title: Multi-tasking with joint semantic spaces for large-scale music annotation and retrieval

abstract: Abstract Music prediction tasks range from predicting tags given a song or clip of audio, predicting the name of the artist, or predicting related songs given a song, clip, artist name or tag. That is, we are interested in every semantic relationship between the different musical concepts in our database. In realistically sized databases, the number of songs is measured in the hundreds of thousands or more, and the number of artists in the tens of thousands or more, providing a considerable challenge to standard machine learning techniques. In this work, we propose a method that scales to such datasets which attempts to capture the semantic similarities between the database items by modelling audio, artist names, and tags in a single low-dimensional semantic embedding space. This choice of space is learnt by optimizing the set of prediction tasks of interest jointly using multi-task learning. Our single model learnt by training on the joint objective function is shown experimentally to have improved accur...


title: An analytical approach for the simulation of realistic low-dose fluoroscopic images

abstract: Purpose ::: The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts.


title: 3D printing based on imaging data: review of medical applications

abstract: PURPOSE ::: Generation of graspable three-dimensional objects applied for surgical planning, prosthetics and related applications using 3D printing or rapid prototyping is summarized and evaluated. ::: ::: ::: MATERIALS AND METHODS ::: Graspable 3D objects overcome the limitations of 3D visualizations which can only be displayed on flat screens. 3D objects can be produced based on CT or MRI volumetric medical images. Using dedicated post-processing algorithms, a spatial model can be extracted from image data sets and exported to machine-readable data. That spatial model data is utilized by special printers for generating the final rapid prototype model. ::: ::: ::: RESULTS ::: Patient-clinician interaction, surgical training, medical research and education may require graspable 3D objects. The limitations of rapid prototyping include cost and complexity, as well as the need for specialized equipment and consumables such as photoresist resins. ::: ::: ::: CONCLUSIONS ::: Medical application of rapid prototyping is feasible for specialized surgical planning and prosthetics applications and has significant potential for development of new medical applications.


title: Path Reducing Watershed for the GPU

abstract: The watershed transform is a popular image segmentation procedure from mathematical morphology used in many applications of computer vision. This paper proposes a novel parallel watershed procedure designed for GPU implementation. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three implementation variants and their parameters are analysed through experiments on 2D and 3D images; a comparison against the state-of-the-art shows a runtime improvement of around 30%. For 3D images of 128 megavoxels execution times of approximately 1.5–2 seconds are achieved.


title: Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible

abstract: Comunicacio presentada a la 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), celebrat els dies 3 i 4 d'agost de 2017 a Vancouver, Canada.


title: Load Frequency Control of an Isolated Micro Grid Using Fuzzy Adaptive Model Predictive Control

abstract: This paper presents a fuzzy adaptive model predictive approach for load frequency control of an isolated micro grid. A generalized state space model of a typical isolated micro grid having controllable and uncontrollable generating power sources is derived, and the same has been utilized to predict the future output and control inputs for the micro grid frequency control. A centralized model predictive control (MPC) is implemented with a single input multi-output system model based on the controllable distributed energy resources in the micro grid. The parameter-driven MPC is made adaptable by dynamically adjusting its parameter $R_{w}$ using fuzzy controller. The proposed fuzzy MPC employs a rule-based fuzzy controller to fuzzify the tuning parameter $R_{w}$ present in the cost function of MPC, which plays an important role in minimizing the frequency deviation in the system. The closed loop system response obtained by the proposed fuzzy MPC has been found faster and adaptable for different scenarios in the system. The effectiveness has been evaluated with performance index integral time square error (ITSE) value and has been compared with MPC with constant tuning parameter value also with the proportional–integral controller response. Thus, the efficacy of using proposed fuzzy MPC in secondary load frequency control has been validated thereof.


title: Repeated pericardiocentesis as palliative treatment for tamponade associated with cardiac lymphoma in a Holstein cow.

