title: Vibration energy harvesting by magnetostrictive material

abstract: A new class of vibration energy harvester based on magnetostrictive material (MsM), Metglas 2605SC, is designed, developed and tested. It contains two submodules: an MsM harvesting device and an energy harvesting circuit. Compared to piezoelectric materials, the Metglas 2605SC offers advantages including higher energy conversion efficiency, longer life cycles, lack of depolarization and higher flexibility to survive in strong ambient vibrations. To enhance the energy conversion efficiency and alleviate the need of a bias magnetic field, Metglas ribbons are transversely annealed by a strong magnetic field along their width direction. To analyze the MsM harvesting device a generalized electromechanical circuit model is derived from Hamilton’s principle in conjunction with the normal mode superposition method based on Euler‐Bernoulli beam theory. The MsM harvesting device is equivalent to an electromechanical gyrator in series with an inductor. In addition, the proposed model can be readily extended to a more practical case of a cantilever beam element with a tip mass. The energy harvesting circuit, which interfaces with a wireless sensor and accumulates the harvested energy into an ultracapacitor, is designed on a printed circuit board (PCB) with plane dimension 25 mm × 35 mm. It mainly consists of a voltage quadrupler, a 3 F ultracapacitor and a smart regulator. The output DC voltage from the PCB can be adjusted within 2.0‐5.5 V. In experiments, the maximum output power and power density on the resistor can reach 200 μW and 900 μ Wc m −3 , respectively, at a low frequency of 58 Hz. For a working prototype under a vibration with resonance frequency of 1.1 kHz and peak acceleration of 8.06 m s −2 (0.82 g), the average power and power density during charging the ultracapacitor can achieve 576 μ Wa nd 606 μ Wc m −3 , respectively, which compete favorably with piezoelectric vibration energy harvesters. (Some figures in this article are in colour only in the electronic version)


title: A Conditional Multiscale Locally Gaussian Texture Synthesis Algorithm

abstract: Exemplar-based texture synthesis is defined as the process of generating, from an input texture sample, new texture images that are perceptually equivalent to the input. In the present work, we model texture self-similarity with conditional Gaussian distributions in the patch space in order to extend the use of stitching techniques. Then, a multiscale texture synthesis algorithm is introduced, where texture patches are modeled at each scale as spatially variable Gaussian vectors in the patch space. The Gaussian distribution for each patch is inferred from the set of its nearest neighbors in the patch space obtained from the input sample. This approach is tested over several real and synthetic texture images, and its results show the effectiveness of the proposed technique for a wide range of textures.


Presenting information on a computer screen: a decision process for the information planner


A Parallel Quasi-Monte Carlo Approach to Pricing American Options


title: Image composition with color harmonization

abstract: Image composition is a very important technique in computer generated imagery (CGI). Besides some factors such as contrast, texture, noise that affect the quality of the composition, color harmony between fore- and background is also an important issue that should be carefully dealt with. However, in the previous image composition techniques, the color harmony between fore- and background is seldom considered. In this paper, an optimization method is presented to deal with the color harmonization problem that used in image composition. A cost function is derived from the observation of the local smoothness of the hue values. And the image is harmonized by minimizing the cost function. A new matching function is presented to select the best matching harmonic schemes, and a new component based pre-harmonization strategy is also presented to preserve the hue distribution of the harmonized images. Our approach overcomes several shortcomings of the existed color harmonization methods. We validate the performance of the method and demonstrate its effectiveness in a variety of experiments.


title: Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

abstract: In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.


title: The system verification methodology for advanced TLM verification

abstract: The IEEE-1800 SystemVerilog [20] system description and verification language integrates dedicated verification features, like constraint random stimulus generation and functional coverage, which are the building blocks of the Universal Verification Methodology (UVM)[3], the emerging standard for electronic systems verification. In this article, we introduce our System Verification Methodology (SVM) as a SystemC library for advanced Transaction Level Modeling (TLM) testbench implementation. As such, we first present SystemC libraries for the support of verification features like functional coverage and constrained random stimulus generation. Thereafter, we introduce the SVM with advanced TLM support based on SystemC and compare it to UVM and related approaches. Finally, we demonstrate the application of our SVM by means of a testbench for a two wheel self-balancing electric vehicle.


title: A CAPTCHA with Tips Related to Alphabets Upper or Lower Case

abstract: Due to the ubiquity of web browsers and the dramatic development of dynamic web pages, web applications become the most popular computing model for providing services over the Internet. Unfortunately, this type of systems is vulnerable to the attacks issued by automated programs. CAPTCHA, a challenge-response test, is the most widely used mechanism to protect web application systems from attacks issued by computer programs. By considering the maintenance cost, most of the CAPTCHA systems employ the text-based challenge in which a string is represented as an image. A user is classified as a human by a text-based CAPTCHA if the user can respond a challenge with the exact string shown in the image. One drawback of a text-based CAPTCHA system is that the string is possibly recognized by a computer program equipped with OCR function. Obfuscating text images can strength the robustness of CAPTCHAs. But, it also degrades the usability of the systems. In this paper, we propose a new text-based CAPTCHA mechanism in which each challenge is associated with a tip providing humans enough information to recognize the alphabets in a highly distorted text image. Therefore, our system provides a balance between the effectiveness and human success rates of text-based CAPTCHAs.


title: FM radio based bistatic radar

abstract: An experimental bistatic radar system is described that detects and tracks targets to ranges in excess of 150 km from the receiver, using echoes from a non-cooperative FM radio transmitter. The system concept and limitations on performance are described, followed by details of the processing used to implement the system. An adaptive filter algorithm is described that is used to efficiently remove interference and strong clutter signals from the receiver channels. A computationally efficient algorithm for target detection using Doppler-sensitive cross-correlation techniques is described. A simple constant false alarm rate algorithm for target detection is described, together with a description of a Kalman filter based target association algorithm. Representative results from the system are provided and compared to truth data derived from air traffic control data.


title: Genetic Risk, Number of Previous Depressive Episodes, and Stressful Life Events in Predicting Onset of Major Depression

abstract: OBJECTIVE: The association between stressful life events and the onset of major depression decreases as the number of previous depressive episodes increases. How do genetic risk factors for major depression impact on this “kindling” phenomenon? In particular, do those at high genetic risk exhibit an increase in the speed of kindling, or are they “prekindled”? METHOD: Using discrete-time survival analysis, the authors examined the interaction between genetic risk, number of previous depressive episodes, and life event exposure in the prediction of episodes of major depression in female-female twin pairs from a population-based registry. The twins were interviewed four times over a 9-year period, producing 92,521 person-months of exposure. RESULTS: The decline in the association between stressful life events and risk for major depression as the number of previous depressive episodes increased was strongest in those at low genetic risk and was weak to absent in those at high genetic risk. In the absence of p...


title: Architecting for DevOps and Continuous Deployment

abstract: Development and Operations (DevOps) in the context of Continuous Deployment (CD) have emerged as an attractive software development movement, which tries to establish a strong connection between development and operations teams. CD is defined as the ability to quickly put new releases into production. We believe that DevOps/CD brings new challenges for architects, which considerably impacts both on their (architectural) design decisions and their organizational responsibilities. We assert that there is an important and urgent need of sufficient research work to gain a deep understanding of how DevOps/CD adoption can influence architecting, architectural decision-making processes and their outcomes in an organization. This PhD research is aimed at understanding and addressing new challenges for designing architectures for supporting DevOps in the context of CD.


title: Web application attack detection and forensics: A survey

abstract: Web application attacks are an increasingly important area in information security and digital forensics. It has been observed that attackers are developing the capability to bypass security controls and launch a large number of sophisticated attacks. Several attempts have been made to address these attacks using a wide range of technology and one of the greatest challenges is responding to new and unknown attacks in an effective way. This study aims to investigate the techniques and solutions used to detect attacks, such as firewalls, intrusion detection systems, honeypots and forensic techniques. Data mining and machine learning techniques, which attempt to address traditional technology shortcomings and produce more effective solutions, are also investigated. It was aimed to contribute to this growing area of research by exploring more intelligent and convenient techniques for web application attack detection by focusing on the data mining techniques in forensics.


