title: Recurrent Neural Network Encoder with Attention for Community Question Answering

abstract: We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical issues such as data sparsity and imbalanced labels, we apply various techniques such as transfer learning and multitask learning. Our experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score compared to an information retrieval-based approach, and achieve comparable performance to a strong handcrafted feature-based method.


title: Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

abstract: We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As opposed to the neural tensor layer that has been adopted recently, the holographic composition provides the benefits of scalable and rich representational learning approach without incurring huge parameter costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified architecture for both deep sentence modeling and semantic matching. Essentially, our model is trained end-to-end whereby the parameters of the LSTM are optimized in a way that best explains the correlation between question and answer representations. In addition, our proposed deep learning architecture requires no extensive feature engineering. Via extensive experiments, we show that HD-LSTM outperforms many other neural architectures on two popular benchmark QA datasets. Empirical studies confirm the effectiveness of holographic composition over the neural tensor layer.


title: Exploring compact reinforcement-learning representations with linear regression

abstract: This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedure in this setting. We explore several applications of this algorithm for learning compact reinforcement-learning representations. We show that KWIK linear regression can be used to learn the reward function of a factored MDP and the probabilities of action outcomes in Stochastic STRIPS and Object Oriented MDPs, none of which have been proven to be efficiently learnable in the RL setting before. We also combine KWIK linear regression with other KWIK learners to learn larger portions of these models, including experiments on learning factored MDP transition and reward functions together.


title: It's a TRaP: Table Randomization and Protection against Function-Reuse Attacks

abstract: Code-reuse attacks continue to evolve and remain a severe threat to modern software. Recent research has proposed a variety of defenses with differing security, efficiency, and practicality characteristics. Whereas the majority of these solutions focus on specific code-reuse attack variants such as return-oriented programming (ROP), other attack variants that reuse whole functions, such as the classic return-into-libc, have received much less attention. Mitigating function-level code reuse is highly challenging because one needs to distinguish a legitimate call to a function from an illegitimate one. In fact, the recent counterfeit object-oriented programming (COOP) attack demonstrated that the majority of code-reuse defenses can be bypassed by reusing dynamically bound functions, i.e., functions that are accessed through global offset tables and virtual function tables, respectively. In this paper, we first significantly improve and simplify the COOP attack. Based on a strong adversarial model, we then present the design and implementation of a comprehensive code-reuse defense which is resilient against reuse of dynamically-bound functions. In particular, we introduce two novel defense techniques: (i) a practical technique to randomize the layout of tables containing code pointers resilient to memory disclosure and (ii) booby trap insertion to mitigate the threat of brute-force attacks iterating over the randomized tables. Booby traps serve the dual purpose of preventing fault-analysis side channels and ensuring that each table has sufficiently many possible permutations. Our detailed evaluation demonstrates that our approach is secure, effective, and practical. We prevent realistic, COOP-style attacks against the Chromium web browser and report an average overhead of 1.1% on the SPEC CPU2006 benchmarks.


title: LFSR-Based Generation of Multicycle Tests

abstract: This paper describes a procedure for computing a multicycle test set whose scan-in states are compressed into seeds for a linear-feedback shift register, and whose primary input vectors are held constant during the application of a multicycle test. The goal of computing multicycle tests is to provide test compaction that reduces both the test application time and the test data volume. To avoid sequential test generation, the procedure uses a single-cycle test set to guide the computation of multicycle tests. The procedure optimizes every multicycle test, and increases the number of faults it detects, by adjusting its seed, primary input vector, and number of functional clock cycles. Optimizing the seed instead of the scan-in state avoids the computation of scan-in states for which seeds do not exist. Experimental results for benchmark circuits are presented to demonstrate the effectiveness of the procedure.


title: Wideband Fabry-Perot Resonator Antenna With Two Complementary FSS Layers

abstract: This paper presents a novel design of a Fabry-Perot (FP) resonator antenna with a wide gain bandwidth in X band. The bandwidth enhancement of the antenna is attributed to the positive reflection phase gradient of an electromagnetic band gap (EBG) structure, which is constructed by the combination of two complementary frequency selective surfaces (FSSs). To explain well the design procedure and approach, the EBG structure is modeled as an equivalent circuit and analyzed using the Smith Chart. Experimental results show that the antenna possesses a relative 3 dB gain bandwidth of 28%, from 8.6 GHz to 11.4 GHz, with a peak gain of 13.8 dBi. Moreover, the gain bandwidth can be well covered by the impedance bandwidth for the reflection coefficient ( ${\rm S} _{11}$ ) below $-10~{\rm dB}$ from 8.6 GHz to 11.2 GHz.


title: Algorithms for connected set cover problem and fault-tolerant connected set cover problem

abstract: Given a set V of elements, S a family of subsets of V, and G a connected graph on vertex set S,a connected set cover (CSC) is a subfamily R of S such that every element in V is covered by at least one set of R, and the subgraph G[R] of G induced by R is connected. If furthermore G[R] is k-connected and every element in V is covered by at least m sets in R, then R is a (k,m)-CSC. In this paper, we present two approximation algorithms for the minimum CSC problem, and one approximation algorithm for the minimum (2,m)-CSC problem. Performance ratios are analyzed. These are the first approximation algorithms for CSC problems in general graphs with guaranteed performance ratios.


title: Constructability and Safety Performance Based Design: A Design and Assessment Tool for the Building Process

abstract: The theme of this contribution fits astride two strands of Construction Management Studies: “health and safety risk management” on construction site and “constructability” design approach. Specifically we faces two problems usually considered separately: the lack of specificity, synergy, effectiveness between safety planning and construction process and the lack of concurrence between design and construction process. The proposed approach assigns to design the key role of optimizing both constructability and safety planning in a unique and simultaneous procedure. The purpose is to improve “working plan” design level by specific construction simulation and the related safety assessment in the perspective of the better constructability performance and final building results. The proposed method is a Construction Management Approach supported by design tool based on working directly on design drawings. Starting from the representation of construction details, their construction can be simulated. This is obtained analyzing construction details by means of progressive drawings that express and simulate the breakdown of construction phases, chronologically processed and logically related. In these specific drawings all the elements of the construction site are graphically placed. The procedure allows both to perform the specific and detailed safety assessment for the realization of construction detail components, and to verify the constructability of them. The safety and constructability analysis is so performed in a graphical way during the design process not waiting the construction phase. So we are still able to intervene, if necessary, with amendments under the designer control.


Developing an Evaluation Framework for Blockchain in the Public Sector : The Example of the German Asylum Process


title: Ontology evolution: a process-centric survey

abstract: Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.


