title: Lean in healthcare: The unfilled promise?

abstract: In an effort to improve operational efficiency, healthcare services around the world have adopted process improvement methodologies from the manufacturing sector, such as Lean Production. In this paper we report on four multi-level case studies of the implementation of Lean in the English NHS. Our results show that this generally involves the application of specific Lean ‘tools’, such as ‘kaizen blitz’ and ‘rapid improvement events’, which tend to produce small-scale and localised productivity gains. Although this suggests that Lean might not currently deliver the efficiency improvements desired in policy, the evolution of Lean in the manufacturing sector also reveals this initial focus on the ‘tool level’. In moving to a more system-wide approach, however, we identify significant contextual differences between healthcare and manufacturing that result in two critical breaches of the assumptions behind Lean. First, the customer and commissioner in the private sector are the one and the same, which is essential in determining ‘customer value’ that drives process improvement activities. Second, healthcare is predominantly designed to be capacity-led, and hence there is limited ability to influence demand or make full use of freed-up resources. What is different about this research is that these breaches can be regarded as not being primarily ‘professional’ in origin but actually more ‘organisational’ and ‘managerial’ and, if not addressed could severely constrain Lean’s impact on healthcare productivity at the systems level.


title: Analyzing spatially-varying blur

abstract: Blur is caused by a pixel receiving light from multiple scene points, and in many cases, such as object motion, the induced blur varies spatially across the image plane. However, the seemingly straight-forward task of estimating spatially-varying blur from a single image has proved hard to accomplish reliably. This work considers such blur and makes two contributions: a local blur cue that measures the likelihood of a small neighborhood being blurred by a candidate blur kernel; and an algorithm that, given an image, simultaneously selects a motion blur kernel and segments the region that it affects. The methods are shown to perform well on a diversity of images.


title: A survey of remote optical photoplethysmographic imaging methods

abstract: In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.


title: Application of singular spectrum analysis and kernel-based extreme learning machine for stock price prediction

abstract: Stock prediction is known as one of the most challenging task in the field of time series analysis. Many existing studies emphasize only in improving accuracy, but not many are concerned about improving speed of the prediction. This paper proposes a stock prediction model using kernel-based extreme learning machine with singular spectrum analysis (SSA-KELM) as a preprocessing tool. The prediction performance of SSA-KELM is compared to five other models. Three stock price data are used to evaluate the performance. The experimental results show that all SSA-based models can outperform non-SSA models. The results are also shown that SSA-KELM can achieve the highest accuracy and the lowest training time among other SSA-based models. The proposed model can therefore be considered as an efficient model for stock price prediction.


title: Scene recognition and weakly supervised object localization with deformable part-based models

abstract: Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based models (DPM's) with latent SVM training [6]. These models have been introduced for fully supervised training of object detectors, but we demonstrate that they are also capable of more open-ended learning of latent structure for such tasks as scene recognition and weakly supervised object localization. For scene recognition, DPM's can capture recurring visual elements and salient objects; in combination with standard global image features, they obtain state-of-the-art results on the MIT 67-category indoor scene dataset. For weakly supervised object localization, optimization over latent DPM parameters can discover the spatial extent of objects in cluttered training images without ground-truth bounding boxes. The resulting method outperforms a recent state-of-the-art weakly supervised object localization approach on the PASCAL-07 dataset.


title: The representation of abstract words: Why emotion matters.

abstract: Although much is known about the representation and processing of concrete concepts, knowledge of what abstract semantics might be is severely limited. In this article we first address the adequacy of the 2 dominant accounts (dual coding theory and the context availability model) put forward in order to explain representation and processing differences between concrete and abstract words. We find that neither proposal can account for experimental findings and that this is, at least partly, because abstract words are considered to be unrelated to experiential information in both of these accounts. We then address a particular type of experiential information, emotional content, and demonstrate that it plays a crucial role in the processing and representation of abstract concepts: Statistically, abstract words are more emotionally valenced than are concrete words, and this accounts for a residual latency advantage for abstract words, when variables such as imageability (a construct derived from dual coding theory) and rated context availability are held constant. We conclude with a discussion of our novel hypothesis for embodied abstract semantics.


title: Classification methods for activity recognition

abstract: Activity recognition is an important subject in di erent areas of research, like e-health and ubiquitous computing. In this paper we give an overview of recent work in the field of activity recognition from both body mounted sensors and ambient sensors. We discuss some of the di erences among approaches and present guidelines for choosing a suitable approach under various circumstances.


title: Software for performance prediction and matching of tractor-implement system

abstract: The matching of tractor-implement system is very difficult task in India because variety of tractor models ranging from 10 to 45kW is prevalent here. To overcome the problem of matching tractor-implement system, a decision support system (DSS) was developed in Visual Basic 6.0 programming language for 2-wheel drive (2WD) tractors. The DSS provides intuitive user interfaces by linking databases such as tractor parameters, tire and implement specifications, soil and operating conditions to support the decision for selection of tractor-implement system. DSS was validated for three different implements (2×30cm moldboard plow; 9–23cm field cultivator and 7–7 offset disc harrow (1.6m)). DSS which calculates draft, drawbar power, slip, tractive efficiency, coefficient of rolling resistance, coefficient of net traction, PTO power required, power utilization efficiency, specific fuel consumption based etc. on input data, can be used effectively in matching of tractor-implement system of various makes and models commercially available in India.


title: Color And Texture Based Wood Inspection With Non-Supervised Clustering

abstract: The appearance of sawn timber has huge natural variations that a human inspector easily compensates for in his brain when determining the types of defects and the grade of each board. However, for automatic wood inspection systems these variations are a major source of complication. For instance, normal wood grain and knots should be reliably discriminated in all circumstances, but simple thresholding based detection methods used in state-of-the-art inspection systems frequently fail even in this apparently straightforward task. In this paper we compare color and texture features in defect detection from lumber. A non-supervised clustering based approach is used for discriminating defects and sound wood. The solution is simple to train, and supports detecting knots and other defects by using multidimensional feature vectors containing texture and color cues for small non-overlapping regions in the image. A key idea is to employ a Self-Organizing Map (SOM) for discriminating between sound wood and defects. The experiments show that color histogram features together with local binary pattern based texture measures are a very promising method. The non-supervised training approach contributes to low error escape and false alarm rates.


title: Effect of Honey and Green Tea Solutions on Streptococcus mutans.

abstract: OBJECTIVES ::: The aim of this cross-sectional in vivo study was to assess the effect of green tea and honey solutions on the level of salivary Streptococcus mutans. ::: ::: ::: STUDY DESIGN ::: A convenient sample of 30 Saudi boys aged 7-10 years were randomly assigned into 2 groups of 15 each. Saliva sample was collected for analysis of level of S. mutans before rinsing. Commercial honey and green tea were prepared for use and each child was asked to rinse for two minutes using 10 mL of the prepared honey or green tea solutions according to their group. Saliva samples were collected again after rinsing. The collected saliva samples were prepared and colony forming unit (CFU) of S. mutans per mL of saliva was calculated. ::: ::: ::: RESULTS ::: The mean number of S. mutans before and after rinsing with honey and green tea solutions were 2.28* 10(8)(2.622*10(8)), 5.64 *10(7)(1.03*10(8)), 1.17*10(9)(2.012*10(9)) and 2.59*10(8) (3.668*10(8)) respectively. A statistically significant reduction in the average number of S. mutans at baseline and post intervention in the children who were assigned to the honey (P=0.001) and green tea (P=0.001) groups was found. ::: ::: ::: CONCLUSIONS ::: A single time mouth rinsing with honey and green tea solutions for two minutes effectively reduced the number of salivary S. mutans of 7-10 years old boys.


title: Tractable dual optimal stochastic model predictive control: An example in healthcare

abstract: Output-Feedback Stochastic Model Predictive Control based on Stochastic Optimal Control for nonlinear systems is computationally intractable because of the need to solve a Finite Horizon Stochastic Optimal Control Problem. However, solving this problem leads to an optimal probing nature of the resulting control law, called dual control, which trades off benefits of exploration and exploitation. In practice, intractability of Stochastic Model Predictive Control is typically overcome by replacement of the underlying Stochastic Optimal Control problem by more amenable approximate surrogate problems, which however come at a loss of the optimal probing nature of the control signals. While probing can be superimposed in some approaches, this is done sub-optimally. In this paper, we examine approximation of the system dynamics by a Partially Observable Markov Decision Process with its own Finite Horizon Stochastic Optimal Control Problem, which can be solved for an optimal control policy, implemented in receding horizon fashion. This procedure enables maintaining probing in the control actions. We further discuss a numerical example in healthcare decision making, highlighting the duality in stochastic optimal receding horizon control.