title: Closed Loop Deep Brain Stimulation for PTSD, Addiction, and Disorders of Affective Facial Interpretation: Review and Discussion of Potential Biomarkers and Stimulation Paradigms

abstract: The treatment of psychiatric diseases with Deep Brain Stimulation (DBS) is becoming more of a reality as studies proliferate the indications and targets for therapies. Opinions on the initial failures of DBS trials for some psychiatric diseases point to a certain lack of finesse in using an Open Loop DBS (OLDBS) system in these dynamic, cyclical pathologies. OLDBS delivers monomorphic input into dysfunctional brain circuits with modulation of that input via human interface at discrete time points with no interim modulation or adaptation to the changing circuit dynamics. Closed Loop DBS (CLDBS) promises dynamic, intrinsic circuit modulation based on individual physiologic biomarkers of dysfunction. Discussed here are several psychiatric diseases which may be amenable to CLDBS paradigms as the neurophysiologic dysfunction is stochastic and not static. Post-Traumatic Stress Disorder (PTSD) has several peripheral and central physiologic and neurologic changes preceding stereotyped hyper-activation behavioral responses. Biomarkers for CLDBS potentially include skin conductance changes indicating changes in the sympathetic nervous system, changes in serum and central neurotransmitter concentrations, and limbic circuit activation. Chemical dependency and addiction have been demonstrated to be improved with both ablation and DBS of the Nucleus Accumbens and as a serendipitous side effect of movement disorder treatment. Potential peripheral biomarkers are similar to those proposed for PTSD with possible use of environmental and geolocation based cues, peripheral signs of physiologic arousal, and individual changes in central circuit patterns. Non-substance addiction disorders have also been serendipitously treated in patients with OLDBS for movement disorders. As more is learned about these behavioral addictions, DBS targets and effectors will be identified. Finally, discussed is the use of facial recognition software to modulate activation of inappropriate responses for psychiatric diseases in which misinterpretation of social cues feature prominently. These include Autism Spectrum Disorder, PTSD, and Schizophrenia – all of which have a common feature of dysfunctional interpretation of facial affective clues. Technological advances and improvements in circuit-based, individual-specific, real-time adaptable modulation, forecast functional neurosurgery treatments for heretofore treatment-resistant behavioral diseases.


title: Analyzing LoRa: A use case perspective

abstract: Low-power wide-area networking (LPWAN) technologies are capable of supporting a large number of Internet of Things (IoT) use cases. While several LPWAN technologies exist, Long Range (LoRa) and its network architecture LoRaWAN, is currently the most adopted technology. LoRa provides a range of physical layer communication settings, such as bandwidth, spreading factor, coding rate, and transmission frequency. These settings impact throughput, reliability, and communication range. As IoT use cases result in varying communication patterns, it is essential to analyze how LoRa's different communication settings impact on real IoT use cases. In this paper, we analyze the impact of LoRa's communication settings on four IoT use cases, e.g. smart metering, smart parking, smart street lighting, and vehicle fleet tracking. Our results demonstrate that the setting corresponding to the fastest data rate achieves up to 380% higher packet delivery ratio and uses 0.004 times the energy compared to other evaluated settings, while being suitable to support the IoT use cases presented here. However, the setting covers a smaller communication area compared to the slow data rate settings. Moreover, we modified the Aloha-based channel access mechanism used by LoRaWAN and our results demonstrate that the modified channel access positively impacts the performance of the different communication settings.


title: The C-terminal domain of the epsilon subunit of the chloroplast ATP synthase is not required for ATP synthesis.

abstract: The epsilon subunit of the ATP synthases from chloroplasts and Escherichia coli regulates the activity of the enzyme and is required for ATP synthesis. The epsilon subunit is not required for the binding of the catalytic portion of the chloroplast ATP synthase (CF1) to the membrane-embedded part (CFo). Thylakoid membranes reconstituted with CF1 lacking its epsilon subunit (CF1-epsilon) have high ATPase activity and no ATP synthesis activity, at least in part because the membranes are very leaky to protons. Either native or recombinant epsilon subunit inhibits ATPase activity and restores low proton permeability and ATP synthesis. In this paper we show that recombinant epsilon subunit from which 45 amino acids were deleted from the C-terminus is as active as full-length epsilon subunit in restoring ATP synthesis to membranes containing CF1-epsilon. However, the truncated form of the epsilon subunit was significantly less effective as an inhibitor of the ATPase activity of CF1-epsilon, both in solution and bound to thylakoid membranes. Thus, the C-terminus of the epsilon subunit is more involved in regulation of activity, by inhibiting ATP hydrolysis, than in ATP synthesis.


title: Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation

abstract: Eight sites participated in the second DARPA off-line intrusion detection evaluation in 1999. Three weeks of training and two weeks of test data were generated on a test bed that emulates a small government site. More than 200 instances of 58 attack types were launched against victim UNIX and Windows NT hosts. False alarm rates were low (less than 10 per day). Best detection was provided by network-based systems for old probe and old denial-of-service (DoS) attacks and by host-based systems for Solaris user-to-root (U2R) attacks. Best overall performance would have been provided by a combined system that used both host- and network-based intrusion detection. Detection accuracy was poor for previously unseen new, stealthy, and Windows NT attacks. Ten of the 58 attack types were completely missed by all systems. Systems missed attacks because protocols and TCP services were not analyzed at all or to the depth required, because signatures for old attacks did not generalize to new attacks, and because auditing was not available on all hosts.


title: The Benefit of Pets and Animal-Assisted Therapy to the Health of Older Individuals

abstract: Many studies utilizing dogs, cats, birds, fish, and robotic simulations of animals have tried to ascertain the health benefits of pet ownership or animal-assisted therapy in the elderly. Several small unblinded investigations outlined improvements in behavior in demented persons given treatment in the presence of animals. Studies piloting the use of animals in the treatment of depression and schizophrenia have yielded mixed results. Animals may provide intangible benefits to the mental health of older persons, such as relief social isolation and boredom, but these have not been formally studied. Several investigations of the effect of pets on physical health suggest animals can lower blood pressure, and dog walkers partake in more physical activity. Dog walking, in epidemiological studies and few preliminary trials, is associated with lower complication risk among patients with cardiovascular disease. Pets may also have harms: they may be expensive to care for, and their owners are more likely to fall. Theoretically, zoonotic infections and bites can occur, but how often this occurs in the context of pet ownership or animal-assisted therapy is unknown. Despite the poor methodological quality of pet research after decades of study, pet ownership and animal-assisted therapy are likely to continue due to positive subjective feelings many people have toward animals.


title: Tailored lighting intervention improves measures of sleep, depression, and agitation in persons with Alzheimer’s disease and related dementia living in long-term care facilities

abstract: Light therapy has shown great promise as a nonpharmacological method to improve symptoms associated with Alzheimer's disease and related dementias (ADRD), with preliminary studies demonstrating that appropriately timed light exposure can improve nighttime sleep efficiency, reduce nocturnal wandering, and alleviate evening agitation. Since the human circadian system is maximally sensitive to short-wavelength (blue) light, lower, more targeted lighting interventions for therapeutic purposes, can be used.The present study investigated the effectiveness of a tailored lighting intervention for individuals with ADRD living in nursing homes. Low-level "bluish-white" lighting designed to deliver high circadian stimulation during the daytime was installed in 14 nursing home resident rooms for a period of 4 weeks. Light-dark and rest-activity patterns were collected using a Daysimeter. Sleep time and sleep efficiency measures were obtained using the rest-activity data. Measures of sleep quality, depression, and agitation were collected using standardized questionnaires, at baseline, at the end of the 4-week lighting intervention, and 4 weeks after the lighting intervention was removed.The lighting intervention significantly (P<0.05) decreased global sleep scores from the Pittsburgh Sleep Quality Index, and increased total sleep time and sleep efficiency. The lighting intervention also increased phasor magnitude, a measure of the 24-hour resonance between light-dark and rest-activity patterns, suggesting an increase in circadian entrainment. The lighting intervention significantly (P<0.05) reduced depression scores from the Cornell Scale for Depression in Dementia and agitation scores from the Cohen-Mansfield Agitation Inventory.A lighting intervention, tailored to increase daytime circadian stimulation, can be used to increase sleep quality and improve behavior in patients with ADRD. The present field study, while promising for application, should be replicated using a larger sample size and perhaps using longer treatment duration.


title: Isotropic Radiation From a Compact Planar Antenna Using Two Crossed Dipoles

abstract: This letter presents a compact printed isotropic-radiated planar antenna for 2.4-GHz wireless local area network (WLAN) applications. The antenna consists of two crossed curved dipoles printed on one side of a substrate. By adjusting the lengths of the two crossed curved dipoles, an isotropic radiation pattern is achieved in the whole band of 2.4-2.48 GHz. The proposed antenna is simulated, fabricated, and measured. The simulation results show that the difference between the maximum and the minimum of gains is smaller than 5 dB in the full space. In the measurement, the difference between the maximum and the minimum of gains is smaller than 6 dB over 97% of the full space. The reflection coefficient and gains are also tested and compared to the simulation results.