abstract: Maintaining a good quality of life for cows with cardiac manifestation of lymphoma may be valuable, especially in high-producing cows. This report describes the medical management of cardiac lymphoma in a cow by means of repeated pericardiocentesis. The cow survived for 34 days and was productive.


title: A Twin Study on Humor Appreciation The Importance of Separating Structure and Content

abstract: The present study focused on genetic and environmental influences on appreciation of structure and content of humor. Monozygotic (n = 135) and dizygotic (n = 60) adult twin pairs rated their trait-like humor appreciation using the 3 WD humor test (Ruch, 1992) which assesses three basic humor stimuli (incongruity-resolution humor; nonsense humor; sexual humor), and two basic components of responses to humor (funniness; aversiveness). Additionally, two indices were derived from these scales, namely structure preference and liking of sexual content (i.e., controlled for humor structure). Intraclass correlations and behavior genetic model-fitting analyses indicated a moderate genetic effect for funniness ratings of liking the sexual content. The remaining funniness scales seemed entirely influenced by environmental effects. Aversiveness scales mainly showed environmental effects represented in reduced CE models, although twin similarity coefficients showed hints of genetic influences as well, which needs to b...


title: Mapping the Intel last-level cache

abstract: Modern Intel processors use an undisclosed hash function to map memory lines into last-level cache slices. In this work we develop a technique for reverse-engineering the hash function. We apply the technique to a 6-core Intel processor and demonstrate that knowledge of this hash function can facilitate cache-based side channel attacks, reducing the amount of work required for profiling the cache by three orders of magnitude. We also show how using the hash function we can double the number of colours used for page-colouring techniques.


title: Imitation Learning of Dual-Arm Manipulation Tasks in Humanoid Robots

abstract: In this paper, we deal with imitation learning of arm movements in humanoid robots. Hidden Markov Models (HMM) are used to generalize movements demonstrated to a robot multiple times. They are trained with the characteristic features (key points) of each demonstration. Using the same HMM, key points that are common to all demonstrations are identified; only those are considered when reproducing a movement. We also show how HMM can be used to detect temporal dependencies between both arms in dual-arm tasks. We created a model of the human upper body to simulate the reproduction of dual-arm movements and generate natural-looking joint configurations from tracked hand paths. Results are presented and discussed.


title: Approximation and Online Algorithms for NFV-Enabled Multicasting in SDNs

abstract: Multicasting is a fundamental functionality of networks for many applications including online conferencing, event monitoring, video streaming, and system monitoring in data centers. To ensure multicasting reliable, secure and scalable, a service chain consisting of network functions (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) usually is associated with each multicast request. Such a multicast request is referred to as an NFV-enabled multicast request. In this paper we study NFV-enabled multicasting in a Software-Defined Network (SDN) with the aims to minimize the implementation cost of each NFV-enabled multicast request or maximize the network throughput for a sequence of NFV-enabled requests, subject to network resource capacity constraints. We first formulate novel NFV-enabled multicasting and online NFV-enabled multicasting problems. We then devise the very first approximation algorithm with an approximation ratio of 2K for the NFV-enabled multicasting problem if the number of servers for implementing the network functions of each request is no more than a constant K (1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an online algorithm with a competitive ratio of O(log n) when K = 1, where n is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms outperform other existing heuristics.


title: Super resolution in CT

abstract: The general framework of super resolution in computed tomography CT system is introduced. Two data acquisition ways before or after the reconstruction respectively are described. Three models including the sinogram model, the in-plane model and the z-axis model, are addressed to adapt super resolution to CT system. The improved iterative back projection algorithm is used in this work. Experimental results based on simulated data, GE performance phantom scanned by GE LightSpeed VCT system, one patient volunteer scanned by TOSHIBA Aquilion system, and a special experimental apparatus demonstrate that super resolution is effective to improve the resolution of CT images. The sinogram model is suitable for future CT system; the in-plane model is restricted to some special clinical diagnoses; and the z-axis model is practicable for current general clinical CT images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 92-101, 2015


