title: Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation

abstract: Recently, a variety of representation learning approaches have been developed in the literature to induce latent generalizable features across two domains. In this paper, we extend the standard hidden Markov models (HMMs) to learn distributed state representations to improve cross-domain prediction performance. We reformulate the HMMs by mapping each discrete hidden state to a distributed representation vector and employ an expectationmaximization algorithm to jointly learn distributed state representations and model parameters. We empirically investigate the proposed model on cross-domain part-ofspeech tagging and noun-phrase chunking tasks. The experimental results demonstrate the effectiveness of the distributed HMMs on facilitating domain adaptation.


title: A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion

abstract: We propose a novel statistical analysis method for functional magnetic resonance imaging (fMRI) to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA). Although ICA has been broadly applied to fMRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studies showing that ICA does not guarantee independence of simultaneously occurring distinct activity patterns in the brain. Instead, sparsity of the signal has been shown to be more promising. This coincides with biological findings such as sparse coding in V1 simple cells, electrophysiological experiment results in the human medial temporal lobe, etc. The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals. A compressed sensing based data-driven sparse generalized linear model is proposed that enables estimation of spatially adaptive design matrix as well as sparse signal components that represent synchronous, functionally organized and integrated neural hemodynamics. Furthermore, a minimum description length (MDL)-based model order selection rule is shown to be essential in selecting unknown sparsity level for sparse dictionary learning. Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.


title: Embedded Collaborative Filtering for"Cold Start"Prediction

abstract: Using only implicit data, many recommender systems fail in general to provide a precise set of recommendations to users with limited interaction history. This issue is regarded as the "Cold Start" problem and is typically resolved by switching to content-based approaches where extra costly information is required. In this paper, we use a dimensionality reduction algorithm, Word2Vec (W2V), originally applied in Natural Language Processing problems under the framework of Collaborative Filtering (CF) to tackle the "Cold Start" problem using only implicit data. This combined method is named Embedded Collaborative Filtering (ECF). An experiment is conducted to determine the performance of ECF on two different implicit data sets. We show that the ECF approach outperforms other popular and state-of-the-art approaches in "Cold Start" scenarios.


Face Recognition Using Principal Component Analysis


title: Copper-catalyzed 1,3-dipolar cycloaddition of methyleneindolinones and N , N '-cyclic azomethine imines

abstract: A copper-catalyzed stereoselective 1,3-dipolar cycloaddition of methyleneindolinones and N,N′-cyclic azomethine imines has been developed under mild reaction conditions. The spiro[pyrazolidin-3,3′-oxindoles] were obtained in moderate to high isolated yields (up to 82%) with good stereoselevtivities (up to 15:1) in the presence of 5 mol% of Cu(OAc)2 at room temperature.


title: Spiral-STC: an on-line coverage algorithm of grid environments by a mobile robot

abstract: We describe an on-line sensor based algorithm for covering planar areas by a square-shaped tool attached to a mobile robot. Let D be the tool size. The algorithm, called Spiral-STC, incrementally subdivides the planar work-area into disjoint D-size cells, while following a spanning tree of the resulting grid. The algorithm covers general grid environments using a path whose length is at most (n + m)D, where n is the number of D-size cells and m /spl les/ n is the number of boundary cells, defined as cells that share at least one point with the grid boundary. We also report that any on-line coverage algorithm generates a covering path whose length is at least (2 - /spl epsiv/)l/sub opt/ in the worst case, where l/sub opt/ is the length of the optimal covering path. Since (n + m)D /spl les/ 2l/sub opt/, Spiral-STC is worst-case optimal. Moreover, m << n in practical environments, and the algorithm generates close-to-optimal covering paths in such environments. Simulation results demonstrate the spiral-like covering patterns typical to the algorithm.


title: Formal Security Analysis of PKCS # 11 and Proprietary Extensions ∗

abstract: PKCS#11 defines an API for cryptographic devices that has been widely adopted in industry. However, it has been shown to be vulnerable to a variety of attacks that could, for example, compromise the sensitive keys stored on the device. In this paper, we set out a formal model of the operation of the API, which differs from previous security API models notably in that it accounts for non-monotonic mutable global state. We give decidability results for our formalism, and describe an implementation of the resulting decision procedure using the model checker NuSMV. We report some new attacks and prove the safety of some configurations of the API in our model. We also analyse proprietary extensions proposed by nCipher (Thales) and Eracom (Safenet), designed to address the shortcomings of PKCS#11.


title: ST-HMP: Unsupervised Spatio-Temporal feature learning for tactile data

abstract: Tactile sensing plays an important role in robot grasping and object recognition. In this work, we propose a new descriptor named Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP) that captures properties of a time series of tactile sensor measurements. It is based on the concept of unsupervised hierarchical feature learning realized using sparse coding. The ST-HMP extracts rich spatio-temporal structures from raw tactile data without the need to predefine discriminative data characteristics. We apply it to two different applications: (1) grasp stability assessment and (2) object instance recognition, presenting its universal properties. An extensive evaluation on several synthetic and real datasets collected using the Schunk Dexterous, Schunk Parallel and iCub hands shows that our approach outperforms previously published results by a large margin.


title: An adaptive peer-to-peer collision warning system

abstract: A cooperative vehicle collision warning system is made possible by the progress in advanced positioning systems and wireless communication technology. We report our work in developing a cooperative intersection collision warning system that is not limited by the requirement of line-of-sight. The novel elements of the system include the use of a dynamic ad hoc wireless network for peer-to-peer data sharing, a new intersection collision warning algorithm, and a flexible and extensible software architecture and system design. The very same system is also capable of collision warning for other scenarios such as frontal and rear-end. Further, the tunable parameters in the algorithm make it possible for the system to adapt to the preference and capability of each individual driver.


title: Disjoint Multi-task Learning Between Heterogeneous Human-Centric Tasks

abstract: Human behavior understanding is arguably one of the most important mid-level components in artificial intelligence. In order to efficiently make use of data, multi-task learning has been studied in diverse computer vision tasks including human behavior understanding. However, multi-task learning relies on task specific datasets and constructing such datasets can be cumbersome. It requires huge amounts of data, labeling efforts, statistical consideration etc. In this paper, we leverage existing single-task datasets for human action classification and captioning data for efficient human behavior learning. Since the data in each dataset has respective heterogeneous annotations, traditional multi-task learning is not effective in this scenario. To this end, we propose a novel alternating directional optimization method to efficiently learn from the heterogeneous data. We demonstrate the effectiveness of our model and show performance improvements on both classification and sentence retrieval tasks in comparison to the models trained on each of the single-task datasets.


title: Coalitions Among Computationally Bounded Agents

abstract: Abstract This paper analyzes coalitions among self-interested agents that need to solve combinatorial optimization problems to operate efficiently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decision-theoretically traded off against computation cost. A normative, application- and protocol-independent theory of coalitions among bounded-rational agents is devised. The optimal coalition structure and its stability are significantly affected by the agents' algorithms' performance profiles and the cost of computation. This relationship is first analyzed theoretically. Then a domain classification including rational and bounded-rational agents is introduced. Experimental results are presented in vehicle routing with real data from five dispatch centers. This problem is NP-complete and the instances are so large that—with current technology—any agent's rationality is bounded by computational complexity.


title: New Algorithms for Bin Packing

abstract: In the bin-packing problem a list L of n numbers are to be packed into unit-capacity bins. For any algorithm S, let r(S) be the maximum ratio S(L)/$L^*$ for large $L^*$, where S(L) denotes the number of bins used by S and $L^*$ denotes the minimum number needed. In this paper we give an on-line O(n log n)-time algorithm RFF with r(RFF) = 5/3, and an off-line polynomial-time algorithm RFFD with r(RFFD) = (11/9)-$\epsilon$ for some fixed $\epsilon$ < 0. These are strictly better respectively than two prominent algorithms -- the First-Fit (FF) which is on-line with r(FF) = 17/10, and the First-Fit-Decreasing (FFD) with r(FFD) = 11/9. Furthermore, it is shown that any on-line algorithm S must have r(S) $\geq$ 3/2. We also discuss the question "how well can an O(n)-time algorithm perform?", showing that, in the generalized d-dimensional bin-packing, any O(n)-time algorithm S must have r(S) $\geq$ d.


