daily for de-escalation, when appropriate (1B); infection source control with attention to the balance of risks and benefits of the chosen method within 12 h of diagnosis (1C); initial fluid resuscitation with crystalloid (1B) and consideration of the addition of albumin in patients who continue to require substantial amounts of crystalloid to maintain adequate mean arterial pressure (2C) and the avoidance of hetastarch formulations (1B); initial fluid challenge in patients with sepsis-induced tissue hypoperfusion and suspicion of hypovolemia to achieve a minimum of 30 mL/kg of crystalloids (more rapid administration and greater amounts of fluid may be needed in some patients (1C); fluid challenge technique continued as long as hemodynamic improvement is based on either dynamic or static variables (UG); norepinephrine as the first-choice vasopressor to maintain mean arterial pressure ≥65 mmHg (1B); epinephrine when an additional agent is needed to maintain adequate blood pressure (2B); vasopressin (0.03 U/min) can be added to norepinephrine to either raise mean arterial pressure to target or to decrease norepinephrine dose but should not be used as the initial vasopressor (UG); dopamine is not recommended except in highly selected circumstances (2C); dobutamine infusion administered or added to vasopressor in the presence of (a) myocardial dysfunction as suggested by elevated cardiac filling pressures and low cardiac output, or (b) ongoing signs of hypoperfusion despite achieving adequate intravascular volume and adequate mean arterial pressure (1C); avoiding use of intravenous hydrocortisone in adult septic shock patients if adequate fluid resuscitation and vasopressor therapy are able to restore hemodynamic stability (2C); hemoglobin target of 7–9 g/dL in the absence of tissue hypoperfusion, ischemic coronary artery disease, or acute hemorrhage (1B); low tidal volume (1A) and limitation of inspiratory plateau pressure (1B) for acute respiratory distress syndrome (ARDS); application of at least a minimal amount of positive end-expiratory pressure (PEEP) in ARDS (1B); higher rather than lower level of PEEP for patients with sepsis-induced moderate or severe ARDS (2C); recruitment maneuvers in sepsis patients with severe refractory hypoxemia due to ARDS (2C); prone positioning in sepsis-induced ARDS patients with a Pao 2/Fio 2 ratio of ≤100 mm Hg in facilities that have experience with such practices (2C); head-of-bed elevation in mechanically ventilated patients unless contraindicated (1B); a conservative fluid strategy for patients with established ARDS who do not have evidence of tissue hypoperfusion (1C); protocols for weaning and sedation (1A); minimizing use of either intermittent bolus sedation or continuous infusion sedation targeting specific titration endpoints (1B); avoidance of neuromuscular blockers if possible in the septic patient without ARDS (1C); a short course of neuromuscular blocker (no longer than 48 h) for patients with early ARDS and a Pao 2/Fi o 2 <150 mm Hg (2C); a protocolized approach to blood glucose management commencing insulin dosing when two consecutive blood glucose levels are >180 mg/dL, targeting an upper blood glucose ≤180 mg/dL (1A); equivalency of continuous veno-venous hemofiltration or intermittent hemodialysis (2B); prophylaxis for deep vein thrombosis (1B); use of stress ulcer prophylaxis to prevent upper gastrointestinal bleeding in patients with bleeding risk factors (1B); oral or enteral (if necessary) feedings, as tolerated, rather than either complete fasting or provision of only intravenous glucose within the first 48 h after a diagnosis of severe sepsis/septic shock (2C); and addressing goals of care, including treatment plans and end-of-life planning (as appropriate) (1B), as early as feasible, but within 72 h of intensive care unit admission (2C). Recommendations specific to pediatric severe sepsis include: therapy with face mask oxygen, high flow nasal cannula oxygen, or nasopharyngeal continuous PEEP in the presence of respiratory distress and hypoxemia (2C), use of physical examination therapeutic endpoints such as capillary refill (2C); for septic shock associated with hypovolemia, the use of crystalloids or albumin to deliver a bolus of 20 mL/kg of crystalloids (or albumin equivalent) over 5–10 min (2C); more common use of inotropes and vasodilators for low cardiac output septic shock associated with elevated systemic vascular resistance (2C); and use of hydrocortisone only in children with suspected or proven “absolute”’ adrenal insufficiency (2C). Strong agreement existed among a large cohort of international experts regarding many level 1 recommendations for the best care of patients with severe sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for this important group of critically ill patients.


