title: Automatic 3D liver location and segmentation via convolutional neural network and graph cut

abstract: PURPOSE ::: Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. ::: ::: ::: METHODS ::: The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. ::: ::: ::: RESULTS ::: The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. ::: ::: ::: CONCLUSIONS ::: The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.


title: Palliative care communication.

abstract: OBJECTIVES ::: To summarize the challenges of teaching, practicing, and learning palliative care communication and offer resources for improving skills and educating others. ::: ::: ::: DATA SOURCES ::: A theoretically grounded, evidence-based communication curriculum called COMFORT (Communication, Orientation and opportunity, Mindful presence, Family, Openings, Relating, and Team). ::: ::: ::: CONCLUSION ::: The COMFORT curriculum is available for free through a Web site, a smartphone/iPad application, and online for continuing education units. ::: ::: ::: IMPLICATIONS FOR NURSING PRACTICE ::: The COMFORT curriculum provides resources to support the expansion and inclusion of palliative care practice not only in oncology, but also in a wide variety of disease contexts.


title: International Pellet Watch: global monitoring of persistent organic pollutants (POPs) in coastal waters. 1. Initial phase data on PCBs, DDTs, and HCHs.

abstract: Samples of polyethylene pellets were collected at 30 beaches from 17 countries and analyzed for organochlorine compounds. PCB concentrations in the pellets were highest on US coasts, followed by western Europe and Japan, and were lower in tropical Asia, southern Africa and Australia. This spatial pattern reflected regional differences in the usage of PCBs and was positively correlated with data from Mussel Watch, another monitoring approach. DDTs showed high concentrations on the US west coast and in Vietnam. In Vietnam, DDT was predominant over its metabolites (DDE and DDD), suggesting the principal source may be current usage of the pesticide for malaria control. High concentrations of pesticide HCHs were detected in the pellets from southern Africa, suggesting current usage of the pesticides in southern Africa. This study demonstrates the utility and feasibility of the International Pellet Watch approach to monitor POPs at a global scale.


title: A link prediction based approach for recommendation systems

abstract: Recommendation Systems are an important tool for aiding discovery of content such as movies, books, and music. Generating personalised recommendations that deliver serendipitous suggestions to the user is a key factor in determining user satisfaction. In this paper, we propose a recommendation algorithm that uses a community-structure based link prediction approach. The proposed link prediction algorithm predicts the links that are most likely to be formed in the network based on two new proposed metrics. These metrics take into account the effects of both the network structure and the attribute information of the items for predicting probable links. The links predicted can be presented as recommendations to the user. An item network consisting of item associations derived from the attribute information usually displays a community structure with items of similar nature being clustered together. Our approach utilises this community structure to expose implicit links in the item network and provides non-obvious recommendations to the user.


title: Multiclass Posterior Probability Support Vector Machines

abstract: Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the multiclass case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.


title: Recent approaches on authorship attribution techniques — An overview

abstract: Authorship analysis deals with the identification of authors which is a problem of text data mining and classification. There are numerous techniques and algorithms that have been published so far, in the field of stylometry. In this regard, the primary objective of the present review is to provide the status of the different studies carried out on authorship analysis based on the important research contributions. The authors have mainly focused on each of the article selected for review (2010–16), by summarizing the authorship detection fields, the corpus, the features of authorship analysis, and authorship attribution techniques used which could provide a platform to distinguish between different research contributions and the diverse techniques used in classifying the author's texts. The details on common tools and authorship attribution techniques published in the recent past would be of importance to the concerned researchers as an aid towards text data mining and future growth in the study of authorship attribution.


1 MECHANISMS OF MORAL DISENGAGEMENT IN TERRORISM


title: Innovation in Healthcare Delivery Systems: A Conceptual Framework

abstract: The healthcare industry has experienced a proliferation of innovations aimed at enhancing life expectancy, quality of life, diagnostic and treatment options, as well as the efficiency and cost effectiveness of the healthcare system. Information technology has played a vital role in the innovation of healthcare systems. Despite the surge in innovation, theoretical research on the art and science of healthcare innovation has been limited. One of the driving forces in research is a conceptual framework that provides researchers with the foundation upon which their studies are built. This paper begins with a definition of healthcare innovation and an understanding of how innovation occurs in healthcare. A conceptual framework is then developed which articulates the intervening variables that drive innovation in healthcare. Based on the proposed definition of healthcare innovation, the dimensions of healthcare innovation, the process of healthcare innovation and the conceptual framework, this paper opens the door for researchers to address several questions regarding innovation in healthcare. If the concept of healthcare innovation can be clarified, then it may become easier for health policymakers and practitioners to evaluate, adopt and procure services in ways that realistically recognize, encourage and give priority to truly valuable healthcare innovations. Lastly, this paper presents 10 research questions that are pertinent to the field of healthcare innovation. It is believed that the answers to these and other such questions will hold the key to future advances in healthcare innovation research.


title: An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes

abstract: In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques.


title: Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

abstract: A novel copy–move forgery detection scheme using adaptive oversegmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. First, the proposed adaptive oversegmentation algorithm segments the host image into nonoverlapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions. Finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the proposed copy–move forgery detection scheme can achieve much better detection results even under various challenging conditions compared with the existing state-of-the-art copy–move forgery detection methods.


title: Large In-Plane and Vertical Piezoelectricity in Janus Transition Metal Dichalchogenides

abstract: Piezoelectricity in 2D van der Waals materials has received considerable interest because of potential applications in nanoscale energy harvesting, sensors, and actuators. However, in all the systems studied to date, strain and electric polarization are confined to the basal plane, limiting the operation of piezoelectric devices. In this paper, based on ab initio calculations, we report a 2D materials system, namely, the recently synthesized Janus MXY (M = Mo or W, X/Y = S, Se, or Te) monolayer and multilayer structures, with large out-of-plane piezoelectric polarization. For MXY monolayers, both strong in-plane and much weaker out-of-plane piezoelectric polarizations can be induced by a uniaxial strain in the basal plane. For multilayer MXY, we obtain a very strong out-of-plane piezoelectric polarization when strained transverse to the basal plane, regardless of the stacking sequence. The out-of-plane piezoelectric coefficient d33 is found to be strongest in multilayer MoSTe (5.7–13.5 pm/V depending on t...


title: A survey of trust computation models for service management in internet of things systems

abstract: Abstract In this paper we survey trust computation models for Internet of things (IoT) systems for the purpose of service management, i.e., whether or not to select an IoT device as a service provider. Future IoT systems will connect the physical world into cyberspace everywhere and everything via billions of smart objects, and are expected to have a high economic impact. To date there is little work on trust computation in IoT environments for service management, especially for dealing with misbehaving owners of IoT devices that provide services to other IoT devices in the system. Our approach is to classify existing trust computation models for service management in IoT systems based on five essential design dimensions for a trust computation model: trust composition, trust propagation, trust aggregation, trust update, and trust formation. We summarize pros and cons of each dimension's options, and highlight the effectiveness of defense mechanisms against malicious attacks. We also summarize the most, least, and little visited trust computation techniques in the literature and provide insight on the effectiveness of trust computation techniques as applying to IoT systems. Finally, we identify gaps in IoT trust computation research and suggest future research directions.


title: A correlated topic model of Science

abstract: Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation even though, for example, a document about genetics is more likely to also be about disease than X-ray astronomy. This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions. In this paper we develop the correlated topic model (CTM), where the topic proportions exhibit correlation via the logistic normal distribution [J. Roy. Statist. Soc. Ser. B 44 (1982) 139--177]. We derive a fast variational inference algorithm for approximate posterior inference in this model, which is complicated by the fact that the logistic normal is not conjugate to the multinomial. We apply the CTM to the articles from Science published from 1990--1999, a data set that comprises 57M words. The CTM gives a better fit of the data than LDA, and we demonstrate its use as an exploratory tool of large document collections.


title: Particle-based fluid simulation for interactive applications

abstract: Realistically animated fluids can add substantial realism to interactive applications such as virtual surgery simulators or computer games. In this paper we propose an interactive method based on Smoothed Particle Hydrodynamics (SPH) to simulate fluids with free surfaces. The method is an extension of the SPH-based technique by Desbrun to animate highly deformable bodies. We gear the method towards fluid simulation by deriving the force density fields directly from the Navier-Stokes equation and by adding a term to model surface tension effects. In contrast to Eulerian grid-based approaches, the particle-based approach makes mass conservation equations and convection terms dispensable which reduces the complexity of the simulation. In addition, the particles can directly be used to render the surface of the fluid. We propose methods to track and visualize the free surface using point splatting and marching cubes-based surface reconstruction. Our animation method is fast enough to be used in interactive systems and to allow for user interaction with models consisting of up to 5000 particles.