title: GA3C: GPU-based A3C for Deep Reinforcement Learning

abstract: We introduce and analyze the computational aspects of a hybrid CPU/GPU implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. Our analysis concentrates on the critical aspects to leverage the GPU's computational power, including the introduction of a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. We also show the potential for the use of larger DNN models on a GPU. Our TensorFlow implementation achieves a significant speed up compared to our CPU-only implementation, and it will be made publicly available to other researchers.


title: Guided Optical Flow Learning

abstract: We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction. They however require the ground truth flow which is usually not accessible except on limited synthetic data. Without the guidance of ground truth optical flow, unsupervised CNNs often perform worse as they are naturally ill-conditioned. We therefore propose a novel framework in which proxy ground truth data generated from classical approaches is used to guide the CNN learning. The models are further refined in an unsupervised fashion using an image reconstruction loss. Our guided learning approach is competitive with or superior to state-of-the-art approaches on three standard benchmark datasets yet is completely unsupervised and can run in real time.


Constructing Meaning in Movement Through its Relationship to Music : A Choreomusical Response to the Sarabande


title: A personalized recommender system based on web usage mining and decision tree induction

abstract: Abstract A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.


title: STDP Learning of Image Patches with Convolutional Spiking Neural Networks

abstract: Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of \textit{convolutional spiking neural networks} is introduced, trained to detect image features with an unsupervised, competitive learning mechanism. Image features can be shared within subpopulations of neurons, or each may evolve independently to capture different features in different regions of input space. We analyze the time and memory requirements of learning with and operating such networks. The MNIST dataset is used as an experimental testbed, and comparisons are made between the performance and convergence speed of a baseline spiking neural network.


title: Inductive Learning of Answer Set Programs from Noisy Examples

abstract: In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.


Movie Classification Using k-Means and Hierarchical Clustering An analysis of clustering algorithms on movie scripts


title: Simple Regret Optimization in Online Planning for Markov Decision Processes

abstract: We consider online planning in Markov decision processes (MDPs). In online planning, the agent focuses on its current state only, deliberates about the set of possible policies from that state onwards and, when interrupted, uses the outcome of that exploratory deliberation to choose what action to perform next. The performance of algorithms for online planning is assessed in terms of simple regret, which is the agent's expected performance loss when the chosen action, rather than an optimal one, is followed. To date, state-of-the-art algorithms for online planning in general MDPs are either best effort, or guarantee only polynomial-rate reduction of simple regret over time. Here we introduce a new Monte-Carlo tree search algorithm, BRUE, that guarantees exponential-rate reduction of simple regret and error probability. This algorithm is based on a simple yet non-standard state-space sampling scheme, MCTS2e, in which different parts of each sample are dedicated to different exploratory objectives. Our empirical evaluation shows that BRUE not only provides superior performance guarantees, but is also very effective in practice and favorably compares to state-of-the-art. We then extend BRUE with a variant of"learning by forgetting."The resulting set of algorithms, BRUE(alpha), generalizes BRUE, improves the exponential factor in the upper bound on its reduction rate, and exhibits even more attractive empirical performance.


title: Characterization of double-layer capacitors (DLCs) for power electronics applications

abstract: The double-layer capacitor (DLC) for power applications is a new device. A simple resistive capacitive equivalent circuit is insufficient to characterize its terminal behavior. Based on physical reasoning, an equivalent circuit is proposed to provide the power electronics engineer with a model for the terminal behavior of the DLC. The equivalent circuit model consists of three RC branches, one of them with a voltage-dependent capacitance. A method to identify the circuit parameters is presented. Measurements of carbon-based DLC's for power applications are presented, analyzed, and the equivalent circuit response is compared with experimental results.


title: Knowledge Extraction from Trained Neural Networks

abstract: Artificial neural networks (ANN) are very efficient in solving various kinds of problems But Lack of explanation capability (Black box nature of Neural Networks) is one of the most important reasons why artificial neural networks do not get necessary interest in some parts of industry. In this work artificial neural networks first trained and then combined with decision trees in order to fetch knowledge learnt in the training process. After successful training, knowledge is extracted from these trained neural networks using decision trees in the forms of IF THEN Rules which we can easily understand as compare to direct neural network outputs. We use decision trees to train on the results set of trained neural network and compare the performance of neural networks and decision trees in knowledge extraction from neural networks. Weka machine learning simulator with version 3.7.5 is used for research purpose. The experimental study is done on bank customers’ data which have 12 attributes and 600 instances. The results study show that although neural networks takes much time in training and testing but are more accurate in classification then decision trees.


title: Automatic Seismic Facies Classification with Kohonen Self Organizing Maps - a Tutorial

abstract: The most popular seismic attributes fall into broad categories - those that are sensitive to lateral changes in waveform and structure, such as coherence and curvature, and those that are sensitive to lithology and fluid properties - such as AVO and impedance inversion. Unfortunately neither of these two attribute families works well in differentiating depositional packages characterized by subtle changes in stratigraphic column or the lateral changes in texture. Automatic seismic facies analysis aims to classify similar seismic traces based on amplitude, phase, frequency and other seismic attributes. This research shows how this technique helps to visualize the variation in rock properties and the possible relationship between these rock types and their seismic expression. There are many different supervised and unsupervised clustering algorithms. In supervised training, the clusters are predefined and patterns are assigned to it in subsequent trainings. Unsupervised clustering is data driven without any a priori information. The self-organizing map (SOM) (Kohonen, 2001) is one of the most effective unsupervised pattern recognition techniques, and is generally used for the automatic identification of seismic facies. We describe a SOM implementation that begins by over-defining the number of initial clusters from the input dataset. These "prototype vectors" are organized using Kohonen SOM neighborhood training rule. These clusters are projected onto a 2D or 3D latent space and are displayed against the continuous 2D or 3D colorbar. Then these clusters are compared back to the input seismic traces and colors are assigned to these seismic traces. The colored output map of seismic facies is then defined by the interpreter's eye and brain, in conjunction with well and other geophysical attributes.


Controlling the Sensitivity of Support Vector Machines


title: Tribological behavior of CrN coating on aluminum alloys deposited by arc ion plating

abstract: Dry sliding wear and friction tests of CrN coating on two types of aluminum alloy substrates, 6061 Al and 7075 Al, deposited by arc ion plating were performed with a ball-on-disk tribometer. The effects of normal load andthe mechanical properties of the substrate on the friction coefficient and wear resistance of CrN coating were investigated. The worn surfaces were observed by scanning electron microscopy. The results show that surface microhardness of CrN-coated 7075 Al is higher than that of CrN-coated 6061 Al. With an increase in normal load, wear volume increases, while the friction coefficient decreases. The friction coefficient of CrN-coated 6061 Al is higher than that of CrN-coated 7075 Al, while the wear resistance of CrN-coated 6061 Al is lower than that of the CrN-coated 7075 Al. This indicates that the substrate mechanical properties have strong influence on the friction coefficient and wear of CrN coating. The main wear mechanism was fragments of CrN coating, caused by apparent plastic deformation of substrate during wear tests.


title: Functional System and Areal Organization of a Highly Sampled Individual Human Brain

abstract: Summary Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected—considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. Video Abstract


title: The Inventory of Personality Organization: Psychometric properties, factorial composition, and criterion relations with affect, aggressive dyscontrol, psychosis proneness, and self-domains in a nonclinical sample.