Business risk evaluation and management of Iranian commercial insurance companies


title: A 28-GHz Quadrature Fractional-N Frequency Synthesizer for 5G Transceivers With Less Than 100-fs Jitter Based on Cascaded PLL Architecture

abstract: This paper introduces a quadrature fractional-N cascaded frequency synthesizer and its phase noise analysis, optimization, and design for future 5G wireless transceivers. The performance improvement of the cascaded phase-locked loop (PLL) over single-stage PLL in terms of jitter and power consumption is theoretically presented and verified with measured results. The cascaded PLL is implemented using a first-stage fractional-N charge-pump PLL followed by a second-stage quadrature dividerless subsampling PLL. The fractional division in the first-stage PLL is implemented using a high-resolution phase mixer for lower quantization noise. Two prototypes of the single-stage PLL and the cascaded PLL were implemented in the 65-nm bulk CMOS process. The 26–32 GHz quadrature cascaded PLL consumes a total of 26.9 mW from 1-V supply and achieves less than 100-fs integrated jitter with −116.2 and −112.6-dBc/Hz phase noise at 1-MHz offset for the integer-N and the fractional-N modes, respectively. The fractional-N single-stage and cascaded PLLs achieve figure-of-merits of −230.58 and −248.75 dB, respectively.


title: Reduction of Gibbs artifacts in magnetic resonance imaging based on Convolutional Neural Network

abstract: In Magnetic Resonance Imaging (MRI), the K-space data is often under-sampled and truncated to shorten the scan time. However, the truncation of K-space also causes Gibbs ringing artifacts in the image, which seriously deteriorates the image quality. Inspired by the recent achievements of deep learning, we propose a novel method to reduce Gibbs artifacts in MRI with Convolutional Neural Network (CNN) in this paper. CNN is trained with a batch of image pairs with and without Gibbs artifacts. Afterwards, images with Gibbs artifacts can be input into the trained network to get the Gibbs-free images. Output of CNN is then transformed into K-space and merged with the sampled K-space data. Finally, inverse Fourier transform is applied to the merged K-space to get the final image. Experiments on both phantoms and real MRI images proved that the proposed method could reduce the Gibbs artifacts to a great degree and keep more image details compared with traditional Tukey filter.


title: GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

abstract: We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end manner. Specifically, geometric relationships are extracted over the predictions of individual modules and then combined as an image reconstruction loss, reasoning about static and dynamic scene parts separately. Furthermore, we propose an adaptive geometric consistency loss to increase robustness towards outliers and non-Lambertian regions, which resolves occlusions and texture ambiguities effectively. Experimentation on the KITTI driving dataset reveals that our scheme achieves state-of-the-art results in all of the three tasks, performing better than previously unsupervised methods and comparably with supervised ones.


title: Socio-technical systems: From design methods to systems engineering

abstract: It is widely acknowledged that adopting a socio-technical approach to system development leads to systems that are more acceptable to end users and deliver better value to stakeholders. Despite this, such approaches are not widely practised. We analyse the reasons for this, highlighting some of the problems with the better known socio-technical design methods. Based on this analysis we propose a new pragmatic framework for socio-technical systems engineering (STSE) which builds on the (largely independent) research of groups investigating work design, information systems, computer-supported cooperative work, and cognitive systems engineering. STSE bridges the traditional gap between organisational change and system development using two main types of activity: sensitisation and awareness; and constructive engagement. From the framework, we identify an initial set of interdisciplinary research problems that address how to apply socio-technical approaches in a cost-effective way, and how to facilitate the integration of STSE with existing systems and software engineering approaches.


title: Pros and cons of online cognitive-behavioural therapy.

abstract: Online cognitive-behavioural therapy (CBT) for depression has the potential to serve as an important addition to the care of people with mild to moderate depression. Although some studies show promising results, the need for proper diagnoses and human guidance must be considered when interpreting the modest effects found in studies with little or no guidance from a therapist.


title: Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System

abstract: This paper addresses a robust method for optimal PID control of Automatic Voltage Regulation (AVR) system to damp terminal voltage oscillation following disturbances in power systems. The optimization is carried out by the Anar- chic Society Optimization (ASO) algorithm which is inspired by a social grouping in which members behave anarchically to improve their situation. The optimal tuning problem of the PID gains to control of AVR system against parametric uncer- tainties is formulated as an optimization problem according to time domain based objective function. It is solved by the ASO algorithm which has a strong ability to find the most optimistic results to reduce control system effort and find optimal design. It is carried out under multiple operation conditions to achieve the desired level of terminal voltage regulation. The effec- tiveness of the optimized PID controller with ACO algorithm is investigated under system parameter changes in comparison with the results of PID controller tuned via of the recently reported optimization method like the craziness based Particle Swarm Optimization (PSO) and Vector Evaluated PSO (VEPSO) approaches. The simulation results is depicted to illustrate robust performance of the proposed ASO technique for enhancement transient stability of AVR system that of the other heuristics methods in the literature. The results analysis reveals that the ASO based PID type AVR control system is effective and provides good terminal voltage oscillations damping ability. Moreover, it is superior that of the other PSO method in terms of accuracy, convergence and computational effort.


title: Long-term blood pressure prediction with deep recurrent neural networks

abstract: Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.


title: Density-Based Clustering Based on Hierarchical Density Estimates

abstract: We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.


title: Gut microbiome development along the colorectal adenoma–carcinoma sequence

abstract: Colorectal cancer, a commonly diagnosed cancer in the elderly, often develops slowly from benign polyps called adenoma. The gut microbiota is believed to be directly involved in colorectal carcinogenesis. The identity and functional capacity of the adenoma- or carcinoma-related gut microbe(s), however, have not been surveyed in a comprehensive manner. Here we perform a metagenome-wide association study (MGWAS) on stools from advanced adenoma and carcinoma patients and from healthy subjects, revealing microbial genes, strains and functions enriched in each group. An analysis of potential risk factors indicates that high intake of red meat relative to fruits and vegetables appears to associate with outgrowth of bacteria that might contribute to a more hostile gut environment. These findings suggest that faecal microbiome-based strategies may be useful for early diagnosis and treatment of colorectal adenoma or carcinoma.


title: Effects of data selection and error specification on the assimilation of AIRS data

abstract: The Atmospheric InfraRed Sounder (AIRS), flying aboard NASA's Aqua satellite with the Advanced Microwave Sounding Unit-A (AMSU-A) and four other instruments, has been providing data for use in numerical weather prediction and data assimilation systems for over three years. The full AIRS data set is currently not transmitted in near-real-time to the prediction/assimilation centres. Instead, data sets with reduced spatial and spectral information are produced and made available within three hours of the observation time. In this paper, we evaluate the use of different channel selections and error specifications. We achieve significant positive impact from the Aqua AIRS/AMSU-A combination during our experimental time period of January 2003. The best results are obtained using a set of 156 channels that do not include any in the H2O band between 1080 and 2100 cm−1. The H2O band channels have a large influence on both temperature and humidity analyses. If observation and background errors are not properly specified, the partitioning of temperature and humidity information from these channels will not be correct, and this can lead to a degradation in forecast skill. Therefore, we suggest that it is important to focus on background error specification in order to maximize the impact from AIRS and similar instruments. In addition, we find that changing the specified channel errors has a significant effect on the amount of data that enters the analysis as a result of quality control thresholds that are related to the errors. However, moderate changes to the channel errors do not significantly impact forecast skill with the 156 channel set. We also examine the effects of different types of spatial data reduction on assimilated data sets and NWP forecast skill. Whether we pick the centre or the warmest AIRS pixel in a 3 × 3 array affects the amount of data ingested by the analysis but does not have a statistically significant impact on the forecast skill. Copyright © Published in 2007 by John Wiley & Sons, Ltd.


title: Characterizing Organ Donation Awareness from Social Media

abstract: Approximately 22 people die every day in the USA due to a lack of organs for transplant. Research suggests that the most effective solution is to increase organ donor rates, current, proposals range from expanding the donor eligibility criteria (donor pool) to performing mass media campaigns. However, little is known about the extent in which activities on social media are associated with aspects (e.g. awareness) of organ donation. Our hypothesis is that social media can be utilized as a sensor to characterize organ donation awareness and population engagement in donation for each different organ. In this sense, we collected Twitter messages (tweets) regarding organ donation, and characterized organ awareness by aggregating tweets from users who mostly mentioned that organ. Similarly, we assessed the relative risk between the cumulative incidence of organrelated conversations inside and outside geographical regions to characterize them regarding organ donation awareness. Our characterization suggests that organ-related conversations on social media seems to be indeed associated with aspects of organ donation such as the co-occurrence of organ transplantations. Also, we found variations regarding the specific organs that are prominently discussed in each geographical region, and that such variations seem to be associated with aspects of organ donation in that region, for instance, the abnormal amount of conversations about kidneys in Kansas. Our findings suggest that the proposed approach has the potential to characterize the awareness of organ donation in real-time.


