title: Brittle-to-ductile transitions in polycrystalline non-metallic materials

abstract: At room temperatures and pressures minerals, ceramics, and rocks have only limited ductility, and this distinguishes them from most metals. The characteristic strength and brittle or ductile deformation behaviour of crystalline solids is determined by the nature of their atomic bonding and crystal structure. Thus Frost & Ashby (1982) have recognized 21 isomechanical groups in their study of deformation mechanism maps to represent the plasticity and creep behaviour of metals and ceramics. The behaviour of the solids belonging to any particular isomechanical group is closely similar and clearly different from that of solids belonging to the other groups. Their study covered the cubic, tetragonal, hexagonal, and trigonal crystal systems, together with the orthorhombic olivines, in particular the forsterite end member. These materials may be broadly classified into seven groups (of which three are the three classes of metals with cubic crystal structures — f.c.c., b.c.c, and h.c.p.), showing a progression of plastic flow properties (Frost & Ashby 1982, Tables 18.3 & 18.4) and of cleavage properties (Kelly, Tyson & Cottrell 1967; Gandhi & Ashby 1979, Table 4 & Fig. 28). These are illustrated in Table 5.1.


title: Multi-Level Graph Layout on the GPU

abstract: This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multi-level scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the GPU requires a data parallel programming model, the challenge is devising a mapping of a naturally unstructured graph into a well-partitioned structured one. This is done by computing a balanced partitioning of a general graph. This algorithm provides a general multi-level scheme, which has the potential to be used not only for computation on the GPU, but also on emerging multi-core architectures. The algorithm manages to compute high quality layouts of large graphs in a fraction of the time required by existing algorithms of similar quality. An application for visualization of the topologies of ISP (Internet service provider) networks is presented.


title: Fusing Spatiotemporal Features and Joints for 3D Action Recognition

abstract: We present a novel approach to 3D human action recognition based on a feature-level fusion of spatiotemporal features and skeleton joints. First, 3D interest points detection and local feature description are performed to extract spatiotemporal motion information. Then the frame difference and pairwise distances of skeleton joint positions are computed to characterize the spatial information of the joints in 3D space. These two features are complementary to each other. A fusion scheme is then proposed to combine them effectively based on the random forests method. The proposed approach is validated on three challenging 3D action datasets for human action recognition. Experimental results show that the proposed approach outperforms the state-of-the-art methods on all three datasets.


title: Clustering Based URL Normalization Technique for Web Mining

abstract: URL (Uniform Resource Locator) normalization is an important activity in web mining. Web data can be retrieved in smoother way using effective URL normalization technique. URL normalization also reduces lot of calculations in web mining activities. A web mining technique for URL normalization is proposed in this paper. The proposed technique is based on content, structure and semantic similarity and web page redirection and forwarding similarity of the given set of URLs. Web page redirection and forward graphs can be used to measure the similarities between the URL’s and can also be used for URL clusters. The URL clusters can be used for URL normalization. A data structure is also suggested to store the forward and redirect URL information.


title: Adaptive Affinity Fields for Semantic Segmentation

abstract: Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks (GAN). We propose a simpler alternative that learns to verify the spatial structure of segmentation during training only. Unlike existing approaches that enforce semantic labels on individual pixels and match labels between neighbouring pixels, we propose the concept of Adaptive Affinity Fields (AAF) to capture and match the semantic relations between neighbouring pixels in the label space. We use adversarial learning to select the optimal affinity field size for each semantic category. It is formulated as a minimax problem, optimizing our segmentation neural network in a best worst-case learning scenario. AAF is versatile for representing structures as a collection of pixel-centric relations, easier to train than GAN and more efficient than CRF without run-time inference. Our extensive evaluations on PASCAL VOC 2012, Cityscapes, and GTA5 datasets demonstrate its above-par segmentation performance and robust generalization across domains.


title: Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

abstract: Problem statement: Question Answering (QA) system is taking an important role in current search engine optimization concept. Natural language processing technique is mostly implemented in QA system for asking user's question and several steps are also followed for conversion of questions to query form for getting a n exact answer. Approach: This paper surveys different types of question answering system based on ontology and semantic web model with different query format. For comparison, the types of input, q uery processing method, input and output format of each system and the performance metrics with its li mitations are analyzed and discussed. Our question answering for automatic learning system architectur e is used to overcome the difficulties raised from the different QA models. Results: The semantic search methodology is implemented by using RDF graph in the application of data structure domain a nd the performance is also analyzed. Answers are retrieved from ontology using Semantic Search approach and question-to-query algorithm is evaluated in our system for analyzing performance evaluation. Conclusion: Performance of question answering system of getting exact result can be improved by u sing semantic search methodology for retrieving answers from ontology model. Our system successfully implements this technique and the system is also used in intelligent manner for automatic learn ing method.


title: Visio-lization: generating novel facial images

abstract: Our goal is to generate novel realistic images of faces using a model trained from real examples. This model consists of two components: First we consider face images as samples from a texture with spatially varying statistics and describe this texture with a local non-parametric model. Second, we learn a parametric global model of all of the pixel values. To generate realistic faces, we combine the strengths of both approaches and condition the local non-parametric model on the global parametric model. We demonstrate that with appropriate choice of local and global models it is possible to reliably generate new realistic face images that do not correspond to any individual in the training data. We extend the model to cope with considerable intra-class variation (pose and illumination). Finally, we apply our model to editing real facial images: we demonstrate image in-painting, interactive techniques for improving synthesized images and modifying facial expressions.


title: Printed document authentication using two level or code

abstract: The availability of high quality copy machines provides a large amount of printed document counterfeits. Numerous authentication techniques based on security printing, graphical codes, hashing or local hashing have been suggested earlier. In this paper, we propose a novel printed document authentication system based on sensitivity of a new two level QR (2LQR) code to copying process. This 2LQR code contains specific textured patterns, which are sensitive to printing and copying processes. Therefore, it can be used to detect unauthorized document duplication. Experimental results show the efficiency of this 2LQR code for copy detection.


title: Hyperthermia in cancer treatment Review Hyperthermia in combined treatment of cancer

abstract: Summary Hyperthermia, the procedure of raising the temperature of tumour-loaded tissue to 40–43°C, is applied as an adjunctive therapy with various established cancer treatments such as radiotherapy and chemotherapy. The potential to control power distributions in vivo has been significantly improved lately by the development of planning systems and other modelling tools. This increased understanding has led to the design of multiantenna applicators (including their transforming networks) and implementation of systems for monitoring of E-fields (eg, electro-optical sensors) and temperature (particularly, online magnetic resonance tomography). Several phase III trials comparing radiotherapy alone or with hyperthermia have shown a beneficial effect of hyperthermia (with existing standard equipment) in terms of local control (eg, recurrent breast cancer and malignant melanoma) and survival (eg, head and neck lymph-node metastases, glioblastoma, cervical carcinoma). Therefore, further development of existing technology and elucidation of molecular mechanisms are justified. In recent molecular and biological investigations there have been novel applications such as gene therapy or immunotherapy (vaccination) with temperature acting as an enhancer, to trigger or to switch mechanisms on and off. However, for every particular temperature-dependent interaction exploited for clinical purposes, sophisticated control of temperature, spatially as well as temporally, in deep body regions will further improve the potential.


title: Learning Regularized LDA by Clustering

abstract: As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between- and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones. To overcome the problem without increasing the number of training samples, we propose making use of the structure of the given training data to regularize the between- and within-class scatter matrices by between- and within-cluster scatter matrices, respectively, and simultaneously. The within- and between-cluster matrices are computed from unsupervised clustered data. The within-cluster scatter matrix contributes to encoding the possible variations in intraclasses and the between-cluster scatter matrix is useful for separating extra classes. The contributions are inversely proportional to the number of training samples per class. The advantages of the proposed method become more remarkable as the number of training samples per class decreases. Experimental results on the AR and Feret face databases demonstrate the effectiveness of the proposed method.


title: Multiview Boosting With Information Propagation for Classification

abstract: Multiview learning has shown promising potential in many applications. However, most techniques are focused on either view consistency, or view diversity. In this paper, we introduce a novel multiview boosting algorithm, called Boost.SH, that computes weak classifiers independently of each view but uses a shared weight distribution to propagate information among the multiple views to ensure consistency. To encourage diversity, we introduce randomized Boost.SH and show its convergence to the greedy Boost.SH solution in the sense of minimizing regret using the framework of adversarial multiarmed bandits. We also introduce a variant of Boost.SH that combines decisions from multiple experts for recommending views for classification. We propose an expert strategy for multiview learning based on inverse variance, which explores both consistency and diversity. Experiments on biometric recognition, document categorization, multilingual text, and yeast genomic multiview data sets demonstrate the advantage of Boost.SH (85%) compared with other boosting algorithms like AdaBoost (82%) using concatenated views and substantially better than a multiview kernel learning algorithm (74%).


