title: Quadriceps strengthening with and without blood flow restriction in the treatment of patellofemoral pain: a double-blind randomised trial

abstract: BACKGROUND ::: Quadriceps strengthening exercises are part of the treatment of patellofemoral pain (PFP), but the heavy resistance exercises may aggravate knee pain. Blood flow restriction (BFR) training may provide a low-load quadriceps strengthening method to treat PFP. ::: ::: ::: METHODS ::: Seventy-nine participants were randomly allocated to a standardised quadriceps strengthening (standard) or low-load BFR. Both groups performed 8 weeks of leg press and leg extension, the standard group at 70% of 1 repetition maximum (1RM) and the BFR group at 30% of 1RM. Interventions were compared using repeated-measures analysis of variance for Kujala Patellofemoral Score, Visual Analogue Scale for 'worst pain' and 'pain with daily activity', isometric knee extensor torque (Newton metre) and quadriceps muscle thickness (cm). Subgroup analyses were performed on those participants with painful resisted knee extension at 60°. ::: ::: ::: RESULTS ::: Sixty-nine participants (87%) completed the study (standard, n=34; BFR, n=35). The BFR group had a 93% greater reduction in pain with activities of daily living (p=0.02) than the standard group. Participants with painful resisted knee extension (n=39) had greater increases in knee extensor torque with BFR than standard (p<0.01). No between-group differences were found for change in Kujala Patellofemoral Score (p=0.31), worst pain (p=0.24), knee extensor torque (p=0.07) or quadriceps thickness (p=0.2). No difference was found between interventions at 6 months. ::: ::: ::: CONCLUSION ::: Compared with standard quadriceps strengthening, low load with BFR produced greater reduction in pain with daily living at 8 weeks in people with PFP. Improvements were similar between groups in worst pain and Kujala score. The subgroup with painful resisted knee extension had larger improvements in quadriceps strength from BFR. ::: ::: ::: TRIAL REGISTRATION NUMBER ::: 12614001164684.


title: Design of a Near-Infrared Device for the Study of Glucose Concentration Measurements

abstract: Maintaining healthy blood glucose concentration levels is advantageous for the prevention of diabetes and obesity. Present day technologies limit such monitoring to patients who already have diabetes. The purpose of this project is to suggest a non-invasive method for measuring blood glucose concentration levels. Such a method would provide useful for even people without illness, addressing preventive care. This project implements near-infrared light of wavelengths 1450nm and 2050nm through the use of light emitting diodes and measures transmittance through solutions of distilled water and d-glucose of concentrations 50mg/dL, 100mg/dL, 150mg/dL, and 200mg/dL by using an InGaAs photodiode. Regression analysis is done. Transmittance results were observed when using near-infrared light of wavelength 1450nm. As glucose concentration increases, output voltage from the photodiode also increases. The relation observed was linear. No significant transmittance results were obtained with the use of 2050nm infrared light due to high absorbance and low power. The use of 1450nm infrared light provides a means of measuring glucose concentration levels.


title: Cost-aware Targeted Viral Marketing in billion-scale networks

abstract: Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the Influence Maximization (IM) problem that seeks a set of k seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient (1 − 1/√e-∊ − e)-approximation algorithm, named BCT, to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the Linear Threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM) in dense networks. In our experiments with a Twitter dataset, containing 1.46 billions of social relations and 106 millions tweets, BCT can identify key influencers in each trending topic in only few minutes.


title: Imputation of Missing Data Using Machine Learning Techniques

abstract: A serious problem in mining industrial data bases is that they are often incomplete, and a significant amount of data is missing, or erroneously entered. This paper explores the use of machine-learning based alternatives to standard statistical data completion (data imputation) methods, for dealing with missing data. We have approached the data completion problem using two well-known machine learning techniques. The first is an unsupervised clustering strategy which uses a Bayesian approach to cluster the data into classes. The classes so obtained are then used to predict multiple choices for the attribute of interest. The second technique involves modeling missing variables by supervised induction of a decision tree-based classifier. This predicts the most likely value for the attribute of interest. Empirical tests using extracts from industrial databases maintained by Honeywell customers have been done in order to compare the two techniques. These tests show both approaches are useful and have advantages and disadvantages. We argue that the choice between unsupervised and supervised classification techniques should be influenced by the motivation for solving the missing data problem, and discuss potential applications for the procedures we are developing.


title: MegaPipe: A New Programming Interface for Scalable Network I/O

abstract: We present MegaPipe, a new API for efficient, scalable network I/O for message-oriented workloads. The design of MegaPipe centers around the abstraction of a channel - a per-core, bidirectional pipe between the kernel and user space, used to exchange both I/O requests and event notifications. On top of the channel abstraction, we introduce three key concepts of MegaPipe: partitioning, lightweight socket (lwsocket), and batching. ::: ::: We implement MegaPipe in Linux and adapt memcached and nginx. Our results show that, by embracing a clean-slate design approach, MegaPipe is able to exploit new opportunities for improved performance and ease of programmability. In microbenchmarks on an 8-core server with 64 B messages, MegaPipe outperforms baseline Linux between 29% (for long connections) and 582% (for short connections). MegaPipe improves the performance of a modified version of memcached between 15% and 320%. For a workload based on real-world HTTP traces, MegaPipe boosts the throughput of nginx by 75%.


title: Designing data governance

abstract: Introduction Organizations are becoming increasingly serious about the notion of "data as an asset" as they face increasing pressure for reporting a "single version of the truth." In a 2006 survey of 359 North American organizations that had deployed business intelligence and analytic systems, a program for the governance of data was reported to be one of the five success "practices" for deriving business value from data assets. In light of the opportunities to leverage data assets as well ensure legislative compliance to mandates such as the Sarbanes-Oxley (SOX) Act and Basel II, data governance has also recently been given significant prominence in practitioners' conferences, such as TDWI (The Data Warehousing Institute) World Conference and DAMA (Data Management Association) International Symposium. The objective of this article is to provide an overall framework for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance approach, strategy and design. Designing data governance requires stepping back from day-to-day decision making and focusing on identifying the fundamental decisions that need to be made and who should be making them. Based on Weill and Ross, we also differentiate between governance and management as follows: • Governance refers to what decisions must be made to ensure effective management and use of IT (decision domains) and who makes the decisions (locus of accountability for decision-making). • Management involves making and implementing decisions. For example, governance includes establishing who in the organization holds decision rights for determining standards for data quality. Management involves determining the actual metrics employed for data quality. Here, we focus on the former. Corporate governance has been defined as a set of relationships between a company's management, its board, its shareholders and other stakeholders that provide a structure for determining organizational objectives and monitoring performance, thereby ensuring that corporate objectives are attained. Considering the synergy between macroeconomic and structural policies, corporate governance is a key element in not only improving economic efficiency and growth, but also enhancing corporate confidence. A framework for linking corporate and IT governance (see Figure 1) has been proposed by Weill and Ross. Unlike these authors, however, we differentiate between IT assets and information assets: IT assets refers to technologies (computers, communication and databases) that help support the automation of well-defined tasks, while information assets (or data) are defined as facts having value or potential value that are documented. Note that in the context of this article, we do not differentiate between data and information. Next, we use the Weill and Ross framework for IT governance as a starting point for our own framework for data governance. We then propose a set of five data decision domains, why they are important, and guidelines for what governance is needed for each decision domain. By operationalizing the locus of accountability of decision making (the "who") for each decision domain, we create a data governance matrix, which can be used by practitioners to design their data governance. The insights presented here have been informed by field research, and address an area that is of growing interest to the information systems (IS) research and practice community.


title: eWOM overload and its effect on consumer behavioral intention depending on consumer involvement

abstract: Online consumer reviews involving experiences, evaluations and opinions on products from previous consumers play two roles - an informant and a recommender. These two roles affect a consumer's intention. However, there is a conflict between two roles when a large number of reviews are offered. From the perspective of the recommender role, consumers have more favorable attitude toward a product as a greater number of positive reviews are offered while consumers may be confronted with too much information, which results in information overload. This study investigates what consequences the tradeoff between two roles has on purchasing intention depending on consumer involvement.


