title: Direct storage hybrid (DSH) inverter: A new concept of intelligent hybrid inverter

abstract: This paper proposes a new configuration of a single-phase hybrid inverter with an integrated battery energy storage, which is suitable for residential households to maximize local consumption of solar energy and thus reduce dependency on grid support. The hybrid inverter is called Direct Storage Hybrid (DSH) Inverter. A transformer-less topology such as HERIC, operating at low frequency to generate a three-level rectangular output voltage, is adopted to connect a photovoltaic (PV) panel to the load and/or the grid. A series active filter is employed to compensate the high harmonic components from the rectangular voltage and provide a sinusoidal voltage. A bidirectional dc/dc converter connects the battery to the PV panel to control the battery state of charge (SoC) and optimize the PV panel operation during both off-grid and grid-connected modes. The DSH inverter can let the battery bypass the dc/dc converter and connect directly to the inverter stage, leading to a significant improvement in throughput efficiency in battery utilization. This paper discusses the operation and loss analysis of the DSH inverter in off-grid mode.


title: Unsupervised cross-modal retrieval through adversarial learning

abstract: The core of existing cross-modal retrieval approaches is to close the gap between different modalities either by finding a maximally correlated subspace or by jointly learning a set of dictionaries. However, the statistical characteristics of the transformed features were never considered. Inspired by recent advances in adversarial learning and domain adaptation, we propose a novel Unsupervised Cross-modal retrieval method based on Adversarial Learning, namely UCAL. In addition to maximizing the correlations between modalities, we add an additional regularization by introducing adversarial learning. In particular, we introduce a modality classifier to predict the modality of a transformed feature. This can be viewed as a regularization on the statistical aspect of the feature transforms, which ensures that the transformed features are also statistically indistinguishable. Experiments on popular multimodal datasets show that UCAL achieves competitive performance compared to state of the art supervised cross-modal retrieval methods.


On the impact of phoneme alignment in DNN-based speech synthesis.


title: Role of aquaculture pond sediments in sequestration of annual global carbon emissions.

abstract: Aquaculture ponds sequester about 16.6MTyr(-1) of organic carbon worldwide representing around 0.21% of annual, global carbon emissions.


title: Feature Learning from Incomplete EEG with Denoising Autoencoder

abstract: An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect.


title: 3. LS-DYNA Anwenderforum, Bamberg 2004 Automotive / Material Crashworthiness analysis of structures made from polymers

abstract: A comparative review of material models for polymers subjected to impact loading is presented. Due to the different material models, polymers are classified in incompressible elastomers, compressible foams and thermoplastics. Elastomers and recoverable foams are based on the same theoretical description: hyperelasticity. We present a material law which allows fast generating of input data based on uniaxial static and dynamic tensile tests at different strain rates. As an application, the deformation behavior of a Hardy disk during a frontal offset impact is investigated. For thermoplastics, we give an overview of suitable material laws and show how the behavior can be characterized approximately by using metal plasticity. On the basis of a three-point bending test, the predictions of the presented approaches are compared critically. Some typical applications of polymer modeling in modern crash simulation are shown by means of pedestrian protection.


title: Heuristics for Connecting Heterogeneous Knowledge via FrameBase

abstract: With recent advances in information extraction techniques, various large-scale knowledge bases covering a broad range of knowledge have become publicly available. As no single knowledge base covers all information, many applications require access to integrated knowledge from multiple knowledge bases. Achieving this, however, is challenging due to differences in knowledge representation. To address this problem, this paper proposes to use linguistic frames as a common representation and maps heterogeneous knowledge bases to the FrameBase schema, which is formed by a large inventory of these frames. We develop several methods to create complex mappings from external knowledge bases to this schema, using text similarity measures, machine learning, and different heuristics. We test them with different widely used large-scale knowledge bases, YAGO2s, Freebase and WikiData. The resulting integrated knowledge can then be queried in a homogeneous way.


title: Perceiving benefits after adversity: The relationship between self-reported posttraumatic growth and creativity.

abstract: Anecdotal and scientific evidence has documented the existence of a relationship between the experience of adversity and creativity. Accounts of the challenges endured by creative individuals suggest that they may have been able to channel their negative experiences as sources of inspiration and motivation for their work. Increased creativity may therefore constitute a manifestation of posttraumatic growth, defined as retrospective perceptions of positive psychological changes that take place following experiences of highly challenging life circumstances. To investigate this hypothesis, the present study tested whether scores on a measure of posttraumatic growth and depreciation related to scores on self-reported measures of creativity in the aftermath of adversity. Results of a path analysis showed that adversity-induced distress predicted self-reported creative growth and breadth in a sample of online participants. Cognitive processing (intrusive/deliberative rumination) as well as domains of posttraumatic growth/ depreciation—in particular, self-reported changes in interpersonal relationships and in the perception of new possibilities for one’s life—mediated the link between self-reported distress and creativity outcomes. This study is the first focused investigation showing that self-reported posttraumatic growth may be manifested through perceptions of increased creativity.


title: A Context-Aware Routing Protocol on Internet of Things Based on Sea Computing Model

abstract: The paper syncretizes the fundamental concept of the Sea Computing model in Internet of Things and the routing protocol of the wireless sensor network, and proposes a new routing protocol CASCR (Context-Awareness in Sea Computing Routing Protocol) for Internet of Things, based on context-awareness which belongs to the key technologies of Internet of Things. Furthermore, the paper describes the details on the protocol in the work flow, data structure and quantitative algorithm and so on. Finally, the simulation is given to analyze the work performance of the protocol CASCR. Theoretical analysis and experiment verify that CASCR has higher energy efficient and longer lifetime than the congeneric protocols. The paper enriches the theoretical foundation and makes some contribution for wireless sensor network transiting to Internet of Things in this research phase.


title: Models for count data with many zeros

abstract: Poisson regression models provide a standard framework for the analysis of count data. In practice, however, count data are often overdispersed relative to the Poisson distribution. One frequent manifestation of overdispersion is that the incidence of zero counts is greater than expected for the Poisson distribution and this is of interest because zero counts frequently have special status. For example, in counting disease lesions on plants, a plant may have no lesions either because it is resistant to the disease, or simply because no disease spores have landed on it. This is the distinction between structural zeros, which are inevitable, and sampling zeros, which occur by chance. In recent years there has been considerable interest in models for count data that allow for excess zeros, particularly in the econometric literature. These models complement more conventional models for overdispersion that concentrate on modelling the variance-mean relationship correctly. Application areas are diverse and have included manufacturing defects (Lambert, 1992), patent applications (Crepon & Duguet, 1997), road safety (Miaou, 1994), species abundance (Welsh et al., 1996; Faddy, 1998), medical consultations


title: Combining displacement mapping methods on the GPU for real-time terrain visualization

abstract: Real-time terrain visualization plays an important role in multiple popular applications. In these applications, displacement mapping algorithms (both per-vertex and per-pixel methods) can be used to improve the accuracy and performance of terrain rendering. Per-vertex methods are usually implemented by means of hardware tessellation, and per-pixel techniques, such as parallax mapping, apply changes at the pixel level using the fragment shader. However, parallax mapping has not still been used in real-time terrain visualization applications due to different reasons. In this paper, we propose a comparison study of different combinations of per-vertex and per-pixel methods. The performance evaluation results reveal that any of the implemented schemes improve the performance of terrain rendering, with respect to the performance yielded by the exclusive use of hardware tessellation. These results validate the proposed schemes as efficient alternatives for real-time terrain visualization applications.


title: Sleepless in Chicago: Tracking the Effects of Adolescent Sleep Loss During the Middle School Years

abstract: The influence of the sleep patterns of 2,259 students, aged 11 to 14 years, on trajectories of depressive symptoms, self-esteem, and grades was longitudinally examined using latent growth cross-domain models. Consistent with previous research, sleep decreased over time. Students who obtained less sleep in sixth grade exhibited lower initial self-esteem and grades and higher initial levels of depressive symptoms. Similarly, students who obtained less sleep over time reported heightened levels of depressive symptoms and decreased self-esteem. Sex of the student played a strong role as a predictor of hours of sleep, self-esteem, and grades. This study underscores the role of sleep in predicting adolescents' psychosocial outcomes and highlights the importance of using idiographic methodologies in the study of developmental processes.