title: Active Plant Wall for Green Indoor Climate Based on Cloud and Internet of Things

abstract: An indoor climate is closely related to human health, well-being, and comfort. Thus, indoor climate monitoring and management are prevalent in many places, from public offices to residential houses. Our previous research has shown that an active plant wall system can effectively reduce the concentrations of particulate matter and volatile organic compounds and stabilize the carbon dioxide concentration in an indoor environment. However, regular plant care is restricted by geography and can be costly in terms of time and money, which poses a significant challenge to the widespread deployment of plant walls. In this paper, we propose a remote monitoring and control system that is specific to the plant walls. The system utilizes the Internet of Things technology and the Azure public cloud platform to automate the management procedure, improve the scalability, enhance user experiences of plant walls, and contribute to a green indoor climate.


title: ETHome: Open-source blockchain based energy community controller

abstract: This paper describes the proof of concept of a blockchain based organization of a local low voltage energy community. The focus of the concept is efficient use of shared resources to minimize external dependence, and not energy trading. A previously proposed control algorithm, which exploits the power dependency of the efficiency of electrical energy storages, is implemented as a smart contract on a private instance of an Ethereum blockchain to coordinate the operation. It is implemented using four connected Raspberry Pis representing the participating households with pre-given electrical load and photovoltaic conversion as well as a battery. Each household runs an Ethereum full node and an interfacing software. Only the energy technology components are simulated, while the blockchain is actually running on the Raspberry Pis in order to mind the full complexity of the technology. The practicability is proved in a test run and positive effects on the efficiency and the self-sufficiency within the community are observed. A first cost-benefit estimate is given and a further research agenda is presented.


title: Natural Language Processing, Electronic Health Records, and Clinical Research

abstract: Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical natural language processing and its common uses for extracting and transforming narrative information in EHR to support clinical research.


title: Exploring user and system requirements of linked data visualization through a visual dashboard approach

abstract: One of the open problems in Semantic Web research is which tools should be provided to users to explore linked data. This is even more urgent now that massive amount of linked data is being released by governments worldwide. The development of single dedicated visualization applications is increasing, but the problem of exploring unknown linked data to gain a good understanding of what is contained is still open. An effective generic solution must take into account the user's point of view, their tasks and interaction, as well as the system's capabilities and the technical constraints the technology imposes. This paper is a first step in understanding the implications of both, user and system by evaluating our dashboard-based approach. Though we observe a high user acceptance of the dashboard approach, our paper also highlights technical challenges arising out of complexities involving current infrastructure that need to be addressed while visualizing linked data. In light of the findings, guidelines for the development of linked data visualization and manipulation are provided.


title: ARTIFICIAL NEURAL NETWORK LEARNING OF NONSTATIONARY BEHAVIOR IN TIME SERIES

abstract: We refine and complement a previously-proposed artificial neural network method for learning hidden signals forcing nonstationary behavior in time series. The method adds an extra input unit to the network and feeds it with the proposed profile for the unknown perturbing signal. The correct time evolution of this new input parameter is learned simultaneously with the intrinsic stationary dynamics underlying the series, which is accomplished by minimizing a suitably-defined error function for the training process. We incorporate here the use of validation data, held out from the training set, to accurately determine the optimal value of a hyperparameter required by the method. Furthermore, we evaluate this algorithm in a controlled situation and show that it outperforms other existing methods in the literature. Finally, we discuss a preliminary application to the real-world sunspot time series and link the obtained hidden perturbing signal to the secular evolution of the solar magnetic field.


title: Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network

abstract: Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs significant number of pixellevel annotated data, which is often unavailable. To address this lack, in this paper, we leverage, on one hand, massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images created through Generative Adversarial Networks. In particular, we propose a semi-supervised framework ,based on Generative Adversarial Networks (GANs), which consists of a generator network to provide extra training examples to a multi-class classifier, acting as discriminator in the GAN framework, that assigns sample a label y from the K possible classes or marks it as a fake sample (extra class). The underlying idea is that adding large fake visual data forces real samples to be close in the feature space, enabling a bottom-up clustering process, which, in turn, improves multiclass pixel classification. To ensure higher quality of generated images for GANs with consequent improved pixel classification, we extend the above framework by adding weakly annotated data, i.e., we provide class level information to the generator. We tested our approaches on several challenging benchmarking visual datasets, i.e. PASCAL, SiftFLow, Stanford and CamVid, achieving competitive performance also compared to state-of-the-art semantic segmentation method


title: The toxic principles of Naematoloma fasciculare.

abstract: Fasciculol E and F were identified as the toxic principles of Naematoloma fasciculare (Nigakuritake), a poisonous mushroom. These compounds caused paralysis and death in mice. The LD50 values of fasciculol E and F to mice were determined as 50 mg/kg and 168 mg/kg (i. p.), respectively.


title: A Taxonomy of Performance Prediction Systems in the Parallel and Distributed Computing Grids

abstract: As Grids are loosely-coupled congregations of geographically distributed heterogeneous resources, the efficient utilization of the resources requires the support of a sound Performance Prediction System (PPS). The performance prediction of grid resources is helpful for both Resource Management Systems and grid users to make optimized resource usage decisions. There have been many PPS projects that span over several grid resources in several dimensions. In this paper the taxonomy for describing the PPS architecture is discussed. The taxonomy is used to categorize and identify approaches which are followed in the implementation of the existing PPSs for Grids. The taxonomy and the survey results are used to identify approaches and issues that have not been fully explored in research.


title: Tracking a hand manipulating an object

abstract: We present a method for tracking a hand while it is interacting with an object. This setting is arguably the one where hand-tracking has most practical relevance, but poses significant additional challenges: strong occlusions by the object as well as self-occlusions are the norm, and classical anatomical constraints need to be softened due to the external forces between hand and object. To achieve robustness to partial occlusions, we use an individual local tracker for each segment of the articulated structure. The segments are connected in a pairwise Markov random field, which enforces the anatomical hand structure through soft constraints on the joints between adjacent segments. The most likely hand configuration is found with belief propagation. Both range and color data are used as input. Experiments are presented for synthetic data with ground truth and for real data of people manipulating objects.


title: A Bayesian Sampling Approach to Exploration in Reinforcement Learning

abstract: We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives exploration by sampling multiple models from the posterior and selecting actions optimistically. It extends previous work by providing a rule for deciding when to re-sample and how to combine the models. We show that our algorithm achieves near-optimal reward with high probability with a sample complexity that is low relative to the speed at which the posterior distribution converges during learning. We demonstrate that BOSS performs quite favorably compared to state-of-the-art reinforcement-learning approaches and illustrate its flexibility by pairing it with a non-parametric model that generalizes across states.


On the Challenges of Translating NLP Research into Commercial Products


title: Estimate air quality based on mobile crowe sensing and big data

abstract: PM2.5 (particulate matter in the atmosphere with a diameter no more than 2.5 microns) in the air can cause great damage to human beings. It's a great challenge to offer a fine-grained and accurate PM2.5 monitoring service in urban areas as the required facilities are very expensive and huge. Since the PM2.5 has a significant scattering effect on visible light, the large-scale user-contributed image data collected by the mobile crowd sensing bring a new opportunity for understanding the urban PM2.5. After the analysis of image data, we find that several image features are very discriminative in PM2.5 inference. In this paper, we propose a fine-grained PM2.5 estimation method based on the random forest model without any PM2.5 measurement devices. We evaluate our approach with experiments based on five data sources: the meteorological data, the traffic data, the records from the monitoring sites, the POIs and the photos collected. The experimental results show that we have a high evaluation accuracy on PM2.5 estimation (precision: 0.875, recall: 0.872), which outperforms existing methods (Logistic, Naive Bayes, Random Tree, and BP ANN).