Family Environment and Juvenile Delinquency


title: Semantic-based topic detection using Markov decision processes

abstract: In the field of text mining, topic modeling and detection are fundamental problems in public opinion monitoring, information retrieval, social media analysis, and other activities. Document clustering has been used for topic detection at the document level. Probabilistic topic models treat topics as a distribution over the term space, but this approach overlooks the semantic information hidden in the topic. Thus, representing topics without loss of semantic information as well as detecting the optimal topic is a challenging task. In this study, we built topics using a network called a topic graph, where the topics were represented as concept nodes and their semantic relationships using WordNet. Next, we extracted each topic from the topic graph to obtain a corpus by community discovery. In order to find the optimal topic to describe the related corpus, we defined a topic pruning process, which was used for topic detection. We then performed topic pruning using Markov decision processes, which transformed topic detection into a dynamic programming problem. Experimental results produced using a newsgroup corpus and a science literature corpus showed that our method obtained almost the same precision and recall as baseline models such as latent Dirichlet allocation and KeyGraph. In addition, our method performed better than the probabilistic topic model in terms of its explanatory power and the runtime was lower compared with all three baseline methods, while it can also be optimized to adapt the corpus better by using topic pruning.


Body Size Moderates the Association Between Gay Community Identification and Body Image Disturbance


title: MEGA2: molecular evolutionary genetics analysis software

abstract: Summary: We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastly extends the capabilities of MEGA version 1 by: (1) facilitating analyses of large datasets; (2) enabling creation and analyses of groups of sequences; (3) enabling specification of domains and genes; (4) expanding the repertoire of statistical methods for molecular evolutionary studies; and (5) adding new modules for visual representation of input data and output results on the Microsoft Windows platform. Availability: http://www.megasoftware.net.


title: Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties

abstract: Online learning has become increasingly popular on handling massive data. The sequential nature of online learning, however, requires a centralized learner to store data and update parameters. In this paper, we consider online learning with distributed data sources. The autonomous learners update local parameters based on local data sources and periodically exchange information with a small subset of neighbors in a communication network. We derive the regret bound for strongly convex functions that generalizes the work by Ram et al. for convex functions. More importantly, we show that our algorithm has intrinsic privacy-preserving properties, and we prove the sufficient and necessary conditions for privacy preservation in the network. These conditions imply that for networks with greater-than-one connectivity, a malicious learner cannot reconstruct the subgradients (and sensitive raw data) of other learners, which makes our algorithm appealing in privacy-sensitive applications.


title: Comprehend DeepWalk as Matrix Factorization

abstract: Word2vec, as an efficient tool for learning vector representation of words has shown its effectiveness in many natural language processing tasks. Mikolov et al. issued Skip-Gram and Negative Sampling model for developing this toolbox. Perozzi et al. introduced the Skip-Gram model into the study of social network for the first time, and designed an algorithm named DeepWalk for learning node embedding on a graph. We prove that the DeepWalk algorithm is actually factoring a matrix M where each entry M_{ij} is logarithm of the average probability that node i randomly walks to node j in fix steps.


title: A comparative study of Cutibacterium (Propionibacterium) acnes clones from acne patients and healthy controls.

abstract: BACKGROUND ::: Cutibacterium (Propionibacterium) acnes is assumed to play an important role in the pathogenesis of acne. ::: ::: ::: OBJECTIVES ::: To examine if clones with distinct virulence properties are associated with acne. ::: ::: ::: METHODS ::: Multiple C. acnes isolates from follicles and surface skin of patients with moderate to severe acne and healthy controls were characterized by multilocus sequence typing. To determine if CC18 isolates from acne patients differ from those of controls in the possession of virulence genes or lack of genes conducive to a harmonious coexistence the full genomes of dominating CC18 follicular clones from six patients and five controls were sequenced. ::: ::: ::: RESULTS ::: Individuals carried one to ten clones simultaneously. The dominating C. acnes clones in follicles from acne patients were exclusively from the phylogenetic clade I-1a and all belonged to clonal complex CC18 with the exception of one patient dominated by the worldwide-disseminated and often antibiotic resistant clone ST3. The clonal composition of healthy follicles showed a more heterogeneous pattern with follicles dominated by clones representing the phylogenetic clades I-1a, I-1b, I-2 and II. Comparison of follicular CC18 gene contents, allelic versions of putative virulence genes and their promoter regions, and 54 variable-length intragenic and inter-genic homopolymeric tracts showed extensive conservation and no difference associated with the clinical origin of isolates. ::: ::: ::: CONCLUSIONS ::: The study supports that C. acnes strains from clonal complex CC18 and the often antibiotic resistant clone ST3 are associated with acne and suggests that susceptibility of the host rather than differences within these clones may determine the clinical outcome of colonization.


title: Immersive Technology for Human-Centric Cyberphysical Systems in Complex Manufacturing Processes: A Comprehensive Overview of the Global Patent Profile Using Collective Intelligence

abstract: Immersive technology for human-centric cyberphysical systems includes broad concepts that enable users in the physical world to connect with the cyberworld with a sense of immersion. Complex systems such as virtual reality, augmented reality, brain-computer interfaces, and brain-machine interfaces are emerging as immersive technologies that have the potential for improving manufacturing systems. Industry 4.0 includes all technologies, standards, and frameworks for the fourth industrial revolution to facilitate intelligent manufacturing. Industrial immersive technologies will be used for smart manufacturing innovation in the context of Industry 4.0’s human machine interfaces. This research provides a thorough review of the literature, construction of a domain ontology, presentation of patent metatrend statistical analysis, and data mining analysis using a technology function matrix and highlights technical and functional development trends using latent Dirichlet allocation (LDA) models. A total of 179 references from the IEEE and IET databases and 2,672 patents are systematically analyzed to identify current trends. The paper establishes an essential foundation for the development of advanced human-centric cyberphysical systems in complex manufacturing processes.


of the Annual Meeting of the Cognitive Science Society Title Timing of Adults ' Utterances and Interpretation of Word Meanings in a Discrepant Labeling Situation Permalink


title: Beyond Data and Model Parallelism for Deep Neural Networks

abstract: The computational requirements for training deep neural networks (DNNs) have grown to the point that it is now standard practice to parallelize training. Existing deep learning systems commonly use data or model parallelism, but unfortunately, these strategies often result in suboptimal parallelization performance. ::: In this paper, we define a more comprehensive search space of parallelization strategies for DNNs called SOAP, which includes strategies to parallelize a DNN in the Sample, Operation, Attribute, and Parameter dimensions. We also propose FlexFlow, a deep learning framework that uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy's performance and is three orders of magnitude faster than prior approaches that have to execute each strategy. We evaluate FlexFlow with six real-world DNN benchmarks on two GPU clusters and show that FlexFlow can increase training throughput by up to 3.8x over state-of-the-art approaches, even when including its search time, and also improves scalability.


Algorithms and Applications for Multitask Learning


title: How nurses and their work environment affect patient experiences of the quality of care: a qualitative study

abstract: Healthcare organisations monitor patient experiences in order to evaluate and improve the quality of care. Because nurses spend a lot of time with patients, they have a major impact on patient experiences. To improve patient experiences of the quality of care, nurses need to know what factors within the nursing work environment are of influence. The main focus of this research was to comprehend the views of Dutch nurses on how their work and their work environment contribute to positive patient experiences. A descriptive qualitative research design was used to collect data. Four focus groups were conducted, one each with 6 or 7 registered nurses in mental health care, hospital care, home care and nursing home care. A total of 26 nurses were recruited through purposeful sampling. The interviews were audiotaped, transcribed and subjected to thematic analysis. The nurses mentioned essential elements that they believe would improve patient experiences of the quality of nursing care: clinically competent nurses, collaborative working relationships, autonomous nursing practice, adequate staffing, control over nursing practice, managerial support and patient-centred culture. They also mentioned several inhibiting factors, such as cost-effectiveness policy and transparency goals for external accountability. Nurses feel pressured to increase productivity and report a high administrative workload. They stated that these factors will not improve patient experiences of the quality of nursing care. According to participants, a diverse range of elements affect patient experiences of the quality of nursing care. They believe that incorporating these elements into daily nursing practice would result in more positive patient experiences. However, nurses work in a healthcare context in which they have to reconcile cost-efficiency and accountability with their desire to provide nursing care that is based on patient needs and preferences, and they experience a conflict between these two approaches. Nurses must gain autonomy over their own practice in order to improve patient experiences.