title: HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation

abstract: Shilling attackers apply biased rating profiles to recommender systems for manipulating online product recommendations. Although many studies have been devoted to shilling attack detection, few of them can handle the hybrid shilling attacks that usually happen in practice, and the studies for real-life applications are rarely seen. Moreover, little attention has yet been paid to modeling both labeled and unlabeled user profiles, although there are often a few labeled but numerous unlabeled users available in practice. This paper presents a Hybrid Shilling Attack Detector, or HySAD for short, to tackle these problems. In particular, HySAD introduces MC-Relief to select effective detection metrics, and Semi-supervised Naive Bayes (SNB_lambda) to precisely separate Random-Filler model attackers and Average-Filler model attackers from normal users. Thorough experiments on MovieLens and Netflix datasets demonstrate the effectiveness of HySAD in detecting hybrid shilling attacks, and its robustness for various obfuscated strategies. A real-life case study on product reviews of Amazon.cn is also provided, which further demonstrates that HySAD can effectively improve the accuracy of a collaborative-filtering based recommender system, and provide interesting opportunities for in-depth analysis of attacker behaviors. These, in turn, justify the value of HySAD for real-world applications.


title: Construction project risk assessment using combined fuzzy and FMEA

abstract: Effectively managing risk is an essential element of successful project management. It is imperative that project management team consider all possible risks to establish corrective actions in the right time. So far, several techniques have been proposed for project risk analysis. Failure Mode and Effect Analysis (FMEA) is recognized as one of the most useful techniques in this field. The main goal is identifying all failure modes within a system, assessing their impact, and planning for corrective actions. In traditional FMEA, the risk priorities of failure modes are determined by using Risk Priority Numbers (RPN), which can be obtained by multiplying the scores of risk factors like occurrence (O), severity (S), and detection (D). This technique has some limitations, though in this paper, Fuzzy logic and Analytical Hierarchy Process (AHP) are used to address the limitations of traditional FMEA. Linguistic variables, expressed in fuzzy numbers, are used to assess the ratings of risk factors O, S, and D. Each factor consists of seven membership functions and on the whole there are 343 rules for fuzzy system. The analytic hierarchy process (AHP) is applied to determine the relative weightings of risk impacts on time, cost, quality and safety. A case study is presented to validate the concept. The feedbacks are showing the advantages of the proposed approach in project risk management.


title: Interpretable Topic Features for Post-ICU Mortality Prediction

abstract: Electronic health records provide valuable resources for understanding the correlation between various diseases and mortality. The analysis of post-discharge mortality is critical for healthcare professionals to follow up potential causes of death after a patient is discharged from the hospital and give prompt treatment. Moreover, it may reduce the cost derived from readmissions and improve the quality of healthcare. ::: ::: Our work focused on post-discharge ICU mortality prediction. In addition to features derived from physiological measurements, we incorporated ICD-9-CM hierarchy into Bayesian topic model learning and extracted topic features from medical notes. We achieved highest AUCs of 0.835 and 0.829 for 30-day and 6-month post-discharge mortality prediction using baseline and topic proportions derived from Labeled-LDA. Moreover, our work emphasized the interpretability of topic features derived from topic model which may facilitates the understanding and investigation of the complexity between mortality and diseases.


Learning by Explaining to Oneself and to Others


title: A large-stroke flexure fast tool servo with new displacement amplifier

abstract: As the rapid progress of science and technology, the free-form surface optical component has played an important role in spaceflight, aviation, national defense, and other areas of the technology. While the technology of fast tool servo (FTS) is the most promising method for the machining of free-form surface optical component. However, the shortcomings of short-stroke of fast tool servo device have constrained the development of free-form surface optical component. To address this problem, a new large-stroke flexible FTS device is proposed in this paper. A series of mechanism modeling and optimal designs are carried out via compliance matrix theory, pseudo-rigid body theory, and Particle Swarm Optimization (PSO) algorithm, respectively. The mechanism performance of the large-stroke FTS device is verified by the Finite Element Analysis (FEA) method. For this study, a piezoelectric (PZT) actuator P-840.60 that can travel to 90 µm under open-loop control is employed, the results of experiment indicate that the maximum of output displacement can achieve 258.3µm, and the bandwidth can achieve around 316.84 Hz. Both theoretical analysis and the test results of prototype uniformly verify that the presented FTS device can meet the demand of the actual microstructure processing.


