title: United States immigration policy and indirect immigration of professionals

abstract: Abstract Professionals migrate to the U.S. from other countries through both direct and indirect channels. The indirect channel, which entails entering the U.S. as a foreign student or visitor and subsequently adjusting visa status to immigrant, has assumed increasing importance over time. The number of adjustments of status can be expected to further increase in the future as the eligible pool, comprised mainly of foreign students, continues to expand. The ratio of adjustments to the eligible pool is also influenced by U.S. immigration policy, which originates in administrative regulations as well as legislative action. U.S. immigration policy affects the ease of immigration and thus the ratio of adjustments to the eligible pool. Variations in immigration policy across countries and over time are found to have statistically significant impacts on this ratio.


title: An inverter packaging scheme for an integrated segmented traction drive system

abstract: The standard voltage source inverter (VSI), widely used in electric vehicle/hybrid electric vehicle (EV/HEV) traction drives, requires a bulky dc bus capacitor to absorb the large switching ripple currents and prevent them from shortening the battery's life. The dc bus capacitor presents a significant barrier to meeting inverter cost, volume, and weight requirements for mass production of affordable EVs/HEVs. The large ripple currents become even more problematic for the film capacitors (the capacitor technology of choice for EVs/HEVs) in high temperature environments as their ripple current handling capability decreases rapidly with rising temperatures. It is shown in previous work that segmenting the VSI based traction drive system can significantly decrease the ripple currents and thus the size of the dc bus capacitor. This paper presents an integrated packaging scheme to reduce the system cost of a segmented traction drive.


title: Metric-based Evaluation of Implemented Software Architectures

abstract: Software systems make up an important part of our daily lives. Just like all man- made objects, the possibilities of a software system are constrained by the choices made during its creation. The complete set of these choices can be referred to as the software architecture of a system. Since the software architecture of a system has a large influence on what can, and cannot, be done with the system, it is important to regularly evaluate this architecture. The purpose of such an evaluation is to create an overview of the strengths and weaknesses of the software architecture, which can then be used to decide whether each weakness is accepted or should be addressed. There is a wide range of software architecture evaluation methods available which can be used to investigate one or more quality aspects of a software architecture. Most of these methods focus on the initial design of the software architecture, there are only a few which specifically target the implemented architecture. Looking at the design alone is not problematic if design and implementation are in sync, but unfortunately there are many occasions in which these two architectures deviate. Moreover, some systems are even built without an initial design at all. In addition, earlier research shows that software architectures are not regularly evaluated in practice, despite the availability of these methods. The reason for this is that the initial effort to start performing software architecture evaluations is considered to be too high for project teams. The goal of this thesis is to lower this initial investment by providing an overview of concrete evaluation attributes, as well as the definition of software metrics that can be used to evaluate these attributes. Our global research approach is that of “industry-as-a-lab”. During our research we have closely collaborated with the Software Improvement Group to design solutions, and to test these solutions on real-world cases. To be able to provide concrete advice we must focus on only a single quality attribute; in this thesis we choose to focus on the maintainability quality attribute of a software system. Following from our goal the research in this thesis is composed of two parts. The focus of the first part is on the identification of architectural attributes that can be used as an indicator for the maintainability of a software system. The result of mining over 40 evaluation reports, interviewing experts, and a validation with various experts is a list of 15 architectural attributes which experts consider to be indicators for the maintainability of a software system. To augment the opinion of the experts we used theories from the field of cognitive psychology to extend an existing model for architectural complexity. This extended model makes it possible to explain why each of the found attributes influence the maintainability of a software system. Based on the attributes and the model we developed a lightweight sanity-check for implemented architectures. This check consists of 28 questions and 28 actions divided over five categories. A person familiar with a system can use this check to get an initial overview of the status of their system within a day, and needs less effort to repeat this evaluation later on. In the second part of our research we focus on the design and validation of metrics related to two quality attributes: balance and independence. These two attributes are related to two of the major building blocks of an implemented architecture; the definition of the components of the system and the relationship these components have with each other. In the ideal case a system is decomposed into a limited set of components on the same level of abstraction, while the dependencies between these components is limited. To quantify the number of components and their level of abstraction we define the Component Balance metric. This metric takes the number of components of a system and the distribution of the volume of the system over these components and outputs a score between zero and one. Interviews with experts and a case-study show that the scores of this metric correlates with scores given by experts. The quantification of the dependencies between the components is done by a Dependency Profile. In such a profile, all code within a component is divided into one of four categories depending on whether a piece of code is dependent upon by, or depends on, code in other components. A large-scale experiment shows that the percentage of code in these categories is correlated with the ratio of local change within a system. Both metrics are implemented in practice to evaluate the usefulness of these two metrics within the context of the evaluation of implemented architectures. The results of a structured observation of experts using the metrics during a period of six months and interviews with 11 experts show that there is room for improvement, but that the two metrics are considered to be useful within this context. The combination of different research methods such as interviews, case-studies, empirical experiments and grounded theory, augmented by experiences taken from practice have lead to research results which are both valid and useful. In this thesis we lower the initial effort needed to start performing architectural evaluations by show- ing which concrete attributes should be taken into account, and how these attributes could be evaluated in a continuous manner. Additionally, we define and validate met- rics for two of these attributes, and show that experts find these metrics useful in the evaluation of implemented architectures within practice.


- The random waypoint city model -: user distribution in a street-based mobility model for wireless network simulations


title: FaceBoxes: A CPU real-time face detector with high accuracy

abstract: Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this challenge, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy. Specifically, our method has a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the Multiple Scale Convolutional Layers (MSCL). The RDCL is designed to enable FaceBoxes to achieve real-time speed on the CPU. The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales. Besides, we propose a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces. As a consequence, the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the number of faces. We comprehensively evaluate this method and present state-of-the-art detection performance on several face detection benchmark datasets, including the AFW, PASCAL face, and FDDB.


title: Crying Without Tears: Dimensions of Crying and Relations With Ocular Dryness and Mental Well-Being in Patients With Sjögren’s Syndrome

abstract: This study examined dimensions of crying and its relations with ocular dryness and mental well-being in patients with Sjögren’s syndrome, a systemic autoimmune disease with dryness as primary symptom. Three-hundred patients with Sjögren’s syndrome completed questionnaires on crying, dryness, and well-being. The crying questionnaire revealed four dimensions: “Cryability” (comprising both crying sensibility and ability to cry), Somatic consequences, Frustration, and Suppression. Compared to 100 demographically-matched control participants from the general population, patients scored low on Cryability and high on Somatic consequences and Frustration. The crying dimensions generally showed significant but weak associations with ocular dryness and mental well-being in patients. This is the first quantitative study indicating that crying problems are more common in patients with Sjögren’s syndrome than in the general population. Perhaps, patients who experience problems with crying could be helped to rely on other ways of expressing emotions than crying in tear-inducing situations.


title: Shame Proneness and Guilt Proneness: Toward the Further Understanding of Reactions to Public and Private Transgressions

abstract: In Study 1, participants completed five extant shame and guilt proneness inventories based on different theoretical conceptions of the difference between shame and guilt. Factor analyses revealed that despite very different theoretical distinctions, the shame proneness subscales loaded on one factor, and the guilt proneness subscales loaded on one factor. In Study 2, we altered scale items so that hypothetical transgressions were committed in either public or private, and likelihood response options were either typical of a “shame-prone response” (negative self-evaluation; avoidance behavior) or a “guilt-prone response” (negative behavior evaluation; approach behavior). Our findings indicate that shame and guilt proneness can be measured both by responses to transgressions (e.g., negative self-evaluation and avoidance responses vs. negative behavior evaluation and approach responses) and the situational context in which the transgression occurs (e.g., public vs. private). We provide recommendations regard...


title: Towards Understanding Adversarial Learning for Joint Distribution Matching

abstract: We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.


title: Study of Density based Algorithms

abstract: Clusters formed on the basis of density are very useful and easy to understand and they do not limit to their shapes. Basically two types of density based algorithms are present. One is density based connectivity which focuses on Density and Connectivity and another is Density function which is total mathematical function. They work best in spatial database. In this paper, we are studying DBSCAN, VDBSCAN, DVBSCAN, UDBSCAN, OPTICS, DBNCLUE, GDBSCA and DBCLASD. We analyze some of the algorithms in terms of meaningful clusters.


title: Between facets and domains: 10 aspects of the Big Five.