title: Cylinder Detection in Large-Scale Point Cloud of Pipeline Plant

abstract: The huge number of points scanned from pipeline plants make the plant reconstruction very difficult. Traditional cylinder detection methods cannot be applied directly due to the high computational complexity. In this paper, we explore the structural characteristics of point cloud in pipeline plants and define a structure feature. Based on the structure feature, we propose a hierarchical structure detection and decomposition method that reduces the difficult pipeline-plant reconstruction problem in R3 into a set of simple circle detection problems in R2. Experiments with industrial applications are presented, which demonstrate the efficiency of the proposed structure detection method.


title: What can AI do for me?: evaluating machine learning interpretations in cooperative play

abstract: Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human users, where the effectiveness of interpretation is measured by how much it improves human performance. We design a grounded, realistic human-computer cooperative setting using a question answering task, Quizbowl. We recruit both trivia experts and novices to play this game with computer as their teammate, who communicates its prediction via three different interpretations. We also provide design guidance for natural language processing human-in-the-loop settings.


title: Information security management objectives and practices: a parsimonious framework

abstract: Purpose – As part of their continuing efforts to establish effective information security management (ISM) practices, information security researchers and practitioners have proposed and developed many different information security standards and guidelines. Building on these previous efforts, the purpose of this study is to put forth a framework for ISM.Design/methodology/approach – This framework is derived from the development of an a priori set of objectives and practices as suggested by literature, standards, and reports found in academia and practice; the refinement of these objectives and practices based on survey data obtained from 354 certified information security professionals; and the examination of interrelationships between the objectives and practices.Findings – The empirical analysis suggests: four factors (information integrity, confidentiality, accountability, and availability) serve as critical information security objectives; most of the security areas and items covered under ISO 17799...


title: DOI: 10.5829/idosi.wasj.2012.19.04.1837 A Survey of Password Attacks and Comparative Analysis on Methods for Secure Authentication

abstract: Passwords play an important role in daily life in various computing applications like ATM machines, internet services, windows login, authentication in mobiles etc. The major aim for using passwords is to restrict unauthorized users to access the system. Passwords are necessary but, still they are not considered much safe to provide the security to the users because of many flaws in the conventional password systems. A large number of attacks on many systems are related to the passwords. This paper describes password attacks and comparative analysis of different authentication methods for awareness of attacks and selection of authentication method in a particular scenario.


title: Algebraic and Graphic Languages for OLAP Manipulations

abstract: This article deals with multidimensional analyses. Analyzed data are designed according to a conceptual model as a constellation of facts and dimensions, which are composed of multi-hierarchies. This model supports a query algebra defining a minimal core of operators, which produce multidimensional tables for displaying analyzed data. This user-oriented algebra supports complex analyses through advanced operators and binary operators. A graphical language, based on this algebra, is also provided to ease the specification of multidimensional queries. These graphical manipulations are expressed from a constellation schema and they produce multidimensional tables.


title: Tackling youth unemployment in sub-Saharan Africa: Is there a role for artisanal and small-scale mining?

abstract: Abstract This paper argues that a formalized and supported artisanal and small-scale mining (ASM) sector could assist immeasurably with reducing the intensity of the youth unemployment crisis which now plagues sub-Saharan Africa. Over the past decade, hundreds of thousands of the region's youth have pursued work in the ASM economy in various capacities, decisions which have brought much-needed stability in a landscape devoid of formal sector job opportunities. Host governments and donors, however, have often condemned these moves, failing to fully appreciate why youth have pursued work in ASM in the first place and the impact it has had on their livelihoods. Whilst by no means the solution to the youth unemployment problem in sub-Saharan Africa, heightened emphasis on formalizing and supporting ASM could certainly buy policymakers and donors some valuable time – at least in the short term – to sufficiently ‘re-think’ their ineffective approaches to tackling the crisis.


title: Building Brand Equity Through Corporate Societal Marketing

abstract: Marketers have shown increasing interest in the use of corporate societal marketing (CSM) programs. In this article, the authors describe six means by which CSM programs can build brand equity: (1) building brand awareness, (2) enhancing brand image, (3) establishing brand credibility, (4) evoking brand feelings, (5) creating a sense of brand community, and (6) eliciting brand engagement. The authors also address three key questions revolving around how CSM programs have their effects, which cause the firm should choose, and how CSM programs should be branded. The authors offer a series of research propositions throughout and conclude by outlining a set of potential future research directions.


title: Applying Model Management to Classical Meta Data Problems

abstract: Model management is a new approach to meta data management that offers a higher level programming interface than current techniques. The main abstractions are models (e.g., schemas, interface definitions) and mappings between models. It treats these


SCADA-specific Intrusion Detection / Prevention Systems : A Survey and Taxonomy


title: Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data

abstract: Models for predicting the risk of cardiovascular (CV) events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restrict the predictive power and generalizability of these risk models to other populations. Electronic health data (EHD) from large health care systems provide access to data on large, heterogeneous, and contemporaneous patient populations. The unique features and challenges of EHD, including missing risk factor information, non-linear relationships between risk factors and CV event outcomes, and differing effects from different patient subgroups, demand novel machine learning approaches to risk model development. In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a CV event within 5 years. In such data, event status may be unknown for some individuals, as the event time is right-censored due to disenrollment and incomplete follow-up. Since many traditional data mining methods are not well-suited for such data, we describe how to modify both modeling and assessment techniques to account for censored observation times. We show that our approach can lead to better predictive performance than the Cox proportional hazards model (i.e., a regression-based approach commonly used for censored, time-to-event data) or a Bayesian network with ad hoc approaches to right-censoring. Our techniques are motivated by and illustrated on data from a large US Midwestern health care system.


title: Real-Time Mobile Cloud Computing: A Case Study in Face Recognition

abstract: Face recognition has received attention from research communities recently. Law enforcement agencies are using facial recognition software as a crime-fighting tool. The quick increasing of Mobile Devices usage and the explosive growth of the mobile applications Mobile face recognition is one of important application. In the same time the mobile devices are facing many challenges in their resources as low computing power, battery life, limited bandwidth and storage. Mobile Cloud Computing (MCC) has been introduced to be a potential technology for mobile services and to solve the mobile resources problem by moving the processing and the storage of data out from mobile devices to the cloud. MCC offers abundant computing power that can be tapped easily. This paper gives explanation of MCC definition, Building private cloud with Open Source cloud OS, Face recognition as MCC Application with presenting the face detection and face recognition algorithms. Our results demonstrate that the proposed architecture is promising for real-time Mobile cloud computing by reducing the overall processing time.


title: Large Scale Detection of Irregularities in Accounting Data

abstract: In recent years, there have been several large accounting frauds where a company's financial results have been intentionally misrepresented by billions of dollars. In response, regulatory bodies have mandated that auditors perform analytics on detailed financial data with the intent of discovering such misstatements. For a large auditing firm, this may mean analyzing millions of records from thousands of clients. This paper proposes techniques for automatic analysis of company general ledgers on such a large scale, identifying irregularities - which may indicate fraud or just honest errors - for additional review by auditors. These techniques have been implemented in a prototype system, called Sherlock, which combines aspects of both outlier detection and classification. In developing Sherlock, we faced three major challenges: developing an efficient process for obtaining data from many heterogeneous sources, training classifiers with only positive and unlabeled examples, and presenting information to auditors in an easily interpretable manner. In this paper, we describe how we addressed these challenges over the past two years and report on experiments evaluating Sherlock.


