title: Linear feature space projections for speaker adaptation

abstract: We extend the well-known technique of constrained maximum likelihood linear regression (MLLR) to compute a projection (instead of a full rank transformation) on the feature vectors of the adaptation data. We model the projected features with phone-dependent Gaussian distributions and also model the complement of the projected space with a single class-independent, speaker-specific Gaussian distribution. Subsequently, we compute the projection and its complement using maximum likelihood techniques. The resulting ML transformation is shown to be equivalent to performing a speaker-dependent heteroscedastic discriminant (or HDA) projection. Our method is in contrast to traditional approaches which use a single speaker-independent projection, and execute speaker adaptation in the resulting subspace. Experimental results on Switchboard show a 3% relative improvement in the word error rate over constrained MLLR in the projected subspace only.


title: Dioscorins from different Dioscorea species all exhibit both carbonic anhydrase and trypsin inhibitor activities.

abstract: Dioscorins, the storage proteins of yarn tubers, from six cultivars of three Dioscorea species including D. batatas, D. alata, and D. pseudojaponica were detected in crude extracts of yam tubers but not leaves by immuno stainings on PVDF membrane with a polyclonal antibody which was raised against 28 kDa dioscorin from Dioscorea batatas Decne. It was demonstrated that the dioscorins from the above-mentioned yam species exhibited both carbonic anhydrase (CA) and trypsin inhibitor activities by activity stainings on 15% SDS-PAGE gels. Dioscorins were identified as glycoproteins by ConA-peroxidase activity staining. Dioscorins from D. batatas Decne contained no zinc atoms detectable by atomic absorption spectrophotornetry. Neither 5mM acetazolamide nor 2,6-pyridinedicarboxylic acid inhibited the CA activity of dioscorins.


A Solution in Search of a Problem : A Method for the Development of Blockchain Use Cases


title: Learning realistic human actions from movies

abstract: The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.


title: Standardization of reagents and methods used in cytological and histological practice with emphasis on dyes, stains and chromogenic reagents

abstract: The need for the standardization of reagents and methods used in the histology laboratory is demonstrated. After definitions of dyes, stains, and chromogenic reagents, existing standards and standards organizations are discussed. This is followed by practical instructions on how to standardize dyes and stains through the preparation of reference materials and the development of chromatographic methods. An overview is presented of the problems concerned with standardization of the Romanowsky-Giemsa stain for cytological and histological application. Finally, the problem of how to convince routine dye and stain users of the need for standardization in their histology laboratories is discussed.


title: Extracting Development Tasks to Navigate Software Documentation

abstract: Knowledge management plays a central role in many software development organizations. While much of the important technical knowledge can be captured in documentation, there often exists a gap between the information needs of software developers and the documentation structure. To help developers navigate documentation, we developed a technique for automatically extracting tasks from software documentation by conceptualizing tasks as specific programming actions that have been described in the documentation. More than 70 percent of the tasks we extracted from the documentation of two projects were judged meaningful by at least one of two developers. We present TaskNavigator, a user interface for search queries that suggests tasks extracted with our technique in an auto-complete list along with concepts, code elements, and section headers. We conducted a field study in which six professional developers used TaskNavigator for two weeks as part of their ongoing work. We found search results identified through extracted tasks to be more helpful to developers than those found through concepts, code elements, and section headers. The results indicate that task descriptions can be effectively extracted from software documentation, and that they help bridge the gap between documentation structure and the information needs of software developers.


Large-scale Multi-class and Hierarchical Product Categorization for an E-commerce Giant


title: LQR control for a quadrotor using unit quaternions: Modeling and simulation

abstract: Nowadays, quadrotors have become a popular UAV research platform because full control can be achieved through speed variations in each and every one of its four rotors. Here, a non-linear dynamic model based on quaternions for attitude is presented as well as its corresponding LQR Gain Scheduling Control. All considerations for the quadrotor movements are described through their state variables. Modeling is carried out through the Newton-Euler formalism. Finally, the control system is simulated and the results shown in a novel and direct unit quaternion. Thus, a successful trajectory and attitude control of a quadrotor is achieved.


title: Application of machine learning techniques for supply chain demand forecasting

abstract: Abstract Full collaboration in supply chains is an ideal that the participant firms should try to achieve. However, a number of factors hamper real progress in this direction. Therefore, there is a need for forecasting demand by the participants in the absence of full information about other participants’ demand. In this paper we investigate the applicability of advanced machine learning techniques, including neural networks, recurrent neural networks, and support vector machines, to forecasting distorted demand at the end of a supply chain (bullwhip effect). We compare these methods with other, more traditional ones, including naive forecasting, trend, moving average, and linear regression. We use two data sets for our experiments: one obtained from the simulated supply chain, and another one from actual Canadian Foundries orders. Our findings suggest that while recurrent neural networks and support vector machines show the best performance, their forecasting accuracy was not statistically significantly better than that of the regression model.


1 NON-FUNCTIONAL REQUIREMENTS IN SOFTWARE ARCHITECTURE PRACTICE


title: Competing "Creatively" in Sponsored Search Markets: The Effect of Rank, Differentiation Strategy, and Competition on Performance

abstract: Although efficiency-enhancing features of online markets have been well studied, much less is known about firms' differentiation strategies in these competitive markets or the outcomes of such differentiation. This study examines competition among firms in online sponsored search markets---one of the fastest growing and most competitive of online markets. We develop and test a model that predicts the clickthrough rate (CTR) of a seller's listing in a sponsored search setting. Drawing on consumer search theory and competitive positioning strategies, we theorize that CTR is jointly driven by a seller's positioning strategy as reflected by the unique selling proposition (USP) in its “ad creative,” by its rank in a sponsored search listing, and by the nature of competition around the focal firm's listing. We use data from a field experiment conducted by a leading firm in the mortgage industry where the firm varied its rank and USP dynamically. Results suggest that sponsored search listings can act as effective customer segmentation mechanisms, consistent with a model of consumer search in directional markets. We further find that the effect on CTR of a firm's positioning strategy and its rank in a listing is strongly moderated by its ability to differentiate itself from adjacent rivals. We discuss the implications of our findings for sellers' strategies in sponsored search markets and for extending the understanding of consumer search behavior in directional markets.


title: Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization

abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.


title: A FINITE-DIFFERENCE TIME-DOMAIN (FDTD) SOFTWARE FOR SIMULATION OF PRINTED CIRCUIT BOARD (PCB) ASSEMBLY

abstract: This paper describes the design of a three-dimensional (3D) finite-difference time-domain (FDTD) simulation software for printed circuit board (PCB) modeling. The flow, the dynamics and important algorithms of the FDTD simulation engine will be shown. The software is developed using object-oriented programming (OOP) approach, to enable code reuse and ease of upgrade in future. The paper begins by looking at how a 3D PCB structure is created using cubes, and proceed to show the inclusion of various lumped components such as resistors, capacitor, inductor and active semiconductor components into the model. The architecture of the FDTD simulation program is then carefully explained. Finally a few sample simulation examples using the software will be illustrated at the end of the paper.