title: Diced Cartilage Versus Solid Grafts in Rhinoplasty

abstract: The use of diced cartilage grafts in reconstructive surgery was first described by Peer [1] in 1943. A number of additional reports describing diced cartilage have followed since then, but the technique has never achieved widespread use. In recent years, however, an interest in using diced cartilage for rhinoplasty procedures has resurfaced. As aesthetic and reconstructive surgeons revisit this technique, it is important that we critically assess the various materials, approaches, and indications applied by clinicians using diced-cartilage augmentation. The use of diced cartilage rather than a solid piece of cartilage graft is an attractive concept because it offers greater flexibility, carries a minimal risk of warping, and obviates the need for a long and straight cartilage graft donor site. Diced cartilage also may be particularly advantageous by enabling the use of residual septum in cases of secondary and revision rhinoplasty or by proving to be a more effective method for delivering conchal cartilage that as a single piece may be inadequate. To prevent problems of palpability and visibility of diced grafts, surgeons have described the use of autogenous, synthetic, or alloplastic wraps to camouflage the cartilage construct. A great deal of controversy currently exists about the various techniques that have been advocated, and the optimal substance or scaffold for delivering diced cartilage has yet to be determined. In 2000, Erol [2] introduced the concept of a ‘‘Turkish Delight,’’ whereby diced cartilage wrapped in Surgicel was used as an adjunct to rhinoplasty. The report by Erol describes the use of Surgicel-wrapped diced cartilage in more than 2,000 rhinoplasty cases, with successful longterm follow-up evaluation. On the other hand, others have had far less success using this approach during rhinoplasty. Daniel and Calvert [3] reported their experience using Surgicel-wrapped diced cartilage for 22 patients involving 14 radix grafts, 4 upper dorsum grafts, and 4 full-length grafts. In all cases, this technique failed to correct the problem due to complete resorption by about 3 months. As a result, Daniel and Calvert abandoned Surgicel and instead used fascia to wrap diced cartilage. In contrast, this patient cohort of fascia-wrapped diced cartilage (n = 20) did not demonstrate any notable resorption but rather required removal of excess amounts of cartilage in one-third of patients due to overcorrection for anticipated volume loss. A recent follow-up article of 79 patients with diced cartilage and fascia demonstrated adequate results at 1to 2-year follow-up evaluations [4]. In this article titled A Novel Autologous Scaffold for Diced Cartilage Grafts in Dorsal Augmentation Rhinoplasty, Bullocks et al. present an innovative approach to rhinoplasty using diced cartilage combined with autologous tissue glue (ATG: platelet-rich plasma ? fibrin glue). The authors report the use of ATG-diced cartilage constructs for 68 rhinoplasty patients requiring dorsal augmentation. During a mean follow-up period of 15 months, no major complications (extrusion, displacement, or infections) were reported, but 11 patients did experience an erythematous reaction lasting 1–4 weeks. The authors report maintenance of dorsal height in all cases, but this was not objectively studied. We believe Bullocks et al. should be congratulated for their work. If diced cartilage is a preferred method of reconstruction, these authors have introduced a novel technique for delivering diced cartilage that may minimize negative factors (i.e., elevated costs, additional donor sites, N. Tabbal (&) O. M. Tepper 521 Park Avenue, New York, NY 10065-8140, USA e-mail: mail@tabbal.us


The social bases of agrarian environmentalism: a comparative analysis of New York and Michigan farm operators.


title: From Needs to Goals and Representations: Foundations for a Unified Theory of Motivation, Personality, and Development

abstract: Drawing on both classic and current approaches, I propose a theory that integrates motivation, personality, and development within one framework, using a common set of principles and mechanisms. The theory begins by specifying basic needs and by suggesting how, as people pursue need-fulfilling goals, they build mental representations of their experiences (beliefs, representations of emotions, and representations of action tendencies). I then show how these needs, goals, and representations can serve as the basis of both motivation and personality, and can help to integrate disparate views of personality. The article builds on this framework to provide a new perspective on development, particularly on the forces that propel development and the roles of nature and nurture. I argue throughout that the focus on representations provides an important entry point for change and growth. (PsycINFO Database Record


ON THE USE OF REDUNDANCY IN SUCCESSIVE APPROXIMATION A / D CONVERTERS


title: State of the art in simulation-based optimisation for maintenance systems

abstract: Simulation based optimisation has been successfully applied to maintenance operations.It has a high potential due to its ability to optimise complex maintenance systems.Much of the research is on optimising PM frequency that will lead to the minimum cost. Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context.


title: Cross-Project and Within-Project Semisupervised Software Defect Prediction: A Unified Approach

abstract: When there exist not enough historical defect data for building an accurate prediction model, semisupervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data are well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only utilize the suitable unlabeled source data. We call CPDP in this scenario as cross-project semisupervised defect prediction (CSDP). Although some within-project semisupervised defect prediction (WSDP) methods have been developed in recent years, there still exists much room for improvement on prediction performance. In this paper, we aim to provide a unified and effective solution for both CSDP and WSDP problems. We introduce the semisupervised dictionary learning technique and propose a cost-sensitive kernelized semisupervised dictionary learning (CKSDL) approach. CKSDL can make full use of the limited labeled defect data and a large amount of unlabeled data in the kernel space. In addition, CKSDL considers the misclassification costs in the dictionary learning process. Extensive experiments on 16 projects indicate that CKSDL outperforms state-of-the-art WSDP methods, using unlabeled cross-project defect data can help improve the WSDP performance, and CKSDL generally obtains significantly better prediction performance than related SSDP methods in the CSDP scenario.


title: Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

abstract: We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.


title: Integrated Magnetic Self-Driven ZVS Nonisolated Full-Bridge Converter

abstract: This paper proposes a high-efficiency high-power-density voltage regulator (VR). An integrated magnetic self-driven full-bridge topology is employed as the main circuit. The proposed VR runs at a 700-kHz switching frequency for a 1 Unit Height (1U) form factor. A novel synchronous rectifier drive method is used to achieve high efficiency. The direct current resistance (DCR) current-sensing method is used for the integrated transformer to achieve adaptive voltage position. Compared with the traditional multiphase buck converter, the proposed solution has higher efficiency, a smaller passive component footprint, and a lower device cost. A 1U 700-kHz VR prototype is built in the laboratory, and it achieves 88.2% efficiency at a 1.2-V/130-A output. The experimental result verifies the theoretical analysis.


title: Increasing TLB reach by exploiting clustering in page translations

abstract: The steadily increasing sizes of main memory capacities require corresponding increases in the processor's translation lookaside buffer (TLB) resources to avoid performance bottlenecks. Large operating system page sizes can mitigate the bottleneck with a smaller TLB, but most OSs and applications do not fully utilize the large-page support in current hardware. Recent work has shown that, while not guaranteed, some virtual-to-physical page mappings exhibit “contiguous” spatial locality in which consecutive virtual pages map to consecutive physical pages. Such locality provides opportunities to coalesce “adjacent” TLB entries for increased reach. We observe that beyond simple adjacent-entry coalescing, many more translations exhibit “clustered” spatial locality in which a group or cluster of nearby virtual pages map to a similarly clustered set of physical pages. In this work, we provide a detailed characterization of the spatial locality among the virtual-to-physical translations. Based on this characterization, we present a multi-granular TLB organization that significantly increases its effective reach and reduces miss rates substantially while requiring no additional OS support. Our evaluation shows that the multi-granular design outperforms conventional TLBs and the recently proposed coalesced TLBs technique.


The Logistic Innovation Approach and The Theory of Inventive Problem Solving


title: Circuitry and plasticity of the dorsal horn – Toward a better understanding of neuropathic pain

abstract: Maladaptive plasticity within the dorsal horn (DH) of the spinal cord is a key substrate for development of neuropathic pain following peripheral nerve injury. Advances in genetic engineering, tracing techniques and opto-genetics are leading to a much better understanding of the complex circuitry of the spinal DH and the radical changes evoked in such circuitry by nerve injury. These changes can be viewed at multiple levels including: synaptic remodeling including enhanced excitatory and reduced inhibitory drive, morphological and electrophysiological changes which are observed both to primary afferent inputs as well as DH neurons, and ultimately circuit-level rewiring which leads to altered connectivity and aberrant processing of sensory inputs in the DH. The DH should not be seen in isolation but is subject to important descending modulation from the brainstem, which is further dysregulated by nerve injury. Understanding which changes relate to specific disease-states is essential, and recent work has aimed to stratify patient populations in a mechanistic fashion. In this review we will discuss how such pathophysiological mechanisms may lead to the distressing sensory phenomena experienced by patients suffering neuropathic pain, and the relationship of such mechanisms to current and potential future treatment modalities.


title: Future Generation Computer Systems 838 (2001) 1–12 A theory and architecture for automating performance diagnosis �

abstract: This prospectus describes research to simplify programing of parallel computers. It focuses specifically on performance diagnosis, the process of finding and explaining sources of inefficiency in parallel programs. Considerable research already has been done to simplify performance diagnosis, but with mixed success. Two elements are missing from existing research: 1. There is no general theory of how expert programers do performance diagnosis. As a result, it is difficult for researchers to compare existing work or fit their work to programers. It is difficult for programers to locate products of existing research that meet their needs. 2. There is no automated, adaptable software to help programers do performance diagnosis. Existing software is either automated but limited to very specific circumstances, or in general, not automated for most tasks. The research described here addresses both of these issues. The research will develop and validate a theory of performance diagnosis, based on general models on diagnostic problem-solving. It will design and evaluate a computer program (called Poirot) that employs the theory to automatically, adaptably support performance diagnosis. © 2001 Elsevier Science B.V. All rights reserved.