Recycling Waste Thermoplastic for Making Lightweight Bricks


title: EduRank: A Collaborative Filtering Approach to Personalization in E-learning

abstract: The growing prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities, backgrounds and styles. There is thus a growing need to accomodate for individual differences in such e-learning systems. This paper presents a new algorithm for personliazing educational content to students that combines collaborative filtering algorithms with social choice theory. The algorithm constructs a “difficulty” ranking over questions for a target student by aggregating the ranking of similar students, as measured by different aspects of their performance on common past questions, such as grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for a target student, rather than ordering them according to predicted performance, which is prone to error. The algorithm was tested on two large real world data sets containing tens of thousands of students and a million records. Its performance was compared to a variety of personalization methods as well as a non-personalized method that relied on a domain expert. It was able to significantly outperform all of these approaches according to standard information retrieval metrics. Our approach can potentially be used to support teachers in tailoring problem sets and exams to individual students and students in informing them about areas they may need to strengthen.


title: The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine

abstract: Automated tongue image segmentation, in Chinese medicine, is difficult due to two special factors: 1) there are many pathological details on the surface of the tongue, which have a large influence on edge extraction; 2) the shapes of the tongue bodies captured from various persons (with different diseases) are quite different, so they are impossible to describe properly using a predefined deformable template. To address these problems, in this paper, we propose an original technique that is based on a combination of a bi-elliptical deformable template (BEDT) and an active contour model, namely the bi-elliptical deformable contour (BEDC). The BEDT captures gross shape features by using the steepest decent method on its energy function in the parameter space. The BEDC is derived from the BEDT by substituting template forces for classical internal forces, and can deform to fit local details. Our algorithm features fully automatic interpretation of tongue images and a consistent combination of global and local controls via the template force. We apply the BEDC to a large set of clinical tongue images and present experimental results.


title: A scene adaptive and signal adaptive quantization for subband image and video compression using wavelets

abstract: The discrete wavelet transform (DWT) provides an advantageous framework of multiresolution space-frequency representation with promising applications in image processing. The challenge as well as the opportunity in wavelet-based compression is to exploit the characteristics of the subband coefficients with respect to both spectral and spatial localities. A common problem with many existing quantization methods is that the inherent image structures are severely distorted with coarse quantization. Observation shows that subband coefficients with the same magnitude generally do not have the same perceptual importance. We propose in this paper a scene adaptive and signal adaptive quantization scheme capable of exploiting the spectral and spatial localization properties resulting from the wavelet transform. The quantization is implemented as maximum a posteriori probability estimation-based clustering in which subband coefficients are quantized to their cluster means, subject to local spatial constraints. The intensity distribution of each cluster within a subband is modeled by an optimal Laplacian source to achieve signal adaptivity, while spatial constraints are enforced by appropriate Gibbs random fields (GRF) to achieve scene adaptivity. With spatially isolated coefficients removed and clustered coefficients retained at the same time, the available bits are allocated to visually important scene structures so that the information loss is least perceptible. Furthermore, the reconstruction noise in the decompressed image can be suppressed using another GRF-based enhancement algorithm.


title: A digital business ecosystem integrated approach for farm management information system

abstract: The concept of Digital Business Ecosystem becomes an important aspect of everyday economy and technologies in this area are becoming a reality considering that results from European Join Collaborative Research Projects (FP6 - FP7) are been integrated within Digital Business Ecosystem. In this context, a Farm Management Information System, designed as a part of this Digital Business Ecosystem, could be the key to solving many current problems and should be designed to sustain agricultural Digital Business Ecosystem development.


title: Accurate and versatile multivariable arbitrary piecewise model regression of nonlinear fluidic muscle behavior

abstract: Wearable exoskeletons and soft robots require actuators with muscle-like compliance. These actuators can benefit from the robust and effective interaction that biological muscles' compliance enables them to have in the uncertainty of the real world. Fluidic muscles are compliant but difficult to control due to their nonlinear behavior. Precise control of these actuators needs accurate models that readily capture this behavior. Here we present the multivariable arbitrary piecewise model regression (MAPMORE) algorithm for automatically creating accurate data-driven, behavior-based models for fluidic muscles. MAPMORE integrates an arbitrary term dictionary based orthogonal forward regression algorithm with piecewise function fusion. We modeled the static and hysteresis force components of a McKibben pneumatic artificial muscle (PAM) and a Peano muscle with MAPMORE, Sarosi's empirical model, and a polynomial model. In all cases, MAPMORE's models had the best mean accuracy of below 15N. This shows it to be an easy to use, accurate, and versatile soft fluidic actuator modeling tool.


title: EXTENDING CORRELATION AND REGRESSION FROM MULTIVARIATE TO FUNCTIONAL DATA ∗

abstract: In this paper, we discuss concepts and methods of functional data analysis. The focus is on the case where a pair of random functions is sampled per experimental unit. We discuss the quantification of the dependency between pairs of random functions by means of functional canonical analysis and linear modeling for L2-processes. Basic concepts of multivariate analysis are extended to the domain of functional data analysis and the conditions under which such an extension is feasible are discussed. Our main results demonstrate how basic properties of canonical correlation and linear regression known from multivariate statistical analysis can be restated for functional data, if appropriate conditions are satisfied.


Supplemental Document for Deep Photo Style Transfer


title: Virtual synchronous generators

abstract: In electricity grids the frequency of the voltage is stabilized by a combination of the rotational inertia (rotating mass) of synchronous power generators in the grid and a control algorithm acting on the rotational speed of a number of major synchronous power generators. When in future small non-synchronous generation units replace a significant part of the synchronous power generation capacity, the total rotational inertia of the synchronous generators is decreased significantly. This causes large frequency variations that can end up in an unstable grid. A way to stabilize the grid frequency is to add virtual rotational inertia to the distributed generators. A virtual inertia can be attained for any generator by adding a short-term energy storage to it, combined with a suitable control mechanism for its power electronics converter. In this way a generator can behave like a ldquoVirtual Synchronous Generatorrdquo (VSG) during short time intervals, and contribute to stabilization of the grid frequency.


title: Queriosity: Automated Data Exploration

abstract: Curiosity, a fundamental drive amongst higher living organisms, is what enables exploration, learning and creativity. In our increasingly data-driven world, data exploration, i.e., Making sense of mounting haystacks of data, is akin to intelligence for science, business and individuals. However, modern data systems -- designed for data retrieval rather than exploration -- only let us retrieve data and ask if it is interesting. This makes knowledge discovery a game of hit-and-trial which can only be orchestrated by expert data scientists. We present the vision toward Queriosity, an automated and personalized data exploration system. Designed on the principles of autonomy, learning and usability, Queriosity envisions a paradigm shift in data exploration and aims to become a a personalized "data robot" that provides a direct answer to what is interesting in a user's data set, instead of just retrieving data. Queriosity autonomously and continuously navigates toward interesting findings based on trends, statistical properties and interactive user feedback.


title: JUG: a JUnit generation, time complexity analysis and reporting tool to streamline grading

abstract: The JUnit Generation (JUG) system provides fast, semiautomated feedback to students. It uses a Java-like script to generate unit tests and time complexity tests, then runs those tests to generate reports. The goals for JUG are improved feedback for students, and decreased preparation and grading time for instructors and grading assistants.