title: PARES: Packet Rewriting on SDN-Enabled Edge Switches for Network Virtualization in Multi-Tenant Cloud Data Centers

abstract: Multi-tenant data centers for cloud computing require the deployment of virtual private networks for tenants in an on-demand manner, providing isolation and security between tenants. To address these requirements, network virtualization techniques such as encapsulation and tunneling have been widely used. However, these approaches inherently incur processing overhead on end-points (such as the host hypervisor), reducing the effective throughput for the tenant virtual network compared to the native network. This problem is exacerbated with increases in line rates, now exceeding 10Gbps. In this paper, we introduce PARES (PAcket REwriting on SDN), a novel technique which uses the packet rewriting feature of SDN switches to provide multi-tenancy in data center networks at edge switches, thereby reducing the load on end-point hypervisors and improving the throughput, compared to tunneling. Experiments in an SDN testbed show that our proposed data center arhictecture with PARES achieves near line-rate multi-tenancy virtualization with 10Gbps links (compared to 20% of line-rate for VXLAN tunneling), without incurring processing overhead at end-point hypervisors or guest servers. Additionally, the paper evaluates the scalability of PARES for ARP protocol handling and with respect to number of SDN flow entries.


title: Context is Everything: Finding Meaning Statistically in Semantic Spaces

abstract: This paper introduces Contextual Salience (CoSal), a simple and explicit measure of a word's importance in context which is a more theoretically natural, practically simpler, and more accurate replacement to tf-idf. CoSal supports very small contexts (20 or more sentences), out-of context words, and is easy to calculate. A word vector space generated with both bigram phrases and unigram tokens reveals that contextually significant words disproportionately define phrases. This relationship is applied to produce simple weighted bag-of-words sentence embeddings. This model outperforms SkipThought and the best models trained on unordered sentences in most tests in Facebook's SentEval, beats tf-idf on all available tests, and is generally comparable to the state of the art. This paper also applies CoSal to sentence and document summarization and an improved and context-aware cosine distance. Applying the premise that unexpected words are important, CoSal is presented as a replacement for tf-idf and an intuitive measure of contextual word importance.


title: DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

abstract: While deep neural networks have led to human-level performance on computer vision tasks, they have yet to demonstrate similar gains for holistic scene understanding. In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models. This paper presents an approach to embed 3D context into the topology of a neural network trained to perform holistic scene understanding. Given a depth image depicting a 3D scene, our network aligns the observed scene with a predefined 3D scene template, and then reasons about the existence and location of each object within the scene template. In doing so, our model recognizes multiple objects in a single forward pass of a 3D convolutional neural network, capturing both global scene and local object information simultaneously. To create training data for this 3D network, we generate partly hallucinated depth images which are rendered by replacing real objects with a repository of CAD models of the same object category. Extensive experiments demonstrate the effectiveness of our algorithm compared to the state-of-the-arts. Source code and data are available at this http URL


title: Iterative Synchronization: EM Algorithm Versus Newton-Raphson Method

abstract: This paper deals with iterative maximum-likelihood synchronization of a scalar parameter. An efficient implementation of the Newton-Raphson (NR) maximum-search method is proposed. Considering the latter implementation, the NR approach is shown to be an attractive alternative to synchronization methods based on the expectation-maximization (EM) algorithm. Simulation results for the case of phase-offset synchronization show that NR method usually increases the speed of convergence of the synchronization algorithm.


title: Analysis of HVDC superimposed harmonic voltage effects on partial discharge behavior in solid dielectric media

abstract: This paper presents an investigation of effects of high voltage direct current (HVDC) superimposed voltage harmonics on partial discharge (PD) behavior. Firstly, a single air filled void embedded in a slab of polymeric insulation was modeled using finite element methods (FEM). PD phenomena activated under the influence of rippled DC voltages were simulated using an interlinked environment of MATLAB and multiphysics simulation package COMSOL. Secondly, the results of the modeling were backed up by experimental tests, where HVDC superimposed harmonics were applied to artificial void samples in the lab and trends in the resultant PD characterized with varying harmonic orders and amplitudes. The results of both modeling and experiment are generally in agreement showing that as the harmonic orders and levels increase, the repetition rate of PD pulses rises in comparison to a purely DC applied voltage having the same peak amplitude. The findings of this study will assist in understanding of PD behavior under HVDC conditions where harmonic rippling occurs on the voltage waveform.


title: Online Segmentation and Clustering From Continuous Observation of Whole Body Motions

abstract: This paper describes a novel approach for incremental learning of human motion pattern primitives through online observation of human motion. The observed time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments are then abstracted into a stochastic model representation and automatically clustered and organized. As new motion patterns are observed, they are incrementally grouped together into a tree structure, based on their relative distance in the model space. The tree leaves, which represent the most specialized learned motion primitives, are then passed back to the segmentation algorithm so that as the number of known motion primitives increases, the accuracy of the segmentation can also be improved. The combined algorithm is tested on a sequence of continuous human motion data that are obtained through motion capture, and demonstrates the performance of the proposed approach.


title: High-Performance Transactions for Persistent Memories

abstract: Emerging non-volatile memory (NVRAM) technologies offer the durability of disk with the byte-addressability of DRAM. These devices will allow software to access persistent data structures directly in NVRAM using processor loads and stores, however, ensuring consistency of persistent data across power failures and crashes is difficult. Atomic, durable transactions are a widely used abstraction to enforce such consistency. Implementing transactions on NVRAM requires the ability to constrain the order of NVRAM writes, for example, to ensure that a transaction's log record is complete before it is marked committed. Since NVRAM write latencies are expected to be high, minimizing these ordering constraints is critical for achieving high performance. Recent work has proposed programming interfaces to express NVRAM write ordering constraints to hardware so that NVRAM writes may be coalesced and reordered while preserving necessary constraints. Unfortunately, a straightforward implementation of transactions under these interfaces imposes unnecessary constraints. We show how to remove these dependencies through a variety of techniques, notably, deferring commit until after locks are released. We present a comprehensive analysis contrasting two transaction designs across three NVRAM programming interfaces, demonstrating up to 2.5x speedup.


title: XMIPP: a new generation of an open-source image processing package for electron microscopy

abstract: X-windows based microscopy image processing package (Xmipp) is a specialized suit of image processing programs, primarily aimed at obtaining the 3D reconstruction of biological specimens from large sets of projection images acquired by transmission electron microscopy. This public-domain software package was introduced to the electron microscopy field eight years ago, and since then it has changed drastically. New methodologies for the analysis of single-particle projection images have been added to classification, contrast transfer function correction, angular assignment, 3D reconstruction, reconstruction of crystals, etc. In addition, the package has been extended with functionalities for 2D crystal and electron tomography data. Furthermore, its current implementation in C++, with a highly modular design of well-documented data structures and functions, offers a convenient environment for the development of novel algorithms. In this paper, we present a general overview of a new generation of Xmipp that has been re-engineered to maximize flexibility and modularity, potentially facilitating its integration in future standardization efforts in the field. Moreover, by focusing on those developments that distinguish Xmipp from other packages available, we illustrate its added value to the electron microscopy community.


title: Significance-Linked Connected Component Analysis for Wavelet Image Coding

abstract: The success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256/spl times/256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.


title: On Successful Team Formation: Statistical Analysis of a Multiplayer Online Game

abstract: Teamwork plays an important role in many areas of today's society, such as business activities. Thus, the question of how to form an effective team is of increasing interest. In this paper we use the team-oriented multiplayer online game Dota 2 to study cooperation within teams and the success of teams. Making use of game log data, we choose a statistical approach to identify factors that increase the chance of a team to win. The factors that we analyze are related to the roles that players can take within the game, the experiences of the players and friendship ties within a team. Our results show that such data can be used to infer social behavior patterns.


title: Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

abstract: Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic. Code and dataset have been made available at: https://usa.honda-ri.com/hevi


title: Quality and productivity outcomes relating to continuous integration in GitHub

abstract: Software processes comprise many steps; coding is followed by building, integration testing, system testing, deployment, operations, among others. Software process integration and automation have been areas of key concern in software engineering, ever since the pioneering work of Osterweil; market pressures for Agility, and open, decentralized, software development have provided additional pressures for progress in this area. But do these innovations actually help projects? Given the numerous confounding factors that can influence project performance, it can be a challenge to discern the effects of process integration and automation. Software project ecosystems such as GitHub provide a new opportunity in this regard: one can readily find large numbers of projects in various stages of process integration and automation, and gather data on various influencing factors as well as productivity and quality outcomes. In this paper we use large, historical data on process metrics and outcomes in GitHub projects to discern the effects of one specific innovation in process automation: continuous integration. Our main finding is that continuous integration improves the productivity of project teams, who can integrate more outside contributions, without an observable diminishment in code quality.


title: Autonomous UAV Navigation Using Reinforcement Learning

abstract: Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research using a UAV with learning capabilities in more important applications, such as wildfire monitoring, or search and rescue missions.


