title: Development of Bluetooth based indoor positioning application

abstract: This paper presents an Android application that is capable for proximity based indoor localization using Bluetooth RSSI. Proximity based localization can be useful for finding well recognizable objects or areas within building. The goal of the system is to locate offices and people in office buildings. The presented algorithm estimates the distance between client and beacon, based on RSSI values and Log-Distance Path Loss Model. Application was tested in a three story building and experimental results demonstrate the applicability of the localization algorithm. The application estimated the position correctly in 88% of the test cases.


title: Protecting Home User Devices with an SDN-Based Firewall

abstract: Internet-connected consumer electronics marketed as smart devices (also known as Internet-of-Things devices) usually lack essential security protection mechanisms. This puts user privacy and security in great danger. One of the essential steps to compromise vulnerable devices is locating them through horizontal port scans. In this paper, we focus on the problem of detecting horizontal port scans in home networks. We propose a software-defined networking (SDN)-based firewall platform that is capable of detecting horizontal port scans. Current SDN implementations (e.g., OpenFlow) do not provide access to packet-level information, which is essential for network security applications, due to performance limitations. Our platform uses FleXight, our proposed new information channel between SDN controller and data path elements to access packet-level information. FleXight uses per-flow sampling and dynamical sampling rate adjustments to provide the necessary information to the controller while keeping the overhead very low. We evaluate our solution on a large real-world packet trace from an ISP and show that our system can identify all attackers and 99% of susceptible victims with only 0.75% network overhead. We also present a detailed usability analysis of our system.


title: Design and Analysis of Star–Delta Hybrid Windings for High-Voltage Induction Motors

abstract: In this paper, a Y-Δ hybrid connection for a high-voltage induction motor is described. Low winding harmonic content is achieved by careful consideration of the interaction between the Y- and Δ-connected three-phase winding sets so that the magnetomotive force (MMF) in the air gap is close to sinusoid. Essentially, the two winding sets operate in a six-phase mode. This paper goes on to verify that the fundamental distribution coefficient for the stator MMF is enhanced compared to a standard three-phase winding set. The design method for converting a conventional double-layer lap winding in a high-voltage induction motor into a Y-Δ hybrid lap winding is described using standard winding theory as often applied to small- and medium-sized motors. The main parameters addressed when designing the winding are the conductor wire gauge, coil turns, and parallel winding branches in the Y and Δ connections. A winding design scheme for a 1250-kW 6-kV induction motor is put forward and experimentally validated; the results show that the efficiency can be raised effectively without increasing the cost.


title: Characterizing and Improving Stability in Neural Style Transfer

abstract: Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize the instability of these methods by examining the solution set of the style transfer objective. We show that the trace of the Gram matrix representing style is inversely related to the stability of the method. Then, we present a recurrent convolutional network for real-time video style transfer which incorporates a temporal consistency loss and overcomes the instability of prior methods. Our networks can be applied at any resolution, do not require optical flow at test time, and produce high quality, temporally consistent stylized videos in real-time.


title: A PageRank-improved ranking algorithm based on cheating similarity and cheating relevance

abstract: Various malicious methods for a website to get more popularity than it deserves are mainly classed into two types, one of which is link-based spam. Mainstream link-based anti-spam algorithms, including ranking algorithms and spam algorithm only count the number and quality of links of a page to identify a spam page, with some of them using a whitelist or a blacklist. This paper proposed a PageRank-improved algorithm, that combined whitelist and blacklist, and used two effect factors — cheating similarity and cheating relevance to get a cheating tendence value. The cheating tendence value was used to modify original PageRank value and reranked sites. Experimental results proved that the new algorithm had a better link-based anti-spam performance.


title: Clustering user preferences using W-kmeans

abstract: Although commonly only document clustering is suggested by Web mining techniques for recommendation systems, one of the various tasks of personalized recommendation is categorization of Web users. In this paper, a method for clustering navigation patterns of Web users is proposed. We adapt the WordNet-enabled W-kmeans algorithm, an enhancement of standard k-means algorithm which uses the external knowledge from WordNet hypernyms and that has been previously used for document clustering, to user profile clustering by analyzing the users¢ historical data. We also investigate the effects this approach has on the recommendation engine by evaluating the overall performance it has in terms of precision -- recall on our online recommendation system.


title: Publication of Investment Treaty Awards: The Qualified Potential of Domestic Access to Information Laws

abstract: Investment treaties and arbitral rules traditionally impose few legally binding duties on States to release investment treaty awards. Despite this, possibly in light of the growing recognition of a human right of access to public information, recent efforts towards transparency in investment arbitration proceedings have led to significant changes in both legal instruments and State practice. However, many States remain reluctant to commit to transparency obligations, or to comply with transparency obligations where they already exist. This article reviews the utility of one lesser-known tool, domestic freedom of information (FOI) laws, in promoting transparency in the particular context of investment arbitration. The article focuses on the Republic of Poland, a State known to be holding a sizeable number of unpublished investment treaty awards. The Polish experience discussed in this article suggests that, despite the many problems encountered, domestic FOI laws do have the potential, even if qualified, to constitute a factor in the growing trend towards transparency.


title: A clinical perspective on a pain neuroscience education approach to manual therapy

abstract: AbstractIn recent years, there has been an increased interest in pain neuroscience education (PNE) in physical therapy. There is growing evidence for the efficacy of PNE to decrease pain, disability, fear-avoidance, pain catastrophization, limited movement, and health care utilization in people struggling with pain. PNE teaches people in pain more about the biology and physiology of their pain experience including processes such as central sensitization, peripheral sensitization, allodynia, inhibition, facilitation, neuroplasticity and more. PNE’s neurobiological model often finds itself at odds with traditional biomedical models used in physical therapy. Traditional biomedical models, focusing on anatomy, pathoanatomy, and biomechanics have been shown to have limited efficacy in helping people understand their pain, especially chronic pain, and may in fact even increase a person’s pain experience by increasing fear-avoidance and pain catastrophization. An area of physical therapy where the biomedical mod...


title: Improved minimax optimisation algorithms and their application in the design of recursive digital filters

abstract: A technique that can improve the performance of available optimisation algorithms, in applications where the objective function involves the sampling of a function with respect to an independent variable, is proposed. The technique is then applied in conjunction with two well known minimax algorithms for the design of recursive digital filters. Extensive experimental results show that the new technique reduces the approximation error significantly and eliminates the problem of spikes in the error function. Although the amount of computation needed to carry out a design for a fixed density of grid points is increased somewhat, improved robustness is achieved, which allows a reduced density of grid points and eliminates waste of computational effort in unsuccessful designs.


title: Learning inter-related visual dictionary for object recognition

abstract: Object recognition is challenging especially when the objects from different categories are visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) algorithm to exploit the visual correlation within a group of visually similar object categories for dictionary learning where a commonly shared dictionary and multiple category-specific dictionaries are accordingly modeled. To enhance the discrimination of the dictionaries, the dictionary learning problem is formulated as a joint optimization by adding a discriminative term on the principle of the Fisher discrimination criterion. As well as presenting the JDL model, a classification scheme is developed to better take advantage of the multiple dictionaries that have been trained. The effectiveness of the proposed algorithm has been evaluated on popular visual benchmarks.


title: A check list of plant species in the urban forestry of Fortaleza, Brazil: where are the native species in the country of megadiversity?

abstract: Although Brazil is recognized as a country of megadiversity many Brazilian cities have an overwhelming number of exotic street trees in their urban environments. Here we investigate this situation in the city of Fortaleza, Brazil’s fifth largest city. Through an extensive qualitative survey, complemented by published quantitative surveys, we developed the most complete inventory done to date in Fortaleza. We registered the largest possible number of plant species which we could record in the public treescape of the city. We recorded 219 tree species, of which 158 were exotic to Fortaleza’s ecosystems. Of the 61 native species, some were represented only by relictual individuals spared during urban growth and only 39 native species were cultivated plants. Most of these 39 cultivated natives are represented by a small number of individuals in the city, while most of common trees are exotic species. We show that many native plants that are potentially suitable for ornamental use in urban settings are being ignored and argue that exotics are overvalued, including some plants that are invasive. We also discuss the consequences of the predominance of alien species for the public perception of native biodiversity. We advocate that increasing the number of native plants in cities has many advantages from an ecological perspective.


title: Online Deep Learning: Learning Deep Neural Networks on the Fly

abstract: Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings. In this paper, we present a new online deep learning framework that attempts to tackle the challenges by learning DNN models of adaptive depth from a sequence of training data in an online learning setting. In particular, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy of our method on large-scale data sets, including both stationary and concept drifting scenarios.


title: Ranking Based Multitask Learning of Scoring Functions

abstract: Scoring functions are an important tool for quantifying properties of interest in many domains; for example, in healthcare, a disease severity scores are used to diagnose the patient’s condition and to decide its further treatment. Scoring functions might be obtained based on the domain knowledge or learned from data by using classification, regression or ranking techniques - depending on the type of supervised information. Although learning scoring functions from collected data is beneficial, it can be challenging when limited data are available. Therefore, learning multiple distinct, but related, scoring functions together can increase their quality as shared regularities may be easier to identify. We propose a multitask formulation for ranking-based learning of scoring functions, where the model is trained from pairwise comparisons. The approach uses mixed-norm regularization to impose structural regularities among the tasks. The proposed regularized objective function is convex; therefore, we developed an optimization approach based on alternating minimization and proximal gradient algorithms to solve the problem. The increased predictive accuracy of the presented approach, in comparison to several baselines, is demonstrated on synthetic data and two different real-world applications; predicting exam scores and predicting tolerance to infections score.