title: Parasitic Layer-Based Radiation Pattern Reconfigurable Antenna for 5G Communications

abstract: The design, prototyping, and characterization of a radiation pattern reconfigurable antenna (RA) targeting 5G communications are presented. The RA is based on a reconfigurable parasitic layer technique in which a driven dipole antenna is located along the central axis of a 3-D parasitic layer structure enclosing it. The reconfigurable parasitic structure is similar to a hexagonal prism, where the top/bottom bases are formed by a hexagonal domed structure. The surfaces of the parasitic structure house electrically small metallic pixels with various geometries. The adjacent pixels are connected by PIN diode switches to change the geometry of the parasitic surface, thus providing reconfigurability in the radiation pattern. This RA is designed to operate over a 4.8–5.2 GHz frequency band, producing various radiation patterns with a beam-steering capability in both the azimuth ( $0 {^{\circ }} ) and elevation planes ( $-18 {^{\circ }} ). Small-cell access points equipped with RAs are used to investigate the system level performances for 5G heterogeneous networks. The results show that using distributed mode optimization, RA equipped small-cell systems could provide up to 29% capacity gains and 13% coverage improvements as compared to legacy omnidirectional antenna equipped systems.


title: Position Paper: Management of Men Complaining of a Small Penis Despite an Actually Normal Size

abstract: ABSTRACT Introduction With the worldwide increase in penile augmentation procedures and claims of devices designed to elongate the penis, it becomes crucial to study the scientific basis of such procedures or devices, as well as the management of a complaint of a small penis in men with a normal penile size. Aim The aim of this work is to study the scientific basis of opting to penile augmentation procedures and to develop guidelines based on the best available evidence for the management of men complaining of a small penis despite an actually normal size. Methods We reviewed the literature and evaluated the evidence about what the normal penile size is, what patients complaining of a small penis usually suffer from, benefits vs. complications of surgery, penile stretching or traction devices, and outcome with patient education and counseling. Repeated presentation and detailed discussions within the Standard Committee of the International Society for Sexual Medicine were performed. Main Outcome Measure Recommendations are based on the evaluation of evidence‐based medical literature, widespread standards committee discussion, public presentation, and debate. Results We propose a practical approach for evaluating and counseling patients complaining of a small‐sized penis. Conclusions Based on the current status of science, penile lengthening procedure surgery is still considered experimental and should only be limited to special circumstances within research or university institutions with supervising ethics committees. Ghanem H, Glina S, Assalian P, and Buvat J. Position paper: Management of men complaining of a small penis despite an actually normal size. J Sex Med 2013;10:294-303.


title: Deep Video Deblurring

abstract: Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on aligning nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task which requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high framerate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.


title: A Simple and Efficient Lane Detection using Clustering and Weighted Regression

abstract: Developing a vision based, efficient and automatic lane detection system from a moving vehicle is a challenging task mainly due to poor quality of lane markings, occlusion created by traffic, complex road geometry and nevertheless, the non-existence of unique models. The focus of this paper is to detect line or curve like segments from a video image taken from moving vehicle and merge them to detect road lane marks. In this work, we propose a method of applying weighted regression to fit a curve in place of traditional line grouping and fitting curve separately. The proposed vision based solution is simple but computationally efficient and hence works in real time on the image sequence captured by camera mounted in the vehicle.