abstract: This report describes 2 studies of the psychometric characteristics of the primary clinical scales of the Inventory of Personality Organization (IPO; O. F. Kernberg & J. F. Clarkin, 1995), which assess reality testing, primitive psychological defenses, and identity diffusion, in a nonclinical sample. The 3 IPO scales display adequate internal consistency and good test-retest reliability. Item-level confirmatory factor analysis supported a two-factor structure of the IPO consistent with O. F. Kernberg's (1984, 1996) model of borderline personality organization. Each of the 3 IPO scales was associated with increased negative affect, aggressive dyscontrol, and dysphoria as well as lower levels of positive affect consistent with Kernberg's model of borderline personality organization. The IPO Reality Testing scale is closely related to various measures of psychotic-like phenomena.


title: Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

abstract: We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals. Solid harmonic wavelets are computed by multiplying solid harmonic functions with Gaussian windows dilated at different scales. Invariant scattering coefficients are obtained by cascading such wavelet transforms with the complex modulus nonlinearity. We study an application of solid harmonic scattering invariants to the estimation of quantum molecular energies, which are also invariant to rigid motion and stable with respect to deformations. A multilinear regression over scattering invariants provides close to state of the art results over small and large databases of organic molecules.


title: Rule-based Expert Systems for Selecting Information Systems Development Methodologies

abstract: Information Systems (IS) are increasingly becoming regarded as crucial to an organization's success. Information Systems Development Methodologies (ISDMs) are used by organizations to structure the information system development process. ISDMs are essential for structuring project participants' thinking and actions; therefore ISDMs play an important role to achieve successful projects. There are different ISDMs and no methodology can claim that it can be applied to any organization. The problem facing decision makers is how to select an appropriate development methodology that may increase the probability of system success. This paper takes this issue into account when study ISDMs and provides a Rule-based Expert System as a tool for selecting appropriate ISDMs. The proposed expert system consists of three main phases to automate the process of selecting ISDMs. Three approaches were used to test the proposed expert system. Face validation through six professors and six IS professionals, predictive validation through twenty four experts and blind validation through nine employees working in IT field. The results show that the proposed system was found to be run without any errors, offered a friendly user interface and its suggestions matching user expectations with 95.8%. It also can help project managers, systems' engineers, systems' developers, consultants, and planners in the process of selecting the suitable ISDM. Finally, the results show that the proposed Rule-based Expert System can facilities the selection process especially for new users and non- specialist in Information System field.


Comment on Tükel et al., “The clinical impact of mood disorder comorbidity on obsessive-compulsive disorder” (Eur Arch Psychiatry Clin Neurosci, 256(4):240–245)


title: Example-based video color grading

abstract: In most professional cinema productions, the color palette of the movie is painstakingly adjusted by a team of skilled colorists -- through a process referred to as color grading -- to achieve a certain visual look. The time and expertise required to grade a video makes it difficult for amateurs to manipulate the colors of their own video clips. In this work, we present a method that allows a user to transfer the color palette of a model video clip to their own video sequence. We estimate a per-frame color transform that maps the color distributions in the input video sequence to that of the model video clip. Applying this transformation naively leads to artifacts such as bleeding and flickering. Instead, we propose a novel differential-geometry-based scheme that interpolates these transformations in a manner that minimizes their curvature, similarly to curvature flows. In addition, we automatically determine a set of keyframes that best represent this interpolated transformation curve, and can be used subsequently, to manually refine the color grade. We show how our method can successfully transfer color palettes between videos for a range of visual styles and a number of input video clips.


title: Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain

abstract: As the core issue of blockchain, the mining requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to high computing power needed. Thus, the development of blockchain in mobile applications is restricted. In this paper, we consider the edge computing as the network enabler for mobile blockchain. In particular, we study optimal pricing-based edge computing resource management to support mobile blockchain applications where the mining process can be offloaded to an Edge computing Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly maximize the profit of the ESP and the individual utilities of different miners. In Stage I, the ESP sets the price of edge computing services. In Stage II, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for uniform and discriminatory pricing schemes. Further, the existence and uniqueness of Stackelberg game are validated for both pricing schemes. At last, the performance evaluation shows that the ESP intends to set the maximum possible value as the optimal price for profit maximization under uniform pricing. In addition, the discriminatory pricing helps the ESP encourage higher total service demand from miners and achieve greater profit correspondingly.


title: Quality of life in adults with congenital adrenal hyperplasia relates to glucocorticoid treatment, adiposity and insulin resistance: United Kingdom Congenital adrenal Hyperplasia Adult Study Executive (CaHASE)

abstract: CONTEXT ::: Quality of life (QoL) has been variously reported as normal or impaired in adults with congenital adrenal hyperplasia (CAH). To explore the reasons for this discrepancy we investigated the relationship between QoL, glucocorticoid treatment and other health outcomes in CAH adults. ::: ::: ::: METHODS ::: Cross-sectional analysis of 151 adults with 21-hydroxylase deficiency aged 18-69 years in whom QoL (assessed using the Short Form Health Survey), glucocorticoid regimen, anthropometric and metabolic measures were recorded. Relationships were examined between QoL, type of glucocorticoid (hydrocortisone, prednisolone and dexamethasone) and dose of glucocorticoid expressed as prednisolone dose equivalent (PreDEq). QoL was expressed as z-scores calculated from matched controls (14,430 subjects from UK population). Principal components analysis (PCA) was undertaken to identify clusters of associated clinical and biochemical features and the principal component (PC) scores used in regression analysis as predictor of QoL. ::: ::: ::: RESULTS ::: QoL scores were associated with type of glucocorticoid treatment for vitality (P=0.002) and mental health (P=0.011), with higher z-scores indicating better QoL in patients on hydrocortisone monotherapy (P<0.05). QoL did not relate to PreDEq or mutation severity. PCA identified three PCs (PC1, disease control; PC2, adiposity and insulin resistance and PC3, blood pressure and mutations) that explained 61% of the variance in observed variables. Stepwise multiple regression analysis demonstrated that PC2, reflecting adiposity and insulin resistance (waist circumference, serum triglycerides, homeostasis model assessment of insulin resistance and HDL-cholesterol), related to QoL scores, specifically impaired physical functioning, bodily pain, general health, Physical Component Summary Score (P<0.001) and vitality (P=0.002). ::: ::: ::: CONCLUSIONS ::: Increased adiposity, insulin resistance and use of prednisolone or dexamethasone are associated with impaired QoL in adults with CAH. Intervention trials are required to establish whether choice of glucocorticoid treatment and/or weight loss can improve QoL in CAH adults.


title: MCDB: a monte carlo approach to managing uncertain data

abstract: To deal with data uncertainty, existing probabilistic database systems augment tuples with attribute-level or tuple-level probability values, which are loaded into the database along with the data itself. This approach can severely limit the system's ability to gracefully handle complex or unforeseen types of uncertainty, and does not permit the uncertainty model to be dynamically parameterized according to the current state of the database. We introduce MCDB, a system for managing uncertain data that is based on a Monte Carlo approach. MCDB represents uncertainty via "VG functions," which are used to pseudorandomly generate realized values for uncertain attributes. VG functions can be parameterized on the results of SQL queries over "parameter tables" that are stored in the database, facilitating what-if analyses. By storing parameters, and not probabilities, and by estimating, rather than exactly computing, the probability distribution over possible query answers, MCDB avoids many of the limitations of prior systems. For example, MCDB can easily handle arbitrary joint probability distributions over discrete or continuous attributes, arbitrarily complex SQL queries, and arbitrary functionals of the query-result distribution such as means, variances, and quantiles. To achieve good performance, MCDB uses novel query processing techniques, executing a query plan exactly once, but over "tuple bundles" instead of ordinary tuples. Experiments indicate that our enhanced functionality can be obtained with acceptable overheads relative to traditional systems.


title: HRM practices and MNC knowledge transfer

abstract: Purpose – The purpose of the paper is to determine and empirically examine the effect of human resource management (HRM) practices on knowledge transfer within multinational corporations.Design/methodology/approach – It is suggested that the employment of human resource practices, which affect absorptive capacity of knowledge receivers and support organizational learning environment, is positively related to the degree of knowledge transfer to the subsidiary. Moreover, the higher degree of knowledge transfer is expected when HRM practices are applied as an integrated system of interdependent practices. Hypotheses derived from these arguments are tested on the data from 92 subsidiaries of Danish multinational corporations (MNCs) located in 11 countries.Findings – Results of the analysis indicated the existence of two groups of HRM practices conducive to knowledge transfer. The simultaneous effect of the first group of HRM practices consisting of “staffing”, “training”, “promotion”, “compensation” and “appr...