title: Analysis and design of a solar charge controller using cuk converter

abstract: This paper presents a scheme for transferring power from the photovoltaic (PV) modules to a battery using a solar charge controller based on a Cuk dc/dc converter. The converter is configured in parallel power transfer mode (PPT) to achieve higher efficiency. PWM control with maximum power point tracking algorithm is used to extract maximum power from the PV modules. Mathematical modelling and field test results using multi crystalline photovoltaic modules have been carried out to verify the effectiveness of the device.


title: A Distributed Agent Based Web Search using a Genetic Algorithm

abstract: Summary In this paper, the problems of current web search engines are analyzed, and the need for a new design is justified. Some ideas on how to improve current web search engines are presented, and then an adaptive method for web meta-search engines with a multi-agent specially the mobile agents is presented to make search engines work more efficiently. In the method, the cooperation between stationary and mobile agents is used to make more efficiency. The meta-search engine gives the user needed documents based on the multi-stage mechanism. The merge of the results obtained from the search engines in the network is done in parallel. Using a reduction parallel algorithm, the efficiency of this method is increased. Furthermore, a feedback mechanism gives the meta-search engine the user’s suggestions about the found documents, which leads to a new query using a genetic algorithm. In the new search stage, more relevant documents are given to the user. The practical experiments were performed in Aglets programming environment. The results achieved from these experiments confirm the efficiency and adaptability of the method.


title: Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework

abstract: We introduce a new approach to unsupervised estimation of feature-rich semantic role labeling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers. When the components are estimated jointly to minimize errors in argument reconstruction, the induced roles largely correspond to roles defined in annotated resources. Our method performs on par with most accurate role induction methods on English and German, even though, unlike these previous approaches, we do not incorporate any prior linguistic knowledge about the languages.


title: A Survey of Online Activity Recognition Using Mobile Phones

abstract: Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.


title: Nonlinear Control of a Tethered UAV: the Taut Cable case

abstract: This paper focuses on the design of a stabilizing control law for an aerial vehicle which is physically connected to a ground station by means of a tether cable. By taking advantage of the tensile force acting along the taut cable, it is shown that the tethered UAV is able to maintain a non-zero attitude while hovering in a constant position. The control objective is to stabilize the desired configuration while simultaneously ensuring that the cable remains taut at all times. This leads to a nonlinear control problem subject to constraints. This paper provides a two-step solution. First, the system is stabilized using a cascade control scheme based on thrust vectoring. Then, constraint satisfaction is guaranteed using a novel Reference Governor scheme.


title: Smart ITS Sensor for the Transportation Planning Based on IoT Approaches Using Serverless and Microservices Architecture

abstract: Currently, there are many challenges in the transportation scope that researchers are attempting to resolve, and one of them is transportation planning. The main contribution of this paper is the design and implementation of an ITS (Intelligent Transportation Systems) smart sensor prototype that incorporates and combines the Internet of Things (IoT) approaches using the Serverless and Microservice Architecture, to help the transportation planning for Bus Rapid Transit (BRT) systems. The ITS smart sensor prototype can detect several Bluetooth signals of several devices (e.g., from mobile phones) that people use while travelling by the BRT system (e.g., in Bogota city). From that information, the ITS smart-sensor prototype can create an O/D (origin/destiny) matrix for several BRT routes, and this information can be used by the Administrator Authorities (AA) to produce a suitable transportation planning for the BRT systems. In addition, this information can be used by the center of traffic management and the AA from ITS cloud services using the Serverless and Microservice architecture.


title: A novel finger and hand pose estimation technique for real-time hand gesture recognition

abstract: Abstract This paper presents a high-level hand feature extraction method for real-time gesture recognition. Firstly, the fingers are modelled as cylindrical objects due to their parallel edge feature. Then a novel algorithm is proposed to directly extract fingers from salient hand edges. Considering the hand geometrical characteristics, the hand posture is segmented and described based on the finger positions, palm center location and wrist position. A weighted radial projection algorithm with the origin at the wrist position is applied to localize each finger. The developed system can not only extract extensional fingers but also flexional fingers with high accuracy. Furthermore, hand rotation and finger angle variation have no effect on the algorithm performance. The orientation of the gesture can be calculated without the aid of arm direction and it would not be disturbed by the bare arm area. Experiments have been performed to demonstrate that the proposed method can directly extract high-level hand feature and estimate hand poses in real-time.


title: An experiment study on stress relaxation of unsaturated lime-treated expansive clay

abstract: Stress relaxation, essentially time-dependent behavior of soil, is crucial to the long-term safety of earth structures. Herein, a series of triaxial relaxation experiments were carried out to advance the understanding of stress relaxation behaviors in the unsaturated lime-treated expansive clay. The roles of the relaxation time, the magnitude of the strain imposed prior to the start of the relaxation process, and the loading mode and rate on the unsaturated relaxation behaviors were thoroughly discussed. The results revealed that the stress relaxation process of tested soils consists of three phases of instantaneous relaxation, decelerated relaxation, and nearly non-relaxation. The initial and residual amounts of the deviatoric stress increased with higher pre-strain and loading rate, while the initial relaxation velocity increased with the increased strain loading rate. It may suggest that the loading rate of the pre-strain contributed to the pattern of the relaxation curves and the duration of time before reaching the nearly non-relaxation phase, while the magnitude of the pre-strain affected significantly the residual amount of the deviatoric stress. The specimen with a small pre-strain produced a large volumetric deformation, while the specimen imposed with a fixed pre-strain imposed at a higher loading rate showed a smaller volumetric strain. In combined multiple relaxation experiments, the specimens exhibited the cumulative damage and the different variations of the volumetric strain in each relaxation, and the amount of relaxed stress before the shear failure of the specimen was larger than that after the failure of the specimen with the same pre-strain. This will provide more comprehensive knowledge for the development of an appropriate constitutional model, incorporated with time effects on the unsaturated lime-treated expansive clay, and for the improvement of the testing method.


title: Continuous Electrowetting of Non-toxic Liquid Metal for RF Applications

abstract: Continuous electrowetting (CEW) is demonstrated to be an effective actuation mechanism for reconfigurable radio frequency (RF) devices that use non-toxic liquid-metal tuning elements. Previous research has shown CEW is an efficient means of electrically inducing motion in a liquid-metal slug, but precise control of the slug's position within fluidic channels has not been demonstrated. Here, the precise positioning of liquid-metal slugs is achieved using CEW actuation in conjunction with channels designed to minimize the liquid-metal surface energy at discrete locations. This approach leverages the high surface tension of liquid metal to control its resting position with submillimeter accuracy. The CEW actuation and fluidic channel design were optimized to create reconfigurable RF devices. In addition, solutions for the reliable actuation of a gallium-based, non-toxic liquid-metal alloy (Galinstan) are presented that mitigate the tendency of the alloy to form a surface oxide layer capable of wetting to the channel walls, inhibiting motion. A reconfigurable slot antenna utilizing these techniques to achieve a 15.2% tunable frequency bandwidth is demonstrated.