What type of happiness are you looking for? - A closer look at detecting mental health from language


title: Dual-energy computed tomography (DECT) in emergency radiology: basic principles, techniques, and limitations

abstract: Recent advances in computed tomography (CT) technology allow for acquisition of two CT datasets with different X-ray spectra. There are different dual-energy computed tomography (DECT) technical approaches such as: the dual-source CT, the fast kilovoltage-switching method, and the sandwich detectors technique. There are various postprocessing algorithms that are available to provide clinically relevant spectral information. There are several clinical applications of DECT that are easily accessible in the emergency setting. In this review article, we aim to provide the emergency radiologist with a discussion on how this new technology works and how some of its applications can be useful in the emergency room setting.


title: A Non-invasive Approach to Trace Architecture Design, Requirements Specification and Agile Artifacts

abstract: Agile processes emphasize iterative delivery rather than assuming the definition of all detailed requirements and architecture up front. This "just enough" approach generally considers user stories and acceptance tests as sufficient documentation for successful system development. However, industry practices have shown that this minimalism is appropriate for projects with short duration and small collocated teams. In the development of large systems, the "just enough" documentation goes beyond the traditional set recommended by the Agile evangelists, due to the diversity of elements to be considered, as for instance geographic distribution of the teams, necessity to comply with industry regulations, strict IT governance programs, integration of the system being developed with others, or even the presence of not-so-agile people in the teams. In this context, a more complex set of artifacts is required to ensure the proper development of systems, such as more detailed requirements documents and architectural specification. In this regard, to support the agile development of large systems, we introduce TraceMan - Traceability Manager as a mechanism for ensuring traceability among user stories, traditional requirements documents, test specifications, architecture design, and source code. We also present an experience report on how TraceMan has been used in the daily activities at John Deere Intelligent Solutions Group (ISG) to support traceability among development artifacts.


title: Where the Action Is: The Foundations of Embodied Interaction

abstract: Computer science as an engineering discipline has been spectacularly successful. Yet it is also a philosophical enterprise in the way it represents the world and creates and manipulates models of reality, people, and action. In this book, Paul Dourish addresses the philosophical bases of human-computer interaction. He looks at how what he calls "embodied interaction" -- an approach to interacting with software systems that emphasizes skilled, engaged practice rather than disembodied rationality -- reflects the phenomenological approaches of Martin Heidegger, Ludwig Wittgenstein, and other twentieth-century philosophers. The phenomenological tradition emphasizes the primacy of natural practice over abstract cognition in everyday activity. Dourish shows how this perspective can shed light on the foundational underpinnings of current research on embodied interaction. He looks in particular at how tangible and social approaches to interaction are related, how they can be used to analyze and understand embodied interaction, and how they could affect the design of future interactive systems.


CONTROL FOR ROBOTIC MANIPULATORS WITH PROPORTIONAL INTEGRAL DERIVATIVE & COMPUTED TORQUE CONTROL


title: Humanoid Robots as Agents of Human Consciousness Expansion

abstract: The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with personality content and cognitive, linguistic, perceptual and behavioral content aimed at enabling loving interactions supportive of human self-transcendence. In September 2017 a small pilot study was conducted, involving the Sophia robot leading human subjects through dialogues and exercises focused on meditation, visualization and relaxation. The pilot was an apparent success, qualitatively demonstrating the viability of the approach and the ability of appropriate human-robot interaction to increase human well-being and advance human consciousness.


title: ENCORE: An energy-aware multicell cooperation in heterogeneous networks with content caching

abstract: Energy saving in cellular systems is increasingly important due to ever-deteriorating global warming. Heterogeneous networks (HetNets) composed of various tiers of cells can attain energy savings thanks to the lower operational and transmit power consumptions of small cells. To address the inter-cell interference problem yet achieving network energy conservation, multicell cooperation facilitating cooperative transmission (also known as coordinated multipoint or CoMP) and sleep mode operation paves a way toward future green HetNets. To further alleviate the induced backhaul energy consumption caused by cooperative transmission, content caching which proactively caches popular files at local storages is regarded as a viable solution. In this paper, we investigate how energy-aware multicell cooperation in HetNets with content caching (ENCORE) can be achieved. On proving that the ENCORE problem is decomposable into two sub-problems, we claim that the place-then-transmit strategy is optimal to the ENCORE problem. Then, we design algorithms for the sub-problems and prove that the total energy consumption achieved by the proposed solution is upper-bounded. Our simulation results demonstrate that the proposed solution outperforms various dynamic clustering approaches in terms of energy savings, and show the impacts of content popularity and cache size on the backhaul energy consumption.


title: Marketing and business performance

abstract: Academics and managers have struggled for many years to understand and delineate the role of marketing in explaining business performance differences between firms. Most of the theory base for any such attempts has to be informed by strategic management theory, since the primary question that strategic management seeks to answer is why some firms outperform others over time. This paper synthesizes three major streams of thought in strategic management with the empirical and theoretical literature on strategic marketing to develop an integrative theory-based conceptual framework linking marketing with firms’ business performance.


title: A Multiport Microsecond Optical Circuit Switch for Data Center Networking

abstract: We experimentally evaluate the network-level switching time of a functional 23-host prototype hybrid optical circuit-switched/electrical packet-switched network for datacenters called Mordia (Microsecond Optical Research Datacenter Interconnect Architecture). This hybrid network uses a standard electrical packet switch and an optical circuit-switched architecture based on a wavelength-selective switch that has a measured mean port-to-port network reconfiguration time of 11.5 $\mu{\rm s}$ including the signal acquisition by the network interface card. Using multiple parallel rings, we show that this architecture can scale to support the large bisection bandwidth required for future datacenters.


title: Probabilistic Programming with Gaussian Process Memoization

abstract: Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can be difficult to apply; all but the simplest classification or regression applications require specification and inference over complex covariance functions that do not admit simple analytical posteriors. This paper shows how to embed Gaussian processes in any higher-order probabilistic programming language, using an idiom based on memoization, and demonstrates its utility by implementing and extending classic and state-of-the-art GP applications. The interface to Gaussian processes, called gpmem, takes an arbitrary real-valued computational process as input and returns a statistical emulator that automatically improve as the original process is invoked and its input-output behavior is recorded. The flexibility of gpmem is illustrated via three applications: (i) robust GP regression with hierarchical hyper-parameter learning, (ii) discovering symbolic expressions from time-series data by fully Bayesian structure learning over kernels generated by a stochastic grammar, and (iii) a bandit formulation of Bayesian optimization with automatic inference and action selection. All applications share a single 50-line Python library and require fewer than 20 lines of probabilistic code each.


title: Soft biometric classification using periocular region features

abstract: With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biometrie classification. We extract gender and ethnicity information from the periocular region images using grayscale pixel intensities and periocular texture computed by Local Binary Patterns as our features and a SVM classifier. Results are presented on the visible spectrum periocular images obtained from the FRGC face dataset. For 4232 periocular images of 404 subjects, we obtain a baseline gender and ethnicity classification accuracy of 93% and 91%, respectively, using 5-fold cross validation. Furthermore, we show that fusion of the soft biometrie information obtained from our classification approach with the texture based periocular recognition approach results in an overall performance improvement.


title: AutoModeling: Integrated Approach for Automated Model Generation by Ensemble Selection of Feature Subset and Classifier

abstract: Feature subset selection and identification of appropriate classification method plays an important role to optimize the predictive performance of supervised machine learning system. Current literature makes isolated attempts to optimize the feature selection and classifier identification. However, feature set has an intrinsic relationship with classification technique and together they form a ‘model’ for classification task. In this paper, we propose AutoModeling that finds optimal learning model and jointly optimize the feature and hypothesis space to maximize performance measure objective function. It is an automated framework of selecting the ensemble model {selected feature subset, selected classifier} from a given superset of features and classifiers learned from given training dataset in a computational efficient manner. We introduce novel relax-greedy search with our proposed patience function as a wrapper feature selection that maximizes the predictive performance and eliminates the classical nesting effect. We perform extensive experimentations on different types of publicly available datasets and AutoModeling demonstrates superior performance over relevant state-of-the-art methods, expert-driven manual methods and deep neural networks.