Recommending citations for academic papers

We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline.


Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)

Massive Online Open Courses (MOOCs) which were introduced in 2008 has since drawn attention around the world for both its advantages as well as criticism on its drawbacks. One of the issues in MOOCs which is the lack of interactivity with the instructor has brought conversational bot into the picture to fill in this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is being developed and integrated into MOOCs website to respond to the learner’s enquiries using text or speech input. MOOC-bot is using the popular Artificial Intelligence Markup Language (AIML) to develop its knowledge base, leverage from AIML’s capability to deliver appropriate responses and can be quickly adapted to new knowledge domains. The system architecture of MOOC-bot consists of knowledge base along with AIML interpreter, chat interface, MOOCs website and Web Speech API to provide speech recognition and speech synthesis capability. The initial MOOC-bot prototype has the general knowledge from the past Loebner Prize winner ALICE, course’s frequent asked questions, and a course’s content offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of MOOC-bot based on the past competition questions from Chatterbox Challenge (CBC) and Loebner Prize has shown that it was able to provide correct answers most of the time during the test and demonstrated the capability to prolong the conversation. The advantages of MOOCbot such as able to provide 24-hour service that can serve different time zones, able to have knowledge in multiple domains, and can be shared by multiple sites simultaneously have outweighed its existing limitations.


Executive Involvement and Participation in the Management of Information Technology

Executive support is often prescribed as critical for fully tapping the benefits of information technology (IT). However, few investigations have attempted to determine what type of executive support is likely or organizationally appropriate. This article puts forward alternative models of executive support. The models are tested by examining chief executive officers’ behaviors in and perceptions of IT activities. CEOs and information systems executives are surveyed and further data collected from industry handbooks and from chairmen’s annual letters to shareholders. The results suggest that executive involvement (a psychological state) is more strongly associated with the firm’s progressive use of IT than executive participation (actual behaviors) in IT activities. Executive involvement is influenced by a CEO’s participation, prevailing organizational conditions, and the executive’s functional background. CEO’s perceptions about the importance of IT in their firms were generally positive, although they participated in IT activities rather infrequently.


Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning

Many problems in sequential decision making and stochastic control often have natural multiscale structure: sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure, particularly beyond a single level of abstraction, has remained a longstanding challenge. We describe a fast multiscale procedure for repeatedly compressing, or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of sub-problems at different scales is automatically determined. Coarsened MDPs are themselves independent, deterministic MDPs, and may be solved using existing algorithms. The multiscale representation delivered by this procedure decouples sub-tasks from each other and can lead to substantial improvements in convergence rates both locally within sub-problems and globally across sub-problems, yielding significant computational savings. A second fundamental aspect of this work is that these multiscale decompositions yield new transfer opportunities across different problems, where solutions of sub-tasks at different levels of the hierarchy may be amenable to transfer to new problems. Localized transfer of policies and potential operators at arbitrary scales is emphasized. Finally, we demonstrate compression and transfer in a collection of illustrative domains, including examples involving discrete and continuous statespaces.


Three-phase power factor correction rectifier applied to wind energy conversion systems

This paper proposes a topology of three-phase controlled rectifier feasible for small wind energy conversion systems. This rectifier uses six wires of the generator and allows for the operation with high power factor, increasing the generator efficiency. One Cycle Control (OCC) was used in the rectifier control. This study is concerned with the operating principle, a design example, and some simulation and experimental results for a 5 kW prototype.


Imbalanced Deep Learning by Minority Class Incremental Rectification

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.


Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.


Data-swapping --a Technique for Disclosure Control

In recent years there has been increasing concern about the confidentiality of computerized databases. This has led to a fast growing interest in the development of techniques for controlling the disclosure of information from such databases about individuals, both natural and legal persons. 3 Much of this interest has been focused on the release of statistical tabulations and microdata files. In this paper, we introduce a new technique, data-swapping, that addresses these interests. This technique can be used to both produce microdata and release statistical tabulations so that confidentiality is not violated. The ideas presented in this report are tentative and have not been tested on real-life data. The illustrations provided in the text are based on small-scale numerical experiments, and serve to illustrate the use of data-swapping for disclosure control. Before the proposed techniques can be used on a large scale, more research will be needed. Directions for this research are pointed out throughout the report. This report is organized in two parts. In Part I, we present the underlying idea of the technique. In Part II, we present some theory and methods.


Sequence to sequence learning for unconstrained scene text recognition

In this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like"g"and"9", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.


Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

BACKGROUND
Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence.

OBJECTIVE
This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention.

METHODS
We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence.

RESULTS
We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p=.004), setup (p<.001), updates (p<.001), frequency of interaction with a counselor (p<.001), the system (p=.003) and peers (p=.017), duration (F=6.068, p=.004), adherence (F=4.833, p=.010) and the number of primary task support elements (F=5.631, p=.005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence.

CONCLUSIONS
Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.


Age-related changes in grey and white matter structure throughout adulthood

Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging.


The effects of a 6-week plyometric training program on agility.

The purpose of the study was to determine if six weeks of plyometric training can improve an athlete's agility. Subjects were divided into two groups, a plyometric training and a control group. The plyometric training group performed in a six week plyometric training program and the control group did not perform any plyometric training techniques. All subjects participated in two agility tests: T-test and Illinois Agility Test, and a force plate test for ground reaction times both pre and post testing. Univariate ANCOVAs were conducted to analyze the change scores (post - pre) in the independent variables by group (training or control) with pre scores as covariates. The Univariate ANCOVA revealed a significant group effect F2,26 = 25.42, p=0.0000 for the T-test agility measure. For the Illinois Agility test, a significant group effect F2,26 = 27.24, p = 0.000 was also found. The plyometric training group had quicker posttest times compared to the control group for the agility tests. A significant group effect F2,26 = 7.81, p = 0.002 was found for the Force Plate test. The plyometric training group reduced time on the ground on the posttest compared to the control group. The results of this study show that plyometric training can be an effective training technique to improve an athlete's agility. Key PointsPlyometric training can enhance agility of athletes.6 weeks of plyometric training is sufficient to see agility results.Ground reaction times are decreased with plyometric training.


Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alternative to spatial convolutions. We fuse shifts and point-wise convolutions to construct end-to-end trainable shift-based modules, with a hyperparameter characterizing the tradeoff between accuracy and efficiency. To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters; we additionally demonstrate the operation's resilience to parameter reduction on ImageNet, outperforming ResNet family members. We finally show the shift operation's applicability across domains, achieving strong performance with fewer parameters on image classification, face verification and style transfer.


Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video

Omnidirectional video records a scene in all directions around one central position. It allows users to select viewing content freely in all directions. Assuming that viewing directions are uniformly distributed, the isotropic observation space can be regarded as a sphere. Omnidirectional video is commonly represented by different projection formats with one or multiple planes. To measure objective quality of omnidirectional video in observation space more accurately, a weighted-to-spherically-uniform quality evaluation method is proposed in this letter. The error of each pixel on projection planes is multiplied by a weight to ensure the equivalent spherical area in observation space, in which pixels with equal mapped spherical area have the same influence on distortion measurement. Our method makes the quality evaluation results more accurate and reliable since it avoids error propagation caused by the conversion from resampling representation space to observation space. As an example of such quality evaluation method, weighted-to-spherically-uniform peak signal-to-noise ratio is described and its performance is experimentally analyzed.