title: Dimming-Feedback Control Method for TRIAC Dimmable LED Drivers

abstract: This paper presents a small-signal ac modeling of a feedback system for a flyback light-emitting diode (LED) driver. From this analysis, a dimming-feedback control method, which has a constant loop gain at dc, is proposed for dimmable LED drivers. Since the proposed method controls the steady-state error of the feedback system, an additional phase angle detection circuit and a LED current adjusting circuit are not required to control the output LED current according to the phase angle of the input voltage in a TRIAC-dimmable LED driver. Therefore, the proposed control method provides a simple structure, small size, and low bill of materials. A prototype of a flyback LED driver with the proposed dimming-feedback control method is implemented and experimented on to verify the validity of the analysis and the proposed control method. The prototype shows that the proposed control method varies the output LED current according to the phase angle of the TRIAC dimmer with no additional circuits. Thus, the proposed control method has high compatibility with TRIAC dimmers and can be applied to any dimmable LED driver with a nondimmable control integrated circuit.


title: Feature Extraction by Non-Parametric Mutual Information Maximization

abstract: We present a method for learning discriminative feature transforms using as criterion the mutual information between class labels and transformed features. Instead of a commonly used mutual information measure based on Kullback-Leibler divergence, we use a quadratic divergence measure, which allows us to make an efficient non-parametric implementation and requires no prior assumptions about class densities. In addition to linear transforms, we also discuss nonlinear transforms that are implemented as radial basis function networks. Extensions to reduce the computational complexity are also presented, and a comparison to greedy feature selection is made.


title: High efficiency and high power factor single-stage balanced forward-flyback converter

abstract: In this paper, a high efficiency and high power factor single-stage balanced forward-flyback converter merging a foward and flyback converter topologies is proposed. The conventional AC/DC flyback converter can achieve a good power factor but it has a high offset current through the transformer magnetizing inductor, which results in a large core loss and low power conversion efficiency. And, the conventional forward converter can achieve the good power conversion efficiency with the aid of the low core loss but the input current dead zone near zero cross AC input voltage deteriorates the power factor. On the other hand, since the proposed converter can operate as the forward and flyback converters during switch on and off periods, respectively, it cannot only perform the power transfer during an entire switching period but also achieve the high power factor due to the flyback operation. Moreover, since the current balanced capacitor can minimize the offset current through the transformer magnetizing inductor regardless of the AC input voltage, the core loss and volume of the transformer can be minimized. Therefore, the proposed converter features a high efficiency and high power factor. To confirm the validity of the proposed converter, theoretical analysis and experimental results from a prototype of 24W LED driver are presented.


title: Towards the Limit of Network Quantization

abstract: Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks. It reduces the number of distinct network parameter values by quantization in order to save the storage for them. In this paper, we design network quantization schemes that minimize the performance loss due to quantization given a compression ratio constraint. We analyze the quantitative relation of quantization errors to the neural network loss function and identify that the Hessian-weighted distortion measure is locally the right objective function for the optimization of network quantization. As a result, Hessian-weighted k-means clustering is proposed for clustering network parameters to quantize. When optimal variable-length binary codes, e.g., Huffman codes, are employed for further compression, we derive that the network quantization problem can be related to the entropy-constrained scalar quantization (ECSQ) problem in information theory and consequently propose two solutions of ECSQ for network quantization, i.e., uniform quantization and an iterative solution similar to Lloyd's algorithm. Finally, using the simple uniform quantization followed by Huffman coding, we show from our experiments that the compression ratios of 51.25, 22.17 and 40.65 are achievable for LeNet, 32-layer ResNet and AlexNet, respectively.


title: Photolysis of bisphenol S in aqueous solutions and the effects of different surfactants

abstract: The photolysis of bisphenol S (BPS) in aqueous solutions was studied under a 20 W UV lamp (λ = 254 nm). The proposed intermediates and possible photolysis pathways of BPS were analyzed, and the effect of three surfactants on this photolysis was determined. UV and liquid chromatography combined with ion-trap time-of-flight mass spectrometry analyses revealed that BPS photolysis mainly occurred in two steps, and the primary photodegradation intermediates or products were p-hydroxybenzenylsulfonic acid, p-hydroxyphenol, and sulfuric acid. The results showed that BPS photolysis fitted well to a first order kinetic equation. The surfactants did not directly participate in BPS photolysis and exerted different effects depending on the structure and concentration of surfactants. The anionic surfactant sodium dodecyl sulfate (SDS) exhibited a strong inhibiting effect on BPS photolysis that increased with increased SDS concentration. At 800 mg/L SDS, the half-life of BPS photolysis was prolonged from 91.2 to 150.7 min and the inhibiting ratio of SDS was 39.5 %. By contrast, the cationic surfactant cetyl trimethyl ammonium bromide (CTAB) showed a promoting effect on BPS photolysis that increased with increased CTAB concentration. At 120 mg/L CTAB, the half-life of BPS photolysis shortened from 91.2 to 63.0 min and the promoting ratio was 44.7 %. On the other hand, the nonionic surfactant polyoxyethylene lauryl ether (PLE) had a relatively weak inhibiting effect at 140 mg/L PLE. At 240 mg/L, the promoting ratio reached 7.9 %.


title: Spectral goodness of fit for network models

abstract: We introduce a new statistic, 'spectral goodness of fit' (SGOF) to measure how well a network model explains the structure of an observed network. SGOF provides an absolute measure of fit, analogous to the standard R-squared in linear regression. Additionally, as it takes advantage of the properties of the spectrum of the graph Laplacian, it is suitable for comparing network models of diverse functional forms, including both fitted statistical models and algorithmic generative models of networks. After introducing, defining, and providing guidance for interpreting SGOF, we illustrate the properties of the statistic with a number of examples and comparisons to existing techniques. We show that such a spectral approach to assessing model fit fills gaps left by earlier methods and can be widely applied.


title: Introducing Serendipity in a Content-Based Recommender System

abstract: Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.


title: Bounded Ideation Theory: A New Model of the Relationship Between Ideaquantity and Idea-quality during Ideation

abstract: This paper presents bounded ideation theory (BIT) to explain the relationship between the number-of-ideas and the number-of-good-ideas contributed during ideation. BIT posits that certain cognitive and physical boundaries cause the ideation function to be an inflected curve that transitions from a positive-but-increasing slope to a positive-but-decreasing slope. We then present implications of the theory for process design


title: Fast frequency acquisition phase-frequency detectors for GSa/s phase-locked loops

abstract: This paper describes two techniques for designing phase-frequency detectors (PFDs) with higher operating frequencies [periods of less than 8/spl times/ the delay of a fan-out-4 inverter (FO-4)] and faster frequency acquisition. Prototypes designed in 0.25-/spl mu/m CMOS process exhibit operating frequencies of 1.25 GHz [=1/(8/spl middot/FO-4)] and 1.5 GHz [=1/(6.7/spl middot/FO-4)] for two techniques, respectively, whereas a conventional PFD operates at <1 GHz [=1/(10/spl middot/FO-4)]. The two proposed PFDs achieve a capture range of 1.7/spl times/ and 1.4/spl times/ the conventional design, respectively.


title: Improving cross-modal and multi-modal retrieval combining content and semantics similarities with probabilistic model

abstract: With the ongoing development of the internet, a large number of multimedia documents containing images and texts have appeared in the daily life of people. Therefore, how to effectively and efficiently conduct cross-modal and multi-modal retrieval is being an important issue. Although some methods have been proposed to deal with the issue, their retrieval processes are confined to a single information source of multimedia documents, such as the representations of images and texts at a semantic level. In this paper, we propose a novel probabilistic model, namely CCSS, which not only combines low-level content and high-level semantics similarities through a first-order Markov chain, but also provides heterogeneous similarity measures for different unimedia types. The ranked list for a query is obtained by highlighting an optimal path across the chain. Content similarity focuses on the internal structure of each modality, while semantics similarity focuses on the semantic correlation between different modalities. Both of them are significant and their combination can be complementary to each other. Multi-class logistic regression and random forests are used to map the original features of each unimedia into a semantic space. According to the query-by-example scenario, the experiments on the Wikipedia dataset show that the performance of our model significantly outperforms those of state-of-the-art approaches for cross-modal retrieval. Additionally, the proposed multi-modal method is also shown to outperform previous systems on image retrieval task.