abstract: Factor analyses of 75 facet scales from 2 major Big Five inventories, in the Eugene-Springfield community sample (N 481), produced a 2-factor solution for the 15 facets in each domain. These findings indicate the existence of 2 distinct (but correlated) aspects within each of the Big Five, representing an intermediate level of personality structure between facets and domains. The authors characterized these factors in detail at the item level by correlating factor scores with the International Personality Item Pool (L. R. Goldberg, 1999). These correlations allowed the construction of a 100-item measure of the 10 factors (the Big Five Aspect Scales [BFAS]), which was validated in a 2nd sample (N 480). Finally, the authors examined the correlations of the 10 factors with scores derived from 10 genetic factors that a previous study identified underlying the shared variance among the Revised NEO Personality Inventory facets (K. L. Jang et al., 2002). The correspondence was strong enough to suggest that the 10 aspects of the Big Five may have distinct biological substrates.


title: A Novel On-Chip Active Dispersive Delay Line (DDL) for Analog Signal Processing

abstract: In this letter, we report the first on-chip design of an active dispersive delay line (DDL) based upon the distributed amplification structure. This distributed amplifier DDL exhibits nanosecond delay variation in the frequency band from 11 to 15 GHz. An on-chip temporal imager is implemented with this active DDL and a linear chirp generator, realized by ramping the control voltage of a voltage controlled oscillator. The experimental data exhibits pulse stretching as well as pulse compression with this system.


title: DETECTING INFORMAL SETTLEMENTS FROM QUICKBIRD DATA IN RIO DE JANEIRO USING AN OBJECT BASED APPROACH

abstract: Informal settlements behave very dynamical over space and time and the number of people living in such housing areas is growing worldwide. The reasons for this dynamical behavior are manifold and are not matter of this article. Nevertheless, informal settlements represent a status quo of housing and living conditions which is from a humanitarian point of view in the most cases below acceptable levels. Therefore, reliable spatial information about informal settlements is vital for any actions of improvement of these living conditions. Since remote sensing data is a well suited data source for mapping and monitoring we demonstrate a methodology to detect informal settlements (favelas) from QuickBird data using an object based approach. On the one hand we therefore use experiences and adapt them which were already made by Hofmann, P. (2001) regarding the image segmentation of an IKONOS scene of Cape Town. On the other hand we resort to a general ontology of informal settlements which we then transfer to a fuzzy-logic rule base which acts as basic classifier of the generated segments. This basic rule base is than extended in a way that features of segregation given by the ontology (namely neighborhood) are applied to the extraction method as an iterative process (i.e. a knowledge based region growing). Finally, we assess the results of the simple and iterative method by comparing them with the results of a manual mapping.


Stock Market Prediction through Technical and Public Sentiment Analysis


title: Design of a Microstrip Low-Pass-Bandpass Diplexer Using Direct-Feed Coupled-Resonator Filter

abstract: Previous systematic low-pass-bandpass diplexer was using coupled-feed line to design its bandpass channel response. However, a coupled-feed line coupled-resonator bandpass filter typically leads to the realization problem in the external quality factor because of the line space limitation between the coupled-feed line and the resonator. Based on our investigation, systematic low-pass-bandpass diplexer design using direct-feed coupled-resonator bandpass filter to relax this problem has never been published in the literature. Therefore, a low-pass-bandpass diplexer using direct-feed coupled-resonator bandpass filter is presented and a systematic matching design is also included in this letter.


title: Social coding in GitHub: transparency and collaboration in an open software repository

abstract: Social applications on the web let users track and follow the activities of a large number of others regardless of location or affiliation. There is a potential for this transparency to radically improve collaboration and learning in complex knowledge-based activities. Based on a series of in-depth interviews with central and peripheral GitHub users, we examined the value of transparency for large-scale distributed collaborations and communities of practice. We find that people make a surprisingly rich set of social inferences from the networked activity information in GitHub, such as inferring someone else's technical goals and vision when they edit code, or guessing which of several similar projects has the best chance of thriving in the long term. Users combine these inferences into effective strategies for coordinating work, advancing technical skills and managing their reputation.


title: Optimisation of quasi-resonant induction cookers

abstract: In this paper, the optimisation possibilities of a quasi-resonant induction cooker are investigated. Simulations show that main losses of the induction cooker occur in the heating coil and the RC-IGBT (power switch). The performance of the coil can be improved mainly by minimising the coil resistance. The IGBT-optimisation is based on the reduction of tail current in soft switching mode. The tail current is reduced by using lifetime engineering and reduction of the IGBT-thickness. The losses of the optimised IGBT are about 30% lower. As a result, the cooling system of the IGBT can be smaller and cheaper. Together with some optimisations of the circuit the overall losses of the system could be improved by almost 10% or 1 percentage point.


title: Learning unions of orthonormal bases with thresholded singular value decomposition

abstract: We propose a new method to learn overcomplete dictionaries for sparse coding structured as unions of orthonormal bases. The interest of such a structure is manifold. Indeed, it seems that many signals or images can be modeled as the superimposition of several layers with sparse decompositions in as many bases. Moreover, in such dictionaries, the efficient block coordinate relaxation (BCR) algorithm can be used to compute sparse decompositions. We show that it is possible to design an iterative learning algorithm that produces a dictionary with the required structure. Each step is based on the coefficients estimation, using a variant of BCR, followed by the update of one chosen basis, using singular value decomposition. We assess experimentally how well the learning algorithm recovers dictionaries that may or may not have the required structure, and to what extent the noise level is a disturbing factor.


title: Implementation of efficient SHA-256 hash algorithm for secure vehicle communication using FPGA

abstract: In this paper, we implement the SHA-256 FPGA hardware module for the security protocol of the IEEE 1609.2 vehicle communication (VC). VC requires high-throughput and low-latency hardware architectures. For fast and efficient design, we exploit parallel structures for preprocessing and hash computation in SHA-256. The proposed design is im- plemented in Vertex-5 and verified for correct hash operation. As a result, we can achieve up to 179.08 MHz with 2796 slices.


title: Methodologies for developing knowledge management systems: an evaluation framework

abstract: Purpose – This paper aims to provide a criteria-based evaluation framework for assessing knowledge management system (KMS) development methodologies. Design/methodology/approach – The evaluation criteria have been elicited based on the features expected from a successful KMS. Furthermore, a number of prominent KMS development methodologies have been scrutinized based on the proposed evaluation framework. Findings – It was demonstrated that the proposed evaluation framework is detailed and comprehensive enough to reveal the strengths and weaknesses of KMS development methodologies. It was also revealed that even though the evaluated methodologies possess certain strong features, they suffer from several shortcomings that need to be addressed. Research limitations/implications – The evaluation framework has not been applied to all existing KMS development methodologies; however, the evaluation does cover the most comprehensive methodologies which exist in the research context. Practical implications – The r...


title: LNG decision making approaches compared.

abstract: Hazard zones associated with LNG handling activities have been a major point of contention in recent terminal development applications. Debate has reflected primarily worst case scenarios and discussion of these. This paper presents results from a maximum credible event approach. A comparison of results from several models either run by the authors or reported in the literature is presented. While larger scale experimental trials will be necessary to reduce the uncertainty, in the interim a set of base cases are suggested covering both existing trials and credible and worst case events is proposed. This can assist users to assess the degree of conservatism present in quoted modeling approaches and model selections.


title: The Streptococcus thermophilus CRISPR/Cas system provides immunity in Escherichia coli

abstract: The CRISPR/Cas adaptive immune system provides resistance against phages and plasmids in Archaea and Bacteria. CRISPR loci integrate short DNA sequences from invading genetic elements that provide small RNA-mediated interference in subsequent exposure to matching nucleic acids. In Streptococcus thermophilus, it was previously shown that the CRISPR1/Cas system can provide adaptive immunity against phages and plasmids by integrating novel spacers following exposure to these foreign genetic elements that subsequently direct the specific cleavage of invasive homologous DNA sequences. Here, we show that the S. thermophilus CRISPR3/Cas system can be transferred into Escherichia coli and provide heterologous protection against plasmid transformation and phage infection. We show that interference is sequence-specific, and that mutations in the vicinity or within the proto-spacer adjacent motif (PAM) allow plasmids to escape CRISPR-encoded immunity. We also establish that cas9 is the sole cas gene necessary for CRISPR-encoded interference. Furthermore, mutation analysis revealed that interference relies on the Cas9 McrA/HNH- and RuvC/RNaseH-motifs. Altogether, our results show that active CRISPR/Cas systems can be transferred across distant genera and provide heterologous interference against invasive nucleic acids. This can be leveraged to develop strains more robust against phage attack, and safer organisms less likely to uptake and disseminate plasmid-encoded undesirable genetic elements.