title: Nursing students' knowledge and attitude on pressure ulcer prevention evidence-based guidelines: A multicenter cross-sectional study

abstract: Summary Background Pressure ulcers still remain a significant problem in many healthcare settings. Poor knowledge and negative attitudes toward pressure ulcer prevention could undesirably affect preventive care strategies. Objective To assess both knowledge and attitudes among nursing students on Pressure Ulcer Prevention Evidence-Based Guidelines. Design A multicenter cross-sectional survey was carried out from December 2012 to August 2013. Settings The study was carried out in seven Italian nursing schools. Participants We involved a convenience sample of nursing students (n=742) Methods Data were collected using two validated questionnaires to assess students' knowledge and attitudes on pressure ulcer prevention. Results The overall Knowledge and Attitude scores were 51.1% (13.3/26) and 76.7% (39.9/52), respectively. We found a weak correlation between total Knowledge scores and total Attitude scores (rho=0.13, p Conclusions Nursing students' knowledge on pressure ulcer prevention was relatively low. However, we observed an association between a high level of education/training experience and higher knowledge scores. Most of the participants showed high attitude scores. These results suggest that positive attitudes toward pressure ulcer prevention may contribute to the compliance with the guidelines in clinical practice.


title: Assessments of global drivers of vaccine hesitancy in 2014—Looking beyond safety concerns

abstract: Vaccine hesitancy has become the focus of growing attention and concern globally despite overwhelming evidence of the value of vaccines in preventing disease and saving the lives of millions of individuals every year. ::: ::: Measuring vaccine hesitancy and its determinants worldwide is important in order to understand the scope of the problem and for the development of evidence-based targeted strategies to reduce hesitancy. ::: ::: Two indicators to assess vaccine hesitancy were developed to capture its nature and scope at the national and subnational level to collect data in 2014: 1) The top 3 reasons for not accepting vaccines according to the national schedule in the past year and whether the response was opinion- or assessment-based and 2) Whether an assessment (or measurement) of the level of confidence in vaccination had taken place at national or subnational level in the previous 5 years. ::: ::: The most frequently cited reasons for vaccine hesitancy globally related to (1) the risk-benefit of vaccines, (2) knowledge and awareness issues, (3) religious, cultural, gender or socio-economic factors. Major issues were fear of side effects, distrust in vaccination and lack of information on immunization or immunization services. The analysis revealed that 29% of all countries had done an assessment of the level of confidence in their country, suggesting that vaccine confidence was an issue of importance. ::: ::: Monitoring vaccine hesitancy is critical because of its influence on the success of immunization programs. To our knowledge, the proposed indicators provide the first global snapshot of reasons driving vaccine hesitancy and depicting its widespread nature, as well as the extent of assessments conducted by countries.


title: MIR IN MATLAB (II): A TOOLBOX FOR MUSICAL FEATURE EXTRACTION FROM AUDIO

abstract: We present the MIRtoolbox, an integrated set of functions written in Matlab, dedicated to the extraction of musical features from audio files. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches – including new strategies we have developed –, that users can select and parametrize. This paper offers an overview of the set of features, related, among others, to timbre, tonality, rhythm or form, that can be extracted with the MIRtoolbox. One particular analysis is provided as an example. The toolbox also includes functions for statistical analysis, segmentation and clustering. Particular attention has been paid to the design of a syntax that offers both simplicity of use and transparent adaptiveness to a multiplicity of possible input types. Each feature extraction method can accept as argument an audio file, or any preliminary result from intermediary stages of the chain of operations. Also the same syntax can be used for analyses of single audio files, batches of files, series of audio segments, multi-channel signals, etc. For that purpose, the data and methods of the toolbox are organised in an object-oriented architecture. 1 MOTIVATION AND APPROACH MIRtoolbox is a Matlab toolbox dedicated to the extraction of musically-related features from audio recordings. It has been designed in particular with the objective of enabling the computation of a large range of features from databases of audio files, that can be subjected to statistical analyses. Few softwares have been proposed in this area. One particularity of our own approach relies in the use of the Matlab computing environment, which offers good visualisation capabilities and gives access to a large variety of other toolboxes. In particular, the MIRtoolbox makes use of functions available in public-domain toolboxes such as the Auditory Toolbox [6], NetLab [5] and SOMtoolbox [10]. Other toolboxes, such as the Statistics toolbox or the Neural Network toolbox from MathWorks, can be directly used for further analyses of the features extracted c © 2007 Austrian Computer Society (OCG). by MIRtoolbox without having to export the data from one software to another. Such computational framework, because of its general objectives, could be useful to the research community in Music Information Retrieval (MIR), but also for educational purposes. For that reason, particular attention has been paid concerning the ease of use of the toolbox. In particular, complex analytic processes can be designed using a very simple syntax, whose expressive power comes from the use of an object-oriented paradigm. The different musical features extracted from the audio files are highly interdependent: in particular, as can be seen in figure 1, some features are based on the same initial computations. In order to improve the computational efficiency, it is important to avoid redundant computations of these common components. Each of these intermediary components, and the final musical features, are therefore considered as building blocks that can been freely articulated one with each other. Besides, in keeping with the objective of optimal ease of use of the toolbox, each building block has been conceived in a way that it can adapt to the type of input data. For instance, the computation of the MFCCs can be based on the waveform of the initial audio signal, or on the intermediary representations such as spectrum, or mel-scale spectrum (see Fig. 1). Similarly, autocorrelation is computed for different range of delays depending on the type of input data (audio waveform, envelope, spectrum). This decomposition of all feature extraction algorithms into a common set of building blocks has the advantage of offering a synthetic overview of the different approaches studied in this domain of research. 2 FEATURE EXTRACTION 2.1 Feature overview Figure 1 shows an overview of the main features implemented in the toolbox. All the different processes start from the audio signal (on the left) and form a chain of operations proceeding to right. Each musical feature is related to one of the musical dimensions traditionally defined in music theory. Boldface characters highlight features related to pitch and tonality. Bold italics indicate features related to rhythm. Simple italics highlight a large set of features that can be associated to timbre and dynamics. Among them, all the operators in grey italics can be Audio signal waveform Zero-crossing rate RMS energy Envelope Low Energy Rate Attack Slope Attack Time Envelope Autocorrelation Tempo Onsets


title: Please Stop Explaining Black Box Models for High Stakes Decisions

abstract: Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable.


Towards Automatic Fake News Detection : Cross-Level Stance Detection in News


ON RANDOM DEEP AUTOENCODERS: EXACT ASYMP-


title: Visual object tracking using adaptive correlation filters

abstract: Although not commonly used, correlation filters can track complex objects through rotations, occlusions and other distractions at over 20 times the rate of current state-of-the-art techniques. The oldest and simplest correlation filters use simple templates and generally fail when applied to tracking. More modern approaches such as ASEF and UMACE perform better, but their training needs are poorly suited to tracking. Visual tracking requires robust filters to be trained from a single frame and dynamically adapted as the appearance of the target object changes. This paper presents a new type of correlation filter, a Minimum Output Sum of Squared Error (MOSSE) filter, which produces stable correlation filters when initialized using a single frame. A tracker based upon MOSSE filters is robust to variations in lighting, scale, pose, and nonrigid deformations while operating at 669 frames per second. Occlusion is detected based upon the peak-to-sidelobe ratio, which enables the tracker to pause and resume where it left off when the object reappears.


title: Systematic optimization of 1 Gbit perpendicular magnetic tunnel junction arrays for 28 nm embedded STT-MRAM and beyond

abstract: This paper demonstrates the co-optimization of all critical device parameters of perpendicular magnetic tunnel junctions (pMTJ) in 1 Gbit arrays with an equivalent bitcell size of 22 F2 at the 28 nm logic node for embedded STT-MRAM. Through thin-film tuning and advanced etching of sub-50 nm (diameter) pMTJ, high device performance and reliability were achieved simultaneously, including TMR = 150 %, Hc > 1350 Oe, Hoff 1012 write cycles). Reliable switching with small temporal variations (< 5 %) was obtained down to 10 ns. In addition, tunnel barrier integrity and high temperature device characteristics were investigated in order to ensure reliable STT-MRAM operation.