title: Image Descriptors from ConvNets: Comparing Global Pooling Methods for Image Retrieval

abstract: A major component of a generic image retrieval pipeline is producing concise and effective descriptors for each image. Previous works have shown impressive results in image retrieval when using descriptors from the black-box output of the fully-connected stage of pretrained Convolutional Neural Networks (ConvNets). However, previous work on descriptors pooled from the deep feature maps from late convolutional layers can produce more discriminative descriptors for generic image retrieval, while being relatively concise. When planning to globally pool such feature maps from a ConvNet, some options to consider are (1) the depth of the network, (2) choice of layer to pool, and (3) the level of dimension reduction. The previous work on global pooling methods uses differing techniques without a clear consensus on which method is best. This motivates us to establish a baseline pipeline from which to compare these options and their effect on retrieval results. Our contribution is a systematic and comprehensive experimental study of different pooling strategies of deep features for image retrieval, and the various options. Our results show that the nature of the dataset (object- heavy or scene-heavy) warrants a different pooling strategy. Significantly, we visualise the level of image discrimination brought by the different pooling methods on the datasets, and show that pooling need not have a priori spatial weights to effectively find objects within the image. The results underline the need to consider the context of the image dataset when developing image retrieval pipelines using ConvNets.


title: Coupled hidden conditional random fields for RGB-D human action recognition

abstract: This paper proposes a human action recognition method via coupled hidden conditional random fields model by fusing both RGB and depth sequential information. The coupled hidden conditional random fields model extends the standard hidden-state conditional random fields model only with one chain-structure sequential observation to multiple chain-structure sequential observations, which are synchronized sequence data captured in multiple modalities. For model formulation, we propose the specific graph structure for the interaction among multiple modalities and design the corresponding potential functions. Then we propose the model learning and inference methods to discover the latent correlation between RGB and depth data as well as model temporal context within individual modality. The extensive experiments show that the proposed model can boost the performance of human action recognition by taking advance of complementary characteristics from both RGB and depth modalities. HighlightsWe propose cHCRF to learn sequence-specific and sequence-shared temporal structure.We contribute a novel RGB-D human action dataset containing 1200 samples.Experiments on 3 popular datasets show the superiority of the proposed method.


Secure Audit Logs with Verifiable Excerpts – Full Version


title: A surgical simulation system for predicting facial soft tissue deformation

abstract: In the field of cranio-maxillofacial (CMF) surgery, surgical simulation is becoming a very powerful tool to plan surgery and simulate surgical results before actually performing a CMF surgical procedure. Reliable prediction of facial soft tissue changes is in particular essential for better preparation and to shorten the time taken for the operation. This paper presents a surgical simulation system to predict facial soft tissue changes caused by the movement of bone segments during CMF surgery. Two experiments were designed to test the feasibility of this simulation system. The test results demonstrate the feasibility of fast and good prediction of post-operative facial appearance, with texture. Our surgical simulation system is applicable to computer-assisted CMF surgery.


title: Fast-and-Light Stochastic ADMM

abstract: The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.


title: SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals

abstract: SemEval-2 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research. This paper defines the task, describes the training and test data and the process of their creation, lists the participating systems (10 teams, 28 runs), and discusses their results.


title: Correlation Power analysis with a leakage model

abstract: A classical model is used for the power consumption of cryptographic devices. It is based on the Hamming distance of the data handled with regard to an unknown but constant reference state. Once validated experimentally it allows an optimal attack to be derived called Correlation Power Analysis. It also explains the defects of former approaches such as Differential Power Analysis.


AERA_Ertmer_Leftwich_3


title: Efficient GPU-based Training of Recurrent Neural Network Language Models Using Spliced Sentence Bunch

abstract: Copyright © 2014 ISCA. Recurrent neural network language models (RNNLMs) are becoming increasingly popular for a range of applications including speech recognition. However, an important issue that limits the quantity of data, and hence their possible application areas, is the computational cost in training. A standard approach to handle this problem is to use class-based outputs, allowing systems to be trained on CPUs. This paper describes an alternative approach that allows RNNLMs to be efficiently trained on GPUs. This enables larger quantities of data to be used, and networks with an unclustered, full output layer to be trained. To improve efficiency on GPUs, multiple sentences are "spliced" together for each mini-batch or "bunch" in training. On a large vocabulary conversational telephone speech recognition task, the training time was reduced by a factor of 27 over the standard CPU-based RNNLM toolkit. The use of an unclustered, full output layer also improves perplexity and recognition performance over class-based RNNLMs.


title: Modern code reviews in open-source projects: which problems do they fix?

abstract: Code review is the manual assessment of source code by humans, mainly intended to identify defects and quality problems. Modern Code Review (MCR), a lightweight variant of the code inspections investigated since the 1970s, prevails today both in industry and open-source software (OSS) systems. The objective of this paper is to increase our understanding of the practical benefits that the MCR process produces on reviewed source code. To that end, we empirically explore the problems fixed through MCR in OSS systems. We manually classified over 1,400 changes taking place in reviewed code from two OSS projects into a validated categorization scheme. Surprisingly, results show that the types of changes due to the MCR process in OSS are strikingly similar to those in the industry and academic systems from literature, featuring the similar 75:25 ratio of maintainability-related to functional problems. We also reveal that 7–35% of review comments are discarded and that 10–22% of the changes are not triggered by an explicit review comment. Patterns emerged in the review data; we investigated them revealing the technical factors that influence the number of changes due to the MCR process. We found that bug-fixing tasks lead to fewer changes and tasks with more altered files and a higher code churn have more changes. Contrary to intuition, the person of the reviewer had no impact on the number of changes.


Presence and Flow in Virtual Environments: An Explorative Study


title: Big Five Personality Factors and Library Anxiety

abstract: Library anxiety is an unpleasant feeling that is experienced in a library location; it has behavioral, psychological, emotional and cognitive effect, which can be harmful for students’ academic career. The purpose of current study was to investigate the relationship between Library anxiety and the Big Five personality factors (neuroticism, extraversion, openness-to-experience, agreeableness, and conscientiousness) using a multivariate approach among students in Ardabil university. The participants were students of Ardabil University of Medical Sciences of which a sample of 580 students was randomly selected. And the assessment methods were revised. The short form of NEO Inventory [1] and the library anxiety questionnaire [2] were used to gather the data. The results showed that Neuroticism increased library anxiety in students, and with increasing the level of education, library anxiety is reduced, whereas by increasing the semester the library anxiety of students increases.


title: Guided Game-Based Learning Using Fuzzy Cognitive Maps

abstract: Fuzzy Cognitive Maps (FCMs) can be used to design game-based learning systems for their excellent ability of concept representation and reasoning. However, they cannot 1) acquire new knowledge from data and 2) correct false prior knowledge, thus reducing the game-based learning ability. This paper utilizes Hebbian Learning Rule to solve the first problem and uses Unbalance Degree to solve the second problem. As a result, an improved FCM gains the ability of self-learning from both data and prior knowledge. The improved FCM, therefore, is intelligent enough to work as a teacher to guide the study process. Based on the improved FCM, a novel game-based learning model is proposed, including a teacher submodel, a learner submodel, and a set of game-based learning mechanisms. The teacher submodel has enough knowledge and intelligence to deduce the answers by the improved FCM. The learner submodel records students' study processes. The game-based learning mechanism realizes the guided game-based learning process with the support of the teacher submodel. A driving training prototype system is presented as a case study to present a way to realize a real system based on the proposed models. Extensive experimental results justify the model in terms of the controlling and guiding the study process of the student.