title: Dynamic Capabilities for Service Innovation: Conceptualization and Measurement

abstract: For both managers and policy makers involved in innovation, capability failures regarding development of new services are a major concern. Efforts to strengthen those capabilities, and evaluation thereof, demand more comprehensive insight in firms’ actual abilities to source ideas and convert them into marketable service propositions. This paper aims to provide clarity by operationalizing a set of dynamic service innovation capabilities (DSICs). We first review how existing conceptualizations adopt recent insights from the dynamic capability view, which emphasizes the need to identify microfoundations corresponding to a limited set of common constructs. One of the encountered conceptualizations, consolidating earlier works in specific service sectors, was found appropriate for gauging DSICs across a wide range of industries. It exemplifies how DSICs can be conceptualized according to the so-called synthesis approach to service innovation by capturing insights on the evolutionary properties of the creation of novel solutions. Secondly, we operationalize a refined version of such DSICs and develop a measurement scale, using two subsamples from a dataset of 391 Dutch firms. The measured capabilities are found to correlate to different extents with performance measures. Our main contribution, a validated scale for five complementary DSICs, opens the way to comparative analyses which are of relevance for further research, management and policy development.


Augmenting Course Material with Open Access Textbooks


1 Aero-Structural Design Optimization of Composite Wind Turbine Blade


title: Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The MetaMap Program

abstract: Abstract ::: The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.


title: Security backlog in Scrum security practices

abstract: The rapid development of software nowadays requires the high speed software product delivery by development teams. In order to deliver the product faster, the development teams make a transformation to their conventional software development lifecycle to agile development method which can enable them towards speedy delivery of software coping with the requirements-change phenomenon. In this scenario, one of the most popular techniques in Agile development is the Scrum methodology which has been criticised in term of its security aspect cycle that ignores the security risk management activity. However, the current practices suggest that security should be considered during all stages of the software development life cycle. In order to address the aforementioned issue, this paper proposes the integration of security principles in development phases using scrum and suggests the element of security backlog that can be used as security features analysis and implementation in scrum phases.


title: Implementation of home automation using eye blink sensor

abstract: The most important source of motivation for continuity of technological developments is the constant demand to improve living standards. Today, in this post-PC era, mobile devices and smart devices are handling daily tasks that traditional desktop and laptop computers once handled. In this study, a smart home automation system design is carried out using a blink sensor. The purpose of this blink sensor is to sense an eye blink in real time. The system analyses the eye blink and differentiates between an intentional blink and a normal eye blink. Here multiple eye blinks are required to control more than one appliance. Software is used to check the correctness of the blink sequence and monitor the instantaneous states of the smart home system. This system can not only be used to save electricity, but also to help tetraplegic patients to lead their own life without anyone else's assistance. Tetraplegia is a condition where people are paralysed below their neck. The proposed system can be easily programmed to add new devices for home automation.


title: Semi Supervised Semantic Segmentation Using Generative Adversarial Network

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


Hyperparameter Optimization of Deep Neural Networks : Combining Hyperband with Bayesian Model Selection


title: Talent Management a Strategy : Identifying Patterns through a Multiple Case Study

abstract: BackgroundExploring the Talent Management literature to understand the importance of Talent Management to strategy and the role the view on talent plays in consideration to Talent Management.Purpos ...


Fast Optimization for t-SNE


title: Comparative study of PID, sliding mode and fuzzy logic controllers for four quadrant operation of switched reluctance motor

abstract: This paper presents the performance comparison of classical PID controller, nonlinear sliding mode controller and modern fuzzy logic controller as applied to the speed control of switched reluctance motor (SRM). Experimental magnetization and static torque data are used for realistic simulation of drive performance. Different control strategies and their effect on dynamic performance of SRM drive system are illustrated in terms of starting and speed reversal time, steady state error in speed, overshoot, load bearing capability, and speed and torque ripple.