Control of Air-Cooled Chiller Condenser Fans using Clustering Neural Networks


title: MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

abstract: With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query the model with their data via an API. However, if the user's input is sensitive, sending it to the server is not an option. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the machine learning model locally on the user's client and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we demonstrate that MLCapsule allows for implementing defenses against advanced attacks on machine learning models such as model stealing/reverse engineering and membership inference.


title: Banana blossom agar (BABA), a new medium to isolate members of the Cryptococcus neoformans/Cryptococcus gattii species complex useful for resource limited countries

abstract: Isolation of representatives of the Cryptococcus neoformans/Cryptococcus gattii species complex can be made using dopamine containing media, such as Niger seed agar and l-DOPA agar. Here, we describe an alternative medium that uses banana flowers. Banana is a dopamine containing fruit and is widely available in tropical and subtropical countries that have high numbers of cryptococcosis patients. This banana blossom-based agar is useful for the enrichment of isolates of the C. neoformans/C. gattii species complex from environmental and clinical materials. The banana blossom agar (BABA) with and without creatinine can differentiate between the melanin forming isolates of the C. neoformans/C. gattii species complex from other yeasts that do not form melanin.


title: Food Recognition: A New Dataset, Experiments, and Results

abstract: We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1027 canteen trays for a total of 3616 food instances belonging to 73 food classes. The food on the tray images has been manually segmented using carefully drawn polygonal boundaries. We have benchmarked the dataset by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class. We have experimented with three different classification strategies using also several visual descriptors. We achieve about 79% of food and tray recognition accuracy using convolutional-neural-networks-based features. The dataset, as well as the benchmark framework, are available to the research community.


title: User modelling in scheduling system with artificial neural networks

abstract: User modelling has become a central subject for anybody interested in understanding how users interact with technology. Personalization is a key issue in an era when there is so much information and so many people interacting in so many ways. Modern users desire a customized experience that adapts itself to their requirements and understands what they need even before they notice it. In order to morph any system into an adapting one, every relevant interaction with its users has to be maintained. Then, a mathematical structure capable of discovering patterns amongst that information is necessary, being able to classify users according to the roles they play. With a correct user categorization, the system knows when, how and what to do to adapt its content, via a mixed-initiative approach. In this paper, an artificial neural network is selected as classifier and users are divided in three roles, from beginners to experts. ADSyS, the target system of this proposal, adapts its content based on who is operating it, providing a higher usability. This guide on how to adapt a system to its users is built as part of ADSyS, but is intended to be generalized as a foundation to other systems.


title: A Framework for Massive Twitter Data Extraction and Analysis

abstract: Social networks surfaced as communication and socialization tools. The vast amount of data these networks generate has led to a growing need of automatic knowledge extraction. The popular nature of these services is ideal for trends discovery. In particular, Twitter offers an open environment where people all around the world share information and opinions, emerging as a real-time repository of knowledge that can be exploited by researchers and applications. We propose an open framework to automatically collect and analyze data from Twitter’s public streams. This is a customizable and extensible framework, so researchers can use it to test new techniques. The framework is complemented with a language-agnostic sentiment analysis module, which provides a set of tools to perform sentiment analysis of the collected tweets. The capabilities of this platform are illustrated with two study cases in Spanish, one related to a high impact event (the Boston Terror Attack), and another one related to regular political activity on Twitter.


System Engineering for Smart Cities-Hybrid-Agile Approach in Smart Cities Procurement


title: On the Equivalence of MMSE and IRC Receiver in MU-MIMO Systems

abstract: In order to combat the residual interference in multiuser multiple-input and multiple-output systems (MU-MIMO), a number of low-complexity interference aware type of receivers have been considered. In this letter, single layer Minimum Mean Square Error (MMSE) and Interference Rejection Combiner (IRC) are applied in MU-MIMO transmission. Based on some very simple but extremely useful matrix manipulations, their equivalence is proven analytically and demonstrated via Monte Carlo simulations.


title: Risk-based security decisions under uncertainty

abstract: This paper addresses the making of security decisions, such as access-control decisions or spam filtering decisions, under uncertainty, when the benefit of doing so outweighs the need to absolutely guarantee these decisions are correct. For instance, when there are limited, costly, or failed communication channels to a policy-decision-point. Previously, local caching of decisions has been proposed, but when a correct decision is not available, either a policy-decision-point must be contacted, or a default decision used. We improve upon this model by using learned classifiers of access control decisions. These classifiers, trained on known decisions, infer decisions when an exact match has not been cached, and uses intuitive notions of utility, damage and uncertainty to determine when an inferred decision is preferred over contacting a remote PDP. Clearly there is uncertainty in the predicted decisions, introducing a degree of risk. Our solution proposes a mechanism to quantify the uncertainty of these decisions and allows administrators to bound the overall risk posture of the system. The learning component continuously refines its models based on inputs from a central policy server in cases where the risk is too high or there is too much uncertainty. We have validated our models by building a prototype system and evaluating it with requests from real access control policies. Our experiments show that over a range of system parameters, it is feasible to use machine learning methods to infer access control policies decisions. Thus our system yields several benefits, including reduced calls to the PDP, reducing latency and communication costs; increased net utility; and increased system survivability.


title: Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics

abstract: Retroviral capsid proteins are conserved structurally but assemble into different morphologies. The mature human immunodeficiency virus-1 (HIV-1) capsid is best described by a 'fullerene cone' model, in which hexamers of the capsid protein are linked to form a hexagonal surface lattice that is closed by incorporating 12 capsid-protein pentamers. HIV-1 capsid protein contains an amino-terminal domain (NTD) comprising seven α-helices and a β-hairpin, a carboxy-terminal domain (CTD) comprising four α-helices, and a flexible linker with a 310-helix connecting the two structural domains. Structures of the capsid-protein assembly units have been determined by X-ray crystallography; however, structural information regarding the assembled capsid and the contacts between the assembly units is incomplete. Here we report the cryo-electron microscopy structure of a tubular HIV-1 capsid-protein assembly at 8 Å resolution and the three-dimensional structure of a native HIV-1 core by cryo-electron tomography. The structure of the tubular assembly shows, at the three-fold interface, a three-helix bundle with critical hydrophobic interactions. Mutagenesis studies confirm that hydrophobic residues in the centre of the three-helix bundle are crucial for capsid assembly and stability, and for viral infectivity. The cryo-electron-microscopy structures enable modelling by large-scale molecular dynamics simulation, resulting in all-atom models for the hexamer-of-hexamer and pentamer-of-hexamer elements as well as for the entire capsid. Incorporation of pentamers results in closer trimer contacts and induces acute surface curvature. The complete atomic HIV-1 capsid model provides a platform for further studies of capsid function and for targeted pharmacological intervention.


title: Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem

abstract: In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the rectified Gaussian scale mixture (R-GSM) to model the sparsity enforcing prior distribution for the solution. The R-GSM prior encompasses a variety of heavy-tailed densities such as the rectified Laplacian and rectified Student's t-distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the expectation-maximization (EM) algorithm. Using the EM based method, we estimate the hyper-parameters and obtain a point estimate for the solution. We refer to the proposed method as rectified sparse Bayesian learning (R-SBL). We provide four R-SBL variants that offer a range of options for computational complexity and the quality of the E-step computation. These methods include the Markov chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate message passing, and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery performance, and is also very robust against the structure of the design matrix.


title: Adversarial Network Compression

abstract: Neural network compression has recently received much attention due to the computational requirements of modern deep models. In this work, our objective is to transfer knowledge from a deep and accurate model to a smaller one. Our contributions are threefold: (i) we propose an adversarial network compression approach to train the small student network to mimic the large teacher, without the need for labels during training; (ii) we introduce a regularization scheme to prevent a trivially-strong discriminator without reducing the network capacity and (iii) our approach generalizes on different teacher-student models.


title: Effect of Full Coverage, Endocrowns, Onlays, Inlays Restorations on Fracture Resistance of Endodontically Treated Molars

abstract: Purpose: This in vitro study compared the fracture resistance of endodontically treated first maxillary molars restored with fiber posts (FRC Postec Plus-Ivoclar Vivadent) and lithium disilicate ceramic (IPS e. Max Press/ Ivoclar Vivadent) full crowns, endocrowns, onlays and inlays with respect to the observed failure modes.