title: Hierarchical skills and cognitive architectures

abstract: Hierarchical Skills and Cognitive Architectures Pat Langley (langley@csli.stanford.edu) Kirstin Cummings (kirstinc@ccrma.stanford.edu) Daniel Shapiro (dgs@stanford.edu) Computational Learning Laboratory, CSLI Stanford University, Stanford, CA 94305 Abstract Previous Research on Hierarchical Skills In this paper, we examine approaches to representing and utilizing hierarchical skills within the context of a cognitive architecture. We review responses to this issue by three established frameworks – ACT-R, Soar, and Prodigy – then present an alternative we have de- veloped within Icarus, another candidate architecture. Unlike most earlier systems, Icarus lets skills refer di- rectly to their subskills and communicate within a single recognize-act cycle. This assumption has implications for the number of cycles required to complete complex tasks. We illustrate our approach with the domain of multi-column subtraction, then discuss related methods and directions for future work in this area. A cognitive architecture (Newell, 1990) specifies the in- frastructure for an intelligent system, indicating those aspects of a cognitive agent that remain unchanged over time and across different application domains. Many proposals for the human cognitive architecture take the form of a production system, which stores long-term knowledge as a set of condition-action rules, encodes short-term elements as declarative list structures, and relies on a recognize-act cycle that matches against, and executes rules that alter, the contents of short-term memory. Soar, ACT-R, and EPIC are all examples of the production-system paradigm, although other archi- tectural frameworks are also possible. Despite the general agreement that cognitive skills are organized hierarchically, there exist different ways to im- plement this basic idea. Within a production-system ar- chitecture, the most natural scheme involves communi- cation between skills and their subskills through the ad- dition of elements to short-term memory. For instance, ACT-R achieves this effect using production rules that match against a generalized goal structure in their condi- tion sides, such as the desire to prove that two triangles are congruent, and, upon firing, create new subgoals, such as the desire to prove that two lines have the same length. This approach requires the explicit addition of goal elements to short-term memory, since this is the only mechanism through which other production rules can be accessed. Soar takes a somewhat different approach to encoding hierarchical skills that relies on multiple problem spaces. For instance, Jones and Laird (1997) report a detailed model of ying an aircraft in combat training scenarios. This system organizes its capabilities in a hierarchical manner, with each node implemented as a Soar problem space with associated states, operators, and goals. To invoke a lower-level problem space, the system adds a new element to short-term memory that refers to that space, and it must take similar action in order to exit. The details differ from those in ACT, but the passing of messages through short-term memory is similar. The Prodigy architecture (Minton et al., 1989) pro- duces cognitive behavior in yet another manner. The system represents knowledge about actions as Strips- like operators, and the central module utilizes means- ends analysis to decompose problems into subproblems. This gives Prodigy the ability to generate hierarchical Introduction and Overview Human skills are organized in a hierarchical fashion. There seems to be general agreement with this claim, as it is consistent not only with experimental findings about the execution and acquisition of skills, but also with introspection about our everyday behavior. Upon request, most people can describe their complex skills at successive levels of aggregation, whether these involve how they drive to work each day, how they cook a meal, or how they write a technical article. What remains an open question is how we should model such skill hierarchies in computational terms. Al- ternative approaches to modeling cognition encode the notion of hierarchy in distinct ways that have different implications for performance and learning. The most interesting positions are those which are embedded in theories of the human cognitive architecture, such as Soar (Laird et al., 1987), ACT-R (Anderson, 1993), and EPIC (Kieras & Meyer, 1997). These frameworks make strong commitments to both the mental representations of knowledge and to the processes that operate on them. In the pages that follow, we consider the challenge of modeling hierarchical skills within a unified theory of cognition. We begin with a brief review of three such ar- chitectures and their responses to this issue, then turn to Icarus, an alternative framework that approaches hier- archical skills from a different perspective. The key issue involves whether one can traverse levels of a hierarchy within a single cognitive cycle. Our illustrative example comes from a familiar cognitive skill that has a hierar- chical organization – multi-column subtraction – but we also consider other models that incorporate multi-level skills. We conclude by discussing related work and our plans to extend the architecture’s capabilities.


title: AdaBoost-based artificial neural network learning

abstract: A boosting-based method of learning a feed-forward artificial neural network (ANN) with a single layer of hidden neurons and a single output neuron is presented. Initially, an algorithm called Boostron is described that learns a single-layer perceptron using AdaBoost and decision stumps. It is then extended to learn weights of a neural network with a single hidden layer of linear neurons. Finally, a novel method is introduced to incorporate non-linear activation functions in artificial neural network learning. The proposed method uses series representation to approximate non-linearity of activation functions, learns the coefficients of nonlinear terms by AdaBoost. It adapts the network parameters by a layer-wise iterative traversal of neurons and an appropriate reduction of the problem. A detailed performances comparison of various neural network models learned the proposed methods and those learned using the least mean squared learning (LMS) and the resilient back-propagation (RPROP) is provided in this paper. Several favorable results are reported for 17 synthetic and real-world datasets with different degrees of difficulties for both binary and multi-class problems.


title: Implementing a Sensor Fusion Algorithm for 3D Orientation Detection with Inertial/Magnetic Sensors

abstract: In this paper a sensor fusion algorithm is developed and implemented for detecting orientation in three dimensions. Tri-axis MEMS inertial sensors and tri-axis magnetometer outputs are used as input to the fusion system. A Kalman filter is designed to compensate the inertial sensors errors by combining accelerometer and gyroscope data. A tilt compensation unit is designed to calculate the heading of the system.


title: A Discriminative Metric Learning Based Anomaly Detection Method

abstract: Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated.


title: A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning

abstract: Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. In this manner, symbolic data mining algorithms can be applied over continuous data and the representation of information is simplified, making it more concise and specific. The literature provides numerous proposals of discretization and some attempts to categorize them into a taxonomy can be found. However, in previous papers, there is a lack of consensus in the definition of the properties and no formal categorization has been established yet, which may be confusing for practitioners. Furthermore, only a small set of discretizers have been widely considered, while many other methods have gone unnoticed. With the intention of alleviating these problems, this paper provides a survey of discretization methods proposed in the literature from a theoretical and empirical perspective. From the theoretical perspective, we develop a taxonomy based on the main properties pointed out in previous research, unifying the notation and including all the known methods up to date. Empirically, we conduct an experimental study in supervised classification involving the most representative and newest discretizers, different types of classifiers, and a large number of data sets. The results of their performances measured in terms of accuracy, number of intervals, and inconsistency have been verified by means of nonparametric statistical tests. Additionally, a set of discretizers are highlighted as the best performing ones.


Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction.


NEMO : Neuro-Evolution with Multiobjective Optimization of Deep Neural Network for Speed and Accuracy


title: Online Minimum-Energy Trajectory Planning and Control on a Straight-Line Path for Three-Wheeled Omnidirectional Mobile Robots

abstract: To maximize total operating time, an online minimum-energy translational and rotational velocity trajectory planning and control system is presented on a straight-line path for three-wheeled omnidirectional mobile robots (TOMRs). We suggest an efficient online trajectory planning algorithm, which minimizes a practical cost function as the energy that is drawn from batteries to motors, which is based on an accurate TOMR dynamic model that includes both the Coriolis force and the actuator motor dynamics. Using Pontryagin's minimum principle, we find the minimum-energy rotational velocity trajectory in analytic form. Then, the minimum-energy translational velocity trajectory is found using a novel algorithm that has a time complexity of O(n), which is based on a linearity condition on the state transition of TOMRs. Moreover, a trajectory control system is implemented using the resolved-acceleration control to validate the actual performance. Simulation and experiment results show that these minimum-energy trajectories can save energy up to 4.76% compared with the energy-efficient trapezoidal velocity profile and up to 5.29% compared with the loss-minimization control.


title: Robust multi-task feature learning

abstract: Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning algorithms have received increasing attention and they have been successfully applied to many applications involving high dimensional data. However, they assume that all tasks share a common set of features, which is too restrictive and may not hold in real-world applications, since outlier tasks often exist. In this paper, we propose a Robust Multi-Task Feature Learning algorithm (rMTFL) which simultaneously captures a common set of features among relevant tasks and identifies outlier tasks. Specifically, we decompose the weight (model) matrix for all tasks into two components. We impose the well-known group Lasso penalty on row groups of the first component for capturing the shared features among relevant tasks. To simultaneously identify the outlier tasks, we impose the same group Lasso penalty but on column groups of the second component. We propose to employ the accelerated gradient descent to efficiently solve the optimization problem in rMTFL, and show that the proposed algorithm is scalable to large-size problems. In addition, we provide a detailed theoretical analysis on the proposed rMTFL formulation. Specifically, we present a theoretical bound to measure how well our proposed rMTFL approximates the true evaluation, and provide bounds to measure the error between the estimated weights of rMTFL and the underlying true weights. Moreover, by assuming that the underlying true weights are above the noise level, we present a sound theoretical result to show how to obtain the underlying true shared features and outlier tasks (sparsity patterns). Empirical studies on both synthetic and real-world data demonstrate that our proposed rMTFL is capable of simultaneously capturing shared features among tasks and identifying outlier tasks.


title: Interplay of metal ions and urease

abstract: Urease, the first enzyme to be crystallized, contains a dinuclear nickel metallocenter that catalyzes the decomposition of urea to produce ammonia, a reaction of great agricultural and medical importance. Several mechanisms of urease catalysis have been proposed on the basis of enzyme crystal structures, model complexes, and computational efforts, but the precise steps in catalysis and the requirement of nickel versus other metals remain unclear. Purified bacterial urease is partially activated via incubation with carbon dioxide plus nickel ions; however, in vitro activation also has been achieved with manganese and cobalt. In vivo activation of most ureases requires accessory proteins that function as nickel metallochaperones and GTP-dependent molecular chaperones or play other roles in the maturation process. In addition, some microorganisms control their levels of urease by metal ion-dependent regulatory mechanisms.