title: A Survey on Various Approaches for Taxonomy Construction

abstract: Objectives : To analysis different approaches for taxonomy construction to improve the knowledge classification, information retrieval and other data mining process. Findings : Taxonomies learning keep getting more important process for knowledge sharing about a domain. It is also used for application development such as knowledge searching, information retrieval. The taxonomy can be build manually but it is a complex process when the data are so large and it also produce some errors while taxonomy construction. There is various automatic taxonomy construction techniques are used to learn taxonomy based on keyword phrases, text corpus and from domain specific concepts etc. So it is required to build taxonomy with less human effort and with less error rate. This paper provides detailed information about those techniques. Methods : The methods such as lexico-syntatic pattern, semi supervised methods, graph based methods, ontoplus, TaxoLearn, Bayesian approach, two-step method, ontolearn and Automatic Taxonomy Construction from Text are analyzed in this paper. Application/Improvements : The findings of this work prove that the TaxoFinder approach provides better result than other approaches.


title: Omni-directional steer-by-wire interface for four wheel independent steering vehicle

abstract: In this paper, an omni-directional steer-by-wire interface for four wheel independent steering vehicle is presented. The proposed steering interface is an extension of a traditional steering interface that provides three steering inputs. By combination of which, driver can control the vehicle in traditional way or omni-directionally without any mode switching operation. The reservation of the conventional steering behavior makes driver easy to adapt the novel steering interface. The force feedback controller is designed to synchronize the extended steering interface and the orientations of wheels so as to improve vehicle handling. Hardware-in-the-loop simulations are conducted to verify the hardware prototype and examine the proposed algorithms.


title: A Hybrid Scheduling Algorithm for Data Intensive Workloads in a MapReduce Environment

abstract: The specific choice of workload task schedulers for Hadoop MapReduce applications can have a dramatic effect on job workload latency. The Hadoop Fair Scheduler (FairS) assigns resources to jobs such that all jobs get, on average, an equal share of resources over time. Thus, it addresses the problem with a FIFO scheduler when short jobs have to wait for long running jobs to complete. We show that even for the FairS, jobs are still forced to wait significantly when the MapReduce system assigns equal sharing of resources due to dependencies between Map, Shuffle, Sort, Reduce phases. We propose a Hybrid Scheduler (HybS) algorithm based on dynamic priority in order to reduce the latency for variable length concurrent jobs, while maintaining data locality. The dynamic priorities can accommodate multiple task lengths, job sizes, and job waiting times by applying a greedy fractional knapsack algorithm for job task processor assignment. The estimated runtime of Map and Reduce tasks are provided to the HybS dynamic priorities from the historical Hadoop log files. In addition to dynamic priority, we implement a reordering of task processor assignment to account for data availability to automatically maintain the benefits of data locality in this environment. We evaluate our approach by running concurrent workloads consisting of the Word-count and Terasort benchmarks, and a satellite scientific data processing workload and developing a simulator. Our evaluation shows the HybS system improves the average response time for the workloads approximately 2.1x faster over the Hadoop FairS with a standard deviation of 1.4x.


title: Blood glucose level prediction based on support vector regression using mobile platforms

abstract: The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body's response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.


title: Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars

abstract: As an initial assessment, over 480,000 labeled virtual images of normal highway driving were readily generated in Grand Theft Auto V's virtual environment. Using these images, a CNN was trained to detect following distance to cars/objects ahead, lane markings, and driving angle (angular heading relative to lane centerline): all variables necessary for basic autonomous driving. Encouraging results were obtained when tested on over 50,000 labeled virtual images from substantially different GTA-V driving environments. This initial assessment begins to define both the range and scope of the labeled images needed for training as well as the range and scope of labeled images needed for testing the definition of boundaries and limitations of trained networks. It is the efficacy and flexibility of a "GTA-V"-like virtual environment that is expected to provide an efficient well-defined foundation for the training and testing of Convolutional Neural Networks for safe driving. Additionally, described is the Princeton Virtual Environment (PVE) for the training, testing and enhancement of safe driving AI, which is being developed using the video-game engine Unity. PVE is being developed to recreate rare but critical corner cases that can be used in re-training and enhancing machine learning models and understanding the limitations of current self driving models. The Florida Tesla crash is being used as an initial reference.


title: The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics

abstract: The currently dominant neuro-cognitive model of Attention Deficit Hyperactivity Disorder (AD/HD) presents the condition as executive dysfunction (EDF) underpinned by disturbances in the fronto-dorsal striatal circuit and associated dopaminergic branches (e.g. meso-cortical). In contrast, motivationally-based accounts focus on altered reward processes and implicate fronto-ventral striatal reward circuits and those meso-limbic branches that terminate in the ventral striatum especially the nucleus accumbens. One such account, delay aversion (DEL), presents AD/HD as a motivational style—characterised by attempts to escape or avoid delay—arising from fundamental disturbances in these reward centres. While traditionally regarded as competing, EDF and DEL models have recently been presented as complimentary accounts of two psycho-patho-physiological subtypes of AD/HD with different developmental pathways, underpinned by different cortico-striatal circuits and modulated by different branches of the dopamine system. In the current paper we describe the development of this model in more detail. We elaborate on the neuro-circuitry possibly underpinning these two pathways and explore their developmental significance within a neuro-ecological framework.


title: Casper the Friendly Finality Gadget

abstract: We introduce Casper, a proof of stake-based finality system which overlays an existing proof of work blockchain. Casper is a partial consensus mechanism combining proof of stake algorithm research and Byzantine fault tolerant consensus theory. We introduce our system, prove some desirable features, and show defenses against long range revisions and catastrophic crashes. The Casper overlay provides almost any proof of work chain with additional protections against block reversions.


title: Photo-real talking head with deep bidirectional LSTM

abstract: Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that is designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM (BLSTM) for audio/visual modeling in our photo-real talking head system. An audio/visual database of a subject's talking is firstly recorded as our training data. The audio/visual stereo data are converted into two parallel temporal sequences, i.e., contextual label sequences obtained by forced aligning audio against text, and visual feature sequences by applying active-appearance-model (AAM) on the lower face region among all the training image samples. The deep BLSTM is then trained to learn the regression model by minimizing the sum of square error (SSE) of predicting visual sequence from label sequence. After testing different network topologies, we interestingly found the best network is two BLSTM layers sitting on top of one feed-forward layer on our datasets. Compared with our previous HMM-based system, the newly proposed deep BLSTM-based one is better on both objective measurement and subjective A/B test.


Physical Human Activity Recognition Using Wearable Sensors


title: Sketching distributed sliding-window data streams

abstract: While traditional data management systems focus on evaluating single, ad hoc queries over static data sets in a centralized setting, several emerging applications require (possibly, continuous) answers to queries on dynamic data that is widely distributed and constantly updated. Furthermore, such query answers often need to discount data that is "stale" and operate solely on a sliding window of recent data arrivals (e.g., data updates occurring over the last 24 h). Such distributed data streaming applications mandate novel algorithmic solutions that are both time and space efficient (to manage high-speed data streams) and also communication efficient (to deal with physical data distribution). In this paper, we consider the problem of complex query answering over distributed, high-dimensional data streams in the sliding-window model. We introduce a novel sketching technique (termed ECM-sketch) that allows effective summarization of streaming data over both time-based and count-based sliding windows with probabilistic accuracy guarantees. Our sketch structure enables point, as well as inner product, queries and can be employed to address a broad range of problems, such as maintaining frequency statistics, finding heavy hitters, and computing quantiles in the sliding-window model. Focusing on distributed environments, we demonstrate how ECM-sketches of individual, local streams can be composed to generate a (low-error) ECM-sketch summary of the order-preserving merging of all streams; furthermore, we show how ECM-sketches can be exploited for continuous monitoring of sliding-window queries over distributed streams. Our extensive experimental study with two real-life data sets validates our theoretical claims and verifies the effectiveness of our techniques. To the best of our knowledge, ours is the first work to address efficient, guaranteed-error complex query answering over distributed data streams in the sliding-window model.