Speaker Verification in Mismatched Conditions with Frustratingly Easy Domain Adaptation


title: A 3D SoC design for H.264 application with on-chip DRAM stacking

abstract: Three-dimensional (3D) on-chip memory stacking has been proposed as a promising solution to the “memory wall” challenge with the benefits of low access latency, high data bandwidth, and low power consumption. The stacked memory tiers leverage through-silicon-vias (TSVs) to communicate with logic tiers, and thus dramatically reduce the access latency and improve the data bandwidth without the constraint of I/O pin count. To demonstrate the feasibility of 3D memory stacking, this paper introduces a 3D System-on-Chip (SoC) for H.264 applications that can make use of multiple memory channels offered by 3D integration. Two logic tiers are stacked together with each having an area of 2.5×5.0mm2, with a 3-layer 8-channel 3D DRAM stacked on the top. The design flow for this 3D SoC is also presented. The prototype chip has been fabricated with GlobalFoundries' 130nm low-power process and Tezzaron's 3D TSV technology. The 3D implementation shows that the 3D ICs can alleviate the pressure from I/O pin count and allow parallel memory accesses through multiple channels.


title: Latent Variable Dialogue Models and their Diversity

abstract: We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the `boring output' issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.


title: The myth of the visual word form area

abstract: Recent functional imaging studies have referred to a posterior region of the left midfusiform gyrus as the “visual word form area” (VWFA). We review the evidence for this claim and argue that neither the neuropsychological nor neuroimaging data are consistent with a cortical region specialized for visual word form representations. Specifically, there are no reported cases of pure alexia who have deficits limited to visual word form processing and damage limited to the left midfusiform. In addition, we present functional imaging data to demonstrate that the so-called VWFA is activated by normal subjects during tasks that do not engage visual word form processing such as naming colors, naming pictures, reading Braille, repeating auditory words, and making manual action responses to pictures of meaningless objects. If the midfusiform region has a single function that underlies all these tasks, then it does not correspond to visual word form processing. On the other hand, if the region participates in several functions as defined by its interactions with other cortical areas, then identifying the neural system sustaining visual word form representations requires identification of the set of regions involved. We conclude that there is no evidence that visual word form representations are subtended by a single patch of neuronal cortex and it is misleading to label the left midfusiform region as the visual word form area.


title: Price effects in online product reviews: an analytical model and empirical analysis

abstract: Consumer reviews may reflect not only perceived quality but also the difference between quality and price (perceived value). In markets where product prices change frequently, these price-influenced reviews may be biased as a signal of product quality when used by consumers possessing no knowledge of historical prices. In this paper, we develop an analytical model that examines the impact of price-influenced reviews on firm optimal pricing and consumer welfare. We quantify the price effects in consumer reviews for different formats of review systems using actual market prices and online consumer ratings data collected for the digital camera ::: ::: market. Our empirical results suggest that unidimensional ratings, commonly used in most review systems, can be substantially biased by price effects. In fact, unidimensional ratings are more closely correlated with ratings of product value than ratings of product quality. Our findings suggest the importance for firms to account for these price effects in their overall marketing strategy and suggest that review systems could better serve consumers by explicitly expanding review dimensions to separate perceived value and perceived quality.


title: Investigating A Generic Paraphrase-Based Approach For Relation Extraction

abstract: Unsupervised paraphrase acquisition has been an active research field in recent years, but its effective coverage and performance have rarely been evaluated. We propose a generic paraphrase-based approach for Relation Extraction (RE), aiming at a dual goal: obtaining an applicative evaluation scheme for paraphrase acquisition and obtaining a generic and largely unsupervised configuration for RE. We analyze the potential of our approach and evaluate an implemented prototype of it using an RE dataset. Our findings reveal a high potential for unsupervised paraphrase acquisition. We also identify the need for novel robust models for matching paraphrases in texts, which should address syntactic complexity and variability.


title: Comparison of Machine Learning Techniques with Classical Statistical Models in Predicting Health Outcomes

abstract: Several machine learning techniques (multilayer and single layer perceptron, logistic regression, least square linear separation and support vector machines) are applied to calculate the risk of death from two biomedical data sets, one from patient care records, and another from a population survey. Each dataset contained multiple sources of information: history of related symptoms and other illnesses, physical examination findings, laboratory tests, medications (patient records dataset), health attitudes, and disabilities in activities of daily living (survey dataset). Each technique showed very good mortality prediction in the acute patients data sample (AUC up to 0.89) and fair prediction accuracy for six year mortality (AUC from 0.70 to 0.76) in individuals from epidemiological database surveys. The results suggest that the nature of data is of primary importance rather than the learning technique. However, the consistently superior performance of the artificial neural network (multi-layer perceptron) indicates that nonlinear relationships (which cannot be discerned by linear separation techniques) can provide additional improvement in correctly predicting health outcomes.


title: A Linguistically Informed Convolutional Neural Network

abstract: Sentiment lexicons and other linguistic knowledge proved to be beneficial in polarity classification. This paper introduces a linguistically informed Convolutional Neural Network (lingCNN), which incorporates this valuable kind of information into the model. We present two intuitive and simple methods: The first one integrates word-level features, the second sentence-level features. By combining both types of features our model achieves results that are comparable to state-of-theart systems.


title: Analysis of data in the form of graphs

abstract: We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges in mining graph data is the definition of a suitable pairwise similarity metric in the space of graphs. We explore two practical solutions to solving this problem: one based on finding subgraph densities, and one using spectral information. The approach is illustrated on three test data sets (ensembles of graphs); two of these are obtained from standard graph generating algorithms, while the graphs in the third example are sampled as dynamic snapshots from an evolving network simulation.


title: On exploring sparsity in widely separated MIMO radar

abstract: The scenario of widely separated multi-input multi-output (MIMO) radar is considered. For a small number of targets, the target returns are sparse in the target space. First, a decoupled Lasso approach is proposed, which by exploiting the structure of the basis matrix decomposes the large size problem into a number of smaller size problems, thus reducing computational complexity. Second, it is shown that by reordering the columns of the basis matrix, group sparsity can be introduced to the returns. This structure can be exploited by a group Lasso approach to achieve significant performance gains.


title: Networked clock synchronization based on second order linear consensus algorithms

abstract: In this paper a distributed algorithm for clock synchronization is proposed. This algorithm is based on an extension of the consensus algorithm able to synchronize a family of double integrators. Since the various clocks may have different drifts, the algorithm needs to be designed so that it can work also in case of heterogeneous double integrators. Through a robust control analysis it is possible to determine the maximum admissible level of heterogeneity yielding synchronization. The first part of the paper is devoted to the analysis of an unrealistic synchronous implementation of the algorithm. However, in the last part of the paper we propose a realistic pseudo-synchronous implementation which is proved to be a perturbation of the synchronous one. From arguments related the center manifold theorem, the stability of the pseudo-synchronous is finally proved.