Developing Expertise in Reading Comprehension: What Should Be Taught? How Should It Be Taught? Technical Report No. 512.


title: Clustering of Vehicle Trajectories

abstract: We present a method that is suitable for clustering of vehicle trajectories obtained by an automated vision system. We combine ideas from two spectral clustering methods and propose a trajectory-similarity measure based on the Hausdorff distance, with modifications to improve its robustness and account for the fact that trajectories are ordered collections of points. We compare the proposed method with two well-known trajectory-clustering methods on a few real-world data sets.


title: Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

abstract: Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.


title: Self-Enabled “Error-Free” Switching Circuit for Spin Transfer Torque MRAM and Logic

abstract: Spin transfer torque (STT) is one of the most promising switching approaches for magnetic tunnel junction (MTJ) nanopillars to build up innovative nonvolatile memory and logic circuits. It presents low critical current (e.g., <; 100 μA at 65 nm), simple switching scheme, and fast-speed; however, it suffers from a number of reliability issues like stochastic switching effects, process voltage temperature (PVT) variations, and erroneous reading etc. The mainstream solution is to enlarge the write pulse duration to reduce error rate, which sacrifices the speed and low power advantages. In this paper, we present a new switching circuit for STT memory and logic, allowing “error-free” as the switching operation becomes deterministic benefiting from the self-enabled mechanism. The switching power efficiency can be also improved thanks to a shorter switching duration. By using an accuracy spice model of STT-MTJ and CMOS 65 nm design-kit, mixed simulations have been performed to demonstrate its high-reliable write/read operations and evaluate its potential area, power, and speed performance.


title: Miro - middleware for mobile robot applications

abstract: Developing software for mobile robot applications is a tedious and error-prone task. Modern mobile robot systems are distributed systems, and their designs exhibit large heterogeneity in terms of hardware, operating systems, communications protocols, and programming languages. Vendor-provided programming environments have not kept pace with recent developments in software technology. Also, standardized modules for certain robot functionalities are beginning to emerge. Furthermore, the seamless integration of mobile robot applications into enterprise information processing systems is mostly an open problem. We suggest the construction and use of object-oriented robot middleware to make the development of mobile robot applications easier and faster, and to foster portability and maintainability of robot software. With Miro, we present such a middleware, which meets the aforementioned requirements and has been ported to three different mobile platforms with little effort. Miro also provides generic abstract services like localization or behavior engines, which can be applied on different robot platforms with virtually no modifications.


title: Software-Defined Fog Network Architecture for IoT

abstract: Rapid increase in number and diversity of Internet-connected devices raises many challenges for the traditional network architecture, which is not designed to support a high level of scalability, real-time data delivery and mobility. To address these issues, in this paper we present a new model of Internet of Things architecture which combines benefits of two emerging technologies: software-defined networking and Fog computing. Software-defined networking implies a logically centralized network control plane, which allows implementation of sophisticated mechanisms for traffic control and resource management. On the other hand, Fog computing enables some data to be analysed and managed at the network edge, thus providing support for applications that require very low and predictable latency. In the paper, we give detailed insight into the system structure and functionality of its main components. We also discuss the benefits of the proposed architecture and its potential services.


title: Compact Dual Band-Notched Printed Monopole Antenna for UWB Application

abstract: A compact printed ultrawideband (UWB) monopole antenna with dual band-notched characteristics is presented. By inserting two I-shaped notches in both sides of the microstrip feed line on the ground plane, additional resonances are excited, and hence the bandwidth is increased up to 123%. Two notched frequency bands are achieved by embedding a pair of Γ-shaped stubs in the radiation patch and a modified G-slot defected ground structure in the feeding line. The designed antenna has a small size of 20 × 18 mm2 and operates over the frequency band between 2.8 and 11.8 GHz for VSWR <; 2, with one notch frequency band at 3.3-3.8 GHz (WiMAX band) and the other at 5.1-6 GHz (WLAN band). The VSWR and radiation patterns of the fabricated antenna are presented, which prove that the designed antenna is a good candidate for various UWB applications.


title: Studying User Footprints in Different Online Social Networks

abstract: With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, Linked In, Twitter and You Tube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users' online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, Friend Feed, and Profilactic, we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler similarity, Word net based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and Linked In. In this paper, we present the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks. User ID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.


title: Automatically discovering local visual material attributes

abstract: Shape cues play an important role in computer vision, but shape is not the only information available in images. Materials, such as fabric and plastic, are discernible in images even when shapes, such as those of an object, are not. We argue that it would be ideal to recognize materials without relying on object cues such as shape. This would allow us to use materials as a context for other vision tasks, such as object recognition. Humans are intuitively able to find visual cues that describe materials. Previous frameworks attempt to recognize these cues (as visual material traits) using fully-supervised learning. This requirement is not feasible when multiple annotators and large quantities of images are involved. In this paper, we derive a framework that allows us to discover locally-recognizable material attributes from crowdsourced perceptual material distances. We show that the attributes we discover do in fact separate material categories. Our learned attributes exhibit the same desirable properties as material traits, despite the fact that they are discovered using only partial supervision.


title: Declarative Name Binding and Scope Rules

abstract: Preprint of paper published in: SLE 2012 - 5th International Conference on Software Language Engineering, 26-28 September 2012, Lecture Notes in Computer Science 7745; doi:10.1007/978-3-642-36089-3_18 In textual software languages, names are used to reference elements like variables, methods, classes, etc. Name resolution analyses these names in order to establish references between definition and use sites of elements. In this paper, we identify recurring patterns for name bindings in programming languages and introduce a declarative metalanguage for the specification of name bindings in terms of namespaces, definition sites, use sites, and scopes. Based on such declarative name binding specifications, we provide a language-parametric algorithm for static name resolution during compile-time. We discuss the integration of the algorithm into the Spoofax Language Workbench and show how its results can be employed in semantic editor services like reference resolution, constraint checking, and content completion.


title: Studies in Immersion Education

abstract: Part 1 Evaluation and assessment: program evaluation and student assessment in British Columbia - a narrative account. Part 2 Curriculum and instruction: teaching grammar in French immersion - an experimental study integrating language and science in immersion classrooms - a case study approach current issues for the immersion language curriculum. Part 3 Teacher education: French immersion teacher education - a study of two programmes French immersion teachers and their professional development needs.


title: Sketch based image retrieval via image-aided cross domain learning

abstract: Existing methods on sketch based image retrieval (SBIR) are usually based on the hand-crafted features whose ability of representation is limited. In this paper, we propose a sketch based image retrieval method via image-aided cross domain learning. First, the deep learning model is introduced to learn the discriminative features. However, it needs a large number of images to train the deep model, which is not suitable for the sketch images. Thus, we propose to extend the sketch training images via introducing the real images. Specifically, we initialize the deep models with extra image data, and then extract the generalized boundary from real images as the sketch approximation. The using of generalized boundary is under the assumption that their domain is similar with sketch domain. Finally, the neural network is fine-tuned with the sketch approximation data. Experimental results on Flicker15 show that the proposed method has a strong ability to link the associated image-sketch pairs and the results outperform state-of-the-arts methods.


title: Unsupervised domain adaptation with joint supervised sparse coding and discriminative regularization term

abstract: Domain adaptation (DA) attempts to enhance the generalization capability of classifier through narrowing the gap of the distributions across domains. This paper focuses on unsupervised domain adaptation where labels are not available in target domain. Most existing approaches explore the domaininvariant features shared by domains but ignore the discriminative information of source domain. To address this issue, we propose a discriminative domain adaptation method (DDA) to reduce domain shift by seeking a common latent subspace jointly using supervised sparse coding (SSC) and discriminative regularization term. Particularly, DDA adapts SSC to yield discriminative coefficients of target data and further unites with discriminative regularization term to induce a common latent subspace across domains. We show that both strategies can boost the ability of transferring knowledge from source to target domain. Experiments on two real world datasets demonstrate the effectiveness of our proposed method over several existing state-of-the-art domain adaptation methods.