Modeling and Generating Random Vectors with Arbitrary Marginal Distributions and Correlation Matrix


title: An introduction to convex optimization for communications and signal processing

abstract: Convex optimization methods are widely used in the design and analysis of communication systems and signal processing algorithms. This tutorial surveys some of recent progress in this area. The tutorial contains two parts. The first part gives a survey of basic concepts and main techniques in convex optimization. Special emphasis is placed on a class of conic optimization problems, including second-order cone programming and semidefinite programming. The second half of the survey gives several examples of the application of conic programming to communication problems. We give an interpretation of Lagrangian duality in a multiuser multi-antenna communication problem; we illustrate the role of semidefinite relaxation in multiuser detection problems; we review methods to formulate robust optimization problems via second-order cone programming techniques


title: Incorporating query expansion and quality indicators in searching microblog posts

abstract: We propose a retrieval model for searching microblog posts for a given topic of interest. We develop a language modeling approach tailored to microblogging characteristics, where redundancy-based IR methods cannot be used in a straightforward manner. We enhance this model with two groups of quality indicators: textual and microblog specific. Additionally, we propose a dynamic query expansion model for microblog post retrieval. Experimental results on Twitter data reveal the usefulness of boolean search, and demonstrate the utility of quality indicators and query expansion in microblog search


title: A Compact Ultra-Wideband Multibeam Antenna System

abstract: A compact UWB (6–18 GHz) multibeam antenna system is proposed. Design procedures comprising of ridged coaxial waveguide, radome with lens properties, and biconical antenna are presented. A novel UWB feed network consisting of a ridged coaxial waveguide with eight inputs has been designed and optimized to achieve minimum reflections as well as desired radiation pattern over the frequency range of operation. The radiating element is a biconical antenna, redesigned and optimized to meet the requirements for radiation characteristics. Another notable improvement made by our design is to employ a radome, which not only enhances the mechanical stability of the biconical antenna and protects the structure, but also it acts as a lens that improves the directivity of the radiating element. Extensive optimization procedures have been applied to all parts of the antenna system to achieve the desired performance. The whole system has been simulated using HFSS full-wave simulator. The measurement results of the fabricated system are in good agreement with simulations.


title: MoWLD: a robust motion image descriptor for violence detection

abstract: Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts.


title: Behavioral consistency of C and Verilog programs using bounded model checking

abstract: We present an algorithm that checks behavioral consistency between an ANSI-C program and a circuit given in Verilog using Bounded Model Checking. Both the circuit and the program are unwound and translated into a formula that represents behavioral consistency. The formula is then checked using a SAT solver. We are able to translate C programs that include side effects, pointers, dynamic memory allocation, and loops with conditions that cannot be evaluated statically. We describe experimental results on various reactive circuits and programs, including a small processor given in Verilog and its Instruction Set Architecture given in ANSI-C.


Expanding Wikidata's Parenthood Information by 178%, or How To Mine Relation Cardinality Information


title: RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG

abstract: Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain–computer interfaces (BCIs). To tackle these barriers, this paper presents a novel algorithm, termed regularized spatio-temporal filtering and classification (RSTFC), for single-trial EEG classification. RSTFC consists of two modules. In the feature extraction module, an $l_{2}$ -regularized algorithm is developed for supervised spatio-temporal filtering of the EEG signals. Unlike the existing supervised spatio-temporal filter optimization algorithms, the developed algorithm can simultaneously optimize spatial and high-order temporal filters in an eigenvalue decomposition framework and thus be implemented highly efficiently. In the classification module, a convex optimization algorithm for sparse Fisher linear discriminant analysis is proposed for simultaneous feature selection and classification of the typically high-dimensional spatio-temporally filtered signals. The effectiveness of RSTFC is demonstrated by comparing it with several state-of-the-arts methods on three brain-computer interface (BCI) competition data sets collected from 17 subjects. Results indicate that RSTFC yields significantly higher classification accuracies than the competing methods. This paper also discusses the advantage of optimizing channel-specific temporal filters over optimizing a temporal filter common to all channels.


title: Average effective SNR mapping in LTE-A uplink

abstract: Link adaptation is an important technique to achieve high throughput in wireless communication systems. Effective SNR mapping (ESM) algorithm is widely used to provide equivalent SNR, which helps to predict the BLER performance. In this paper, we propose a new ESM - average effective SNR mapping (AESM) algorithm, which is applicable and efficient in LTE-A uplink. Compared with the well-known exponential ESM (EESM) algorithm, AESM not only provides good approximation, but also has lower complexity by getting rid of the calibration procedure, and replacing exponential and logarithmic calculation with simply averaging algorithm. We study the relationship between AESM and EESM, and demonstrate that the value of AESM is greater than that of EESM, and the difference between them is monotonically decreasing and non-decreasing function of the calibration factor and the variance of the mapped SNRs or SINRs, respectively. Finally, we apply AESM to link adaptation in LTE-A uplink, and the results illustrate that effective SNR obtained by AESM is almost the same with EESM and the performance of link adaptation is as good as expected.


title: Do Children Perceive Whether a Robotic Peer is Learning or Not?

abstract: Social robots are being used to create better educational scenarios, thereby fostering children»s learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children»s handwriting skills. Exploiting the benefits of the learning-by-teaching method, the system provides a scenario in which a child acts as a teacher and corrects the handwriting difficulties of the robotic agent. To explore the children»s perception towards this social robot and the effect on their learning, we have conducted a multi-session study with children that compared two contrasting competencies in the robot: 'learning' vs 'non-learning' and presented as two conditions in the study. The results suggest that the children learned more in the learning condition compared with the non-learning condition and their learning gains seem to be affected by their perception of the robot. The results did not lead to any significant differences in the children»s perception of the robot in the first two weeks of interaction. However, by the end of the 4th week, the results changed. The children in the learning condition gave significantly higher writing ability and overall performance scores to the robot compared with the non-learning condition. In addition, the change in the robot»s learning capabilities did not show to affect their perceived intelligence, likability and friendliness towards it.


title: A Survey of Blockchain Security Issues and Challenges

abstract: Blockchain technologies is one of the most popular issue in recent years, it has already changed people's lifestyle in some area due to its great influence on many business or industry, and what it can do will still continue cause impact in many places. Although the feature of blockchain technologies may bring us more reliable and convenient services, the security issues and challenges behind this innovative technique is also an important topic that we need to concern.


A Phrase-Based HMM Approach To Document/Abstract Alignment


title: Nerve entrapment syndromes in the upper extremity contralateral to amputation.

abstract: Five cases are reported of upper extremity amputation with no metabolic disease. Patients experienced pain, paresthesia and weakness in the intact extremity associated with electrophysiologic evidence of entrapment neuropathies. All patients did heavy manual work, and all had carpal tunnel syndrome. One patient also had cubital tunnel syndrome and compression of the medial cord of the brachial plexus in the axilla, and another patient had cubital tunnel syndrome and axillary neuropathy. Surgery did not relieve symptoms of carpal tunnel and cubital tunnel syndromes for prosthesis users until the figure-8 harness was changed. Patients who did not use a prosthesis felt relief of symptoms following surgical release. Possible mechanisms which produce nerve entrapment syndromes in patients with upper extremity amputations are use of one limb for heavy manual work over prolonged periods, direct compression of neural structures from the axilla loop of a figure-8 harness, and compression of neural structures in the axilla resulting in entrapment at a distal site. Changing the figure-8 harness should be considered prior to surgical decompression for patients who have upper extremity amputations with entrapment syndromes.


title: PM-PM: PatchMatch With Potts Model for Object Segmentation and Stereo Matching

abstract: This paper presents a unified variational formulation for joint object segmentation and stereo matching, which takes both accuracy and efficiency into account. In our approach, depth-map consists of compact objects, each object is represented through three different aspects: 1) the perimeter in image space; 2) the slanted object depth plane; and 3) the planar bias, which is to add an additional level of detail on top of each object plane in order to model depth variations within an object. Compared with traditional high quality solving methods in low level, we use a convex formulation of the multilabel Potts Model with PatchMatch stereo techniques to generate depth-map at each image in object level and show that accurate multiple view reconstruction can be achieved with our formulation by means of induced homography without discretization or staircasing artifacts. Our model is formulated as an energy minimization that is optimized via a fast primal-dual algorithm, which can handle several hundred object depth segments efficiently. Performance evaluations in the Middlebury benchmark data sets show that our method outperforms the traditional integer-valued disparity strategy as well as the original PatchMatch algorithm and its variants in subpixel accurate disparity estimation. The proposed algorithm is also evaluated and shown to produce consistently good results for various real-world data sets (KITTI benchmark data sets and multiview benchmark data sets).


title: COOLCAT: an entropy-based algorithm for categorical clustering

abstract: In this paper we explore the connection between clustering categorical data and entropy: clusters of similar poi lower entropy than those of dissimilar ones. We use this connection to design an incremental heuristic algorithm, COOLCAT, which is capable of efficiently clustering large data sets of records with categorical attributes, and data streams. In contrast with other categorical clustering algorithms published in the past, COOLCAT's clustering results are very stable for different sample sizes and parameter settings. Also, the criteria for clustering is a very intuitive one, since it is deeply rooted on the well-known notion of entropy. Most importantly, COOLCAT is well equipped to deal with clustering of data streams(continuously arriving streams of data point) since it is an incremental algorithm capable of clustering new points without having to look at every point that has been clustered so far. We demonstrate the efficiency and scalability of COOLCAT by a series of experiments on real and synthetic data sets.