A Variational Principle for Graphical Models


title: HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection

abstract: We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by inducing the Markov Blanket of the variable to be classified or predicted. A wide variety of biomedical tasks with different characteristics were used for an empirical evaluation. Namely, (i) bioactivity prediction for drug discovery, (ii) clinical diagnosis of arrhythmias, (iii) bibliographic text categorization, (iv) lung cancer diagnosis from gene expression array data, and (v) proteomics-based prostate cancer detection. State-of-the-art algorithms for each domain were selected for baseline comparison. Results: (1) HITON reduces the number of variables in the prediction models by three orders of magnitude relative to the original variable set while improving or maintaining accuracy. (2) HITON outperforms the baseline algorithms by selecting more than two orders-ofmagnitude smaller variable sets than the baselines, in the selected tasks and datasets.


title: Is L2 a Good Loss Function for Neural Networks for Image Processing

abstract: Neural networks are becoming central in several areas of computer vision and image processing. Different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention by the research community: the default and most common choice is L2. This can be particularly limiting in the context of image processing, since L2 correlates poorly with perceived image quality. ::: In this paper we bring attention to alternative choices. We study the performance of several losses, including perceptually-motivated losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even for the same network architecture.


title: Designing smart home interfaces for the elderly

abstract: In this paper we highlight the importance of tailoring the design of dialogue systems to the targeted user group. We propose a human-centered design cycle and report the results from a survey conducted among the intended users of a smart home for the elderly.


Aspect extraction from product reviews using category hierarchy information


title: Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking

abstract: We present an improved framework for real-time segmentation and tracking by fusing depth and RGB color data. We are able to solve common problems seen in tracking and segmentation of RGB images, such as occlusions, fast motion, and objects of similar color. Our proposed real-time mean shift based algorithm outperforms the current state of the art and is significantly better in difficult scenarios.


title: The yices smt solver

abstract: SMT stands for Satisfiability Modulo Theories. An SMT solver decides the satisfiability of propositionally complex formulas in theories such as arithmetic and uninterpreted functions with equality. SMT solving has numerous applications in automated theorem proving, in hardware and software verification, and in scheduling and planning problems. This paper describes Yices, an efficient SMT solver developed at SRI International. Yices supports a rich combination of first-order theories that occur frequently in software and hardware modeling: arithmetic, uninterpreted functions, bit vectors, arrays, recursive datatypes, and more. Beyond pure SMT solving, Yices can solve weighted MAX-SMT problems, compute unsatisfiable cores, and construct models. Yices is the main decision procedure used by the SAL model checking environment, and it is being integrated to the PVS theorem prover. As a MAX-SMT solver, Yices is the main component of the probabilistic consistency engine used in SRI’s CALO system.


title: Estimating robot end-effector force from noisy actuator torque measurements

abstract: This paper discusses two ways to estimate the interaction force at the end-effector of a robot. The first approach that is presented combines filtered dynamic equations with a recursive least squares estimation algorithm to provide a smoothened force signal, which is useful in the (common) case of noisy torque measurements.


title: Using Crowd-Source Based Features from Social Media and Conventional Features to Predict the Movies Popularity

abstract: Predicting the success of movies has been of interest to economists and investors (media and production houses) as well as predictive analysts. A number of attributes such as cast, genre, budget, production house, PG rating affect the popularity of a movie. Social media such as Twitter, YouTube etc. are major platforms where people can share their views about the movies. This paper describes experiments in predictive analysis using machine learning algorithms on both conventional features, collected from movies databases on Web as well as social media features (text comments on YouTube, Tweets). The results demonstrate that the sentiments harnessed from social media and other social media features can predict the success with more accuracy than that of using conventional features. We achieved best value of 77% and 61% using selected social media features for Rating and Income prediction respectively, whereas selected conventional features gave results of 76.2% and 52% respectively. More it was found that the blend of both types of attributes (conventional and those collected from social media) can outperform the existing approaches in this domain.


title: A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility

abstract: This paper shows how we can combine logical representations of actions and decision theory in such a manner that seems natural for both. In partitular we assume an axiomatization of the domain in terms of situation calculus, using what is essentially Reiter's solution to the frame problem, in terms of the completion of the axioms defining the state change. Uncertainty is handled in terms of the independent choice logic, which allows for independent choices and a logic program that gives the consequences of the choices. As part of the consequences are a specification of the utility of (final) states. The robot adopts robot plans, similar to the GOLOG programming language. Within this logic, we can define the expected utility of a conditional plan, based on the axiomadzation of the actions, the uncertainty and the utility. The 'planning' problem is to find the plan with the highest expected utility. This is related to recent structured representations for POMDPs; here we use stochastic situation calculus rules to specify the state transition function and the reward/value function. Finally we show that with stochastic frame axioms, actions representations in probabilistic STRIPS are exponentially larger than using the representation proposed here.


title: Joint iterative guidance filtering for compressed depth images

abstract: In the general 3D scene, the correlation of depth image and corresponding color image exists, so many filtering methods have been proposed to improve the quality of depth images according to this correlation. Unlike the conventional methods, in this paper both depth and color information can be jointly employed to improve the quality of compressed depth image by the way of iterative guidance. Firstly, due to noises and blurring in the compressed image, a depth pre-filtering method is essential to remove artifact noises. Considering that the received geometry structure in the distorted depth image is more reliable than its color image, the color information is merged with depth image to get depth-merged color image. Then the depth image and its corresponding depth-merged color image can be used to refine the quality of the distorted depth image using joint iterative guidance filtering method. Therefore, the efficient depth structural information included in the distorted depth images are preserved relying on depth itself, while the corresponding color structural information are employed to improve the quality of depth image. We demonstrate the efficiency of the proposed filtering method by comparing objective and visual quality of the synthesized image with many existing depth filtering methods.


title: Toward detecting emotions in spoken dialogs

abstract: The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. This paper explores the detection of domain-specific emotions using language and discourse information in conjunction with acoustic correlates of emotion in speech signals. The specific focus is on a case study of detecting negative and non-negative emotions using spoken language data obtained from a call center application. Most previous studies in emotion recognition have used only the acoustic information contained in speech. In this paper, a combination of three sources of information-acoustic, lexical, and discourse-is used for emotion recognition. To capture emotion information at the language level, an information-theoretic notion of emotional salience is introduced. Optimization of the acoustic correlates of emotion with respect to classification error was accomplished by investigating different feature sets obtained from feature selection, followed by principal component analysis. Experimental results on our call center data show that the best results are obtained when acoustic and language information are combined. Results show that combining all the information, rather than using only acoustic information, improves emotion classification by 40.7% for males and 36.4% for females (linear discriminant classifier used for acoustic information).


title: Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

abstract: We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach that models future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. Future frame synthesis is challenging, as it involves low- and high-level image and motion understanding. We propose a novel network structure, namely a Cross Convolutional Network to aid in synthesizing future frames; this network structure encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold videos. We also show that our model can be applied to tasks such as visual analogy-making, and present an analysis of the learned network representations.


title: Freedom to roam: a study of mobile device adoption and accessibility for people with visual and motor disabilities

abstract: Mobile devices provide people with disabilities new opportunities to act independently in the world. However, these empowering devices have their own accessibility challenges. We present a formative study that examines how people with visual and motor disabilities select, adapt, and use mobile devices in their daily lives. We interviewed 20 participants with visual and motor disabilities and asked about their current use of mobile devices, including how they select them, how they use them while away from home, and how they adapt to accessibility challenges when on the go. Following the interviews, 19 participants completed a diary study in which they recorded their experiences using mobile devices for one week. Our results show that people with visual and motor disabilities use a variety of strategies to adapt inaccessible mobile devices and successfully use them to perform everyday tasks and navigate independently. We provide guidelines for more accessible and empowering mobile device design.


title: Frequency Conversion Technology & its Application

abstract: Frequency conversion technology is the main composition of electric and electronic technology.It is mainly used in the fields of spead adjusting of alternator and power supply.The technology can reduce the volume of power supply equipment,improve its working efficiency and generate the great economic benefit. With the devolepment of science and technology,it will be more widely used.It can promote the development of relevant technology and other relevant technology can also lead to the advance of the frequency conversion.


title: Captology: a critical review

abstract: This critical review of B.J. Fogg's book Persuasive Technology regards captology as an eclectic and formative work. It summarises two other reviewers' work and identifies several new strengths. It scrutinises Fogg's functional triad - computers functioning as tools, media and social actors - and some categorical changes are recommended. It investigates further Johnson's concerns about specific ethical omissions, nominating a new term, compusuasion, for the resultant but unintended, exogenous behaviour/attitude change effects of captological design. The review commences to more carefully define what constitutes persuasion and draws attention to the distinction between persuasion techniques in general and the behavioural changes that result from advocacy and education. The reviewer concludes that a fundamental ethic be that the designer's intent be exposed at the commencement of the user's engagement with the program and proffers the idea of persuasion resulting in a new conviction, induced by others, as a helpful definition of persuasion.