title: Low complexity bit parallel architectures for polynomial basis multiplication over GF(2m)

abstract: Representing the field elements with respect to the polynomial (or standard) basis, we consider bit parallel architectures for multiplication over the finite field GF(2m). In this effect, first we derive a new formulation for polynomial basis multiplication in terms of the reduction matrix Q. The main advantage of this new formulation is that it can be used with any field defining irreducible polynomial. Using this formulation, we then develop a generalized architecture for the multiplier and analyze the time and gate complexities of the proposed multiplier as a function of degree m and the reduction matrix Q. To the best of our knowledge, this is the first time that these complexities are given in terms of Q. Unlike most other articles on bit parallel finite field multipliers, here we also consider the number of signals to be routed in hardware implementation and we show that, compared to the well-known Mastrovito's multiplier, the proposed architecture has fewer routed signals. The proposed generalized architecture is further optimized for three special types of polynomials, namely, equally spaced polynomials, trinomials, and pentanomials. We have obtained explicit formulas and complexities of the multipliers for these three special irreducible polynomials. This makes it very easy for a designer to implement the proposed multipliers using hardware description languages like VHDL and Verilog with minimum knowledge of finite field arithmetic.


title: Approximate Computing for Low Power and Security in the Internet of Things

abstract: To save resources for Internet of Things (IoT) devices, a proposed approach segments operands and corresponding basic arithmetic operations that can be carried out by approximate function units for almost all applications. The approach also increases the security of IoT devices by hiding information for IP watermarking, digital fingerprinting, and lightweight encryption.


title: The Big Picture of UX is Missing in Scrum Projects

abstract: The Scrum development process has gained increasing popularity during the last decade. At the same time user experience (UX) has emerged as an important quality feature. However, the integration of UX related activities into Scrum projects has not been without problems, and this area needs to be further examined. This paper describes the results from two in depth interviews with knowledgeable UX specialists working in Scrum projects in the prodauct development industry. It describes their ways of working generally with UX, their experiences from UX evaluations and the challenges encountered from their UX work. The main concern when working with UX in Scrum projects is that the big picture of UX is often lacking. Finally, the paper discusses the differences and similarities between the experiences from the UX specialists.


title: KUKA Sunrise Toolbox: Interfacing Collaborative Robots With MATLAB

abstract: Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa equipped with the KUKA Sunrise.OS controller is a good example of a collaborative/sensitive robot. This paper presents a MATLAB Toolbox, the KUKA Sunrise Toolbox (KST), to interface KUKA Sunrise.OS using MATLAB. The KST contains functionalities for networking, real-time control, point-to-point motion, setters and getters of parameters and physical interaction. KST includes more than 50 functions and runs on a remote computer connected with the KUKA Sunrise controller via transmission control Protocol/Internet Protocol (TCP/IP). The KST potentialities are demonstrated in three use cases.


title: A Study of Recent Contributions on Information Extraction

abstract: This paper reports on modern approaches in Information Extraction (IE) and its two main sub-tasks of Named Entity Recognition (NER) and Relation Extraction (RE). Basic concepts and the most recent approaches in this area are reviewed, which mainly include Machine Learning (ML) based approaches and the more recent trend to Deep Learning (DL) based methods.


title: Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

abstract: We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ -Learning algorithm to get control policy in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.


title: Natural language question answering over RDF: a graph data driven approach

abstract: RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a national language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.


title: An introduction to exponential random graph (p ∗ ) models for social networks

abstract: This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph


title: Cave or Community? An Empirical Examination of 100 Mature Open Source Projects

abstract: Starting with Eric Raymond's groundbreaking work, "The Cathedral and the Bazaar", open-source software (OSS) has commonly been regarded as work produced by a community of developers. Yet, given the nature of software programs, one also hears of developers with no lives that work very hard to achieve great product results. In this paper, I sought empirical evidence that would help us understand which is more common - the cave (i.e., lone producer) or the community. Based on a study of the top 100 mature products on Sourceforge, I find a few surprising things. First, most OSS programs are developed by individuals, rather than communities. The median number of developers in the 100 projects I looked at was 4 and the mode was 1 - numbers much lower than previous numbers reported for highly successful projects! Second, most OSS programs do not generate a lot of discussion. Third, products with more developers tend to be viewed and downloaded more often. Fourth, the number of developers associated with a project was positively correlated to the age of the project. Fifth, the larger the project, the smaller the percent of project administrators.