title: Service Oriented Middleware for the Internet of Things: A Perspective ⋆

abstract: The Internet of Things plays a central role in the foreseen shift of the Internet to the Future Internet, as it incarnates the drastic expansion of the In- ternet network with non-classical ICT devices. It will further be a major source of evolution of usage, due to the penetration in the user's life. As such, we en- vision that the Internet of Things will cooperate with the Internet of Services to provide users with services that are aware of their surrounding environment. The supporting service-oriented middleware shall then abstract the functionalities of Things as services as well as provide the needed interoperability and flexibil- ity, through a loose coupling of components and composition of services. Still, core functionalities of the middleware, namely service discovery and composi- tion, need to be revisited to meet the challenges posed by the Internet of Things. Challenges in particular relate to the ultra large scale, heterogeneity and dynam- ics of the Internet of Things that are far beyond the ones of today's Internet of Services. In addition, new challenges also arise, pertaining to the physical-world aspect that is central to the IoT. In this paper, we survey the major challenges posed to service-oriented middleware towards sustaining a service-based Internet of Things, together with related state of the art. We then concentrate on the spe- cific solutions that we are investigating within the INRIA ARLES project team as part of the CHOReOS European project, discussing new approaches to overcome the challenges particular to the Internet of Things.


title: Test-driven learning: intrinsic integration of testing into the CS/SE curriculum

abstract: Test-driven learning (TDL) is an approach to teaching computer programming that involves introducing and exploring new concepts through automated unit tests. TDL offers the potential of teaching testing for free, of improving programmer comprehension and ability, and of improving software quality both in terms of design quality and reduced defect density.This paper introduces test-driven learning as a pedagogical tool. It will provide examples of how TDL can be incorporated at multiple levels in computer science and software engineering curriculum for beginning through professional programmers. In addition, the relationships between TDL and test-driven development will be explored.Initial evidence indicates that TDL can improve student comprehension of new concepts while improving their testing skills with no additional instruction time. In addition, by learning to construct programs in a test-driven manner, students are expected to be more likely to develop their own code with a test-driven approach, likely resulting in improved software designs and quality.


title: Human preferences for robot-human hand-over configurations

abstract: Handing over objects to humans is an essential capability for assistive robots. While there are infinite ways to hand an object, robots should be able to choose the one that is best for the human. In this paper we focus on choosing the robot and object configuration at which the transfer of the object occurs, i.e. the hand-over configuration. We advocate the incorporation of user preferences in choosing hand-over configurations. We present a user study in which we collect data on human preferences and a human-robot interaction experiment in which we compare hand-over configurations learned from human examples against configurations planned using a kinematic model of the human. We find that the learned configurations are preferred in terms of several criteria, however planned configurations provide better reachability. Additionally, we find that humans prefer hand-overs with default orientations of objects and we identify several latent variables about the robot's arm that capture significant human preferences. These findings point towards planners that can generate not only optimal but also preferable hand-over configurations for novel objects.


title: Big Data Classification using Fuzzy K-Nearest Neighbor

abstract: becomes troublesome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this paper is to classify big data using Fuzzy K-Nearest Neighbor classifier, and to provide a comparative study between the results of the proposed systems and the method reviewed in the literature. In this paper we implemented the Fuzzy KNearest Neighbor method using the MapReduce paradigm to process on big data. Results on different data sets show that the proposed Fuzzy K-Nearest Neighbor method outperforms a better performance than the method reviewed in the literature.


title: Development of a Social Media Maturity Model -- A Grounded Theory Approach

abstract: Social media has become part of everyday lives. Discussions about whether social media is relevant to organizations no longer exist, instead, discussions on how to successfully adopt social media prevail. With much uncertainty on what it takes to reach excellence in social media use, companies often start with an uncoordinated approach, not taking into account all potential threats. However, little research is done on the diverse components of an organization that are influenced by the use of social media. Furthermore, a common understanding on when a company is "ready" for social media application is missing. Thus, this work seeks to describe a generic approach on assessing social media maturity. Applying grounded theory, a first version for a maturity model is developed which will serve as a tool for introducing and assessing organizational social media activities, including a set of relevant demographic information, organizational prerequisites, as well as dimensions of maturity.


title: Denoising point sets via L0 minimization

abstract: We present an anisotropic point cloud denoising method using L 0 minimization. The L 0 norm directly measures the sparsity of a solution, and we observe that many common objects can be defined as piecewise smooth surfaces with a small number of features. Hence, we demonstrate how to apply an L 0 optimization directly to point clouds, which produces sparser solutions and sharper surfaces than either the L 1 or L 2 norm. Our method can faithfully recover sharp features while at the same time smoothing the remaining regions even in the presence of large amounts of noise.


title: Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation

abstract: There is a lack of enough attention to the patients' hand posture. When robots implement assistive training, patients are often asked to grip a handle tightly, which may induce strong hand muscle contractions with the hand at an abnormal posture. If we ignore proper control of the muscle tension of subject's hand, the flexibility of hand/fingers may decrease and the robot training may potentially cause abnormal muscle tone. On the other hand, since the patient's fingers are already in an abnormal posture, properly aligning the joint and making the robot easy to attach is important. However, an existing multi-DOF hand exoskeleton systems may be difficult for the patients to put it on. the purpose of this paper was to design a patient-friendly mechanism drive by a single motor and attached to the whole-arm rehab robot for the hand and finger opening and closing functions. Using our 8+2 DOF whole-arm robot including this hand opening and closing mechanism, a novel integrated rehabilitation is performed including 1) strenuous stretching of the MCP-thumb joints and other spastic joints of the upper limb; 2) active assistive exercise is provided to improve voluntary neuromuscular control by using robot-computer games with a griping task; 3) outcome evaluations including the cross-coupling torques between the fingers/thumb and the other joints during hand opening/closing and other upper limb movements.


Can Microblogs Predict Music Charts? An Analysis of the Relationship Between #Nowplaying Tweets and Music Charts


title: A Task-Type-Based Algorithm for the Energy-Aware Profit Maximizing Scheduling Problem in Heterogeneous Computing Systems

abstract: In this paper, we design an efficient algorithm for the energy-aware profit maximizing scheduling problem, where the high performance computing system administrator is to maximize the profit per unit time. The running time of the proposed algorithm is depending on the number of task types, while the running time of the previous algorithm is depending on the number of tasks. Moreover, we prove that the worst-case performance ratio is close to 2, which maybe the best result. Simulation experiments show that the proposed algorithm is more accurate than the previous method.


title: Retweet Prediction with Attention-based Deep Neural Network

abstract: On Twitter-like social media sites, the re-posting statuses or tweets of other users are usually considered to be the key mechanism for spreading information. How to predict whether a tweet will be retweeted by a user has received increasing attention in recent years. Previous methods studied the problem using various linguistic features, personal information of users, and many other manually constructed features to achieve the task. Usually, feature engineering is a laborious task, we require to obtain the external sources and they are difficult or not always available. Recently, deep learning methods have been used in the industry and research community for their ability to learn optimal features automatically and in many tasks, deep learning methods can achieve state-of-the art performance, such as natural language processing, computer vision, image classification and so on. In this work, we proposed a novel attention-based deep neural network to incorporate contextual and social information for this task. We used embeddings to represent the user, the user's attention interests, the author and tweet respectively. To train and evaluate the proposed methods, we also constructed a large dataset collected from Twitter. Experimental results showed that the proposed method could achieve better results than the previous state-of-the-art methods.


title: It's always April fools' day!: On the difficulty of social network misinformation classification via propagation features

abstract: Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.


title: Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control

abstract: A humanoid robot is expected to be a rational form of machine to act in the real human environment and support people through interaction with them. Current humanoid robots, however, lack in adaptability, agility, or high-mobility enough to meet the expectations. In order to enhance high-mobility, the humanoid motion should be generated in real-time in accordance with the dynamics, which commonly requires a large amount of computation and has not been implemented so far. We have developed a real-time motion generation method that controls the center of gravity (COG) by indirect manipulation of the zero moment point (ZMP). The real-time response of the method provides humanoid robots with high-mobility. In the paper, the algorithm is presented. It consists of four parts, namely, the referential ZMP planning, the ZMP manipulation, the COG velocity decomposition to joint angles, and local control of joint angles. An advantage of the algorithm lies in its applicability to humanoids with a lot of degrees of freedom. The effectiveness of the proposed method is verified by computer simulations.