title: Syntactically Based Sentence Processing Classes: Evidence from Event-Related Brain Potentials

abstract: Theoretical considerations and diverse empirical data from clinical, psycholinguistic, and developmental studies suggest that language comprehension processes are decomposable into separate subsystems, including distinct systems for semantic and grammatical processing. Here we report that event-related potentials (ERPs) to syntactically well-formed but semantically anomalous sentences produced a pattern of brain activity that is distinct in timing and distribution from the patterns elicited by syntactically deviant sentences, and further, that different types of syntactic deviance produced distinct ERP patterns. ::: ::: Forty right-handed young adults read sentences presented at 2 words/sec while ERPs were recorded from over several positions between and within the hemispheres. Half of the sentences were semantically and grammatically acceptable and were controls for the remainder, which contained sentence medial words that violated (1) semantic expectations, (2) phrase structure rules, or (3) WH-movement constraints on Specificity and (4) Subjacency. As in prior research, the semantic anomalies produced a negative potential, N400, that was bilaterally distributed and was largest over posterior regions. The phrase structure violations enhanced the N125 response over anterior regions of the left hemisphere, and elicited a negative response (300-500 msec) over temporal and parietal regions of the left hemisphere. Violations of Specificity constraints produced a slow negative potential, evident by 125 msec, that was also largest over anterior regions of the left hemisphere. Violations of Subjacency constraints elicited a broadly and symmetrically distributed positivity that onset around 200 msec. The distinct timing and distribution of these effects provide biological support for theories that distinguish between these types of grammatical rules and constraints and more generally for the proposal that semantic and grammatical processes are distinct subsystems within the language faculty.


title: Fully Convolutional Adaptation Networks for Semantic Segmentation

abstract: The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets. Nevertheless, collecting expert labeled datasets especially with pixel-level annotations is an extremely expensive process. An appealing alternative is to render synthetic data (e.g., computer games) and generate ground truth automatically. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. In this paper, we facilitate this issue from the perspectives of both visual appearance-level and representation-level domain adaptation. The former adapts source-domain images to appear as if drawn from the"style"in the target domain and the latter attempts to learn domain-invariant representations. Specifically, we present Fully Convolutional Adaptation Networks (FCAN), a novel deep architecture for semantic segmentation which combines Appearance Adaptation Networks (AAN) and Representation Adaptation Networks (RAN). AAN learns a transformation from one domain to the other in the pixel space and RAN is optimized in an adversarial learning manner to maximally fool the domain discriminator with the learnt source and target representations. Extensive experiments are conducted on the transfer from GTA5 (game videos) to Cityscapes (urban street scenes) on semantic segmentation and our proposal achieves superior results when comparing to state-of-the-art unsupervised adaptation techniques. More remarkably, we obtain a new record: mIoU of 47.5% on BDDS (drive-cam videos) in an unsupervised setting.


title: Neurofuzzy Model Based Predictive Control Of Weld Fusion Zone Geometry

abstract: A closed-loop system is developed to control the weld fusion, which Is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances.


Managing the Unmanageable: How IS Research Can Contribute to the Scholarship of Cyber Projects


title: Robust solutions of uncertain linear programs

abstract: We treat in this paper linear programming (LP) problems with uncertain data. The focus is on uncertainty associated with hard constraints: those which must be satisfied, whatever is the actual realization of the data (within a prescribed uncertainty set). We suggest a modeling methodology whereas an uncertain LP is replaced by its robust counterpart (RC). We then develop the analytical and computational optimization tools to obtain robust solutions of an uncertain LP problem via solving the corresponding explicitly stated convex RC program. In particular, it is shown that the RC of an LP with ellipsoidal uncertainty set is computationally tractable, since it leads to a conic quadratic program, which can be solved in polynomial time.


title: Analyzing natural human language from the point of view of dynamic of a complex network

abstract: We present complex network model for semantic representation of human language.Contextual scope from texts are inferred from physical measures in complex networks.The complex network modeled presents behavior of scale-free networks.Human language has modular characteristic in the relationship between words.The relationship between words of human language is a sparse space. With increasing amount of information, mainly due to the explosive growth of Internet, the demand for applications of automatic text analysis has also grown. One of the tools that has increased in importance in the understanding of problems related to this area are complex networks. This tool merges graph theory and statistical methods for modeling important problems. In several research fields, complex networks are studied from the various points of view, such as: topology of networks, extraction of physical features and statistics, specific applications, comparison of metrics and study of physical phenomena. Linguistic is one area that has received great attention, particularly due to its close relationship with issues arising from the emergence of large text databases. Thus, many studies have emerged for modeling of complex networks in this area, increasing the demand for efficient algorithms for feature extraction, network dynamic observation and comparison of behavior for different types of languages. Some works for specific languages such as English, Chinese, French, Spanish, Russian and Arabic, have discussed the semantic aspects of these languages. On the other hand, as an important feature of a network we can highlight the computation of average clustering coefficient. This measure has a physical impact on the network topology studies and consequently on the conclusions about the semantics of a language. However its computational time is of O(n3), making its computing prohibitive for large current databases. This paper presents as main contribution a modeling of two complex networks: the first one, in English, is constructed from a specific medical database; the second, in Portuguese, from a journalistic manually annotated database. Our paper then presents the study of the dynamics of these two networks. We show their small-world behavior and the influence of hubs, suggesting that these databases have a high degree of Modularity, indicating specific contexts of words. Also, a method for efficient clustering coefficient computation is presented, and can be applied to large current databases. Other features such as fraction of reciprocal connections and average connection density are also calculated and discussed for both networks.


title: Corpora for Automatically Learning to Map Natural Language Questions into SQL Queries

abstract: Automatically translating natural language into machine-readable instructions is one of major interesting and challenging tasks in Natural Language (NL) Processing. This problem can be addressed by using machine learning algorithms to generate a function that find mappings between natural language and programming language semantics. For this purpose suitable annotated and structured data are required. In this paper, we describe our method to construct and semi-automatically annotate these kinds of data, consisting of pairs of NL questions and SQL queries. Additionally, we describe two different datasets obtained by applying our annotation method to two well-known corpora, GEOQUERIES and RESTQUERIES . Since we believe that syntactic levels are important, we also generate and make available relational pairs represented by means of their syntactic trees whose lexical content has been generalized. We validate the quality of our corpora by experimenting with them and our machine learning models to derive automatic NL/SQL translators. Our promising results suggest that our corpora can be effectively used to carry out research in the field of natural language interface to database.


title: What Makes a Style: Experimental Analysis of Fashion Prediction

abstract: In this work, we perform an experimental analysis of the differences of both how humans and machines see and distinguish fashion styles. For this purpose, we propose an expert-curated new dataset for fashion style prediction, which consists of 14 different fashion styles each with roughly 1,000 images of worn outfits. The dataset, with a total of 13,126 images, captures the diversity and complexity of modern fashion styles. We perform an extensive analysis of the dataset by benchmarking a wide variety of modern classification networks, and also perform an in-depth user study with both fashion-savvy and fashion-naive users. Our results indicate that, although classification networks are able to outperform naive users, they are still far from the performance of savvy users, for which it is important to not only consider texture and color, but subtle differences in the combination of garments.


title: Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease

abstract: Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) — 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.


title: BlockStore: A Secure Decentralized Storage Framework on Blockchain

abstract: In order to ensure faster audits, higher transparency and security, many applications are being designed using blockchains. We propose BlockStore, a secure decentralized storage framework using blockchain technology. The primary motivation is efficient utilization of storage resources of users. Users often have un-utilized or underutilized storage in their devices. They can choose to host their storage resources when they are not in use. Users rent storage from the host for a fee for a fixed period of time and release back after the time expires. BlockStore keeps track of un-utilized storage of hosts in Space Wallet, a structure that helps in assigning storage to renters on request. The ownership of storage can be proved by logging all storage transactions in a public ledger (the blockchain), which can be verified by any user. A host cannot host the same storage to two users at the same time, nor can it tamper with the data of the renter. Renters cannot frame a host of cheating. BlockStore uses proofs of storage and data possession to verify that the hosts do not tamper with data and penalizes parties for misbehavior. Users can encrypt data for privacy. Payment and penalty are handled using smart contracts. BlockStore differs from existing solutions, by providing stronger audit that detects and penalizes misbehaving parties earlier than existing schemes.