title: Obesity as a cardiovascular risk factor

abstract: Abstract The prevalence of overweight and obesity continues to increase rapidly in the United States, with more than half of all adults currently overweight or obese. In general, people become obese because of a combination of inherited genes and a lifestyle consisting of low levels of physical activity and consumption of excess calories. Obesity, especially the central or visceral type, is a predisposing factor for the development of type 2 diabetes mellitus, hypertension, and cardiovascular disease (CVD). Obesity and type 2 diabetes are associated with insulin resistance. The relation among obesity, insulin resistance, and CVD appears to develop at a relatively young age. Central obesity is linked with hyperinsulinemia, insulin resistance, dyslipidemia, and proinflammatory and prothrombotic clinical states. Adipose tissue synthesizes and secretes biologically active molecules that may affect CVD risk factors. These chemical messengers include adiponectin, resistin, leptin, plasminogen activator inhibitor–1, tumor necrosis factor–α, and interleukin-6. In overweight and obese individuals, weight loss may improve insulin sensitivity, leading to reduction in risk factors for CVD and, consequently, the potential for cardiovascular events. Agents that improve insulin sensitivity, such as the thiazolidinediones, have been shown to reduce visceral obesity. Decreases in visceral adipose tissue contribute to improvements in insulin sensitivity and blood pressure, and weight loss reduces serum levels of triglycerides and low-density lipoprotein cholesterol while increasing serum levels of high-density lipoprotein cholesterol. Reduction of risk factors suggests that the development of cardiovascular disease will be reduced by the improvement of insulin sensitivity and weight loss.


title: Real time DDoS detection using fuzzy estimators

abstract: We propose a method for DDoS detection by constructing a fuzzy estimator on the mean packet inter arrival times. We divided the problem into two challenges, the first being the actual detection of the DDoS event taking place and the second being the identification of the offending IP addresses. We have imposed strict real time constraints for the first challenge and more relaxed constraints for the identification of addresses. Through empirical evaluation we confirmed that the detection can be completed within improved real time limits and that by using fuzzy estimators instead of crisp statistical descriptors we can avoid the shortcomings posed by assumptions on the model distribution of the traffic. In addition we managed to obtain results under a 3 sec detection window.


Improving Design of Systems Supporting Creativity-intensive Processes – A Cross-industry Focus Group Evaluation


title: Alleviating overfitting for polysemous words for word representation estimation using lexicons

abstract: Though there are some works on improving distributed word representations using lexicons, the improper over-fitting of the words that have multiple meanings is a remaining issue deteriorating the learning when lexicons are used, which needs to be solved. An alternative method is to allocate a vector per sense instead of a vector per word. However, the word representations estimated in the former way are not as easy to use as the latter one. Our previous work uses a probabilistic method to alleviate the overfitting, but it is not robust with a small corpus. In this paper, we propose a new neural network to estimate distributed word representations using a lexicon and a corpus. We add a lexicon layer in the continuous bag-of-words model and a threshold node after the output of the lexicon layer. The threshold rejects the unreliable outputs of the lexicon layer that are less likely to be the same with their inputs. In this way, it alleviates the overfitting of the polysemous words. The proposed neural network can be trained using negative sampling, which maximizing the log probabilities of target words given the context words, by distinguishing the target words from random noises. We compare the proposed neural network with the continuous bag-of-words model, the other works improving it, and the previous works estimating distributed word representations using both a lexicon and a corpus. The experimental results show that the proposed neural network is more efficient and balanced for both semantic tasks and syntactic tasks than the previous works, and robust to the size of the corpus.


title: DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

abstract: Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy. Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.


title: Fast Multi-Instance Multi-Label Learning

abstract: In multi-instance multi-label learning (MIML), one object is represented by multiple instances and simultaneously associated with multiple labels. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderatesized data. To efficiently handle large data sets, we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIML fast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 30K bags and 270K instances, where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output semantics.


title: From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation

abstract: Abstract We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.


title: Chatterbots, tinymuds, and the turing test entering the loebner prize competition

abstract: The Turing Test was proposed by Alan Turing in 1950; he called it the Imitation Game. In 1991 Hugh Loebner started the Loebner Prize competition, offering a $100,000 prize to the author of the first computer program to pass an unrestricted Turing test. Annual competitions are held each year with smaller prizes for the best program on a restricted Turing test. This paper describes the development of one such Turing System, including the technical design of the program and its performance on the first three Loebner Prize competitions. We also discuss the program's four year development effort, which has depended heavily on constant interaction with people on the Internet via Tinymuds (multiuser network communication servers). Finally, we discuss the design of the Loebner competition itself, and address its usefulness in furthering the development of Artificial Intelligence.


title: Genetics of autism spectrum disorder

abstract: Autism is a highly heritable complex neurodevelopmental disorder characterized by distinct impairments of cognitive function in the field of social interaction and speech development. Different approaches have been undertaken worldwide to identify susceptibility loci or genes for autism spectrum disorders. No clear conclusions can be made today about genetic loci involved in these disorders. The review will focus on relevant results from the last decade of research with emphasis on whole genome screens and association studies.


title: Aspect-oriented programming using reflection and metaobject protocols

abstract: Some of the original inspiration for Aspect-Oriented Programming (AOP) [KLM97] draws from the research in dynamic, reflective object-oriented languages and metaobject protocols (MOPs) [KdB91]. A MOP lets the programmer delve “under the covers” and programmatically affect basic language mechanisms such as dynamic method dispatch and class instantiation. These powerful facilities enable the sort of crosscutting metaprogramming that AOP strives to deliver. We believe that there are two reasons why research on providing aspect-oriented programming features to the programmer has strayed from its metaobject protocol roots:


Let the Church Be the Church: The Voice and Mission of the People of God


title: Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images

abstract: BackgroundAnterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation.MethodsThe developed framework comprises of five computational steps to segment the ATFL ligament region. Initially, region of interest is selected from the original image, which is followed by the adaptive histogram equalization to enhance the contrast level of the ultrasound image. The enhanced contrast image is further optimized by the particle swarm optimization algorithm. Thereafter, the optimized image is processed by the Chan–Vese method to extract the ATFL region through curve evolution; then the resultant image smoothed by morphological operation. The algorithm is tested on 25 subjects’ datasets and the corresponding performance metrics are evaluated to demonstrate its clinical applicability.ResultsThe performance of the developed framework is evaluated based on various measurement metrics. It was found that estimated computational performance of the developed framework is 12 times faster than existing Chan–Vese method. Furthermore, the developed framework yielded the average sensitivity of 98.3 %, specificity of 96.6 % and accuracy of 96.8 % as compared to the manual segmentation. In addition, the obtained distance using Hausdorff is 14.2 pixels and similarity index by Jaccard is 91 %, which are indicating the enhanced performance whilst segmented area of ATFL region obtained from five normal (average Pixels—16,345.09), five tear (average Pixels—14,940.96) and five thickened (average Pixels—12,179.20) subjects’ datasets show good performance of developed framework to be used in clinical practices.ConclusionsOn the basis of obtained results, the developed framework is computationally more efficient and more accurate with lowest rate of coefficient of variation (less than 5 %) that indicates the highest clinical significance of this research in the assessment of ATFL injuries.


title: TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

abstract: Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands users' questions and converts them to SQL queries automatically. In this paper we present a novel approach, TypeSQL, which views this problem as a slot filling task. Additionally, TypeSQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when users' queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.


title: Action Design Research and Visualization Design

abstract: In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly account for the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into addressing this issue. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design work, while aiming to ensure reliability of the process and design, and emphasizing opportunities for conducting research. We explore the utility of ADR in increasing the reliability of applied visualization design research by: describing ADR in the language and constructs developed within the visualization community; comparing ADR to existing visualization methodologies; and analyzing a recent design study retrospectively through the lens of ADR's framework and principles.


title: Toward crowdsourcing micro-level behavior annotations: the challenges of interface, training, and generalization

abstract: Research that involves human behavior analysis usually requires laborious and costly efforts for obtaining micro-level behavior annotations on a large video corpus. With the emerging paradigm of crowdsourcing however, these efforts can be considerably reduced. We first present OCTAB (Online Crowdsourcing Tool for Annotations of Behaviors), a web-based annotation tool that allows precise and convenient behavior annotations in videos, directly portable to popular crowdsourcing platforms. As part of OCTAB, we introduce a training module with specialized visualizations. The training module's design was inspired by an observational study of local experienced coders, and it enables an iterative procedure for effectively training crowd workers online. Finally, we present an extensive set of experiments that evaluates the feasibility of our crowdsourcing approach for obtaining micro-level behavior annotations in videos, showing the reliability improvement in annotation accuracy when properly training online crowd workers. We also show the generalization of our training approach to a new independent video corpus.