Comparative Study and Enhancement of Camera Tampering Detection Algorithms

Recently the use of video surveillance systems is widely increasing. Different places are equipped by camera surveillances such as hospitals, schools, airports, museums and military places in order to ensure the safety and security of the persons and their property. Therefore it becomes significant to guarantee the proper working of these systems. Intelligent video surveillance systems equipped by sophisticated digital camera can analyze video information's and automatically detect doubtful actions. The camera tampering detection algorithms may indicate that accidental or suspicious activities have occurred and that causes the abnormality works of the video surveillance. It uses several techniques based on image processing and computer vision. In this paper, comparative study of performance of four algorithms that can detect abnormal disturbance for video surveillance is presented also a new proposed method is exposed.


Deception : The Role of Consequences

Deception is part of many economic interactions. Business people, politicians, diplomats, lawyers, and students in the experimental laboratory who make use of private information do not always do so honestly. This observation indicates that behavior often rejects the moral approach to deception. As St. Augustine wrote, “To me, however, it seems certain that every lie is a sin. . . ” (St. Augustine, 421). Later, philosophers like Immanuel Kant (1787) again adopted this uncompromising moral stance when arguing against lying. At the other extreme, economic theory is built on the assumption of “homo economicus,” a figure who acts selfishly and is unconcerned about the well-being of others. An implication of this assumption is that lies will be told whenever it is beneficial for the liar, regardless of their effect on the other party. Another implication is that there is no negative outcome associated with lying per se. This assumption is very useful in many economic models. Consider contract theory, where it is assumed that without an explicit contract, neither side will fulfill its respective obligations. For example, George Akerlof’s (1970) paper on asymmetric information and the market for lemons assumes that sellers of used cars will always lie if it is in their benefit to do so. In the mechanism design literature (e.g., Bengt Holmstrom, 1979), the standard assumption is that people will tell the truth only if this is incentive-compatible given material outcomes. In the literature on tax evasion, the choice of whether to avoid paying taxes is considered a decision under uncertainty; cost is treated as a product of the probability of being caught and the cost of punishment, whereas benefit is simply the money saved by avoiding payment. However, there is no cost associated with the very act of lying (Michael Alingham and Agnar Sandmo, 1972). Another example is the game theoretic treatment of “cheap talk” (Crawford and Joel Sobel, 1982). An intermediate approach is taken by utilitarian philosophers (e.g., Jeremy Bentham, 1789). Utilitarianism prescribes that, when choosing whether to lie, one should weigh benefits against harm, and happiness against unhappiness. As Martin Luther stated, “What harm would it do, if a man told a good strong lie for the sake of the good and for the Christian church. . . a lie out of necessity, a useful lie, a helpful lie, such lies would not be against God, he would accept them.” Similarly to the economic theory approach, this type of calculation implies that lies, apart from their resultant harm and benefit, are in themselves neutral. A lie and a truthful statement that achieve the same monetary payoffs (for both sides) are considered * Graduate School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, IL 60637 (e-mail: uri.gneezy.gsb.uchicago.edu). I thank Douglas Bernheim and two anonymous reviewers for insightful comments that considerably improved the paper. I also thank Andreas Blume, Gary Charness, Rachel Croson, Martin Dufwenberg, Georg Kirchsteiger, David Levine, Muriel Niederle, Yuval Rottenstreich, Maurice Schweitzer, Richard Thaler, George Wu, and seminar participants at numerous universities for their comments and suggestions. Ira Leybman provided valuable help in running the experiment. I became interested in deception when my father was terminally ill and his physicians created in him a belief that they considered to be untrue. I dedicate this paper to his memory. 1 Important deviations from this assumption in economic modeling are found in Kenneth Arrow’s (1972) discussion of trust, Gary Becker’s (1976) modeling of altruistic preferences, and Akerlof’s (1982) study of the fair-wage hypothesis. For a general discussion, see Becker (1993): “The economic approach I refer to does not assume that individuals are motivated solely by selfishness or material gain. It is a method of analysis, not an assumption about particular motivations. Along with others, I have tried to pry economists away from narrow assumptions about self-interest. Behavior is driven by a much richer set of values and preferences” (p. 385). 2 Note that this does not mean that a completely selfish person will always lie. There may be strategic reasons not to lie. For example, see the David Kreps and Robert Wilson (1982) discussion of reputation and imperfect information; see also Vincent P. Crawford (2003). 3 Cited by his secretary, in a letter in Max Lenz, ed., Briefwechsel Landgraf Phillips des Grossmuthigen von Hessen mit Bucer, Vol. 1.