title: A Survey of Recent Trends in Testing Concurrent Software Systems

abstract: Many modern software systems are composed of multiple execution flows that run simultaneously, spanning from applications designed to exploit the power of modern multi-core architectures to distributed systems consisting of multiple components deployed on different physical nodes. We collectively refer to such systems as concurrent systems . Concurrent systems are difficult to test, since the faults that derive from their concurrent nature depend on the interleavings of the actions performed by the individual execution flows. Testing techniques that target these faults must take into account the concurrency aspects of the systems. The increasingly rapid spread of parallel and distributed architectures led to a deluge of concurrent software systems, and the explosion of testing techniques for such systems in the last decade. The current lack of a comprehensive classification, analysis and comparison of the many testing techniques for concurrent systems limits the understanding of the strengths and weaknesses of each approach and hampers the future advancements in the field. This survey provides a framework to capture the key features of the available techniques to test concurrent software systems, identifies a set of classification criteria to review and compare the available techniques, and discusses in details their strengths and weaknesses, leading to a thorough assessment of the field and paving the road for future progresses.


title: Violence against nurses and its impact on stress and productivity.

abstract: The purpose of this study was to examine how violence from patients and visitors is related to emergency department (ED) nurses' work productivity and symptoms of post-traumatic stress disorder (PTSD). Researchers have found ED nurses experience a high prevalence of physical assaults from patients and visitors. Yet, there is little research which examines the effect violent events have on nurses' productivity, particularly their ability to provide safe and compassionate patient care. A cross-sectional design was used to gather data from ED nurses who are members of the Emergency Nurses Association in the United States. Participants were asked to complete the Impact of Events Scale-Revised and Healthcare Productivity Survey in relation to a stressful violent event. Ninety-four percent of nurses experienced at least one posttraumatic stress disorder symptom after a violent event, with 17% having scores high enough to be considered probable for PTSD. In addition, there were significant indirect relationships between stress symptoms and work productivity. Workplace violence is a significant stressor for ED nurses. Results also indicate violence has an impact on the care ED nurses provide. Interventions are needed to prevent the violence and to provide care to the ED nurse after an event.


title: Speech and gesture share the same communication system

abstract: Humans speak and produce symbolic gestures. Do these two forms of communication interact, and how? First, we tested whether the two communication signals influenced each other when emitted simultaneously. Participants either pronounced words, or executed symbolic gestures, or emitted the two communication signals simultaneously. Relative to the unimodal conditions, multimodal voice spectra were enhanced by gestures, whereas multimodal gesture parameters were reduced by words. In other words, gesture reinforced word, whereas word inhibited gesture. In contrast, aimless arm movements and pseudo-words had no comparable effects. Next, we tested whether observing word pronunciation during gesture execution affected verbal responses in the same way as emitting the two signals. Participants responded verbally to either spoken words, or to gestures, or to the simultaneous presentation of the two signals. We observed the same reinforcement in the voice spectra as during simultaneous emission. These results suggest that spoken word and symbolic gesture are coded as single signal by a unique communication system. This signal represents the intention to engage a closer interaction with a hypothetical interlocutor and it may have a meaning different from when word and gesture are encoded singly.


title: Iterated register coalescing

abstract: An important function of any register allocator is to target registers so as to eliminate copy instructions. Graph-coloring register allocation is an elegant approach to this problem. If the source and destination of a move instruction do not interfere, then their nodes can be coalesced in the interference graph. Chaitin's coalescing heuristic could make a graph uncolorable (i.e., introduce spills); Briggs et al. demonstrated a conservative coalescing heuristic that preserves colorability. But Briggs's algorithm is too conservative and leaves too many move instructions in our programs. We show how to interleave coloring reductions with Briggs's coalescing heuristic, leading to an algorithm that is safe but much more aggressive.


title: xGEMs: Generating Examplars to Explain Black-Box Models

abstract: This work proposes xGEMs or manifold guided exemplars, a framework to understand black-box classifier behavior by exploring the landscape of the underlying data manifold as data points cross decision boundaries. To do so, we train an unsupervised implicit generative model -- treated as a proxy to the data manifold. We summarize black-box model behavior quantitatively by perturbing data samples along the manifold. We demonstrate xGEMs' ability to detect and quantify bias in model learning and also for understanding the changes in model behavior as training progresses.


title: Monthly flow forecast for Mississippi River basin using artificial neural networks

abstract: Long-term monthly flow forecasts are essential for decision making in a river basin system. Many studies have already been reported on monthly as well as seasonal forecast using artificial neural networks (ANNs). This study demonstrates that monthly forecasts can be significantly improved if the input variables in ANN models are chosen with due consideration, even if number of training patterns are less. Monthly forecast models up to 12-month lead-time have been developed for Mississippi River in USA. It is seen that direct forecast with only antecedent flows as inputs does not improve the result. It is better to develop individual models for each month separately with information from previous years for the same month. Further, the forecast is found to significantly improve if the difference in predicted and actual flows is also included as one of the input variables (i.e. error updating), particularly where there is a clearly observed pattern in the historical information.


title: Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients

abstract: Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Common signs and symptoms include fever, increased heart rate, increased breathing rate, and confusion. Sepsis is difficult to predict, diagnose, and treat. Patients who develop sepsis have an increased risk of complications and death and face higher health care costs and longer hospitalization. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). In this paper, we look at the problem of early detection of sepsis by using temporal data mining. We focus on the use of knowledge-based temporal abstraction to create meaningful interval-based abstractions, and on time-interval mining to discover frequent interval-based patterns. We used 2,560 cases derived from the MIMIC-III database. We found that the distribution of the temporal patterns whose frequency is above 10% discovered in the records of septic patients during the last 6 and 12 hours before onset of sepsis is significantly different from that distribution within a similar period, during an equivalent time window during hospitalization, in the records of non-septic patients. This discovery is encouraging for the purpose of performing an early diagnosis of sepsis using the discovered patterns as constructed features.


title: Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear Programming

abstract: Securing the networks of large organizations is technically challenging due to the complex configurations and constraints. Managing these networks requires rigorous and comprehensive analysis tools. A network administrator needs to identify vulnerable configurations, as well as tools for hardening the networks. Such networks usually have dynamic and fluidic structures, thus one may have incomplete information about the connectivity and availability of hosts. In this paper, we address the problem of statically performing a rigorous assessment of a set of network security defense strategies with the goal of reducing the probability of a successful large-scale attack in a dynamically changing and complex network architecture. We describe a probabilistic graph model and algorithms for analyzing the security of complex networks with the ultimate goal of reducing the probability of successful attacks. Our model naturally utilizes a scalable state-of-the-art optimization technique called sequential linear programming that is extensively applied and studied in various engineering problems. In comparison to related solutions on attack graphs, our probabilistic model provides mechanisms for expressing uncertainties in network configurations, which is not reported elsewhere. We have performed comprehensive experimental validation with real-world network configuration data of a sizable organization.


title: Automatic segmentation of MR brain images with a convolutional neural network

abstract: Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.


title: An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment

abstract: In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.


title: English and communication skills for the global engineer

abstract: Engineering graduates require an ever-increasing range of skills to maintain relevance with the global environment of the new millennium. Communication skills are an vital component of this, recognised by academia and industry alike. English language skills are also important given its widespread status across the globe as a lingua franca. Indeed, multilingual skills are considered a salient element in the make-up of the new global engineer. English for specific purposes focuses the learner's attention on the particular terminology and communication skills required in the international professional field. Communication skills development is discussed in the paper, with examples given of different methods of teaching and assessment. The impacts on communication skills development include various elements, including gender equality. A lack of sufficient communication skills serves only to undermine the image of the engineer, but this can be tackled by engaging features of emotional intelligence (EQ) in the education of engineers. EQ offers various components that can improve communication skills and emphasise a more experiential approach to learning.


title: Underwater Wireless Optical Communication Channel Modeling and Performance Evaluation using Vector Radiative Transfer Theory

abstract: This paper presents the modeling of an underwater wireless optical communication channel using the vector radiative transfer theory. The vector radiative transfer equation captures the multiple scattering nature of natural water, and also includes the polarization behavior of light. Light propagation in an underwater environment encounters scattering effect creating dispersion which introduces inter-symbol-interference to the data communication. The attenuation effect further reduces the signal to noise ratio. Both scattering and absorption have adverse effects on underwater data communication. Using a channel model based on radiative transfer theory, we can quantify the scattering effect as a function of distance and bit rate by numerical Monte Carlo simulations. We also investigate the polarization behavior of light in the underwater environment, showing the significance of the cross-polarization component when the light encounters more scattering.