title: Revisiting password rules: facilitating human management of passwords

abstract: Password rules were established in the context of past security concerns. Recent work in computer security challenges the conventional wisdom of expert password advice, such as change your passwords often, do not reuse your passwords, or do not write your passwords down. The effectiveness of these rules for protecting user accounts against real world attacks is questioned. We review the latest research examining password rules for general-purpose user authentication on the web, and discuss the arguments behind the continued acceptance or the rejection of the rules based on empirical evidence and solid justifications. Following the review, we recommend an updated set of password rules.


title: Bitcoin Will Bite the Dust

abstract: Bitcoin is the most radical innovation in the monetary space for a very long time. It is an entirely private monetary system that runs itself and does not depend on trust in any central authority to honor its promises. Instead, it relies on trust in the Bitcoin community or network that verifies transactions and maintains the integrity of the system. This system of distributed trust creates bitcoins and produces an automatic, tamper-proof bitcoin money supply process. (1) As such, it avoids the dangers of discretionary monetary policy--namely, quantitative easing, manipulated interest rates, and the need to rely on wise men or women to withstand political pressure or successfully forecast the future. Indeed, under Bitcoin there is no monetary policy at all. There is just an automatic monetary rule dictated by the Bitcoin protocol designed in 2009 by an anonymous programmer using the alias Satoshi Nakamoto. Bitcoin has been widely hailed as a success and has won a substantial following. Unfortunately, the underlying economics of Bitcoin mean that it is unsustainable and in all likelihood will be remembered as a failed experiment--at best a pointer to some superior successor. A first-pass intuition into Bitcoin can be obtained from a comparison with the stone money in Milton Friedman's (1992) case study, "The Island of Stone Money." In this story, the people of the island of Yap in Micronesia used as money large round limestone disks transported from the nearby island of Palau. These were too heavy to conveniently move around, so they were placed in prominent places. When ownership was to be transferred (e.g., as part of a dowry, inheritance, or ransom payment), the current owner would publicly announce the change in ownership but the stone would typically remain where it was and the islanders would maintain a collective memory of the ownership history of the stones. This collective memory ensured that there was no dispute over who owned which stones. Similarly, in Bitcoin, the record of all transactions, the "blockchain," is also public knowledge and is regarded as die definitive record of who owns which bitcoins. Both the stone money and Bitcoin share a critical feature that is highly unusual for a monetary system: both systems operate via a decentralized collective memory. On February 11, 2009, Nakamoto gave an explanation of the thinking behind Bitcoin in an e-mail announcing its launch: "The root problem with conventional currency is all the trust that is required to make it work. The central bank must be trusted not to debase the currency, but the history of fiat currencies is full of breaches of that trust.... With e-currency based on cryptographic proof, without die need to trust a third-party middleman, money can be secure and transactions complete." Cryptocurrencies, however, face the problem of "double-spending." As Nakamoto notes, "Any owner could try to re-spend an already spent coin by [digitally] signing it again to another owner. The usual solution is for a trusted company with a central database to check for double-spending, but that just gets back to the trust model.... Bitcoin's solution is to use a peer-to-peer network to check for double-spending." Consequently, "the result is a distributed system with no single point of failure." (2) Kevin Dowd is Professor of Finance and Economics at Durham University in the United Kingdom and a partner at Cobden Partners. Martin Hutchinson is a journalist and author of the Bear's Lair column (www.tbwns.com/category/the-bears-lair). The authors thank Ferdinando Ametrano, Gavin Andresen, Raadhiyah Anees, Steve Baker MP, Roger Brown, Dave Campbell, Akin Fernandez, Dominic Frisby, Jim Harper, Doug Jackson, Gordon Kerr, Jim Rapp, Eric Samieski, Lawrence H. White and Basil Zafiriou for much helpful feedback. We note that several of our readers have expressed serious reservations about our analysis and conclusions. Any remaining mistakes are the authors' own. …


title: Ray tracing deformable scenes using dynamic bounding volume hierarchies

abstract: The most significant deficiency of most of today's interactive ray tracers is that they are restricted to static walkthroughs. This restriction is due to the static nature of the acceleration structures used. While the best reported frame rates for static geometric models have been achieved using carefully constructed kd-trees, this article shows that bounding volume hierarchies (BVHs) can be used to efficiently ray trace large static models.More importantly, the BVH can be used to ray trace deformable models (sets of triangles whose positions change over time) with little loss of performance. A variety of efficiency techniques are used to achieve this performance, but three algorithmic changes to the typical BVH algorithm are mainly responsible. First, the BVH is built using a variant of the surface area heuristic conventionally used to build kd-trees. Second, the topology of the BVH is not changed over time so that only the bounding volumes need to be refit from frame-to-frame. Third, and most importantly, packets of rays are traced together through the BVH using a novel integrated packet-frustum traversal scheme. This traversal scheme elegantly combines the advantages of both packet traversal and frustum traversal and allows for rapid hierarchy descent for packets that hit bounding volumes as well as rapid exits for packets that miss. A BVH-based ray tracing system using these techniques is shown to achieve performance for deformable models comparable to that previously available only for static models.


title: Interactive Computer Graphics A Top Down Approach With Open Gl

abstract: Thank you for downloading interactive computer graphics a top down approach with open gl. Maybe you have knowledge that, people have search hundreds times for their favorite books like this interactive computer graphics a top down approach with open gl, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their computer.


title: Sigma*: symbolic learning of input-output specifications

abstract: We present Sigma*, a novel technique for learning symbolic models of software behavior. Sigma* addresses the challenge of synthesizing models of software by using symbolic conjectures and abstraction. By combining dynamic symbolic execution to discover symbolic input-output steps of the programs and counterexample guided abstraction refinement to over-approximate program behavior, Sigma* transforms arbitrary source representation of programs into faithful input-output models. We define a class of stream filters---programs that process streams of data items---for which Sigma* converges to a complete model if abstraction refinement eventually builds up a sufficiently strong abstraction. In other words, Sigma* is complete relative to abstraction. To represent inferred symbolic models, we use a variant of symbolic transducers that can be effectively composed and equivalence checked. Thus, Sigma* enables fully automatic analysis of behavioral properties such as commutativity, reversibility and idempotence, which is useful for web sanitizer verification and stream programs compiler optimizations, as we show experimentally. We also show how models inferred by Sigma* can boost performance of stream programs by parallelized code generation.


title: Discretization: An Enabling Technique

abstract: Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers which are more concise to represent and specify, easier to use and comprehend as they are closer to a knowledge-level representation than continuous values. Many studies show induction tasks can benefit from discretization: rules with discrete values are normally shorter and more understandable and discretization can lead to improved predictive accuracy. Furthermore, many induction algorithms found in the literature require discrete features. All these prompt researchers and practitioners to discretize continuous features before or during a machine learning or data mining task. There are numerous discretization methods available in the literature. It is time for us to examine these seemingly different methods for discretization and find out how different they really are, what are the key components of a discretization process, how we can improve the current level of research for new development as well as the use of existing methods. This paper aims at a systematic study of discretization methods with their history of development, effect on classification, and trade-off between speed and accuracy. Contributions of this paper are an abstract description summarizing existing discretization methods, a hierarchical framework to categorize the existing methods and pave the way for further development, concise discussions of representative discretization methods, extensive experiments and their analysis, and some guidelines as to how to choose a discretization method under various circumstances. We also identify some issues yet to solve and future research for discretization.