title: Acoustic Echo Cancellation for Surround Sound using Perceptually Motivated Convergence Enhancement

abstract: Acoustic echo cancellation (AEC) has become an essential and well-known enabling technology for hands-free communication and human-machine interfaces. AEC for two or more reproduction channels aims at identifying the echo paths between the microphone and each audio reproduction source in order to cancel the associated echo contribution. A number of preprocessing methods have been proposed to decorrelate stereo audio signals in order to enable an unambiguous identification of each echo path and to thus ensure robustness to changing sound source locations. While several of these methods provide enough decorrelation to achieve proper AEC convergence in the stereo case, considerations of subjective sound quality have frequently not been addressed adequately. This paper compares the performance of several methods in terms of both convergence speed and aspects of sound perception, and proposes a novel signal decorrelation approach with attractive properties. The superior performance of the proposed method is demonstrated for 5.1 surround sound reproduction.


title: Macrolide, lincosamide, and streptogramin B resistance in lipophilic Corynebacteria inhabiting healthy human skin.

abstract: Corynebacteria exist as part of human skin microbiota. However, under some circumstances, they can cause opportunistic infections. The subject of the study was to examine the macrolide-lincosamide-streptogramin B (MLSB) antibiotic resistance in 99 lipophilic strains of Corynebacterium genus isolated from the skin of healthy men. Over 70% of the tested strains were resistant to erythromycin and clindamycin. All of which demonstrated a constitutive type of MLSB resistance mechanism. In all strains, there were being investigated the erm(A), erm(B), erm(C), erm(X), lin(A), msr(A), and mph(C) genes that could be responsible for the different types of resistance to marcolides, lincosamides, and streptogramin B. In all strains with the MLSB resistance phenotype, the erm(X) gene was detected. None of the other tested genes were discovered. Strains harboring the erm(X) gene were identified using a phenotypic method based on numerous biological and biochemical tests. Identification of the chosen strains was compared with the results of API Coryne, MALDI-TOF MS, and 16S rDNA sequencing methods. Only 7 out of the 23 investigated resistant strains provided successful results in all the used methods, showing that identification of this group of bacteria is still a great challenge. The MLSB resistance mechanism was common in most frequently isolated from healthy human skin Corynebacterium tuberculostearicum and Corynebacterium jeikeium strains. This represents a threat as these species are also commonly described as etiological factors of opportunistic infections.


title: Scene Graph Generation via Conditional Random Fields

abstract: Despite the great success object detection and segmentation models have achieved in recognizing individual objects in images, performance on cognitive tasks such as image caption, semantic image retrieval, and visual QA is far from satisfactory. To achieve better performance on these cognitive tasks, merely recognizing individual object instances is insufficient. Instead, the interactions between object instances need to be captured in order to facilitate reasoning and understanding of the visual scenes in an image. Scene graph, a graph representation of images that captures object instances and their relationships, offers a comprehensive understanding of an image. However, existing techniques on scene graph generation fail to distinguish subjects and objects in the visual scenes of images and thus do not perform well with real-world datasets where exist ambiguous object instances. In this work, we propose a novel scene graph generation model for predicting object instances and its corresponding relationships in an image. Our model, SG-CRF, learns the sequential order of subject and object in a relationship triplet, and the semantic compatibility of object instance nodes and relationship nodes in a scene graph efficiently. Experiments empirically show that SG-CRF outperforms the state-of-the-art methods, on three different datasets, i.e., CLEVR, VRD, and Visual Genome, raising the Recall@100 from 24.99% to 49.95%, from 41.92% to 50.47%, and from 54.69% to 54.77%, respectively.


title: Learning Without Memorizing

abstract: Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model. The key problem in this task is the requirement of storing data (e.g. images) associated with existing classes, while teaching the classifier to learn new classes. However, this is impractical as it increases the memory requirement at every incremental step, which makes it impossible to implement IL algorithms on edge devices with limited memory. Hence, we propose a novel approach, called `Learning without Memorizing (LwM)', to preserve the information about existing (base) classes, without storing any of their data, while making the classifier progressively learn the new classes. In LwM, we present an information preserving penalty: Attention Distillation Loss (L_{AD}), and demonstrate that penalizing the changes in classifiers' attention maps helps to retain information of the base classes, as new classes are added. We show that adding L_{AD} to the distillation loss which is an existing information preserving loss consistently outperforms the state-of-the-art performance in the iILSVRC-small and iCIFAR-100 datasets in terms of the overall accuracy of base and incrementally learned classes.


title: A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal

abstract: Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A^2Net) with lightweight architectures to remove raindrops from single images. Instead of directly cascading convolutional layers, we design an adjacent aggregation architecture to better fuse features for rich representations generation, which can lead to high quality images reconstruction. To further simplify the learning process, we utilize a problem-specific knowledge to force the network focus on the luminance channel in the YUV color space instead of all RGB channels. By combining adjacent aggregating operation with color space transformation, the proposed A^2Net can achieve state-of-the-art performances on raindrop removal with significant parameters reduction.


title: From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

abstract: Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organizations admit compromises on a daily basis. Many misuse and anomaly based intrusion detection systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this paper uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communication.


title: NFC-Based Traceability in the Food Chain

abstract: In recent years, many events related to food and public health, such as bovine spongiform encephalopathy (BSE, commonly known as mad cow disease), dioxin, bird flu and swine flu, brought the issue of food security to the center of international attention. Many countries established strict rules to ensure the food traceability “from farm to fork” to meet the demand of consumer safety and to ensure public health. Consequently, the concepts of controlled supply chain, brand of quality and traceability of food products have had a strong evolution. Companies in the food production sector must evolve and change their organizational and management chain to satisfy increasingly stringent government rules and to respond to the requirements of the market. From this point of view, NFC (Near Field Communication) has all the requirements to become the main technology for traceability in the various food sectors. This paper proposes a solution to gather information throughout the entire food supply chain and bring it directly to the consumer. The architecture consists of a complex identification system based on NFC tags, which cumulates data during all phases of the production process. The aim of this work is to keep track of the information generated during food processing, not only for traceability purposes, but also for enhancing and optimizing production. The data generated by the traceability system are collected in a cloud database through apps on Android smartphones. The information is accessible by the consumers through a public Android application. The pork supply chain has been used as an application example of the proposed methodology.


Task-technology Fit Aware Expectation-confirmation Model towards Understanding of MOOCs Continued Usage Intention


Arx : A DBMS with Semantically Secure Encryption Rishabh


title: Extracting Tree-Structured Representations of Trained Networks

abstract: A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Our algorithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that TREPAN is able to produce decision trees that maintain a high level of fidelity to their respective networks while being comprehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large networks and problems with high-dimensional input spaces.


title: On the Reliable Detection of Concept Drift from Streaming Unlabeled Data

abstract: New classifier-independent, dynamic, unsupervised approach for detecting concept drift.Reduced number of false alarms and increased relevance of drift detection.Results comparable to supervised approaches, which require fully labeled streams.Our approach generalizes the notion of margin density, as a signal to detect drifts.Experiments on cybersecurity datasets, show efficacy for detecting adversarial drifts. Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. At the same time, it produces performance comparable to that of a fully labeled drift detector. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability.


title: Analysis of voltage droop control method for dc microgrids with Simulink: Modelling and simulation

abstract: This work presents a perfomance study of a dc microgrid when it is used a voltage droop technique to regulated the grid voltage and to control the load sharing between different sources. A small model of a dc microgrid comprising microsources and loads was implemented in the Simulink/Matlab environment. Some aspects about centralized (master-slave) and descentralized (voltage droop) control strategies as well as the procedures to design the controllers, with and without droop control, are presented and discussed. Simulation results obtained with the digital model of the dc microgrid with three microsources will be presented to validate the effectiveness of the voltage droop strategy, applied to proportional and proportional-integral controllers, to regulate the microgrid voltage.


