title: Equality of Education and Citizenship: Challenges of European Integration

abstract: What kind of equality among Europeans does equal citizenship require, especially regarding education? In particular, is there good reason to insist of equality of education among Europeans—and if so, equality of what? To what extent should the same knowledge base and citizenship norms be taught across state borders and religious and other normative divides? At least three philosophical issues merit attention: (a) The requirements of multiple democratic citizenships beyond the nation state; (b) how to respect diversity while securing such equality and inculcating commitments to justice and norms of citizenship, and (c) The multiple reasons for equality of various kinds among political equals living in a Union as compared to a unitary state. The article responds on the basis of several arguments in favour of certain kinds of equality. All Union citizens must enjoy a high minimum level of education, and all pupils must be informed concerning the various ways of life prevalent in Europe. Furthermore, there must be standards for securing equality of opportunity across the EU, though it is difficult to measure under multiculturalism. Citizens must also be socialised to certain ‘citizenship norms’. This shared basis to be taught in schools should avoid contested religious or philosophical premises as far as possible. Yet the school system should socialise pupils to three commitments: to the just domestic and European institutions and hence the legislation they engender, to principles that justify these institutions; and to a political theory that grounds these principles in a conception of the proper role of individuals, of member states and of the Union. I also argue that equality of result is not a plausible normative requirement among Europeans, while equality of opportunity is. The paper concludes with some comments on the lessons to be drawn for ‘Global’ citizenship.


title: Orientation-boosted Voxel Nets for 3D Object Recognition

abstract: Recent work has shown good recognition results in 3D object recognition using 3D convolutional networks. In this paper, we show that the object orientation plays an important role in 3D recognition. More specifically, we argue that objects induce different features in the network under rotation. Thus, we approach the category-level classification task as a multi-task problem, in which the network is trained to predict the pose of the object in addition to the class label as a parallel task. We show that this yields significant improvements in the classification results. We test our suggested architecture on several datasets representing various 3D data sources: LiDAR data, CAD models, and RGB-D images. We report state-of-the-art results on classification as well as significant improvements in precision and speed over the baseline on 3D detection.


High-Performance Transaction Processing in SAP HANA


title: Gamification Solutions to Enhance Software User Engagement—A Systematic Review

abstract: ABSTRACTGamification is the use of video-game mechanics and elements in nongame contexts to enhance user engagement and performance. The purpose of this study is to conduct a systematic review to have an in-depth investigation into the existing gamification solutions targeted at solving user engagement problems in different categories of software. We carried out this systematic review by proposing a framework of gamifying process, which is the basis for comparison of existing gamification solutions. In order to report the review, the primary studies are categorized according to the following: a) gamified software and their platforms; b) elements of the gamifying process; c) gamification solutions in each software type; d) gamification solutions for software user engagement problems; e) gamification solutions in general; and f) effects of gamification on software user engagement and performance. Based on the search procedure and criteria, a total of 78 primary studies were extracted. Most of the studies fo...


Paraphrasing Revisited with Neural Machine Translation


title: Deep Transfer Learning for Cross-domain Activity Recognition

abstract: Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar $K$ source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.


title: New frontiers in the rubber hand experiment: when a robotic hand becomes one’s own

abstract: The rubber hand illusion is an experimental paradigm in which participants consider a fake hand to be part of their body. This paradigm has been used in many domains of psychology (i.e., research on pain, body ownership, agency) and is of clinical importance. The classic rubber hand paradigm nevertheless suffers from limitations, such as the absence of active motion or the reliance on approximate measurements, which makes strict experimental conditions difficult to obtain. Here, we report on the development of a novel technology—a robotic, user- and computer-controllable hand—that addresses many of the limitations associated with the classic rubber hand paradigm. Because participants can actively control the robotic hand, the device affords higher realism and authenticity. Our robotic hand has a comparatively low cost and opens up novel and innovative methods. In order to validate the robotic hand, we have carried out three experiments. The first two studies were based on previous research using the rubber hand, while the third was specific to the robotic hand. We measured both sense of agency and ownership. Overall, results show that participants experienced a “robotic hand illusion” in the baseline conditions. Furthermore, we also replicated previous results about agency and ownership.


title: Radiographic landmarks for surgical reconstruction of the anterolateral ligament of the knee

abstract: PURPOSE ::: To determine the radiographic landmarks of the anterolateral ligament (ALL) on the femur and tibia to assist in intraoperative graft placement during ALL reconstruction. ::: ::: ::: METHODS ::: The footprints of the ALL, fibular collateral ligament (FCL), popliteus insertion, lateral gastrocnemius insertion, and Gerdy's tubercle were isolated and centrally marked with tantalum beads in thirteen fresh-frozen cadaveric knees. Measurements were taken from the true lateral fluoroscopic images. On the femur, the mean distances from the ALL origin to the FCL origin and from the ALL origin to the popliteus insertion were measured. On the tibia, the mean distances from the ALL insertion to Gerdy's tubercle and from the ALL insertion to the lateral tibial plateau were measured. Furthermore, radiographic descriptions of the ALL origin and insertion were developed. ::: ::: ::: RESULTS ::: The ALL origin on the femur averaged 3.3 ± 1.5 mm anterior-distal to the FCL origin in one anatomical variant and 5.4 ± 1.4 mm posterior-proximal to the FCL origin in a second variant. The ALL origin was 9.9 ± 2.7 mm from the popliteus insertion. The ALL origin is described as overlying the posterior femoral cortical line, between Blumensaat's line and a line from the posterior condylar articular edge parallel to Blumensaat's line. The ALL insertion on the tibia averaged 24.7 ± 4.5 mm posterior to Gerdy's tubercle and 11.5 ± 2.9 mm distal to the lateral tibial plateau. The tibial ALL insertion is described between the posterior tibial cortical line and a parallel line drawn down from the apex of the tibial spine, and overlying a line drawn perpendicular to the posterior tibial cortical line starting from the apex of the posterior tibial condyles. ::: ::: ::: CONCLUSIONS ::: Using direct lateral fluoroscopy, radiographic landmarks of the ALL origin and insertion have been described.


title: A shape-based similarity measure for time series data with ensemble learning

abstract: This paper introduces a shape-based similarity measure, called the angular metric for shape similarity (AMSS), for time series data. Unlike most similarity or dissimilarity measures, AMSS is based not on individual data points of a time series but on vectors equivalently representing it. AMSS treats a time series as a vector sequence to focus on the shape of the data and compares data shapes by employing a variant of cosine similarity. AMSS is, by design, expected to be robust to time and amplitude shifting and scaling, but sensitive to short-term oscillations. To deal with the potential drawback, ensemble learning is adopted, which integrates data smoothing when AMSS is used for classification. Evaluative experiments reveal distinct properties of AMSS and its effectiveness when applied in the ensemble framework as compared to existing measures.


title: Human emotion recognition from videos using spatio-temporal and audio features

abstract: In this paper, we present human emotion recognition systems based on audio and spatio-temporal visual features. The proposed system has been tested on audio visual emotion data set with different subjects for both genders. The mel-frequency cepstral coefficient (MFCC) and prosodic features are first identified and then extracted from emotional speech. For facial expressions spatio-temporal features are extracted from visual streams. Principal component analysis (PCA) is applied for dimensionality reduction of the visual features and capturing 97 % of variances. Codebook is constructed for both audio and visual features using Euclidean space. Then occurrences of the histograms are employed as input to the state-of-the-art SVM classifier to realize the judgment of each classifier. Moreover, the judgments from each classifier are combined using Bayes sum rule (BSR) as a final decision step. The proposed system is tested on public data set to recognize the human emotions. Experimental results and simulations proved that using visual features only yields on average 74.15 % accuracy, while using audio features only gives recognition average accuracy of 67.39 %. Whereas by combining both audio and visual features, the overall system accuracy has been significantly improved up to 80.27 %.