title: Implementation of scalable K-Means++ clustering for passengers temporal pattern analysis in public transportation system (BRT Trans Jogja case study)

abstract: The popularity of Bus Rapid Transit (BRT) makes Trans Jogja an alternative of a mass public transportation system for urban mobility. However, without supervision on temporal patterns of passenger's behavior in Trans Jogja on supply and demand, it will result in the decreases of the number of BRT users and the increasing number of private vehicle users, so that traffic jams remain difficult to avoid. Smart Card Automated Fare Collection System (SCAFCS) which is currently used as e-ticketing in Trans Jogja public transport can be used to analyze passengers pattern with data mining approaches. This paper applied SCAFCS data preprocessing with data warehouse mechanism and implemented Hadoop Platform as distributed computing to improve K-Means++ clustering performance on large datasets scalability; in this case, SCAFCS Trans Jogja has a large dataset (volume) and rapid growth data (velocity). Scalable K-Means++ algorithm generates five clusters with characteristics in number of clusters, namely: Very Low, Low, Average, High, Very High. The clusters were used to analyze passengers pattern based on the dimensions of time (temporal), segmentation of passengers (structure) to determine the variability of passengers based on the card they used and transaction peak on boarding location (spatio). Experimental and testing setup was performed by comparing Sum of Square Error (SSE) which is the total squared error of k cluster at the centroid on three algorithms, simple K-Means, K-Means++ and K-Means++ implementation using Hadoop Platform as parallel and distributed computing. K-Means++ with Hadoop Platform implementation generates smaller SSE value than simple K-Means and K-Means++ algorithms; that shows it has good SSE value.


title: Neural GPUs Learn Algorithms

abstract: Learning an algorithm from examples is a fundamental problem that has been widely studied. Recently it has been addressed using neural networks, in particular by Neural Turing Machines (NTMs). These are fully differentiable computers that use backpropagation to learn their own programming. Despite their appeal NTMs have a weakness that is caused by their sequential nature: they are not parallel and are are hard to train due to their large depth when unfolded. ::: We present a neural network architecture to address this problem: the Neural GPU. It is based on a type of convolutional gated recurrent unit and, like the NTM, is computationally universal. Unlike the NTM, the Neural GPU is highly parallel which makes it easier to train and efficient to run. ::: An essential property of algorithms is their ability to handle inputs of arbitrary size. We show that the Neural GPU can be trained on short instances of an algorithmic task and successfully generalize to long instances. We verified it on a number of tasks including long addition and long multiplication of numbers represented in binary. We train the Neural GPU on numbers with upto 20 bits and observe no errors whatsoever while testing it, even on much longer numbers. ::: To achieve these results we introduce a technique for training deep recurrent networks: parameter sharing relaxation. We also found a small amount of dropout and gradient noise to have a large positive effect on learning and generalization.


title: Accessible presentation of information for people with visual disabilities

abstract: Personal computers, palm top computers, media players and cell phones provide instant access to information from around the world. There are a wide variety of options available to make that information available to people with visual disabilities, so many that choosing one for use in any given context can often feel daunting to someone new to the field of accessibility. This paper reviews tools and techniques for the presentation of textual, graphic, mathematic and web documents through audio and haptic modalities to people with visual disabilities.


title: Genetic algorithms to optimise the time to make stock market investment

abstract: The application of Artificial Intelligence described in this article is intended to resolve the issue of speculation on the stock market. Genetic Algorithms is the technique that is used, with the article focusing on the different ways that chromosomes can be designed and on how the pertinent evaluation mechanism is established. The problem will be based on the speculation systems that are typical of Technical Analysis.


title: Chronic pretreatment with American ginseng berry and its polyphenolic constituents attenuate oxidant stress in cardiomyocytes.

abstract: The acute anti-oxidant and protective effect of American ginseng berry extract (AGBE) has been demonstrated in cultured cardiomyocytes in our previous study. In the current study we evaluated if a chronic pretreatment of cultured cardiomyocytes with AGBE can alter the cellular antioxidant potential. Chick embryo cardiomyocytes were treated with AGBE (0.5-2.5 mg/ml) for up to 72 h. The treated cells were then exposed to exogenously added hydrogen peroxide (H(2)O(2); 500 microM). The oxidant-mediated injury was measured using a fluorescent probe 2',7'-dichlorofluorescin diacetate (DCFH/DA) while cell death was measured using propidium iodide (PI) staining. The non-treated (control) cells exposed to H(2)O(2) showed significant increase in DCF- and PI-mediated fluorescence suggesting significant oxidative injury and cell death. Pretreatment with AGBE demonstrated a significant attenuation of DCF fluorescence (p<0.005) with AGBE 0.5 mg/ml showing a 17% decrease, AGBE 1.0 mg/ml showing a 26% decrease, and AGBE 2.5 mg/ml showing a 49% decrease from control DCF fluorescence following a 72 h pretreatment. Cell death caused by H(2)O(2) was also significantly attenuated in AGBE-pretreated cells in a concentration- and time-dependent manner (p<0.005). We also demonstrated that active polyphenolic constituents in AGBE, caffeic acid and chlorogenic acid, appear to contribute significantly to AGBE's protective effects. Finally, catalase inhibition resulted in a significantly increased fluorescence in AGBE-treated cells compared to the control. The results suggest that pretreatment with AGBE upregulates peroxide detoxifying mechanisms, which could affect intracellular oxidant dynamics in cardiomyocytes.


title: Treatment effects of the bionator and high-pull facebow combination followed by fixed appliances in patients with increased vertical dimensions

abstract: Introduction: The purpose of this study was to evaluate the effectiveness of a first phase of bionator and high-pull facebow treatment followed by a second phase of fixed appliance therapy in growing subjects with increased vertical dimensions. Methods: The records of 24 subjects with high-angle skeletal relationships (mean MPA value ∼30°) treated consecutively with this protocol were examined. Cephalometric measurements were compared with those obtained from 23 sets of records of an untreated group matched according to age, gender, vertical skeletal relationships, and time intervals between records. The matched group of patients was from the University of Michigan Elementary and Secondary School Growth Study. Lateral cephalograms were analyzed prior to the start of treatment (T1, mean age 9.1 years), at the start of phase 2 treatment (T2, mean age 11.9 years), and after phase 2 treatment (T3, mean age 14.7 years). The total treatment duration (phase 1, retention, and phase 2) for the treated group was 5.5 years, whereas the control group total time interval averaged 5.6 years. Results: As to sagittal relationships, no significant differences were found between treated subjects and controls at the end of the 2-phase treatment for all measurements. Counterintuitively, the bionator and high-pull headgear combination worsened the hyperdivergent facial pattern at a clinically significant level, as shown by analysis of final facial forms. The treated group exhibited a significantly larger MPA value than controls (2.5°) as well as a larger inclination of the Frankfort horizontal to the occlusal plane (2.8°). Conclusions: Based on the analysis of this sample, the examined therapeutic protocol does not appear to be a recommendable option for treatment of subjects with increased vertical dimensions.


title: FPGA implementation of a smart home lighting control system

abstract: Intelligent homes are nowadays equipped with all sort of smart devices for the control of appliances and domotic applications. One of the main aims of these devices is, apart from the comfort of having automated systems, the efficient use of energy. This paper reports on the design and FPGA implementation of a smart lighting system for application in small smart homes. Unlike those systems using pervasive remote controls, the proposed system is based on automatic detection of application conditions, and it requires minimum programming by the end user.


title: DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

abstract: We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images. Similar to other challenges in computer vision domain such as DAVIS and COCO, DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.


title: High Accuracy Myanmar Handwritten Character Recognition using Hybrid approach through MICR and Neural Network

abstract: This paper contributes an effective recognition approach for Myanmar Handwritten Characters. In this article, Hybrid approach use ICR and OCR recognition through MICR (Myanmar Intelligent Character Recognition) and backpropagation neural network. MICR is one kind of ICR. It composed of statistical/semantic information and final decision is made by voting system. In Hybrid approach, the features of statistical and semantic information of MICR have been used in back-propagation neural network as input nodes. So it needs a few input nodes to use. The back-propagation algorithm has been used to train the feed-forward neural network and adjustment of weights to require the desired output. The purpose of Hybrid approach to achieve the high accuracy rates and very fast recognition rate compare with other recognition systems. The experiments were carried out on 1000 words samples of different writer. Using Hybrid approach, over-all recognition accuracy of 95% was obtained.


title: Linked education: interlinking educational resources and the Web of data

abstract: Research on interoperability of technology-enhanced learning (TEL) repositories throughout the last decade has led to a fragmented landscape of competing approaches, such as metadata schemas and interface mechanisms. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web and offers a large potential to solve interoperability issues in the field of TEL. In this paper, we describe a general approach to exploit the wealth of already existing TEL data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. This approach has been implemented in the context of the mEducator project where data from a number of open TEL data repositories has been integrated, exposed and enriched by following Linked Data principles.