title: Acoustic, Phonetic, and Discriminative Approaches to Automatic Language Identification*

abstract: Formal evaluations conducted by NIST in 1996 demonstrated that systems that used parallel banks of tokenizer-dependent language models produced the best language identification performance. Since that time, other approaches to language identification have been developed that match or surpass the performance of phone-based systems. This paper describes and evaluates three techniques that have been applied to the language identification problem: phone recognition, Gaussian mixture modeling, and support vector machine classification. A recognizer that fuses the scores of three systems that employ these techniques produces a 2.7% equal error rate (EER) on the 1996 NIST evaluation set and a 2.8% EER on the NIST 2003 primary condition evaluation set. An approach to dealing with the problem of out-of-set data is also discussed.


title: A new LDA-based face recognition system which can solve the small sample size problem

abstract: A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.


title: Iris Liveness Detection by Relative Distance Comparisons

abstract: The focus of this paper is on presentation attack detection for the iris biometrics, which measures the pattern within the colored concentric circle of the subjects' eyes, to authenticate an individual to a generic user verification system. Unlike previous deep learning methods that use single convolutional neural network architectures, this paper develops a framework built upon triplet convolutional networks that takes as input two real iris patches and a fake patch or two fake patches and a genuine patch. The aim is to increase the number of training samples and to generate a representation that separates the real from the fake iris patches. The smaller architecture provides a way to do early stopping based on the liveness of single patches rather than the whole image. The matching is performed by computing the distance with respect to a reference set of real and fake examples. The proposed approach allows for real-time processing using a smaller network and provides equal or better than state-of-the-art performance on three benchmark datasets of photo-based and contact lens presentation attacks.


title: An Overview of Google Brain and Its Applications

abstract: Machine learning is quickly becoming a major field of research for many technology companies. Google, perhaps, is at the forefront of this movement and have instituted an entire research team called Google Brain to explore the technical aspect and applications of large scale neural networks. Thus far, the group has developed advancements in the areas of natural language recognition, open-source deep learning software, and healthcare related uses for computer vision assisted diagnosis.


title: Torque calculation of PM brushless DC motor and influence of structure parameter on torque

abstract: Accurate calculation of electromagnetic torque has great significance for design of motor and its control system. The influence of different stator and rotor structures on the torque of PMDC brushless motor is analyzed by means of ANSYS software, and the strategy of minimizing torque pulsation and increasing output is discussed.


title: Learning Depth from Single Images with Deep Neural Network Embedding Focal Length

abstract: Learning depth from a single image, as an important issue in scene understanding, has attracted a lot of attention in the past decade. The accuracy of the depth estimation has been improved from conditional Markov random fields, non-parametric methods, to deep convolutional neural networks most recently. However, there exist inherent ambiguities in recovering 3D from a single 2D image. In this paper, we first prove the ambiguity between the focal length and monocular depth learning, and verify the result using experiments, showing that the focal length has a great influence on accurate depth recovery. In order to learn monocular depth by embedding the focal length, we propose a method to generate synthetic varying-focal-length dataset from fixed-focal-length datasets, and a simple and effective method is implemented to fill the holes in the newly generated images. For the sake of accurate depth recovery, we propose a novel deep neural network to infer depth through effectively fusing the middle-level information on the fixed-focal-length dataset, which outperforms the state-of-the-art methods built on pretrained VGG. Furthermore, the newly generated varying-focallength dataset is taken as input to the proposed network in both learning and inference phases. Extensive experiments on the fixed- and varying-focal-length datasets demonstrate that the learned monocular depth with embedded focal length is significantly improved compared to that without embedding the focal length information.


title: clcNet: Improving the Efficiency of Convolutional Neural Network Using Channel Local Convolutions

abstract: Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN). We suggest that these models can be considered as special cases of a generalized convolution operation, named channel local convolution(CLC), where an output channel is computed using a subset of the input channels. This definition entails computation dependency relations between input and output channels, which can be represented by a channel dependency graph(CDG). By modifying the CDG of grouped convolution, a new CLC kernel named interlaced grouped convolution (IGC) is created. Stacking IGC and GC kernels results in a convolution block (named CLC Block) for approximating regular convolution. By resorting to the CDG as an analysis tool, we derive the rule for setting the meta-parameters of IGC and GC and the framework for minimizing the computational cost. A new CNN model named clcNet is then constructed using CLC blocks, which shows significantly higher computational efficiency and fewer parameters compared to state-of-the-art networks, when being tested using the ImageNet-1K dataset. Source code is available at this https URL .


title: A Compact Dual-Band Feed for Reflector Antennas Based on Choke Horn and Circular Eleven Antenna

abstract: A novel dual-band feed for reflector antennas is presented. This feed is a combination of a novel circular Eleven feed for C band and a compact choke horn for Ku band. The combined feed has been characterized in terms of total feed efficiency of reflector antenna and coupling coefficient of C and Ku bands (< -18 dB) over all operating frequencies. An experimental model has been manufactured and measured. The measured results agree well with the simulated ones. The dual-band feed is compact in size and can be a good candidate for satellite terminals and other applications.


title: Design of outer rotor type induction motor having high power density for in-wheel system

abstract: In this paper, the optimal design of outer rotor type induction motor having high power density was carried out for electric vehicle traction systems. Because of lower magnetic flux density of induction motor compare to permanent magnet synchronous motor, it is difficult to achieve the high power density. Therefore the Controlled filling (CF) die casting method was proposed and adapted for high density rotor bar. because the CF die casting increase the filling ratio of rotor bar, the secondary resistance of motor is decreased. Hence we can achieve the high output power density. Moreover, the high power density design of induction motor carried out varying with stack height, stator / rotor slot combination, slot shape, lamination, winding pattern, turns and material property of rotor bar. The design and performance of a new type of induction motor capable of extremely high power was described this paper with result of 0.53kw/kg.


title: Optimal detection of changepoints with a linear computational cost

abstract: We consider the problem of detecting multiple changepoints in large data sets. Our focus is on applications where the number of changepoints will increase as we collect more data: for example in genetics as we analyse larger regions of the genome, or in finance as we observe time-series over longer periods. We consider the common approach of detecting changepoints through minimising a cost function over possible numbers and locations of changepoints. This includes several established procedures for detecting changing points, such as penalised likelihood and minimum description length. We introduce a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost which, under mild conditions, is linear in the number of observations. This compares favourably with existing methods for the same problem whose computational cost can be quadratic or even cubic. In simulation studies we show that our new method can be orders of magnitude faster than these alternative exact methods. We also compare with the Binary Segmentation algorithm for identifying changepoints, showing that the exactness of our approach can lead to substantial improvements in the accuracy of the inferred segmentation of the data.


title: Leakage-Resilient Cryptography

abstract: We construct a stream-cipher S whose implementation is secure even if a bounded amount of arbitrary (adversarially chosen) information on the internal state ofS is leaked during computation. This captures all possible side-channel attacks on S where the amount of information leaked in a given period is bounded, but overall can be arbitrary large. The only other assumption we make on the implementation of S is that only data that is accessed during computation leaks information. The stream-cipher S generates its output in chunks K1, K2, . . . and arbitrary but bounded information leakage is modeled by allowing the adversary to adaptively chose a function fl : {0,1}* rarr {0, 1}lambda before Kl is computed, she then gets fl(taul) where taul is the internal state ofS that is accessed during the computation of Kg. One notion of security we prove for S is that Kg is indistinguishable from random when given K1,..., K1-1,f1(tau1 ),..., fl-1(taul-1) and also the complete internal state of S after Kg has been computed (i.e. S is forward-secure). The construction is based on alternating extraction (used in the intrusion-resilient secret-sharing scheme from FOCS'07). We move this concept to the computational setting by proving a lemma that states that the output of any PRG has high HILLpseudoentropy (i.e. is indistinguishable from some distribution with high min-entropy) even if arbitrary information about the seed is leaked. The amount of leakage lambda that we can tolerate in each step depends on the strength of the underlying PRG, it is at least logarithmic, but can be as large as a constant fraction of the internal state of S if the PRG is exponentially hard.