title: Using causal discovery for feature selection in multivariate numerical time series

abstract: Time series data contains temporal ordering, which makes its feature selection different from the normal feature selection. Feature selection in multivariate time series has two tasks: identifying the relevant features and finding their effective window sizes of lagged values. The methods extended from normal feature selection methods do not solve this two-dimensional feature selection problem since they do not take lagged observations of features into consideration. In this paper, we present a method using the Granger causality discovery to identify causal features with effective sliding window sizes in multivariate numerical time series. The proposed method considers the influence of lagged observations of features on the target time series. We compare our proposed feature selection method with several normal feature selection methods on multivariate time series data using three well-known modeling methods. Our method outperforms other methods for predicting future values of target time series. In a real world case study on water quality monitoring data, we show that the features selected by our method contain four out of five features used by domain experts, and prediction performance on our features is better than that on features of domain experts using three modeling methods.


title: Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

abstract: We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network, resulting in two disjoint sub-networks. Each sub-network is trained to perform a difficult task – predicting one subset of the data channels from another. Together, the sub-networks extract features from the entire input signal. By forcing the network to solve cross-channel prediction tasks, we induce a representation within the network which transfers well to other, unseen tasks. This method achieves state-of-the-art performance on several large-scale transfer learning benchmarks.


title: Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning

abstract: Applying reinforcement learning in physical-world tasks is extremely challenging. It is commonly infeasible to sample a large number of trials, as required by current reinforcement learning methods, in a physical environment. This paper reports our project on using reinforcement learning for better commodity search in Taobao, one of the largest online retail platforms and meanwhile a physical environment with a high sampling cost. Instead of training reinforcement learning in Taobao directly, we present our environment-building approach: we build Virtual-Taobao, a simulator learned from historical customer behavior data, and then we train policies in Virtual-Taobao with no physical sampling costs. To improve the simulation precision, we propose GAN-SD (GAN for Simulating Distributions) for customer feature generation with better matched distribution; we propose MAIL (Multiagent Adversarial Imitation Learning) for generating better generalizable customer actions. To further avoid overfitting the imperfection of the simulator, we propose ANC (Action Norm Constraint) strategy to regularize the policy model. In experiments, Virtual-Taobao is trained from hundreds of millions of real Taobao customers’ records. Compared with the real Taobao, Virtual-Taobao faithfully recovers important properties of the real environment. We further show that the policies trained purely in Virtual-Taobao, which has zero physical sampling cost, can have significantly superior real-world performance to the traditional supervised approaches, through online A/B tests. We hope this work may shed some light on applying reinforcement learning in complex physical environments.


title: Image deblurring using inertial measurement sensors

abstract: We present a deblurring algorithm that uses a hardware attachment coupled with a natural image prior to deblur images from consumer cameras. Our approach uses a combination of inexpensive gyroscopes and accelerometers in an energy optimization framework to estimate a blur function from the camera's acceleration and angular velocity during an exposure. We solve for the camera motion at a high sampling rate during an exposure and infer the latent image using a joint optimization. Our method is completely automatic, handles per-pixel, spatially-varying blur, and out-performs the current leading image-based methods. Our experiments show that it handles large kernels -- up to at least 100 pixels, with a typical size of 30 pixels. We also present a method to perform "ground-truth" measurements of camera motion blur. We use this method to validate our hardware and deconvolution approach. To the best of our knowledge, this is the first work that uses 6 DOF inertial sensors for dense, per-pixel spatially-varying image deblurring and the first work to gather dense ground-truth measurements for camera-shake blur.


title: Sufficient Sample Sizes for Multilevel Modeling

abstract: An important problem in multilevel modeling is what constitutes a sufficient sample size for accurate estimation. In multilevel analysis, the major restriction is often the higher-level sample size. In this paper, a simulation study is used to determine the influence of different sample sizes at the group level on the accuracy of the estimates (regression coefficients and variances) and their standard errors. In addition, the influence of other factors, such as the lowest-level sample size and different variance distributions between the levels (different intraclass correlations), is examined. The results show that only a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second-level standard errors. In all of the other simulated conditions the estimates of the regression coefficients, the variance components, and the standard errors are unbiased and accurate.


title: Dual Polarized Dual Antennas for 1.7–2.1 GHz LTE Base Stations

abstract: A novel antenna with both horizontal and vertical polarizations is proposed for 1.7–2.1 GHz LTE band small cell base stations. Horizontal polarization is achieved by using the Vivaldi antennas at the main PCB board in azimuth plane, whereas the vertical polarization is obtained using the rectangular monopole with curved corners in proximity of the horizontal elements. A prototype antenna associated with 8-elements (four horizontal and four vertical) is fabricated on the FR4 substrate with the thickness of 0.2 cm and 0.12 cm for Vivaldi and monopole antennas, respectively. Experimental results have validated the design procedure of the antenna with a volume of 14 $\times $ 14 $\times $ 4.5 cm $^{3}$ and indicated the realization of the requirements for the small cell base station applications.


title: Review: Semantic web services: Standards, applications, challenges and solutions

abstract: To share Knowledge coming from disparate and heterogeneous environments, we propose the use of semantic Web services to provide a common Knowledge format and meaning. Semantic Web services would require careful usage of combined technologies. On one hand, XML Web services technology because of their system agnostic nature and the ease of integration and, on the other hand, the semantic Web can define and link Web data in a way that it can be understood and used by software agents. This mixed technology may be a solution to functional interoperability, technical interoperabilty, semantic interoperability and flexible development in heterogeneous environments using the Internet as the main infrastructure. In this paper a survey of semantic Web services is realized to show that they ensure interoperability. Four aspects of Web services are presented: (1) Standards of XML Web services (eXtended Markup Language Web services) and their limits, (2) Languages and Tools of Semantic Annotation, (3) Web Services Composition, and (4) Performance Evaluation. Observations on some new challenges brought by semantic Web services are cited. Furthermore, a comparative study is presented, over the Internet and Intranet, based on numerical results using a discrete event between semantic Web services and distributed middleware, e.g. CORBA (Common Object Request Broker Architecture) and JAVA RMI (JAVA Remote Method Invocation).


title: Statistical Analysis-Based Error Models for the Microsoft Kinect™ Depth Sensor

abstract: The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat surface, and horizontal (x) and vertical (y) errors are measured using a novel 3D checkerboard. Results show that the stochastic nature of the Kinect measurement error is affected mostly by the depth at which the object being sensed is located, though radial factors must be considered, as well. Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view. The resulting models are compared against existing Kinect error models, and through these comparisons, the proposed model is shown to be a more sophisticated and precise characterization of the Kinect error distributions.


title: Losing Control: On the Effectiveness of Control-Flow Integrity under Stack Attacks

abstract: Adversaries exploit memory corruption vulnerabilities to hijack a program's control flow and gain arbitrary code execution. One promising mitigation, control-flow integrity (CFI), has been the subject of extensive research in the past decade. One of the core findings is that adversaries can construct Turing-complete code-reuse attacks against coarse-grained CFI policies because they admit control flows that are not part of the original program. This insight led the research community to focus on fine-grained CFI implementations. In this paper we show how to exploit heap-based vulnerabilities to control the stack contents including security-critical values used to validate control-flow transfers. Our investigation shows that although program analysis and compiler-based mitigations reduce stack-based vulnerabilities, stack-based memory corruption remains an open problem. Using the Chromium web browser we demonstrate real-world attacks against various CFI implementations: 1)~against CFI implementations under Windows 32-bit by exploiting unprotected context switches, and 2)~against state-of-the-art fine-grained CFI implementations (IFCC and VTV) in the two premier open-source compilers under Unix-like operating systems. Both 32 and 64-bit x86 CFI checks are vulnerable to stack manipulation. Finally, we provide an exploit technique against the latest shadow stack implementation.


title: D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59

abstract: One recent interest in computer-aided diagnosis of neurological diseases is to predict the clinical scores from brain images. Most existing methods usually estimate multiple clinical variables separately, without considering the useful correlation information among them. On the other hand, nearly all methods use only one modality of data (mostly structural MRI) for regression, and thus ignore the complementary information among different modalities. To address these issues, in this paper, we present a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Our method contains three major subsequent steps: (1) a multi-task feature selection which selects the common subset of relevant features for the related multiple clinical variables from each modality; (2) a kernel-based multimodal data fusion which fuses the above-selected features from all modalities; (3) a support vector regression which predicts multiple clinical variables based on the previously learnt mixed kernel. Experimental results on ADNI dataset with both imaging modalities (MRI and PET) and biological modality (CSF) validate the efficacy of the proposed M3T learning method.