POMDP Models for Assistive Technology


title: A 76–81 GHz active phase shifter for phased array automotive radar in 65nm CMOS

abstract: This paper demonstrates an active phase shifter in 65nm CMOS for phased array radar systems, covering dual radar bands of 76 GHz and 79 GHz. The phase shifter achieves 360-degree phase variation using a vector combining method. 4-phase quadrature signals are produced using a coupled-line type 90-degree hybrid circuit and two single-to-differential amplifiers designed with a neutralization technique for stability and gain enhancement in wider frequency range. The active phase shifter has a significantly higher phase resolution to achieve fine control of four variable gain amplifier bias voltages using high bit DACs. The phase shifter has an average gain of 2.3 to 4.3 dB at 76-81GHz, with a gain variation of less than 1.3dB for each frequency across all phase states. In addition, a novel electronic beam scanning radar system configuration is proposed. Using the proposed radar system configuration with the active phase shifter, it is possible to distinguish more targets compared to conventional DBF systems.


title: Study on application modes of military Internet of Things (MIOT)

abstract: Recently, the Internet of Things (IOT) has obtained rapid development and has a significant impact on the military field. This paper first proposes a conception of military internet of things (MIOT) and analyzes the architecture of MIOT in detail. Then, three modes of MIOT, i.e., information sensing, information transmission and information serving, are respectively studied to show various military domain applications. Finally, an application assumption of MIOT from the weapon control aspect is given to validate the proposed application modes.


title: Fuzzy approach to detect learning style using McCarthy model as a tool for e-learning system

abstract: This study suggests a soft computing approach to extract knowledge which can decide learner's learning style in e-leaming environment The approach uses the rules of the learning style instrument(LSI) designed by Marlene Lefever which is based on the McCarthy Model There are e-learning system which detect the learning style of a particular learner in a crisp manner. Due to the fuzzy nature of the learner a fuzzy approach is used to decide the learning style of the learner. The proposed method has used the McCarthy Model as a tool to detect the learning style of the student. This increase the efficiency of the learning because most of the systems are based on the notion that each student has only one learning style which is not true.


title: It's Mine, Don't Touch!: interactions at a large multi-touch display in a city centre

abstract: We present data from detailed observations of CityWall, a large multi-touch display installed in a central location in Helsinki, Finland. During eight days of installation, 1199 persons interacted with the system in various social configurations. Videos of these encounters were examined qualitatively as well as quantitatively based on human coding of events. The data convey phenomena that arise uniquely in public use: crowding, massively parallel interaction, teamwork, games, negotiations of transitions and handovers, conflict management, gestures and overt remarks to co-present people, and "marking" the display for others. We analyze how public availability is achieved through social learning and negotiation, why interaction becomes performative and, finally, how the display restructures the public space. The multi-touch feature, gesture-based interaction, and the physical display size contributed differentially to these uses. Our findings on the social organization of the use of public displays can be useful for designing such systems for urban environments.


title: Malignant neoplasms of the sinonasal tract: report of 71 patients and literature review and analysis

abstract: BACKGROUND ::: The present study aimed to report the characteristics, prognostic factors, and treatment outcomes of 71 cases of malignant neoplasms of the sinonasal tract and literature review and analysis of major report series. ::: ::: ::: METHODS ::: Seventy-one consecutive patients diagnosed with primary malignant neoplasm of the sinonasal tract that were treated and followed up at a university hospital between May 2000 and March 2008 were selected for the present study. Thirty-four patients were treated with surgery followed by a combination of chemotherapy and radiotherapy, 15 with surgery alone, 14 with combined radiotherapy and chemotherapy, six with radiotherapy alone, and two with surgery followed by radiotherapy. To find out the major series of related studies over the last 20 years, a literature review of PubMed was performed. In all, we found 42 major series including 8,164 patients with malignant neoplasms of the sinonasal tract. ::: ::: ::: RESULTS ::: There were 35 women and 36 men ranging in age from 5 to 80 years, with a median age of 55 years at diagnosis. The primary sites included were paranasal sinuses in 51 and nasal cavity in 20. There were one case of stage I, 20 of stage II, 27 of stage III, and 23 of stage IV. Epithelial tumors constituted 65% of all neoplasms. After a median follow-up of 39 months for surviving patients, 33 patients are alive and without disease, eight are alive with disease, and 30 patients died due to disease. Local recurrence was the most frequent treatment failure. The 5-year disease-free, local control, and overall survival rates were 42.1%, 59.5%, and 54.5%, respectively. On univariate analysis, cervical lymph nodes involvement, primary tumor size, histologic type, response to therapy, and stage of disease were independent prognostic factors for overall survival. In the literature review and by analyzing the data collection from 42 major reported series, the median age was 57 years and male/female ratio was 1.8. Epithelial tumors consisted of 69% of all malignant neoplasms of sinonasal tract and stages III and IV disease constituted 74.7% of all stages. Local recurrence was the dominant treatment failure in nearly all series. Five-year local control and overall survival rates were 56% and 45.5%, respectively. ::: ::: ::: CONCLUSIONS ::: In this review and by analyzing the large data collection of recent major reported series, we found that malignant neoplasms of the sinonasal tract tend to present at locally advanced stage, with a high frequency of local failure and a moderate to poor outcome. More effective local treatment for improving the local control and overall survival is needed.


title: Early life risk factors for obesity in childhood: cohort study

abstract: OBJECTIVE ::: To identify risk factors in early life (up to 3 years of age) for obesity in children in the United Kingdom. ::: ::: ::: DESIGN ::: Prospective cohort study. ::: ::: ::: SETTING ::: Avon longitudinal study of parents and children, United Kingdom. ::: ::: ::: PARTICIPANTS ::: 8234 children in cohort aged 7 years and a subsample of 909 children (children in focus) with data on additional early growth related risk factors for obesity. ::: ::: ::: MAIN OUTCOME MEASURES ::: Obesity at age 7 years, defined as a body mass index (3) 95th centile relative to reference data for the UK population in 1990. ::: ::: ::: RESULTS ::: Eight of 25 putative risk factors were associated with a risk of obesity in the final models: parental obesity (both parents: adjusted odds ratio, 10.44, 95% confidence interval 5.11 to 21.32), very early (by 43 months) body mass index or adiposity rebound (15.00, 5.32 to 42.30), more than eight hours spent watching television per week at age 3 years (1.55, 1.13 to 2.12), catch-up growth (2.60, 1.09 to 6.16), standard deviation score for weight at age 8 months (3.13, 1.43 to 6.85) and 18 months (2.65, 1.25 to 5.59); weight gain in first year (1.06, 1.02 to 1.10 per 100 g increase); birth weight, per 100 g (1.05, 1.03 to 1.07); and short (< 10.5 hours) sleep duration at age 3 years (1.45, 1.10 to 1.89). ::: ::: ::: CONCLUSION ::: Eight factors in early life are associated with an increased risk of obesity in childhood.


title: Humor Recognition and Humor Anchor Extraction

abstract: Humor is an essential component in personal communication. How to create computational models to discover the structures behind humor, recognize humor and even extract humor anchors remains a challenge. In this work, we first identify several semantic structures behind humor and design sets of features for each structure, and next employ a computational approach to recognize humor. Furthermore, we develop a simple and effective method to extract anchors that enable humor in a sentence. Experiments conducted on two datasets demonstrate that our humor recognizer is effective in automatically distinguishing between humorous and non-humorous texts and our extracted humor anchors correlate quite well with human annotations.


title: An optical see-through head mounted display with addressable focal planes

abstract: Most existing stereoscopic head mounted displays (HMDs), presenting a pair of stereoscopic images at a fixed focal distance, lack the ability to correctly render the naturally coupled accommodation and convergence cues. Psychophysical studies have shown that such displays may cause many adverse consequences such as visual fatigue, diplopic vision, degraded oculomotor response, and depth perception errors. In this paper, we present a see-through HMD with addressable focal planes utilizing a novel active optical element - a liquid lens. The element, with a varying optical power from -5 to 20 diopters, is able to address the focal distance of the HMD from infinity to the near point of the eye. A monocular prototype was built from off-the-shelf elements and experimental results are presented to validate the proposed designs. We also describe both subjective and objective measurements of the accommodation responses of the viewer to the focal distances presented by the prototype.