Recognition and pose estimation of rigid transparent objects with a kinect sensor


title: A technique for disentangling temporal source and sediment variations from radioactive isotope measurements with depth

abstract: A quantitative procedure is developed which permits the singular separation of effects due to source variations in time from those due to variations in sedimentation rate in observed profiles of any radioactive isotope concentration with subsurface depth. Five excess activity 210 Pb profiles collected from Amazonian deltaic sediments are used to illustrated the general applicability and utility of the technique to diverse variations of profile patterns that are otherwise difficult to comprehend


title: Evaluation and management of insomnia in menopause

abstract: Insomnia is a problem with complex and multifactorial etiologies that requires both standardized and individualized treatment interventions. Specific targets of treatment may include hyperarousal, poor sleep habits, underlying mood disorders, sedative overuse, pain and general medical problems, circadian dysrhythmias, sleep apnea, and restless legs syndrome. Optimal treatment also will incorporate stress management, coping strategies, enhancement of relationships, and promoting lifestyle changes that facilitate sleep.


title: Cognitive architectures: Research issues and challenges

abstract: In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area.


title: Semi-Supervised and Latent-Variable Models of Natural Language Semantics

abstract: This thesis focuses on robust analysis of natural language semantics. A primary bottleneck for semantic processing of text lies in the scarcity of high-quality and large amounts of annotated data that provide complete information about the semantic structure of natural language expressions. In this dissertation, we study statistical models tailored to solve problems in computational semantics, with a focus on modeling structure that is not visible in annotated text data. We first investigate supervised methods for modeling two kinds of semantic phenomena in language. First, we focus on the problem of paraphrase identification, which attempts to recognize whether two sentences convey the same meaning. Second, we concentrate on shallow semantic parsing, adopting the theory of frame semantics (Fillmore, 1982). Frame semantics offers deep linguistic analysis that exploits the use of lexical semantic properties and relationships among semantic frames and roles. Unfortunately, the datasets used to train our paraphrase and frame-semantic parsing models are too small to lead to robust performance. Therefore, a common trait in our methods is the hypothesis of hidden structure in the data. To this end, we employ conditional loglinear models over structures, that are firstly capable of incorporating a wide variety of features gathered from the data as well as various lexica, and secondly use latent variables to model missing information in annotated data. Our approaches towards solving these two problems achieve state-of-the-art accuracy on standard corpora. For the frame-semantic parsing problem, we present fast inference techniques for jointly modeling the semantic roles of a given predicate. We experiment with linear program formulations, and use a commercial solver as well as an exact dual decomposition technique that breaks the role labeling problem into several overlapping components. Continuing with the theme of hypothesizing hidden structure in data for modeling natural language semantics, we present methods to leverage large volumes of unlabeled data to improve upon the shallow semantic parsing task. We work within the framework of graph-based semi-supervised learning, a powerful method that associates similar natural language types, and helps propagate supervised annotations to unlabeled data. We use this framework to improve frame-semantic parsing performance on unknown predicates that are absent in annotated data. We also present a family of novel


title: Newsworthy Rumor Events: A Case Study of Twitter

abstract: Rumor events differ in how and where they originate, what topics they address, the emotions they invoke, and how they engage their audience. In this paper, we study various semantic aspects of rumors and analyze the motivational and functional roles they play. Using Twitter as a case study, we develop a framework to characterize rumors. Our characterization covers intrinsic and extrinsic factors, tweet and event-level, as well as usage analysis. We determine the roles various user-types play and analyze rumor propagation from both a re-tweeting and burstiness perspective.


title: Joint state and measurement time-delay estimation of nonlinear state space systems

abstract: Sensor fusion algorithms often assume perfect time synchronization of the sensor clocks. In a practical sensoractuator setup this is often difficult to achieve which in turn can give rise to systematic errors in the sensor fusion. In this article we suggest how the effect of the synchronization error from an unknown static or slowly varying measurement time-delays in a nonlinear state space system can be handled by linearizing the measurement equation in time. Based on the linearization an augmented system is constructed from which the system states and the delays can be jointly estimated. Expressions for the system, measurement, and covariance matrices of the augmented system are derived. Finally, the feasibility of the suggested approach is demonstrated by an example and a Monte-Carlo simulation.


title: FinDroidHR: Smartwatch Gesture Input with Optical Heartrate Monitor

abstract: We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.


title: Aversive events and aggression

abstract: There is perhaps no finding in psychology that is more consistent than the human motivation to avoid negative experiences and seek out positive ones. The current review details some of the aggression-related consequences that result from failures to avoid these negative experiences. Attention is paid to the theoretical processes at work that produce such effects. A review is conducted of the empirical literature detailing animal and human studies, in the lab and field. Lastly, we briefly discuss future directions in research that may advance our understanding of such effects.


title: A comparison of hole-filling methods in 3D

abstract: Abstract This paper presents a review of the most relevant current techniques that deal with hole-filling in 3D models. Contrary to earlier reports, which approach mesh repairing in a sparse and global manner, the objective of this review is twofold. First, a specific and comprehensive review of hole-filling techniques (as a relevant part in the field of mesh repairing) is carried out. We present a brief summary of each technique with attention paid to its algorithmic essence, main contributions and limitations. Second, a solid comparison between 34 methods is established. To do this, we define 19 possible meaningful features and properties that can be found in a generic hole-filling process. Then, we use these features to assess the virtues and deficiencies of the method and to build comparative tables. The purpose of this review is to make a comparative hole-filling state-of-the-art available to researchers, showing pros and cons in a common framework.


title: Group emotion recognition with individual facial emotion CNNs and global image based CNNs

abstract: This paper presents our approach for group-level emotion recognition in the Emotion Recognition in the Wild Challenge 2017. The task is to classify an image into one of the group emotion such as positive, neutral or negative. Our approach is based on two types of Convolutional Neural Networks (CNNs), namely individual facial emotion CNNs and global image based CNNs. For the individual facial emotion CNNs, we first extract all the faces in an image, and assign the image label to all faces for training. In particular, we utilize a large-margin softmax loss for discriminative learning and we train two CNNs on both aligned and non-aligned faces. For the global image based CNNs, we compare several recent state-of-the-art network structures and data augmentation strategies to boost performance. For a test image, we average the scores from all faces and the image to predict the final group emotion category. We win the challenge with accuracies 83.9% and 80.9% on the validation set and testing set respectively, which improve the baseline results by about 30%.


title: Learning Dynamic Robot-to-Human Object Handover from Human Feedback

abstract: Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.


Design of Low Cost Stair Climbing Robot Using Arduino


title: Cyber Risk for the Financial Sector: A Framework for Quantitative Assessment

abstract: Cyber risk has emerged as a key threat to financial stability, following recent attacks on financial institutions. This paper presents a novel documentation of cyber risk around the world for financial institutions by analyzing the different types of cyber incidents (data breaches, fraud and business disruption) and identifying patterns using a variety of datasets. The other novel contribution that is outlined is a quantitative framework to assess cyber risk for the financial sector. The framework draws on a standard VaR type framework used to assess various types of stability risk and can be easily applied at the individual country level. The framework is applied in this paper to the available cross-country data and yields illustrative aggregated losses for the financial sector in the sample across a variety of scenarios ranging from 10 to 30 percent of net income.


title: 2084 -- Safe New World: Designing Ubiquitous Interactions

abstract: This paper investigates a concept for highly ubiquitous game interactions in pervasive games. Pervasive gaming is increasingly popular, but steadily improving mobile and ubiquitous technologies (e.g. smartwatches) have yet to be utilised to their full potential in this area. For this purpose, we implemented 2084 Safe New World; a pervasive game that allows particularly ubiquitous gameplay through micro interactions of varying duration. In a lab study, different interaction techniques based on gestures and touch input were compared on two mobile devices regarding usability and game input observability. A second study evaluated the player experience under more realistic circumstances; in particular, it examined how well the game can be integrated into everyday life, and tested boundaries of social acceptance of ubiquitous interactions in a pervasive spy game.


A high speed 8 transistor full adder design using novel 3 transistor


title: Modeling and Understanding Human Routine Behavior

abstract: Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful-that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.


title: Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels

abstract: This paper presents cross-lingual models for dependency parsing using the first release of the universal dependencies data set. We systematically compare annotation projection with monolingual baseline models and study the effect of predicted PoS labels in evaluation. Our results reveal the strong impact of tagging accuracy especially with models trained on noisy projected data sets. This paper quantifies the differences that can be observed when replacing gold standard labels and our results should influence application developers that rely on crosslingual models that are not tested in realis-


MalwareTextDB: A Database for Annotated Malware Articles