DADIL : Data Augmentation for Domain-Invariant Learning


title: Lane-Change Detection From Steering Signal Using Spectral Segmentation and Learning-Based Classification

abstract: In order to formulate a high-level understanding of driver behavior from massive naturalistic driving data, an effective approach is needed to automatically process or segregate data into low-level maneuvers. Besides traditional computer vision processing, this study addresses the lane-change detection problem by using vehicle dynamic signals (steering angle and vehicle speed) extracted from the CAN-bus, which is collected with 58 drivers around Dallas, TX area. After reviewing the literature, this study proposes a machine learning-based segmentation and classification algorithm, which is stratified into three stages. The first stage is preprocessing and prefiltering, which is intended to reduce noise and remove clear left and right turning events. Second, a spectral time-frequency analysis segmentation approach is employed to generalize all potential time-variant lane-change and lane-keeping candidates. The final stage compares two possible classification methods—1) dynamic time warping feature with k -nearest neighbor classifier and 2) hidden state sequence prediction with a combined hidden Markov model. The overall optimal classification accuracy can be obtained at 80.36% for lane-change-left and 83.22% for lane-change-right. The effectiveness and issues of failures are also discussed. With the availability of future large-scale naturalistic driving data, such as SHRP2, this proposed effective lane-change detection approach can further contribute to characterize both automatic route recognition as well as distracted driving state analysis.


title: The psychology of curiosity: A review and reinterpretation

abstract: Research on curiosity has undergone 2 waves of intense activity. The 1st, in the 1960s, focused mainly on curiosity's psychological underpinnings. The 2nd, in the 1970s and 1980s, was characterized by attempts to measure curiosity and assess its dimensionality. This article reviews these contributions with a concentration on the 1st wave. It is argued that theoretical accounts of curiosity proposed during the 1st period fell short in 2 areas: They did not offer an adequate explanation for why people voluntarily seek out curiosity, and they failed to delineate situational determinants of curiosity. Furthermore, these accounts did not draw attention to, and thus did not explain, certain salient characteristics of curiosity: its intensity, transience, association with impulsivity, and tendency to disappoint when satisfied. A new account of curiosity is offered that attempts to address these shortcomings. The new account interprets curiosity as a form of cognitively induced deprivation that arises from the perception of a gap in knowledge or understanding. Curiosity is the most superficial of all the affections; it changes its object perpetually; it has an appetite which is very sharp, but very easily satisfied; and it has always an appearance of giddiness, rest


title: Review. Machine learning techniques for traffic sign detection

abstract: An automatic road sign detection system localizes road signs from within images captured by an on-board camera of a vehicle, and support the driver to properly ride the vehicle. Most existing algorithms include a preprocessing step, feature extraction and detection step. This paper arranges the methods applied to road sign detection into two groups: general machine learning, neural networks. In this review, the issues related to automatic road sign detection are addressed, the popular existing methods developed to tackle the road sign detection problem are reviewed, and a comparison of the features of these methods is composed.


title: REAL-TIME MEDICAL MONITORING SYSTEM DESIGN BASED ON INTRA-BODY COMMUNICATION

abstract: With soaring of the sub-health population around the world, the traditional medical treatment has not met the increasing demand; it is urgent to create a modern nursing technology to provide service for human being. With development of the sensor technology and continuous improvement of the wireless communication technology, the new medical monitoring system has been gradually released from the traditional human-labor nursing. On the basis of development of the intra-body communication technology, a wireless, wound-free, portable medical system with real-time monitoring is proposed in this paper. In combination with the remote diagnosis system in the modern medical treatment, the sensor technology and the intra-body communication technology can be utilized to provide all-sided and real-time monitoring for individuals. Because data fusion can be effectively implemented between the systems and WAN & LAN, the model of the medical monitoring system for families is also presented in this paper, which offers guidance for further promotion of real-time medical monitoring.


title: Neutrosophic Association Rule Mining Algorithm for Big Data Analysis

abstract: Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold.


title: Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs

abstract: In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2–3 primitives) than kinematic patterns from on-ground locomotion (νm4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware.


title: Identifying emerging influential Nodes in evolving networks: Exploiting strength of weak nodes

abstract: Identifying emerging influential or popular node/item in future on network is a current interest of the researchers. Most of previous works focus on identifying leaders in time evolving networks on the basis of network structure or node's activity separate way. In this paper, we have proposed a hybrid model which considers both, node's structural centrality and recent activity of nodes together. We consider that the node is active when it is receiving more links in a given recent time window, rather than in the whole past life of the node. Furthermore our model is flexible to implement structural rank such as PageRank and webpage click information as activity of the node. For testing the performance of our model, we adopt the PageRank algorithm and linear preferential attachment based model as the baseline methods. Experiments on three real data sets (i.e Movielens, Netflix and Facebook wall post data set), we found that our model shows better performance in terms of finding the emerging influential nodes that were not popular in past.


title: GeniBux - event based intelligent Forex trading strategy enhancer

abstract: The Foreign Currency Exchange market (Forex) is the largest financial market in the world with the highest daily trading volume. A highly volatile complex behavior is seen during the time the market is open and understanding market trend patterns solves an enormous amount of problems pertaining to prediction and decision making. Many often traders write trading strategies to identify the significant patterns they have recognized. This concept directly involves trading automation or algorithmic trading but building such a versatile algorithm is quite challenging unless you have a person to suggest improvements in your own algorithm. Therefore, in this paper we propose an intelligent system called Genibux will assist Forex traders to improve their strategies. The system suggests improvements with justifications for the maximum gain of profit. Genibux is mainly based on Complex Event Processing and it has been implemented with highly comprehensible Genibux Strategy Language (GSL) together with Machine Learning and classifying algorithms enclosed in a set of highly interactive interfaces. Most importantly, it performs exceedingly well and depicts more profit gains through Genibux improved trading strategies.


title: Object class segmentation of RGB-D video using recurrent convolutional neural networks

abstract: Abstract Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results.


title: PVDF hydrophone for the measurement of shock waves (lithotripsy)

abstract: Focused fields of finite-amplitude ultrasonic pulses occurring in lithotripsy can be measured with hydrophones made of the piezoelectric polymer polyvinylidene fluoride. Extreme requirements for the hydrophones-peak pressure of more than 20 MPa with a risetime of less than 50 ns (shock pulses)-lead to a special construction of the hydrophone where sensitive area and metal electrodes are well separated spatially. The signal from the sensitive area is capacitively coupled to the metal electrodes by a dielectric liquid medium. With a thin, 25- mu m PVDF foil, and an unmetalized sensitive area of 3 mm diameter, a hydrophone of 2*10/sup -2/ V/MPa sensitivity with a bandwidth near 20 MHz is achieved. More than 10/sup 5/ focused shock pulses of approximately=20 MPa were measured without a significant decrease in sensitivity of the hydrophone. >


title: Defining textual entailment

abstract: Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of textâ€ based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails Hâ€‰=â€‰df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is contextâ€ sensitive, nontransitive, and nonmonotonic.


title: Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor

abstract: The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities.


Leaf Disease Grading by Machine Vision and Fuzzy Logic


title: Single-path provisioning with multi-path recovery in flexgrid optical networks

abstract: The new flexgrid technology, in opposition to the fixed grid one traditionally used in wavelength switched optical networks (WSON), allows allocating the spectral bandwidth needed to convey heterogeneous client demand bitrates in a flexible manner so that the optical spectrum can be managed much more efficiently. In this paper we propose a new recovery scheme, called single-path provisioning multi-path recovery (SPP-MPR), specifically designed for flexgrid-based optical networks. It provisions single-paths to serve the bitrate requested by client demands and combines protection and restoration schemes to jointly recover, in part or totally, that bitrate in case of failure. We define the bitrate squeezed recovery optimization (BRASERO) problem to maximize the bitrate which is recovered in case of failure of any single fiber link. A mixed integer linear programming (MILP) formulation is provided. Exhaustive numerical experiments carried out over two network topologies and realistic traffic scenarios show that the efficiency of the proposed SPP-MPR scheme approaches that of restoration mechanisms while providing recovery times as short as protection schemes.


title: Modeling the Predictive Social Mind

abstract: The social mind is tailored to the problem of predicting the mental states and actions of other people. However, social cognition researchers have only scratched the surface of the predictive social mind. We discuss here a new framework for explaining how people organize social knowledge and use it for social prediction. Specifically, we propose a multilayered framework of social cognition in which two hidden layers – the mental states and traits of others – support predictions about the observable layer – the actions of others. A parsimonious set of psychological dimensions structures each layer, and proximity within and across layers guides social prediction. This simple framework formalizes longstanding intuitions from social cognition, and in doing so offers a generative model for deriving new hypotheses about predictive social cognition.