Accurate Supervised and Semi-Supervised Machine Reading for Long Documents.


title: End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

abstract: Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in most studies, there is an inconsistency between the model optimization criterion and the evaluation criterion on the enhanced speech. For example, in measuring speech intelligibility, most of the evaluation metric is based on a short-time objective intelligibility (STOI) measure, while the frame based minimum mean square error (MMSE) between estimated and clean speech is widely used in optimizing the model. Due to the inconsistency, there is no guarantee that the trained model can provide optimal performance in applications. In this study, we propose an end-to-end utterance-based speech enhancement framework using fully convolutional neural networks (FCN) to reduce the gap between the model optimization and evaluation criterion (true targets). Because of the utterance-based optimization, temporal correlation information of long speech segments, or even at the entire utterance level, can be considered when perception-based objective functions are used for the direct optimization. As an example, we implement the proposed FCN enhancement framework to optimize the STOI measure. Experimental results show that the STOI of test speech is better than conventional MMSE-optimized speech due to the consistency between the training and evaluation target. Moreover, by integrating the STOI in model optimization, the performance of the automatic speech recognition (ASR) system on the enhanced speech is also substantially improved compared to those generated by the MMSE criterion.


title: Design of patch array antennas for future 5G applications

abstract: The purpose of the paper is to investigate the design of rectangular patch antenna arrays fed by miscrostrip and coaxial lines at 28 GHz for future 5G applications. Our objective is to design a four element antenna array with a bandwidth higher than 1 GHz and a maximum radiation gain. The performances of the rectangular 4∗1 and 2∗2 patch antenna arrays designed on Rogers RT/Duroid 5880 substrate were optimized and the simulation results reveal that the performance of 4∗1 antenna array fed by microstrip line is better than 2∗2 antenna array fed by coaxial cable. We obtained for the topology of 4∗1 rectangular patch array antenna a bandwidth of 2.15 GHz and 1.3 GHz respectively with almost similar gains of the order of 13.3 dBi.


title: Stream programming on general-purpose processors

abstract: In this paper we investigate mapping stream programs (i.e., programs written in a streaming style for streaming architectures such as Imagine and Raw) onto a general-purpose CPU. We develop and explore a novel way of mapping these programs onto the CPU. We show how the salient features of stream programming such as computation kernels, local memories, and asynchronous bulk memory loads and stores can be easily mapped by a simple compilation system to CPU features such as the processor caches, simultaneous multi-threading, and fast inter-thread communication support, resulting in an executable that efficiently uses CPU resources. We present an evaluation of our mapping on a hyperthreaded Intel Pentium 4 CPU as a canonical example of a general-purpose processor. We compare the mapped stream program against the same program coded in a more conventional style for the general-purpose processor. Using both micro-benchmarks and scientific applications we show that programs written in a streaming style can run comparably to equivalent programs written in a traditional style. Our results show that coding programs in a streaming style can improve performance on today's machines and smooth the way for significant performance improvements with the deployment of streaming architectures.


title: No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities

abstract: Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we show that once the Wikipedia entities mentioned in a corpus of textual assertions are linked, this can further enable the detection and fine-grained typing of the unlinkable entities. Our proposed method for detecting unlinkable entities achieves 24% greater accuracy than a Named Entity Recognition baseline, and our method for fine-grained typing is able to propagate over 1,000 types from linked Wikipedia entities to unlinkable entities. Detection and typing of unlinkable entities can increase yield for NLP applications such as typed question answering.


title: New AC–DC Power Factor Correction Architecture Suitable for High-Frequency Operation

abstract: This paper presents a novel ac–dc power factor correction (PFC) power conversion architecture for a single-phase grid interface. The proposed architecture has significant advantages for achieving high efficiency, good power factor, and converter miniaturization, especially in low-to-medium power applications. The architecture enables twice-line-frequency energy to be buffered at high voltage with a large voltage swing, enabling reduction in the energy buffer capacitor size and the elimination of electrolytic capacitors. While this architecture can be beneficial with a variety of converter topologies, it is especially suited for the system miniaturization by enabling designs that operate at high frequency (HF, 3–30 MHz). Moreover, we introduce circuit implementations that provide efficient operation in this range. The proposed approach is demonstrated for an LED driver converter operating at a (variable) HF switching frequency (3–10 MHz) from 120 V $_{\mathrm{\rm ac}}$ , and supplying a 35 V $_{\mathrm{dc}}$ output at up to 30 W. The prototype converter achieves high efficiency (92%) and power factor (0.89), and maintains a good performance over a wide load range. Owing to the architecture and HF operation, the prototype achieves a high “box” power density of 50 W/in $^3$ (“displacement” power density of 130 W/in $^3$ ), with miniaturized inductors, ceramic energy buffer capacitors, and a small-volume EMI filter.


title: Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy

abstract: Somatic cancer mutation detection at ultra-low variant allele frequencies (VAFs) is an unmet challenge that is intractable with current state-of-the-art mutation calling methods. Specifically, the limit of VAF detection is closely related to the depth of coverage, due to the requirement of multiple supporting reads in extant methods, precluding the detection of mutations at VAFs that are orders of magnitude lower than the depth of coverage. Nevertheless, the ability to detect cancer-associated mutations in ultra low VAFs is a fundamental requirement for low-tumor burden cancer diagnostics applications such as early detection, monitoring, and therapy nomination using liquid biopsy methods (cell-free DNA). Here we defined a spatial representation of sequencing information adapted for convolutional architecture that enables variant detection at VAFs, in a manner independent of the depth of sequencing. This method enables the detection of cancer mutations even in VAFs as low as 10x-4^, >2 orders of magnitude below the current state-of-the-art. We validated our method on both simulated plasma and on clinical cfDNA plasma samples from cancer patients and non-cancer controls. This method introduces a new domain within bioinformatics and personalized medicine – somatic whole genome mutation calling for liquid biopsy.


Convergent and discriminant validation by the multitrait-multimethod matrix.


title: Developer Experience: Concept and Definition

abstract: New ways of working such as globally distributed development or the integration of self-motivated external developers into software ecosystems will require a better and more comprehensive understanding of developers' feelings, perceptions, motivations and identification with their tasks in their respective project environments. User experience is a concept that captures how persons feel about products, systems and services. It evolved from disciplines such as interaction design and usability to a much richer scope that includes feelings, motivations, and satisfaction. Similarly, developer experience could be defined as a means for capturing how developers think and feel about their activities within their working environments, with the assumption that an improvement of the developer experience has positive impacts on characteristics such as sustained team and project performance. This article motivates the importance of developer experience, sketches related approaches from other domains, proposes a definition of developer experience that is derived from similar concepts in other domains, describes an ongoing empirical study to better understand developer experience, and finally gives an outlook on planned future research activities.


title: An approximation algorithm for the hamiltonian walk problem on maximal planar graphs

abstract: Abstract A hamiltonian walk of a graph is a shortest closed walk that passes through every vertex at least once, and the length is the total number of traversed edges. The hamiltonian walk problem in which one would like to find a hamiltonian walk of a given graph is NP-complete. The problem is a generalized hamiltonian cycle problem and is a special case of the traveling salesman problem. Employing the techniques of divide-and-conquer and augmentation, we present an approximation algorithm for the problem on maximal planar graphs. The algorithm finds, in O(p2) time, a closed spanning walk of a given arbitrary maximal planar graph, and the length of the obtained walk is at most 3 2 (p − 3) if the graph has p (≥ 9) vertices. Hence the worst-case bound is 3 2 .


title: Lung carcinoma with rhabdoid cells: a clinicopathological study and survival analysis of 14 cases

abstract: AIMS ::: We determined the clinicopathological features of primary lung carcinomas with rhabdoid cells by defining the immunophenotype of rhabdoid cells and analysing survival. ::: ::: ::: METHODS AND RESULTS ::: Rhabdoid cells are distinctive in having an eccentric nucleus and a large intracytoplasmic inclusion on routinely stained sections. Based on the number of rhabdoid cells, 45 cases of large cell carcinoma were divided into the following three types: lung tumour with a rhabdoid phenotype (LTRP) (n=4), lung carcinoma with a small number of rhabdoid cells (LCSR) (n=10), large cell carcinoma containing no rhabdoid cells (LCNR) (n=31). LTRP is composed of at least 10% rhabdoid cells. In LCSR the percentage of rhabdoid cells is less than 10%. LTRP and LCSR are associated with locally advanced disease. Immunohistochemical stains were positive for epithelial markers in all LTRP and eight LCSR, for neuroendocrine markers in one LTRP and three LCSR. The outcome is worse for patients with LTRP than LCSR or LCNR. LCSR shows a trend close to LCNR. Stage-matched survival analysis, however, revealed no statistically significant difference among the histological subtypes. ::: ::: ::: CONCLUSIONS ::: Rhabdoid cells are heterogeneous except for epithelial markers and vimentin positivity. Less than 5% of rhabdoid cells has a negligible effect on prognosis.


title: Pattern-based development of Domain-Specific Modelling Languages

abstract: Model-Driven Engineering (MDE) promotes the use of models to conduct all phases of software development in an automated way. Models are frequently defined using Domain-Specific Modelling Languages (DSMLs), which many times need to be developed for the domain at hand. However, while constructing DSMLs is a recurring activity in MDE, there is scarce support for gathering, reusing and enacting knowledge for their design and implementation. This forces the development of every new DSML to start from scratch. To alleviate this problem, we propose the construction of DSMLs and their modelling environments aided by patterns which gather knowledge of specific domains, design alternatives, concrete syntax, dynamic semantics and functionality for the modelling environment. They may have associated services, realized via components. Our approach is supported by a tool that enables the construction of DSMLs through the application of patterns, and synthesizes a graphical modelling environment according to them.