title: Software‐defined networking (SDN): a survey

abstract: With the advent of cloud computing, many new networking concepts have been introduced to simplify network management and bring innovation through network programmability. The emergence of the software-defined networking (SDN) paradigm is one of these adopted concepts in the cloud model so as to eliminate the network infrastructure maintenance processes and guarantee easy management. In this fashion, SDN offers real-time performance and responds to high availability requirements. However, this new emerging paradigm has been facing many technological hurdles; some of them are inherent, while others are inherited from existing adopted technologies. In this paper, our purpose is to shed light on SDN related issues and give insight into the challenges facing the future of this revolutionary network model, from both protocol and architecture perspectives. Additionally, we aim to present different existing solutions and mitigation techniques that address SDN scalability, elasticity, dependability, reliability, high availability, resiliency, security, and performance concerns. Copyright © 2017 John Wiley & Sons, Ltd.


title: Optimal Robust Tuning for 1DoF PI/PID Control Unifying FOPDT/SOPDT Models

abstract: Abstract The aim of the paper is to present tuning equations for one-degree-of-freedom (1DoF) proportional integral (PI) and proportional integral derivative (PID) controllers. These are based on a performance/robustness trade-off analysis with first- and second-order plus deadtime models. On the basis of this analysis a tuning method is developed for 1DoF PI and PID controllers for servo and regulatory control that allows designing closed-loop control systems with a specified M S robustness that at the same time have the best possible IAE performance. The control system robustness is adjusted varying only the controller proportional gain.


title: Efficient Euclidean distance transform algorithm of binary images in arbitrary dimensions

abstract: In this paper, we propose an efficient algorithm, i.e., PBEDT, for short, to compute the exact Euclidean distance transform (EDT) of a binary image in arbitrary dimensions. The PBEDT is based on independent scan and implemented in a recursive way, i.e., the EDT of a d-dimensional image is able to be computed from the EDTs of its (d-1)-dimensional sub-images. In each recursion, all of the rows in the current dimensional direction are processed one by one. The points in the current processing row and their closest feature points in (d-1)-dimensional sub-images can be shown in a Euclidean plane. By using the geometric properties of the perpendicular bisector, the closest feature points of (d-1)-dimensional sub-images are easily verified so as to lead to the EDT of a d-dimensional image after eliminating the invalid points. The time complexity of the PBEDT algorithm is linear in the amount of both image points and dimensions with a small coefficient. Compared with the state-of-the-art EDT algorithms, the PBEDT algorithm is much faster and more stable in most cases.


title: Formalizing information security knowledge

abstract: Unified and formal knowledge models of the information security domain are fundamental requirements for supporting and enhancing existing risk management approaches. This paper describes a security ontology which provides an ontological structure for information security domain knowledge. Besides existing best-practice guidelines such as the German IT Grundschutz Manual also concrete knowledge of the considered organization is incorporated. An evaluation conducted by an information security expert team has shown that this knowledge model can be used to support a broad range of information security risk management approaches.


GermEval-2014: Nested Named Entity Recognition with Neural Networks


title: Epidermal barrier in hereditary ichthyoses, atopic dermatitis, and psoriasis

abstract: Several skin disorders are associated with impaired skin barrier function. Primary dysfunction is caused by monogenic defects in key components of the epidermis (for example ichthyoses). Secondary barrier impairment occurs in inflammatory dermatoses marked by disturbed epidermal homeostasis (eczema, psoriasis, etc.). In these disorders, inflammation impedes the synthesis or maintenance of skin barrier components. Recent evidence suggests a combination of primary and secondary barrier dysfunction in atopic dermatitis and, to a lesser extent, also in psoriasis. In the future, subtypes of atopic dermatitis may likely be defined, in which one or the other is prevalent.


Incorporating Topic Aspects for Online Comment Convincingness Evaluation


Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention


title: Gaining and sustaining competitive advantage with activity based cost management system

abstract: In the era of competitive global environment and technology‐based organizations managers are, more than ever, pressured to find ways to maintain their competitive advantage. In order to achieve and sustain a competitive advantage, managers must examine the internal processes of their companies. The firm produces products or provide services by performing a set of activities that create value. Increasing the value of these activities can increase the competitive advantage of a firm. There are several methods that have been developed for adding value to the core activities in the firm. One method that is gaining popularity among companies is activity‐based cost management (ABCM). ABCM as a system can provide managers with a strategic view of the activities that are essential to the competitive nature of the enterprise.


title: PIM and personality: what do our personal file systems say about us?

abstract: Individual differences are prevalent in personal information management (PIM). There is large variation between individuals in how they structure and retrieve information from personal archives. These differences make it hard to develop general PIM tools. However we know little about the origins of these differences. We present two studies evaluating whether differences arise from personality traits, by exploring whether different personalities structure personal archives differently. The first exploratory study asks participants to identify PIM cues that signal personality traits. While the aim was to identify cues, these cues also proved surprisingly accurate indicators of personality. In a second study, to evaluate these cues, we directly measure relations between structure and traits. We demonstrate that Conscientiousness predicts file organization, particularly PC users' desktops. Neurotic people may also keep more desktop files. One implication is that systems might be customized for different personalities. We also advance personality theory, showing that personal digital artifacts signal personality.


title: Visual Domain Adaptation: A survey of recent advances

abstract: In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model have different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to mitigate this degradation. In this article, we provide a survey of domain adaptation methods for visual recognition. We discuss the merits and drawbacks of existing domain adaptation approaches and identify promising avenues for research in this rapidly evolving field.


title: Do Gravitational Fields Have Mass? Or on the Nature of Dark Matter

abstract: As has been shown before (a brief comment will be given in the text), relativistic mass and relativistic time dilation of moving bodies are equivalent as well as time and mass in the rest frame. This implies that the time dilation due to the gravitational field is combined with inertial and gravitational mass as well and permits the computation of the gravitational action of the vacuum constituting the gravitational field in any distance from the source of the field. Theoretical predictions are compared with experimental results and it is shown that many known astrophysical and gravitational phenomena, especially the so-called dark or missing matter, owe their existence to the gravitational effects of the mass of the field-vacuum.


title: Symbolic and semisymbolic analysis of electronic circuit in maple

abstract: This paper presents a usage of Maple program for electric and electronic circuit analysis. The special package PraCAn has been used for this purpose. This contribution introduces possibilities of the package for circuit linearization and following ac symbolic or semisymbolic analyses. Simplification of the active part models is done by their instance parameters to obtain useful results for dominant circuit behavior. This is demonstrated on elementary examples of electronic circuit analysis. The package was developed at the Department of Circuit Theory, CTU for research and teaching support.


title: Using google distance for query expansion in expert finding

abstract: Expert finding, which identifies people with relevant knowledge or skills, is one of the most important issues under many circumstances. In this paper, we propose a method that utilizes Normalized Google Distance (NGD) with some global factors to enhance the relevance between initial query and extended query, and to improve the accuracy of the search results of the expert finding system. Results of a numerical study show that the NGD-based method has a higher accuracy than the methods proposed in the literature, and that the NGD-based method is more effective as the number of top results, N, increases. Moreover, the precision rate of our NGD-based method is, on average, higher than that of the other methods in the literature by 5%.


title: An Overview of Graph Databases

abstract: Graph databases are used for social networking and website link structure as graphs are used for storing connections among users. For example, social networking sites like Facebook, LinkedIn, and Twitter etc. It has more complicated networks of relationships, so to represent these databases using relational model is not efficient because of large number of table joining. Several well known graph databases are available such as Neo4j, flockdb, allegrograph, sones, trinity, hypergraphdb, infinitegraph, infogrid, orientdb, DEX. In this paper, we have presented comparison of current graph databases and it has been observed that Neo4j is the best in all current current graph databases. General Terms Graph databases.


title: A High-Efficiency Multimode Li–Ion Battery Charger With Variable Current Source and Controlling Previous-Stage Supply Voltage

abstract: A high-efficiency multimode Li-ion battery charger with variable current source and controlling previous-stage supply voltage is presented in this paper. Using variable current source can achieve the goal of constant-current mode to charge the battery and control previous-stage supply voltage which could increase the efficiency of the multimode battery charger. Moreover, the charging mode adopted in this charger is applied by two types of dual-mode strategy decided by the value of the equivalent series resistance of the Li-ion battery. This technique will reduce the damage of Li-ion battery. The Li-ion battery charger is designed with 0.35-mum CMOS double-poly four-metal processes. The experimental results show that the charger works well and the theoretical analysis can be confirmed. The average power efficiency of the multimode Li-Ion battery charger can be up to 91.2% under the average power of 1.24 W, and the accuracy of the adaptive reference voltage is up to 97.3%. The chip area is only 1.32 times 0.95 mm2.


title: Reflexes of grammar in patterns of language change

abstract: When one form replaces another over time in a changing language, the new form does not occur equally often in all linguistic contexts. Linguists have generally assumed that those contexts in which the new form is more common are those in which the form first appears and in which it advances most rapidly. However, evidence from several linguistic changes (most importantly the rise of the periphrastic auxiliary do in late Middle English) shows that the general assumption is false. Instead, at least for syntactic cases, change seems to proceed at the same rate in all contexts. Contexts change together because they are merely surface manifestations of a single underlying change in grammar. Differences in frequency of use of a new form across contexts reflect functional and stylistic factors, which are constant across time and independent of grammar.