A robust method for automatic player detection in sport videos


title: Concept of fuzzy planar graphs

abstract: Fuzzy graph is now a very important research area due to its wide application. Fuzzy multigraph and fuzzy planar graphs are two subclasses of fuzzy graph theory. In this paper, we define both of these graphs and studied a lot of properties. A very close association of fuzzy planar graph is fuzzy dual graph. This is also defined and studied several properties. The relation between fuzzy planar graph and fuzzy dual graph is also established.


title: The Influence of the size and Shape of Molecules and Particles on their Electrophoretic Mobility

abstract: Abstract As a general rule the electrophoretic mobility of molecules and particles is solely governed by the potential at their surface of shear (ζ-potential) and does not depend on their size or shape. There are hawever two sets of exceptional conditions under which the size or shape of molecules and particles may influence their electrophoretic mobility. These are: 1) Conditions under which the ratio between the dimensions of the molecules or particles and the thickness of their diffuse ionic double layer is such that the distortion of the electric field by the particles is neither at its minimum nor at its maximum. 2) Conditions under which the molecules or particles are migrating under the influence of an electric field inside a porous medium, the size of the pores of which is such that they impede the larger molecules or particles more than the smaller ones.


title: Applications of surface structuring with lasers

abstract: Surface properties are significant in determining the applications of a material, and the modifications of surface properties have been drawing wide attention. The properties attracting the most attention include hydrophilicity, microhardness, optical properties, and tribological properties. To modify these surface characteristics, numerous methods have been developed and investigated over the years; however, they have limitations such as the involvement of toxic chemicals or the lack of spatial resolution. Free from these drawbacks, laser surface structuring is a promising method for surface property modifications. It provides high temporal and spatial resolution, noncontact processing and hence can maintain a high degree of purity. It also delivers a high quantum of energy to induce a very high heating and cooling rate, thermal gradient and resolidification rate. In this paper, laser surface structuring is reviewed in two fronts: the topography change, in which a desired surface topography is created, and the microstructure change, which alters the surface crystallinity and crystal structure. Simultaneous modification of topography and microstructure is also considered. Recent studies on the modifications of surface properties and applications caused by laser structuring are reviewed. Future perspectives are also presented.Surface properties are significant in determining the applications of a material, and the modifications of surface properties have been drawing wide attention. The properties attracting the most attention include hydrophilicity, microhardness, optical properties, and tribological properties. To modify these surface characteristics, numerous methods have been developed and investigated over the years; however, they have limitations such as the involvement of toxic chemicals or the lack of spatial resolution. Free from these drawbacks, laser surface structuring is a promising method for surface property modifications. It provides high temporal and spatial resolution, noncontact processing and hence can maintain a high degree of purity. It also delivers a high quantum of energy to induce a very high heating and cooling rate, thermal gradient and resolidification rate. In this paper, laser surface structuring is reviewed in two fronts: the topography change, in which a desired surface topography is created, a...


title: Hierarchical traffic control for partially decentralized coordination of multi AGV systems in industrial environments

abstract: This paper deals with decentralized coordination of Automated Guided Vehicles (AGVs). We propose a hierarchical traffic control algorithm, that implements path planning on a two layer architecture. The high-level layer describes the topological relationships among different areas of the environment. In the low-level layer, each area includes a set of fixed routes, along which the AGVs have to move. An algorithm is also introduced for the automatic definition of the route map itself. The coordination among the AGVs is obtained exploiting shared resources (i.e. centralized information) and local negotiation (i.e. decentralized coordination). The proposed strategy is validated by means of simulations using real plant.


title: Genome sequence and analysis of methylotrophic yeast Hansenula polymorpha DL1

abstract: BackgroundHansenula polymorpha DL1 is a methylotrophic yeast, widely used in fundamental studies of methanol metabolism, peroxisome biogenesis and function, and also as a microbial cell factory for production of recombinant proteins and metabolic engineering towards the goal of high temperature ethanol production.ResultsWe have sequenced the 9 Mbp H. polymorpha DL1 genome and performed whole-genome analysis for the H. polymorpha transcriptome obtained from both methanol- and glucose-grown cells. RNA-seq analysis revealed the complex and dynamic character of the H. polymorpha transcriptome under the two studied conditions, identified abundant and highly unregulated expression of 40% of the genome in methanol grown cells, and revealed alternative splicing events. We have identified subtelomerically biased protein families in H. polymorpha, clusters of LTR elements at G + C-poor chromosomal loci in the middle of each of the seven H. polymorpha chromosomes, and established the evolutionary position of H. polymorpha DL1 within a separate yeast clade together with the methylotrophic yeast Pichia pastoris and the non-methylotrophic yeast Dekkera bruxellensis. Intergenome comparisons uncovered extensive gene order reshuffling between the three yeast genomes. Phylogenetic analyses enabled us to reveal patterns of evolution of methylotrophy in yeasts and filamentous fungi.ConclusionsOur results open new opportunities for in-depth understanding of many aspects of H. polymorpha life cycle, physiology and metabolism as well as genome evolution in methylotrophic yeasts and may lead to novel improvements toward the application of H. polymorpha DL-1 as a microbial cell factory.


title: Design of An anthropomorphic tendon-driven robotic finger

abstract: The flexor digitorum profundus, extensor digitorum communis and lumbrical muscle of the human hand play a significant role in the movement of the finger. The structure consisting of these muscles and tendons is important to consider an anthropomorphic tendon-driven finger. However, there are some problems to apply the structure found in humans to robotic fingers using mechanical elements. One of them is that the origin of the lumbrical muscle is not on any bones but on the tendon of the flexor digitorum profundus. Another is the non-constant length of the moment arm of the lateral band at the proximal interphalangeal (PIP) joint. We propose a design based on the kinematic model proposed by Leijnse et al. [1] considering the equalization of the joint torques. The proposed model can be easily realized by a structure consisting of actuators fixed to a base and a tendon-pulley system that maintains the function of those three muscle and their tendons.


title: AJANA: A General Framework for Source-Code-Level Interprocedural Dataflow Analysis of AspectJ Software

abstract: Aspect-oriented software presents new challenges for the designers of static analyses. Our work aims to establish systematic foundations for dataflow analysis of AspectJ software. We propose a control- and data-flow program representation for AspectJ programs, as basis for subsequent interprocedural dataflow analyses. The representation is built at the source code level and captures the semantic intricacies of various pointcut designators, multiple applicable advices per joint point, dynamic advices, and general flow of data to, from, and between advices. We also propose two dataflow analyses for AspectJ software: (1) a novel object effect analysis based on a flow- and context-sensitive must-alias analysis, and (2) a dependence analysis used for constructing the system dependence graph for slicing, refactoring, change impact analysis, etc. Both analyses are representative of a general category of dataflow analyses referred to as interprocedural distributed environment (IDE) problems. The two analyses are built on top of the proposed representation, and take into account the complex flow of control and data due to aspect-oriented features. We present a study of the proposed techniques on 37 program versions, using our Ajana analysis framework which is based on the abc AspectJ compiler. The results show that the representation can be built efficiently, that it is superior to an approach based on the woven bytecode, and that it enables analyses that are both faster and more precise. These findings strongly indicate that the proposed approach is a promising candidate for a foundation upon which various inter-procedural analyses for AspectJ can be designed and built.