title: Throughput Modeling and Analysis of Random Access in Narrowband Internet of Things

abstract: Narrowband Internet of Things (NB-IoT) is one of the most promising technologies for low-power, wide-area, and low-traffic applications. In NB-IoT, random access is implemented in media access control layer to resolve the channel contention conflict among multiple user equipments (UEs), and is crucial to the throughput performance of NB-IoT. Previous results in long-term evolution cannot be directly applied due to specification differences. In this paper, we take the first attempt to systematically analyze the performance of random access in NB-IoT. First, after extensively studying the backoff mechanism, we characterize the probability that a UE initiates random access, the probability that a packet is transmitted successfully and the probability that a channel is busy. Then, we define each UE’s buffer as a first-in-first-out queue. We employ Markov chain to model retransmission number caused by collisions and the length of the queue simultaneously. By exploiting the characteristic of the steady-state distribution of the Markov chain, the above three probabilities in steady state can be obtained explicitly. Based on these probabilities, we calculate the system throughput in terms of UE number, packet generation rate, retransmission number, and the length of the queue. Finally, we investigate the system throughput and conduct extensive simulations under various parameters, which validate our analysis.


title: Optimization at Container Terminals: Status, Trends and Perspectives

abstract: International sea-freight container transportation has grown dramatically over the last years and container terminals represent nowadays a key actor in the global shipping network. Terminal managers have to face with an increasing competitiveness among terminals, which require more and more efficiency in container operations both along the quayside and within the yard: the objective is usually to minimize ships turnaround time, one of the main indicators of the terminal performance for the shipping companies. Moreover, the minimization of operational costs directly entails the achievement of competitive terminal fares, thus increasing the attractiveness for new customers. Operations research methods and techniques are therefore worth being used in optimizing terminal operations. In this work, we firstly give an overview of decision problems which arise in the management of a container terminal (e.g. berth allocation, quay crane scheduling, storage policies and strategies, transfer operations, ship stowage planning) and provide a review of recent papers in the OR literature. Then, starting from a collaboration with some of the busiest ports in Europe, we identify some critical issues: in particular, we discuss the impact that gate and transshipment operations have on the yard. We also focus on competition and cooperation issues among port market players and decision makers. Finally, we conclude by suggesting possible research tracks and open issues.


title: Harmonic current control for Interior Permanent Magnet Synchronous Machines applying current controller design method by using complex vectors

abstract: This paper proposes a cross-coupling decoupling PI controller based on a complex vector notation for the harmonic current control system of Interior Permanent Magnet Synchronous Machines. The proposed method could achieve decoupling control in synchronous reference frame rotated with the harmonic order, and could improve a performance of harmonic current control system, e.g. transient response. In addition, the proposed current controller is designed by using only controller bandwidth and fundamental parameters such as armature resistance and d-q axis inductances, therefore the proposed method is not effected by identification errors of spatial harmonics and has low parameter sensitivity. Effectiveness of the proposed method is revealed by simulation results and experimental results.


title: Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway

abstract: Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas. Using deep convolutional neural networks, we can now quantitatively demonstrate that there is indeed an explicit gradient for feature complexity in the ventral pathway of the human brain. Our approach also allows stimulus features of increasing complexity to be mapped across the human brain, providing an automated approach to probing how representations are mapped across the cortical sheet. Finally, it is shown that deep convolutional neural networks allow decoding of representations in the human brain at a previously unattainable degree of accuracy, providing a more sensitive window into the human brain.


title: State of the art of network protocol reverse engineering tools

abstract: Communication protocols enable structured information exchanges between different entities. A description, at different levels of detail, is necessary for many applications, such as interoperability or security audits. When such a description is not available, one can resort to protocol reverse engineering to infer the format of exchanged messages or a model of the protocol. During the past 12 years, several tools have been developed in order to automate, entirely or partially, the protocol inference process. Each of those tools has been developed with a specific application goal for the inferred model, leading to specific needs, and thus different strengths and limitations. After identifying key challenges, the paper presents a survey of protocol reverse engineering tools developed in the last decade. We consider tools focusing on the inference of the format of individual messages or of the grammar of sequences of messages. Finally, we propose a classification of these tools according to different criteria, that is aimed at providing relevant insights about the techniques used by each of these tools and comparatively to other tools, for the classification of messages, the inference of their format or of the grammar of the protocol. This classification also permits to identify technical areas that are not sufficiently explored so far and that require further development in the future.


title: Highly accurate gaze estimation using a consumer RGB-D sensor

abstract: Determining the direction in which a person is looking is an important problem in a wide range of HCI applications. In this paper we describe a highly accurate algorithm that performs gaze estimation using an affordable and widely available device such as Kinect. The method we propose starts by performing accurate head pose estimation achieved by fitting a person specific morphable model of the face to depth data. The ordinarily competing requirements of high accuracy and high speed are met concurrently by formulating the fitting objective function as a combination of terms which excel either in accurate or fast fitting, and then by adaptively adjusting their relative contributions throughout fitting. Following pose estimation, pose normalization is done by re-rendering the fitted model as a frontal face. Finally gaze estimates are obtained through regression from the appearance of the eyes in synthetic, normalized images. Using EYEDIAP, the standard public dataset for the evaluation of gaze estimation algorithms from RGB-D data, we demonstrate that our method greatly outperforms the state of the art.


title: Nmap: A Novel Neighborhood Preservation Space-filling Algorithm

abstract: Space-filling techniques seek to use as much as possible the visual space to represent a dataset, splitting it into regions that represent the data elements. Amongst those techniques, Treemaps have received wide attention due to its simplicity, reduced visual complexity, and compact use of the available space. Several different Treemap algorithms have been proposed, however the core idea is the same, to divide the visual space into rectangles with areas proportional to some data attribute or weight. Although pleasant layouts can be effectively produced by the existing techniques, most of them do not take into account relationships that might exist between different data elements when partitioning the visual space. This violates the distance-similarity metaphor, that is, close rectangles do not necessarily represent similar data elements. In this paper, we propose a novel approach, called Neighborhood Treemap (Nmap), that seeks to solve this limitation by employing a slice and scale strategy where the visual space is successively bisected on the horizontal or vertical directions and the bisections are scaled until one rectangle is defined per data element. Compared to the current techniques with the same similarity preservation goal, our approach presents the best results while being two to three orders of magnitude faster. The usefulness of Nmap is shown by two applications involving the organization of document collections and the construction of cartograms illustrating its effectiveness on different scenarios.


Juvenile Breast Hypertrophy: A Successful Breast Reduction of 14.9% Body Weight without Recurrence in a 5-Year Follow-Up


title: Techniques for Handling Error in User-Estimated Execution Times During Resource Management on Systems Processing MapReduce Jobs

abstract: In our previous work, we described a resource allocation and scheduling technique for processing an open stream of MapReduce jobs with SLAs (characterized by an earliest start time, an execution time, and a deadline) called the Hadoop Constraint Programming based Resource Management technique (HCP-RM). Since the user-estimated job execution times are used to perform resource allocation and scheduling, error/inaccuracies in the execution times can hinder the ability of HCP-RM from making effective scheduling decisions, leading to a degradation in system performance. This paper focuses on improving the robustness of HCP-RM by introducing a mechanism to handle errors/inaccuracies in user estimates of job execution times that are submitted as part of the job's SLA. A Prescheduling Error Handling technique (PSEH) is devised to adjust the user-estimated execution times of the jobs to make them more accurate before they are used by the resource management algorithm. Results of experiments conducted on a Hadoop cluster deployed on Amazon EC2 demonstrate the effectiveness of the PSEH technique in improving system performance.


title: Stabilization of Lateral Motion in Passive Dynamic Walking

abstract: Passive dynamic walking refers to a class of bipedal machines that are able to walk down a gentle slope with no external control or energy input. The legs swing naturally as pendula, and conservation of angular momentum governs the contact of the swing foot with the ground. Previous machines have been limited to planar motions. We extend the planar motions to allow for tilting side to side (roll motion). Passive walking cycles exist, but the roll motion is unstable, resembling that of an inverted pendulum. The instability is due to mismatching of roll velocity with the ground contact conditions. Several strategies are presented for stabilizing this motion, of which the quasi-static control of step width is determined to be both simple and efficient.