title: Perception markup language: towards a standardized representation of perceived nonverbal behaviors,” in Intelligent Virtual Agents

abstract: Modern virtual agents require knowledge about their environment, the interaction itself, and their interlocutors' behavior in order to be able to show appropriate nonverbal behavior as well as to adapt dialog policies accordingly. Recent achievements in the area of automatic behavior recognition and understanding can provide information about the interactants' multimodal nonverbal behavior and subsequently their affective states. In this paper, we introduce a perception markup language (PML) which is a first step towards a standardized representation of perceived nonverbal behaviors. PML follows several design concepts, namely compatibility and synergy, modeling uncertainty, multiple interpretative layers, and extensibility, in order to maximize its usefulness for the research community. We show how we can successfully integrate PML in a fully automated virtual agent system for healthcare applications.


Configuration Tool for ARINC 653 Operating Systems Eu


title: Learning Multi-level Features For Sensor-based Human Action Recognition

abstract: Abstract This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action. The Max-margin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework. The proposed method achieves the state-of-the-art performances, 88.7%, 98.8% and 72.6% (weighted F 1 score) respectively, on Skoda, WISDM and OPP datasets.


title: A Boosted Particle Filter: Multitarget Detection and Tracking

abstract: The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost proposal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual players. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system.


title: An empirical analysis of semantic network in online crowdfunding: evidence from kickstarter

abstract: Crowdfunding, which helps getting financial sources for niche market entrepreneurs, lowers the entry barrier to start a social and for-profit project. However, it is widely believed that overwhelmingly successful projects, called "blockbuster" projects, would have a significant impact on the overall crowdfunding projects. In theoretical perspectives, there have been two countervailing effects of blockbuster, cannibalization effects and spill-over effects. Therefore, this motivates our research to investigate the competitions among projects within same categories. We expect to fill in the gaps of prior literatures which concluded that the blockbuster project has the same effects on all the other projects within same categories. As a major contribution of this paper, we obtain market structure insights from the creator generated contents based on a text-mining approach and combine it with semantic network analysis to assess the degree to which the latent competition causes cannibalize or spill-over effects inside the same category. Our study uses unique data set, compiled by Python based self-developed crawler. The dataset includes 148,398 projects and 2,915,821 records of daily pledged amount. Our analysis of the concurrent impact of blockbuster project shows the much larger spillover effects to the inside cluster relative to the outside clusters. However, this spillover effect to the outside cluster significantly increase as the magnitude to the inside cluster in the long run perspective. The results of this paper suggest practical marketing implications to entrepreneurs and platform.


title: Bayesian analysis of neuroimaging data in FSL.

abstract: Abstract Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.


Literature Review: e-Leadership


title: Design of Force-Reflection Joystick System for VR-Based Simulation *

abstract: In this paper, we develop a force-reflection joystick system for VR-based simula- tion, including a 2-DOF joystick and the related software. The developed force-reflection joystick, as a kind of haptic device, provides two-way communication in both position and force, and is very helpful for the user to interact with a simulation system. By con- sidering the main factors in designing a force-reflection joystick, we build the joystick to be with the bandwidth, precision, and output torque comparable to the advanced com- mercial joystick. We also perform workspace analysis, system identification, and system modeling to better connect the joystick with the simulation system. To make it suitable for various applications, the software is developed to generate virtual motion constraints, so that the joystick is confined to operate within the workspace that corresponds to task requirements. In other words, the joystick may behave like a manipulative device spe- cific for the given task. For demonstration, in experiments, we use the developed joy- stick system to emulate a virtual manual gearshift system.


title: Thermal Gravitational Waves

abstract: There is a lot of current interest in sources of gravitational waves and active ongoing projects to detect such ra- diation, such as the LIGO project. These are long wavelength, low frequency gravitational waves. LISA would be sensitive to much longer wavelengths and lower fluxes. However compact stellar objects can generate high frequency (10 16 -10 21 Hz) thermal gravitational radiation, which in the case of hot neutron stars can be high. Also white dwarfs and main-sequence stars can generate such radiation from plasma-Coulomb collisions. Again gamma ray bursts and relativis- tic jets could also be sources of such radiation. Terminal stages of evaporating black holes could also generate high fre- quency gravitational radiation. A comparative study is made of the thermal gravitational wave emission from all of the above sources, and the background flux is estimated. The earliest phases of the universe close to the Planck scale would also leave remnant thermal gravitational waves. The integrated thermal gravitational flux as the universe expands is also estimated and compared with that from all the discrete sources discussed above. Possible schemes to detect such sources of high frequency thermal gravitational radiation are discussed and the physical principles involved are elaborated. Due to Coulomb collisions in the core of the stars, ther- mal gravitational waves can be generated. These thermal gravitational waves can arise in white dwarfs and neutron stars due to the fermion collisions in the dense degenerate Fermi gas. Such high frequency thermal gravitational waves are also produced during the collisions in a gamma ray burst or in the jets of a rotating black hole.


title: A practical approach to E-mail spam filters to protect data from advanced persistent threat

abstract: Time based Self-destructing email mainly aims at protecting data privacy. In this paper we discussed the spear phishing process as a part of advanced persistent threat attack which gathers information and targets an individual or organization. It implements of social engineering techniques to gather data regarding recipient. Malicious emails are sent by combining the psychological and technical tricks, where phishing emails contains web-links that provoke the recipient to click on them, these links contains websites that are infected with malware. We also concentrated on Spam Emails and Targeted Malicious E-mails. In this paper we discussed recipient side detection techniques, such as spam or Junk mail filters using mathematical concept of Bayesian spam filtering. We contribute a clear indication of behavioral structure of Advanced Persistent Threat and a self-destructive mechanism is adopted as Defense System to protect sensitive confidential data from intruders. A mathematical approach is given along with the computational practical analysis and experimental result.


title: Comparative study and analysis of network intrusion detection tools

abstract: Security has a significant influence in network management. One of the most common way to secure information in the computer from malicious use is IDS Intrusion detection system(IDS) is most prominent to secure a computer and network against intrusion. IDSs primarily intended to preserve the availability, confidentiality and Integrity(CAI)of network and computer. IDS can be broadly classified in two categories: Network intrusion detection system (NIDS) and Host intrusion detection system(HIDS). NIDS is main part of any network security architecture, which monitors network traffic for predefined suspicious activity or patterns and alert system administrators. Nowadays, many IDSs tools are available such as commercial as well as open source tools. Open source tools promotes a global access through free license. In paper we found study of three popular NIDS tools : Snort, Suricata, Bro.


title: An efficient algorithm for discovering frequent subgraphs

abstract: Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in various application areas. However, as data mining techniques are being increasingly applied to nontraditional domains, existing frequent pattern discovery approaches cannot be used. This is because the transaction framework that is assumed by these algorithms cannot be used to effectively model the data sets in these domains. An alternate way of modeling the objects in these data sets is to represent them using graphs. Within that model, one way of formulating the frequent pattern discovery problem is that of discovering subgraphs that occur frequently over the entire set of graphs. We present a computationally efficient algorithm, called FSG, for finding all frequent subgraphs in large graph data sets. We experimentally evaluate the performance of FSG using a variety of real and synthetic data sets. Our results show that despite the underlying complexity associated with frequent subgraph discovery, FSG is effective in finding all frequently occurring subgraphs in data sets containing more than 200,000 graph transactions and scales linearly with respect to the size of the data set.


title: Movie-DiC: a Movie Dialogue Corpus for Research and Development

abstract: This paper describes Movie-DiC a Movie Dialogue Corpus recently collected for research and development purposes. The collected dataset comprises 132,229 dialogues containing a total of 764,146 turns that have been extracted from 753 movies. Details on how the data collection has been created and how it is structured are provided along with its main statistics and characteristics.