title: Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models

abstract: The goal of this paper is to simulate the benefits of jointly applying active learning (AL) and semi-supervised training (SST) in a new speech recognition application. Our data selection approach relies on confidence filtering, and its impact on both the acoustic and language models (AM and LM) is studied. While AL is known to be beneficial to AM training, we show that it also carries out substantial improvements to the LM when combined with SST. Sophisticated confidence models, on the other hand, did not prove to yield any data selection gain. Our results indicate that, while SST is crucial at the beginning of the labeling process, its gains degrade rapidly as AL is set in place. The final simulation reports that AL allows a transcription cost reduction of about 70% over random selection. Alternatively, for a fixed transcription budget, the proposed approach improves the word error rate by about 12.5% relative.


title: HAIRY POLYP on the dorsum of the tongue – detection and comprehension of its possible dinamics

abstract: The formation of a Hairy Polyp on the dorsum of the tongue is a rare condition that may hinder vital functions such as swallowing and breathing due to mechanical obstruction. The authors present the situation on a child with an approach of significant academic value. Imaging diagnostics with the application of a topical oral radiocontrastant was used to determine the extent of the tumor. Performed treatment was complete excision and diagnostics was confirmed with anatomopathological analysis. The patient was controlled for five months and, showing no signs of relapse, was considered free from the lesion. Accurate diagnostics of such a lesion must be performed in depth so that proper surgical treatment may be performed. The imaging method proposed has permitted the visualization of the tumoral insertion and volume, as well as the comprehension of its threatening dynamics.


title: CCA based multi-view feature selection for multi-omics data integration

abstract: Recent technological advances in high-throughput omics technologies and their applications in genomic medicine have opened up outstanding opportunities for individualized medicine. However, several challenges arise in the integrative analysis of such data including heterogeneity and high dimensionality of the omics data. In this study, we present a novel multi-view feature selection algorithm based on the well- known canonical correlation analysis (CCA) statistical method for jointly selecting discriminative features from multi-omics data sources (multi-views). Our results demonstrate that models for predicting kidney renal clear cell carcinoma (KIRC) survival using our proposed method for jointly selecting discriminative features from copy number alteration (CNA), gene expression RNA-Seq, and reverse-phase protein arrays (RPPA) views outperform models trained using single-view data as well as three integrated models developed using data fusion approaches including CCA-based feature fusion.


title: Myoelectric Control Based on a Generic Musculoskeletal Model: Toward a Multi-User Neural-Machine Interface

abstract: This study aimed to develop a novel electromyography (EMG)-based neural-machine interface (NMI) that is user-generic for continuously predicting coordinated motion between metacarpophalangeal (MCP) and wrist flexion/extension. The NMI requires a minimum calibration procedure that only involves capturing maximal voluntary muscle contraction for the monitored muscles for individual users. At the center of the NMI is a user-generic musculoskeletal model based on the experimental data collected from 6 able-bodied (AB) subjects and 9 different upper limb postures. The generic model was evaluated on-line on both AB subjects and a transradial amputee. The subjects were instructed to perform a virtual hand/wrist posture matching task with different upper limb postures. The on-line performance of the generic model was also compared with that of the musculoskeletal model customized to each individual user (called "specific model"). All subjects accomplished the assigned virtual tasks while using the user-generic NMI, although the AB subjects produced better performance than the amputee subject. Interestingly, compared to the specific model, the generic model produced comparable completion time, a reduced number of overshoots, and improved path efficiency in the virtual hand/wrist posture matching task. The results suggested that it is possible to design an EMG-driven NMI based on a musculoskeletal model that could fit multiple users, including upper limb amputees, for predicting coordinated MCP and wrist motion. The present new method might address the challenges of existing advanced EMG-based NMI that require frequent and lengthy customization and calibration. Our future research will focus on evaluating the developed NMI for powered prosthetic arms.


title: Technology‐Based Communication and the Development of Interpersonal Competencies Within Adolescent Romantic Relationships: A Preliminary Investigation

abstract: This study investigated longitudinal associations between adolescents’ technology-based communication and the development of interpersonal competencies within romantic relationships. A school-based sample of 487 adolescents (58% girls; Mage = 14.1) participated at two time points, one year apart. Participants reported (1) proportions of daily communication with romantic partners via traditional modes (in person, on the phone) versus technological modes (text messaging, social networking sites) and (2) competence in the romantic relationship skill domains of negative assertion and conflict management. Results of cross-lagged panel models indicated that adolescents who engaged in greater proportions of technology-based communication with romantic partners reported lower levels of interpersonal competencies one year later, but not vice versa; associations were particularly strong for boys.


title: A survey on data leakage prevention systems

abstract: Protection of confidential data from being leaked to the public is a growing concern among organisations and individuals. Traditionally, confidentiality of data has been preserved using security procedures such as information security policies along with conventional security mechanisms such as firewalls, virtual private networks and intrusion detection systems. Unfortunately, these mechanisms lack pro-activeness and dedication towards protecting confidential data, and in most cases, they require predefined rules by which protection actions are taken. This can result in serious consequences, as confidential data can appear in different forms in different leaking channels. Therefore, there has been an urge to mitigate these drawbacks using more efficient mechanisms. Recently, data leakage prevention systems (DLPSs) have been introduced as dedicated mechanisms to detect and prevent the leakage of confidential data in use, in transit and at rest. DLPSs use different techniques to analyse the content and the context of confidential data to detect or prevent the leakage. Although DLPSs are increasingly being designed and developed as standalone products by IT security vendors and researchers, the term still ambiguous. In this study, we have carried out a comprehensive survey on the current DLPS mechanisms. We explicitly define DLPS and categorise active research directions in this field. In addition, we suggest future directions towards developing more consistent DLPSs that can overcome some of the weaknesses of the current ones. This survey is an updated reference on DLPSs, that can benefit both academics and professionals.


title: Learning Shared Representations in Multi-task Reinforcement Learning

abstract: We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space. Since the environment does not change, there is potentially a lot of common ground amongst tasks and learning to solve them individually seems extremely wasteful. In this paper, we explicitly model and learn this shared structure as it arises in the state-action value space. We will show how one can jointly learn optimal value-functions by modifying the popular Value-Iteration and Policy-Iteration procedures to accommodate this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization. this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization.


title: Effect of Bacillus-based direct-fed microbials on Eimeria maxima infection in broiler chickens

abstract: The effect of dietary Bacillus-based direct-fed microbials (DFMs; eight single strains designated as Bs2084, LSSAO1, 3AP4, Bs18, 15AP4, 22CP1, Bs27, and Bs278, and one multiple-strain DFM product [AVICORR™]) on growth performance, intestinal lesions, and innate and acquired immunities were evaluated in broiler chickens following Eimeria maxima (EM) infection. EM-induced reduction of body weight gain and intestinal lesions were significantly decreased by addition of 15AP4 or Bs27 into broiler diets compared with EM-infected control birds. Serum nitric oxide levels were increased in infected chickens fed with Bs27, but lowered in those given Bs2084, LSSAO1, 3AP4 or 15AP4 compared with the infected controls. Recombinant coccidial antigen (3-1E)-stimulated spleen cell proliferation was increased in chickens given Bs27, 15AP4, LSSAO1, 3AP4, or Bs18, compared with the infected controls. Finally, all experimental diets increased concanavalin A-induced splenocyte mitogenesis in infected broilers compared with the nonsupplemented and infected controls. In summary, dietary Bacillus subtilis-based DFMs reduced the clinical signs of experimental avian coccidiosis and increased various parameters of immunity in broiler chickens in a strain-dependent manner.


title: An information theoretic framework for active de-anonymization in social networks based on group memberships

abstract: In this paper, a new mathematical formulation for the problem of de-anonymizing social network users by actively querying their membership in social network groups is introduced. In this formulation, the attacker has access to a noisy observation of the group membership of each user in the social network. When an unidentified victim visits a malicious website, the attacker uses browser history sniffing to make queries regarding the victim's social media activity. Particularly, it can make polar queries regarding the victim's group memberships and the victim's identity. The attacker receives noisy responses to her queries. The goal is to de-anonymize the victim with the minimum number of queries. Starting with a rigorous mathematical model for this active de-anonymization problem, an upper bound on the attacker's expected query cost is derived, and new attack algorithms are proposed which achieve this bound. These algorithms vary in computational cost and performance. The results suggest that prior heuristic approaches to this problem provide sub-optimal solutions.