title: Online Social Network Dependency: Theoretical Development and Testing of Competing Models

abstract: The proliferation of new social media technologies has changed the behavioral patterns of online users. This study aims at investigating the structure and dimensionality of the Online Social Network Dependency (OSN Dependency). We tested the competing models built upon the cognitive-behavioral model and the biopsychogical framework of addiction. Our findings suggested that OSN Dependency can be explained by a higher-order factor model with seven first-order factors (i.e. mood alternation, social benefit, negative outcomes, compulsivity, excessive time, withdrawal, and interpersonal control) and two correlated second-order factors (i.e. social components and intrapersonal components). The model provides a good-fit to the data, reflecting logical consistency. Implications of the current investigation for practice and research are provided.


title: Improving Cross-Domain Chinese Word Segmentation with Word Embeddings

abstract: Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our code and data available on Github.


title: An evaluation of techniques for grabbing and manipulating remote objects in immersive virtual environments

abstract: Grabbing and manipulating virtual objects is an important user interaction for immersive virtual environments. We present implementations and discussion of six techniques which allow manipulation of remote objects. A user study of these techniques was performed which revealed their characteristics and deficiencies, and led to the development of a new class of techniques. These hybrid techniques provide distinct advantages in terms of ease of use and efficiency because they consider the tasks of grabbing and manipulation separately. CR Categories and Subject Descriptors: 1.3.7 [Computer Graphics] :Three-Dimensional Graphics and Realism - Virtual Reality; 1.3.6 [Computer Graphics]:Methodology and Techniques - Interaction Techniques.


title: Radio Propagation Measurements and WINNER II Parameterization for a Shopping Mall at 60 GHz

abstract: In this paper, we derive parameters of the WINNER II channel model for a shopping mall environment at 60 GHz band, covering both line-of-sight and obstructed-line-of-sight scenarios. The model parameters are derived mostly based on channel measurements reported in this paper. Due to a large measurement bandwidth and the highly specular nature of the channel in the shopping mall, only weak clustering effects of propagation paths are found. Still, we demonstrate that the WINNER II model structure is applicable to 60 GHz channel modeling since the model reproduces the measured channels well.


title: Statistical Extraction and Visualization of Topics in the Qur'an Corpus

abstract: Unsupervised machine learning techniques are described and applied on the mildly preprocessed Arabic text of the Holy Qur’an, with promising results. Topic modelling based on nonnegative matrix factorization was used to successfully extract meaningful topics underlying the set of 6236 verses in the corpus. Data visualization using t-SNE dimensionality reduction correctly grouped verses of the Holy Qur’an into clusters based on theme and word usage. This accessible paper begins with an introductory view of machine learning, and includes motivating descriptions of the implemented techniques before presenting a summary of findings. A graphical display combining the results of topic modelling and data visualization demonstrates the consistency of the studied models.


Investigation of Nonmotor Symptoms in First-Degree Relatives of Patients with Different Clinical Types of Parkinson's Disease


title: Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health

abstract: Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content. Methods: Our case study is the ongoing Horyzons site (5-year randomised controlled trial for youth recovering from early psychosis), which is powered by MOST. We outline the motivation underlying the project and the web application’s foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system. Conclusions: To date, the innovative MOST system has demonstrated viability in a series of clinical research trials. Given the data-driven opportunities afforded by the software system, observed usage patterns and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms.


title: A Survey on Pre-Processing in Image Matting

abstract: Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask (Trimap). This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories: static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.


title: Integrating Prior Information into Subspace Identification Methods

abstract: Integrating prior information into subspace identification methods improves their usability for industrial data, where experimental data by them self are in many cases not good enough to give a proper model. The identification experiments in the industrial environment are limited by the economical and safety reasons. However, in practical applications, there is often strong prior information about the identified system, which can be exploited in the identification. The presented algorithm formulates subspace identification as a multi-step predictor optimization. Reformulation to the Bayesian framework allows to incorporate prior information. The paper is completed with the application to the experimental data from the oil burning steam boiler with the rated power of 100 MW.


title: Theory of Inventive Problem Solving Applied To Business Process Management Projects (BPM-TRIZ)

abstract: Many companies look for business process management as a way to put into practice their strategic goals, thus expecting to anticipate and adequate themselves for potential environment changes. However, it is possible to find a set of innovation barriers when the process management methodology is executed, such as the existence of conflicting project goals, the inherited level of business processes variance and the specialist's psychological inertia. Those barriers often decrease the level of efficiency and quality of projects, by the application of completely intuitive methods, and by the development of compromise solutions. It is proposed in this article that the Theory of Inventive Problem Solving (TRIZ) be applied to business process management projects, specifically to the proposal and implementation stages. The main goal is to reduce or even eliminate the innovation barrier's effects on such projects. Inventive principles can be used as a guidance to reach the ideal final result, and inventive solutions complying with contradictory goals can be developed in a systematic way. Necessary analogies are proposed and validated, on a business process development project, to adapt the TRIZ method on a non-technical environment. Preliminary results are presented, and partial conclusions are stated.


title: Visualization of oceanographic applications using a common data model

abstract: Due to the rapid development of ocean observation technology, the volume of ocean data is increasing exponentially. To better utilize these data, more and more computational methods have been applied to exploring, sharing, and processing such data. Although great efforts have been made, there is an urgent need for a single platform containing a set of visualization components for every kind of ocean data. This paper presents a distributed, scalable and service-oriented visualization platform. The development of the platform is based on a common data model, by which ocean data with different spatial and temporal distribution can be organized and accessed in a uniform fashion, which sets a solid foundation for designing and implementing all kinds of visualization components and facilitates collaborative oceanographic research. The platform is run in China Ocean Observation System (COOS), and enables easy and convenient development of all kinds of visualization components for professionals and the general public and greatly advances ocean data sharing and scientific research collaboration.


title: Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS

abstract: Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.


Automatic Chord Transcription from Audio Using Computational Models of Musical Context


title: A comparison of spotlight synthetic aperture radar image formation techniques

abstract: Spotlight synthetic aperture radar images can be formed from the complex phase history data using two main techniques: (1) polar-to-cartesian interpolation followed by two-dimensional inverse Fourier transform (2DFFT), and (2) convolution backprojection (CBP). CBP has been widely used to reconstruct medical images in computer aided tomography, and only recently has been applied to form synthetic aperture radar imagery. It is alleged that CBP yields higher quality images because (1) all the Fourier data are used and (2) the polar formatted data is used directly to form a 2D Cartesian image and therefore 2D interpolation is not required. This report compares the quality of images formed by CBP and several modified versions of the 2DFFT method. We show from an image quality point of view that CBP is equivalent to first windowing the phase history data and then interpolating to an exscribed rectangle. From a mathematical perspective, we should expect this conclusion since the same Fourier data are used to form the SAR image. We next address the issue of parallel implementation of each algorithm. We dispute previous claims that CBP is more readily parallelizable than the 2DFFT method. Our conclusions are supported by comparing execution times between massively parallel implementations of both algorithms, showing that both experience similar decreases in computation time, but that CBP takes significantly longer to form an image.