title: Fatal Lumbar Puncture: Fact Versus Fiction--An Approach to a Clinical Dilemma

abstract: Recent reports and commentaries have emphasized the alleged risk of cerebral herniation complicating lumbar puncture performed to diagnose acute bacterial meningitis. Instead, knowledge of facts relevant to the disease process can provide a rational and reassuring approach to management. All cases of purulent meningitis are associated with increased intracranial pressure, but herniation is a rare complication (5%). Despite suggestions to the contrary, cranial computed tomography (CT) is normal in most cases of purulent meningitis, including those with subsequent herniation. Additionally, CT may be associated with long-term radiation effects. An accurate clinical history combined with recognition of the early systemic and neurologic findings of bacterial meningitis will indicate a safe setting for performance of a diagnostic lumbar puncture with little likelihood of complicating herniation. In contrast, in patients in whom the disease process has progressed to the neurologic findings associated with impending cerebral herniation, a delay of the diagnostic procedure is indicated. In this latter circumstance, a different approach in management can be developed.


title: Effect of Various Superheat Condition on Working Condition of Air Cooled Chiller

abstract: Application of energy efficient chiller, compressor, air handling unit, evaporator etc are some indicators of the energy conservation in Heating, Ventilating, and Air Conditiong (HVAC) systems. The condition of superheat vapor is the form of energy savings optimization in this paper. The present work aims to find the effect of various superheat temperature condition on an air cooled chiller simulation using refrigerant 22 numerically and experimentally. This investigation includes the analysis of the effect of degree of superheat toward the heat absorbed in coil of evaporator, heat rejected to the environment in condenser, power input in compressor and performance coefficient (COP) of the refrigeration system using numerical software and experimentally. The superheat temperature of refrigerant vapor in this experiment were between 4.6 to 7.6 degree Celcius. For given condition of superheat vapor, the working parameter of chiller are tabulated and investigated by comparing numerical and experimental data. The investigation reveals under 1% of COP of the system decrease to make sure the vapor which is enter the compressor is superheated vapor. The optimum condition of degree of superheat in the investigation were 5,05 degree Celcius.


title: Dependency-to-Dependency Neural Machine Translation

abstract: Recent research has proven that syntactic knowledge is effective to improve the performance of neural machine translation (NMT). Most previous work focuses on leveraging either source or target syntax in the recurrent neural network (RNN) based encoder–decoder model. In this paper, we simultaneously use both source and target dependency tree to improve the NMT model. First, we propose a simple but effective syntax-aware encoder to incorporate source dependency tree into NMT. The new encoder enriches each source state with dependence relations from the tree. Then, we propose a novel sequence-to-dependence framework. In this framework, the target translation and its corresponding dependence tree are jointly constructed and modeled. During decoding, the tree structure is used as context to facilitate word generations. Finally, we extend the sequence-to-dependence framework with the syntax-aware encoder to build a dependence-NMT model and apply the dependence-based framework to the Transformer. Experimental results on several translation tasks show that both source and target dependence structures can improve the translation quality and their effects can be accumulated.


Leveraging Multiple Domains for Sentiment Classification


title: IllumiRoom: peripheral projected illusions for interactive experiences

abstract: IllumiRoom is a proof-of-concept system that augments the area surrounding a television with projected visualizations to enhance traditional gaming experiences. We investigate how projected visualizations in the periphery can negate, include, or augment the existing physical environment and complement the content displayed on the television screen. Peripheral projected illusions can change the appearance of the room, induce apparent motion, extend the field of view, and enable entirely new physical gaming experiences. Our system is entirely self-calibrating and is designed to work in any room. We present a detailed exploration of the design space of peripheral projected illusions and we demonstrate ways to trigger and drive such illusions from gaming content. We also contribute specific feedback from two groups of target users (10 gamers and 15 game designers); providing insights for enhancing game experiences through peripheral projected illusions.


title: Key challenges for developing a Socially Assistive Robotic (SAR) solution for the health sector

abstract: To help meet the health and caregiving needs of an increasingly ageing population, Engineering is developing SARA, a ’’Socially Assistive Robotic Solution for Ambient assisted living’’, as an extension of its commercial AREAS Enterprise Resource Planning suite for the Health sector. SARA is a complex distributed home automation and robotics system providing health monitoring and (socially interactive) assistance in daily living tasks to the elderly (and their caregivers) at home – with the aim to prolong their autonomy and delay their institutionalization. While advances in artificial intelligence and internet-of-things technologies are converging to make SARA (and similar automated solutions) ever more technically feasible and economically viable, there nevertheless remain significant technological challenges to overcome. This paper introduces SARA, describes some of the key challenges faced in its development, and explains how smart end-to-end IoT interoperability, connectivity and security can help tackle these challenges.


title: Monocular Vision for Long-term Micro Aerial Vehicle State Estimation: A Compendium

abstract: The recent technological advances in Micro Aerial Vehicles (MAVs) have triggered great interest in the robotics community, as their deployability in missions of surveillance and reconnaissance has now become a realistic prospect. The state of the art, however, still lacks solutions that can work for a long duration in large, unknown, and GPS-denied environments. Here, we present our visual pipeline and MAV state-estimation framework, which uses feeds from a monocular camera and an Inertial Measurement Unit (IMU) to achieve real-time and onboard autonomous flight in general and realistic scenarios. The challenge lies in dealing with the power and weight restrictions onboard a MAV while providing the robustness necessary in real and long-term missions. This article provides a concise summary of our work on achieving the first onboard vision-based power-on-and-go system for autonomous MAV flights. We discuss our insights on the lessons learned throughout the different stages of this research, from the conception of the idea to the thorough theoretical analysis of the proposed framework and, finally, the real-world implementation and deployment. Looking into the onboard estimation of monocular visual odometry, the sensor fusion strategy, the state estimation and self-calibration of the system, and finally some implementation issues, the reader is guided through the different modules comprising our framework. The validity and power of this framework are illustrated via a comprehensive set of experiments in a large outdoor mission, demonstrating successful operation over flights of more than 360 m trajectory and 70 m altitude change.


title: High Q TE01 mode DR cavity filters for wireless base stations

abstract: This paper summarizes the state-of-the-art of high Q TE01 mode DR cavity filters for PCS wireless base station applications. The mode chart and cavity Q are computed for typical commercially-available DR materials. A new approach to suppress the spurious response of the DR cavity filter is proposed and the advantage is analyzed. Experimental 8-pole and 6-pole quasi-elliptic function filters show the typical performances. Special techniques on cross-coupling techniques are used to realize a three-pole elliptic function and a 5-pole canonical asymmetric filter. The 5-pole canonical asymmetric filter, we believe, has never been realized before.


title: Autonomous website categorization with pre-defined dictionary

abstract: In this technology emerging era, the number of websites is increasing dramatically. The content and category of information are overflowing the Internet World. Finding the right information from almost a billion of websites is considerably hard, but finding the accurate and quality one is even harder. Hence, the need of website categorization's demand is increasing tremendously. Unfortunately, the website categorization techniques in previous works are still immature and not good enough to satisfy the need. Additionally, a training dataset is a limitation of supervised learning algorithm and unsupervised learning algorithm also have a complex algorithm. Regularly, they can categorize into only 1 category but the content usually contains various types. Therefore, in this paper, we propose the simple yet powerful algorithm for website categorization which can give a multi-category results with confidence level, distributed systems supported, and does not even need to be trained because the algorithm uses word frequency in the content of each website to match with the categories in a pre-defined dictionary. The result shows that the accuracy of our proposed algorithm is over 95% when tested with Reuters dataset. The comparison of our algorithm and another Text Analysis API shows that our algorithm has more accuracy with less computation time. The accuracy can also be increased by improving the pre-defined dictionary and filtering noise words.