title: Motor Schema — Based Mobile Robot Navigation

abstract: Motor schemas serve as the basic unit of behavior specifica tion for the navigation of a mobile robot. They are multiple concurrent processes that operate in conjunction with asso ciated perceptual schemas and contribute independently to the overall concerted action of the vehicle. The motivation be hind the use of schemas for this domain is drawn from neuro- scientific, psychological, and robotic sources. A variant of the potential field method is used to produce the appropriate velocity and steering commands for the robot. Simulation re sults and actual mobile robot experiments demonstrate the feasibility of this approach.


title: Compact Dual – Band Antenna for 2.4/5.2/5.8-GHz WLAN Applications

abstract: A compact dual – band antenna for 2.4/5.2/5.8-GHz wireless local area network (WLAN) applications ispresented. The antenna consists of two elements, a C shapedelement and a E shaped element. Both of these elements result inexcitation of two resonant modes for dual – band operation. TheC shaped element is fed directly with coaxial probe. The Eshaped element is coupled with ground plane using a connectingcylindrical via. A thorough parametric study is performed tooptimize the antenna performance. The designed antennaprovides bandwidths of about 114MHz (2.32GHz – 2.46GHz) over the lower band and 2.58GHz (4.48GHz – 7.46GHz) over theupper band covering the WLAN frequencies. The antennaradiation pattern shows a broadside pattern over entirebandwidth with maximum gain of about 1.5dBi. The size of theantenna is reduced by 50% in terms of area compared to theprevious research.


title: An 82.4% efficiency package-bondwire-based four-phase fully integrated buck converter with flying capacitor for area reduction

abstract: Multi-phase converters have become a topic of great interest due to the high output power capacity and output ripple cancellation effect. They are even more beneficial to nowadays high-frequency fully integrated converters with output capacitor integrated on-chip. As one of the dominant chip area consumers, reducing the size of the on-chip decoupling capacitors directly leads to cost down. It is reported that a 5× capacitor area reduction can be achieved with a four-phase converter compared to a single-phase one [1]. However, the penalty is obvious. Every extra phase comes with an inductor, which is also counted as cost and becomes more dominant with increase in the number of phases.


title: Comparing inertia weights and constriction factors in particle swarm optimization

abstract: The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.


title: Predicting large population data cumulative match characteristic performance from small population data ?

abstract: Given a biometric feature-space, in this paper we present a method to predict cumulative match characteristic (CMC) curve performance for a large population of individuals using a significantly smaller population to make the prediction. This is achieved by mathematically modelling the CMC curve. For a given biometric technique that extracts measurements of individuals to be used for identification, the CMC curve shows the probability of recognizing that individual within a database of measurements that are extracted from multiple individuals. As the number of individuals in the database increase, the probabilities displayed on the CMC curve decrease, which indicate the decreasing ability of the biometric technique to recognize individuals. Our mathematical model replicates this effect, and allows us to predict the identification performance of a technique as more individuals are added without physically needing to extract measurements from more individuals.


title: Current trends on ICT technologies for enterprise information systems

abstract: As business conditions change rapidly, the need for integrating business and technical systems calls for novel ICT frameworks and solutions to remain concurrent in highly competitive markets. A number of problems and issues arise in this regard. In this paper, four big challenges of enterprise information systems (EIS) are defined and discussed: (1) data value chain management; (2) context awareness; (3) usability, interaction and visualization; and (4) human learning and continuous education. Major contributions and research orientations of ICT technologies are elaborated based on selected key issues and lessons learned. First, the semantic mediator is proposed as a key enabler for dealing with semantic interoperability. Second, the context-aware infrastructures are proposed as a main solution for making efficient use of EIS to offer a high level of customization of delivered services and data. Third, the product avatar is proposed as a contribution to an evolutionary social, collaborative and product-centric and interaction metaphor with EIS. Fourth, human learning solutions are considered to develop individual competences in order to cope with new technological advances. The paper ends with a discussion on the impact of the proposed solutions on the economic and social landscape and proposes a set of recommendations as a perspective towards next generation of information systems.


Bilingual Word Embeddings with Bucketed CNN for Parallel Sentence Extraction


title: Evaluating a threefold intervention framework for assisting researchers in literature review and manuscript preparatory tasks

abstract: Purpose ::: ::: ::: ::: ::: Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.


title: Comparative Study of Reliability of Ferroelectric and Anti-Ferroelectric Memories

abstract: With the discovery of the ferroelectric (FE) properties within HfO2, the scaling gap between state-of-the-art technology nodes and non-volatile memories based on FE materials can be bridged. In addition to non-volatility, new memory concepts should guarantee sufficient endurance and operation stability. However, in contrast to optimized perovskite based FEs, binary oxide based FE memories still show changes in the memory window (MW) followed by either hard breakdown or closure of the MW. Recently, we have shown that anti-FE (AFE) materials exhibit very stable and significantly higher endurance with respect to the FE counterparts. Inspired by the robustness and remarkable cycling performance of the AFE materials, we analyze the remaining reliability aspects of these devices. By characterizing the pure film properties of capacitor stacks and switching performance when integrated into devices, we compare and investigate temperature stability, imprint, retention, and variability of both FE and AFE memories. We investigate if the lower energetic barrier to be overcome together with partial switching and lower switching induced stress are responsible for the higher endurance of the AFE with respect to the FE based memories. By utilizing charge trapping and charge pumping tests together with leakage current spectroscopy in combination with comprehensive modeling we check that assumption. Moreover, we identify the interfacial buffer layer as the weakest link of these devices.


title: Indoor positioning techniques based on wireless LAN

abstract: As well as delivering high speed internet, Wireless LAN (WLAN) can be used as an effective indoor positioning system. It is competitive in terms of both accuracy and cost compared to similar systems. To date, several signal strength based techniques have been proposed. Researchers at the University of New South Wales (UNSW) have developed several innovative implementations of WLAN positioning systems. This paper describes the techniques used and details the experimental results of the research.


title: Toward Automatic Time-Series Forecasting Using Neural Networks

abstract: Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.


title: 3D-IC thermo-compression collective bonding process using high temperature stage

abstract: As one of the methods to stack the 3D-IC fast, the collective bonding process using TCB (thermo-compression bonder) attracts attention [1-3]. In the collective bonding process, we can improve the throughput considerably by postbonding the multilayered pre-bonded chips at a time. However, when the number of the stacked chips increased, we found that the temperature difference between the upper layer and lower layer became large and the good solder connection was not obtained in all the layer in conventional TCB. Therefore, we reported that the collective bonding could be realized by using the heat insulation stage which can prevent an outflow of the heat from the bonding head [1]. By using the heat insulation stage, we could reduce the temperature difference to less than 10oC for four layers, but it was necessary to reduce temperature difference more, depending on the kind of NCF. Besides, it was difficult to reduce difference of temperature when, for example, the number of the stacked chips increased more than eight layers. In this presentation, we report about new collective bonding process that is able to reduce the temperature difference by using high temperature backup stage. We could enable the high temperature process of the stage by using the wafer-handling mechanism which lifts a substrate from the stage every one bonding cycle.


title: The brain as a dream state generator: an activation-synthesis hypothesis of the dream process

abstract: Recent research in the neurobiology of dreaming sleep provides new evidence for possible structural and functional substrates of formal aspects of the dream process. The data suggest that dreaming sleep is physiologically determined and shaped by a brain stem neuronal mechanism that can be modeled physiologically and mathematically. Formal features of the generator processes with strong implications for dream theory include periodicity and automaticity of forebrain activation, suggesting a preprogrammed neural basis for dream mentation in sleep; intense and sporadic activation of brain stem sensorimotor circuits including reticular, oculomotor, and vestibular neurons, possibly determining spatiotemporal aspects of dream imagery; and shifts in transmitter ratios, possibly accounting for dream amnesia. The authors suggest that the automatically activated forebrain synthesizes the dream by comparing information generated in specific brain stem circuits with information stored in memory.