title: Generating Counterfactual Explanations with Natural Language

abstract: Natural language explanations of deep neural network decisions provide an intuitive way for a AI agent to articulate a reasoning process. Current textual explanations learn to discuss class discriminative features in an image. However, it is also helpful to understand which attributes might change a classification decision if present in an image (e.g.,"This is not a Scarlet Tanager because it does not have black wings.") We call such textual explanations counterfactual explanations, and propose an intuitive method to generate counterfactual explanations by inspecting which evidence in an input is missing, but might contribute to a different classification decision if present in the image. To demonstrate our method we consider a fine-grained image classification task in which we take as input an image and a counterfactual class and output text which explains why the image does not belong to a counterfactual class. We then analyze our generated counterfactual explanations both qualitatively and quantitatively using proposed automatic metrics.


title: Financing the Millennium Development Goals: Is more public spending the best way to meet poverty reduction targets?

abstract: The strategy for expanding public services through donor funding is flawed and is doomed to fail. Donor funding necessitates increased internal public expenditure. This shifts resources away from the export-oriented private sector which would bring about economic growth and provide a basis for a more comprehensive poverty reduction. There are also absorptive limitations in the use of large donor aid. Donor funds distort Governments budget allocation pattern cohesion and stability. The funding gap envisaged under MDGs by donor aid advocates ranges between 6.4% to 13.6% of Ugandas GDP which are enormous. Large donor aid will lead to contraction of the private sector appreciation of the shilling and falling prices of agricultural produce. It would mean that the Government would have to depend on donors for over 60% of its budget. And yet donor funding is volatile and unpredictable. The strategy of achieving MDGs through large donor aid should be abandoned. Instead as projected in the long-termexpenditure framework Uganda should concentrate on the current budget policy to increase economic growth. In 10 years revenue will have increased by 150% and health expenditure will be about 15% of the Government expenditure. (authors)


title: Study on zero-crossing conducted noise issue of totem-pole bridgeless PFC converter

abstract: This paper analyzes the conducted noise generation mechanism of totem-pole bridgeless PFC converter in consideration of frame ground capacitor and LISN at every input voltage zero-crossing. The spike current occurs at zero-crossing and causes severe common mode noise. The frequency range on noise spectrum caused by zero-crossing issue is discussed by mathematical calculation. Furthermore, the experimental measurements have been achieved to verify the theoretical analysis. Finally, a method is proposed to solve the spike current issue and furthermore suppress conducted noise.


title: Tiled and clustered forward shading: supporting transparency and MSAA

abstract: We present details of Tiled and Clustered Forward Shading in its application to rendering transparent geometry and using Multi Sampling Anti Aliasing (MSAA). We detail how transparency and MSAA is supported, and present performance results measured on modern GPUs. Previous techniques for handling large numbers of lights are usually based on deferred shading [Andersson 2009; Lauritzen 2010]. However, deferred shading techniques struggle with impractically large frame buffers when MSAA is used, and make supporting transparency difficult. In addition, deferred shading makes it more difficult to support custom shaders on geometry. Tiled Forward Shading is a new and highly practical approach to real-time shading scenes with thousands of light sources, introduced by Olsson and Assarsson in 2011 [2011]. Their results, measured on an GTX 280 GPU, indicated that tiled forward shading was impractically slow. Performance on more recent GPUs has improved considerably (approaching that of tiled deferred), which opens up the possibility of using the technique to support transparency and MSAA. Clustered Shading further extends tiled shading by adding depth partitioning [Olsson et al. 2012]. We show how Clustered Forward Shading can be extended to support transparency efficiently. Forward shading naturally supports both transparency and MSAA, which has been shown in previous work. However, the performance and implementation details have not previously been investigated. We provide details on how to construct the light grid for use with transparency. When the transparent geometry is considered, the depth range optimization cannot be fully used. Instead, only a more conventional hierarchical depth test can be used. The grid structure can be built once, and quickly pruned to prepare a more efficient instance for opaque geometry. However, as each transparent layer must consider all the lights in the tile, performance does not scale linearly with the depth complexity, but far worse (Figure 1, right). To improve on this we extend clustered forward shading by constructing the grid using a pre-pass over all geometry (not just opaque), and flagging clusters as a side effect. This allows us to quickly find the unique clusters used. As clusters contain only space around actual samples that need shading, efficiency is much better (Figure 1, left). For deferred shading a single 1080p, 16x MSAA, 16-bit float RGBA buffer requires over 250Mb of memory. In addition, each sample may need to be shaded individually, effectively running shading at a per-sample frequency. For forward shading, no G-Buffers are required and MSAA is trivially enabled. A brief performance and memory comparison is shown in Figure 2, showing that clustered forward outperforms tiled forward by more than 2 times, and also outperforms tiled deferred, if MSAA is used.


title: Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

abstract: In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed "Chemception", a deep CNN for the prediction of chemical properties, using just the images of 2D drawings of molecules. We develop Chemception without providing any additional explicit chemistry knowledge, such as basic concepts like periodicity, or advanced features like molecular descriptors and fingerprints. We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40,000 compounds. When compared to multi-layer perceptron (MLP) deep neural networks trained with ECFP fingerprints, Chemception slightly outperforms in activity and solvation prediction and slightly underperforms in toxicity prediction. Having matched the performance of expert-developed QSAR/QSPR deep learning models, our work demonstrates the plausibility of using deep neural networks to assist in computational chemistry research, where the feature engineering process is performed primarily by a deep learning algorithm.


title: An improved Poisson Surface Reconstruction algorithm

abstract: In this paper we describe an improved Poisson Surface Reconstruction algorithm. The Poisson Surface Reconstruction method considers all the points at once, without resorting to heuristic spatial partitioning or blending, and use the Marching Cubes algorithm to get the representations. We introduce an improved Marching Cubes algorithm to Poisson Surface Reconstruction method to generate more realizing representations. The improved MC algorithm includes interpolating between data points and fast searching based on data segmentation. Experimenting with publicly available scan data, we demonstrate reconstruction of surface with greater detail and more efficiency.


Ontology Mediation, Merging and Alignment


title: Learning Syntactic Patterns for Automatic Hypernym Discovery

abstract: Semantic taxonomies such as WordNet provide a rich source of knowledge for natural language processing applications, but are expensive to build, maintain, and extend. Motivated by the problem of automatically constructing and extending such taxonomies, in this paper we present a new algorithm for automatically learning hypernym (is-a) relations from text. Our method generalizes earlier work that had relied on using small numbers of hand-crafted regular expression patterns to identify hypernym pairs. Using "dependency path" features extracted from parse trees, we introduce a general-purpose formalization and generalization of these patterns. Given a training set of text containing known hypernym pairs, our algorithm automatically extracts useful dependency paths and applies them to new corpora to identify novel pairs. On our evaluation task (determining whether two nouns in a news article participate in a hypernym relationship), our automatically extracted database of hypernyms attains both higher precision and higher recall than WordNet.


title: Effective heterogeneous similarity measure with nearest neighbors for cross-media retrieval

abstract: Emerging multimedia content including images and texts are always jointly utilized to describe the same semantics. As a result, cross-media retrieval becomes increasingly important, which is able to retrieve the results of the same semantics with the query but with different media types. In this paper, we propose a novel heterogeneous similarity measure with nearest neighbors (HSNN). Unlike traditional similarity measures which are limited in homogeneous feature space, HSNN could compute the similarity between media objects with different media types. The heterogeneous similarity is obtained by computing the probability for two media objects belonging to the same semantic category. The probability is achieved by analyzing the homogeneous nearest neighbors of each media object. HSNN is flexible so that any traditional similarity measure could be incorporated, which is further regarded as the weak ranker. An effective ranking model is learned from multiple weak rankers through AdaRank for cross-media retrieval. Experiments on the wikipedia dataset show the effectiveness of the proposed approach, compared with state-of-the-art methods. The cross-media retrieval also shows to outperform image retrieval systems on a unimedia retrieval task.


title: Applying Reinforcement Learning and Supervised Learning Techniques to Play Hearthstone

abstract: Hearthstone is the most popular digital trading card game nowadays. This paper addresses the problem of applying machine learning methods for the development of an agent, which plays Hearthstone. We show how to separate a large action space of Hearthstone in two parts, one of which is quite small, in order to apply reinforcement learning. Also we develop a new treewalking algorithm which allows us to consider only the most promising game states of Hearthstone in order to determine the best action sequence on a given turn. Besides that, we propose a new way to train and build a game state metric using supervised learning techniques. We develop four agents based on these proposals. Two of them show better results compared to previous works. Lastly, we show that the best developed agent can compete with game experts.