title: THE AGE OF THE UNIVERSE

abstract: IT Is not easy to decide whether the Ionians can properly be called scientists or whether they were simply philosophers. We incline to call them the first scientists because they were concerned to give a rational discussion of such problems as the nature of matter; we hesitate because their answers, so far as our slender records show, were qualitative and not based on an experimental method. But it is clearly true that however much we wish to restrict the term' science ', the greater part of its history and much of its current practice consists in research associated with that very problem : and it is also true that the foremost contributors to the field are still wrestling with difficulties that are at least partly philosophical, such as the prediction-requirements of a satisfactory physical theory, or the reality of the neutrino. One is at first inclined to say that the Ionians were asking a scientific question, but that they failed to give a really scientific answer. Perhaps a better description would be that they were asking a question which is partly scientific and partly philosophical, and that they had a very much simpler conception than we have of the sort of answer that can be given to each part, both in kind and in detail. The other great problem that concerned the Greeks (as it had the Babylonians and the Egyptians before them) was the nature of the universe. Each of these problems had one particularly important sub-problem : in the first case, the origin of living matter; in the second, the originl of the universe. The importance of these topics lies not only in their scientific interest but also in the fact that they represent in a sense the last stakes that the world-religions have in science. The origin of life is connected with the existence of the soul and the freedom of the will; the origin of the universe is connected with the existence of a God. It is now doubtful whether any research concerning the nature of life will directly support or conflict with the official interpretation of any important creeds, though a century ago it did. But experimental production of living creatures would undoubtedly affect the climate of religious belief. Just as evolutionary evidence made it impossible to interpret the book of Genesis literally, i.e. to accept the separate creation of man, so biological creation 181


title: Early Sensitivity to Arguments: How Preschoolers Weight Circular Arguments

abstract: Observational studies suggest that children as young as 2 years can evaluate some of the arguments people offer them. However, experimental studies of sensitivity to different arguments have not yet targeted children younger than 5 years. The current study aimed at bridging this gap by testing the ability of preschoolers (3-, 4-, and 5-year-olds) to weight arguments. To do so, it focused on a common type of fallacy—circularity—to which 5-year-olds are sensitive. The current experiment asked children—and, as a group control, adults—to choose between the contradictory opinions of two speakers. In the first task, participants of all age groups favored an opinion supported by a strong argument over an opinion supported by a circular argument. In the second task, 4- and 5-year-olds, but not 3-year-olds or adults, favored the opinion supported by a circular argument over an unsupported opinion. We suggest that the results of these tasks in 3- to 5-year-olds are best interpreted as resulting from the combination of two mechanisms: (a) basic skills of argument evaluations that process the content of arguments, allowing children as young as 3 years to favor non-circular arguments over circular arguments, and (b) a heuristic that leads older children (4- and 5-year-olds) to give some weight to circular arguments, possibly by interpreting these arguments as a cue to speaker dominance.


title: MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

abstract: Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.


title: A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model

abstract: We describe an accelerated hardware neuron being capable of emulating the adaptive exponential integrate-and-fire neuron model. Firing patterns of the membrane stimulated by a step current are analyzed in transistor level simulations and in silicon on a prototype chip. The neuron is destined to be the hardware neuron of a highly integrated wafer-scale system reaching out for new computational paradigms and opening new experimentation possibilities. As the neuron is dedicated as a universal device for neuroscientific experiments, the focus lays on parameterizability and reproduction of the analytical model.


title: Cross-library API recommendation using web search engines

abstract: Software systems are often built upon third party libraries. Developers may replace an old library with a new library, for the consideration of functionality, performance, security, and so on. It is tedious to learn the often complex APIs in the new library from the scratch. Instead, developers may identify the suitable APIs in the old library, and then find counterparts of these APIs in the new library. However, there is typically no such cross-references for APIs in different libraries. Previous work on automatic API recommendation often recommends related APIs in the same library. In this paper, we propose to mine search results of Web search engines to recommend related APIs of different libraries. In particular, we use Web search engines to collect relevant Web search results of a given API in the old library, and then recommend API candidates in the new library that are frequently appeared in the Web search results. Preliminary results of generating related C# APIs for the APIs in JDK show the feasibility of our approach.


title: Blind image deconvolution using constrained variance maximization

abstract: This paper describes an algorithm based on constrained variance maximization for the restoration of a blurred image. Blurring is a smoothing process by definition. Accordingly, the deblurring filter shall be able to perform as a high pass filter, which increases the variance. Therefore, we formulate a variance maximization object function for the deconvolution filter. Using principal component analysis (PCA), we find the filter maximizing the object function. PCA is more than just a high pass filter; by maximizing the variances, it is able to perform the decorrelation, by which the original image is extracted from the mixture (the blurred image). Our approach was experimentally compared with the adaptive Lucy-Richardson maximum likelihood (ML) algorithm. The comparative results on both synthesized and real blurred images are included.


Structure and function of hemocyanin


title: Soft intersection near-rings with its applications

abstract: In this paper, we first define soft intersection near-ring (soft int near-ring) by using intersection operation of sets. This new notion can be regarded as a bridge among soft set theory, set theory and near-ring theory, since it shows how a soft set effects on a near-ring structure by means of intersection and inclusion of sets. We then derive its basic properties with illustrative examples. Moreover, we obtain some analog of classical near-ring theoretic concepts for soft int near-ring and give the applications of soft int near-ring to near-ring theory.


title: Hyperglycemia in the intensive care unit: is insulin the only option?

abstract: Current guidelines advocate the use of insulin for the management of hyperglycemia in the hospital setting. However, insulin is limited by a narrow therapeutic window, frequent errors, a need for expertise and systems-based monitoring, and lack of specificity for metabolic abnormalities that occur during critical illness. As a result, non-insulin alternatives have garnered increasing interest for managing hyperglycemia in the hospital. However, non-insulin therapies have had safety and tolerability concerns, patients may still need insulin for glycemic control, and there have been limited outcomes data supporting their use. In the study by Christiansen and colleagues in the previous issue of Critical Care, pre-admission metformin therapy was associated with reduced mortality in critically ill patients with type 2 diabetes. The mortality benefit persisted after controlling for other variables, and was particularly prominent when metformin was continued during admission. Furthermore, the reduction in mortality was observed despite a slightly increased prevalence of lactic acidosis in metformin users. The protective effects of metformin are purported to be related to pleiotropic, possibly anti-inflammatory mechanisms, raising the question of benefit in patients without diabetes. Thus, the findings warrant a re-appraisal of the risks and benefits of metformin use during critical illness. However, in order to justify the revision of multiple guidelines and changes in product labeling, clinical trials in carefully selected patient populations are indicated.


title: When kids' toys breach mobile phone security

abstract: Touch-based verification --- the use of touch gestures (e.g., swiping, zooming, etc.) to authenticate users of touch screen devices --- has recently been widely evaluated for its potential to serve as a second layer of defense to the PIN lock mechanism. In all performance evaluations of touch-based authentication systems however, researchers have assumed naive (zero-effort) forgeries in which the attacker makes no effort to mimic a given gesture pattern. In this paper we demonstrate that a simple "Lego" robot driven by input gleaned from general population swiping statistics can generate forgeries that achieve alarmingly high penetration rates against touch-based authentication systems. Using the best classification algorithms in touch-based authentication, we rigorously explore the effect of the attack, finding that it increases the Equal Error Rates of the classifiers by between 339% and 1004% depending on parameters such as the failure-to-enroll threshold and the type of touch stroke generated by the robot. The paper calls into question the zero-effort impostor testing approach used to benchmark the performance of touch-based authentication systems.