title: Single-Inductor Multi-Output (SIMO) DC-DC Converters With High Light-Load Efficiency and Minimized Cross-Regulation for Portable Devices

abstract: A load-dependant peak-current control single-inductor multiple-output (SIMO) DC-DC converter with hysteresis mode is proposed. It includes multiple buck and boost output voltages. Owing to the adaptive adjustment of the load-dependant peak-current control technique and the hysteresis mode, the cross-regulation can be minimized. Furthermore, a new delta-voltage generator can automatically switch the operating mode from pulse width modulation (PWM) mode to hysteresis mode, thereby avoiding inductor current accumulation when the total power of the buck output terminals is larger than that of the boost output terminals. The proposed SIMO DC-DC converter was fabricated in TSMC 0.25 mum 2P5M technology. The experimental results show high conversion efficiency at light loads and small cross-regulation within 0.35%. The power conversion efficiency varies from 80% at light loads to 93% at heavy loads.


title: Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM

abstract: Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is used as the building block in energy-based deep generative models. Due to numerical stability and quantifiability of the likelihood, RBM is commonly used with Bernoulli units. Here, we consider an alternative member of exponential family RBM with leaky rectified linear units -- called leaky RBM. We first study the joint and marginal distributions of leaky RBM under different leakiness, which provides us important insights by connecting the leaky RBM model and truncated Gaussian distributions. The connection leads us to a simple yet efficient method for sampling from this model, where the basic idea is to anneal the leakiness rather than the energy; -- i.e., start from a fully Gaussian/Linear unit and gradually decrease the leakiness over iterations. This serves as an alternative to the annealing of the temperature parameter and enables numerical estimation of the likelihood that are more efficient and more accurate than the commonly used annealed importance sampling (AIS). We further demonstrate that the proposed sampling algorithm enjoys faster mixing property than contrastive divergence algorithm, which benefits the training without any additional computational cost.


title: How good is my GAN?

abstract: Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification---GAN-train and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two measures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.


title: Handwritten Tamil Character Recognition and Conversion using Neural Network

abstract: Hand written Tamil Character recognition refers to the process of conversion of handwritten Tamil character into Unicode Tamil character. The scanned image is segmented into paragraphs using spatial space detection technique, paragraphs into lines using vertical histogram, lines into words using horizontal histogram, and words into character image glyphs using horizontal histogram. The extracted features considered for recognition are given to Support Vector Machine, Self Organizing Map, RCS, Fuzzy Neural Network and Radial Basis Network. Where the characters are classified using supervised learning algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts. This character recognition finds applications in document analysis where the handwritten document can be converted to editable printed document. This approach can be extended to recognition and reproduction of hand written documents in South Indian languages. In the training set, a recognition rate of 100% was achieved and in the test set the recognized speed for each character is 0.1sec and accuracy is 97%. Understandably, the training set produced much higher recognition rate than the test set. Structure analysis suggested that the proposed system of RCS with back propagation network is given higher recognition rate. Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size and orientation angle of the characters. Among different branches of handwritten character recognition it is easier to recognize English alphabets and numerals than Tamil characters. Many researchers have also applied the excellent generalization capabilities offered by ANNs to the recognition of characters. Many studies have used fourier descriptors and Back Propagation Networks for classification tasks. Fourier descriptors were used in to recognize handwritten numerals. Neural Network approaches were used to classify tools. There have been only a few attempts in the past to address the recognition of printed or handwritten Tamil Characters. However, less attention had been given to Indian language recognition. Some efforts have been reported in the literature


title: Balanced Graph Partitioning with Apache Spark

abstract: A significant part of the data produced every day by online services is structured as a graph. Therefore, there is the need for efficient processing and analysis solutions for large scale graphs. Among the others, the balanced graph partitioning is a well known NP-complete problem with a wide range of applications. Several solutions have been proposed so far, however most of the existing state-of-the-art algorithms are not directly applicable in very large-scale distributed scenarios. A recently proposed promising alternative exploits a vertex-center heuristics to solve the balance graph partitioning problem. Their algorithm is massively parallel: there is no central coordination, and each node is processed independently. Unfortunately, we found such algorithm to be not directly exploitable in current BSP-like distributed programming frameworks. In this paper we present the adaptations we applied to the original algorithm while implementing it on Spark, a state-of-the-art distributed framework for data processing.


title: A Comparative Evaluation of String Similarity Metrics for Ontology Alignment

abstract: Ontology alignment is regarded as the most perspective way to achieve semantic interoperability among heterogeneous data. The majority of state of art ontology alignment systems used one or more string similarity metrics, while the performance of these metrics were not given much attention. In this paper we flrst analyze naming variations in competing ontologies, then we evaluate a wide range of string similarity metrics, from the experimental result we can get some heuristic strategies to achieve better alignment results with regard to efiectiveness and e‐ciency.


title: Effect of eliminating rotor iron on a mega-watt halbach permanent magnet synchronous generator for wind turbine applications

abstract: Permanent Magnet Synchronous Generators (PMSGs) are increasingly considered for large, direct-drive Mega-Watt level wind energy conversion systems. Highefficiency, high-power density, low maintenance, and high reliability are some cited benefits of PMSGs making them attractive for onshore and offshore wind farms. However, as the PMSG's power level increases so would its weight and size. This research attempted to reduce the size and weight of PMSGs by using Halbach magnetic array instead of a conventional array. An analytical model of a MW Halbach array PMSG has been developed. This Halbach PMSG was optimized using a five-input particle swarm optimization algorithm. The use of Hablach array allowed the ability to reduce backside iron, which resulted in a reduction of overall active material weight, without effecting performance.


title: Simultaneous Deep Transfer Across Domains and Tasks

abstract: Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings.


IDENTIFICATION AND DETECTION OF THE PROCESS FAULT IN A CEMENT ROTARY KILN BY EXTREME LEARNING MACHINE AND ANT COLONY OPTIMIZATION


title: Kernel generative topographic mapping

abstract: A kernel version of Generative Topographic Mapping, a model of the manifold learning family, is defined in this paper. Its ability to adequately model non-i.i.d. data is illustrated in a problem concerning the identification of protein subfamilies from protein sequences.


Deep Learning for Chemical Compound Stability Prediction


title: Crystallization and preliminary X-ray analysis of the major cellulose-binding domain of the cellulosome from Clostridium thermocellum.

abstract: The cellulose-binding domain from the scaffoldin subunit of the cellulosome from Clostridium thermocellum strain YS has been expressed in Escherichia coli, purified to homogeneity, and crystallized. Crystals were grown by vapor diffusion using polyethylene glycol as precipitant. They belong to the monoclinic space group C2 with unit cell dimensions of a = 64.68 A, b = 50.36 A, c = 96.27 A; beta = 99.43 degrees, and density packing considerations suggest that the asymmetric unit contains two molecules. The crystals diffract beyond 2.0 A resolution using a laboratory rotating anode source.


title: Theory and applications of HVAC control systems – A review of model predictive control (MPC)

abstract: This work presents a literature review of control methods, with an emphasis on the theory and applications of model predictive control (MPC) for heating, ventilation, and air conditioning (HVAC) systems. Several control methods used for HVAC control are identified from the literature review, and a brief survey of each method is presented. Next, the performance of MPC is compared with that of other control approaches. Factors affecting MPC performance (including control configuration, process type, model, optimization technique, prediction horizon, control horizon, constraints, and cost function) are elaborated using specific examples from the literature. The gaps in MPC research are identified, and future directions are highlighted.


title: Treatment planning of the edentulous mandible

abstract: Edentulism can be a debilitating handicap. Zarb described endentulous individuals who could not function as 'denture cripples'.(1) Most difficulty with complete denture prostheses arises from the inability to function with the mandibular prostheses. Factors that adversely affect successful use of a complete denture on the mandible include: 1) the mobility of the floor of the mouth, 2) thin mucosa lining the alveolar ridge, 3) reduced support area and 4) the motion of the mandible (Figs 1,2). These factors alone can explain the difficulty of wearing a denture on the mandibular arch compared to the maxillary arch. The maxilla exhibits much less mobility on the borders of the denture than the mandible, moreover having a stable palate with thick fibrous tissues available to support the prostheses and resist occlusal forces. These differences explain most of the reasons why patients experience difficulty with using a complete denture on the mandibular arch compared to the maxillary arch.