title: An Integrated Conceptual Model for Information System Security Risk Management and Enterprise Architecture Management based on TOGAF, ArchiMate, IAF and DoDAF

abstract: Risk management is today a major steering tool for any organization wanting to deal with Information System (IS) security. However, IS Security Risk Management (ISSRM) remains difficult to establish and maintain, mainly in a context of multi-regulations with complex and inter-connected IS. We claim that a connection with Enterprise Architecture Management (EAM) contributes to deal with these issues. A first step towards a better integration of both domains is to define an integrated EAM-ISSRM conceptual model. Among the steps of the research method followed to define such an integrated EAM-ISSRM conceptual, this technical report presents the whole outputs (through alignment tables) of the conceptual alignment between concepts used to model EA (based on ArchiMate, TOGAF, IAF and DoDAF) and concepts of the ISSRM domain model.


title: Familia: An Open-Source Toolkit for Industrial Topic Modeling

abstract: Familia is an open-source toolkit for pragmatic topic modeling in industry. Familia abstracts the utilities of topic modeling in industry as two paradigms: semantic representation and semantic matching. Efficient implementations of the two paradigms are made publicly available for the first time. Furthermore, we provide off-the-shelf topic models trained on large-scale industrial corpora, including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word Embedding (TWE). We further describe typical applications which are successfully powered by topic modeling, in order to ease the confusions and difficulties of software engineers during topic model selection and utilization.


title: Discussant response to ‘Mirroring the voice from Garcia to the present day: Some insights into singing voice registers’

abstract: PEVOC6 took as its theme ‘Mirroring the Voice’ to mark the occasion, 150 years before, of Manuel Garcia's publication in London of his work on the singing voice using what is now called, following him, a laryngoscope. This also was the special theme of the final plenary session contribution to the Conference by Nathalie Henrich—but with particular reference to singing voice registers. Her paper and conference presentation have two complementary parts. The first gives an overview of the development of the ideas that have their roots in Garcia's work; the second concentrates on modern developments and attempts to provide an acceptable path to the working definition of the concept of register and to give a basis for the taxonomic description of register types.


title: Software Defects and their Impact on System Availability: A Study of Field Failures in Operating Systems

abstract: Defects reported between 1986 and 1989 in the MVS operating system are studied in order to gain the insight needed to provide a clear strategy for avoiding or tolerating them. Typical defects (regular) are compared to those that corrupt a program's memory (overlay), given that overlays are considered by field services to be particularly hard to find and fix. It is shown that the impact of an overlay defect is, on average, much higher than that of a regular defect, that boundary conditions and allocation management are the major causes of overlay defects, not timing, and that most overlays are small and corrupt data near the data that the programmer meant to update. Further analysis is provided on defects in fixes to other defects, failure symptoms, and the impact of defects on customers. >


title: Affect and the Computer Game Player: The Effect of Gender, Personality, and Game Reinforcement Structure on Affective Responses to Computer Game-Play

abstract: Previous research on computer games has tended to concentrate on their more negative effects (e.g., addiction, increased aggression). This study departs from the traditional clinical and social learning explanations for these behavioral phenomena and examines the effect of personality, in-game reinforcement characteristics, gender, and skill on the emotional state of the game-player. Results demonstrated that in-game reinforcement characteristics and skill significantly effect a number of affective measures (most notably excitement and frustration). The implications of the impact of game-play on affect are discussed with reference to the concepts of "addiction" and "aggression."


title: Context-Aware Online Learning for Course Recommendation of MOOC Big Data

abstract: The Massive Open Online Course (MOOC) has expanded significantly in recent years. With the widespread of MOOC, the opportunity to study the fascinating courses for free has attracted numerous people of diverse educational backgrounds all over the world. In the big data era, a key research topic for MOOC is how to mine the needed courses in the massive course databases in cloud for each individual student accurately and rapidly as the number of courses is increasing fleetly. In this respect, the key challenge is how to realize personalized course recommendation as well as to reduce the computing and storage costs for the tremendous course data. In this paper, we propose a big data-supported, context-aware online learning-based course recommender system that could handle the dynamic and infinitely massive datasets, which recommends courses by using personalized context information and historical statistics. The context-awareness takes the personal preferences into consideration, making the recommendation suitable for people with different backgrounds. Besides, the algorithm achieves the sublinear regret performance, which means it can gradually recommend the mostly preferred and matched courses to students. In addition, our storage module is expanded to the distributed-connected storage nodes, where the devised algorithm can handle massive course storage problems from heterogeneous sources of course datasets. Comparing to existing algorithms, our proposed algorithms achieve the linear time complexity and space complexity. Experiment results verify the superiority of our algorithms when comparing with existing ones in the MOOC big data setting.


title: Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

abstract: Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.


title: Development of word-based text compression algorithm for Indonesian language document

abstract: Information technology is growing very rapidly, in particular for data handling. Data is a valuable asset for everyone, especially for larger companies with branches in several places. Data transmission from headquarters to branch offices make the company must provide good tools to do it. These companies also need tools that can be used to compress data to reduce their size. The main idea of the word-based encoding is to extract each word of the source text, then it is checked whether containing capital letters or not. After that, it is checked if there is a symbol or number. The particle will be separated from the basic word using stemming algorithm. Symbols, numbers and affixes will be indexed in the basic dictionary. The basic word will also be checked whether it exists in the basic dictionary or not. If there is not a match, then the word will be stored in the supplement dictionary. The experiment was conducted on the text file with the size from about 10K bytes up to 500K bytes with 16-bits length codewords. The result shows that the compression ratio of the proposed method is comparable with the previous ones, while its processing time is much better than the Reversed Sequence of Characters on LZW method.


title: Mining Activity Clusters From Low-level Event Logs

abstract: Process mining techniques have proven to be a valuable tool for analyzing the execution of business processes. They rely on logs that identify events at an activity level, i.e., most process mining techniques assume that the information system explicitly supports the notion of activities/tasks. This is often not the case and only low-level events are being supported and logged. For example, users may provide different pieces of data which together constitute a single activity. The technique introduced in this paper uses clustering algorithms to derive activity logs from lower-level data modification logs, as produced by virtually every information system. This approach was implemented in the context of the ProM framework and its goal is to widen the scope of processes that can be analyzed using existing process mining techniques.


title: Effectiveness of Top Management Support in Enterprise Systems Success: A Contingency Perspective of Fit between Leadership Style and System Life-Cycle

abstract: Top management support has been identified as one of the most important factors in the success of enterprise systems (ES). However, few studies have addressed the issue of what type of leadership is most effective in which phase of the ES life-cycle. Given the different challenges to leadership in the different phases of an ES, a ‘one-style-fits-all’ approach is clearly inadequate. In this study, we analyze the contingent fit between the recognized leadership styles and the phases in the ES life-cycle. The evidence from a multi-case study provides support for our contingency propositions that transformational leadership fits best with the adoption phase, transactional leadership fits best with the implementation phase, and two variations of combined transformational and transactional leadership styles are most effective in the assimilation and extension phases. This study breaks new theoretical ground in information systems literature by highlighting the contingencies of leadership effectiveness in the success of ES at different phases. It also provides prescriptive insights for top executives in terms of who to put in charge and what type of leadership style to look for when considering adopting and implementing new ES, assimilating the implemented systems, or contemplating integrations with business partners.


title: A Bayesian Network Approach to Ontology Mapping

abstract: This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.


title: Toward Characterizing the Productivity Benefits of Very Large Displays

abstract: Larger display surfaces are becoming increasingly available due to multi-monitor capability built into many systems, in addition to the rapid decrease in their costs. However, little is known about the performance benefits of using these larger surfaces compared to traditional single-monitor displays. In addition, it is not clear that current software designs and interaction techniques have been properly tuned for these larger surfaces. A preliminary user study was carried out to provide some initial evidence about the benefits of large versus small display surfaces for complex, multi-application office work. Significant benefits were observed in the use of a prototype, larger display, in addition to significant positive user preference and satisfaction with its use over a small display. In addition, design guidelines for enhancing user interaction across large display surfaces were identified. User productivity could be significantly enhanced in future graphical user interface designs if developed with these findings in mind.