title: Speed limit traffic sign detection and recognition

abstract: Since carelessness about the maximum speed allowed in roads has caused many fatal accidents, an intelligent system to detect and inform it automatically, seems to be very useful. In this paper, a system is developed to detect and recognize the speed limit traffic signs, which takes digital images from driving scenes and then the pictures are being filtered and searched to find the circular speed limit sign. The new method proposed, that is named "hierarchical Hough transform" can optimize the solution of accuracy/complexity problem. The powerful tool of neural network is then applied to recognize the pattern (number) which has been well separated by the previous method. Some results from natural scenes are shown during the process presentation


Multi party computations: past and present


title: Application of Feature Selection Methods in Educational Data Mining

abstract: recent years, web based learning has emerged as a new field of research due to growth of network and communication technology. These learning systems generate a large volume of student data. Data mining algorithms may be applied on this data set to study interesting patterns. As an example, student enrollment data and his past examination records could be used to predict his grades in the term end examination. However this prediction could mean examining a lot of features of the student data resulting in creation of a model with high computational complexity. In this context this work first defines a student data set with 309 records and 14 features collected by a survey from various graduation level students majoring in Computer Science under University of Calcutta. Different feature selection algorithms are applied on this data set. The best results are obtained by Correlation Based Feature Selection algorithm with 8 features. Subsequently classification algorithms may be applied on this feature subset for predicting student grades.


title: Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

abstract: Much of the user-generated content on social media is provided by ordinary people telling stories about their daily lives. We develop and test a novel method for learning fine-grained common-sense knowledge from these stories about contingent (causal and conditional) relationships between everyday events. This type of knowledge is useful for text and story understanding, information extraction, question answering, and text summarization. We test and compare different methods for learning contingency relation, and compare what is learned from topic-sorted story collections vs. general-domain stories. Our experiments show that using topic-specific datasets enables learning finer-grained knowledge about events and results in significant improvement over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topic-sorted stories are judged as contingent.


title: Empirical investigation of IEEE 802.11ad network

abstract: The IEEE 802.11ad standard allows wireless devices to operate in the unlicensed spectrum band of 60 GHz. By utilizing the channel with 2.16 GHz width, the devices are able to transmit at multi-Gigabit data rates that potentially satisfy demanding requirements of quality of services. Additionally, the advent of off-the-shelf IEEE 802.11ad device motivates research efforts to exploit this 60 GHz opportunity for applications. Although much has been understood from the previous works, there is still a lack of insight into building IEEE 802.11ad networks, especially the ones with multi-Gigabit links. Address the issue, this work investigates the feasibility of multi-Gigabit IEEE 802.11ad networks, as well as, the factors that influence network performance in a typical office environment. By experimental study with off-the-shelf IEEE 802.11ad hardware, we can identify the conditions that the TCP and UDP throughput of IEEE 802.11ad link reach multi-Gigabit levels. Moreover, we show and discuss the negative effects of both co-channel and adjacent channel interference on the IEEE 802.11ad network's performance.


title: Improving stabilization of passive walking using chaos

abstract: Passive walking is defined as walking down a shallow slope without using any muscular contraction as an active controller. Based on this definition, which was made to present the simple possible models of human gait, some knee-less models have been proposed. These simple passive models have the ability to show several different dynamics such as periodic and chaotic behaviors. Previously, the periodic behavior has been studied and considered as the only stable response, but recent dynamical analysis on human locomotion characteristics such as stride length exhibit that this process has chaotic behavior. The purpose of this paper is to demonstrate that the knee-less passive-dynamic models has the ability to show chaotic behavior as a stable and acceptable answer using a chaotic function in heel strike condition. Represented chaotic model's parameters have been examined and compared concluding that the novel model is robust to trivial changes of slope or initial conditions.


title: Towards Open Domain Event Extraction from Twitter: REVEALing Entity Relations

abstract: In the past years social media services received content contributions from millions of users, making them a fruitful source for data analysis. In this paper we present a novel approach for mining Twitter data in order to extract factual information concerning trending events. Our approach is based on relation extraction between named entities, such as people, organizations and locations. The experiments and the obtained results suggest that relation extraction can help in extracting events in social media, when combined with pre and post-processing steps.


title: Bounding Multiple Gaussians Uncertainty with Application to Object Tracking

abstract: This paper proves the uncertainty bound for the multiple Gaussian functions, termed multiple Gaussians Uncertainty (MGU), which significantly generalizes the uncertainty principle for the single Gaussian function. First, as a theoretical contribution, we prove that the momentum (velocity) and position for the sum of multiple Gaussians wave function are theoretically bounded. Second, as for a practical application, we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework. By integrating MGU with a given object tracker, we demonstrate that uncertainty principle can provide remarkable robustness in tracking. Extensive experiments are done to show that the proposed MGU can significantly help base trackers overcome the object drifting and reach state-of-the-art results.


Controllable Text Generation


Backward transition radiation in the extreme ultraviolet region as a tool for the transverse beam profile diagnostic


title: An IoT-Oriented Data Storage Framework in Cloud Computing Platform

abstract: The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.


title: When Is It Too Early for Single Sport Specialization?

abstract: Over the past 15 years, there has been an increase in youth sports participation with a concomitant increase in early year-round training in a single sport. Many factors contribute to the desire of parents and coaches to encourage early single sport specialization, including the desire to give the young athlete an edge in competition, pursuit of scholarships, and potential professional status, and the ability to label a young athlete as elite at an early age. Despite these perceived advantages, some data suggest that early sport specialization does not lead to a competitive advantage over athletes who participate in multiple sports. Although the data are limited, there is some evidence that early sport specialization may put the young athlete at risk for overuse injuries. The focus of this review is to highlight the evidence regarding early sport specialization and risk for injury; discuss the risk factors for overuse injury in high-risk sports including ice hockey, swimming, gymnastics, and baseball; and discuss future potential research that would help define the risk of injury for young athletes who participate in early sport specialization.


title: The Personality Inventory for DSM-5 Short Form (PID-5-SF): psychometric properties and association with big five traits and pathological beliefs in a Norwegian population

abstract: Abstract Background With the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), an alternative model for personality disorders based on personality dysfunction and pathological personality traits was introduced. The Personality Inventory for DSM-5 (PID-5) is a 220-item self-report inventory designed to assess the personality traits of this model. Recently, a short 100-item version of the PID-5 (PID-5-SF) has been developed. The aim of this study was to investigate the score reliability and structure of the Norwegian PID-5-SF. Further, criterion validity with the five factor model of personality (FFM) and pathological personality beliefs was examined. Methods A derivation sample of university students ( N = 503) completed the PID-5, the Big Five Inventory (BFI), and the Personality Beliefs Questionnaire – Short Form (PBQ-SF), whereas a replication sample of 127 students completed the PID-5-SF along with the aforementioned measures. Results The short PID-5 showed overall good score reliability and structural validity. The associations with FFM traits and pathological personality beliefs were conceptually coherent and similar for the two forms of the PID-5. Conclusions The results suggest that the Norwegian PID-5 short form is a reliable and efficient measure of the trait criterion of the alternative model for personality disorders in DSM-5.


title: Detecting product review spammers using rating behaviors

abstract: This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.


title: A GPS/GIS method for travel mode detection in New York City

abstract: Abstract Handheld GPS provides a new technology to trace people’s daily travels and has been increasingly used for household travel surveys in major cities worldwide. However, methodologies have not been developed to successfully manage the enormous amount of data generated by GPS, especially in a complex urban environment such as New York City where urban canyon effects are significant and transportation networks are complicated. We develop a GIS algorithm that automatically processes the data from GPS-based travel surveys and detects five travel modes (walk, car, bus, subway, and commuter rail) from a multimodal transportation network in New York City. The mode detection results from the GIS algorithm are checked against the travel diaries from two small handheld GPS surveys. The combined success rate is a promising 82.6% (78.9% for one survey and 86.0% for another). Challenges we encountered in the mode detection process, ways we developed to meet these challenges, as well as possible future improvement to the GPS/GIS method are discussed in the paper, in order to provide a much-needed methodology to process GPS-based travel data for other cities.