Ensemble forecasting of species distributions.

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.


Robot dynamics and control

This chapter presents an introduction to the dynamics and control of robot manipulators. We derive the equations of motion for a general open-chain manipulator and, using the structure present in the dynamics , construct control laws for asymptotic tracking of a desired trajectory. In deriving the dynamics, we will make explicit use of twists for representing the kinematics of the manipulator and explore the role that the kinematics play in the equations of motion. We assume some familiarity with dynamics and control of physical systems.


Glove Based User Interaction Techniques for Augmented Reality in an Outdoor Environment

This paper presents a set of pinch glove-based user interface tools for an outdoor wearable augmented reality computer system. The main form of user interaction is the use of hand and head gestures. We have developed a set of augmented reality information presentation techniques. To support direct manipulation, the following three selection techniques have been implemented: two-handed framing, line of sight and laser beam. A new glove-based text entry mechanism has been developed to support symbolic manipulation. A scenario for a military logistics task is described to illustrate the functionality of this form of interaction.


A Survey of Augmented Reality

This paper surveys the field of augmented reality (AR), in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.


Design of High Performance Permanent-Magnet Synchronous Wind Generators

This paper is devoted to the analysis and design of high performance permanent-magnet synchronous wind generators (PSWGs). A systematic and sequential methodology for the design of PMSGs is proposed with a high performance wind generator as a design model. Aiming at high induced voltage, low harmonic distortion as well as high generator efficiency, optimal generator parameters such as pole-arc to pole-pitch ratio and stator-slot-shoes dimension, etc. are determined with the proposed technique using Maxwell 2-D, Matlab software and the Taguchi method. The proposed double three-phase and six-phase winding configurations, which consist of six windings in the stator, can provide evenly distributed current for versatile applications regarding the voltage and current demands for practical consideration. Specifically, windings are connected in series to increase the output voltage at low wind speed, and in parallel during high wind speed to generate electricity even when either one winding fails, thereby enhancing the reliability as well. A PMSG is designed and implemented based on the proposed method. When the simulation is performed with a 6 Ω load, the output power for the double three-phase winding and six-phase winding are correspondingly 10.64 and 11.13 kW. In addition, 24 Ω load experiments show that the efficiencies of double three-phase winding and six-phase winding are 96.56% and 98.54%, respectively, verifying the proposed high performance operation. OPEN ACCESS Energies 2014, 7 7106


Fog Computing for the Internet of Mobile Things: Issues and Challenges

The Internet of Things (IoT) conceives a world where everyday objects are able to join the Internet and exchange data as well as process, store, collect them from the surrounding environment, and actively intervene on it. An unprecedented number of services may be envisioned by exploiting the Internet of Things. Fog Computing, which is also known as Edge Computing, was proposed in 2012 as the ideal paradigm to support the resource-constrained IoT devices in data processing and information delivery. Indeed, the Fog, which does not replace the centralized Cloud but cooperates with it, distributes Cloud Computing technologies and principles anywhere along the Cloud-to-Things continuum and particularly at the network edge. The Fog proximity to the IoT devices allows for several advantages that must be continuously guaranteed, also when end devices move from one place to another. In this paper, we aim at examining in depth what it means to provide mobility support in a Fog environment and at investigating what are the main challenges to be faced. Besides, in order to highlight the importance of this topic in everyday life, we provide the reader with three scenarios where there is an integration between the IoT and Fog Computing, and in which mobility support is essential. We finally point out the main research directions in the field.