title: An Efficient Algorithm for Mining High-Utility Itemsets with Discount Notion

abstract: High-utility itemset mining has attracted significant attention from the research community. Identifying high-utility itemsets from a transaction database can help business owners to earn a better profit by promoting the sales of high-utility itemsets. The technique also finds applications in web-click stream analysis, biomedical data analysis, mobile E-commerce etc. Several algorithms have been proposed to mine high-utility itemsets from a transaction database. However, these algorithms assume that items have a constant profit associated with them and don't embed the notion of discount into the utility-mining framework. In this paper, we integrate the notion of discount in state-of-the-art utility-mining algorithms and propose an algorithm for efficiently mining high-utility itemsets. We conduct extensive experiments on real and synthetic datasets and our results show that our proposed algorithm outperforms the state-of-the-art algorithms in terms of total execution time and number of itemsets that need to be explored.


title: Cross-Entropy Method for Reinforcement Learning

abstract: Reinforcement Learning methods have been succesfully applied to various optimalization problems. Scaling this up to real world sized problems has however been more of a problem. In this research we apply Reinforcement Learning to the game of Tetris which has a very large state space. We not only try to learn policies for Standard Tetris but try to learn parameterized policies for Generalized Tetris, which varies from game to game in field size and the chance of a single block occuring. In comparison to a non-parameterized policy we find that a parameterized policy is able to outperform the non-parameterized policy. The increased complexity of learning such a policy using the Cross-Entropy method however reaches a limit at which the policy is too complex to learn and performance drops below the non-parameterized policy.


title: SMERS: Music Emotion Recognition Using Support Vector Regression

abstract: Music emotion plays an important role in music retrieval, mood detection and other music-related applications. Many issues for music emotion recognition have been addressed by different disciplines such as physiology, psychology, cognitive science and musicology. We present a support vector regression (SVR) based music emotion recognition system. The recognition process consists of three steps: (i) seven distinct features are ex- tracted from music; (ii) those features are mapped into eleven emotion categories on Thayer's two-dimensional emotion model; (iii) two regression functions are trained using SVR and then arousal and valence values are pre- dicted. We have tested our SVR-based emotion classifier in both Cartesian and polar coordinate system empirically. The result indicates the SVR classifier in the polar repre- sentation produces satisfactory result which reaches 94.55% accuracy superior to the SVR (in Cartesian) and other machine learning classification algorithms such as


title: Broadband True-Time-Delay Microwave Lenses Based on Miniaturized Element Frequency Selective Surfaces

abstract: We present a new technique for designing low-profile, ultrawideband, true-time-delay (TTD) equivalent microwave lenses. Such a lens is composed of numerous spatial time-delay units (TDUs) distributed over a planar surface. Each spatial TDU is the unit cell of an appropriately designed miniaturized-element frequency selective surface and provides a frequency-independent time delay within the frequency band of interest. Two TTD lens prototypes with focal length to aperture dimension (f/D) ratios of 1 and 1.6 are designed, fabricated, and experimentally characterized at X-band. The 3-dB gain bandwidths of these lenses are respectively 7.5-11.6 and 7.8-11.5 GHz. Each fabricated lens has an overall thickness of 4.76 mm, which corresponds to ~ 0.150λ0, where λ0 is the free-space wavelength at the center frequency of operation. Each lens uses spatial TDUs with physical dimensions of 6 × 6 mm2, or ~ 0.19λ0 × 0.19λ0. Both lenses have a system fidelity factor close to 1, when excited with a broadband pulse. Furthermore, due to their true-time-delay equivalent behavior, the fabricated lenses do not suffer from chromatic aberration within their operational bands. When used in a beam-scanning antenna system, each lens demonstrates an excellent scanning performance in a field of view of ± 60°.


title: Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

abstract: One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed problem in previous approaches is the existence of highly non-uniform misprediction costs. While for model evaluation these costs have been taken into account through recently proposed business-aware offline metrics -- such as the Utility metric which measures the impact on advertiser profit -- this is not the case when training the models themselves. In this paper, to bridge the gap, we formally analyze the relationship between optimizing the Utility metric and the log loss, which is considered as one of the state-of-the-art approaches in conversion modeling. Our analysis motivates the idea of weighting the log loss with the business value of the predicted outcome. We present and analyze a new cost weighting scheme and show that significant gains in offline and online performance can be achieved.


title: Multi-view Inverse Rendering Under Arbitrary Illumination and Albedo

abstract: 3D shape reconstruction with multi-view stereo (MVS) relies on a robust evaluation of photo consistencies across images. The robustness is ensured by isolating surface albedo and scene illumination from the shape recovery, i.e. shading and colour variation are regarded as a nuisance in MVS. This yields a gap in the qualities between the recovered shape and the images used. We present a method to address it by jointly estimating detailed shape, illumination and albedo using the initial shape robustly recovered by MVS. This is achieved by solving the multi-view inverse rendering problem using the geometric and photometric smoothness terms and the normalized spherical harmonics illumination model. Our method allows spatially-varying albedo and per image illumination without any prerequisites such as training data or image segmentation. We demonstrate that our method can clearly improve the 3D shape and recover illumination and albedo on real world scenes.


title: ECT Evaluation by an Error Measurement System According to IEC 60044-8 and 61850-9-2

abstract: A high-accuracy error measurement system for calibration of digital-output equipped electronic current transformers (ECTs) is described. The system has been developed in conformity with the requirements for digital information networks in IEC 61850-9-2 and for accuracy class and error measurement procedures in IEC 60044-8. The system has also been designed to achieve simultaneous and synchronous samplings between an ECT/MU and a reference measuring unit, a procedure for capturing and filtering sampled value (SV) packets and data processing using the discrete Fourier transform with error compensations for an integrating analog-to-digital converter. The system employs a prototype commercial digital-output equipped ECT consisting of an optical current transformer (OCT) and a merging unit (MU). The error sources of the system are discussed for the error compensations and uncertainty estimations. Based on the principle of the system, the accuracy and temperature dependence of the prototype OCT/MU are evaluated and are confirmed to be within the limits of errors of accuracy class 0.2S.


That complex whole : culture and the evolution of human behavior by Lee Cronk


Peer ratings and assessment quality in crowd-based innovation processes


title: Multidimensional assessment of coping: A critical evaluation.

abstract: Several existing self-report measures of coping and the relevant research using these instruments are reviewed. Many of these coping measures suffer from a variety of psychometric weaknesses. A self-report instrument, the Multidimensional Coping Inventory (MCI), was constructed that identifies 3 types of coping styles: task-oriented, emotion-oriented, and avoidance-oriented coping


title: Wideband and Low-Profile Omnidirectional Circularly Polarized Antenna With Slits and Shorting-Vias

abstract: A novel wideband and low-profile omnidirectional circularly polarized (CP) patch antenna is proposed. The antenna is constructed on a via-shorted circular patch with slits cut on the bottom and top planes. By loading vias on the circular patch antenna, the ${\rm TM}_{01}$ mode is generated and coupled to the original ${\rm TM}_{02}$ mode. Then, a wide bandwidth is achieved. Moreover, the asymmetrically placed slits will generate a horizontal polarization, while the nonslit monopolar antenna will provide a vertical polarization. With a quadrature phase difference and equal amplitudes for the two orthogonal polarizations, a CP radiation could then be obtained. A prototype is fabricated and measured. With a low profile of $0.028{\lambda _0}$ , the antenna has a 10-dB return-loss impedance bandwidth of 16.6% and a 3-dB axial-ratio bandwidth of 14.8%.


title: Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

abstract: Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was motivated by two real-world applications in computational sustainability: one studies the spatial correlation among multiple bird species using the eBird data and the other models multi-dimensional landscape composition and human footprint in the Amazon rainforest with satellite images. We show that MEDL_CVAE captures rich dependency structures, scales better than previous methods, and further improves on the joint likelihood taking advantage of very large datasets that are beyond the capacity of previous methods.