Algorithms for Graph Similarity and Subgraph Matching


title: Altruistic scheduling in multi-resource clusters

abstract: Given the well-known tradeoffs between fairness, performance, and efficiency, modern cluster schedulers often prefer instantaneous fairness as their primary objective to ensure performance isolation between users and groups. However, instantaneous, short-term convergence to fairness often does not result in noticeable long-term benefits. Instead, we propose an altruistic, long-term approach, CARBYNE, where jobs yield fractions of their allocated resources without impacting their own completion times. We show that leftover resources collected via altruisms of many jobs can then be rescheduled to further secondary goals such as application-level performance and cluster efficiency without impacting performance isolation. Deployments and large-scale simulations show that CARBYNE closely approximates the state-of-the-art solutions (e.g., DRF [27]) in terms of performance isolation, while providing 1:26× better efficiency and 1:59× lower average job completion time.


title: Integration of World Knowledge for Natural Language Understanding

abstract: Preliminaries.- Natural Language Understanding and World Knowledge.- Sources of World Knowledge.- Reasoning for Natural Language Understanding.- Knowledge Base Construction.- Ensuring Consistency.- Abductive Reasoning with the Integrative Knowledge Base.- Evaluation.- Conclusion.


title: How to get a Singleton in Eiffel?

abstract: Can the Singleton pattern [Gamma95] be turned into a reusable component? To help answer this question, we have reviewed existing implementations and tried to improve them. This article explains the difficulties of having a single-instance class in Eiffel and proposes language extensions, namely once creation procedures, which would be satisfactory in most cases, or frozen classes.


title: Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation

abstract: Natural language generation (NLG) is an important component in spoken dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements. The proposed model can be jointly trained both sentence planning and surface realization to produce natural language utterances. The model was extensively assessed on four different NLG domains, in which the experimental results showed that the proposed generator consistently outperforms the previous methods on all the NLG domains.


title: Fundamental development of a virtual reality simulator for four-arm disaster rescue robot OCTOPUS

abstract: This paper presents a virtual reality (VR) simulator for four-arm disaster response robot OCTOPUS, which has high capable of both mobility and workability. OCTOPUS has 26 degrees of freedom (DOF) and is currently teleoperated by two operators, so it is quite difficult to operate OCTOPUS. Thus, we developed a VR simulator for training operation, developing operator support system and control strategy. Compared with actual robot and environment, VR simulator can reproduce them at low cost and high efficiency. The VR simulator consists of VR environment and human-machine interface such as operation-input and video- and sound-output, based on robot operation system (ROS) and Gazebo. To enhance work performance, we implement indicators and data collection functions. Four tasks such as rough terrain passing, high-step climbing, obstacle stepping over, and object transport were conducted to evaluate OCTOPUS itself and our VR simulator. The results indicate that operators could complete all the tasks but the success rate differed in tasks. Smooth and stable operations increased the work performance, but sudden change and oscillation of operation degraded it. Cooperating multi-joint adequately is quite important to execute task more efficiently.


title: Potential determinants of heavier internet usage

abstract: Despite its uses, the Internet is liable to be abused. “Internet Addiction” is a newly proposed construct, derived form DSM-IV criteria for substance abuse. As a very recent phenomenon, excess internet use probably arises through pre-existing mechanisms. The addictive element may be the search for stimulation through interactive services, or the Internet may serve the purpose of an escape from real-life difficulties. We therefore considered the extent to which sensation seeking or poor self-esteem predicts heavier Internet use. Fifty participants, recruited through the Internet or the Internet Addiction Support Group, completed an Internet Related Problem Scale, the MMPI-2 Addiction Potential Scale, the Coopersmith Self-Esteem Inventory and the Sensation Seeking Scale. The Internet Related Problem Scale showed a moderate level of internal consistency and demonstrated construct validity, predicting hours of Internet use and having a relationship with the Addiction Potential Scale. While poorer self-esteem predicted greater scores on the Internet Related Problem Scale, impulsivity did not. Researchers need to re-assess previous conceptualizations of the typical “computer addict” as a highly educated, male introvert with a constant need for intellectual stimulation (Shotton, 1991).


title: Programmable Synaptic Chip for Electronic Neural Networks

abstract: A binary synaptic matrix chip has been developed for electronic neural networks. The matrix chip contains a programmable 32×32 array of "long channel" NMOSFET binary connection elements implemented in a 3-um bulk CMOS process. Since the neurons are kept off-chip, the synaptic chip serves as a "cascadable" building block for a multi-chip synaptic network as large as 512×512 in size. As an alternative to the programmable NMOSFET (long channel) connection elements, tailored thin film resistors are deposited, in series with FET switches, on some CMOS test chips, to obtain the weak synaptic connections. Although deposition and patterning of the resistors require additional processing steps, they promise substantial savings in silcon area. The performance of a synaptic chip in a 32- neuron breadboard system in an associative memory test application is discussed.


title: LLC converters with automatic resonant frequency tracking based on synchronous rectifier (SR) gate driving signals

abstract: In this paper, a novel switching frequency control scheme to track the resonant frequency point (f 0 ) is proposed. First, the synchronous rectifier (SR) gate driving signals are well tuned with proposed universal adaptive SR driving scheme by eliminating the body diode conduction. Then, the novel pulse width locked loop with digital implementation is presented to minimize the pulse width difference between main switch and well tuned SR gate driving signals, making LLC always operate at f 0 point.


title: Is it Finger or Wrist Dexterity That is Missing in Current Hand Prostheses?

abstract: Building prostheses with dexterous motor function equivalent to that of the human hand is one of the ambitious goals of bioengineers. State of art prostheses lack several degrees of freedom (DoF) and force the individuals to compensate for them by changing the motions of their arms and body. However, such compensatory movements often result in residual limb pain and overuse syndromes. Significant efforts were spent in designing artificial hands with multiple allowed grasps but little work has been done with regards to wrist design, regardless the fact that the wrist contributes significantly to the execution of upper limb motor tasks. We hypothesized that a single DoF hand with wrist flexion/extension allowed function comparable to a highly performant multi DoF hand without wrist flexion/extension. To assess this we compared four emulated architectures of hand-wrist prostheses using the Southampton Hand Assessment Procedure and evaluating the extent of compensatory movements with unimpaired subjects wearing ortheses. Our findings show indeed that shifting the dexterity from the hand to the wrist could preserve the ability of transradial amputees in performing common tasks with limited effect on the compensatory movements. Hence, this study invites rehabilitation engineers to focus on novel artificial wrist architectures.


title: FACE RECOGNITION TECHNIQUES: CLASSIFICATION AND COMPARISONS

abstract: Human brains can remember and recognize a vast array of faces, getting a computer to do the same is difficult but in modern world there would be many uses of such systems. Face reorganization has been a fast growing, challenging and interesting area in real time application. It can be widely use for image and film processing; this requires computational models for the identification of the face. This model should be easy and simple when implemented. In this paper we will review the different methods for face recognition, their advantages and disadvantages.


title: Helicopter noise experiments in an urban environment

abstract: In two series of helicopter noise experiments, sound‐pressure‐level recordings were made on the ground while a helicopter flew over (i) an array of microphones placed in an open field, and (ii) a similar array placed in the center of a city street surrounded by tall buildings. For given helicopter altitude and airspeed, it was found that the flyover noise recorded in the street, although initially lower, built up rapidly as the aircraft approached such that the peak recorded noise was actually more intense than that recorded in the open field. This result is in qualitative accord with the results of previous laboratory scale‐model experiments performed by Lyon and Pande. The differences between the two sets of field data are attributed in major part to the fact that a reverberant sound field builds up in the street during flyover. This enhancement is less pronounced for higher flight altitudes. A simple theory based on geometrical acoustics and statistical concepts is described that quantitatively explain...