Evaluating CNN and LSTM for Web Attack Detection

Web attack detection is the key task for network security. To tackle this hard problem, this paper explores the deep learning methods, and evaluates convolutional neural network, long-short term memory and their combination method. By comparing with the traditional methods, experimental results show that deep learning methods can greatly outperform the traditional methods. Besides, we also analyze the different factors influencing the performance. This work can be a good guidance for researcher to design network architecture and parameters for detecting web attack in practice.


Optimising ring oscillator-based true random number generators concept on FPGA

Random Number Generators are widely used in cryptographic applications (symmetric and asymmetric encryption, digital certificate generation, etc.) and other related areas. True Random Number Generators (TRNG) are based on physical non-deterministic phenomenon. Most of these generators are based on a large number of interconnected high frequency ring oscillators (RO), which are most commonly implemented in FPGA. This paper describes an optimized solution which increases the speed and the complexity of these ring oscillator-based TRNG. The proposed solution combines a personalized approach on the Ring Oscillator scheme, based on preprocessed raw data and a well-known one, based on the desynchronization of a large number of free running ROs, therefore minimizing the resource used within the FPGA. The paper covers the most known statistical testes (provided by the National Institute of Standards and Technology - NIST) applied on a large amount of data and proves the stability of a generator by providing positive experimental results that were obtained from measurements using different operating frequencies.


Profanity in media associated with attitudes and behavior regarding profanity use and aggression.

OBJECTIVE
We hypothesized that exposure to profanity in media would be directly related to beliefs and behavior regarding profanity and indirectly to aggressive behavior.

METHODS
We examined these associations among 223 adolescents attending a large Midwestern middle school. Participants completed a number of questionnaires examining their exposure to media, attitudes and behavior regarding profanity, and aggressive behavior.

RESULTS
Results revealed a positive association between exposure to profanity in multiple forms of media and beliefs about profanity, profanity use, and engagement in physical and relational aggression. Specifically, attitudes toward profanity use mediated the relationship between exposure to profanity in media and subsequent behavior involving profanity use and aggression.

CONCLUSIONS
The main hypothesis was confirmed, and implications for the rating industry and research field are discussed.


An architecture pattern for safety critical automated driving applications: Design and analysis

Introduction of automated driving increases complexity of automotive systems. As a result, architecture design becomes a major concern for ensuring non-functional requirements such as safety, and modifiability. In the ISO 26262 standard, architecture patterns are recommended for system development. However, the existing architecture patterns may not be able to answer requirements of automated driving completely. When applying these patterns in the automated driving context, modification and analysis of these patterns are needed. In this paper, we present a novel architecture pattern for safety critical automated driving functions. In addition, we propose a generic approach to compare our pattern with a number of existing ones. The comparison results can be used as a basis for project specific architectural decisions. Our Safety Channel pattern is validated by its implementation for a real-life truck platooning application.


Visual Tracking via Boolean Map Representations

In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of oriented gradients and raw color features, of which each one is characterized by a set of Boolean maps generated by uniformly thresholding their values. The Boolean maps effectively encode multi-scale connectivity cues of the target with different granularities. The fine-grained Boolean maps capture spatially structural details that are effective for precise target localization while the coarse-grained ones encode global shape information that are robust to large target appearance variations. Finally, all the Boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel, which is fed into a logistic regression classifier with online update, and the target location is estimated within a particle filter framework. The proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state-of-the-art tracking methods on a large benchmark dataset of 50 image sequences.


Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.