title: Exact Model Knowledge and Direct Adaptive Controllers on Ball and Beam

abstract: This paper presents analysis and implementation of Exact Model Knowledge (EMK) and Direct Adaptive control schemes on the 4th order ball and beam system in which the dynamics of the ball position and the dynamics of the beam angle are cascaded. For the controller analysis the error dynamic equations for ball position and the beam angle are derived for both cases. Following, experimental studies are conducted based on the proposed control approaches and it is presented that constant and sinusoidal references for the ball position are tracked asymptotically.


title: One Knowledge Graph to Rule Them All? Analyzing the Differences Between DBpedia, YAGO, Wikidata & co.

abstract: Public Knowledge Graphs (KGs) on the Web are considered a valuable asset for developing intelligent applications. They contain general knowledge which can be used, e.g., for improving data analytics tools, text processing pipelines, or recommender systems. While the large players, e.g., DBpedia, YAGO, or Wikidata, are often considered similar in nature and coverage, there are, in fact, quite a few differences. In this paper, we quantify those differences, and identify the overlapping and the complementary parts of public KGs. From those considerations, we can conclude that the KGs are hardly interchangeable, and that each of them has its strenghts and weaknesses when it comes to applications in different domains.


title: Avatar-Net: Multi-scale Zero-Shot Style Transfer by Feature Decoration

abstract: Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images. However, the trade-off between the generalization and efficiency in existing methods impedes a high quality zero-shot style transfer in real-time. In this paper, we resolve this dilemma and propose an efficient yet effective Avatar-Net that enables visually plausible multi-scale transfer for arbitrary style. The key ingredient of our method is a style decorator that makes up the content features by semantically aligned style features from an arbitrary style image, which does not only holistically match their feature distributions but also preserve detailed style patterns in the decorated features. By embedding this module into an image reconstruction network that fuses multi-scale style abstractions, the Avatar-Net renders multi-scale stylization for any style image in one feed-forward pass. We demonstrate the state-of-the-art effectiveness and efficiency of the proposed method in generating high-quality stylized images, with a series of applications include multiple style integration, video stylization and etc.


title: Implementation and validation of Multimedia Broadcast Multicast Service for LTE/LTE-advanced in OpenAirInterface platform

abstract: Evolved Multimedia Broadcast Multicast Service (eMBMS) is a point-to-multipoint content delivery solution designed for LTE/LTE-A. It distributes efficiently the broadcast and multicast service to a massive number of mobile devices located in a given geographical area. eMBMS can be used to boost the network's capability for providing high-quality multimedia service in high user-density area such a stadium during an entertainment or sport event. The aim of this paper is to validate and evaluate the performance of eMBMS following the 3GPP standard (release 10) implemented in the context of the OpenAirInterface SDR platforms. The difference between the OpenAirInterface in-lab system validation platform with respect to existing simulation/emulation tools such as OPNET, matlab, NS-2/3 is that firstly it is built with a high level of realism supporting real-time operation (based on RTAI and RT-Preempt); secondly it is part of the integration and validation chain for a real-time RF experimentation. The results show that eMBMS performance in the OpenAirInterface satisfies the requirement of 3GPP standard in terms of BLER. Furthermore, the theoretical user throughput proposed by 3GPP have been validated through experimentation.


Le structuralisme en linguistique


title: Emotion Cause Detection with Linguistic Construction in Chinese Weibo Text

abstract: To identify the cause of emotion is a new challenge for researchers in nature language processing. Currently, there is no existing works on emotion cause detection from Chinese micro-blogging (Weibo) text. In this study, an emotion cause annotated corpus is firstly designed and developed through anno- tating the emotion cause expressions in Chinese Weibo Text. Up to now, an emotion cause annotated corpus which consists of the annotations for 1,333 Chinese Weibo is constructed. Based on the observations on this corpus, the characteristics of emotion cause expression are identified. Accordingly, a rule- based emotion cause detection method is developed which uses 25 manually complied rules. Furthermore, two machine learning based cause detection me- thods are developed including a classification-based method using support vec- tor machines and a sequence labeling based method using conditional random fields model. It is the largest available resources in this research area. The expe- rimental results show that the rule-based method achieves 68.30% accuracy rate. Furthermore, the method based on conditional random fields model achieved 77.57% accuracy which is 37.45% higher than the reference baseline method. These results show the effectiveness of our proposed emotion cause detection method.


title: Glomus tumor of the penis

abstract: Glomus tumors of the penis are extremely rare. A patient with multiple regional glomus tumors involving the penis is reported. A 16-year-old boy presented with the complaint of painless penile masses and resection of the lesions was performed. The pathologic diagnosis was glomus tumor of the penis. This is the ninth case of glomus tumor of the penis to be reported in the literature.


title: Health care's human crisis - RX for an evolving profession.

abstract: In 2001, the Robert Wood Johnson Foundation commissioned a study on the nursing shortage in the United States to gain a better understanding of the drivers and inform the Foundation's response. This article discusses the impetus for, and a brief summary of, the resulting report, "Health Care's Human Crisis: The American Nursing Shortage," published by the Foundation in 2002. It examines the historical, social, cultural, and economic factors that drive the nursing shortage. A new framework, namely a continuum of responses, is offered for understanding the myriad activities being undertaken in response to the problem, pointing the way to long-term, sustainable solutions. Recommendations and implications for nursing leaders are discussed.


title: Big Data Analytics in Chemical Engineering.

abstract: Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.


title: Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

abstract: Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.


Toward Improving Solar Panel Efficiency using Reinforcement Learning


title: Team Boundary Issues Across Multiple Global Firms

abstract: Numerous methodological issues arise when studying teams that span multiple boundaries. The main purpose of this paper is to raise awareness about the challenges of conducting field research on teams in global firms. Based on field research across multiple firms (software development, product development, financial services, and high technology), we outline five types of boundaries that we encountered in our field research (geographical, functional, temporal, identity, and organizational)and discuss methodological issues in distinguishing the effects of one boundary where multiple boundaries exist. We suggest that it is important to: (1) appropriately measure the boundary of interest to the study, (2) assess and control for other influential boundaries within and across teams, and (3)distinguish the effects of each boundary on each team outcome of interest. Only through careful attention to methodology can we properly assess the effects of team boundaries and appreciate their research and practical implications for designing and using information systems to support collaborative work.


title: A Unified Probabilistic Framework for Real-Time Depth Map Fusion

abstract: This paper proposes a unified probabilistic framework for real-time depth map fusion. By modeling the depth imaging process as a random experiment, the depth map fusion is converted into probability density function (pdf) estimation. The depth fusion problem is decoupled into four parts: the fusion space, the influence term, the visibility term and the confidence term. We combine these four terms in a unified probabilistic framework, and apply the framework in two cases to evaluate the performance. In the first case, multiple stereo vision cameras are used to acquire multiple depth map streams from multiple viewpoints simultaneously in real time. In the second case, two cameras and a Kinect are combined to provide two depth map streams. In both cases, strategies for each part of the framework are presented to perform real-time depth fusion. Experimental results show that the proposed framework is promising for real-time depth map fusion.


title: Dual Active Bridge-Based Battery Charger for Plug-in Hybrid Electric Vehicle With Charging Current Containing Low Frequency Ripple

abstract: Manufacturers want high power density for the on-board battery chargers of plug-in hybrid electric vehicles. Wide bandgap devices can be used to shrink other passive components by increasing the switching frequency, but the bulk dc link capacitor of the ac–dc power factor correction stage, becomes one of the major barriers to higher power density, because its volume depends on the ripple power at the double line frequency in a dc current charging system. However, if this double line frequency ripple flows into the battery, the dc link capacitance can be significantly reduced. This charging scheme, named as sinusoidal charging in this paper, is analyzed and implemented based on a two-stage battery charging system, which is comprised of one full bridge ac–dc stage and one dual active bridge dc–dc stage. We further find that converter loss causes ripple power imbalance and bigger dc link capacitance. Therefore, the impact of converter loss on the ripple power balance is analyzed, and a feedback control on the dc link voltage ripple is proposed based on this analysis in order to further reduce the dc link capacitance. The effectiveness of the proposed solutions is verified in both Si-based and GaN-based charging systems.