Training Mixture Models at Scale via Coresets


title: Parsing and Hypergraphs

abstract: While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis, which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra's algorithm can be used to construct a probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as is allowed by the inherent ordering of probabilistic dependencies.


title: FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

abstract: Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Recently, deep learning has drawn much attention in computer vision because of its exceptional performance in image classification tasks. For this reason, its application to connectomic analyses holds great promise, as well. In this paper, we introduce a novel deep neural network architecture, FusionNet, for the automatic segmentation of neuronal structures in connectomics data. FusionNet leverages the latest advances in machine learning, such as semantic segmentation and residual neural networks, with the novel introduction of summation-based skip connections to allow a much deeper network architecture for a more accurate segmentation. We demonstrate the performance of the proposed method by comparing it with state-of-the-art electron microscopy (EM) segmentation methods from the ISBI EM segmentation challenge. We also show the segmentation results on two different tasks including cell membrane and cell body segmentation and a statistical analysis of cell morphology.


title: Hybrid mobile apps in the Google play store: an exploratory investigation

abstract: One of the most intriguing challenges in mobile apps development is its fragmentation with respect to mobile platforms (e.g., Android, Apple iOS, Windows Phone). Large companies like IBM and Adobe and a growing community of developers advocate hybrid mobile apps development as a possible solution to mobile platforms fragmentation. Hybrid mobile apps are consistent across platforms and built on web standards. How hybrid apps are performing in production settings is still highly debated, with limited objective evidence.In this paper, we present the first realistic investigation into mobile hybrid apps through a solid empirical strategy. Our goal is exploratory and we aim at identifying, analysing, and understanding the traits and distinctions of publicly available hybrid mobile apps within their real-life context. The study has been conducted by mining 11,917 free apps and 3,041,315 reviews from the Google Play Store, and analyzing them from both a technical and end users' perception perspective. The results of this study build an objective and reproducible snapshot about how hybrid mobile development is performing "in the wild" in real projects.


title: A Virtual SDN-Enabled LTE EPC Architecture: A Case Study for S-/P-Gateways Functions

abstract: The recent initiative of Network Functions Virtualization (NFV) aims to deliver any data- plane processing or control-plane function in high volume data centers or network elements to decrease operational cost and increase deployment flexibility. In order to dynamically direct traffic flows between respective network elements, Software Defined Networking (SDN) can be seen as one enabler. In this paper, we focus on mobile core network nodes such as the MME, HSS, S- and P- Gateway as standardized for the LTE Evolved Packet Core (EPC). One straightforward solution for a virtualized EPC architecture would be to move all EPC network nodes completely into a data center and handle the data traffic via SDN-enabled switches. However, this solution would keep the conventional monolithic architecture unchanged. A possible split in the EPC functionality between a centralized data center and operator's transport network elements could be needed to provide the desired flexibility, performance and TCO reduction. Therefore, we have analyzed the EPC nodes and classified their functions according to their impact on data-plane and control-plane processing. We propose a mapping for these functions on four alternative deployment frameworks based on SDN and OpenFlow (OF). In addition, we investigate the current OF implementation's capability to realize basic core operations such as QoS, data classification, tunneling and charging. Our analysis shows that functions, which involve high data packet processing such as tunneling, have more potential to be kept on the data-plane network element, i.e. realized by an OpenFlow Switch. We argue for an enhanced OF network element NE+, which contains additional network functions next to the basic OpenFlow protocol.


title: Multi-Hop Knowledge Graph Reasoning with Reward Shaping

abstract: Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used for training, the agent can be misled by spurious search trajectories that incidentally lead to the correct answer. We propose two modeling advances to address both issues: (1) we reduce the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts; (2) we counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks. Our approach significantly improves over existing path-based KGQA models on several benchmark datasets and is comparable or better than embedding-based models.


title: Open-end multi-level six-phase machine drive system with three three-phase DC-link converters

abstract: A novel multi-level AC six-phase motor drive is developed in this paper. The scheme is based on three conventional 2-level three-phase voltage source inverters (VSIs) supplying the open-end windings of a dual three-phase motor (six-phase induction machine). The proposed inverter is capable of supply the machine with multi-level voltage waveforms. The developed system is compared with the conventional solution and it is demonstrated that the drive system permits to reduce the harmonic distortion of the machine currents, to reduce the total semiconductor losses and to decrease the power processed by converter switches. The system model and the Pulse-Width Modulation (PWM) strategy are presented. The experimental verification was obtained by using IGBTs with dedicated drives and a digital signal processor (DSP) with plug-in boards and sensors.


title: Probing user opinions in an indirect way: an aspect based sentiment analysis of game reviews

abstract: This paper presents a method for gathering and evaluating user attitudes towards previously released video games. A three-part video game franchise was selected, and all user reviews of these games were collected. The most frequently mentioned words of the game were derived from this dataset through word frequency analysis. The words, called "aspects" were then further analyzed through a manual aspect based sentiment analysis. The final analysis showed that the rating of user reviews to a high degree correlate with the sentiment of the aspect in question. This knowledge is valuable for a developer who wishes to learn more about previous games success or failure factors.


title: Trigeminal Autonomic Cephalalgias: Paroxysmal Hemicrania, SUNCT/SUNA, and Hemicrania Continua

abstract: The trigeminal autonomic cephalalgias (TACs) are a group of primary headache disorders that include cluster headache (CH), paroxysmal hemicrania (PH), and short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing/cranial autonomic features (SUNCT/SUNA). Hemicrania continua (HC) is often included with this group, although the second edition of The International Classification of Headache Disorders did not link the entities. Trigeminal autonomic cephalalgias are generally characterized by relatively short-lasting attacks of severe pain and lateralized associated features including the pain, cranial autonomic symptoms, and where present, migrainous symptoms, such as photophobia. Paroxysmal hemicrania has intermediate duration and intermediate attack frequency. Short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing has the shortest attack duration and the highest attack frequency. Hemicrania continua has a continuous pain with exacerbations that can include cranial autonomic symptoms as part of the phenotype. The syndromes share much in their pathophysiology and investigation paths; however, their treatment is distinct, so that the accurate differentiation is important for optimal management.


title: Data-driven pause prediction for speech synthesis in storytelling style speech

abstract: In the storyteller speech, pauses plays a significant role in introducing suspense and climax. Pauses are used to emphasize keywords, emotion-salient words and separate the phrases in the utterance. The objective of this work is to predict the position and duration of the pauses in the synthesized speech from the text-to-speech system. We analyzed the pause patterns in storyteller speech and classified the pauses into three different categories, that is, short, medium and long pauses. A data driven three stage pause prediction model is proposed. In the first stage, the model is built properly to identify the pause position within an utterance using a set of word-level features. In the second stage, the pauses are classified into three different categories using a set of syllable-level features. In the final stage, a regression predictor is trained to predict the pause duration for each category. We conducted both objective and subjective tests to evaluate the proposed method. The subjective evaluation showed that subjects are perceiving a noticeable difference in the synthesized speech using the proposed method.


title: Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security

abstract: Blockchain may help solve several complex problems related to securing the integrity and trustworthiness of rapid, distributed, complex energy transactions and data exchanges. In a move towards grid resilience, blockchain commoditizes trust and enables automated smart contracts to support auditable multiparty transactions based on predefined rules between distributed energy providers and customers. Blockchain based smart contracts also help remove the need to interact with third-parties, facilitating the adoption and monetization of distributed energy transactions and exchanges, both energy flows as well as financial transactions. This may help reduce transactive energy costs and increase the security and sustainability of distributed energy resource (DER) integration, helping to remove barriers to a more decentralized and resilient power grid. This paper explores the application of blockchain and smart contracts to improve smart grid cyber resiliency and secure transactive energy applications.


title: XDvision: Dense outdoor perception for autonomous vehicles

abstract: Robust perception is the cornerstone of safe and environmentally-aware autonomous navigation systems. Autonomous robots are expected to recognise the objects in their surroundings under a wide range of challenging environmental conditions. This problem has been tackled by combining multiple sensor modalities that have complementary characteristics. This paper proposes an approach to multi-sensor-based robotic perception that leverages the rich and dense appearance information provided by camera sensors, and the range data provided by active sensors independently of how dense their measurements are. We introduce a framework we call XDvision where colour images are augmented with dense depth information obtained from sparser sensors such as lidars. We demonstrate the utility of our framework by comparing the performance of a standard CNN-based image classifier fed with image data only with the performance of a two-layer multimodal CNN trained using our augmented representation.