title: Architectural geometry

abstract: Around 2005 it became apparent in the geometry processing community that freeform architecture contains many problems of a geometric nature to be solved, and many opportunities for optimization which however require geometric understanding. This area of research, which has been called architectural geometry, meanwhile contains a great wealth of individual contributions which are relevant in various fields. For mathematicians, the relation to discrete differential geometry is significant, in particular the integrable system viewpoint. Besides, new application contexts have become available for quite some old-established concepts. Regarding graphics and geometry processing, architectural geometry yields interesting new questions but also new objects, e.g. replacing meshes by other combinatorial arrangements. Numerical optimization plays a major role but in itself would be powerless without geometric understanding. Summing up, architectural geometry has become a rewarding field of study. We here survey the main directions which have been pursued, we show real projects where geometric considerations have played a role, and we outline open problems which we think are significant for the future development of both theory and practice of architectural geometry. Graphical abstractDisplay Omitted HighlightsThis is a survey paper on 'architectural geometry' as a geometric discipline, together with references to real projects.


title: A Crowd-Powered System for Fashion Similarity Search

abstract: Driven by the needs of customers and industry, online fashion search and analytics are recently gaining much attention. As fashion is mostly expressed by visual content, the analysis of fashion images in online social networks is a rich source of possible insights on evolving trends and customer preferences. Although a plethora of visual content is available, the modeling of clothes’ physics and movement, the implicit semantics in fashion designs, and the subjectivity of their interpretation pose difficulties to fully automated solutions for fashion search and analysis. In this article, we present the design and evaluation of a crowd-powered system for fashion similarity search from Twitter, supporting trend analysis for fashion professionals. The system enables fashion similarity search based on specific human-based similarity criteria. This is achieved by implementing a novel machine--crowd workflow that supports complex tasks requiring highly subjective judgments where multiple true solutions may coexist. We discuss how this leads to a novel class of crowd-powered systems for which the output of the crowd is not used to verify the automatic analysis but is the desired outcome. Finally, we show how this kind of crowd involvement enables a novel kind of similarity search and represents a crucial factor for the acceptance of system results by the end user.


Angina due to obstructive coronary artery disease in association with peripheral artery disease


title: CUDT: A CUDA Based Decision Tree Algorithm

abstract: Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.


title: A V-Band Beam-Steering Antenna on a Thin-Film Substrate With a Flip-Chip Interconnection

abstract: We propose a planar V-band beam-steering antenna based on a millimeter-wave (mm-wave) system-on-package technology using advanced thin-film technology on a silicon mother board. The thin-film substrate has the capability of integrated passive elements and flip-chip interconnection. Space-consuming components such as a microstrip Rotman lens and patch antennas are implemented on a low-loss, low-cost, and low-dielectric-constant material, benzocyclobutane. V-band monolithic microwave integrated circuits, a power amplifier, and a SP4T switch, are flip-chipped on the thin-film substrate while minimizing parasitic effects. The fabricated antenna module shows if-plane beam steering at four angles, plusmn6deg and plusmn20deg. The measured effective isotropic radiated power is in the range of 17.3-18 dBm. To our knowledge, this is the first demonstration of a planar mm-wave beam-steering antenna module on a thin-film substrate incorporating integrated terminating loads and flip-chip interconnections.


title: Extreme learning machine: algorithm, theory and applications

abstract: Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks. Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems. ELM is based on empirical risk minimization theory and its learning process needs only a single iteration. The algorithm avoids multiple iterations and local minimization. It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate. In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications. It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future.


title: High-Throughput Ring-LWE Cryptoprocessors

abstract: This paper presents the design of ring learning with errors (LWE) cryptoprocessors using number theoretic transform (NTT) cores and Gaussian samplers based on the inverse transform method. The NTT cores are designed using radix-2 and radix-8 decimation-in-frequency NTT algorithms and pipeline architectures. The designed Gaussian samplers are an optimized parallel implementation of the inverse transform method and they use a pipeline architecture to generate a sample every clock cycle after the latency period, that is, the output is obtained in a fixed time achieving timing-attack-resistant ring-LWE cryptoprocessors. Also, taking into account the national institute of standards and technology recommendation, a random number generator is designed to generate the input of the Gaussian sampler. The cryptoprocessors were synthesized on the field-programmable gate array EP4SGX230KF40C2 and verified in hardware using the DE4 board and the SignalTap tool. According to the obtained synthesis results, for dimension 512, the three cryptoprocessors perform the encryption in 9.33, 5.16, and $1.73~\boldsymbol {\mu }\text{s}$ and the decryption in 4.59, 2.78, and $1.04~\boldsymbol {\mu }\text{s}$ . We compared the designed cryptoprocessors with other ones presented in the literature, and from this comparison, we can conclude that they have the highest throughput, but they require more area resources than other previous ones.


title: Conductance and congestion in power law graphs

abstract: It has been observed that the degrees of the topologies of several communication networks follow heavy tailed statistics. What is the impact of such heavy tailed statistics on the performance of basic communication tasks that a network is presumed to support? How does performance scale with the size of the network? We study routing in families of sparse random graphs whose degrees follow heavy tailed distributions. Instantiations of such random graphs have been proposed as models for the topology of the Internet at the level of Autonomous Systems as well as at the level of routers. Let n be the number of nodes. Suppose that for each pair of nodes with degrees du and dv we have O(du dv) units of demand. Thus the total demand is O(n2). We argue analytically and experimentally that in the considered random graph model such demand patterns can be routed so that the flow through each link is at most O(n log2 n). This is to be compared with a bound O(n2) that holds for arbitrary graphs. Similar results were previously known for sparse random regular graphs, a.k.a. "expander graphs." The significance is that Internet-like topologies, which grow in a dynamic, decentralized fashion and appear highly inhomogeneous, can support routing with performance characteristics comparable to those of their regular counterparts, at least under the assumption of uniform demand and capacities. Our proof uses approximation algorithms for multicommodity flow and establishes strong bounds of a generalization of "expansion," namely "conductance." Besides routing, our bounds on conductance have further implications, most notably on the gap between first and second eigenvalues of the stochastic normalization of the adjacency matrix of the graph.


title: Loopy belief propagation: Convergence and effects of message errors

abstract: Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or from stochastic approximation methods. The introduction of such errors into the BP message computations has the potential to affect the solution obtained adversely. We analyze the effect resulting from message approximation under two particular measures of error, and show bounds on the accumulation of errors in the system. This analysis leads to convergence conditions for traditional BP message passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing.


title: Efficient algorithms for finding maximum matching in graphs

abstract: This paper surveys the techniques used for designing the most efficient algorithms for finding a maximum cardinality or weighted matching in (general or bipartite) graphs. It also lists some open problems concerning possible improvements in existing algorithms and the existence of fast parallel algorithms for these problems.


title: Light field mapping: efficient representation and hardware rendering of surface light fields

abstract: A light field parameterized on the surface offers a natural and intuitive description of the view-dependent appearance of scenes with complex reflectance properties. To enable the use of surface light fields in real-time rendering we develop a compact representation suitable for an accelerated graphics pipeline. We propose to approximate the light field data by partitioning it over elementary surface primitives and factorizing each part into a small set of lower-dimensional functions. We show that our representation can be further compressed using standard image compression techniques leading to extremely compact data sets that are up to four orders of magnitude smaller than the input data. Finally, we develop an image-based rendering method, light field mapping, that can visualize surface light fields directly from this compact representation at interactive frame rates on a personal computer. We also implement a new method of approximating the light field data that produces positive only factors allowing for faster rendering using simpler graphics hardware than earlier methods. We demonstrate the results for a variety of non-trivial synthetic scenes and physical objects scanned through 3D photography.


title: Torque-dependent compliance control in the joint space of an operational robotic machine for motor therapy

abstract: This paper is focused on the design of interaction control of robotic machines for rehabilitation motor therapy of the upper limb. The control approach tries to address requirements deriving from the application scenario and adopts a bio-inspired approach for regulating robot behavior in the interaction with the patient. The coactivation-based compliance control law in the joint space [Zollo, L, et al., 2003] is resumed and a new control law for regulating robot compliance in the free space is proposed, that is borrowed from studies on the biological mechanisms of regulation of the elastic properties of a healthy human arm. Moreover, a direct force control loop is added, in order to tune the level of force in the interaction with the patient. The control law is tested on a purposively developed simulation tool, which models the dynamics of the MIT-MANUS robot interacting with a human subject. The capability of the control system of counterbalancing incorrect movements depending on the level of pathology is finally validated.


title: Reduction in the cholinesterase activity of the rat anococcygeus muscle produced by corticosterone.

abstract: 1 ::: Administration of corticosterone caused a 47% reduction in the cholinesterase (ChE) activity of homogenates of the rat anococcygeus muscle. ::: ::: 2 ::: ChE activity was also reduced by morphine withdrawal and this effect was abolished by the corticosteroid synthesis inhibitor, metyrapone. ::: ::: 3 ::: A single dose of reserpine reduced ChE activity in normal but not in adrenalectomized rats. ::: ::: 4 ::: ChE activity was increased by adrenalectomy or by metyrapone treatment. ::: ::: 5 ::: The mechanism of the corticosteroid-induced reduction in ChE activity is discussed. ::: ::: 6 ::: The reduction in ChE activity produced by corticosterone, morphine withdrawal, or a single dose of reserpine might explain the leftward shift of the dose-% response curve to acetylcholine produced by these procedures in the isolated anococcygeus muscle of the rat.