Online enrollment in Medicaid and SCHIP.


title: Development of an upper limb exoskeleton powered via pneumatic electric hybrid actuators with bowden cable

abstract: In this paper, we introduce our ongoing work on the development of an upper body exoskeleton robot, driven by a pneumatic-electric hybrid actuation system. Since the limb of an exoskeleton robot needs to have small inertia to achieve agility and safety, using a heavy actuator is not preferable. Furthermore, we need to use backdrivable actuators that can generate sufficiently large torques to support user movements. These two requirements may seem contradictory. In order to cope with this development problem, we use a hybrid actuation system composed of Pneumatic Artificial Muscles (PAMs) and small-size electromagnetic motors. Although we and other research groups have already presented the advantage of the hybrid actuation system, we newly propose the usage of Bowden cable in a hybrid actuator to transmit the force generated by the PAMs to joints of our exoskeleton robot so that we can design a compact upper limb with small inertia. In addition, small size electric motors are mechanically connected to joints in order to compensate uncertainty generated by the PAM dynamics and the Bowden cable. We demonstrate that the proposed joint is backdrivable with the capability of large torque generation for the gravity compensation task both in One-DOF system with a dummy weight and right arm of the upper body exoskeleton with a mannequin arm. We also show the right arm exoskeleton can be moved using a torque input, extracted from sensory information via a goniometer.


title: Low cost design & development of conical horn feed for parabolic reflector antenna

abstract: We have designed the unusual types of feed antenna for parabolic dish that is used for both reception and transmission purpose. This different frequency band performance having horn and helical feed, works for the parabolic reflector antenna. We have worked on frequency band between 4.8 GHz to 5.9 GHz for horn type of feed. Here function of the horn is to produce uniform phase front with a larger aperture than that of the waveguide and hence greater directivity. Parabolic dish antenna is the most commonly and widely used antenna in communication field mainly in satellite and radar communication.


title: Differences in Antibiotic Resistance Patterns of Enterococcus faecalis and Enterococcus faecium Strains Isolated from Farm and Pet Animals

abstract: The prevalence of acquired resistance in 146 Enterococcus faecium and 166 Enterococcus faecalis strains from farm and pet animals, isolated in 1998 and 1999 in Belgium, against antibiotics used for growth promotion and for therapy was determined. Acquired resistance against flavomycin and monensin, two antibiotics used solely for growth promotion, was not detected. Avoparcin (glycopeptide) resistance was found sporadically in E. faecium only. Avilamycin resistance was almost exclusively seen in strains from farm animals. Resistance rates were higher in E. faecium strains from broiler chickens than in strains from other animal groups with tylosin and virginiamycin and in E. faecalis as well as in E. faecium strains with narasin and bacitracin. Resistance against ampicillin was mainly found among E. faecium strains from pets and was absent in E. faecalis. Tetracycline resistance occurred most often in strains from farm animals, while enrofloxacin resistance, only found in E. faecalis, occurred equally among strains from all origins. Resistance against gentamicin was very rare in broiler strains, whereas resistance rates were high in strains from other origins. It can be concluded that resistance against antibiotics used solely for growth promotion was more prevalent in E. faecium strains than in E. faecalis strains. With few exceptions, resistance against the different categories of antibiotics was more prevalent in strains from farm animals than in those from pets.


title: Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition

abstract: Convolutional Neural Network (CNN) has led to significant progress in face recognition. Currently most CNNbased face recognition methods follow a two-step pipeline, i.e. a detected face is first aligned to a canonical one predefined by a mean face shape, and then it is fed into a CNN to extract features for recognition. The alignment step transforms all faces to the same shape, which can cause loss of geometrical information which is helpful in distinguishing different subjects. Moreover, it is hard to define a single optimal shape for the following recognition, since faces have large diversity in facial features, e.g. poses, illumination, etc. To be free from the above problems with an independent alignment step, we introduce a Recursive Spatial Transformer (ReST) module into CNN, allowing face alignment to be jointly learned with face recognition in an end-to-end fashion. The designed ReST has an intrinsic recursive structure and is capable of progressively aligning faces to a canonical one, even those with large variations. To model non-rigid transformation, multiple ReST modules are organized in a hierarchical structure to account for different parts of faces. Overall, the proposed ReST can handle large face variations and non-rigid transformation, and is end-to-end learnable and adaptive to input, making it an effective alignment-free face recognition solution. Extensive experiments are performed on LFW and YTF datasets, and the proposed ReST outperforms those two-step methods, demonstrating its effectiveness.


title: A 0.7-V 0.6- $\mu \text{W}$ 100-kS/s Low-Power SAR ADC With Statistical Estimation-Based Noise Reduction

abstract: This paper presents a power-efficient noise reduction technique for successive approximation register analog-to-digital converters (ADCs) based on the statistical estimation theory. It suppresses both comparator noise and quantization error by accurately estimating the ADC conversion residue. It allows a high signal-to-noise ratio (SNR) to be achieved with a noisy low-power comparator and a relatively low resolution digital-to-analog converter (DAC). The proposed technique has low hardware complexity, requiring no change to the standard ADC operation except for repeating the least significant bit (LSB) comparisons. Three estimation schemes are studied and the optimal Bayes estimator is chosen for a prototype 11-b ADC in 65-nm CMOS. The measured SNR is improved by 7 dB with the proposed noise reduction technique. Overall, it achieves 10.5-b effective number of bits while operating at 100 kS/s and consuming $0.6~\mu \text{W}$ from a 0.7-V power supply.


Indoor Localization and Tracking using Wi-Fi Access Points


title: On the Repeatability of 3D Point Cloud Segmentation Based on Interest Points

abstract: Object recognition systems that use 3D point cloud as the input data are potentially subjected to the problems of signal attenuation at a local level, or occlusions in cluttered scenes. In an attempt to develop more robust methods in handling these problems, the present paper introduces the notion of repeatable regions through a 3D region segmentation algorithm based on the extraction of repeatable interest points. A segmentation method presented is presented which is capable of segmenting 3D images of free-form objects using piece-wise boundary curves and regions reconstructed from extracted interest points. An experimental evaluation was devised to confirm the repeatability of segments in various realistic scenes, including cluttered and partially occluded scene. Three different 3D free-form objects in seven 2.5D scenes were tested in the experiment, with results showing that out of the top 15 selected regions from each 3D model, an average of six repeatable segmented regions with at least one correctly segmented region were recorded for each scene. This shows that highly repeatable regions can be localized and used to drive robust object recognition in 3D data.


title: Circuit Design of a High Speed and Low Power CMOS Continuous-time Current Comparator

abstract: Current comparator is a fundamental component of current-mode analog integrated circuits. A novel high-performance continuous-time CMOS current comparator is proposed in this paper, which comprises one CMOS complementary amplifier, two resistive-load amplifiers and two CMOS inverters. A MOS resistor is used as the CMOS complementary amplifier's negative feedback. Because the voltage swings of the CMOS complementary amplifier are reduced by low input and output resistances, the delay time of the current comparator is shortened. Its power consumption can be reduced rapidly with the increase of input current. Simulation results based on 1.2 μm CMOS process model show the speed of the novel current comparator is comparable with those of the existing fastest CMOS current comparators, and its power consumption is the lowest, so it has the smallest power-delay product. Furthermore, the new current comparator occupies small area and is process-robust, so it is very suitable to high-speed and low-power applications.


title: The MIT super mini cheetah: A small, low-cost quadrupedal robot for dynamic locomotion

abstract: We present the MIT Super Mini Cheetah, a small (sub-10kg) and low-cost (sub-10k$) quadrupedal robot for dynamic locomotion. The robot can control vertical and horizontal force and impedance at each foot and performs dynamically stable walking, jumping, pronking, turning and braking using simple force and impedance trajectories. We present design specifications for dynamic legs which were used to guide the selection of leg components (e.g., motor, gearbox, and limb geometry). We demonstrate experimental evidence of accurate force control during foot-ground impact and present video of locomotion gaits performed by the robot. To the best of our knowledge, this is the first small (sub-10kg), power autonomous quadrupedal robot to perform such a wide range of ballistic locomotion behaviors and demonstrate foot force control during impact.


title: Bilateral hip disarticulation in paraplegics

abstract: Background: Decubitus ulcers are severe challenges to paraplegic patients, as well as to the medical providers caring for such disabled persons. Severe, chronic infection often can lead to death, especially in developing countries. Sometimes, hip disarticulation is the most appropriate surgical response to chronic ulceration. Objective: Report the results of bilateral disarticulations in 3 patients, one in Cambodia, and two in Australia. Methods: Chart reviews, examinations, and interviews with the patients were conducted to identify appropriate details of the indications and results. Results: Severe decubitus ulcers were successfully treated using the technique, without undue surgical complications. The general health of the patients was much improved. Conclusion: Bilateral hip disarticulations were performed as last stage salvage operations in three patients, who expressed satisfaction with the results, even though some problems with balance and recurrent ulceration persisted.


title: Mining idioms from source code

abstract: We present the first method for automatically mining code idioms from a corpus of previously written, idiomatic software projects. We take the view that a code idiom is a syntactic fragment that recurs across projects and has a single semantic purpose. Idioms may have metavariables, such as the body of a for loop. Modern IDEs commonly provide facilities for manually defining idioms and inserting them on demand, but this does not help programmers to write idiomatic code in languages or using libraries with which they are unfamiliar. We present Haggis, a system for mining code idioms that builds on recent advanced techniques from statistical natural language processing, namely, nonparametric Bayesian probabilistic tree substitution grammars. We apply Haggis to several of the most popular open source projects from GitHub. We present a wide range of evidence that the resulting idioms are semantically meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. Manual examination of the most common idioms indicate that they describe important program concepts, including object creation, exception handling, and resource management.


title: HiText: Text Reading with Dynamic Salience Marking

abstract: The staggering amounts of content readily available to us via digital channels can often appear overwhelming. While much research has focused on aiding people at selecting relevant articles to read, only few approaches have been developed to assist readers in more efficiently reading an individual text. In this paper, we present HiText, a simple yet effective way of dynamically marking parts of a document in accordance with their salience. Rather than skimming a text by focusing on randomly chosen sentences, students and other readers can direct their attention to sentences determined to be important by our system. For this, we rely on a deep learning-based sentence ranking method. Our experiments show that this results in marked increases in user satisfaction and reading efficiency, as assessed using TOEFL-style reading comprehension tests.