O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs


title: A complex network approach to robustness and vulnerability of spatially organized water distribution networks

abstract: In this work, water distribution systems are regarded as large sparse planar graphs with complex network characteristics and the relationship between important topological features of the network (i.e. structural robustness and loop redundancy) and system resilience, viewed as the antonym to structural vulnerability, are assessed. Deterministic techniques from complex networks and spectral graph theory are utilized to quantify well-connectedness and estimate loop redundancy in the studied benchmark networks. By using graph connectivity and expansion properties, system robustness against node/link failures and isolation of the demand nodes from the source(s) are assessed and network tolerance against random failures and targeted attacks on their bridges and cut sets are analyzed. Among other measurements, two metrics of meshed-ness and algebraic connectivity are proposed as candidates for quantification of redundancy and robustness, respectively, in optimization design models. A brief discussion on the scope and limitations of the provided measurements in the analysis of operational reliability of water distribution systems is presented.


title: Rotor design of a high-speed Permanent Magnet Synchronous Machine rating 100,000 rpm at 10kW

abstract: Rotors of electrical high speed machines are subject to high stress, limiting the rated power of the machines. This paper describes the design process of a high-speed rotor of a Permanent Magnet Synchronous Machine (PMSM) for a rated power of 10kW at 100,000 rpm. Therefore, at the initial design the impact of the rotor radius to critical parameters is analyzed analytically. In particular, critical parameters are mechanical stress due to high centrifugal forces and natural bending frequencies. Furthermore, air friction losses, heating the rotor and the stator additionally, are no longer negligible compared to conventional machines and must be considered in the design process. These mechanical attributes are controversial to the electromagnetic design, increasing the effective magnetic air gap, for example. Thus, investigations are performed to achieve sufficient mechanical strength without a significant reduction of air gap flux density or causing thermal problems. After initial design by means of analytical estimations, an optimization of rotor geometry and materials is performed by means of the finite element method (FEM).


title: Stick to the Script: Monitoring The Policy Compliance of SDN Data Plane

abstract: Software defined networks provide new opportunities for automating the process of network debugging. Many tools have been developed to verify the correctness of network configurations on the control plane. However, due to software bugs and hardware faults of switches, the correctness of control plane may not readily translate into that of data plane. To bridge this gap, we present VeriDP, which can monitor "whether actual forwarding behaviors are complying with network configurations". Given that policies are well-configured, operators can leverage VeriDP to monitor the correctness of the network data plane. In a nutshell, VeriDP lets switches tag packets that they forward, and report tags together with headers to the verification server before the packets leave the network. The verification server pre-computes all header-to-tag mappings based on the configuration, and checks whether the reported tags agree with the mappings. We prototype VeriDP with both software and hardware OpenFlow switches, and use emulation to show that VeriDP can detect common data plane fault including black holes and access violations, with a minimal impact on the data plane.


title: Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM

abstract: Bitwise operations are an important component of modern day programming. Many widely-used data structures (e.g., bitmap indices in databases) rely on fast bitwise operations on large bit vectors to achieve high performance. Unfortunately, in existing systems, regardless of the underlying architecture (e.g., CPU, GPU, FPGA), the throughput of such bulk bitwise operations is limited by the available memory bandwidth. ::: We propose Buddy, a new mechanism that exploits the analog operation of DRAM to perform bulk bitwise operations completely inside the DRAM chip. Buddy consists of two components. First, simultaneous activation of three DRAM rows that are connected to the same set of sense amplifiers enables us to perform bitwise AND and OR operations. Second, the inverters present in each sense amplifier enables us to perform bitwise NOT operations, with modest changes to the DRAM array. These two components make Buddy functionally complete. Our implementation of Buddy largely exploits the existing DRAM structure and interface, and incurs low overhead (1% of DRAM chip area). ::: Our evaluations based on SPICE simulations show that, across seven commonly-used bitwise operations, Buddy provides between 10.9X---25.6X improvement in raw throughput and 25.1X---59.5X reduction in energy consumption. We evaluate three real-world data-intensive applications that exploit bitwise operations: 1) bitmap indices, 2) BitWeaving, and 3) bitvector-based implementation of sets. Our evaluations show that Buddy significantly outperforms the state-of-the-art.


title: Distinguishing and contrasting two strategies for design science research

abstract: This paper distinguishes and contrasts two design science research strategies in information systems. In the first strategy, a researcher constructs or builds an IT meta-artefact as a general solution concept to address a class of problem. In the second strategy, a researcher attempts to solve a client’s specific problem by building a concrete IT artefact in that specific context and distils from that experience prescriptive knowledge to be packaged into a general solution concept to address a class of problem. The two strategies are contrasted along 16 dimensions representing the context, outcomes, process and resource requirements.


title: A randomized linear-time algorithm to find minimum spanning trees

abstract: We present a randomized linear-time algorithm to find a minimum spanning tree in a connected graph with edge weights. The algorithm uses random sampling in combination with a recently discovered linear-time algorithm for verifying a minimum spanning tree. Our computational model is a unit-cost random-access machine with the restriction that the only operations allowed on edge weights are binary comparisons.


title: Revision on fuzzy artificial potential field for humanoid robot path planning in unknown environment

abstract: Path planning in a completely known environment has been experienced various ways. However, in real world, most humanoid robots work in unknown environments. Robots' path planning by artificial potential field and fuzzy artificial potential field methods are very popular in the field of robotics navigation. However, by default humanoid robots lack range sensors; thus, traditional artificial potential field approaches needs to adopt themselves to these limitations. This paper investigates two different approaches for path planning of a humanoid robot in an unknown environment using fuzzy artificial potential (FAP) method. In the first approach, the direction of the moving robot is derived from fuzzified artificial potential field whereas in the second one, the direction of the robot is extracted from some linguistic rules that are inspired from artificial potential field. These two introduced trajectory design approaches are validated though some software and hardware in the loop simulations and the experimental results demonstrate the superiority of the proposed approaches in humanoid robot real-time trajectory planning problems.


title: Insights into layout patterns of mobile user interfaces by an automatic analysis of android apps

abstract: Mobile phones recently evolved into smartphones that provide a wide range of services. One aspect that differentiates smartphones from their predecessor is the app model. Users can easily install third party applications from central mobile application stores. In this paper we present a process to gain insights into mobile user interfaces on a large scale. Using the developed process we automatically disassemble and analyze the 400 most popular free Android applications. The results suggest that the complexity of the user interface differs between application categories. Further, we analyze interface layouts to determine the most frequent interface elements and identify combinations of interface widgets. The most common combination that consists of three nested elements covers 5.43% of all interface elements. It is more frequent than progress bars and checkboxes. The ten most frequent patterns together cover 21.13% of all interface elements. They are all more frequent than common widget including radio buttons and spinner. We argue that the combinations identified not only provide insights about current mobile interfaces, but also enable the development of new optimized widgets.


title: Pattern specification and application in metamodels in ecore

abstract: The increased use of domain-specific languages (DSLs) and the absence of adequate tooling to take advantage of commonalities among DSLs has led to a situation where the same structure is duplicated in multiple DSLs. This observation has lead to the work described in this paper: an investigation of methods and tools for pattern specification and application and two extensions of a state-of-the-art tool for patterns in DSLs, DSL-tao. The extensions make patterns more understandable and they also make the tool suitable for more complex pattern applications. The first extension introduces a literal specification for patterns and the second extension introduces a merge function for the application of patterns. These two extensions are demonstrated on an often-occurring pattern in DSLs.