On Fast Dropout and its Applicability to Recurrent Networks

Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modelling of RNNs, mostly motivated by adressing the problems of the vanishing and exploding gradients. The control of overfitting has seen considerably less attention. This paper contributes to that by analyzing fast dropout, a recent regularization method for generalized linear models and neural networks from a back-propagation inspired perspective. We show that fast dropout implements a quadratic form of an adaptive, per-parameter regularizer, which rewards large weights in the light of underfitting, penalizes them for overconfident predictions and vanishes at minima of an unregularized training loss. The derivatives of that regularizer are exclusively based on the training error signal. One consequence of this is the absence of a global weight attractor, which is particularly appealing for RNNs, since the dynamics are not biased towards a certain regime. We positively test the hypothesis that this improves the performance of RNNs on four musical data sets.


The structure of creative cognition in the human brain

Creativity is a vast construct, seemingly intractable to scientific inquiry-perhaps due to the vague concepts applied to the field of research. One attempt to limit the purview of creative cognition formulates the construct in terms of evolutionary constraints, namely that of blind variation and selective retention (BVSR). Behaviorally, one can limit the "blind variation" component to idea generation tests as manifested by measures of divergent thinking. The "selective retention" component can be represented by measures of convergent thinking, as represented by measures of remote associates. We summarize results from measures of creative cognition, correlated with structural neuroimaging measures including structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS). We also review lesion studies, considered to be the "gold standard" of brain-behavioral studies. What emerges is a picture consistent with theories of disinhibitory brain features subserving creative cognition, as described previously (Martindale, 1981). We provide a perspective, involving aspects of the default mode network (DMN), which might provide a "first approximation" regarding how creative cognition might map on to the human brain.


From Partition-Based Clustering to Density-Based Clustering: Fast Find Clusters With Diverse Shapes and Densities in Spatial Databases

Spatial data clustering has played an important role in the knowledge discovery in spatial databases. However, due to the increasing volume and diversity of data, conventional spatial clustering methods are inefficient even on moderately large data sets, and usually fail to discover clusters with diverse shapes and densities. To address these challenges, we propose a two-phase clustering method named KMDD (clustering by combining K-means with density and distance-based method) to fast find clusters with diverse shapes and densities in spatial databases. In the first phase, KMDD uses a partition-based algorithm (K-means) to cluster the data set into several relatively small spherical or ball-shaped subclusters. After that, each subcluster is given a local density; to merge subclusters, KMDD utilizes the idea that genuine cluster cores are characterized by a higher density than their neighbor subclusters and by a relatively large distance from subclusters with higher densities. Extensive experiments on both synthetic and real-world data sets demonstrate that the proposed algorithm has a near-linear time complexity with respect to the data set size and dimension, and has the capability to find clusters with diverse shapes and densities.


Fair Client Puzzles from the Bitcoin Blockchain

Client puzzles have been proposed as a mechanism for proving legitimate intentions by providing “proofs of work”, which can be applied to discourage malicious usage of resources. A typical problem of puzzle constructions is the difference in expected solving time on different computing platforms. We call puzzles which can be solved independently of client computing resources fair client puzzles. We propose a construction for client puzzles requiring widely distributed computational effort for their solution. These puzzles can be solved using the mining process of Bitcoin, or similar cryptocurrencies. Adapting existing definitions, we show that our puzzle construction satisfies formal requirements of client puzzles under reasonable assumptions. We describe a way of transforming our client puzzles for use in denial of service scenarios and demonstrate a practical construction.


Topology and Analysis of Three-Phalanx COSA Finger Based on Linkages for Humanoid Robot Hands

The design of a novel linkage three-phalanx finger was proposed in this paper. The finger is based on the concept of COSA (coupling and self-adaptation) and also two-phalanx COSA finger. As a combination of rigid coupled finger and self-adaptive under-actuated finger, the COSA finger has comprehensive grasping ability with human-like motion. Available topology designs were discussed in this paper and then mechanical design in details is given. Motion and grasping patterns of the finger were illustrated and compared with traditional coupled finger and under-actuated finger. Moreover, stability of grasp was simulated by static analysis. The three-phalanx finger with advantages of coupling and self-adaptation can be widely applied in hands for prosthetic limbs or humanoid robots.