title: TUIMOD — MODULAR OBJECTS FOR TANGIBLE USER INTERFACES

abstract: This paper describes the design and construction of TUImod, a modular system of basic elements generated by rapid-prototyping techniques that can be combined in various ways into human distinguishable and computer trackable physical objects with specific physical properties. The described system is used in our tangible desk environment for data exploration applications.


title: Criminal network analysis and visualization

abstract: A new generation of data mining tools and applications work to unearth hidden patterns in large volumes of crime data.


title: Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

abstract: Justification ::: Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004–2009), which builds upon previous expertise from the INTERPRET project (2000–2002) has allowed such an evaluation to take place.


title: SimRank: a measure of structural-context similarity

abstract: The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.


title: Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks

abstract: This article examines whether firm-level idiosyncratic shocks propagate in production networks. We identify idiosyncratic shocks with the occurrence of natural disasters. We find that affected suppliers impose substantial output losses on their customers, especially when they produce specific inputs. These output losses translate into significant market value losses, and they spill over to other suppliers. Our point estimates are economically large, suggesting that input specificity is an important determinant of the propagation of idiosyncratic shocks in the economy. JEL Codes: L14, E23, E32.


An overview of current ontology meta-matching solutions


title: IoT based energy and gas economic home automation system using Raspberry Pi 3

abstract: Energy is the most extensive need in today's world where we are wasting our energy deliberately or abstractedly. But we have to stop maltreatment to our energy. In this prospective system, a process is developed through which power will be supplied after ensuring the existence of human and gas leakage will be apprised immediately. Here, Raspberry Pi 3 and the WeMOS D1 mini is the master apparatus of this system supported by many other microcontrollers like PIR sensor, gas sensor etc. when human is not encountered by the PIR sensor for a fixed time, controlling the relay module through connection Raspberry Pi will ensure power disconnection (like: light and fans will go turn off which left turned on by the user abstractly). On the other hand, while the PIR sensor is detecting human/animal presence in a room, power(like: light, fan etc.) will be turned on through the relay module and also apprise the user about the human existence. Besides, when gas leakage is detected by Gas sensor it makes the WeMOS known about this, which transfers signal to raspberry Pi and it apprises the user immediately through GMS module. Thus, the user can restrain the unauthorized access to his home and also can rescue his home from unwanted accidents.


title: Antenna diversity in mobile communications

abstract: The conditions for antenna diversity action are investigated. In terms of the fields, a condition is shown to be that the incident field and the far field of the diversity antenna should obey (or nearly obey) an orthogonality relationship. The role of mutual coupling is central, and it is different from that in a conventional array antenna. In terms of antenna parameters, a sufficient condition for diversity action for a certain class of high gain antennas at the mobile, which approximates most practical mobile antennas, is shown to be zero (or low) mutual resistance between elements. This is not the case at the base station, where the condition is necessary only. The mutual resistance condition offers a powerful design tool, and examples of new mobile diversity antennas are discussed along with some existing designs.


title: An Improved Automatic Algorithm for Global Eddy Tracking Using Satellite Altimeter Data

abstract: In this paper, we propose a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data. This method integrates both physical and geometric eddy properties (including the distance between eddies, the area and amplitude of eddy, and the shape of the eddy edge) via the output of detection and the calculation of Hausdorff distance, which could describe the similarity between eddy boundaries. We applied the proposed hybrid method to several previously reported eddies and compared the results with those from two traditional tracking methods. A quantitative comparison indicates that the hybrid algorithm can better reveal eddy signals in terms of their spatial scale, amplitude, lifespan, and splitting. The hybrid method was used for global mesoscale eddies tracking from 1993 to 2015. Global distributions of net eddy numbers revealed that the sources of eddies are located along the eastern boundaries of the world oceans, while the sinks of eddies are located along the western boundaries. The lifespan distribution of eddies exhibited steep growth from high and low latitudes to middle latitudes. A clear divergent pathway demonstrates that cyclonic/anticyclonic eddies tend to travel poleward/equatorward in the world oceans.


title: Probiotic bacteria: safety, functional and technological properties.

abstract: During the past two decades probiotic (health promoting) micro-organisms have been increasingly included in various types of food products, especially in fermented milks. Several aspects, including safety, functional and technological characteristics, have to be taken into consideration in the selection process of probiotic micro-organisms. Safety aspects include specifications such as origin (healthy human GI-tract), non-pathogenicity and antibiotic resistance characteristics. Functional aspects include viability and persistence in the GI-tract, immunomodulation, antagonistic and antimutagenic properties. Before probiotic strains, chosen on the basis of their good safety and functional characteristics, can benefit the consumer, they must first be able to be manufactured under industrial conditions. Furthermore, they have to survive and retain their functionality during storage, and also in the foods into which they are incorporated without producing off-flavours. Factors related to the technological and sensory aspects of probiotic food production are of utmost importance since only by satisfying the demands of the consumer can the food industry succeed in promoting the consumption of functional probiotic products in the future.


title: Postpartum Depression: What We Know

abstract: Postpartum depression (PPD) is a serious mental health problem. It is prevalent, and offspring are at risk for disturbances in development. Major risk factors include past depression, stressful life events, poor marital relationship, and social support. Public health efforts to detect PPD have been increasing. Standard treatments (e.g., Interpersonal Psychotherapy) and more tailored treatments have been found effective for PPD. Prevention efforts have been less consistently successful. Future research should include studies of epidemiological risk factors and prevalence, interventions aimed at the parenting of PPD mothers, specific diathesis for a subset of PPD, effectiveness trials of psychological interventions, and prevention interventions aimed at addressing mental health issues in pregnant women. © 2009 Wiley Periodicals, Inc. J Clin Psychol 65:1–12, 2009.


title: Development of Embedded Control System of Micro-Flow Variable Plunger Pump Based on Frequency Control

abstract: In order to break through the traditional flow change way of the variable plunger pump, a kind of micro-flow variable plunger pump with stepper motor is designed; and on this basis, the main control electric circuit of the pump is developed by taking AT89C52 single-chip microprocessor as MCU and making use of the keyboard and display circuit and three internal timers of AT89C52; the driving circuit is developed by using chapped wave driving model implemented by the chips of L297 and L298N; the software of the system is also developed. The joint debugging results of hardware and software indicate that the developed embedded control system not only implements the numerical driving of the pump, but also carries out the frequency control of the pump by changing the input frequency and duty ratio, additionally, the control of the whole system is flexible and the operation is simple.


title: Analysis of attacks against the security of keyless-entry systems for vehicles and suggestions for improved designs

abstract: Remote control of vehicle functions using a handheld electronic device became a popular feature for vehicles. Such functions include, but are not limited to, locking, unlocking, remote start, window closures, and activation of an alarm. As consumers enjoy the remote access and become more comfortable with the remote functions, original equipment manufacturers (OEMs) have started looking for new features to simplify and reduce the user interface for vehicle access. These new features will provide users with an additional level of comfort without requiring them to touch or press any button on any remote devices to gain access to the vehicle. While this extra level of comfort is a desirable feature, it introduces several security threats against the vehicle's keyless-entry system. This paper describes a number of attacks against the security of keyless-entry systems of vehicles and also presents analyzes of several attacks and compares the vulnerability of the system under different attacks. At the end, some suggestions for improved design are proposed.


title: Low-cost Autonomous Navigation System Based on Optical Flow Classification

abstract: This work presents a low-cost robot, controlled by a Raspberry Pi, whose navigation system is based on vision. The strategy used consisted of identifying obstacles via optical flow pattern recognition. Its estimation was done using the Lucas-Kanade algorithm, which can be executed by the Raspberry Pi without harming its performance. Finally, an SVM-based classifier was used to identify patterns of this signal associated with obstacles movement. The developed system was evaluated considering its execution over an optical flow pattern dataset extracted from a real navigation environment. In the end, it was verified that the acquisition cost of the system was inferior to that presented by most of the cited works, while its performance was similar to theirs.


title: A 28GHz Bulk-CMOS dual-polarization phased-array transceiver with 24 channels for 5G user and basestation equipment

abstract: Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.