title: Efficient virtual memory for big memory servers

abstract: Our analysis shows that many "big-memory" server workloads, such as databases, in-memory caches, and graph analytics, pay a high cost for page-based virtual memory. They consume as much as 10% of execution cycles on TLB misses, even using large pages. On the other hand, we find that these workloads use read-write permission on most pages, are provisioned not to swap, and rarely benefit from the full flexibility of page-based virtual memory. To remove the TLB miss overhead for big-memory workloads, we propose mapping part of a process's linear virtual address space with a direct segment, while page mapping the rest of the virtual address space. Direct segments use minimal hardware---base, limit and offset registers per core---to map contiguous virtual memory regions directly to contiguous physical memory. They eliminate the possibility of TLB misses for key data structures such as database buffer pools and in-memory key-value stores. Memory mapped by a direct segment may be converted back to paging when needed. We prototype direct-segment software support for x86-64 in Linux and emulate direct-segment hardware. For our workloads, direct segments eliminate almost all TLB misses and reduce the execution time wasted on TLB misses to less than 0.5%.


Effect of molecular weight of hyaluronic acid (HA) on viscoelasticity and particle texturing feel of HA dermal biphasic fillers


Influence of arc chamber parameters on passive resonance circuit of HVDC circuit breakers


title: A Gradient-Based Reinforcement Learning Approach to Dynamic Pricing in Partially-Observable Environments

abstract: As more companies are beginning to adopt the e-business model, it becomes easier for buyers to compare prices at multiple sellers and choose the one that charges the best price for the same item or service. As a result, the demand for the goods of a particular seller is becoming more unstable, since other sellers are regularly offering discounts that attract large fractions of buyers. Therefore, it becomes more important for each seller to switch from static to dynamic pricing policies that take into account observable characteristics of the current demand and the state of the seller's resources. This paper presents a Reinforcement Learning algorithm that can tune parameters of a seller's dynamic pricing policy in a gradient direction (thus converging to the optimal parameter values that maximize the revenue obtained by the seller) even when the seller's environment is not fully observable. This algorithm is evaluated using a simulated Grid market environment, where customers choose a Grid Service Provider (GSP) to which they want to submit a computing job based on the posted price and expected delay information at each GSP.


title: A New Scale to Measure Executive Servant Leadership: Development, Analysis, and Implications for Research

abstract: This article introduces a new scale to measure executive servant leadership, situating the need for this scale within the context of ethical leadership and its impacts on followers, organizations and the greater society. The literature on servant leadership is reviewed and servant leadership is compared to other concepts that share dimensions of ethical leadership (e.g., transformational, authentic, and spiritual leadership). Next, the Executive Servant Leadership Scale (ESLS) is introduced, and its contributions and limitations discussed. We conclude with an agenda for future research, describing ways the measure can be used to test hypotheses about organizational moral climate, ethical organizational culture, corporate responsibility, and institutional theory.


Analyzing HTTP requests for web intrusion detection


title: Culture and the Brain

abstract: The goal of this article is to highlight recent work examining how culture affects neural activation. We suggest a framework for cultural neuroscience in which there are two objectives: culture mapping—or the mapping function from patterns characteristic of cultures to their neural processing—and source analysis—or the attempt to determine the sources of observed commonalities and differences. We review links between culture and the brain across fundamental domains of cognitive and social psychology.


title: Banking Sector Reforms and Commercial Bank Performance in China

abstract: The Chinese banking sector has gone through a consistent process of reforms which includes liberalization of the business to foreign banks, privatization and listing bank shares, allowing diversifications into intermediary business beyond the traditional deposit and loan activities. In the mean time, the changing structures of the banking sector and the favourable economic condition in the recent decade have promoted the development of the commercial banks. Using a sample of 80 Chinese commercial banks for the period from 2000 to 2008, we investigate the determinants of bank performance. Aggregate index measures of performance are constructed based on proxy variables that assess the quality of assets, capital adequacy, profitability, and liquidity. Potential determinants at bank level, industry level, and macroeconomic level are examined in the panel data models. We find that city commercial banks out-perform the national banks which include state owned big four banks and joint-stock banks. Our evidence also suggests lower financial leverage, higher off-balance sheet activities, and larger size of the bank are associated with better performance. At industry level, concentration of the banking sector has allowed better performance due to less competitive environment. The liberalization of the banking business to foreign banks in 2003 has an encouraging effect on the banking sector, although the evidence is not statistically significant. At macroeconomic level, higher per capita GDP and lower unemployment has been significantly related to better bank performance.


title: 5 Mbps optical wireless communication with error correction coding for underwater sensor nodes

abstract: One issue with underwater sensors is how to efficiently transfer large amounts of data collected by the node to an interrogating platform such as an underwater vehicle. It is often impractical to make a physical connection between the node and the vehicle which suggests an acoustic or optical wireless solution. For large amounts of data, the high bandwidth of underwater optical wireless is an advantage. A small, low-cost platform to demonstrate the potential of an optical wireless communications interface for underwater sensor nodes is demonstrated. To enhance the reliability and robustness of the optical wireless communication digital signal processing and error correction techniques are used. The system was tested in 3 and 7.7 meter tanks at 5 Mbps with the turbidity of the water controlled by the addition of Maalox.


title: Pattern-Reconfigurable, Flexible, Wideband, Directive, Electrically Small Near-Field Resonant Parasitic Antenna

abstract: A pattern-reconfigurable, flexible, wideband, directive, electrically small near-field resonant parasitic (NFRP) antenna is presented. The antenna consists of a pair of Egyptian axe dipole NFRP elements, together with a pair of shaped metallic strips that act as the driven element and are fed by a coaxial cable. These NFRP and driven elements are designed to achieve compactness. Two pairs of p-i-n diodes are integrated into the driven element to enable the pattern reconfigurability. The antenna has two switchable directive endfire states, each pointed in direct opposition to the other. Examples of the evolution of the antenna are used to illustrate its operating principles. A prototype of the optimized design operating in a frequency range centered at 1.8 GHz was fabricated and measured. The simulation and experimental results are in good agreement. The antenna exhibits a wide ~13.1% impedance bandwidth and a ~4.42 dBi peak realized gain in both pattern-reconfigurable states while maintaining its electrically small size: $\textit {ka} \sim 0.94$ . The flexibility of this antenna is demonstrated under different bending conditions by mounting it on cylinders with several different radii, and the results confirm that its performance characteristics are maintained under all of them.


title: Hate Speech Dataset from a White Supremacy Forum

abstract: Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive rise of user-generated web content on social media, the amount of hate speech is also steadily increasing. Over the past years, interest in online hate speech detection and, particularly, the automation of this task has continuously grown, along with the societal impact of the phenomenon. This paper describes a hate speech dataset composed of thousands of sentences manually labelled as containing hate speech or not. The sentences have been extracted from Stormfront, a white supremacist forum. A custom annotation tool has been developed to carry out the manual labelling task which, among other things, allows the annotators to choose whether to read the context of a sentence before labelling it. The paper also provides a thoughtful qualitative and quantitative study of the resulting dataset and several baseline experiments with different classification models. The dataset is publicly available.


title: Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

abstract: The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7\% on Cityscapes (street scene parsing), 71.3\% on PASCAL-Person-Part (person-part segmentation), and 87.9\% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems.