title: The self as a moral agent: Linking the neural bases of social agency and moral sensitivity

abstract: Abstract The human brain is inherently able to understand the world in moral ways, endowing most of us with an intuitive sense of fairness, concern for others, and observance of cultural norms. We have argued that this moral sensitivity ability depends on a sophisticated integration of cognitive, emotional, and motivational mechanisms, which are modulated by individual experience in different cultural milieus. Different lines of investigation on agency and morality have pointed to overlapping neural systems. Therefore, understanding the relationships between morality and agency may provide key insights into the mechanisms underlying human behavior in several clinical and societal settings. We used functional MRI to investigate the contribution of agency and of specific moral emotions to brain activation using action scripts. Results showed that emotionally neutral agency recruited neural networks previously associated with agency, intentionality and moral cognition, encompassing ventral and subgenual sect...


title: Image denoising using group sparsity residual and external nonlocal self-similarity prior

abstract: Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, due to a majority of reconstruction methods only exploit the nonlocal self-similarity (NSS) prior of the degraded observation image, it is very challenging to reconstruct the latent clean image directly from the noisy observation. In this paper we propose a novel model for image denoising via group sparsity residual and external NSS prior. To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual. Due to the fact that the groups contain a large amount of NSS information of natural images, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture model (GMM) learning and the group sparse coefficients of noisy image are used to approximate the estimation. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.


title: Semantic Annotation and Retrieval of Music and Sound Effects

abstract: We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model the joint probability of acoustic features and words. We collect a data set of 1700 human-generated annotations that describe 500 Western popular music tracks. For each word in a vocabulary, we use this data to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our ldquoquery-by-textrdquo system can retrieve appropriate songs for a large number of musically relevant words. We also show that our audition system is general by learning a model that can annotate and retrieve sound effects.


title: Learning Beyond Human Expertise with Generative Models for Dental Restorations

abstract: Computer vision has advanced significantly that many discriminative approaches such as object recognition are now widely used in real applications. We present another exciting development that utilizes generative models for the mass customization of medical products such as dental crowns. In the dental industry, it takes a technician years of training to design synthetic crowns that restore the function and integrity of missing teeth. Each crown must be customized to individual patients, and it requires human expertise in a time-consuming and labor-intensive process, even with computer-assisted design software. We develop a fully automatic approach that learns not only from human designs of dental crowns, but also from natural spatial profiles between opposing teeth. The latter is hard to account for by technicians but important for proper biting and chewing functions. Built upon a Generative Adversar-ial Network architecture (GAN), our deep learning model predicts the customized crown-filled depth scan from the crown-missing depth scan and opposing depth scan. We propose to incorporate additional space constraints and statistical compatibility into learning. Our automatic designs exceed human technicians' standards for good morphology and functionality, and our algorithm is being tested for production use.


title: MoBio: A 5 DOF trans-humeral robotic prosthesis

abstract: In this paper, a 5 DOF trans-humeral robotic prosthesis: MoBio is proposed. MoBio includes 2 DOF at wrist which is rare in other trans-humeral prostheses. Through anthropometric features MoBio prosthetic arm can achieve elbow flexion/extension, forearm supination/pronation, wrist radial/ulnar deviation, wrist flexion/extension and compound motion of thumb and index finger. An EMG based control method which uses EMG signals of the biceps brachii and triceps brachii, is used with a motion switching mechanism to control the prosthesis. Experimental results have verified the usability and effectiveness of MoBio in performing Activities of Daily Living.


title: Robust Principal Component Analysis on Graphs

abstract: Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first issue with a sparse penalty term. The second issue can be handled with the matrix factorization model, which is however non-convex. Besides, PCA based clustering can also be enhanced by using a graph of data similarity. In this article, we introduce a new model called 'Robust PCA on Graphs' which incorporates spectral graph regularization into the Robust PCA framework. Our proposed model benefits from 1) the robustness of principal components to occlusions and missing values, 2) enhanced low-rank recovery, 3) improved clustering property due to the graph smoothness assumption on the low-rank matrix, and 4) convexity of the resulting optimization problem. Extensive experiments on 8 benchmark, 3 video and 2 artificial datasets with corruptions clearly reveal that our model outperforms 10 other state-of-the-art models in its clustering and low-rank recovery tasks.


title: Analyzing entities and topics in news articles using statistical topic models

abstract: Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a novel combination of statistical topic models and named-entity recognizers to jointly analyze entities mentioned (persons, organizations and places) and topics discussed in a collection of 330,000 New York Times news articles. We demonstrate an analytic framework which automatically extracts from a large collection: topics; topic trends; and topics that relate entities.


title: From Network Sharing to Multi-tenancy: The 5G Network Slice Broker

abstract: The ever-increasing traffic demand is pushing network operators to find new cost-efficient solutions toward the deployment of future 5G mobile networks. The network sharing paradigm was explored in the past and partially deployed. Nowadays, advanced mobile network multi-tenancy approaches are increasingly gaining momentum, paving the way toward further decreasing capital expenditure and operational expenditure (CAPEX/OPEX) costs, while enabling new business opportunities. This article provides an overview of the 3GPP standard evolution from network sharing principles, mechanisms, and architectures to future on-demand multi-tenant systems. In particular, it introduces the concept of the 5G Network Slice Broker in 5G systems, which enables mobile virtual network operators, over-the-top providers, and industry vertical market players to request and lease resources from infrastructure providers dynamically via signaling means. Finally, it reviews the latest standardization efforts, considering remaining open issues for enabling advanced network slicing solutions, taking into account the allocation of virtualized network functions based on ETSI NFV, the introduction of shared network functions, and flexible service chaining.


title: Preparation, characterization and in vivo evaluation of pH sensitive, safe quercetin-succinylated chitosan-alginate core-shell-corona nanoparticle for diabetes treatment.

abstract: The study aims for development of an efficient polymeric carrier for evaluating pharmaceutical potentialities in modulating the drug profile of quercetin (QUE) in anti-diabetic research. Alginate and succinyl chitosan are focused in this investigation for encapsulating quercetin into core-shell nanoparticles through ionic cross linking. The FT-IR, XRD, NMR, SEM, TEM, drug entrapment and loading efficiency are commenced to examine the efficacy of the prepared nanoparticles in successful quercetin delivery. Obtained results showed the minimum particle size of ∼91.58nm and ∼95% quercetin encapsulation efficiently of the particles with significant pH sensitivity. Kinetics of drug release suggested self-sustained QUE release following the non-fickian trend. A pronounced hypoglycaemic effect and efficient maintenance of glucose homeostasis was evident in diabetic rat after peroral delivery of these quercetin nanoparticles in comparison to free oral quercetin. This suggests the fabrication of an efficient carrier of oral quercetin for diabetes treatment.


title: A systematic approach to modeling impedances and current distribution in planar magnetics

abstract: Planar magnetic components using printed-circuit-board windings are attractive due to their high repeatability, good thermal performance and usefulness for realizing intricate winding patterns. To enable higher system integration at high switching frequency, more sophisticated methods that can rapidly and accurately model planar magnetics are needed. This paper develops a lumped circuit model that captures the impact of skin and proximity effects on current distribution and electromagnetic fields in planar magnetics. This enables accurate predictions of impedances, losses, stored reactive energy and current sharing among parallel windings. This lumped model is also a circuit domain representation of electromagnetic interactions. It can be used to simulate circuits incorporating planar magnetics, to visualize the electromagnetic fields, and to extract parameters for magnetic models by simulations. The modeling results match with previous theories and finite-element-modeling results. A group of planar magnetic devices, including transformers and inductors with various winding patterns, are prototyped and measured to validate the proposed approach.


title: Design, Simulation, and Experiments for an Improved Coaxial High-Voltage Vacuum Insulator in TPG700 for High-Power Microwave Generation

abstract: An improved coaxial high-voltage vacuum insulator applied in a Tesla-type generator, model TPG700, has been designed and tested for high-power microwave (HPM) generation. The design improvements include: changing the connection type of the insulator to the conductors from insertion to tangential, making the insulator thickness uniform, and using Nylon as the insulation material. Transient field simulation shows that the electric field (E-field) distribution within the improved insulator is much more uniform and that the average E-field on the two insulator surfaces is decreased by approximately 30% compared with the previous insulator at a voltage of 700 kV. Key structures such as the anode and the cathode shielding rings of the insulator have been optimized to significantly reduce E-field stresses. Aging experiments and experiments for HPM generation with this insulator were conducted based on a relativistic backward-wave oscillator. The preliminary test results show that the output voltage is larger than 700 kV and the HPM power is about 1 GW. Measurements show that the insulator is well within allowable E-field stresses on both the vacuum insulator surface and the cathode shielding ring.