title: Using Reinforcement Learning to Achieve Two Wheeled Self Balancing Control

abstract: The non-linear, unstable system of the two wheeled self-balancing robot has made it a popular research subject within the past decade. This paper outlines the design of a two wheeled robot with self balancing control systems using Reinforcement Learning. The BeagleBone Black platform was used to design the two wheeled robot. Along with the motor, the robot was also equipped with an accelerometer and gyroscope. Using the Q-Learning method, adjustments to the motor were made according to the dip angle and the angular velocity at that given time to return the robot to balance. The experimental results show that using this reinforcement learning method, the robot has the ability to quickly return to a balanced state under any dip angle.


title: Diagnostic assessment of the borg MOEA for many-objective product family design problems

abstract: The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the e-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.


title: Design of Tracked Vehicle with Passive Mechanism for Uneven Terrain

abstract: This paper describes design of tracked vehicle which can adapt rough terrain using passive link mechanism. The vehicle has two track modules at both sides which have four-bar link mechanism with passive spring elements. The passive spring elements of the link mechanism provide the restoring force which helps the vehicle to be changed to stable configuration when overcoming uneven terrains. The two track modules are connected with rotary joint so that it provides adaptability to laterally located terrain roughness. Simulating under various conditions, we verify our design concept and determine critical design parameter. We manufactured prototype vehicle with determined design parameters from simulation results. The vehicle has size of 295 mmtimes210 mmtimes105 mm, and weight of the vehicle is 1.31 kg. The prototype equips two 2.17 W DC motors as driving motors. We conducted experiments with manufactured prototype under various terrain conditions. The terrain conditions include steps, stairs, trench, and unstructured terrain. In experiments, this vehicle shows good overcoming ability for the tested terrain conditions


title: Preliminary idea for a converged fixed and mobile network infrastructure with 5G using Radio-over-Fiber technology and an Opto-Electronic Oscillator in the millimeter-wave range

abstract: Radio-over-Fiber (RoF) is becoming increasingly popular for the transportation of wireless signals due to it having a couple of advantages in the millimeter-wave communication architecture. This paper focuses on bringing a new approach to produce high-frequency signals in the millimeter-wave region for Radio-over-Fiber technology. It also gives information regarding an Opto-Electronics Oscillator (OEO) that has been designed for 3 GHz to produce signals with long-term stability, a high side-mode suppression ratio with a low phase noise and can be a key component for 5G converged networks.


title: Skin Microbiome: Looking Back to Move Forward

abstract: Trillions of bacteria, fungi, viruses, archaea, and small arthropods colonize the skin surface, collectively comprising the skin microbiome. Generations of researchers have classified these microbes as transient versus resident, beneficial versus pathogenic, and collaborators versus adversaries. Culturing and direct sequencing of microbial inhabitants identified distinct populations present at skin surface sites. Herein, we explore the history of this field, describe findings from the current molecular sequencing era, and consider the future of investigating how microbes and antimicrobial therapy contribute to human health.


title: TTS Synthesis with Bidirectional LSTM based Recurrent Neural Networks

abstract: Feed-forward, Deep neural networks (DNN)-based text-tospeech (TTS) systems have been recently shown to outperform decision-tree clustered context-dependent HMM TTS systems [1, 4]. However, the long time span contextual effect in a speech utterance is still not easy to accommodate, due to the intrinsic, feed-forward nature in DNN-based modeling. Also, to synthesize a smooth speech trajectory, the dynamic features are commonly used to constrain speech parameter trajectory generation in HMM-based TTS [2]. In this paper, Recurrent Neural Networks (RNNs) with Bidirectional Long Short Term Memory (BLSTM) cells are adopted to capture the correlation or co-occurrence information between any two instants in a speech utterance for parametric TTS synthesis. Experimental results show that a hybrid system of DNN and BLSTM-RNN, i.e., lower hidden layers with a feed-forward structure which is cascaded with upper hidden layers with a bidirectional RNN structure of LSTM, can outperform either the conventional, decision tree-based HMM, or a DNN TTS system, both objectively and subjectively. The speech trajectory generated by the BLSTM-RNN TTS is fairly smooth and no dynamic constraints are needed.


title: Understanding JavaScript event-based interactions

abstract: Web applications have become one of the fastest growing types of software systems today. Despite their popularity, understanding the behaviour of modern web applications is still a challenging endeavour for developers during development and maintenance tasks. The challenges mainly stem from the dynamic, event-driven, and asynchronous nature of the JavaScript language. We propose a generic technique for capturing low-level event-based interactions in a web application and mapping those to a higher-level behavioural model. This model is then transformed into an interactive visualization, representing episodes of triggered causal and temporal events, related JavaScript code executions, and their impact on the dynamic DOM state. Our approach, implemented in a tool called Clematis, allows developers to easily understand the complex dynamic behaviour of their application at three different semantic levels of granularity. The results of our industrial controlled experiment show that Clematis is capable of improving the task accuracy by 61%, while reducing the task completion time by 47%.


title: 3D Body Shapes Estimation from Dressed-Human Silhouettes

abstract: Estimation of 3D body shapes from dressed-human photos is an important but challenging problem in virtual fitting. We propose a novel automatic framework to efficiently estimate 3D body shapes under clothes. We construct a database of 3D naked and dressed body pairs, based on which we learn how to predict 3D positions of body landmarks (which further constrain a parametric human body model) automatically according to dressed-human silhouettes. Critical vertices are selected on 3D registered human bodies as landmarks to represent body shapes, so as to avoid the time-consuming vertices correspondences finding process for parametric body reconstruction. Our method can estimate 3D body shapes from dressed-human silhouettes within 4 seconds, while the fastest method reported previously need 1 minute. In addition, our estimation error is within the size tolerance for clothing industry. We dress 6042 naked bodies with 3 sets of common clothes by physically based cloth simulation technique. To the best of our knowledge, We are the first to construct such a database containing 3D naked and dressed body pairs and our database may contribute to the areas of human body shapes estimation and cloth simulation.


title: A Deep Learning Approach to Block-based Compressed Sensing of Images

abstract: Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires significantly less memory to store the sensing matrix. In this paper we present a deep learning approach for block-based CS, in which a fully-connected network performs both the block-based linear sensing and non-linear reconstruction stages. During the training phase, the sensing matrix and the non-linear reconstruction operator are \emph{jointly} optimized, and the proposed approach outperforms state-of-the-art both in terms of reconstruction quality and computation time. For example, at a 25% sensing rate the average PSNR advantage is 0.77dB and computation time is over 200-times faster.


title: Pericardial disease: value of CT and MR imaging.

abstract: UNLABELLED ::: The pericardium represents an important focus of morbidity and mortality in patients with cardiovascular disease. Fortunately, in recent years knowledge regarding this enigmatic part of the heart and the diagnosis of related diseases has substantially advanced. To a large extent, this can be attributed to the availability of several noninvasive cardiac imaging modalities. Transthoracic echocardiography, which combines structural and physiologic assessment, is the first-line technique for examination of patients suspected of having or known to have pericardial disease; however, cardiac computed tomography (CT) and magnetic resonance (MR) imaging are becoming increasingly popular for the study of this part of the heart. Modern multidetector CT scanners merge acquisition speed and high spatial and contrast resolution, with volumetric scanning to provide excellent anatomic detail of the pericardium. Multidetector CT is by far the modality of choice for depiction of pericardial calcifications. MR imaging is probably the best imaging modality for the acquisition of a comprehensive view of the pericardial abnormalities. MR imaging combines cardiac and pericardial anatomic assessment with tissue characterization and appraisal of the effects of pericardial abnormalities on cardiac performance. This review aims to elucidate the role of the pericardium and its interaction with the remainder of the heart in normal and pathologic conditions. It focuses on the rapidly evolving insights regarding pericardial disease provided by modern imaging modalities, not infrequently necessitating reconsideration of evidence that has thus far been taken for granted. ::: ::: ::: SUPPLEMENTAL MATERIAL ::: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13121059/-/DC1.