title: Seq2Seq Mimic Games: A Signaling Perspective

abstract: We study the emergence of communication in multiagent adversarial settings inspired by the classic Imitation game. A class of three player games is used to explore how agents based on sequence to sequence (Seq2Seq) models can learn to communicate information in adversarial settings. We propose a modeling approach, an initial set of experiments and use signaling theory to support our analysis. In addition, we describe how we operationalize the learning process of actor-critic Seq2Seq based agents in these communicational games.


title: Characterizing flash memory: anomalies, observations, and applications

abstract: Despite flash memory's promise, it suffers from many idiosyncrasies such as limited durability, data integrity problems, and asymmetry in operation granularity. As architects, we aim to find ways to overcome these idiosyncrasies while exploiting flash memory's useful characteristics. To be successful, we must understand the trade-offs between the performance, cost (in both power and dollars), and reliability of flash memory. In addition, we must understand how different usage patterns affect these characteristics. Flash manufacturers provide conservative guidelines about these metrics, and this lack of detail makes it difficult to design systems that fully exploit flash memory's capabilities. We have empirically characterized flash memory technology from five manufacturers by directly measuring the performance, power, and reliability. We demonstrate that performance varies significantly between vendors, devices, and from publicly available datasheets. We also demonstrate and quantify some unexpected device characteristics and show how we can use them to improve responsiveness and energy consumption of solid state disks by 44% and 13%, respectively, as well as increase flash device lifetime by 5.2x.


title: Coupled inductor based single-phase bridgeless PFC boost rectifier with auxiliary circuit assisted ZVS

abstract: Front end PFC (power factor correction) boost ac-dc converter is an intrinsic part of applications like telecom servers, electric vehicle (EV) charging, and microgrids. In this paper a bridgeless PFC boost rectifier with two dc/dc boost circuits topology is employed to convert universal ac input to a fixed and regulated output dc voltage. High efficiency and power density are key figure-of-merits of boost PFC ac-dc converters. In this converter the conduction losses are reduced owing to the bridgeless topology and the boost diode reverse recovery is eliminated using MOSFETs for synchronous rectification. The converter operates in continuous conduction mode (CCM), at a switching frequency of 500 kHz. At high switching frequency and at CCM, soft-switching is necessary, therefore a passive auxiliary circuit is used to achieve zero voltage switching (ZVS). In order to increase the power density, the boost and the auxiliary inductors are magnetically coupled with a very low coupling coefficient. This coupling reduces the volume of the magnetics, although there is some loss due to the additional circulating current resulting from the coupling, however this loss is not that significant. Although passive auxiliary circuit adds more components, however at 500 kHz switching frequency these passive components do not occupy significant space. The soft-switching improves the efficiency and reduces EMI noise and cooling requirements. The detailed analysis, design and simulation results using PSIM are provided to validate the theoretical claims.


title: The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles

abstract: The Grand Cooperative Driving Challenge (GCDC), with the aim to boost the introduction of cooperative automated vehicles by means of wireless communication, is presented. Experiences from the previous edition of GCDC, which was held in Helmond in the Netherlands in 2011, are summarized, and an overview and expectations of the challenges in the 2016 edition are discussed. Two challenge scenarios, cooperative platoon merge and cooperative intersection passing, are specified and presented. One demonstration scenario for emergency vehicles is designed to showcase the benefits of cooperative driving. Communications closely follow the newly published cooperative intelligent transport system standards, while interaction protocols are designed for each of the scenarios. For the purpose of interoperability testing, an interactive testing tool is designed and presented. A general summary of the requirements on teams for participating in the challenge is also presented.


title: Both your intention and mine are reflected in the kinematics of my reach-to-grasp movement

abstract: The aim of the present study is to ascertain whether in a social context the kinematic parameters are influenced by the stance of the participants. In particular, we consider two basic modes of social cognition, namely cooperation and competition. Naive subjects were asked either to cooperate or to compete with a partner (a professional female actor), whose attitude could be either congruent or incongruent with the task instructions. Thus, on congruent conditions, subjects cooperated or competed with a partner showing a congruent cooperative or competitive attitude. On incongruent trials, the partner assumed an attitude that was manifestly in contrast with the instruction: competitive for the cooperative task, cooperative for the competitive task. We hypothesized that this mismatch between partner's attitude and instruction would produce a sort of unexpected social situation, affecting the kinematics of reach-to-grasp movement performed by the agents. If cooperative and competitive kinematic patterns are sensitive to the partner's attitude, then we should expect that an incongruent attitude have the potency to determine a reversal in kinematic patterning. Results revealed that for the incongruent trials the specific kinematic patterns for cooperation and competition found for the congruent conditions where modified according to the incongruent attitude assumed by the model actor. We suggest that this 'attitude' contagion is part of a sophisticated system that allows us to infer about the intention to act in a social context.


title: Improving Software Development Using Scrum Model by Analyzing Up and Down Movements On The Sprint Burn Down Chart - Proposition for Better Alternatives

abstract: Among various models, now a day, to address changing customer’s needs, commercial software developers intend to use iterative processes. Agile software development processes are built on the foundation of iterative development. One of the agile development processes is Scrum, which is an iterative incremental process of software development. Besides meeting varying needs of customer, timely delivery of product is important too. To predict when all of the work will be completed the developers use Burn Down Chart which represents work left to do vs. time. Theoretically, progression of time should leave less amount of task, the chart will look like straight line and the sprint will remain constant. However, in software industry, the irregularities on burn down chart are common which are the ups and downs occurring due to introducing new story points, new technology, wrong estimation of resources, cost and schedule. These reasons in turn affect the cost of the project, which in turn pull down the quality of the product because of failed to attain the defined sprint goal. To avoid such irregularities in development the better solution might be use of sprint burn up chart or modified Phil Goodwin and Russ Rufer’s burn down chart.


title: Voice Onset Time merger and development of tonal contrast in Seoul Korean stops: A corpus study

abstract: Abstract This paper is an apparent-time study of sound change in a three-way laryngeal contrast of Korean stops. The data are drawn from a read speech corpus distributed by the National Institute of the Korean Language. Voice Onset Time (VOT) of sentence initial stops and f0 of the first two vowels of the sentence produced by 117 Seoul Korean speakers were measured to determine how VOT and f0 realization of stop categories vary by speakers' age (range: 19–71) and gender. This is the first large-scale study of its kind based on data gathered from Seoul residents. The results replicate previous findings that the VOT values of aspirated stops are shortening in younger speakers' speech with the VOT difference between aspirated and lenis stops reducing accordingly and that this change is more advanced in female speech than in male speech. The novel finding of the study is that there is a trend of enhanced f0 distinction between aspirated and lenis stops, whereby the f0 distinction is amplified and extends further into the phrase in the speech of younger compared to older speakers and in the speech of female compared to male speakers. The results confirm that the establishment of tonal contrast and the loss of VOT distinction are taking place in tandem rather than in separate stages. The result also indicates that f0 enhancement as sound change is mediated by structural categories, namely all [+spread glottis] sounds including /h/ rather than narrowly targeting threatened segmental contrast in stop consonants.


title: Challenges in teaching a graduate course in applied cryptography

abstract: We describe our experience in creating and teaching a graduate course in cryptography at the Johns Hopkins University. We emphasize on the importance of such a course for a graduate student in an information security program. We discuss the course implementation and discuss the challenges in teaching this course to students from varied backgrounds. Based on our observations, we suggest specific improvements that could be applied to this course in the future.


title: Editing beyond articles: diversity & dynamics of teamwork in open collaborations

abstract: We report a study of Wikipedia in which we use a mixed-methods approach to understand how participation in specialized workgroups called WikiProjects has changed over the life of the encyclopedia. While previous work has analyzed the work of WikiProjects in supporting the development of articles within particular subject domains, the collaborative role of WikiProjects that do not fit this conventional mold has not been empirically examined. We combine content analysis, interviews and analysis of edit logs to identify and characterize these alternative WikiProjects and the work they do. Our findings suggest that WikiProject participation reflects community concerns and shifts in the community's conception of valued work over the past six years. We discuss implications for other open collaborations that need flexible, adaptable coordination mechanisms to support a range of content creation, curation and community maintenance tasks.