title: Exploring the taxonomie and associative link between emotion and function for robot sound design

abstract: Sound is a medium that conveys functional and emotional information in a form of multilayered streams. With the use of such advantage, robot sound design can open a way for being more efficient communication in human-robot interaction. As the first step of research, we examined how individuals perceived the functional and emotional intention of robot sounds and whether the perceived information from sound is associated with their previous experience with science fiction movies. The sound clips were selected based on the context of the movie scene (i.e., Wall-E, R2-D2, BB8, Transformer) and classified as functional (i.e., platform, monitoring, alerting, feedback) and emotional (i.e., positive, neutral, negative). A total of 12 participants were asked to identify the perceived properties for each of the 30 items. We found that the perceived emotional and functional messages varied from those originally intended and differed by previous experience.


title: © 2008 INFORMS Reducing Delays for Medical Appointments: A Queueing Approach

abstract: Many primary care offices and other medical practices regularly experience long backlogs for appointments. These backlogs are exacerbated by a significant level of last-minute cancellations or “no-shows,” which have the effect of wasting capacity. In this paper, we conceptualize such an appointment system as a single-server queueing system in which customers who are about to enter service have a state-dependent probability of not being served and may rejoin the queue. We derive stationary distributions of the queue size, assuming both deterministic as well as exponential service times, and compare the performance metrics to the results of a simulation of the appointment system. Our results demonstrate the usefulness of the queueing models in providing guidance on identifying patient panel sizes for medical practices that are trying to implement a policy of “advanced access.”


title: Efficient Behavior of Small-World Networks

abstract: We introduce the concept of efficiency of a network as a measure of how efficiently it exchanges information. By using this simple measure, small-world networks are seen as systems that are both globally and locally efficient. This gives a clear physical meaning to the concept of "small world," and also a precise quantitative analysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.


Artificial neural networks in mammography: Application to decision making in the diagnosis of breast cancer


title: Automating biomedical data science through tree-based pipeline optimization

abstract: Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning—pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators—such as synthetic feature constructors—that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.


title: Conceptual Commitments of the LIDA Model of Cognition

abstract: Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad -based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses “conceptual commitments” and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.


A Comparative Study of Web Search Service Performance


title: Femtosecond-Long Pulse-Based Modulation for Terahertz Band Communication in Nanonetworks

abstract: Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. Nanonetworks are the enabling technology of long-awaited applications such as advanced health monitoring systems or high-performance distributed nano-computing architectures. The peculiarities of novel plasmonic nano-transceivers and nano-antennas, which operate in the Terahertz Band (0.1-10 THz), require the development of tailored communication schemes for nanonetworks. In this paper, a modulation and channel access scheme for nanonetworks in the Terahertz Band is developed. The proposed technique is based on the transmission of one-hundred-femtosecond-long pulses by following an asymmetric On-Off Keying modulation Spread in Time (TS-OOK). The performance of TS-OOK is evaluated in terms of the achievable information rate in the single-user and the multi-user cases. An accurate Terahertz Band channel model, validated by COMSOL simulation, is used, and novel stochastic models for the molecular absorption noise in the Terahertz Band and for the multi-user interference in TS-OOK are developed. The results show that the proposed modulation can support a very large number of nano-devices simultaneously transmitting at multiple Gigabits-per-second and up to Terabits-per-second, depending on the modulation parameters and the network conditions.


title: High Performance of Hash-based Signature Schemes

abstract: Hash-based signature schemes, whose security is ::: based on properties of the underlying hash functions, are promising ::: candidates to be quantum-safe digital signatures schemes. In ::: this work, we present a software implementation of two recent ::: standard proposals for hash-based signature schemes, Leighton ::: and Micali Signature (LMS) scheme and Extended Merkle ::: Signature Scheme (XMSS), using a set of AVX2 instructions on ::: Intel processors. The implementation uses several optimization ::: techniques for speeding up the underlying hash functions SHA2 ::: or SHA3, and other building block functions which lead to high ::: performance for signature operations on both schemes. On an ::: Intel Skylake processor, using a tree of height 60 with 12 layers, ::: the signing operation for XMSS takes 3,841,199 cycles (1,043 ::: signatures per second) at 128-bit security level (against quantum ::: attacks). For an equivalent security, the LMS system computes ::: a signature in 1,307,376 cycles (3,065 signatures per second). We ::: also provide the first comparative performance results for signing ::: and verification of both schemes using different parameters. ::: The results of our implementation indicate that both schemes ::: LMS and XMSS can achieve high performance using vector ::: instructions on modern processors.


Ontology Learning


title: A Stratified Analysis of Bayesian Optimization Methods

abstract: Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses inaccessible to theoretical analysis. We define two metrics for comparing the performance of Bayesian optimization methods and propose a ranking mechanism for summarizing performance within various genres or strata of test functions. These test functions serve to mimic the complexity of hyperparameter optimization problems, the most prominent application of Bayesian optimization, but with a closed form which allows for rapid evaluation and more predictable behavior. This offers a flexible and efficient way to investigate functions with specific properties of interest, such as oscillatory behavior or an optimum on the domain boundary.


title: Reversible image data hiding based on gradient adjusted prediction

abstract: The present study illustrates a new lossless data hiding method for digital images using image prediction technique. In the proposed method which is based on gradient-adjusted prediction (GAP), first prediction errors are computed and then the error values are slightly modified through shifting the prediction errors. The modified errors are used for embedding the data. Experimental results of present research have demonstrated that the proposed method called shifted gradient-adjusted prediction error (SGAPE) is capable of hiding more secret data with absolutely high PSNR.


title: MRAP: a novel MapReduce-based framework to support HPC analytics applications with access patterns

abstract: Due to the explosive growth in the size of scientific data sets, data-intensive computing is an emerging trend in computational science. Many application scientists are looking to integrate data-intensive computing into computational-intensive High Performance Computing facilities, particularly for data analytics. We have observed several scientific applications which must migrate their data from an HPC storage system to a data-intensive one. There is a gap between the data semantics of HPC storage and data-intensive system, hence, once migrated, the data must be further refined and reorganized. This reorganization requires at least two complete scans through the data set and then at least one MapReduce program to prepare the data before analyzing it. Running multiple MapReduce phases causes significant overhead for the application, in the form of excessive I/O operations. For every MapReduce application that must be run in order to complete the desired data analysis, a distributed read and write operation on the file system must be performed. Our contribution is to extend Map-Reduce to eliminate the multiple scans and also reduce the number of pre-processing MapReduce programs. We have added additional expressiveness to the MapReduce language to allow users to specify the logical semantics of their data such that 1) the data can be analyzed without running multiple data pre-processing MapReduce programs, and 2) the data can be simultaneously reorganized as it is migrated to the data-intensive file system. Using our augmented MapReduce system, MapReduce with Access Patterns (MRAP), we have demonstrated up to 33% throughput improvement in one real application, and up to 70% in an I/O kernel of another application.


title: Focal dystonia and the Sensory-Motor Integrative Loop for Enacting (SMILE)

abstract: Performing accurate movements requires preparation, execution, and monitoring mechanisms. The first two are coded by the motor system, and the latter by the sensory system. To provide an adaptive neural basis to overt behaviors, motor and sensory information has to be properly integrated in a reciprocal feedback loop. Abnormalities in this sensory-motor loop are involved in movement disorders such as focal dystonia, a hyperkinetic alteration affecting only a specific body part and characterized by sensory and motor deficits in the absence of basic motor impairments. Despite the fundamental impact of sensory-motor integration mechanisms on daily life, the general principles of healthy and pathological anatomic-functional organization of sensory-motor integration remain to be clarified. Based on the available data from experimental psychology, neurophysiology, and neuroimaging, we propose a bio-computational model of sensory-motor integration: the Sensory-Motor Integrative Loop for Enacting (SMILE). Aiming at direct therapeutic implementations and with the final target of implementing novel intervention protocols for motor rehabilitation, our main goal is to provide the information necessary for further validating the SMILE model. By translating neuroscientific hypotheses into empirical investigations and clinically relevant questions, the prediction based on the SMILE model can be further extended to other pathological conditions characterized by impaired sensory-motor integration.