title: Can You Hear Me Now? Musical Training Shapes Functional Brain Networks for Selective Auditory Attention and Hearing Speech in Noise

abstract: Even in the quietest of rooms, our senses are perpetually inundated by a barrage of sounds, requiring the auditory system to adapt to a variety of listening conditions in order to extract signals of interest (e.g., one speaker’s voice amidst others). Brain networks that promote selective attention are thought to sharpen the neural encoding of a target signal, suppressing competing sounds and enhancing perceptual performance. Here, we ask: does musical training benefit cortical mechanisms that underlie selective attention to speech? To answer this question, we assessed the impact of selective auditory attention on cortical auditory-evoked response variability in musicians and nonmusicians. Outcomes indicate strengthened brain networks for selective auditory attention in musicians in that musicians but not nonmusicians demonstrate decreased prefrontal response variability with auditory attention. Results are interpreted in the context of previous work from our laboratory documenting perceptual and subcortical advantages in musicians for the hearing and neural encoding of speech in background noise. Musicians’ neural proficiency for selectively engaging and sustaining auditory attention to language indicates a potential benefit of music for auditory training. Given the importance of auditory attention for the development of language-related skills, musical training may aid in the prevention, habilitation and remediation of children with a wide range of attention-based language and learning impairments.


title: Carrier-based PWM technique applied to single-phase asymmetric NPC inverters with three levels on input and seven on output, using FPGA

abstract: This paper presents a carrier-based pulse width modulation (PWM) technique applied to three-level NPC single-phase asymmetric multilevel inverter. The PWM technique along with the asymmetrical dc voltage condition allows for seven levels on the output AC voltage, providing two levels more than the equivalent symmetrical single phase inverter. Therefore, the Total Harmonic Distortion (THD) of the output voltage is highly reduced. To verify the proposed PWM strategy, simulations were performed and an experimental prototype was implemented, using a Field Programmable Gate Array device (FPGA) for the PWM modulator. The simulation and experimental results demonstrate the effectiveness of the modulation strategy.


title: High performance polymer solar cells with as-prepared zirconium acetylacetonate film as cathode buffer layer

abstract: Low-work-function active metals are commonly used as cathode in polymer solar cells (PSCs), but sensitivity of the active metals towards moisture and oxygen results in poor stability of the devices. Therefore, solution-proceessable and stable cathode buffer layer is of great importance for the application of PSCs. Here we demonstrate high performance PSCs by employing as-prepared zirconium acetylacetonate (a-ZrAcac) film spin-cast from its ethanol solution as cathode buffer layer. The PSCs based on a low bandgap polymer PBDTBDD as donor and PC60BM as acceptor with a-ZrAcac/Al cathode demonstrated an average power conversion efficiency (PCE) of 8.75% which is significantly improved than that of the devices with traditional Ca/Al cathode. The improved photovoltaic performance is benefitted from the decreased series resistance and enhanced light harvest of the PSCs with the a-ZrAcac/Al cathode. The results indicate that a-ZrAcac is a promising high performance cathode buffer layer for fabricating large area flexible PSCs.


title: Properties and Architecture of Drugs and Natural Products Revisited

abstract: Computer-based analysis revealed that natural products exhibit a remarkable structural diversity of molecular frameworks and scaffolds that could be systematically exploited for combinatorial synthesis. Natural products offer a rich pool of unique molecular frameworks that complement "drug space". They possess desirable druglike properties rendering them ideal starting points for molecular design considerations. This review provides an overview of chemotype diversity and molecular properties of collections of drugs and druglike molecules, pure natural products, and natural product- derived compounds. Compared to druglike molecules, pure natural products contain more oxygen atoms and chiral cen- ters, and have less aromatic atoms on average. Among the natural product library we identified more than one thousand scaffolds that were not contained in any other compound set analyzed. This outcome provides a basis for the design of new natural product-derived compound libraries. Our study demonstrates that computational chemical biology can assist in finding suitable molecular entities in collections of natural products for drug discovery.


Explorer Merlin : An Open Source Neural Network Speech Synthesis System


title: The Web Never Forgets: Persistent Tracking Mechanisms in the Wild

abstract: We present the first large-scale studies of three advanced web tracking mechanisms - canvas fingerprinting, evercookies and use of "cookie syncing" in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing. Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls --- such as failing to clear state on multiple browsers at once - in which a single lapse in judgement can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.


Inter-individual Differences in Heart Rate Variability Are Associated with Inter-individual Differences in Empathy and Alexithymia


title: © Asia-Pacific Society for Computers in Education DESIGNING A DESKTOP VIRTUAL REALITY-BASED LEARNING ENVIRONMENT WITH EMOTIONAL CONSIDERATION

abstract: Many studies related to the use of virtual reality in education are focused on the cognitive aspects with little consideration given to the emotional domain. Thus, this study aims to identify the salient linkages between learners’ emotions and design elements of a desktop virtual reality-based learning environment by employing Kansei Engineering concepts. A courseware related to the teaching of road safety skills to young learners was designed and developed to be used as a case for the study. Ten specimens of the courseware, which highlights different design elements, were presented to 90 students from three randomly selected secondary schools. They were required to rate their feelings towards the specimens using the provided checklist that consists of 30 words related to emotions. The gathered data were then analysed using Principal Component Analysis and Partial Least Squares analysis. The results revealed that the most influential design elements in inducing positive emotions are environment richness and coaching. Ultimately, the uncovered linkages could be used to inform future design of emotionally sound desktop VR-learning environments.


title: Detection and recognition of urban road markings using images

abstract: While road lane markings detection was extensively studied, in particular for intelligent vehicle applications, the detection and recognition of all kind of marking such as arrows, crosswalks, zebras, words, pictograms, continuous and discontinuous lane markings was drastically less studied. However, it has many potential applications in the design of advanced driver assistance systems, as well as for asset management along itineraries. An algorithm is proposed which is based on the following processing steps: marking pixel extraction, detection using connected components before Inverse Perspective Mapping and recognition based on the comparison with a single pattern or with repetitive rectangular patterns. The proposed algorithm is able to detect and recognize repetitive markings (such as crosswalks) as well as single patterns (such as arrows). We believe that the proposed algorithm can be extended easily to solve the problem of the identification of all types of markings.


title: HermiT: A Highly-Efficient OWL Reasoner

abstract: HermiT is a new OWL reasoner based on a novel “hypertableau” calculus. The new calculus addresses performance problems due to nondeterminism and model size—the primary sources of complexity in state-of-the-art OWL reasoners. The latter is particularly important in practice, and it is achieved in HermiT with an improved blocking strategy and and an optimization that tries to reuse existing individuals rather than generating new ones. HermiT also incorporates a number of other novel optimizations, such as a more efficient approach to handling nominals, and various techniques for optimizing ontology classification. Our tests show that HermiT is usually much faster than other reasoners when classifying complex ontologies, and it is already able to classify a number of ontologies which no other reasoner has been able to handle.


title: Solid-state Marx Generator Design with an Energy Recovery Reset Circuit for Output Transformer Association

abstract: This paper presents a hybrid fully integrated solid- state Marx generator circuit, which has been developed for high-frequency (kHz), high-voltage (kV) applications needing rectangular pulses. The proposed circuit takes advantage of the intensive use of power semiconductor switches to increase the performance of the classical Marx circuit, strongly reducing losses and increasing the pulse repetition frequency. In addition, to further increase the output pulse, the proposed topology is enhanced with an energy recovery reset circuit that enables the use of an output pulse transformer, and recovers the transformer magnetizing energy, during the pulse off state, back to the energy storage capacitors.


title: Segmentation of Dermoscopy Images Using Wavelet Networks

abstract: This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.