title: Deep reinforcement learning-based scheduling for roadside communication networks

abstract: The proper design of a vehicular network is the key expeditor for establishing an efficient Intelligent Transportation System, which enables diverse applications associated with traffic safety, traffic efficiency, and the entertainment of commuting passengers. In this paper, we address both safety and Quality-of-Service (QoS) concerns in a green Vehicle-to-Infrastructure communication scenario. Using the recent advances in training deep neural networks, we present a deep reinforcement learning model, namely deep Q-network, that learns an energy-efficient scheduling policy from high-dimensional inputs corresponding to the characteristics and requirements of vehicles residing within a RoadSide Unit's (RSU) communication range. The realized policy serves to extend the lifetime of the battery-powered RSU while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform both random and greedy scheduling benchmarks.


title: Laundering Sexual Deviance: Targeting Online Pornography through Anti-money Laundering

abstract: This paper concentrates on cyber-pornography/obscenity, which encompasses online publications or distribution of sexually explicit material in breach of the English obscenity and indecency laws. After examining the major deficiencies of the attempts to restrict illegal pornographic representations, the authors aim to highlight that the debate regarding their availability in the Internet era neglects the lucrative nature of the circulation of such material, which can be also targeted through anti-money laundering. Rising profits fuel the need to recycle the money back into the legal financial system, with a view to concealing their illegal origin. Anti-money laundering laws require disclosure of any isuspicion¢ related to money laundering, thus opening another door for law enforcement to reach the criminal.


title: Semantically Consistent Regularization for Zero-Shot Recognition

abstract: The role of semantics in zero-shot learning is considered. The effectiveness of previous approaches is analyzed according to the form of supervision provided. While some learn semantics independently, others only supervise the semantic subspace explained by training classes. Thus, the former is able to constrain the whole space but lacks the ability to model semantic correlations. The latter addresses this issue but leaves part of the semantic space unsupervised. This complementarity is exploited in a new convolutional neural network (CNN) framework, which proposes the use of semantics as constraints for recognition. Although a CNN trained for classification has no transfer ability, this can be encouraged by learning an hidden semantic layer together with a semantic code for classification. Two forms of semantic constraints are then introduced. The first is a loss-based regularizer that introduces a generalization constraint on each semantic predictor. The second is a codeword regularizer that favors semantic-to-class mappings consistent with prior semantic knowledge while allowing these to be learned from data. Significant improvements over the state-of-the-art are achieved on several datasets.


title: The optimal routing problem in the context of battery-powered electric vehicles

abstract: Electric vehicles (EV) powered by batteries will play a sig- nificant role in the road traffic of the future. The unique characteristics of such EVs – limited cruising range, long recharge times, and the ability to regain energy during deceleration – require novel routing algorithms, since the task is now to determine the most economical route rather than just the shortest one. This paper proposes extensions to general shortest- path algorithms that address the problem of energy-optimal routing. Specifically, we (i) formalize energy-efficient routing in the presence of rechargeable batteries as a special case of the constrained shortest path problem (CSPP) with hard and soft constraints, and (ii) present an adap- tion of a general shortest path algorithm (using an energy graph, i.e., a graph with a weight function representing the energy consumption) that respects the given constraints and has a worst case complexity of O(n3). The presented algorithms have been implemented and evaluated within a prototypic navigation system for energy-efficient routing.


title: SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment

abstract: This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval2014. Participation was open to systems based on any approach. Systems were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (i) semantic relatedness and (ii) entailment. The task attracted 21 teams, most of which participated in both subtasks. We received 17 submissions in the relatedness subtask (for a total of 66 runs) and 18 in the entailment subtask (65 runs).


title: Smarter eyewear: using commercial EOG glasses for activity recognition

abstract: Smart eyewear computing is a relatively new subcategory in ubiquitous computing research, which has enormous potential. In this paper we present a first evaluation of soon commercially available Electrooculography (EOG) glasses (J!NS MEME) for the use in activity recognition. We discuss the potential of EOG glasses and other smart eye-wear. Afterwards, we show a first signal level assessment of MEME, and present a classification task using the glasses. We are able to distinguish of 4 activities for 2 users (typing, reading, eating and talking) using the sensor data (EOG and acceleration) from the glasses with an accuracy of 70 % for 6 sec. windows and up to 100 % for a 1 minute majority decision. The classification is done user-independent. The results encourage us to further explore the EOG glasses as platform for more complex, real-life activity recognition systems.


title: Automated reassembly of fragmented images

abstract: We address the problem of reassembly of images from a collection of their fragments. The image reassembly problem is formulated as a combinatorial optimization problem and image assembly is then done by finding an optimal ordering of fragments. We present implementation results showing that images can be reconstructed with high accuracy even when there are thousands of fragments and multiple images involved.


title: AXIAL-FLUX PERMANENT-MAGNET MOTOR DESIGN FOR ELECTRIC VEHICLE DIRECT DRIVE USING SIZING EQUATION AND FINITE ELEMENT ANALYSIS

abstract: The design process of a double-sided slotted TORUS axial- ∞ux permanent-magnet (AFPM) motor suitable for direct drive of electric vehicle (EV) is presented. It used sizing equation and Finite Element Analysis (FEA). AFPM motor is a high-torque-density motor easily mounted compactly onto a vehicle wheel, fltting the wheel rim perfectly. A preliminary design is a double-sided slotted AFPM motor with 6 rotor poles for high torque-density and stable rotation. In determining the design requirements, a simple vehicle-dynamics model that evaluates vehicle performance through the typical cruising trip of an automobile was considered. To obtain, with the highest possible torque, the initial design parameters of the motor, AFPM's fundamental theory and sizing equation were applied. Vector Field Opera-3D 14.0 commercial software ran the FEA of the motor design, evaluating and enhancing accuracy of the design parameters. Results of the FEA simulation were compared with those obtained from the sizing equation; at no-load condition, the ∞ux density at every part of the motor agreed. The motor's design meets all the requirements and limits of EV, and flts the shape and size of a classical-vehicle wheel rim. The design process is comprehensive and can be used for an arbitrary EV with an arbitrary cruising scenario.


title: Management of information security indicators under a cognitive security model

abstract: Organizations face daily different computer security threats, which makes necessary to have a Decision Support System (DSS) that allows generating knowledge to define strategies to minimize risk. The information security management supported in DSS requires time-consuming manual processes, analysis of large amounts of data and the experience of security specialists. The use of alternatives such as bigdata, machine learning, data analytics, and data visualization can reduce the time of manual processes and the automation of data analysis, contributing the inclusion of intelligence to decision support systems. This study presents a conceptual framework based on the three components of information security: people, technology and processes, with a cognitive approach; emphasizing the aspect of technology in the management of indicators through the integration of concepts such as bigdata, machine learning, data analytics, and data visualization to strengthen the DSS for the generation of knowledge and to help security specialists to make decisions at a strategic level.


Tenets , implications and tenability of three theories


title: Evaluating shallow and deep networks for secure shell (ssh)traffic analysis

abstract: The family of recurrent neural network (RNN) mechanisms are largely used for the various tasks in natural language processing, speech recognition, image processing and many others due to they established as a powerful mechanism to capture dynamic temporal behaviors in arbitrary length of large-scale sequence data. This paper attempts to know the effectiveness of various RNN mechanisms on the traffic classification specifically for Secure Shell (SSH) protocol by modeling the feature sets of statistical flows as time-series obtained from various public and private traces. These traces are from NIMS (Network Information Management and Security Group), DARPA (Defense Advanced Research Projects Agency) 1999 Week1, DARPA 1999 Week3, MAWI (Measurement and Analysis on the WIDE Internet), and NLANR (National Laboratory for Applied Network Research) Active Measurement Project (AMP). A various configurations of network topologies, network parameters and network structures are used for family of RNN architectures to identify an optimal architecture. The experiments are run up to 1000 epochs with learning rate in the range [0.01-05] on both the binary and multiclass classification settings. RNN mechanisms have performed well in comparison to the other classical machine learning algorithms. Moreover, long short-term memory (LSTM) mechanism is a modified RNN, as achieved highest accuracy in cross-validation and testing of binary and multi-class classification cases. The background reason to that is, RNN mechanisms have capability to capture the dynamic temporal dependencies by storing information and updating them, when it is necessary across time-steps.