title: Semi-supervised machine learning approach for DDoS detection

abstract: Even though advanced Machine Learning (ML) techniques have been adopted for DDoS detection, the attack remains a major threat of the Internet. Most of the existing ML-based DDoS detection approaches are under two categories: supervised and unsupervised. Supervised ML approaches for DDoS detection rely on availability of labeled network traffic datasets. Whereas, unsupervised ML approaches detect attacks by analyzing the incoming network traffic. Both approaches are challenged by large amount of network traffic data, low detection accuracy and high false positive rates. In this paper we present an online sequential semi-supervised ML approach for DDoS detection based on network Entropy estimation, Co-clustering, Information Gain Ratio and Exra-Trees algorithm. The unsupervised part of the approach allows to reduce the irrelevant normal traffic data for DDoS detection which allows to reduce false positive rates and increase accuracy. Whereas, the supervised part allows to reduce the false positive rates of the unsupervised part and to accurately classify the DDoS traffic. Various experiments were performed to evaluate the proposed approach using three public datasets namely NSL-KDD, UNB ISCX 12 and UNSW-NB15. An accuracy of 98.23%, 99.88% and 93.71% is achieved for respectively NSL-KDD, UNB ISCX 12 and UNSW-NB15 datasets, with respectively the false positive rates 0.33%, 0.35% and 0.46%.


title: Location Privacy in Mobile Big Data: User Identifiability via Habitat Region Representation

abstract: Mobile big data collected by mobile network operators is of interest to many research communities and industries for its remarkable values. However, such spatiotemporal information may lead to a harsh threat to subscribers’ privacy. This work focuses on subscriber privacy vulnerability assessment in terms of user identifiability across two datasets with significant detail reduced mobility representation. In this paper, we propose an innovative semantic spatiotemporal representation for each subscriber based on the geographic information, termed as daily habitat region, to approximate the subscriber’s daily mobility coverage with far lesser information compared with original mobility traces. The daily habitat region is realized via convex hull extraction on the user’s daily spatiotemporal traces. As a result, user identification can be formulated to match two records with the maximum similarity score between two convex hull sets, obtained by our proposed similarity measures based on cosine distance and permutation hypothesis test. Experiments are conducted to evaluate our proposed innovative mobility representation and user identification algorithms, which also demonstrate that the subscriber’s mobile privacy is under a severe threat even with significantly reduced spatiotemporal information.


title: On the Emerging Coexistence of Edge, Fog and Cloud Computing paradigms in Real-Time Internets-of-EveryThings which operate in the Big-Squared Data space

abstract: This report motivates the roles of Cloud computing, Edge computing, and the hierarchically distributed cooperative Fog computing, for the real-time analysis of big-data in Internets-of-Everything (IoEs). IoEs are enhanced Internets of Things (IoTs) which integrate people, process, data and heterogeneous “Things”: compute, storage, and sensor/actuator hardware. The ubiquitousness of IoE devices, the ever-increasing amount of big data in IoEs, and the need for real-time computing in IoEs have motivated the problem of distributed data storage and analysis. With trillions (big-data scale) of IoE devices on the verge of being deployed in tomorrow's ever-connected and autonomous society, and with the expected big-data generated by each such IoE device (typically image data of the order of tens and hundreds of gigabytes per day), we are rapidly approaching Big-Squared data dimensions. The power consumption of traditional cloud data centers are already about 70% of all power generated, and it will increase exponentially if Cloud computing is the only solution for tomorrow's IoEs. Moreover, the big-squared data from merging IoEs will create network and compute level bottlenecks that will be impractical from a realtime standpoint, especially in case of rapid mobility in IoEs. Hence, the need for distributed hierarchical Fog computing and associated data management. We survey key features of emerging IoEs, the existing big-data computing and storage frameworks, and point out their capabilities and deficiencies. Finally, we discuss the design and implementation of our Fog computing architecture.


title: A Zero Phase Delay Power Divider Using Zero Phase Impedance Transformer

abstract: The 3-dB power divider is one of the most common three-port device in microwave systems, which equally splits the signal at its two output ports. In previous Wilkinson power divider design, the phase delay between the input and output ports is not considered into the design requirements. However, the zero-phase shifting power divider attracts great interest in modern microwave systems. In this letter, a 3-dB power divider with zero-phase delay is designed based on a zero-phase impedance transformer. Instead of metamaterial transmission line, a transmission line stub is added to connect the isolation resistor, and a capacitor is insert between the output ports and the end of main branch to cancel out the imaginary part of even–odd mode impedances. With this new design, it releases the layout restriction that the two branches route around to connect to lumped element resistor. Closed-form design equations are derived and a prototype device is then simulated and measured. The measurement results agree well with the simulation results.


title: Contextual spoken language understanding using recurrent neural networks

abstract: We present a contextual spoken language understanding (contextual SLU) method using Recurrent Neural Networks (RNNs). Previous work has shown that context information, specifically the previously estimated domain assignment, is helpful for domain identification. We further show that other context information such as the previously estimated intent and slot labels are useful for both intent classification and slot filling tasks in SLU. We propose a step-n-gram model to extract sentence-level features from RNNs, which extract sequential features. The step-n-gram model is used together with a stack of Convolution Networks for training domain/intent classification. Our method therefore exploits possible correlations among domain/intent classification and slot filling and incorporates context information from the past predictions of domain/intent and slots. The proposed method obtains new state-of-the-art results on ATIS and improved performances over baseline techniques such as conditional random fields (CRFs) on a large context-sensitive SLU dataset.


title: Cannabidiol in Humans—The Quest for Therapeutic Targets

abstract: Cannabidiol (CBD), a major phytocannabinoid constituent of cannabis, is attracting growing attention in medicine for its anxiolytic, antipsychotic, antiemetic and anti-inflammatory properties. However, up to this point, a comprehensive literature review of the effects of CBD in humans is lacking. The aim of the present systematic review is to examine the randomized and crossover studies that administered CBD to healthy controls and to clinical patients. A systematic search was performed in the electronic databases PubMed and EMBASE using the key word "cannabidiol". Both monotherapy and combination studies (e.g., CBD + ∆9-THC) were included. A total of 34 studies were identified: 16 of these were experimental studies, conducted in healthy subjects, and 18 were conducted in clinical populations, including multiple sclerosis (six studies), schizophrenia and bipolar mania (four studies), social anxiety disorder (two studies), neuropathic and cancer pain (two studies), cancer anorexia (one study), Huntington's disease (one study), insomnia (one study), and epilepsy (one study). Experimental studies indicate that a high-dose of inhaled/intravenous CBD is required to inhibit the effects of a lower dose of ∆9-THC. Moreover, some experimental and clinical studies suggest that oral/oromucosal CBD may prolong and/or intensify ∆9-THC-induced effects, whereas others suggest that it may inhibit ∆9-THC-induced effects. Finally, preliminary clinical trials suggest that high-dose oral CBD (150-600 mg/d) may exert a therapeutic effect for social anxiety disorder, insomnia and epilepsy, but also that it may cause mental sedation. Potential pharmacokinetic and pharmacodynamic explanations for these results are discussed.


title: Model-based blind estimation of reverberation time: application to robust ASR in reverberant environments

abstract: This paper presents a method for blind estimation of reverberation times in reverberant enclosures. The proposed algorithm is based on a statistical model of short-term log-energy sequences for echo-free speech. Given a speech utterance recorded in a reverberant room, it computes a Maximum Likelihood estimate of the room full-band reverberation time. The estimation method is shown to require little data and to perform satisfactorily. The method has been successfully applied to robust automatic speech recognition in reverberant environments by model selection. For this application, the reverberation time is first estimated from the reverberated speech utterance to be recognized. The estimation is then used to select the best acoustic model out of a library of models trained in various artificial reverberant conditions. Speech recognition experiments in simulated and real reverberant environments show the efficiency of our approach which outperforms standard channel normalization techniques.


title: Ileal meconium plugs.

abstract: Eleven cases of neonatal intestinal obstruction associated with a white ileal meconium plug are described; 6 of these presented with complications--such as, giant meconium pseudocyst, perforation, volvulus, or atresia. Most of these complications are presumed to have arisen during the intrauterine period. Only one patient could be relieved of the ileal meconium plug by enemas. The condition of ileal meconium plug is not as benign as a meconium plug in the rectum or distal colon. A plea is made to restrict the name meconium plug syndrome to cases in which the meconium plug is white and chalky and the consequent intestinal obstruction can be relieved by enemas, without evidence of intestinal dysfunction in later life.