title: Lock-Free Cuckoo Hashing

abstract: This paper presents a lock-free cuckoo hashing algorithm, to the best of our knowledge this is the first lock-free cuckoo hashing in the literature. The algorithm allows mutating operations to operate concurrently with query ones and requires only single word compare-and-swap primitives. Query of items can operate concurrently with others mutating operations, thanks to the two-round query protocol enhanced with a logical clock technique. When an insertion triggers a sequence of key displacements, instead of locking the whole cuckoo path, our algorithm breaks down the chain of relocations into several single relocations which can be executed independently and concurrently with other operations. A fine tuned synchronization and a helping mechanism for relocation are designed. The mechanisms allow high concurrency and provide progress guarantees for the data structure's operations. Our experimental results show that our lock-free cuckoo hashing performs consistently better than two efficient lock-based hashing algorithms, the chained and the hopscotch hash-map, in different access pattern scenarios.


title: TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation

abstract: Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing the log-likelihood of the tag predictions in the training set. In this manner, we can optimally combine a collection of image similarity metrics that cover different aspects of image content, such as local shape descriptors, or global color histograms. We also introduce a word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall of rare words. We investigate the performance of different variants of our model and compare to existing work. We present experimental results for three challenging data sets. On all three, TagProp makes a marked improvement as compared to the current state-of-the-art.


title: Antibacterial properties of nanoparticles

abstract: Antibacterial agents are very important in the textile industry, water disinfection, medicine, and food packaging. Organic compounds used for disinfection have some disadvantages, including toxicity to the human body, therefore, the interest in inorganic disinfectants such as metal oxide nanoparticles (NPs) is increasing. This review focuses on the properties and applications of inorganic nanostructured materials and their surface modifications, with good antimicrobial activity. Such improved antibacterial agents locally destroy bacteria, without being toxic to the surrounding tissue. We also provide an overview of opportunities and risks of using NPs as antibacterial agents. In particular, we discuss the role of different NP materials.


title: Next generation 650V CSTBTTM with improved SOA fabricated by an advanced thin wafer technology

abstract: Using an advanced thin wafer technology, we have successfully fabricated the next generation 650V class IGBT with an improved SOA and maintaining the narrow distribution of the electrical characteristics for industrial applications. The applied techniques were the finer pattern transistor cell, the thin wafer process and the optimized back side doping concentration profiles. With the well organized back-side wafer process, the practically large chip has achieved without any sacrifice of the production yield. As a results, V CEsat -E off trade-off relationship and an Energy of Short Circuit by active Area (E SC /A) are improved in comparison with the conventional Punch Through (PT) structure.


title: A Parallel Approach to Compression and Decompression of Triangle Meshes using the GPU

abstract: Most state-of-the-art compression algorithms use complex connectivity traversal and prediction schemes, which are not efficient enough for online compression of large meshes. In this paper we propose a scalable massively parallel approach for compression and decompression of large triangle meshes using the GPU. Our method traverses the input mesh in a parallel breadth-first manner and encodes the connectivity data similarly to the well known cut-border machine. Geometry data is compressed using a local prediction strategy. In contrast to the original cut-border machine, we can additionally handle triangle meshes with inconsistently oriented faces. Our approach is more than one order of magnitude faster than currently used methods and achieves competitive compression rates.


title: DC-DC converter based on the switched-coupled-inductor quadratic boost converter and diode-capacitor Dickson multiplier

abstract: The very high voltage ratio has been achieved by introducing the switched-coupled-inductor to the quadratic boost converter and combining it with the diode-capacitor Dickson multiplier. The operating principles and steady-state analyses of continuous conduction mode are performed. The formulas to calculate the maximal voltage stresses on the transistor and on the diodes are provided. Simulation and experimental results of the 50W prototype confirm the theoretical analysis and the advantages of the proposed converter.


title: Deep End2End Voxel2Voxel Prediction

abstract: Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been in the area of video classification and detection, i.e., problems involving the prediction of a single class label or a handful of output variables per video. Furthermore, while deep networks are commonly recognized as the best models to use in these domains, there is a widespread perception that in order to yield successful results they often require time-consuming architecture search, manual tweaking of parameters and computationally intensive pre-processing or post-processing methods. ::: In this paper we challenge these views by presenting a deep 3D convolutional architecture trained end to end to perform voxel-level prediction, i.e., to output a variable at every voxel of the video. Most importantly, we show that the same exact architecture can be used to achieve competitive results on three widely different voxel-prediction tasks: video semantic segmentation, optical flow estimation, and video coloring. The three networks learned on these problems are trained from raw video without any form of preprocessing and their outputs do not require post-processing to achieve outstanding performance. Thus, they offer an efficient alternative to traditional and much more computationally expensive methods in these video domains.


title: Scaling effect of spin-torque nano-oscillators

abstract: We have investigated the impact of device size on the characteristics of spin-torque nano-oscillators by studying the frequency and linewidth of the oscillator as a function of current density utilizing micromagnetic simulations at room temperature. Upon reduction of the device size from 40 down to 10 nm, thermal noise flattens the frequency and linewidth trends with current density and lowers the threshold current density of the oscillation. The magnetization vector trajectories reveal that irregular oscillations exist in smaller devices. Our findings suggest that a 20 × 20 nm2 spin-torque oscillator could be a viable candidate for a magnetic read sensor.We have investigated the impact of device size on the characteristics of spin-torque nano-oscillators by studying the frequency and linewidth of the oscillator as a function of current density utilizing micromagnetic simulations at room temperature. Upon reduction of the device size from 40 down to 10 nm, thermal noise flattens the frequency and linewidth trends with current density and lowers the threshold current density of the oscillation. The magnetization vector trajectories reveal that irregular oscillations exist in smaller devices. Our findings suggest that a 20 × 20 nm2 spin-torque oscillator could be a viable candidate for a magnetic read sensor.


title: JPEG image classification in digital forensic via DCT coefficient analysis

abstract: From the digital forensics point of view, image forgery is considered as evidence that could provide a major breakthrough in the investigation process. Additionally, the development of storage device technologies has increased storage space significantly. Thus a digital investigator can be overwhelmed by the amount of data on storage devices that needs to be analysed. In this paper, we propose a model for classifying bulk JPEG images produced by the data carving process or other means into three different classes to solve the problem of identifying forgery quickly and effectively. The first class is JPEG images that contain errors or corrupted data, the second class is JPEG images that contain forged regions, and the third is JPEG images that have no signs of corruption or forgery. To test the proposed model, some experiments were conducted on our own dataset in addition to CASIA V2 image forgery dataset. The experiments covered different types of forgery technique. The results yielded around 88% accuracy rate in the classification process using five different machine learning methods on CASIA V2 dataset. It can be concluded that the proposed model can help investigators to automatically classify JPEG images, which reduce the time needed in the overall digital investigation process.


title: Combining Satellite Imagery and GPS Data for Road Extraction

abstract: Road extraction is a fundamental problem in remote sensing and mapping. Recent advances in Convolution Neural Network (CNN) have contributed significant improvements in automatic road extraction from satellite imagery, albeit prediction gaps challenge post-processing. Some of the gaps are hard to bridge by satellite imagery alone due to dense vegetation, road construction, and building shadows. In this paper, we combine satellite imagery with GPS data to improve road extraction quality. Our dataset includes 100cm pixel resolution satellite imagery and 192-hour taxi GPS traces from the urban area of Beijing. Experimenting with various layers to combine GPS data, our CNN model outperforms the RGB-only model by nearly 13% on mean IoU.


title: Design and optimization of high frequency (250 kHz) planar transformer for micro solar converter application

abstract: With increasing demand of renewable energy, it is need of the hour to get highly efficient, low profile, light weight and high power density converters. High switching frequency in power converters results in high power density and reduction in magnetic components size. These benefits incorporate with additional losses and parasitics. Hence, conventional technology and magnetic components have been failed to meet the requirements of the future converters. Therefore, a technology is needed to provide a compact design solution with minimum losses. Recently planar technology emerged as a solution of this problem. In this paper an optimized design and modelling of a high frequency (kHz) planar power transformer for micro solar converters (200W) application has been presented. Power losses calculation, design optimization and modelling have been carried out through ANSYS 2D Finite Element Analysis (FEA) simulation. Finite element analysis shows that energy efficiency of designed transformer is 97% for a power level of 200W at 250 kHz frequency.


title: Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction

abstract: Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on relation extraction are mostly supervised. The clear drawback of supervised methods is the need of training data: labeled data is expensive to obtain, and there is often a mismatch between the training data and the data the system will be applied to. This is the problem of domain adaptation. In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic analysis (LSA) and (ii) structured kernels to improve the adaptability of relation extractors to new text genres/domains. The empirical evaluation on ACE 2005 domains shows that a suitable combination of syntax and lexical generalization is very promising for domain adaptation.