title: End-to-end learning for music audio

abstract: Content-based music information retrieval tasks have traditionally been solved using engineered features and shallow processing architectures. In recent years, there has been increasing interest in using feature learning and deep architectures instead, thus reducing the required engineering effort and the need for prior knowledge. However, this new approach typically still relies on mid-level representations of music audio, e.g. spectrograms, instead of raw audio signals. In this paper, we investigate whether it is possible to apply feature learning directly to raw audio signals. We train convolutional neural networks using both approaches and compare their performance on an automatic tagging task. Although they do not outperform a spectrogram-based approach, the networks are able to autonomously discover frequency decompositions from raw audio, as well as phase-and translation-invariant feature representations.


title: Mining Frequent Route Patterns Based on Personal Trajectory Abstraction

abstract: Frequent route pattern mining from personal trajectory data is the basis of location awareness and location services. However, because personal trajectory data is highly uncertain, most existing approaches are only capable of finding short and incomplete route patterns. In this paper, a novel approach is proposed for the discovery of frequent route patterns based on trajectory abstraction. First, trajectory partition, location extraction, data simplification, and common segment discovery are used to abstract trajectory data, convert these trajectories into common segment temporal sequences (STS) and generate 1-frequent itemsets . Then, a pattern mining algorithm is proposed based on the spatial-temporal adjacency relationship. This algorithm uses the constraint mechanism and bidirectional projected database to mine frequent route patterns from STS. Based on the real GeoLife trajectory data, the experimental results indicate that the proposed method has better performance and can find longer route patterns than other currently available methods.


title: A cMUT probe for ultrasound-guided focused ultrasound targeted therapy

abstract: Ultrasound-mediated targeted therapy represents a promising strategy in the arsenal of modern therapy. Capacitive micromachined ultrasonic transducer (cMUT) technology could overcome some difficulties encountered by traditional piezoelectric transducers. In this study, we report on the design, fabrication, and characterization of an ultrasound-guided focused ultrasound (USgFUS) cMUT probe dedicated to preclinical evaluation of targeted therapy (hyperthermia, thermosensitive liposomes activation, and sonoporation) at low frequency (1 MHz) with simultaneous ultrasonic imaging and guidance (15 to 20 MHz). The probe embeds two types of cMUT arrays to perform the modalities of targeted therapy and imaging respectively. The wafer-bonding process flow employed for the manufacturing of the cMUTs is reported. One of its main features is the possibility of implementing two different gap heights on the same wafer. All the design and characterization steps of the devices are described and discussed, starting from the array design up to the first in vitro measurements: optical (microscopy) and electrical (impedance) measurements, arrays' electroacoustic responses, focused pressure field mapping (maximum peak-to-peak pressure = 2.5 MPa), and the first B-scan image of a wire-target phantom.


title: Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality

abstract: Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs.


title: Evaluation of tracking algorithm performance without ground-truth data

abstract: Visual tracking is a topic on which a lot of scientific work has been carried out in the last years. An important aspect of tracking algorithms is the performance evaluation, which has been carried out typically through hand-labeled ground-truth data. Since the manual generation of ground truth is a time-consuming, error-prone and tedious task, recently many researchers have focused their attention on self-evaluation techniques for performance analysis. In this paper we propose a novel tool that enables image processing researchers to test the performance of tracking algorithms without resorting to hand-labeled ground truth data. The proposed approach consists of computing a set of features describing shape, appearance and motion of the tracked objects and combining them through a naive Bayesian classifier, in order to obtain a probability score representing the overall evaluation of each tracking decision. The method was tested on three different targets (vehicles, humans and fish) with three different tracking algorithms and the results show how this approach is able to reflect the quality of the performed tracking.


title: Automotive lead-acid battery state-of-health monitoring system

abstract: In this paper, we present a system to estimate the health of a lead-acid car battery and warn the driver of upcoming battery failure in the near future. Most existing lead-acid battery state of health (SOH) estimation systems measure the battery impedance by sensing the voltage and current of a battery. However, current sensing is costly for parts and/or labor. A method to estimate SOH by sensing the battery voltage during a cranking event was proposed. Our system intends to improve a previous method. It measures the battery voltage during a cranking event, processes critical voltage points to estimate the SOH of the battery, and warns the driver of a potential upcoming failure.


title: A 5.58 nW Crystal Oscillator Using Pulsed Driver for Real-Time Clocks

abstract: A 5.58 nW real-time clock using a crystal oscillator is presented. In this circuit, the amplifier used in a traditional circuit is replaced with pulsed drivers. The pulse is generated with precise timing using a DLL. With this approach, an extremely low oscillation amplitude of 160 mV results in low-power operation. This low-amplitude oscillation is sustained robustly using additional supply voltages: a lower supply for the final drive stage and a higher supply used for pulses that drive the final drive stage, which ensures low on -resistance necessary for reliable operation. The different supply levels are generated on-chip by a switched capacitor network (SCN) from a single supply. The circuit has been tested at different supply voltages and temperatures. It shows a minimum power consumption of 5.58 nW and power supply sensitivity of 30.3 ppm/V over supply voltage of 0.94–1.2 V, without degrading the crystal’s temperature dependency: between $- {{20}}\;^\circ {\rm C}\;{\text{and}}\;{{80}}\;^\circ {\rm C} $ . Moreover, its performance as a real-time clock has been verified by measurement of an Allan deviation of $1.16 \times {10^{ - 8}}$ .


title: Closed-Form Expressions for the Equivalent Circuit Model of Square-Waveguide T-Junctions and Its Application in Ortho-Mode Transducer Design

abstract: In this paper, closed-form expressions for the equivalent circuit model of square-waveguide T-junctions are presented. The square-waveguide T-junction is first modeled as a generalized admittance matrix and then reduced to a general five-port network. It is theoretically seen that the general five-port network can be simplified as a combination of a two-port subnetwork and a three-port subnetwork through the mode-matching analysis. Accurate closed-form expressions are derived with proper approximations to calculated the admittance parameters of these two subnetworks. A short-circuited branch ortho-mode transducer is then designed in a circuit simulator using the proposed equivalent circuit model. Measured results exhibit an excellent agreement with those from circuit simulation.


title: Security Analysis of Mobile Two-Factor Authentication Schemes

abstract: Two-factor authentication (2FA) schemes aim at strengthening the security of login password-based authentication by deploying secondary authentication tokens. In this context, mobile 2FA schemes require no additional hardware (e.g., a smartcard) to store and handle the secondary authentication token, and hence are considered as a reasonable trade-off between security, usability and costs. They are widely used in online banking and increasingly deployed by Internet service providers. In this article, we investigate 2FA implementations of several well-known Internet service providers such as Google, Dropbox, Twitter and Facebook. We identify various weaknesses that allow an attacker to easily bypass 2FA, even when the secondary authentication token is not under attacker's control. We then go a step further and present a more general attack against mobile 2FA schemes. Our attack relies on a cross-platform infection that subverts control over both end points (PC and a mobile device) involved in the authentication protocol. We apply this attack in practice and successfully circumvent diverse schemes: SMS-based TAN solutions of four large banks, one instance of a visual TAN scheme, 2FA login verification systems of Google, Dropbox, Twitter and Facebook accounts, and the Google Authenticator app currently used by 32 third-party service providers. Finally, we cluster and analyze hundreds of real-world malicious Android apps that target mobile 2FA schemes and show that banking Trojans already deploy mobile counterparts that steal 2FA credentials like TANs.


title: A Short Review of Ethical Challenges in Clinical Natural Language Processing

abstract: Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.