title: Enhancement of Anaerobic Digestion Using Particulate Growth Systems

abstract: Attached media reactors are used for enhancement of wastewater treatment processes including anaerobic condition. Selection of a suitable biofilm carrier is a compelling method to improve anaerobic digestion systems. This study investigates the performance of four fibrous biofilms installed in batch biogas reactors for treatment of cow manure. BioCords HS1, HS2, LS1, and LS2 are manufactured by Bishop Water Technologies, ON, Canada. Effluents and attached growth media were analyzed after batch experiment; methane production, methane yield, transfer efficiencies, organic and solid removal efficiencies, pH, and attached volatile suspended solid (VSS) were measured; VSS attached to biofilms mainly correlated with the specific surface area of each biofilm. Additionally, SEM (scanning electron microscopy) was used for further understanding of biofilm formation process for BioCords and the dissimilarity in their performance. The results indicated that BioCord LS2 had positive impact on achieving higher methane production and removal efficiencies compared to other support media utilized in batch reactors. It was also demonstrated from the experiment that BioCord LS2 potentially could generate higher methane production than conventional batch bioreactor.


title: Feature based graph SLAM with high level representation using rectangles

abstract: In mobile robotics, feature based maps are very popular for the representation of the environment. Some of the main advantages of these maps are final compactness and expressivity, aspects that make storage easier and simplify higher level reasoning. Most existing approaches, however, stick to low level features such as points, segments and sometimes circles, corners or splines. This paper presents the incorporation of rectangles as higher level features in a feature based graph Simultaneous Localization and Mapping (SLAM) framework for the consideration of the structure of the environment in the mapping process. A new method for building maps of rectangles in mobile robotics is proposed.A generic feature based graph SLAM framework for different types of features and extraction methods is used.Incorrect structure constraints can be removed.Rectangles can be detected from segments obtained from laser data even in conditions of only partial visibility.Properties such as parallelism and orthogonality can be preserved, and the resulting maps are compact and convenient for human interpretation.


A Learning Design Recommendation System Based on Markov Decision Processes


title: Domain-Adaptive generative adversarial networks for sketch-to-photo inversion

abstract: Generating photo-realistic images from multiple style sketches is one of challenging tasks in image synthesis with important applications such as facial composite for suspects. While machine learning techniques have been applied for solving this problem, the requirement of collecting sketch and face photo image pairs would limit the use of the learned model for rendering sketches of different styles. In this paper, we propose a novel deep learning model of Domain-adaptive Generative Adversarial Networks (DA-GAN). The design of DA-GAN performs cross-style sketch-to-photo inversion, which mitigates the difference across input sketch styles without the need to collect a large number of sketch and face image pairs for training purposes. In experiments, we show that our method is able to produce satisfactory results as well as performing favorably against state-of-the-art approaches.


title: Sensing the future of HCI: touch, taste, and smell user interfaces

abstract: Touch, taste, and smell are an underexplored design space in the context of interactive experiences. By understanding the ways in which our senses process information and how they relate to one another, it will be possible to create richer experiences for humantechnology interactions. We need to design interfaces that allow the stimulation of unexplored sensory inputs as well as interfaces that take into account the relationships between the senses. Digital Taste Interface is a method for simulating the sensation of taste by actuating the human tongue through electrical and thermal stimulation. AromaShooter is a smell-delivery device, contains six scent cartridges and connects to a computer via USB. Taste+ is an interface where the user can augment the flavors of food and beverages by applying weak and controlled electrical pulses on their tongue using electronically enhanced everyday utensils such as spoons and beverage bottles. It is crucial to determine the meaningful design space for multisensory interactive experiences. At the same time, it is also key to understand the unique properties of each sensory modality before designing for their sensory integration in the design of interactive systems.


title: Microservices - Lightweight Service Descriptions for REST Architectural Style.