title: Development of Inchworm In-Pipe Robot Based on Self-Locking Mechanism

abstract: In-pipe robots are used to carry sensors and some other repairing instruments to perform inspection and maintenance jobs inside pipelines. In this paper, a self-locking mechanism is presented to improve the traction ability of in-pipe robots and avoid their traditional limitations. The structure of this type of in-pipe robot is presented and some critical design issues on the principle of self-locking mechanism are discussed. Prototypes of 19 mm diameter were produced, and related experiments were performed on specially designed test platform. The traction ability of the proposed in-pipe robot was measured experimentally to be 15.2 N, far beyond its maximum static friction of 0.35 N with the inner surface of pipeline. It means that this development has broken the traditional limitation of in-pipe robot whose traction ability was smaller than its maximum static friction with the pipeline.


title: Understanding Volunteer AT Fabricators: Opportunities and Challenges in DIY-AT for Others in e-NABLE

abstract: We present the results of a study of e-NABLE, a distributed, collaborative volunteer effort to design and fabricate upper-limb assistive technology devices for limb-different users. Informed by interviews with 14 stakeholders in e-NABLE, including volunteers and clinicians, we discuss differences and synergies among each group with respect to motivations, skills, and perceptions of risks inherent in the project. We found that both groups are motivated to be involved in e-NABLE by the ability to use their skills to help others, and that their skill sets are complementary, but that their different perceptions of risk may result in uneven outcomes or missed expectations for end users. We offer four opportunities for design and technology to enhance the stakeholders' abilities to work together.


title: Tablet use in schools: a critical review of the evidence for learning outcomes

abstract: The increased popularity of tablets in general has led to uptake in education. We critically review the literature reporting use of tablets by primary and secondary school children across the curriculum, with a particular emphasis on learning outcomes. The systematic review methodology was used, and our literature search resulted in 33 relevant studies meeting the inclusion criteria. A total of 23 met the minimum quality criteria and were examined in detail 16 reporting positive learning outcomes, 5 no difference and 2 negative learning outcomes. Explanations underlying these observations were analysed, and factors contributing to successful uses of tablets are discussed. While we hypothesize how tablets can viably support children in completing a variety of learning tasks across a range of contexts and academic subjects, the fragmented nature of the current knowledge base, and the scarcity of rigorous studies, makes it difficult to draw firm conclusions. The generalizability of evidence is limited, and detailed explanations as to how, or why, using tablets within certain activities can improve learning remain elusive. We recommend that future research moves beyond exploration towards systematic and in-depth investigations building on the existing findings documented here.


title: New VF-power system architecture and evaluation for future aircraft

abstract: Conventional aircraft power system is a constant-frequency (CF) supply based on mechanical-regulated constant-speed mechanism that has relatively low efficiency. Replacing the CF system with variable-frequency (VF) power improves overall efficiency, and reduce the system's weight and volume. However, this creates a new tier of requirements and design challenges. Novel VF-power architecture is developed with minimization of the power losses throughout the stages of power conversions. Optimal partitioning and grouping of onboard AC loads has been discussed with specific system data. New VF-input multi-functional power converters are also briefly discussed.


title: A new type of multi-way microwave power divider based on Bagley Polygon power divider

abstract: A new type of power divider with simple design theory is proposed based on Bagley Polygon power divider. The design theory is explained by using the equivalent circuit model. Comparing with the Bagley Polygon power divider, it takes less area on substrate board and the output ports can be neatly arranged on the board. The theoretical results are presented and discussed.


title: Local masking in natural images: A database and analysis

abstract: Studies of visual masking have provided a wide range of important insights into the processes involved in visual coding. However, very few of these studies have employed natural scenes as masks. Little is known on how the particular features found in natural scenes affect visual detection thresholds and how the results obtained using unnatural masks relate to the results obtained using natural masks. To address this issue, this paper describes a psychophysical study designed to obtain local contrast detection thresholds for a database of natural images. Via a three-alternative forced-choice experiment, we measured thresholds for detecting 3.7 cycles/° vertically oriented log-Gabor noise targets placed within an 85 × 85-pixels patch (1.9° patch) drawn from 30 natural images from the CSIQ image database (Larson & Chandler, Journal of Electronic Imaging, 2010). Thus, for each image, we obtained a masking map in which each entry in the map denotes the root mean squared contrast threshold for detecting the log-Gabor noise target at the corresponding spatial location in the image. From qualitative observations we found that detection thresholds were affected by several patch properties such as visual complexity, fineness of textures, sharpness, and overall luminance. Our quantitative analysis shows that except for the sharpness measure (correlation coefficient of 0.7), the other tested low-level mask features showed a weak correlation (correlation coefficients less than or equal to 0.52) with the detection thresholds. Furthermore, we evaluated the performance of a computational contrast gain control model that performed fairly well with an average correlation coefficient of 0.79 in predicting the local contrast detection thresholds. We also describe specific choices of parameters for the gain control model. The objective of this database is to provide researchers with a large ground-truth dataset in order to further investigate the properties of the human visual system using natural masks.


title: Selective switching median filter for the removal of salt & pepper impulse noise

abstract: In this paper, a new median based filtering algorithm is presented for the removal of impulse noise from digital images. A good analysis of the limitations of the top ranking median filters, the progressive switching median filter, PSMF and the rank-order based adaptive median filter, RAMF is made and are overcome very effectively by the proposed filter which cleans the impulse corruptions of a digital image in two distinct phases of impulse detection and impulse correction. The detection phase identifies the corrupted pixels into a flag image by a spatial rank ordered approach and the correction phase modifies the corrupted pixels identified in the flag image by a more suitable rank ordered value by considering the neighboring features. The filter shows better performance than most other impulse filters, and in particular, that of the top ranking RAMF and PSMF in terms of computational aspect and objectively. The filtering technique gives good results with less patchy effects from images corrupted at higher noise levels and the results are more appealing with good fidelity at lower noise levels.


title: A generalized construction of integrated speech recognition transducers

abstract: We showed in previous work that weighted finite-state transducers provide a common representation for many components of a speech recognition system and described general algorithms for combining these representations to build a single optimized and compact transducer integrating all these components, directly mapping from HMM states to words. This approach works well for certain well-controlled input transducers, but presents some problems related to the efficiency of composition and the applicability of determinization and weight-pushing with more general transducers. We generalize our prior construction of the integrated speech recognition transducer to work with an arbitrary number of component transducers and, to a large extent, release the constraints imposed on the type of input transducers by providing more general solutions to these problems. This generalization allowed us to deal with cases where our prior optimization did not apply. Our experiments in the AT&T HMIHY 0300 task and an AT&T VoiceTone task show the efficiency of our generalized optimization technique. We report a 1.6 recognition speed-up in the HMIHY 0300 task, 1.8 speed-up in a VoiceTone task using a word-based language model, and 1.7 using a class-based model.


title: Sudden death in individuals in hobble restraints during paramedic transport.

abstract: For patient and personnel safety, agitated and violent individuals are sometime physically restrained during out-of-hospital ambulance transport. We report two cases of unexpected death in restrained, agitated individuals while they were being trans-ported by advanced life support ambulance. Both patients had been placed in hobble restraints by law enforcement. At autopsy, toxicologic analysis revealed nonlethal levels of amphetamines in one patient and nonlethal levels of ethanol, cocaine, and amphetamines in the other. In both cases the cause of death was determined to be positional asphyxiation during restraint for excited delirium. Physicians and emergency service personnel should be aware of the potential complications of using physical restraints for control of agitated patients.


title: Six-stator spherical induction motor for balancing mobile robots

abstract: This paper describes the design, construction, and operation of a closed-loop spherical induction motor (SIM) ball wheel for a balancing mobile robot (ballbot). Following earlier work, this new design has a smaller rotor and higher torques due to the use of six stators in a skewed layout. Actuation and sensing kinematics as well as control methods are presented. In its current implementation, torques of up to 8 Nm are produced by the motor with rise and decay times of 100 ms. Results are presented supporting its potential as a prime mover for mobile robots.


title: Indoor Localization of Mobile Robots Through QR Code Detection and Dead Reckoning Data Fusion

abstract: Many techniques for robot localization rely on the assumption that both process and measurement noises are uncorrelated, white, and normally distributed. However, if this assumption does not hold, these techniques are no longer optimal and, in addition, the maximum estimation errors can be hardly kept under control. In this paper, this problem is addressed by means of a tailored extended H $_\infty$ filter (EHF) fusing odometry and gyroscope data with position and heading measurements based on quick response (QR) code landmark recognition. In particular, it is shown that, by properly tuning EHF parameters and by using an adaptive mechanism to avoid finite escape time phenomena, it is possible to achieve a higher localization accuracy than using other dynamic estimators, even if QR codes are detected sporadically. Also, the proposed approach ensures a good tradeoff in terms of computational burden, convergence time, and deployment complexity.