title: Noise-robust double-talk detection based on normalized cross correlation and a noise offset

abstract: This paper presents a noise-robust double-talk detection algorithm based on normalized cross-correlation and a noise offset. The noise offset alleviates undesirable influence by the background noise existing in the microphone signal. It is estimated from the echo-cancelled signal when its autocorrelation is low and its power is smaller than the echo-replica power. A detection threshold of the new normalized cross-correlation is adaptively controlled based on the echo-to-NES (near-end speech) ratio. Simulation results demonstrate superior performance of the new algorithm.


title: Mistaking minds and machines: How speech affects dehumanization and anthropomorphism.

abstract: Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person's actual mental experience-a humanlike voice-affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text's creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text's creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual cues to text (i.e., seeing a person perform a script in a subtitled video clip), did not increase the likelihood of inferring a human creator compared with only reading text, suggesting that defining features of personhood may be conveyed more clearly in speech (Experiments 1 and 2). Removing the naturalistic paralinguistic cues that convey humanlike capacity for thinking and feeling, such as varied pace and intonation, eliminates the humanizing effect of speech (Experiment 4). We discuss implications for dehumanizing others through text-based media, and for anthropomorphizing machines through speech-based media. (PsycINFO Database Record


title: Time-Delay Compensation by Communication Disturbance Observer for Bilateral Teleoperation Under Time-Varying Delay

abstract: This paper presents the effectiveness of a time-delay compensation method based on the concept of network disturbance and communication disturbance observer for bilateral teleoperation systems under time-varying delay. The most efficient feature of the compensation method is that it works without time-delay models (model-based time-delay compensation approaches like Smith predictor usually need time-delay models). Therefore, the method is expected to be widely applied to network-based control systems, in which time delay is usually unknown and time varying. In this paper, the validity of the time-delay compensation method in the cases of both constant delay and time-varying delay is verified by experimental results compared with Smith predictor.


Extracting Drug-Drug Interactions with Attention CNNs


title: Improved Topological Fiducial Tracking in the reacTIVision System

abstract: This paper describes reacTIVision: a camera based two dimensional fiducial (marker) tracking system developed for the reacTable*, a table based tangible musical instrument. Key features of reacTIVision include: (1) the ability to track a large number of fiducials with faster than real-time performance and (2) fiducial size may be varied depending on the number of distinct fiducial identities required. We describe recent advances in our implementation of topologybased fiducial recognition, including a generalised method for accurately computing fiducial location and orientation. A method of graph naming known as left heavy depth sequences is applied to the identification of topologically distinct fiducials. Also discussed is our approach to generating fiducial images for the system, in which we employ evolutionary computation to produce compact fiducials with specific geometric properties.


title: 3D Action Recognition from Novel Viewpoints

abstract: We propose a human pose representation model that transfers human poses acquired from different unknown views to a view-invariant high-level space. The model is a deep convolutional neural network and requires a large corpus of multiview training data which is very expensive to acquire. Therefore, we propose a method to generate this data by fitting synthetic 3D human models to real motion capture data and rendering the human poses from numerous viewpoints. While learning the CNN model, we do not use action labels but only the pose labels after clustering all training poses into k clusters. The proposed model is able to generalize to real depth images of unseen poses without the need for re-training or fine-tuning. Real depth videos are passed through the model frame-wise to extract viewinvariant features. For spatio-temporal representation, we propose group sparse Fourier Temporal Pyramid which robustly encodes the action specific most discriminative output features of the proposed human pose model. Experiments on two multiview and three single-view benchmark datasets show that the proposed method dramatically outperforms existing state-of-the-art in action recognition.


title: Unsupervised Monocular Depth Estimation with Left-Right Consistency

abstract: Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Ex-ploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.


title: A risk management framework for cloud computing

abstract: Although cloud computing has the advantages of cost-saving, efficiency and scalability, it also brings about many security issues. Because almost all software, hardware, and application data are deployed and stored in the cloud platforms, there is often the distrust between users and cloud suppliers. To resolve the problem, this paper proposes a risk management framework on the basis of the previous work. The framework consists of five components: user requirement self-assessment, cloud service providers desktop assessment, risk assessment, third-party agencies review, and continuous monitoring. By means of the framework, the cloud service suppliers can better understand the user's requirements, and the trust between the users and the suppliers is more easily acquired.


title: 1 Highly Scalable Appearance-Only SLAM –

abstract: We describe a new formulation of appearance-only SLAM suitable for very large scale navigation. The system navigates in the space of appearance, assigning each new observation to either a new or previously visited location, without reference to metric position. The system is demonstrated performing reliable online appearance mapping and loop closure detection over a 1,000 km trajectory, with mean filter update times of 14 ms. The 1,000 km experiment is more than an order of magnitude larger than any previously reported result. The scalability of the system is achieved by defining a sparse approximation to the FAB-MAP model suitable for implementation using an inverted index. Our formulation of the problem is fully probabilistic and naturally incorporates robustness against perceptual aliasing. The 1,000 km data set comprising almost a terabyte of omni-directional and stereo imagery is available for use, and we hope that it will serve as a benchmark for future systems.


title: Common factors influence bee foraging in urban and wildland landscapes

abstract: Bees are important flower-visiting insects that display differential occurrences at food resources throughout urban and wildland landscapes. This study examined the visitation rates and foraging patterns of eight taxonomic groups of bees that are common to California poppies, Eschscholzia californica, in both landscape types. Bee occurrence was documented in relation to floral resource characteristics (patch area, poppy coverage, and poppy density), local landscape characteristics (distance to the wildland-urban interface, distance to riparian areas, distance to green space, and land use), and regional landscape context (urban versus wildland). Similar abundance and richness measures were recorded at both urban and wildland poppy patches, but community composition varied in each landscape. Bumble bees were more abundant at poppies in the wildland whereas species in the family Halictidae (sweat bees) were more abundant at poppies within the urban landscape. Resource patch size and density consistently correlated with increased bee presence for all bee types foraging in the wildland. Individual patterns of occurrence in the urban landscape were somewhat divergent; the foraging dynamics of larger bodied-bees (Bombus vosnesenskii and Megachile species) correlated significantly with resource patch size and density, whereas smaller-bodied bees (family Halictidae and Andrena species) were influenced by landscape characteristics such as distance to the wildland-urban interface and distance to riparian areas. In summary, the surrounding landscape had an influence on community composition, but the magnitude of the floral resource present at a site and factors relating to foraging energetics were dominant drivers of local occurrence. These results suggest that management strategies that provide dense and abundant floral resources should be successful in attracting bees, irrespective of their location within the urban matrix.


title: A Brief Tour Through Provenance in Scientific Workflows and Databases

abstract: Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.


title: Deep reinforcement learning with experience replay based on SARSA

abstract: SARSA, as one kind of on-policy reinforcement learning methods, is integrated with deep learning to solve the video games control problems in this paper. We use deep convolutional neural network to estimate the state-action value, and SARSA learning to update it. Besides, experience replay is introduced to make the training process suitable to scalable machine learning problems. In this way, a new deep reinforcement learning method, called deep SARSA is proposed to solve complicated control problems such as imitating human to play video games. From the experiments results, we can conclude that the deep SARSA learning shows better performances in some aspects than deep Q learning.


title: The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

abstract: Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people’s independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application.


title: Correlates of Academic Procrastination: Discomfort, Task Aversiveness, and Task Capability

abstract: Abstract The relationships among five aspects of academic procrastination—behavioral delay, personal upset about the delay, task aversiveness, task capability, and the desire to reduce behavioral delay—were investigated in 10th-grade Israeli students (N = 195). Upset about delay was weakly related to delay itself, and—unlike delay—was strongly related to perceived capability to perform academic tasks and to the desire to change delaying behavior. Students delayed more on academic tasks labeled unpleasant than pleasant, were neutral in between, and were correspondingly more upset about the former than the latter. They more frequently acknowledged reasons for academic procrastination that were less threatening to their self-image (e.g., problems in time management) than reasons that were more threatening (e.g., lack of ability). Interest in reducing delay is related more to self-perceived ability to handle tasks than to time spent procrastinating or reasons given for procrastinating.