title: Millimeter-Wave Integrated Circuits in 65-nm CMOS

abstract: We present the design and measurement results of millimeter-wave integrated circuits implemented in 65-nm baseline CMOS. Both active and passive test structures were measured. In addition, we present the design of an on-chip spiral balun and the transition from CPW to the balun and report transistor noise parameter measurement results at V-band. Finally, the design and measurement results of two amplifiers and a balanced resistive mixer are presented. The 40-GHz amplifier exhibits 14.3 dB of gain and the 1-dB output compression point is at +6-dBm power level using a 1.2 V supply with a compact chip area of 0.286 mm2. The 60-GHz amplifier achieves a measured noise figure of 5.6 dB at 60 GHz. The AM/AM and AM/PM results show a saturated output power of +7 dBm using a 1.2 V supply. In downconversion, the balanced resistive mixer achieves 12.5 dB of conversion loss and +5 dBm of 1-dB input compression point. In upconversion, the measured conversion loss was 13.5 dB with -19 dBm of 1-dB output compression point.


title: Exploring Prior Knowledge for Pedestrian Detection

abstract: In this paper, we aim to explore the role of prior knowledge for pedestrian detection. The main idea is to integrate human body priors into the design of features. To this end, we propose the symmetric features and cross-channel features so as to capture the specific information of human body. Experimental results demonstrate that our detector achieves state-of-the-art performance. What’s more, the evaluation results on "scale" subsets of Caltech-USA show that our detector performs best at medium scale and therefore has great potential to be integrated into real-world applications.


title: Action Schema Networks: Generalised Policies with Deep Learning

abstract: In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight-sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet's learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.


title: Specifying real-time properties with metric temporal logic

abstract: This paper is motivated by the need for a formal specification method for real-time systems. In these systemsquantitative temporal properties play a dominant role. We first characterize real-time systems by giving a classification of such quantitative temporal properties. Next, we extend the usual models for temporal logic by including a distance function to measure time and analyze what restrictions should be imposed on such a function. Then we introduce appropriate temporal operators to reason about such models by turning qualitative temporal operators into (quantitative) metric temporal operators and show how the usual quantitative temporal properties of real-time systems can be expressed in this metric temporal logic. After we illustrate the application of metric temporal logic to real-time systems by several examples, we end this paper with some conclusions.


title: SimpleScalar: An Infrastructure for

abstract: Designers can execute programs on software models to validate a proposed hardware design's performance and correctness, while programmers can use these models to develop and test software before the real hardware becomes available. Three critical requirements drive the implementation of a software model: performance, flexibility, and detail. Performance determines the amount of workload the model can exercise given the machine resources available for simulation. Flexibility indicates how well the model is structured to simplify modification, permitting design variants or even completely different designs to be modeled with ease. Detail defines the level of abstraction used to implement the model's components. The SimpleScalar tool set provides an infrastructure for simulation and architectural modeling. It can model a variety of platforms ranging from simple unpipelined processors to detailed dynamically scheduled microarchitectures with multiple-level memory hierarchies. SimpleScalar simulators reproduce computing device operations by executing all program instructions using an interpreter. The tool set's instruction interpreters also support several popular instruction sets, including Alpha, PPC, x86, and ARM.


title: Ranking Attacks Based on Vulnerability Analysis

abstract: Now that multiple-known attacks can affect one software product at the same time, it is necessary to rank and prioritize those attacks in order to establish a better defense. The purpose of this paper is to provide a set of security metrics to rank attacks based on vulnerability analysis. The vulnerability information is retrieved from a vulnerability management ontology, which integrates commonly used standards like CVE, CWE, CVSS, and CAPEC. Among the benefits of ranking attacks through the method proposed here are: a more effective mitigation or prevention of attack patterns against systems, a better foundation to test software products, and a better understanding of vulnerabilities and attacks.


title: Semantic indexing of Arabic texts for information retrieval system

abstract: As part of information retrieval systems (IRS) and in the context of the use of ontologies for documents and queries indexing, we propose and evaluate in this paper the contribution of this approach applied to Arabic texts. To do this we indexed a corpus of Arabic text using Arabic WordNet. The disambiguation of words was performed by applying the Lesk algorithm. The results obtained by our experiment allowed us to deduct the contribution of this approach in IRS for Arabic texts.


DevOps for Developers


title: Design and implementation of OFDM signal processing on PSoC microcontroller

abstract: In this paper, we describe the construction of OFDM transmission system that implements the OFDM signal processing to PSoC5 microcomputer. In the future, we aim to build the receiving system that corresponds to the transmitter with multiple microcontrollers. Examining multi-level digital modulation causes to increase the amount of data per OFDM signal, then can improve the transmission rate. Moreover, we would like to design and implement the OFDM systems on PSoC microcontroller over several wireless channels.


title: ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT

abstract: Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.


title: Ray tracing height fields

abstract: We present a novel surface reconstruction algorithm which can directly reconstruct surfaces with different levels of smoothness in one framework from height fields using 3D discrete grid ray tracing. Our algorithm exploits the 2.5D nature of the elevation data and the regularity of the rectangular grid from which the height field surface is sampled. Based on this reconstruction method, we also develop a hybrid rendering method which has the features of both rasterization and ray tracing. This hybrid method is designed to take advantage of GPUs newly available flexibility and processing power.


title: Medical image denoising using convolutional neural network: a residual learning approach

abstract: In medical imaging, denoising is very important for analysis of images, diagnosis and treatment of diseases. Currently, image denoising methods based on deep learning are effective, where the methods are however limited for the requirement of training sample size (i.e., not successful enough for small data size). Using small sample size, we design deep feed forward denoising convolutional neural networks by studying the model in deep framework, learning approach and regularization approach for medical image denoising. More specifically, we use residual learning as a learning approach and batch normalization as regularization in the deep model. Unlike most of the other image denoising approaches which directly learn the latent clean images, the residual learning approach learns the noise from the noisy images instead of the latent clean images where the denoised images are obtained by subtracting the learned residual from the noisy image. Moreover, batch normalization is integrated with residual learning to improve model learning accuracy and training time. We compute the quality of the reconstructed or denoised image in standard image quality metrics, peak signal to noise ratio and structural similarity and compare our model performance with some medical image denoising techniques. Experimental results reveal that our approach has better performance than some other methods.


title: Compact Patch Antenna Design for Outdoor RF Energy Harvesting in Wireless Sensor Networks

abstract: In this paper, two compact patch antenna designs for a new application - outdoor RF energy harvesting in powering a wireless soil sensor network - are presented. The first design is a low-profile folded shorted patch antenna (FSPA), with a small ground plane and wide impedance bandwidth. The second design is a novel FSPA structure with four pairs of slot embedded into its ground plane. Performance of both antennas was first simulated using CST Microwave Studio. Antenna prototypes were then fabricated and tested in the anechoic chamber and in their actual operating environment - an outdoor field. It was found that the FSPA with slotted ground plane achieved a comparable impedance bandwidth to the first design, with an overall size reduction of 29%. Simulations were also carried out to investigate the effects of different design parameters on the performance of the proposed slotted ground plane FSPA.


title: Querying semantic web data with SPARQL

abstract: The Semantic Web is the initiative of the W3C to make information on the Web readable not only by humans but also by machines. RDF is the data model for Semantic Web data, and SPARQL is the standard query language for this data model. In the last ten years, we have witnessed a constant growth in the amount of RDF data available on the Web, which have motivated the theoretical study of some fundamental aspects of SPARQL and the development of efficient mechanisms for implementing this query language. Some of the distinctive features of RDF have made the study and implementation of SPARQL challenging. First, as opposed to usual database applications, the semantics of RDF is open world, making RDF databases inherently incomplete. Thus, one usually obtains partial answers when querying RDF with SPARQL, and the possibility of adding optional information if present is a crucial feature of SPARQL. Second, RDF databases have a graph structure and are interlinked, thus making graph navigational capabilities a necessary component of SPARQL. Last, but not least, SPARQL has to work at Web scale! RDF and SPARQL have attracted interest from the database community. However, we think that this community has much more to say about these technologies, and, in particular, about the fundamental database problems that need to be solved in order to provide solid foundations for the development of these technologies. In this paper, we survey some of the main results about the theory of RDF and SPARQL putting emphasis on some research opportunities for the database community.


title: Automatically Generating Wikipedia Articles: A Structure-Aware Approach

abstract: In this paper, we investigate an approach for creating a comprehensive textual overview of a subject composed of information drawn from the Internet. We use the high-level structure of human-authored texts to automatically induce a domain-specific template for the topic structure of a new overview. The algorithmic innovation of our work is a method to learn topic-specific extractors for content selection jointly for the entire template. We augment the standard perceptron algorithm with a global integer linear programming formulation to optimize both local fit of information into each topic and global coherence across the entire overview. The results of our evaluation confirm the benefits of incorporating structural information into the content selection process.