title: Neuroevolution of hierarchical reservoir computers

abstract: Reservoir Computers such as Echo State Networks (ESN) represent an alternative recurrent neural network model that provides fast training and state-of-the-art performances for supervised learning problems. Classic ESNs suffer from two limitations; hyperparameter selection and learning of multiple temporal and spatial scales. To learn multiple scales, hierarchies are proposed, and to overcome manual tuning, optimisation is used. However, the autonomous design of hierarchies and optimisation of multi-reservoir systems has not yet been demonstrated. In this work, an evolvable architecture is proposed called Reservoir-of-Reservoirs (RoR) where sub-networks of neurons (ESNs) are interconnected to form the reservoir. The design of each sub-network, hyperparameters, global connectivity and its hierarchical structure are evolved using a genetic algorithm (GA) called the Microbial GA. To evaluate the RoR and microbial GA, single networks and other hierarchical ESNs are evolved. The results show the microbial GA can dramatically increase the performance of single networks compared to other optimisation techniques. The evolutionary process also leads to competitive results with RoRs and other hierarchical ESNs, despite having fewer connections than a single network. In the final section, it is revealed that the RoR architecture may learn generalised features other architectures cannot, offering improvements in network generalisation to other tasks.


title: Secure Cloud-Based EHR System Using Attribute-Based Cryptosystem and Blockchain

abstract: To achieve confidentiality, authentication, integrity of medical data, and support fine-grained access control, we propose a secure electronic health record (EHR) system based on attribute-based cryptosystem and blockchain technology. In our system, we use attribute-based encryption (ABE) and identity-based encryption (IBE) to encrypt medical data, and use identity-based signature (IBS) to implement digital signatures. To achieve different functions of ABE, IBE and IBS in one cryptosystem, we introduce a new cryptographic primitive, called combined attribute-based/identity-based encryption and signature (C-AB/IB-ES). This greatly facilitates the management of the system, and does not need to introduce different cryptographic systems for different security requirements. In addition, we use blockchain techniques to ensure the integrity and traceability of medical data. Finally, we give a demonstrating application for medical insurance scene.


title: How psychotherapy changes the brain – the contribution of functional neuroimaging

abstract: A thorough investigation of the neural effects of psychotherapy is needed in order to provide a neurobiological foundation for widely used treatment protocols. This paper reviews functional neuroimaging studies on psychotherapy effects and their methodological background, including the development of symptom provocation techniques. Studies of cognitive behavioural therapy (CBT) effects in obsessive-compulsive disorder (OCD) were consistent in showing decreased metabolism in the right caudate nucleus. Cognitive behavioural therapy in phobia resulted in decreased activity in limbic and paralimbic areas. Interestingly, similar effects were observed after successful intervention with selective serotonin reuptake inhibitors (SSRI) in both diseases, indicating commonalities in the biological mechanisms of psycho- and pharmacotherapy. These findings are discussed in the context of current neurobiological models of anxiety disorders. Findings in depression, where both decreases and increases in prefrontal metabolism after treatment and considerable differences between pharmacological and psychological interventions were reported, seem still too heterogeneous to allow for an integrative account, but point to important differences between the mechanisms through which these interventions attain their clinical effects. Further studies with larger patient numbers, use of standardised imaging protocols across studies, and ideally integration with molecular imaging are needed to clarify the remaining contradictions. This effort is worthwhile because functional imaging can then be potentially used to monitor treatment effects and aid in the choice of the optimal therapy. Finally, recent advances in the functional imaging of hypnosis and the application of neurofeedback are evaluated for their potential use in the development of psychotherapy protocols that use the direct modulation of brain activity as a way of improving symptoms.


title: A Library of Skills and Behaviors for Smart Mobile Assistant Robots in Automotive Assembly Lines

abstract: In the automotive industry, market demands tend to require more variants of products while life cycles become shorter. This means an increasing complexity in automotive assembly lines. Therefore, flexible assistant systems will be needed in the future to optimally support human workers. In this work, a library of skills for human-robot collaboration is developed and behaviors are introduced as additional module in a skill framework that allows intuitive programming of smart robots.


title: Written Devnagari Characters with Regular Expressions & Minimum Edit Distance Method

abstract: Regular expressions are extremely useful, because they allow us to work with text in terms of patterns. They are considered the most sophisticated means of performing operations such as string searching, manipulation, validation, and formatting in all applications that deal with text data. Character recognition problem scenarios in sequence analysis that are ideally suited for the application of regular expression algorithms. This paper describes a use of regular expressions in this problem domain, and demonstrates how the effective use of regular expressions that can serve to facilitate more efficient and more effective character recognition.


title: AN ALGORITHM TO GROUP DEFECTS ON PRINTED CIRCUIT BOARD FOR AUTOMATED VISUAL INSPECTION

abstract: Due to disadvantages in manual inspection, an automated visual inspection system is needed to ::: eliminate subjective aspects and provides fast and quantitative assessment of printed circuit board (PCB). Up to ::: the present, there has been a lot of work and research concentrated on PCB defect detection. PCB defects ::: detection is necessary for verification of the characteristics of PCB to make sure it is in conformity with the ::: design specifications. However, besides the need to detect the defects, it is also essential to classify these defects ::: so that the source of these defects can be identified. Unfortunately, this area has been neglected and not been ::: given enough attention. Hence, this study proposes an algorithm to group the defects found on bare PCB. Using ::: a synthetically generated PCB image, the algorithm is able to group 14 commonly known PCB defects into five ::: groups. The proposed algorithm includes several image processing operations such as image subtraction, image ::: adding, logical XOR and NOT, and flood fill operator


title: Genética, HLA-B27 y espondilitis anquilosante: 40 años

abstract: Ankylosing spondylitis (AS) is a prototypical inflammatory disease of the locomotor system affecting axial skeleton. It is part of the general group of spondyloarthopathies (SpA). Its strong association with histocompatibility antigen HLA-B27 is known since 1973. However, HLA-B27 contribution to AS genetic risk is approximately 16%. Therefore, other genes are necessarily involved in the pathogenesis of the disease. Genomic development and the possibility of making genome wide screening have contributed enormously to the study of the disease. In this paper, we describe the actual knowledge about AS genetic risk, which has contributed to understand the influence of HLA-B27 on the etiology and pathogenesis of the disease. We also intend to foresee how these findings will result in an improvement of patients’ quality of life


title: Deep Echo State Network (DeepESN): A Brief Survey

abstract: The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers, i.e. on the bias of depth in RNNs architectural design. In this paper, we summarize the advancements in the development, analysis and applications of deepESNs.


title: Research Review: Evaluating and reformulating the developmental taxonomic theory of antisocial behaviour

abstract: BACKGROUND: The developmental taxonomic theory proposes that there are two subtypes of antisocial behaviour. The first is a neurodevelopmental disorder which emerges in early childhood and follows a life-course persistent course, whereas the second emerges in adolescence, remits in early adulthood and reflects peer processes such as mimicry of antisocial peers. The aim of this review was to evaluate the developmental taxonomic theory in the light of recent empirical research. ::: ::: METHODS: We conducted a comprehensive literature review comparing these subtypes of antisocial behaviour based on searches on PubMed and other scientific databases covering the period from 1993 to 2013. We focused on research encompassing psychiatric epidemiology, personality assessment, neuropsychology, neuroendocrinology, genetics, and structural and functional neuroimaging. Sixty one empirical studies were identified that investigated one of these forms of antisocial behaviour separately or explicitly compared childhood-onset and adolescence-onset forms of antisocial behaviour. ::: ::: RESULTS: Empirical research provides support for the hypothesis that life-course persistent antisocial behaviour is a neurodevelopmental disorder which emerges in the transactions between individual vulnerabilities and environmental adversity. In contrast to the developmental taxonomic theory, however, empirical findings suggest that severe antisocial behaviour that emerges in adolescence frequently has a negative prognosis and is rarely limited to the adolescent period. In addition, both forms of antisocial behaviour are associated with emotion processing deficits, changes in brain structure and function, alterations in cortisol secretion, and atypical personality traits (such as increased callous-unemotional traits). ::: ::: CONCLUSIONS: We conclude that the developmental taxonomic theory is in need of revision, as differences between life-course persistent and adolescence-onset forms of antisocial behaviour appear to be quantitative, rather than qualitative, in nature. In addition, evidence is accumulating that adolescence-onset antisocial behaviour may also be a neurodevelopmental disorder. To account for the similarities between these groups, despite the differences in their age-of-onset, we propose that the quality of the child's early environment moderates the relationship between individual vulnerabilities and the age-of-onset of antisocial behaviour.