title: Failures in Psychodynamic Psychotherapy

abstract: This article addresses the issue of failures in psychodynamic psychotherapy. Drawing on the clinical and research literatures, and utilizing our clinical experiences, we first describe and define criteria for success and failure in treatment. We then review five factors that can lead to failure: client factors, therapist factors, technical factors, relationship factors, and environmental factors. We illustrate our presentation with a case example, and conclude by discussing ways in which the likelihood of failures in psychodynamic treatment can be lowered. & 2011 Wiley Periodicals, Inc. J Clin Psychol: In Session 67:1‐10, 2011.


title: Dynamic shared control for human-wheelchair cooperation

abstract: Shared control is a common used method for human and wheelchair cooperation. However, most of the previous shared control methods didn't think much of the effect caused by the difference in the user's control ability. The control weight of a user in these methods is irrelevant to the user's capability or the driving conditions. In this paper, a dynamic shared control method is proposed to adapt wheelchair's assistance to the variations of user performance and the environmental changes. Three evaluation indices including safety, comfort and obedience are designed to evaluate wheelchair performance in real time. A minimax multi-objective optimization algorithm is adopted to calculate the user's control weight. The results of lab experiments and elderly home field tests show that this method can adapt the degree of wheelchair's autonomy to the user's control ability and it makes driving wheelchair much easier for elder people.


title: Differential Evolution Training Algorithm for Feed-Forward Neural Networks

abstract: An evolutionary optimization method over continuous search spaces, differential evolution, has recently been successfully applied to real world and artificial optimization problems and proposed also for neural network training. However, differential evolution has not been comprehensively studied in the context of training neural network weights, i.e., how useful is differential evolution in finding the global optimum for expense of convergence speed. In this study, differential evolution has been analyzed as a candidate global optimization method for feed-forward neural networks. In comparison to gradient based methods, differential evolution seems not to provide any distinct advantage in terms of learning rate or solution quality. Differential evolution can rather be used in validation of reached optima and in the development of regularization terms and non-conventional transfer functions that do not necessarily provide gradient information.


title: Modeling, identifying, and simulating Dynamic Adaptive Streaming over HTTP

abstract: As HTTP-based streaming video applications have grown to become a major source of Internet traffic, and as the new ISO standard Dynamic Adaptive Streaming over HTTP (DASH) gains industry acceptance, researchers need the ability to both study real-world viewing data and simulate realistic DASH streams. The first effort is complicated by the fact that network researchers are often restricted to anonymized, header-only traces. The second effort is difficult since researchers are currently limited to three undesirable options for generating DASH traffic: (1) encode and store movies, which is both labor-and storage-intensive, (2) parameterize DASH traffic, which is open to criticism, or (3) stream movies from a service such as Netflix, which is prohibitively expensive if a researcher wants to simulate many simultaneous viewers. In this paper we present our work towards developing a model for DASH traffic and show how the model can be applied to identify DASH streams from anonymized, header-only traces using a combination of Hadoop and Hive. We then describe how the insight gained from our work will be used in a simulator that can recreate the DASH payloads of real movies using a format that requires only a few kilobytes of storage per movie.


title: A geometric framework for channel network extraction from LiDAR: nonlinear diffusion and geodesic paths

abstract: [1] A geometric framework for the automatic extraction of channels and channel networks from high-resolution digital elevation data is introduced in this paper. The proposed approach incorporates nonlinear diffusion for the preprocessing of the data, both to remove noise and to enhance features that are critical to the network extraction. Following this preprocessing, channels are defined as curves of minimal effort, or geodesics, where the effort is measured on the basis of fundamental geomorphological characteristics such as flow accumulation area and isoheight contours curvature. The merits of the proposed methodology, and especially the computational efficiency and accurate localization of the extracted channels, are demonstrated using light detection and ranging (lidar) data of the Skunk Creek, a tributary of the South Fork Eel River basin in northern California.


title: Using saddle points for subpixel feature detection in camera calibration targets

abstract: The determination of reliable features with subpixel accuracy is an important requirement of any camera calibration algorithm. This paper offers a simple and computationally attractive method for extracting X-junctions with subpixel precision from images of calibration checkerboards by showing how the saddle points associated with them can be determined without any surface fitting required by standard feature detection algorithms.


title: An implementation of the codelet model

abstract: Chip architectures are shifting from few, faster, functionally heavy cores to abundant, slower, simpler cores to address pressing physical limitations such as energy consumption and heat expenditure. As architectural trends continue to fluctuate, we propose a novel program execution model, the Codelet model, which is designed for new systems tasked with efficiently managing varying resources. The Codelet model is a fine-grained dataflow inspired model extended to address the cumbersome resources available in new architectures. In the following, we define the Codelet execution model as well as provide an implementation named DARTS. Utilizing DARTS and two predominant kernels, matrix multiplication and the Graph 500's breadth first search, we explore the validity of fine-grain execution as a promising and viable execution model for future and current architectures. We show that our runtime is on par or performs better than AMD's highly-optimized parallel library for matrix multication, outperforming it on average by 1.40× with a speedup up to 4×. Our implementation of the parallel BFS outperforms Graph 500's reference implementation (with or without dynamic scheduling) on average by 1.50× with a speed up of up to 2.38×.


title: Cyber Threat Hunting Through the Use of an Isolation Forest

abstract: Most intrusion detection systems use supervised machine learning algorithms which allow them to detect only recorded types of malicious attacks. This paper applies a fundamentally different approach to the problem, exploiting Isolation Forests, an unsupervised machine learning algorithm in a new context. One of the most important advantages of the algorithm is that it can identify and record novel intrusion models. We conduct experiments using HTTP log data to explore the algorithm's accuracy under various conditions. We empirically determine the optimal values for the algorithm's parameters and prove that the originally suggested standard Isolation Forest's parameters do not always produce optimal results. Furthermore, we explore which HTTP features achieve the best results for differentiating between malicious and normal data by running a genetic algorithm. After applying the established results, we achieve approximately 300% increase in the accuracy and we decrease the requested time of the algorithm by nearly 50%.


title: Adaptive Self-Tuning Techniques for Performance Tuning of Database Systems: A Fuzzy-Based Approach

abstract: Self-tuning of Database Management Systems(DBMS) offers important advantages such as improved performance, reduced Total Cost of Ownership(TCO), eliminating the need for an exert Database Administrator(DBA) and improved business prospects. Several techniques have been proposed by researchers and the database vendors to self-tune the DBMS. However, the research focus was confined to physical tuning techniques and the algorithms used in existing methods for self-tuning of memory need analysis of large statistical data. As result, these approaches are not only computationally expensive but also do not adapt well to highly unpredictable workload types and user-load patterns. Hence, in this paper a fuzzy based self-tuning approach has been proposed wherein, three inputs namely, Buffer-Hit-Ratio, Number of Users and Database size are extracted from the Database management system as sensor inputs that indicate degradation in performance and key tuning parameters called the effectors are altered according to the fuzzy-rules. The fuzzy rules are framed after a detailed study of impact of each tuning parameter on the response-time of user queries. The proposed self-tuning architecture is based on Monitor, Analyze, Plan and Execute(MAPE) feedback control loop framework [1] and has been tested under various workload types. The results have been validated by comparing the performance of the proposed self-tuning system with the auto-tuning feature of commercial database systems. The results show significant improvement in performance under various workload-types, user-load variations.


Optimal Neighborhood Kernel Clustering with Multiple Kernels.