Anatomic Study of the Retaining Ligaments of the Face and Applications for Facial Rejuvenation


title: Hyperdraw: a new computer aided drawing system based on prediction of drawing action

abstract: Traditional pens and papers are often the most preferable drawing materials for industrial designers, though computer aided design (CAD) systems are widely used in industrial design field. Such CAD systems are too strict for designers to draw their sketches freely and lack some real feeling of drawing. Somehow, especially in industrial design field, designers need to be assisted when they are drawing sketches since it is difficult even for professional designers to draw beautiful lines and curves every time they move their pens. The authors introduce a new computer aided drawing system, which is called Hyperdraw, in this paper. The Hyperdraw system "reads" its user's mind: it predicts and curves/lines that the user is intending to draw on a canvas and shows guide on the canvas by using mixed reality (MR) technology.


Association between stress-induced arousal and nocturnal sleep: a preliminary study


title: Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks

abstract: In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of their polarity in an end-to-end trainable neural network. We conduct experiments with different neural architectures and word representations on the recent GermEval 2017 dataset. We were able to show considerable performance gains by using the joint modeling approach in all settings compared to pipeline approaches. The combination of a convolutional neural network and fasttext embeddings outperformed the best submission of the shared task in 2017, establishing a new state of the art.


title: Targeted Syntactic Evaluation of Language Models

abstract: We present a dataset for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.


title: Are Your Training Datasets Yet Relevant? - An Investigation into the Importance of Timeline in Machine Learning-Based Malware Detection

abstract: In this paper, we consider the relevance of timeline in the construction of datasets, to highlight its impact on the performance of a machine learning-based malware detection scheme. Typically, we show that simply picking a random set of known malware to train a malware detector, as it is done in many assessment scenarios from the literature, yields significantly biased results. In the process of assessing the extent of this impact through various experiments, we were also able to confirm a number of intuitive assumptions about Android malware. For instance, we discuss the existence of Android malware lineages and how they could impact the performance of malware detection in the wild.


title: Shot Boundary Detection and Keyframe Extraction Based on Scale Invariant Feature Transform

abstract: In shot boundary detection, the key technology is to compute the visual content discontinuity values between consecutive video frames. In this paper, a unified framework is proposed to detect the shot boundaries and extract the keyframes of a shot. Firstly, the Scale invariant feature transform (SIFT) is adopted to compute the visual content discontinuity values. Then a new method, which is called the Local Double Threshold Shot Boundary Detection (LDT-SBD), is used to detect shot boundaries. Lastly, two mechanisms are proposed to extract keyframe. Experimental results show the framework is effective and has a good performance.


title: Evaluating ResNeXt Model Architecture for Image Classification

abstract: In recent years, deep learning methods have been successfully applied to image classification tasks. Many such deep neural networks exist today that can easily differentiate cats from dogs. One such model is the ResNeXt model that uses a homogeneous, multi-branch architecture for image classification. This paper aims at implementing and evaluating the ResNeXt model architecture on subsets of the CIFAR-10 dataset. It also tweaks the original ResNeXt hyper-parameters such as cardinality, depth and base-width and compares the performance of the modified model with the original. Analysis of the experiments performed in this paper show that a slight decrease in depth or base-width does not affect the performance of the model much leading to comparable results.


title: Generative Code Modeling with Graphs

abstract: Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. The generative procedure interleaves grammar-driven expansion steps with graph augmentation and neural message passing steps. An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.


title: Data center energy efficient resource scheduling

abstract: The information and communication technology (ICT) sector has grown exponentially in the recent years. An essential component of the ICT organizations is constituted by the data centers that are densely populated with redundant servers and communicational links to ensure the provision of 99.99 % availability of services; a fact responsible for the heavy energy consumption by data centers. For energy economy, the redundant elements can be powered off based on the current workload within the data center. We propose a Data Center-wide Energy-Efficient Resource Scheduling framework (DCEERS) that schedules data center resources according to the current workload of the data center. Minimum subset of resources to service the current workload are calculated by solving the Minimum Cost Multi Commodity Flow (MCMCF) using the Benders decomposition algorithm. The Benders decomposition algorithm is scalable: it solves the MCMCF problem in linear time for large data center environments. Our simulated results depict that DCEERS achieves better energy efficiency than the previous data center resource scheduling methodologies.


title: From Axial to Road-Centre Lines: A New Representation for Space Syntax and a New Model of Route Choice for Transport Network Analysis

abstract: Axial analysis is one of the fundamental components of space syntax. The space syntax community has suggested that it picks up qualities of configurational relationships between spaces not illuminated by other representations. However, critics have questioned the absolute necessity of axial lines to space syntax, as well as the exact definition of axial lines. Why not another representation? In particular, why not road-centre lines, which are easily available in many countries for use within geographical information systems? Here I propose that a recently introduced method of analysis, angular segment analysis, can marry axial and road-centre line representations, and in doing so reflect a cognitive model of how route choice decisions may be made. I show that angular segment analysis can be applied generally to road-centre line segments or axial segments, through a simple length-weighted normalisation procedure that makes values between the two maps comparable. I make comparative quantitative assessments for a real urban system, not just investigating angular analysis between axial and road-centre line networks, but also including more intuitive measures based on metric (or block) distances between locations. I show that the new angular segment analysis algorithm produces better correlation with observed vehicular flow than both standard axial analysis and metric distance measures. The results imply that there is no reason why space syntax inspired measures cannot be combined with transportation network analysis representations in order to create a new, cognitively coherent, model of movement in the city.


title: Data mining technique for identification of diagnostic biomarker to predict Schizophrenia disorder

abstract: In recent days, researchers are actively analysing the human brain to understand the underlying mechanism of heterogeneous psychiatric conditions. Schizophrenia is a severe neurological disorder which has been characterized by varying symptoms namely hallucinations, delusions and cognitive problems. In this paper, we have investigated the resting state fMRI images of 15 normal controls and 12 Schizophrenia patients by constructing functional connectome through image preprocessing techniques namely Realignment, temporal correction, filtering, etc., The parcellation of neuroimage is performed based on Automated Anatomical Labelling(AAL) atlas and 74 regions of interest(ROI) are identified. Functional connectome of each subject includes Pearson correlation values of mean time courses obtained between the regions. These region to region functional connectivity is considered as features and the feature selection technique namely Fisher filtering, ReliefF filtering and Runs filtering are applied. Then the features which are found by different filtering techniques are fed as input to the supervised non linear classifiers namely Random forest, C4.5, Cost sensitive classification and regression tree and K-Nearest Neighbour classification algorithm. These algorithms have produced classification rules which are used in the prediction of Schizophrenia disorder. C4.5 has achieved the higher predictive accuracy of 93% with leave-one out cross-validation and the predominant feature or diagnostic biomarker is obtained from the rule. This feature is one among the commonly identified feature of different feature selection techniques. The work has shown that the biomarker corresponds to the alterations in the functional connectivity between the brain regions namely Rolandic operculum and Postcentral gyrus of brain's left hemisphere which is involved in sensorimotor function of human.