On the all-pairs-shortest-path problem


title: A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia

abstract: We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB). Given the KB, our goal is to comprehend a natural language query and provide corresponding accurate answers. Focusing on solving the non-aggregation questions, in this paper, we construct a subgraph of the knowledge base from the detected entities and propose a graph traversal method to solve both the semantic item mapping problem and the disambiguation problem in a joint way. Compared with existing work, we simplify the process of query intention understanding and pay more attention to the answer path ranking. We evaluate our method on a non-aggregation question dataset and further on a complete dataset. Experimental results show that our method achieves best performance compared with several state-of-the-art systems.


title: Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition

abstract: Fine-grained car recognition aims to recognize the category information of a car, such as car make, car model, or even the year of manufacture. A number of recent studies have shown that a deep convolutional neural network (DCNN) trained on a large-scale data set can achieve impressive results at a range of generic object classification tasks. In this paper, we propose a spatially weighted pooling (SWP) strategy, which considerably improves the robustness and effectiveness of the feature representation of most dominant DCNNs. More specifically, the SWP is a novel pooling layer, which contains a predefined number of spatially weighted masks or pooling channels. The SWP pools the extracted features of DCNNs with the guidance of its learnt masks, which measures the importance of the spatial units in terms of discriminative power. As the existing methods that apply uniform grid pooling on the convolutional feature maps of DCNNs, the proposed method can extract the convolutional features and generate the pooling channels from a single DCNN. Thus minimal modification is needed in terms of implementation. Moreover, the parameters of the SWP layer can be learned in the end-to-end training process of the DCNN. By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature. We advance the state-of-the-art results by improving the accuracy from 92.6% to 93.1% on the Stanford Cars-196 data set and 91.2% to 97.6% on the recent CompCars data set. We have also tested the proposed method on two additional large-scale data sets with impressive results observed.


Capturing Value from Data: Revenue Models for Data-Driven Services


title: Five-Level Reduced-Switch-Count Boost PFC Rectifier With Multicarrier PWM

abstract: A multilevel boost PFC (Power Factor Correction) rectifier is presented in this paper controlled by cascaded controller and multicarrier pulse width modulation technique. The presented topology has less active semiconductor switches compared to similar ones reducing switching losses as well as the number of required gate drives that would shrink manufactured box significantly. A simple controller has been implemented on the studied converter to generate a constant voltage at the output while generating a five-level voltage waveform at the input without connecting the load to the neutral point of the DC bus capacitors. Multicarrier PWM technique has been used to produce switching pulses from control signal. Multi-level voltage waveform harmonics has been analyzed comprehensively which affects the size of input current and required filters directly. Full simulation and experimental results confirm the good dynamic performance of the proposed five-level PFC boost rectifier in delivering power from AC grid to the DC loads while correcting the power factor at the AC side as well as reducing the current harmonics remarkably.


title: Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots

abstract: Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process.


title: Regenerative Braking System of Electric Vehicle Driven by Brushless DC Motor

abstract: Regenerative braking can improve energy usage efficiency and can prolong the driving distance of electric vehicles (EVs). A creative regenerative braking system (RBS) is presented in this paper. The RBS is adapted to brushless dc (BLDC) motor, and it emphasizes on the distribution of the braking force, as well as BLDC motor control. In this paper, BLDC motor control utilizes the traditional proportional-integral-derivative (PID) control, and the distribution of braking force adopts fuzzy logic control. Because the fuzzy reasoning is slower than PID control, the braking torque can be real-time controlled by PID control. In comparison to other solutions, the new solution has better performance in regard to realization, robustness, and efficiency. Then, this paper presents the simulation results by analyzing the battery state of charge, braking force, and dc bus current under the environment of MATLAB and Simulink. The simulation results show that the fuzzy logic and PID control can realize the regenerative braking and can prolong the driving distance of EVs under the condition of ensuring braking quality. At last, it is verified that the proposed method is realizable for practical implementation.


title: More bilateral, more anterior: Alterations of brain organization in the large-scale structural network in Chinese dyslexia

abstract: Abnormalities in large-scale brain networks have been recently reported in dyslexia; however, it remains unclear whether these abnormalities are congenital (due to dyslexia per se) or arise later in development. Here, structural magnetic resonance imaging data of 17 Chinese reading disabled (RD) and 17 age-matched typically developing (TD) children were used to construct cortical thickness (sensitive to postnatal development) and surface area (sensitive to prenatal development) networks. In the thickness network, compared to TD, RD showed reduced nodal network properties (e.g., degree and betweenness) in the left hemisphere along with enhanced nodal properties mainly in the right hemisphere. As for the surface area network, compared to TD, RD demonstrated lower nodal properties in the posterior brain regions and higher nodal properties in the anterior brain regions. Furthermore, hubs in both the thickness and surface area networks in RD were more distributed in frontal areas and less distributed in parietal areas, whereas TD showed the opposite pattern. Altogether, these findings indicate that the aberrant structural connectivity in the dyslexic individuals was not only due to a late developmental effect reflected in the altered thickness network, but may also be a congenital effect during prenatal development, reflected in the altered surface network.


Patterns as Formulas : Patterns in the Digital Humanities


title: Distributed Arithmetic for FIR Filter Design on FPGA

abstract: This paper presents a distributed arithmetic (DA) for highly efficient multiplier-less FIR filter designed on FPGA. First, the theory of the distributed arithmetic is described. Furthermore, a modification of the DA based on the look up table (LUT) and filter structure to implement the high-order filter hardware-efficient on FPGA is introduced. The proposed filter has been designed and synthesized with ISE 7.1, and implemented with a 4VLX40FF668 FPGA device. Our results show that the proposed DA architecture can implement FIR filters with the smaller resource usage and similar speed in comparison to the previous DA architecture.


title: Transferring Subspaces Between Subjects in Brain-Computer Interfacing

abstract: Compensating changes between a subjects' training and testing session in brain-computer interfacing (BCI) is challenging but of great importance for a robust BCI operation. We show that such changes are very similar between subjects, and thus can be reliably estimated using data from other users and utilized to construct an invariant feature space. This novel approach to learning from other subjects aims to reduce the adverse effects of common nonstationarities, but does not transfer discriminative information. This is an important conceptual difference to standard multi-subject methods that, e.g., improve the covariance matrix estimation by shrinking it toward the average of other users or construct a global feature space. These methods do not reduces the shift between training and test data and may produce poor results when subjects have very different signal characteristics. In this paper, we compare our approach to two state-of-the-art multi-subject methods on toy data and two datasets of EEG recordings from subjects performing motor imagery. We show that it can not only achieve a significant increase in performance, but also that the extracted change patterns allow for a neurophysiologically meaningful interpretation.


title: Publication Venue Recommendation Based on Paper Abstract

abstract: We consider the problem of matching the topics of a scientific paper with those of possible publication venues for that paper. While every researcher knows the few top-level venues for his specific fields of interest, a venue recommendation system may be a significant aid when starting to explore a new research field. We propose a venue recommendation system which requires only title and abstract, differently from previous works which require full-text and reference list: hence, our system can be used even in the early stages of the authoring process and greatly simplifies the building and maintenance of the knowledge base necessary for generating meaningful recommendations. We assessed our proposal using a standard metric on a dataset of more than 58000 papers: the results show that our method provides recommendations whose quality is aligned with previous works, while requiring much less information from both the paper and the knowledge base.