title: Google's Deep Web crawl

abstract: The Deep Web, i.e., content hidden behind HTML forms, has long been acknowledged as a significant gap in search engine coverage. Since it represents a large portion of the structured data on the Web, accessing Deep-Web content has been a long-standing challenge for the database community. This paper describes a system for surfacing Deep-Web content, i.e., pre-computing submissions for each HTML form and adding the resulting HTML pages into a search engine index. The results of our surfacing have been incorporated into the Google search engine and today drive more than a thousand queries per second to Deep-Web content. ::: ::: Surfacing the Deep Web poses several challenges. First, our goal is to index the content behind many millions of HTML forms that span many languages and hundreds of domains. This necessitates an approach that is completely automatic, highly scalable, and very efficient. Second, a large number of forms have text inputs and require valid inputs values to be submitted. We present an algorithm for selecting input values for text search inputs that accept keywords and an algorithm for identifying inputs which accept only values of a specific type. Third, HTML forms often have more than one input and hence a naive strategy of enumerating the entire Cartesian product of all possible inputs can result in a very large number of URLs being generated. We present an algorithm that efficiently navigates the search space of possible input combinations to identify only those that generate URLs suitable for inclusion into our web search index. We present an extensive experimental evaluation validating the effectiveness of our algorithms.


title: COSS Losses in 600 V GaN Power Semiconductors in Soft-Switched, High- and Very-High-Frequency Power Converters

abstract: We report losses from charging and discharging the parasitic output capacitor, ${\rm C}_{\rm OSS}$ , in Gallium Nitride (GaN) power devices with voltage ratings over 600 ${\rm V}_{\rm DS}$ . These losses are of particular importance in soft-switched circuits used at MHz switching frequencies, where the output capacitance of the device is charged and discharged once per switching cycle during the device's off-time. This process is assumed lossless. We measure ${\rm C}_{\rm OSS}$ losses from 5–35 MHz sine, square, and Class- $\Phi _{2}$ waveshapes in enhancement-mode and cascode devices, and find that losses are present in all tested devices, equal or greater than conduction losses at MHz frequencies, and exponentially increasing with $\mathbf{dV/dt}$ . The cascode device outperforms the e-mode devices under 300 V, but the e-mode devices are preferred above this operating voltage. Furthermore, we show that, within a device family, losses scale linearly with output energy storage. Packaging appears to have only a minor effect on these losses. Finally, we demonstrate 10 MHz, 200 W dc–dc converters with varying device configurations, showing that, even with constant circulating currents, moving to larger devices with lower ${\rm R}_{\rm DS,ON}$ actually degrades efficiency in certain applications due to ${\rm C}_{\rm OSS}$ losses. In the high-voltage, high-frequency range, these reported losses must be optimized simultaneously with conduction losses on a per-application basis.


title: Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique

abstract: Social networks have developed as a promising point for everybody to communicate with their interested friend and share their opinions, photos, and videos. Also, it has been an upcoming research field and has picked an established position globally. In this paper, we considered depression problems among various Facebook users. Already, a number of researchers have studied and applied many techniques to detect depression, but still need to detect accurately from social network data. So, we investigate the possibility to utilize Facebook data and apply KNN (k-nearest neighbors) classification technique for detecting depressive emotions. We do believe that our investigation and approach might be helpful to raise consciousness in online social network users.


title: Broad coverage paragraph segmentation across languages and domains

abstract: This article considers the problem of automatic paragraph segmentation. The task is relevant for speech-to-text applications whose output transcipts do not usually contain punctuation or paragraph indentation and are naturally difficult to read and process. Text-to-text generation applications (e.g., summarization) could also benefit from an automatic paragaraph segementation mechanism which indicates topic shifts and provides visual targets to the reader. We present a paragraph segmentation model which exploits a variety of knowledge sources (including textual cues, syntactic and discourse-related information) and evaluate its performance in different languages and domains. Our experiments demonstrate that the proposed approach significantly outperforms our baselines and in many cases comes to within a few percent of human performance. Finally, we integrate our method with a single document summarizer and show that it is useful for structuring the output of automatically generated text.


title: Distance and Density Clustering for Time Series Data

abstract: Clustering is an important branch in the field of data mining as well as statistical analysis and is widely used in exploratory analysis. Many algorithms exist for clustering in the Euclidean space. However, time series clustering introduces new problems, such as inadequate distance measure, inaccurate cluster center description, lack of efficient and accurate clustering techniques. When dealing with time series data, Dynamic Time Warping (DTW) is an accepted and effective distance measure. For cluster updates and representation, DTW Barycenter Averaging (DBA) algorithm being a global averaging method using DTW and has proven to be an effective averaging method for time series data. In this paper, we propose a Distance Density clustering method that is a medoid-based clustering with time series data density consideration which provides clustering results in a hierarchy fashion. First, we introduce two clustering initialization techniques, from the time series similarity matrix we use majority voting to determine either the nearest or the furthest time series as the initial clustering seed. By doing so, our clustering method is deterministic, and the clustering results can always be reproduced. In the Distance Density clustering algorithm, we use medoids because it is a more representative alternative to the statistical mean, especially with time series data where the mean value is often non-existent. The time series density is a virtual density based on time series similarity; this can find more natural splits in a dataset and also the number of clusters does not need to be determined a priori. Experiments using the Distance Density clustering technique on the UCR dataset demonstrates that clustering initialization is crucial in obtaining stable and better results than random initialization on average, and is also more accurate than traditional distance clustering.


title: Analysis and Design of Low-Jitter Digital Bang-Bang Phase-Locked Loops

abstract: Digital phase-locked loops based on bang-bang phase detectors are attractive candidates for low-jitter clock-frequency multiplication. Unfortunately, the coarse quantization of phase error makes these systems prone to the generation of limit cycles appearing as unwanted spurs in the spectrum. The random noise contributed by building blocks and acting as dithering signal can eliminate those spurs. The quantitative analysis of those phenomena becomes more involved when a DCO with relaxed intrinsic resolution, such as a ΔΣ-DCO is employed, and when practical spectra of random noise sources are considered. In this work, the expression of jitter is calculated in closed-form taking into account the quantization, introduced by both phase detector and DCO, and the phase noise of DCO, with both 1/f 2 and 1/f 3 components. Combining these results, a closed-form expression of the total output jitter as a function of loop parameters and noise sources is developed which suggests a minimum-jitter design strategy. The proposed analysis and optimization are validated both numerically and experimentally on a 320-MHz digital bang-bang PLL fabricated in a 65-nm CMOS process.


title: Diversity and heritability of the maize rhizosphere microbiome under field conditions.

abstract: The rhizosphere is a critical interface supporting the exchange of resources between plants and their associated soil environment. Rhizosphere microbial diversity is influenced by the physical and chemical properties of the rhizosphere, some of which are determined by the genetics of the host plant. However, within a plant species, the impact of genetic variation on the composition of the microbiota is poorly understood. Here, we characterized the rhizosphere bacterial diversity of 27 modern maize inbreds possessing exceptional genetic diversity grown under field conditions. Randomized and replicated plots of the inbreds were planted in five field environments in three states, each with unique soils and management conditions. Using pyrosequencing of bacterial 16S rRNA genes, we observed substantial variation in bacterial richness, diversity, and relative abundances of taxa between bulk soil and the maize rhizosphere, as well as between fields. The rhizospheres from maize inbreds exhibited both a small but significant proportion of heritable variation in total bacterial diversity across fields, and substantially more heritable variation between replicates of the inbreds within each field. The results of this study should facilitate expanded studies to identify robust heritable plant-microbe interactions at the level of individual polymorphisms by genome wide association, so that plant-microbiome interactions can ultimately be incorporated into plant breeding.


title: Process planning and offline programming for robotic remote laser welding systems

abstract: The paper introduces a complete offline programming toolbox for remote laser welding RLW which provides a semi-automated method for computing close-to-optimal robot programs. A workflow is proposed for the complete planning process, and new models and algorithms are presented for solving the optimisation problems related to each step of the workflow: the sequencing of the welding tasks, path planning, workpiece placement, calculation of inverse kinematics and the robot trajectory, as well as for generating the robot program code. The paper summarises the results of an industrial case study on the assembly of a car door using RLW technology, which illustrates the feasibility and the efficiency of the proposed approach.