title: Evaluation and recommendations on IPv6 for the Internet of Things

abstract: This article presents some key achievements and recommendations from the IoT6 European research project on IPv6 exploitation for the Internet of Things (IoT). It highlights the potential of IPv6 to support the integration of a global IoT deployment including legacy systems by overcoming horizontal fragmentation as well as more direct vertical integration between communicating devices and the cloud.


title: Sequence-to-Sequence Model with Attention for Time Series Classification

abstract: Encouraged by recent waves of successful applications of deep learning, some researchers have demonstrated the effectiveness of applying convolutional neural networks (CNN) to time series classification problems. However, CNN and other traditional methods require the input data to be of the same dimension which prevents its direct application on data of various lengths and multi-channel time series with different sampling rates across channels. Long short-term memory (LSTM), another tool in the deep learning arsenal and with its design nature, is more appropriate for problems involving time series such as speech recognition and language translation. In this paper, we propose a novel model incorporating a sequence-to-sequence model that consists two LSTMs, one encoder and one decoder. The encoder LSTM accepts input time series of arbitrary lengths, extracts information from the raw data and based on which the decoder LSTM constructs fixed length sequences that can be regarded as discriminatory features. For better utilization of the raw data, we also introduce the attention mechanism into our model so that the feature generation process can peek at the raw data and focus its attention on the part of the raw data that is most relevant to the feature under construction. We call our model S2SwA, as the short for Sequence-to-Sequence with Attention. We test S2SwA on both uni-channel and multi-channel time series datasets and show that our model is competitive with the state-of-the-art in real world tasks such as human activity recognition.


title: Quadruplet Networks for Sketch-Based Image Retrieval

abstract: Freehand sketches are a simple and powerful tool for communication. They are easily recognized across cultures and suitable for various applications. In this paper, we use deep convolutional neural networks (ConvNets) to address sketch-based image retrieval (SBIR). We first train our ConvNets on sketch and image object recognition in a large scale benchmark for SBIR (the sketchy database). We then conduct a comprehensive study of ConvNets features for SBIR, using a kNN similarity search paradigm in the ConvNet feature space. In contrast to recent SBIR works, we propose a new architecture the quadruplet networks which enhance ConvNet features for SBIR. This new architecture enables ConvNets to extract more robust global and local features. We evaluate our approach on three large scale datasets. Our quadruplet networks outperform previous state-of-the-art on two of them by a significant margin and gives competitive results on the third. Our system achieves a recall of 42.16% (at k=1) for the sketchy database (more than 5% improvement), a Kendal score of 43.28Τb on the TU-Berlin SBIR benchmark (close to 6Τb improvement) and a mean average precision (MAP) of 32.16% on Flickr15k (a category level SBIR benchmark).


title: Development of eating behaviors among children and adolescents.

abstract: The prevalence of obesity among children is high and is increasing. We know that obesity runs in families, with children of obese parents at greater risk of developing obesity than children of thin parents. Research on genetic factors in obesity has provided us with estimates of the proportion of the variance in a population accounted for by genetic factors. However, this research does not provide information regarding individual development. To design effective preventive interventions, research is needed to delineate how genetics and environmental factors interact in the etiology of childhood obesity. Addressing this question is especially challenging because parents provide both genes and environment for children. An enormous amount of learning about food and eating occurs during the transition from the exclusive milk diet of infancy to the omnivore9s diet consumed by early childhood. This early learning is constrained by children9s genetic predispositions, which include the unlearned preference for sweet tastes, salty tastes, and the rejection of sour and bitter tastes. Children also are predisposed to reject new foods and to learn associations between foods9 flavors and the postingestive consequences of eating. Evidence suggests that children can respond to the energy density of the diet and that although intake at individual meals is erratic, 24-hour energy intake is relatively well regulated. There are individual differences in the regulation of energy intake as early as the preschool period. These individual differences in self-regulation are associated with differences in child-feeding practices and with children9s adiposity. This suggests that child-feeding practices have the potential to affect children9s energy balance via altering patterns of intake. Initial evidence indicates that imposition of stringent parental controls can potentiate preferences for high-fat, energy-dense foods, limit children9s acceptance of a variety of foods, and disrupt children9s regulation of energy intake by altering children9s responsiveness to internal cues of hunger and satiety. This can occur when well-intended but concerned parents assume that children need help in determining what, when, and how much to eat and when parents impose child-feeding practices that provide children with few opportunities for self-control. Implications of these findings for preventive interventions are discussed.


title: The novel stress simulation method for contemporary DRAM capacitor arrays

abstract: The increasing of aspect ratio in DRAM capacitors causes structural instabilities and device failures as the generation evolves. Conventionally, two-dimensional and three-dimensional models are used to solve these problems by optimizing thin film thickness, material properties and structure parameters; however, it is not enough to analyze the latest failures associated with large-scale DRAM capacitor arrays. Therefore, beam-shell model based on classical beam and shell theories is developed in this study to simulate diverse failures. It enables us to solve multiple failure modes concurrently such as supporter crack, capacitor bending, and storage-poly fracture.


title: Explanation of Two Anomalous Results in Statistical Mediation Analysis

abstract: Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M, a, increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y, b, was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a. Implications of these findings are discussed.


title: Person Identification in Natural Static Postures Using Kinect

abstract: Automatic person identification using un-obtrusive methods are of immense importance in the area of computer vision. Anthropometric approaches are robust to external factors including environmental illumination and obstructions due to hair, spectacles, hats or any other wearable. Recently, there have been efforts made on people identification using walking pattern of the skeleton data obtained from Kinect. In this paper we investigate the possibility of identification using static postures namely sitting and standing. Existing gait based identifications, mostly rely on the dynamics of the joints of the skeleton data. In case of static postures the motion information is not available, hence the identification mainly relies on the static distance information between the joints. Moreover, the variation of pose in a particular posture makes the identification more challenging. The proposed methodology, initially sub-divides the body-parts into static, dynamic and noisy parts followed by a combinatorial element responsible for selectively extracting features for each of those parts. Finally a radial basis function support vector machine classifier is used to perform the training and testing for the identification. Results indicate an identification accuracy of more than 97 % in terms of F-score for 10 people using a dataset created with various poses of natural sitting and standing posture.


title: The foot abduction characteristics following Steenbeek foot abduction brace

abstract: PURPOSE ::: We prospectively investigated the foot abduction characteristics following Steenbeek foot abduction brace (SFAB) use in corrected clubfeet. The foot abduction achievable in SFAB with knee flexion and extension was calculated to find the effectiveness and stretch exerted by it. ::: ::: ::: METHODS ::: Only children with corrected idiopathic clubfeet using SFAB for greater than 3 months were enrolled. The foot abduction with and without brace in knee extended and flexed positions was measured. Hip range of motion (ROM) with and without brace was also recorded. ::: ::: ::: RESULTS ::: The average age of 42 children ( 62 feet) was 24.25 months (range: 5-48 months). There was difference in foot abduction of 22.2° in knee extension and flexion with SFAB on. A significant change in foot stretch of 25.5° observed when the knee was moved from extended to flexed position indicated SFAB dynamicity. The SFAB was found to be an effective orthosis as it brought the corrected clubfoot into maximum abduction permissible in the foot during the phase of knee flexion. The tibial rotation accounted for a major component (61%) of apparent foot abduction with the brace on. A hip ROM of 52.2° was required for SFAB function. ::: ::: ::: CONCLUSION ::: SFAB is a dynamic brace that functions better in flexed knee position. It is able to induce a near equivalent actual abduction available in the foot in flexed position of knee. There is a significant component of tibial external rotation in SFAB-induced foot abduction. SFAB function is also dependent on hip mechanics.