title: Modeling affordances using Bayesian networks

abstract: Affordances represent the behavior of objects in terms of the robot's motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic systems, since it is at the core of many higher level skills such as imitation. In this paper, we propose a general affordance model based on Bayesian networks linking actions, object features and action effects. The network is learnt by the robot through interaction with the surrounding objects. The resulting probabilistic model is able to deal with uncertainty, redundancy and irrelevant information. We evaluate the approach using a real humanoid robot that interacts with objects.


title: An Indirect Genetic Algorithm for a Nurse Scheduling Problem

abstract: This paper describes a Genetic Algorithms (GAs) approach to a manpower-scheduling problem arising at a major UK hospital. Although GAs have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical GAs paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.


title: Simulation of Cumuliform Clouds Based on Computational Fluid Dynamics

abstract: Simulation of natural phenomena is one of the important research fields in computer graphics. In particular, clouds play an important role in creating images of outdoor scenes. Fluid simulation is effective in creating realistic clouds because clouds are the visualization of atmospheric fluid. In this paper, we propose a simulation technique, based on a numerical solution of the partial differential equation of the atmospheric fluid model, for creating animated cumulus and cumulonimbus clouds with features formed by turbulent vortices.


title: Design, simulation and optimization of a novel DC electroosmotic micro-infusion pump

abstract: This paper illustrates the design, numerical simulation and optimization of a novel microfluidic electroosmotic infusion pump with low voltage requirement and high flow rate. This micropump is equipped with four microelectrodes that are embedded in the curved side surfaces of the microchannel. The electrode array consists of two sets of microelectrodes in the shape of a quadrant of a circle arranged nearby the inlet and outlet. Inner microchannels through the micropump are placed in purpose of electroosmotic force improvement and flow rate increasing. Numerical analysis of inner microchannels effect on pumping efficiency is carried out by means of two sets of simulations. First, the width of the inner microchannels is kept constant at 20 μm while the length is varied within 0-210 μm. The highest achieved flow rate is 40.4 μl/min at 40 μm of inner microchannels length. Next, the length is kept constant at 110 μm whilst the width of the inner microchannels is swept within 12.5-240 μm. The best achieved pumping efficiency is 37 μl/min at 37.7 μm inner microchannels width. Keywords—microfluidics; electroosmotic micropump; flow rate


title: Defining thalamic nuclei and topographic connectivity gradients in vivo

abstract: The thalamus consists of multiple nuclei that have been previously defined by their chemoarchitectual and cytoarchitectual properties ex vivo. These form discrete, functionally specialized, territories with topographically arranged graduated patterns of connectivity. However, previous in vivo thalamic parcellation with MRI has been hindered by substantial inter-individual variability or discrepancies between MRI derived segmentations and histological sections. Here, we use the Euclidean distance to characterize probabilistic tractography distributions derived from diffusion MRI. We generate 12 feature maps by performing voxel-wise parameterization of the distance histograms (6 feature maps) and the distribution of three-dimensional distance transition gradients generated by applying a Sobel kernel to the distance metrics. We use these 12 feature maps to delineate individual thalamic nuclei, then extract the tractography profiles for each and calculate the voxel-wise tractography gradients. Within each thalamic nucleus, the tractography gradients were topographically arranged as distinct non-overlapping cortical networks with transitory overlapping mid-zones. This work significantly advances quantitative segmentation of the thalamus in vivo using 3T MRI. At an individual subject level, the thalamic segmentations consistently achieve a close relationship with a priori histological atlas information, and resolve in vivo topographic gradients within each thalamic nucleus for the first time. Additionally, these techniques allow individual thalamic nuclei to be closely aligned across large populations and generate measures of inter-individual variability that can be used to study both basic function and pathological processes in vivo.


title: Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis.

abstract: Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables.


title: A 30.5 dBm 48% PAE CMOS Class-E PA With Integrated Balun for RF Applications

abstract: -Integration of the power amplifier together with signal processing in a transmitter is still missing in demanding RF commercial products. Issues preventing PA integration include LO pulling phenomena, thermal dissipation, and power efficiency. In this work we investigate high efficiency watt range Class-E PAs and integrated baluns. In particular, insights in the design of a fully differential cascode topology for high efficiency and reliable operation are provided and a narrowband lumped element balun, employing minimum number of integrated inductors for minimum power loss, is introduced. Two versions have been manufactured using a 0.13 pm CMOS technology. The first comprises the driver, and a differential PA connected to an external low-loss commercial balun. Experiments prove 31 dBm delivered output power, with 58% PAE and 67% drain efficiency, at 1.7 GHz. The second version adopts the same driver and PA and also integrates the balun. Experiments prove 30.5 dBm delivered output power, with 48% PAE and 55% drain efficiency, at 1.6 GHz.


title: Improving scalability of apache spark-based scale-up server through docker container-based partitioning

abstract: We propose an Apache Spark-based scale-up server architecture using Docker container-based partitioning method to improve performance scalability. The performance scalability problem of Apache Spark-based scale-up servers is due to garbage collection(GC) and remote memory access overheads when the servers are equipped with significant number of cores and Non-Uniform Memory Access(NUMA). The proposed method minimizes the problems using Docker container-based architecture effectively partitioning the original scale-up server into small logical servers. Our evaluation study based on benchmark programs revealed that the partitioning method showed performance improvement by ranging from 1.1x through 1.7x on a 120 core scale-up system. Our proof-of-concept scale-up server architecture provides the basis towards complete and practical design of partitioning-based scale-up servers showing performance scalability.


title: Quantitative evaluation of software packages for single-molecule localization microscopy

abstract: This Analysis reports a comparison of current software packages for single-molecule localization in localization-based super-resolution imaging. Performance of the participating software on synthetic, biologically inspired ground-truth data was assessed by multiple criteria.


title: Software Process Improvement and Roadmapping – A Roadmap for Implementing IEC 62304 in Organizations Developing and Maintaining Medical Device Software

abstract: Organizations engaged in medical device software are required to demonstrate compliance with a set of medical device standards and regulations before the device can be marketed. One such standard IEC 62304, Medical device software – Software life cycle processes, is a standard that defines the processes that are required to be executed in order to develop safe software. Demonstrating compliance with IEC 62304 can be problematic for organizations that are new to or have limited experience in the domain. The standard defines what processes must be carried out, but does not state how. This paper presents a research method for generating a roadmap that will guide organizations in the implementation of IEC 62304.


An introduction to molecular pathology


WTO Judicial Interpretation


title: Activated carbon adsorption of PAHs from vegetable oil used in soil remediation.

abstract: Vegetable oil has been proven to be advantageous as a non-toxic, cost-effective and biodegradable solvent to extract polycyclic aromatic hydrocarbons (PAHs) from contaminated soils for remediation purposes. The resulting vegetable oil contained PAHs and therefore required a method for subsequent removal of extracted PAHs and reuse of the oil in remediation processes. In this paper, activated carbon adsorption of PAHs from vegetable oil used in soil remediation was assessed to ascertain PAH contaminated oil regeneration. Vegetable oils, originating from lab scale remediation, with different PAH concentrations were examined to study the adsorption of PAHs on activated carbon. Batch adsorption tests were performed by shaking oil-activated carbon mixtures in flasks. Equilibrium data were fitted with the Langmuir and Freundlich isothermal models. Studies were also carried out using columns packed with activated carbon. In addition, the effects of initial PAH concentration and activated carbon dosage on sorption capacities were investigated. Results clearly revealed the effectiveness of using activated carbon as an adsorbent to remove PAHs from the vegetable oil. Adsorption equilibrium of PAHs on activated carbon from the vegetable oil was successfully evaluated by the Langmuir and Freundlich isotherms. The initial PAH concentrations and carbon dosage affected adsorption significantly. The results indicate that the reuse of vegetable oil was feasible.


title: Knowledge representation and reasoning of XML with ontology

abstract: Today XML has reached a wide acceptance as the data exchange format for e-commerce. Unfortunately, XML covers the syntactic level, but lacks semantics. Ontology can represent shared domain knowledge and enable semantic interoperability. Therefore, in this paper, we propose an approach for representing and reasoning on XML with ontologies. The formal definitions of XML Schemas and ontologies are given. On this basis, for representing XML with ontologies, we propose an approach which can translate the XML Schema into the ontology. Based on the constructed ontologies, we study how to reason on XML by means of ontologies, so that the reasoning problems of XML (e.g., conformance, inclusion, and equivalence) may be reasoned through the reasoning mechanism of ontologies.