title: Leaf-spine architecture for OTN switching

abstract: The application of a leaf-spine switching architecture is considered for Layer 1 circuit switching, such as with OTN ODU-based switching. Simple relationships are derived for the achievable leaf-spine switch capacity and scalability, given the number and capacity of individual leaf and spine switch elements, and bandwidth for leaf-spine interlinks. Considerations for optimizing cost with respect to the partitioning of leaf and spine switch elements and fabric interlink bandwidth are analyzed and discussed, along with considerations for protection against equipment failures. Simulation results are then provided for different leaf-spine configurations and switching degrees.


title: Maximizing Expected Model Change for Active Learning in Regression

abstract: Active learning is well-motivated in many supervised learning tasks where unlabeled data may be abundant but labeled examples are expensive to obtain. The goal of active learning is to maximize the performance of a learning model using as few labeled training data as possible, thereby minimizing the cost of data annotation. So far, there is still very limited work on active learning for regression. In this paper, we propose a new active learning framework for regression called Expected Model Change Maximization (EMCM), which aims to choose the examples that lead to the largest change to the current model. The model change is measured as the difference between the current model parameters and the updated parameters after training with the enlarged training set. Inspired by the Stochastic Gradient Descent (SGD) update rule, the change is estimated as the gradient of the loss with respect to a candidate example for active learning. Under this framework, we derive novel active learning algorithms for both linear regression and nonlinear regression to select the most informative examples. Extensive experimental results on the benchmark data sets from UCI machine learning repository have demonstrated that the proposed algorithms are highly effective in choosing the most informative examples and robust to various types of data distributions.


title: RelEx---Relation extraction using dependency parse trees

abstract: Motivation: The discovery of regulatory pathways, signal cascades, metabolic processes or disease models requires knowledge on individual relations like e.g. physical or regulatory interactions between genes and proteins. Most interactions mentioned in the free text of biomedical publications are not yet contained in structured databases. ::: ::: Results: We developed RelEx, an approach for relation extraction from free text. It is based on natural language preprocessing producing dependency parse trees and applying a small number of simple rules to these trees. We applied RelEx on a comprehensive set of one million MEDLINE abstracts dealing with gene and protein relations and extracted ~150 000 relations with an estimated perfomance of both 80% precision and 80% recall. ::: ::: Availability: The used natural language preprocessing tools are free for use for academic research. Test sets and relation term lists are available from our website (http://www.bio.ifi.lmu.de/publications/RelEx/). ::: ::: Contact: katrin.fundel@bio.ifi.lmu.de


title: OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit

abstract: Various open-source toolkits exist for speech recognition and speech processing. These toolkits have brought a great benefit to the research community, i.e. speeding up research. Yet, no such freely available toolkit exists for automatic affect recognition from speech. We herein introduce a novel open-source affect and emotion recognition engine, which integrates all necessary components in one highly efficient software package. The components include audio recording and audio file reading, state-of-the-art paralinguistic feature extraction and plugable classification modules. In this paper we introduce the engine and extensive baseline results. Pre-trained models for four affect recognition tasks are included in the openEAR distribution. The engine is tailored for multi-threaded, incremental on-line processing of live input in real-time, however it can also be used for batch processing of databases.


title: A touch controller using differential sensing method for on-cell capacitive touch screen panel systems

abstract: A touch controller is proposed for on-cell capacitive touch screen panel systems. The proposed IC adopts the differential sensing method to enhance the dynamic range of sensing voltage and to be robust to display noise. The measurement results show that the maximum reporting rate, jitter tolerance, and signal-to-noise ratio (SNR) are 140 Hz, ±0.3 mm, and 12 dB, respectively, when evaluated with a 13.3-inch wide extended graphics array (WXGA) liquid crystal display (LCD) panel with the on-cell touch screen. The proposed IC fabricated via 0.35 μm CMOS process technology occupies a silicon area of 4 mm × 5 mm and consumes a power of 19 mW when the supply voltage is 3.3 V.


title: Measuring Latency in Virtual Environments

abstract: Latency of interactive computer systems is a product of the processing, transport and synchronisation delays inherent to the components that create them. In a virtual environment (VE) system, latency is known to be detrimental to a user's sense of immersion, physical performance and comfort level. Accurately measuring the latency of a VE system for study or optimisation, is not straightforward. A number of authors have developed techniques for characterising latency, which have become progressively more accessible and easier to use. In this paper, we characterise these techniques. We describe a simple mechanical simulator designed to simulate a VE with various amounts of latency that can be finely controlled (to within 3ms). We develop a new latency measurement technique called Automated Frame Counting to assist in assessing latency using high speed video (to within 1ms). We use the mechanical simulator to measure the accuracy of Steed's and Di Luca's measurement techniques, proposing improvements where they may be made. We use the methods to measure latency of a number of interactive systems that may be of interest to the VE engineer, with a significant level of confidence. All techniques were found to be highly capable however Steed's Method is both accurate and easy to use without requiring specialised hardware.


title: Analysis of Ego Network Structure in Online Social Networks

abstract: Results about offline social networks demonstrated that the social relationships that an individual (ego) maintains with other people (alters) can be organised into different groups according to the ego network model. In this model the ego can be seen as the centre of a series of layers of increasing size. Social relationships between ego and alters in layers close to ego are stronger than those belonging to more external layers. Online Social Networks are becoming a fundamental medium for humans to manage their social life, however the structure of ego networks in these virtual environments has not been investigated yet. In this work we contribute to fill this gap by analysing a large data set of Facebook relationships. We filter the data to obtain the frequency of contact of the relationships, and we check - by using different clustering techniques - whether structures similar to those found in offline social networks can be observed. The results show a strikingly similarity between the social structures in offline and Online Social Networks. In particular, the social relationships in Facebook share three of the most important features highlighted in offline ego networks: (i) they appear to be organised in four hierarchical layers, (ii) the sizes of the layers follow a scaling factor near to three, and (iii) the number of active social relationships is close to the well-known Dunbar's number. These results strongly suggest that, even if the ways to communicate and to maintain social relationships are changing due to the diffusion of Online Social Networks, the way people organise their social relationships seems to remain unaltered.


title: Migrating Legacy Systems: Gateways, interfaces and the incremental approach

abstract: Many businesses are burdened by legacy ISs--existing systems that contain valuable data but lack the power or the agility to meet current organizational requirements. Since such systems are by nature resistant to modification, the only way an enterprise can gain full use of the data within one is to move to a completely new IS--ideally, one that won't become a legacy itself in a few years. This book discusses the challenges and provides a framework for a workable system migration, based on incremental modifications that allow the manager to control cost and risk. Focusing on data migration, the authors outline key techniques for transforming a legacy IS into a flexible system that meets current needs and is adaptable to unpredictable future changes. Because designing, developing, testing, and deploying a new system all at once would be extremely risky, the authors advocate a step-by-step strategy that migrates the system to a decomposable target environment while the current IS remains completely operational. Their flexible 11-step method allows any IS to be migrated incrementally, using gateways to access data reliably and accurately without any downtime. Because each company's IS is as unique as its business strategy, the authors focus on the choices and pitfalls that may be encountered in an IS migration. Drawing on over 45 years of collective IS experience, they illustrate their work with two complete case studies of migration projects they worked on themselves. Their versatile solution is effective for any migration, including those to a client/server environment using relational DBMSs and 4GLs. Features a special survey of migration tools by Jennifer Schmidt. Note: the authors would be very interested in receiving a brief report on any successful legacy IS migration project using their methodology or any other, or of significant problems encountered in a migration attempt. As such projects take years, success can apply to the first significant increment.