BitTorrent Traffic D etection with Deep Packet Inspection and Deep Flow Inspection


title: EAC-Net: A Region-Based Deep Enhancing and Cropping Approach for Facial Action Unit Detection

abstract: In this paper, we propose a deep learning basedapproach for facial action unit detection by enhancing andcropping the regions of interest. The approach is implementedby adding two novel nets (layers): the enhancing layers and thecropping layers, to a pretrained CNN model. For the enhancinglayers (the E-Net), we designed an attention map based on faciallandmark features and applied it to a pretrained neural networkto conduct enhanced learning. For the cropping layers (the CNet), we crop facial regions around the detected landmarksand design convolutional layers to learn deeper features foreach facial region. We then fuse the E-Net and the C-Net toobtain our Enhancing and Cropping (EAC) Net, which canlearn both feature enhancing and region cropping functions.Our approach shows significant improvement in performancecompared to the state-of-the-art methods applied to BP4D andDISFA AU datasets.


title: Lattice code decoder for space-time codes

abstract: We explore the lattice sphere packing representation of a multi-antenna system and the algebraic space-time (ST) codes. We apply the sphere decoding (SD) algorithm to the resulted lattice code. For the uncoded system, SD yields, with small increase in complexity, a huge improvement over the well-known V-BLAST detection algorithm. SD of algebraic ST codes exploits the full diversity of the coded multi-antenna system, and makes the proposed scheme very appealing to take advantage of the richness of the multi-antenna environment. The fact that the SD does not depend on the constellation size, gives rise to systems with very high spectral efficiency, maximum-likelihood performance, and low decoding complexity.


title: Reducing Complexity with Simplicity - Usability Methods for Industry 4.0

abstract: At RWTH Aachen University the research cluster “Production Technology for High-Wage Countries” engages in advancing the polylemma of production. In many cases engineers and physicists develop simulation tools, machine interfaces, and data exploration tools but lack essential training in HumanComputer Interaction. Without proper training the interaction of visual, cognitive and task complexity can lead to solutions that are valuable only to the developers themselves, but are not usable without extensive training. We show the most critical ergonomic factors for developing software in a scientific engineering setting that focuses on complex problems. We present an overview of usability methods as well as complexity reduction methods and their applicability in engineering software design. We present an exemplary study for the case of supply chain management, where the approaches were successfully integrated into a serious game not only serving as an investigatory tool but also as a training utility for supply chain managers.


title: Independent component analysis , A new concept ? *

abstract: Abstract The independent component analysis (ICA) of a random vector consists of searching for a linear transformation that minimizes the statistical dependence between its components. In order to define suitable search criteria, the expansion of mutual information is utilized as a function of cumulants of increasing orders. An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time. The concept of ICA may actually be seen as an extension of the principal component analysis (PCA), which can only impose independence up to the second order and, consequently, defines directions that are orthogonal. Potential applications of ICA include data analysis and compression, Bayesian detection, localization of sources, and blind identification and deconvolution.


title: Edgar Allan Poe and neurology

abstract: Edgar Allan Poe was one of the most celebrated writers of all time. He published several masterpieces, some of which include references to neurological diseases. Poe suffered from recurrent depression, suggesting a bipolar disorder, as well as alcohol and drug abuse, which in fact led to his death from complications related to alcoholism. Various hypotheses were put forward, including Wernicke's encephalopathy.


title: A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

abstract: Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.


title: The Struggle for District-Based Health Information Systems in South Africa

abstract: This article describes the initial period (1994-2001) of an ongoing action research project to develop health information systems to support district management in South Africa. The reconstruction of the health sector in postapartheid South Africa striving for equity in health service delivery and building of a decentralized structure based on health districts. In terms of information systems (IS) development, this reform process translates into standardization of health data in ways that inscribe the goals of the new South Africa by enhancing local control and integration of information handling. We describe our approach to action research and use concepts from actor-network and structuration theories in analyzing the case material. In the detailed description and analysis of the process of IS development provided, we focus on the need to balance standardization and local flexibility (localization); standardization is thus seen as bottom-up alignment of an array of heterogeneous actors. Building on a soc...


title: Power-Efficient VLC Transmitter Based on Pulse-Width Modulated DC–DC Converters and the Split of the Power

abstract: Visible light communication (VLC) has gained relevance during the last few years. It consists in using high-brightness LEDs (HB-LEDs) both for lighting and transmitting information by changing the light intensity rapidly. However, there are some bottlenecks that are slowing down the deployment of this technology. One of the most important problems is that the HB-LED drivers proposed for addressing high data rates in VLC achieve poor power efficiency. Since these HB-LED drivers must be able to reproduce fast current waveforms, the use of linear power amplifiers has been adopted, which clearly damages the power efficiency of HB-LED lighting. In order to alleviate this problem, an HB-LED driver made up of two DC–DC power converters is presented in this work. One of them is responsible for performing the communication functionality by operating at high switching frequency (10 MHz), whereas the second converter fulfills the illumination functionality by ensuring a certain biasing point. The split of the power allows us to minimize the power delivered by the fast-response DC–DC power converter, which suffers from high switching losses. Thus, the overall efficiency can be maximized for each particular communication scenario and for scenarios with changing conditions (i.e., mobile transmitter and/or receiver, presence of mobile obstacles, etc.). In this sense, how the lighting level and the communication signal power affect both the power efficiency and the communication efficiency is deeply analyzed in the experimental section. The implemented prototype achieves an overall efficiency around 90%. In addition, the proposed VLC transmitter is able to reproduce a wide range of digital modulation schemes, including the preferred one for wireless communications: orthogonal frequency division multiplexing.


title: A Cascaded Spatial Transformer Network for Oriented Equipment Detection in Thermal Images

abstract: Equipment detection is a fundamental step towards automatic diagnosis in electric power industry. In this paper, we propose a novel approach to detect equipment parts, which are often oriented in thermal images due to the use of hand-held thermographic cameras. The proposed approach integrates a cascaded spatial transformer network (STN) and a proposal-driven detection network together. The cascaded STN introduces a rotation transform globally so that the rigid body constraint between parts is implicitly considered. Moreover, the cascaded scheme is also robust to large variation in orientation. The designed STN and the detection network are first trained separately to get good initialization weights. Further, these two networks are fine-tuned in an end-to-end manner. Experiments demonstrate that both the cascaded scheme and the end-to-end fine-tuning highly boost the detection performance.


title: Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos

abstract: Abnormal event detection in large videos is an important task in research and industrial applications, which has attracted considerable attention in recent years. Existing methods usually solve this problem by extracting local features and then learning an outlier detection model on training videos. However, most previous approaches merely employ hand-crafted visual features, which is a clear disadvantage due to their limited representation capacity. In this paper, we present a novel unsupervised deep feature learning algorithm for the abnormal event detection problem. To exploit the spatiotemporal information of the inputs, we utilize the deep three-dimensional convolutional network (C3D) to perform feature extraction. Then, the key problem is how to train the C3D network without any category labels. Here, we employ the sparse coding results of the hand-crafted features generated from the inputs to guide the unsupervised feature learning. Specifically, we define a multilevel similarity relationship between these inputs according to the statistical information of the shared atoms. In the following, we introduce the quadruplet concept to model the multilevel similarity structure, which could be used to construct a generalized triplet loss for training the C3D network. Furthermore, the C3D network could be utilized to generate the features for sparse coding again, and this pipeline could be iterated for several times. By jointly optimizing between the sparse coding and the unsupervised feature learning, we can obtain robust and rich feature representations. Based on the learned representations, the sparse reconstruction error is applied to predicting the anomaly score of each testing input. Experiments on several publicly available video surveillance datasets in comparison with a number of existing works demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.


title: Flickr circles: Mining socially-aware aesthetic tendency

abstract: Aesthetic tendency discovery is a useful and interesting application in social media. This paper proposes to categorize large-scale Flickr users into multiple circles. Each circle contains users with similar aesthetic interests (e.g., landscapes or abstract paintings). We notice that: 1) an aesthetic model should be flexible as different visual features may be used to describe different image sets, and 2) the numbers of photos from different users varies significantly and some users have very few photos. Therefore, a regularized topic model is proposed to quantify user's aesthetic interest as a distribution in the latent space. Then, a graph is built to describe the similarity of aesthetic interests among users. Obviously, densely connected users are with similar aesthetic interests. Thus an efficient dense subgraph mining algorithm is adopted to group users into different circles. Experiments show that our approach accurately detects circles on an image set crawled from over 60,000 Flickr users.