title: Volcano deformation measurement using persistent scatterer interferometry with atmospheric delay corrections

abstract: In this paper, we present the deformation measurements in volcanic area with consideration of atmospheric delay corrections using multi-stack persistent scatterer interferometry (PSI). The 3-D numerical weather model data from European Centre for Medium-Range Weather Forecasts (ECMWF) is used to estimate the atmospheric delay along the SAR observation ray. A validation is performed for the weather model data based atmospheric delay by comparison with the total zenith delay (ZPD) measurements from the Regional Reference Frame Sub-Commission for Europe (EUREF) permanent GNSS network. The slant range delay (SPD) on each persistent scatterer (PS) is estimated and the atmospheric phase in the differential interferogram is calculated and compensated before the PS estimation. An evident improvement of the deformation results are shown in the paper.


title: Alignment-based compositional semantics for instruction following

abstract: This paper describes an alignment-based model for interpreting natural language instructions in context. We approach instruction following as a search over plans, scoring sequences of actions conditioned on structured observations of text and the environment. By explicitly modeling both the low-level compositional structure of individual actions and the high-level structure of full plans, we are able to learn both grounded representations of sentence meaning and pragmatic constraints on interpretation. To demonstrate the model’s flexibility, we apply it to a diverse set of benchmark tasks. On every task, we outperform strong task-specific baselines, and achieve several new state-of-the-art results.


title: Distribution and expression of tissue inhibitors of metalloproteinase in dorsal root entry zone and dorsal column after dorsal root injury.

abstract: To understand whether tissue inhibitors of metalloproteinase (TIMPs) contribute to the failure of regenerating sensory axons to enter the spinal cord, we used in situ hybridization and immunocytochemistry to examine the expression of TIMP1, TIMP2, and TIMP3 in the dorsal root, dorsal root entry zone (DREZ), and dorsal column after dorsal root injury in adult rats. We found that the three TIMPs and their mRNAs were up-regulated in a time-, region-, and cell-type-specific manner. Strong up-regulation of all three TIMPs was seen in the injured dorsal roots. TIMP2 was also significantly up-regulated in the DREZ and degenerating dorsal column, where TIMP1 and TIMP3 showed only moderate up-regulation. Most cells up-regulating the TIMPs in the DREZ and degenerating dorsal column were reactive astrocytes, but TIMP2 was also up-regulated by microglia/macrophages, especially at long postoperative survival times. These results suggest that TIMPs may be involved in controlling tissue remodelling following dorsal root injury and that manipulation of the expression of TIMPs may provide a means of promoting axonal regeneration into and within the injured spinal cord.


title: A 500 nA quiescent, 100 mA maximum load CMOS low-dropout regulator

abstract: Ultra low quiescent, wide output current range low-dropout regulators (LDO) are in high demand in portable applications to extend battery lives. This paper presents a 500 nA quiescent, 0 to 100 mA load, 3.5–7 V input to 3 V output LDO in a digital 0.35 μm 2P3M CMOS technology. The challenges in designing with nano-ampere of quiescent current are discussed, namely the leakage, the parasitics, and the excessive DC gain. CMOS super source follower voltage buffer and input excessive gain reduction are then proposed. The LDO is internally compensated using Ahuja method with a minimum phase margin of 55° across all load conditions. The maximum transient voltage variation is less than 150 and 75 mV when used with 1 and 10 μF external capacitor. Compared with existing work, this LDO achieves the best transient flgure-of-merit with close to best dynamic current efficiency (maximum-to-quiescent current ratio).


title: A long-term investigation of the comprehension of OOP concepts by novices

abstract: This article describes research on the learning of object-oriented programming (OOP) by novices. During two academic years, we taught OOP to high school students, using Java and BlueJ. Our approach to teaching featured: objects-first, teaching composed classes relatively early, deferring the teaching of main methods, and focusing on class structure before algorithms. The research used a constructivist qualitative research methodology using observations and field notes, audio and video recordings, and an analysis of artifacts such as homework assignments. The findings were divided into four primary categories: class vs. object, instantiation and constructors, simple vs. composed classes, and program flow. In total, 58 conceptions and difficulties were identified. Nevertheless, at the end of the courses, the students understood the basic principles of OOP. The two main contributions of this research are: (i) the breadth and depth of its investigation into the concepts held by novices studying OOP, and (ii) ...


title: A series elastic actuator as a new load-sensitive continuously variable transmission mechanism for control actuation systems

abstract: Electrical motors are often used to actuate mechanisms with varying speed and under varying load e.g., missile control actuation systems or robot extremities. Sizing of the motor is critical in these applications where space is limited. Varying speed and load cause poor utilization of the motor if the reduction ratio is constant. This requires employment of a larger motor than the smallest possible with ideal variable transmission. To realize better utilization of the motor, we propose a series elastic actuator that works as a load sensitive continuously variable transmission system. This is achieved by combining a pin in slot mechanism with an elastic output shaft. The proposed mechanism is modeled and simulated using example missile flight data and a typical robot finger loading scenario. Results are compared to the constant reduction ratio case, and advantages of the proposed mechanism are shown as decreased actuator size and power.


title: Flutter: Scheduling tasks closer to data across geo-distributed datacenters

abstract: Typically called big data processing, processing large volumes of data from geographically distributed regions with machine learning algorithms has emerged as an important analytical tool for governments and multinational corporations. The traditional wisdom calls for the collection of all the data across the world to a central datacenter location, to be processed using data-parallel applications. This is neither efficient nor practical as the volume of data grows exponentially. Rather than transferring data, we believe that computation tasks should be scheduled where the data is, while data should be processed with a minimum amount of transfers across datacenters. In this paper, we design and implement Flutter, a new task scheduling algorithm that improves the completion times of big data processing jobs across geographically distributed datacenters. To cater to the specific characteristics of data-parallel applications, we first formulate our problem as a lexicographical min-max integer linear programming (ILP) problem, and then transform it into a nonlinear program with a separable convex objective function and a totally unimodular constraint matrix, which can be solved using a standard linear programming solver efficiently in an online fashion. Our implementation of Flutter is based on Apache Spark, a modern framework popular for big data processing. Our experimental results have shown that we can reduce the job completion time by up to 25%, and the amount of traffic transferred among datacenters by up to 75%.


title: Knowledge of extraction from trained neural network by using decision tree

abstract: Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. As the number of hidden neurons increase, mean squared error and mean absolute percentage error decrease, and more time they need to deal with the dataset, that is result of investigation from neural network architectures. Decision tree induction generally performs better in knowledge extraction result with accuracy and precision level from 84.07 to 93.17 percent. The extracted rule can be used to explaining the process of the neural network systems and also can be applied in other systems like expert systems.


title: A comprehensive categorical and bibliometric analysis of published research articles on pediatric pain from 1975 to 2010.

abstract: The field of pediatric pain research began in the mid-1970s and has undergone significant growth and development in recent years as evidenced by the variety of books, conferences, and journals on the topic and also the number of disciplines engaged in work in this area. Using categorical and bibliometric meta-trend analysis, this study offers a synthesis of research on pediatric pain published between 1975 and 2010 in peer-reviewed journals. Abstracts from 4256 articles, retrieved from Web of Science, were coded across 4 categories: article type, article topic, type and age of participants, and pain stimulus. The affiliation of the first author and number of citations were also gathered. The results suggest a significant increase in the number of publications over the time period investigated, with 96% of the included articles published since 1990 and most research being multiauthored publications in pain-focused journals. First authors were most often from the United States and affiliated with a medical department. Most studies were original research articles; the most frequent topics were pain characterization (39.86%), pain intervention (37.49%), and pain assessment (25.00%). Clinical samples were most frequent, with participants most often characterized as children (6-12 years) or adolescents (13-18 years) experiencing chronic or acute pain. The findings provide a comprehensive overview of contributions in the field of pediatric pain research over 35 years and offers recommendations for future research in the area.


A Verified MATLAB Toolbox for the Dempster-Shafer Theory