title: Efficient Hardware Architecture of SHA-256 Algorithm for Trusted Mobile Computing

abstract: We present a compact SHA-256 hardware architecture suitable for the Trusted Mobile Platform (TMP), which requires low-area and low-power characteristics. The built-in hardware engine to compute a hash algorithm in TMP is one of the most important circuit blocks and contributes the performance of the whole platform because it is used as key primitives supporting platform integrity and command authentication. Unlike personal computers, mobile platform have very stringent limitations with respect to available power, physical circuit area, and cost. Therefore, special architecture and design methods for a compact hash hardware module are required. Our SHA-256 hardware can compute 512-bit data block using 8,588 gates on a 0.25μm CMOS process. The highest operation frequency and throughput of the proposed architecture are 136MHz and 142Mbps, which satisfies processing requirement for the mobile application.


title: Efficient reductions for imitation learning

abstract: Imitation Learning, while applied successfully on many large real-world problems, is typically addressed as a standard supervised learning problem, where it is assumed the training and testing data are i.i.d.. This is not true in imitation learning as the learned policy influences the future test inputs (states) upon which it will be tested. We show that this leads to compounding errors and a regret bound that grows quadratically in the time horizon of the task. We propose two alternative algorithms for imitation learning where training occurs over several episodes of interaction. These two approaches share in common that the learner’s policy is slowly modified from executing the expert’s policy to the learned policy. We show that this leads to stronger performance guarantees and demonstrate the improved performance on two challenging problems: training a learner to play 1) a 3D racing game (Super Tux Kart) and 2) Mario Bros.; given input images from the games and corresponding actions taken by a human expert and near-optimal planner respectively.


title: Deep learning based classification for paddy pests & diseases recognition

abstract: Pests and diseases are a threat to paddy production, especially in Indonesia, but identification remains to be a challenge in massive scale and automatically. Increasing smartphone usage and deep learning advance create an opportunity to answer this problem. Collecting 4,511 images from four language using search engines, and augment it to develop diverse data set. This dataset fed into CaffeNet model and processed with Caffe framework. Experiment result in the model achieved accuracy 87%, which is higher than random selection 7.6%.


title: Data-Driven Decentralized Optimal Power Flow

abstract: The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework facilitates the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. Our methodology provides a natural extension to decide what buses a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to active distribution networks.


title: Performance evaluation of RF based Inter Satellite communication Link at 60 GHz

abstract: Inter-satellite links provide direct connectivity between two or more satellites, thus eliminating the need for intermediate ground stations while sending data for various wireless applications. This paper proposes the investigations on performance evaluation of Radio Frequency (RF) signal at 60 GHz utilizing Matlab/Scilab computation. A block level model is designed at 60 GHz for estimation of satellite RF link along with the link budget calculator. The operation of the link budget calculator features the calculation of effective noise temperature, noise power, Effective Isotropic Radiative Power(EIRP), Free Space Loss(FSL) and received power of the RF Inter-Satellite Link which further provides an important ground for the Inter-Satellite Link block level design. The power spectrum, constellation diagram and eye-diagram at both transmitter and receiver side for the RF link is evaluated with a separation distance of 500 km and 1000 km.


title: Churn prediction model for effective gym customer retention

abstract: In the fitness industry, rolling gym membership contracts allow customers to terminate a contract with little advanced notice. Customer churn prediction is a well known area in Machine Learning research. Many companies, however, face a data science skills gap when trying to translate this research onto their own datasets and IT infrastructure. In this paper we present a series of experiments that aim to predict customer behaviour, in order to increase gym utilisation and customer retention. We use two off-the-shelf machine learning platforms, so that we can evaluate whether these platforms, used by non ML experts, can help companies improve their services.


Quality Scheme Assessment in the Clustering Process


title: Internet routing anomaly detection and visualization

abstract: Diagnosing inter-domain routing problems in the Internet is hard. BGP, the defacto inter-domain glue, is designed for routing, not diagnosis. It is extremely chatty - the most minor connectivity change produces hundreds of BGP messages and a major peering loss can generate millions - and making sense of the deluge of data remains challenging. We have developed statistical techniques to extract the large-scale structure of BGP events and visualization techniques to display that structure in operationally meaningful ways. These tools can be used to detect routing anomalies in real-time. We show case studies of routing instabilities at a Tier-1 ISP and a large institutional network, automatically diagnosed by our tools. We present drawbacks in using BGP events alone to understand inter-domain routing, and discuss how to solve them through the integration of additional data sources.


title: "Climate Change" Frames Detection and Categorization Based on Generalized Concepts

abstract: The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for long time in political science and communications research. Media framing offers "interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts. In this paper, we develop a new type of textual features that generalize (subject, verb, object) triplets extracted from text, by clustering them into high-level concepts. We utilize these concepts as features to detect frames in text. Our corpus comprises more than 45,000 climate change related sentences. Expert coders annotated those sentences as frame/non-frame and framed sentences were mapped into one of four general frame categories: solution, problem threat, cause, and motivation. Compared to unigram and bigram based models, classification using our generalized concepts yielded better discriminating features and a higher accuracy classifier with a 12% boost (i.e. from 74% to 83% in f-measure) for frame/no frame detection.


title: Non-constrained Blood Pressure Monitoring Using ECG and PPG for Personal Healthcare

abstract: Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. Arterial blood pressure (BP) was estimated from pulse transit time (PTT) and PPG waveform. PTT is a time interval between an R-wave of electrocardiography (ECG) and a photoplethysmography (PPG) signal. This method does not require an aircuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject. PTT computed between the ECG R-wave and the maximum first derivative PPG was strongly correlated with systolic blood pressure (SBP) (R = -0.712) compared with other PTT values, and the diastolic time proved to be appropriate for estimation diastolic blood pressure (DBP) (R = -0.764). The percent errors of SBP using the individual regression line (4-11%) were lower than those using the regression line obtained from all five subjects (9-14%). On the other hand, the DBP estimation did not show much difference between the individual regression (4-10%) and total regression line (6-10%). Our developed device had a total size of 7 x 13.5 cm and was operated by single 3-V battery. Biosignals can be measured for 72 h continuously without external interruptions. Through a serial network communication, an external personal computer can monitor measured waveforms in real time. Our proposed method can be used for non-constrained, thus continuous BP monitoring for the purpose of personal healthcare.


Modeling heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees


title: Last Line of Defense: A Novel IDS Approach Against Advanced Threats in Industrial Control Systems

abstract: Industrial control systems are becoming increasingly interconnected, and with it their vulnerability to malicious actors. While intrusion detection systems are suited to detect network-based attacks, they remain unable to detect more sophisticated attacks against control systems, for example a compromise of the PLCs. This paper makes the case that the evolving landscape of threats such as the Stuxnet malware requires an alternative approach to intrusion detection in industrial control systems. We argue that effective control of such advanced threats needs to happen in the last link of the control network, hence building a last line of defense. A proof of concept of this new paradigm was implemented for the control system of a dredging vessel, and we describe main lessons learned and pose open research questions we find based on these experiences for ICS intrusion detection.


title: Quintic G²-Splines for the Iterative Steering of Vision-Based Autonomous Vehicles

abstract: This paper presents a new motion planning primitive to be used for the iterative steering of vision-based autonomous vehicles. This primitive is a parameterized quintic spline, denoted as η-spline, that allows interpolating an arbitrary sequence of points with overall second-order geometric (G 2 -) continuity. Issues such as completeness, minimality, regularity, symmetry, and flexibility of these G 2 -splines are addressed in the exposition. The development of the new primitive is tightly connected to the inversion control of nonholonomic car-like vehicles. The paper also exposes a supervisory strategy for iterative steering that integrates feedback vision data processing with the feedforward inversion control.