The Effect of Dynamic Seating on Classroom Behavior for Students in a


title: Developmental and Cognitive Characteristics of “High-Level Potentialities” (Highly Gifted) Children

abstract: This study covers the interesting field of the development in gifted children which is often neglected in pediatrics because psychomotor development data are still rare, since “gifted” children are generally noticed towards the end of their primary schooling by IQ measurement. Developmental studies have shown the evidence from several fields that children identified as “high-level potentialities” or “intellectually gifted” develop sensory, locomotor, neuropsychological, and language skills earlier than typically expected. The hypothesis is offered that the earlier development originates from biological processes affecting the physical development of the brain and in turn even intellectual abilities are developed earlier, potentially allowing for advanced development. Further it is discussed how these developmental advances interact with the social environment and in certain circumstances may entail increased risk for developing socioemotional difficulties and learning disabilities that often go unaddressed due to the masking by the advance intellectual abilities.


title: Bus-based content downloading for Vehicular Ad Hoc Networks

abstract: Vehicular Ad Hoc Networks (VANETs) represent promising technologies for comfort driving and entertainment applications which rely heavily on the data downloading. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the download requirements from multi-vehicles at the same time. This paper proposes a Bus-Based Content Downloading (BBCD) which aims to maximize the volume of downloaded data from bus to vehicles while the download opportunity fairness of each vehicle is taken into consideration. By predicting the number of buses which the vehicle would encounter in its future path and estimating the connection duration that the vehicle stays in the coverage of bus, the proposed BBCD schedules the download service for vehicles slot by slot such that the volume of downloaded data can be guaranteed while achieving the download opportunity fairness. The effectiveness of the proposed algorithm is evaluated by extensive simulations.


title: Female Orgasm(s): One, Two, Several

abstract: ABSTRACT Introduction There is general agreement that it is possible to have an orgasm thru the direct simulation of the external clitoris. In contrast, the possibility of achieving climax during penetration has been controversial. Methods Six scientists with different experimental evidence debate the existence of the vaginally activated orgasm (VAO). Main Outcome Measure To give reader of The Journal of Sexual Medicine sufficient data to form her/his own opinion on an important topic of female sexuality. Results Expert #1, the Controversy's section Editor, together with Expert #2, reviewed data from the literature demonstrating the anatomical possibility for the VAO. Expert #3 presents validating women's reports of pleasurable sexual responses and adaptive significance of the VAO. Echographic dynamic evidence induced Expert # 4 to describe one single orgasm, obtained from stimulation of either the external or internal clitoris, during penetration. Expert #5 reviewed his elegant experiments showing the uniquely different sensory responses to clitoral, vaginal, and cervical stimulation. Finally, the last Expert presented findings on the psychological scenario behind VAO. Conclusion The assumption that women may experience only the clitoral, external orgasm is not based on the best available scientific evidence. Jannini EA, Rubio‐Casillas A, Whipple B, Buisson O, Komisaruk BR, and Brody S. Female orgasm(s): one, two, several. J Sex Med 2012;9:956–965.


title: DiCE: The Infinitely Differentiable Monte-Carlo Estimator

abstract: The score function estimator is widely used for estimating gradients of stochastic objectives in Stochastic Computation Graphs (SCG), eg. in reinforcement learning and meta-learning. While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order gradients is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order gradient involves increasingly cumbersome graph manipulations. Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for higher-order gradient estimators. To address all these shortcomings in a unified way, we introduce DiCE, which provides a single objective that can be differentiated repeatedly, generating correct gradient estimators of any order in SCGs. Unlike SL, DiCE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DiCE both through a proof and through numerical evaluation of the DiCE gradient estimates. We also use DiCE to propose and evaluate a novel approach for multi-agent learning. Our code is available at this https URL


title: Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning

abstract: This prospective study with 464 older adolescents (14 to 19 years at Time 1; 16 to 21 years at Time 2) tested the structural paths of influence through which perceived self-efficacy for affect regulation operates in concert with perceived behavioral efficacy in governing diverse spheres of psychosocial functioning. Self-efficacy to regulate positive and negative affect is accompanied by high efficacy to manage one's academic development, to resist social pressures for antisocial activities, and to engage oneself with empathy in others' emotional experiences. Perceived self-efficacy for affect regulation essentially operated mediationally through the latter behavioral forms of self-efficacy rather than directly on prosocial behavior, delinquent conduct, and depression. Perceived empathic self-efficacy functioned as a generalized contributor to psychosocial functioning. It was accompanied by prosocial behavior and low involvement in delinquency but increased vulnerability to depression in adolescent females.


title: Nonlinear Component Analysis as a Kernel Eigenvalue Problem

abstract: A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.


title: Clustering with Bregman Divergences: an Asymptotic Analysis

abstract: Clustering, in particular $k$-means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of $k$-means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters $k$ is large. We establish quantization rates and describe the limiting distribution of the centers as $k\to \infty$, extending well-known results for $k$-means clustering.


title: Dynamics in socio-technical systems: Typology of change processes and contrasting case studies

abstract: This paper deals with fundamental change processes in socio-technical systems. It offers a typology of changes based on a multi-level perspective of innovation. Three types of change processes are identified: reproduction, transformation and transition. ‘Reproduction’ refers to incremental change along existing trajectories. ‘Transformation’ refers to a change in the direction of trajectories, related to a change in rules that guide innovative action. ‘Transition’ refers to a discontinuous shift to a new trajectory and system. Using the multi-level perspective, the underlying mechanisms of these change processes are identified. The transformation and transition processes are empirically illustrated by two contrasting case studies: the hygienic transition from cesspools to integrated sewer systems (1870–1930) and the transformation in waste management (1960–2000) in the Netherlands.


History of the Continuum in the 20th Century


title: Evolution of human mate choice

abstract: This article provides a review of evolutionary theory and empirical research on mate choices in nonhuman species and uses it as a frame for understanding the how and why of human mate choices. The basic principle is that the preferred mate choices and attendant social cognitions and behaviors of both women and men, and those of other species, have evolved to focus on and exploit the reproductive potential and reproductive investment of members of the opposite sex. Reproductive potential is defined as the genetic, material, and/or social resources an individual can invest in offspring, and reproductive investment is the actual use of these resources to enhance the physical and social well‐being of offspring. Similarities and differences in the mate preferences and choices of women and men are reviewed and can be understood in terms of similarities and differences in the form of reproductive potential that women and men have to offer and their tendency to use this potential for the well‐being of children.


title: A high-efficient LLCC series-parallel resonant converter

abstract: A high efficient LLCC-type resonant dc-dc converter is discussed in this paper for a low-power photovoltaic application. Emphasis is put on the different design mechanisms of the resonant tank. At the same time soft switching of the inverter as well as the rectifier bridge are regarded. Concerning the design rules, a new challenge is solved in designing a LLCC-converter with voltage-source output. Instead of the resonant elements, ratios of them, e.g. the ratio of inductances L s /L p is considered as design parameters first. Furthermore, the derived design rule for the transformer-inductor device fits directly into the overall LLCC-design. Due to the nature of transformers, i.e. the relation of the inductances L s /L p is only a function of geometry, this design parameter is directly considered by geometry. Experimental results demonstrate the high efficiency.


title: Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications

abstract: In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.


Employing EM and Pool-Based Active Learning for Text Classification


title: Neural mechanisms of motivated forgetting

abstract: Not all memories are equally welcome in awareness. People limit the time they spend thinking about unpleasant experiences, a process that begins during encoding, but that continues when cues later remind someone of the memory. Here, we review the emerging behavioural and neuroimaging evidence that suppressing awareness of an unwelcome memory, at encoding or retrieval, is achieved by inhibitory control processes mediated by the lateral prefrontal cortex. These mechanisms interact with neural structures that represent experiences in memory, disrupting traces that support retention. Thus, mechanisms engaged to regulate momentary awareness introduce lasting biases in which experiences remain accessible. We argue that theories of forgetting that neglect the motivated control of awareness omit a powerful force shaping the retention of our past.


title: Multiresolution Deep Belief Networks

abstract: Motivated by the observation that coarse and ne resolutions of an image reveal dierent structures in the underlying visual phenomenon, we present a model based on the Deep Belief Network (DBN) which learns features from the multiscale representation of images. A Laplacian Pyramid is rst constructed for each image. DBNs are then trained separately at each level of the pyramid. Finally, a top level RBM combines these DBNs into a single network we call the Multiresolution Deep Belief Network (MrDBN). Experiments show that MrDBNs generalize better than standard DBNs on NORB classication and TIMIT phone recognition. In the domain of generative learning, we demonstrate the superiority of MrDBNs at modeling face images.