A Survey of SDN Security Research


title: On the automatic classification of app reviews

abstract: App stores like Google Play and Apple AppStore have over 3 million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. The majority of the reviews, however, is rather non-informative just praising the app and repeating to the star ratings in words. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and text ratings. For this, we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with simple text classification and natural language preprocessing of the text--particularly with bigrams and lemmatization--the classification precision for all review types got up to 88---92 % and the recall up to 90---99 %. Multiple binary classifiers outperformed single multiclass classifiers. Our results inspired the design of a review analytics tool, which should help app vendors and developers deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders. We describe the tool main features and summarize nine interviews with practitioners on how review analytics tools including ours could be used in practice.


title: Analyzing, Detecting, and Exploiting Sentiment in Web Queries

abstract: The Web contains an increasing amount of biased and opinionated documents on politics, products, and polarizing events. In this article, we present an indepth analysis of Web search queries for controversial topics, focusing on query sentiment. To this end, we conduct extensive user assessments and discriminative term analyses, as well as a sentiment analysis using the SentiWordNet thesaurus, a lexical resource containing sentiment annotations. Furthermore, in order to detect the sentiment expressed in queries, we build different classifiers based on query texts, query result titles, and snippets. We demonstrate the virtue of query sentiment detection in two different use cases. First, we define a query recommendation scenario that employs sentiment detection of results to recommend additional queries for polarized queries issued by search engine users. The second application scenario is controversial topic discovery, where query sentiment classifiers are employed to discover previously unknown topics that trigger both highly positive and negative opinions among the users of a search engine. For both use cases, the results of our evaluations on real-world data are promising and show the viability and potential of query sentiment analysis in practical scenarios.


title: WebRTC technology overview and signaling solution design and implementation

abstract: This paper describes the WebRTC technology and implementation of WebRTC client, server and signaling. Main parts of the WebRTC API are described and explained. Signaling methods and protocols are not specified by the WebRTC standards, therefore in this study we design and implement a novel signaling mechanism. The corresponding message sequence chart of the WebRTC communication behavior describes a communication flow between peers and the server. The server application is implemented as a WebSocket server. The client application demonstrates the use of the WebRTC API for achieving real-time communication. Benefits and future development of the WebRTC technology are mentioned.


title: Frequency-Synchronized Resonant Converters for the Supply of Multiwinding Coils in Induction Cooking Appliances

abstract: The induction heating principle has been successfully introduced for about 20 years in domestic cooking appliances. The technical developments now concern the improvement of the current appliances and the introduction of new functions. So, one tendency is the design of multiwinding induction coils that are very adaptive concerning the shape and the power capability. This paper describes different original converter topologies designed to supply multiwinding coils, and, in addition, multicoil systems. The proposed topologies are based on particular use and associations of zero-voltage switching series-resonant converters. They are all characterized by a frequency-synchronized operating mode, in order to prevent any acoustic harm that could result from beating frequency-interferences


title: On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs

abstract: This paper discusses how to efficiently choose from n unknown distributions the k ones whose means are the greatest by a certain metric, up to a small relative error. We study the topic under two standard settings—multi-armed bandits and hidden bipartite graphs—which differ in the nature of the input distributions. In the former setting, each distribution can be sampled (in the i.i.d. manner) an arbitrary number of times, whereas in the latter, each distribution is defined on a population of a finite size m (and hence, is fully revealed after m samples). For both settings, we prove lower bounds on the total number of samples needed, and propose optimal algorithms whose sample complexities match those lower bounds.


title: A Virtual Machine Placement Taxonomy

abstract: Cloud computing data enters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions, all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.


title: Fab: Content-based, collaborative recommendation

abstract: A passive, integrating electromagnetic radiation power dosimeter. A radiofrequency or microwave antenna is combined with a diode detector/rectifier, a squaring circuit, and a electrochemical storage cell to provide an apparatus for determining the average energy of electromagnetic radiation incident on a surface. After a particular period of irradiation, the dosimeter can be interrogated electrically or visually, depending on the type of electrochemical cell employed, to yield the desired information. The apparatus has a substantially linear response to the electromagnetic power density over a wide range of electromagnetic field, and all of the energy required to record the incident energy is supplied by the electromagnetic field.


title: Algorithms for energy disaggregation

abstract: In this project we compare three different solutions to the energy disaggregation problem. We test the algorithms on a reference dataset, and try to learn about the requirements and feasibility of energy disaggregation as a potential commercial product.


title: From Monolith to Microservices: Lessons Learned on an Industrial Migration to a Web Oriented Architecture

abstract: MGDIS SA is a software editing company that underwent a major strategic and technical change during the past three years, investing 17 300 man. Days rewriting its core business software from monolithic architecture to a Web Oriented Architecture using microservices. The paper presents technical lessons learned during and from this migration by addressing three crucial questions for a successful context-adapted migration towards a Web Oriented Architecture: how to determine (i) the most suitable granularity of micro-services, (ii) the most appropriate deployment and (iii) the most efficient orchestration?


Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network


title: Analysis of the contribution of FTO, NPC1, ENPP1, NEGR1, GNPDA2 and MC4Rgenes to obesity in Mexican children

abstract: Background: Recent genome wide association studies (GWAS) and previous positional linkage studies have identified more than 50 single nucleotide polymorphisms (SNPs) associated with obesity, mostly in Europeans. We aimed to assess the contribution of some of these SNPs to obesity risk and to the variation of related metabolic traits, in Mexican children. Methods: The association of six European obesity-related SNPs in or near FTO, NPC1, ENPP1, NEGR1, GNPDA2 and MC4R genes with risk of obesity was tested in 1,463 school-aged Mexican children (Ncases= 514; Ncontrols = 949). We also assessed effects of these SNPs on the variation of body mass index (BMI), fasting serum insulin levels, fasting plasma glucose levels, total cholesterol and triglyceride levels, in a subset of 1,171 nonobese Mexican children. Results: We found a significant effect of GNPDA2 rs10938397 on risk of obesity (odds ratio [OR] =1.30; P=1.34×10 -3 ). Furthermore, we found nominal associations between obesity risk or BMI variation and the following SNPs: ENPP1 rs7754561, MC4R rs17782313 and NEGR1 rs2815752. Importantly, the at-risk alleles of both MC4R rs17782313 and NPC1 rs1805081 showed significant effect on increased fasting glucose levels (β=0.36 mmol/L;P=1.47×10 -3 )a nd decreased fasting serum insulin levels (β=�0.10 μU/mL; P=1.21×10 -3 ), respectively. Conclusion: Our present results suggest that some obesity-associated SNPs previously reported in Europeans also associate with risk of obesity, or metabolic quantitative traits, in Mexican children. Importantly, we found new associations between MC4R and fasting glucose levels, and between NPC1 and fasting insulin levels.


title: Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition

abstract: It is well known that how to extract dynamical features is a key issue for video based face analysis. In this paper, we present a novel approach of facial action units (AU) and expression recognition based on coded dynamical features. In order to capture the dynamical characteristics of facial events, we design the dynamical haar-like features to represent the temporal variations of facial events. Inspired by the binary pattern coding, we further encode the dynamic haar-like features into binary pattern features, which are useful to construct weak classifiers for boosting learning. Finally the Adaboost is performed to learn a set of discriminating coded dynamic features for facial active units and expression recognition. Experiments on the CMU expression database and our own facial AU database show its encouraging performance.


title: Derivation of simplified hybrid model and speed control for Travelling Wave Ultrasonic Motor

abstract: A methodology to design an attractive robust controller for traveling wave ultrasonic motor (TWUSM) is followed on this work. This methodology takes into account several non-linear effects, such as the friction effect, those occur in the ultrasonic motor mechanism. To achieve that, our first objective is to derive a simplified hybrid model (PSHM) that catch the main behavior of the motor during normal operation, including the stator/rotor contact interface, and that can be used to design speed and position control laws. Finally, based on this model, a Fuzzy logic controller (FLC) is designed in order to achieve robustness against several uncertainties (i.e. load torque variations, motor heating, parameters uncertainties...), and a popular Shinsei USR60 is used experimentally to validate the proposed methodology.


Learning MultiGoal Inverse Kinematics in Humanoid Robot