title: A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point Cloud Classification

abstract: The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. Most works in deep learning have achieved a great success on regular input representations, but they are hard to be directly applied to classify point clouds due to the irregularity and inhomogeneity of the data. In this paper, a deep neural network with spatial pooling (DNNSP) is proposed to classify large-scale point clouds without rasterization. The DNNSP first obtains the point-based feature descriptors of all points in each point cluster. The distance minimum spanning tree-based pooling is then applied in the point feature representation to describe the spatial information among the points in the point clusters. The max pooling is next employed to aggregate the point-based features into the cluster-based features. To assure the DNNSP is invariant to the point permutation and sizes of the point clusters, the point-based feature representation is determined by the multilayer perception (MLP) and the weight sharing for each point is retained, which means that the weight of each point in the same layer is the same. In this way, the DNNSP can learn the features of points scaled from the entire regions to the centers of the point clusters, which makes the point cluster-based feature representations robust and discriminative. Finally, the cluster-based features are input to another MLP for point cloud classification. We have evaluated qualitatively and quantitatively the proposed method using several airborne laser scanning and terrestrial laser scanning point cloud data sets. The experimental results have demonstrated the effectiveness of our method in improving classification accuracy.


title: Learning to predict crisp boundaries

abstract: Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries. Highly imbalanced categories of boundary versus background in training data is one of main reasons for the above problem. In this work, the aim is to make CNNs produce sharp boundaries without post-processing. We introduce a novel loss for boundary detection, which is very effective for classifying imbalanced data and allows CNNs to produce crisp boundaries. Moreover, we propose an end-to-end network which adopts the bottom-up/top-down architecture to tackle the task. The proposed network effectively leverages hierarchical features and produces pixel-accurate boundary mask, which is critical to reconstruct the edge map. Our experiments illustrate that directly making crisp prediction not only promotes the visual results of CNNs, but also achieves better results against the state-of-the-art on the BSDS500 dataset (ODS F-score of .815) and the NYU Depth dataset (ODS F-score of .762).


title: Automatic formal verification of MPI-based parallel programs

abstract: The Toolkit for Accurate Scientific Software (TASS) is a suite of tools for the formal verification of MPI-based parallel programs used in computational science. TASS can verify various safety properties as well as compare two programs for functional equivalence. The TASS front end takes an integer n ≥ 1 and a C/MPI program, and constructs an abstract model of the program with n processes. Procedures, structs, (multi-dimensional) arrays, heap-allocated data, pointers, and pointer arithmetic are all representable in a TASS model. The model is then explored using symbolic execution and explicit state space enumeration. A number of techniques are used to reduce the time and memory consumed. A variety of realistic MPI programs have been verified with TASS, including Jacobi iteration and manager-worker type programs, and some subtle defects have been discovered. TASS is written in Java and is available from http://vsl.cis.udel.edu/tass under the Gnu Public License.


title: Robust Angle Invariant 1D Barcode Detection

abstract: Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method.


title: Elimination of the hysteresis effect of PAM actuator: modelling and experimental studies

abstract: 2), judder-free and resistant to dirt and dust, therefore these actuators are widely used in industrial environment besides electric motors or hydraulic actuators. One of the most mentioned drawbacks of PAMs is the existence of hysteresis. In this paper two experiments related to hysteresis phenomena of PAMs are described. The goal is to present a precise static force model for the hysteresis loop in the force-contraction (force-relative displacement) curve and a LabVIEW based sliding mode controller for hysteresis independent accurate positioning. Using the approximation algorithm for the force produced by the Fluidic Muscle the correlation coefficient R of nearly 1 and 0,01 mm accuracy in positioning is reached.


title: Energizing 5G: Near- and Far-Field Wireless Energy and Data Trantransfer as an Enabling Technology for the 5G IoT

abstract: We are surrounded in our daily lives by a multitude of small, relatively inexpensive computing devices, many equipped with communication and sensing features. From these has evolved the concept of "pervasive intelligence" [1], [2], a basis from we can envision our future world as an Internet of Things/Internet of Everything (IoT/IoE), in terms of both a consumer IoT/IoE (interconnected devices within an individual's environment) and the Industrial IoT (interconnectedness to improve business-to-business services, mainly through machineto-machine interactions) [3].


title: Opportunistic beam training with hybrid analog/digital codebooks for mmWave systems

abstract: Millimeter wave (mmWave) communication is one solution to provide more spectrum than available at lower carrier frequencies. To provide sufficient link budget, mmWave systems will use beamforming with large antenna arrays at both the transmitter and receiver. Training these large arrays using conventional approaches taken at lower carrier frequencies, however, results in high overhead. In this paper, we propose a beam training algorithm that efficiently designs the beamforming vectors with low training overhead. Exploiting mmWave channel reciprocity, the proposed algorithm relaxes the need for an explicit feedback channel, and opportunistically terminates the training process when a desired quality of service is achieved. To construct the training beamforming vectors, a new multi-resolution codebook is developed for hybrid analog/digital architectures. Simulation results show that the proposed algorithm achieves a comparable rate to that obtained by exhaustive search solutions while requiring lower training overhead when compared to prior work.


title: On the Functions Realized by Stochastic Computing Circuits

abstract: Stochastic computing (SC) employs conventional logic circuits to implement analog-style arithmetic functions acting on digital bit-streams. It exploits the advantages of analog computation -powerful basic operations, high operating speed, and error tolerance- in important applications such as sensory image processing and neuromorphic systems. At the same time, SC exhibits the analog drawbacks of low precision and complex underlying behavior. Although studied since the 1960s, many of SC"s fundamental properties are not well known or well understood. This paper presents, in a uniform manner and notation, what is known about the relations between the logical and stochastic behavior of stochastic circuits. It also considers how correlation among input bit-streams and the presence of memory elements influences stochastic behavior. Some related research challenges posed by SC are also discussed.


title: Comprehensive security management system for Hadoop platforms

abstract: The growing popularity and development of big data technologies bring serious threats to the security of big data. Building a secure big data platform is the foundation for protecting the security of big data. In this paper, we focus on a comprehensive security management system for Hadoop platforms. Firstly, we discuss the security requirements of big data and extract key technologies of big data security in terms of authentication, authorization and access control, data hiding and encryption, network security, and system security. Then we design a security management system for Hadoop platforms and develop corresponding security modules. As a result, this management system can implement security protection for Hadoop platforms and unified management of security services.


title: Are Hypomineralized Primary Molars and Canines Associated with Molar-Incisor Hypomineralization?

abstract: PURPOSE:The purpose of this study was to evaluate the prevalence of and relationship between hypomineralized second primary molars (HSPM) and hypomineralized primary canines (HPC) with molar-incisor hypomineralization (MIH) in 1,963 schoolchildren. METHODS:The European Academy of Paediatric Dentistry (EAPD) criterion was used for scoring HSPM/HPC and MIH. Only children with four permanent first molars and eight incisors were considered in calculating MIH prevalence (n equals 858); for HSPM/HPC prevalence, only children with four primary second molars (n equals 1,590) and four primary canines (n equals 1,442) were considered. To evaluate the relationship between MIH/HSPM, only children meeting both criteria cited were considered (n equals 534), as was true of MIH/HPC (n equals 408) and HSPM/HPC (n equals 360; chi-square test and logistic regression). RESULTS:The prevalence of MIH was 14.69 percent (126 of 858 children). For HSPM and HPC, the prevalence was 6.48 percent (103 of 1,592) and 2.22 percent (32 of 1,442), respectively. A significant relationship was observed between MIH and both HSPM/HPC (P<0.001). The odds ratio for MIH based on HSPM was 6.31 (95 percent confidence interval [CI] equals 2.59 to 15.13) and for HPC was 6.02 (95 percent CI equals 1.08 to 33.05). CONCLUSION:The results led to the conclusion that both hypomineralized second primary molars and hypomineralized primary canines are associated with molar-incisor hypomineralization, because children with HSPM/HPC are six times more likely to develop MIH.


title: Efficient methods to compute genomic predictions.

abstract: Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.