abstract: Current web has a vast number of applications available that offer users a wide domain of services. Most services, however, cannot be machine processed, which limits service composition for application and mashup development. Research on Semantic Web Services contributes to the improvement of interoperability and composition of applications and services. Many approaches cover service description by following paradigms such as Web Services and REST architectural style, allowing describing any kind of service for its use by an automatic agent, but sometimes using these solutions can be a time-consuming task. This paper introduces Microservices, a lightweight service classification framework for REST architectural style. Microservices do not attempt to describe every possible service, but to provide a way to describe a set of services in a simple way. Microservice descriptions consist of a set of terms that represent service features. After describing features semantically, microservices framework allows generating detailed service descriptions, which allows reusing common feature descriptions across different services. A use case that adapts heterogeneous search services to produce a standard interface using microservices is described.


title: Optimal Residential Demand Response for Multiple Heterogeneous Homes With Real-Time Price Prediction in a Multiagent Framework

abstract: Demand response (DR) is a recent effort to improve the efficiency of the electricity market and the stability of the power system. A successful implementation relies on both appropriate policy design and enabling technology. This paper presents a multiagent system to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HAs) and a retailer agent (RA). The HA is able to predict and control electricity load demand. A real-time price prediction model is developed for the HA and the RA. The optimal control of electricity consumption is formulated into a convex programming problem to minimize electricity payment and waiting time under real-time pricing. Simulation results show that the peak-to-average power ratio and electricity payments are significantly reduced using the proposed algorithms. The HA, with the proposed optimal control algorithms, can be embedded into a home energy management system to make intelligent decisions on behalf of homeowners responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable the implementation of DR.


title: Weak complicial sets, a simplicial weak omega-category theory. Part I: basic homotopy theory

abstract: This paper develops the foundations of a simplicial theory of weak omega-categories, which builds upon the insights originally expounded by Ross Street in his 1987 paper on oriented simplices. The resulting theory of weak complicial sets provides a common generalisation of the theories of (strict) omega-categories, Kan complexes and Joyal's quasi-categories. We generalise a number of results due to the current author with regard to complicial sets and strict omega-categories to provide an armoury of well behaved technical devices, such as joins and Gray tensor products, which will be used to study these the weak omega-category theory of these structures in a series of companion papers. In particular, we establish their basic homotopy theory by constructing a Quillen model structure on the category of stratified simplicial sets whose fibrant objects are the weak complicial sets. As a simple corollary of this work we provide an independent construction of Joyal's model structure on simplicial sets for which the fibrant objects are the quasi-categories.


title: CyTIME: Cyber Threat Intelligence ManagEment framework for automatically generating security rules

abstract: It is becoming increasingly necessary for organizations to build a Cyber Threat Intelligence (CTI) platform to fight against sophisticated attacks. To reduce the risk of cyber attacks, security administrators and/or analysts can use a CTI platform to aggregate relevant threat information about adversaries, targets and vulnerabilities, analyze it and share key observations from the analysis with collaborators. In this paper, we introduce CyTIME (Cyber Threat Intelligence ManagEment framework) which is a framework for managing CTI data. CyTIME can periodically collect CTI data from external CTI data repositories via standard interfaces such as Trusted Automated Exchange of Indicator Information (TAXII). In addition, CyTIME is designed to automatically generate security rules without human intervention to mitigate discovered new cybersecurity threats in real time. To show the feasibility of CyTIME, we performed experiments to measure the time to complete the task of generating the security rule corresponding to a given CTI data. We used 1,000 different CTI files related to network attacks. Our experiment results demonstrate that CyTIME automatically generates security rules and store them into the internal database within 12.941 seconds on average (max = 13.952, standard deviation = 0.580).


title: The Matlab-Simulink model for monitoring of the power loss in inverters which supplies a BLDC motor

abstract: This paper presents the concept and the design of a Matlab-Simulink model for monitoring and measurement of the losses that appear in a static converter with MOSFET transistors, of the fully controlled three-phase bridge type. The model was inserted in a drive system of a BLDC motor which operates a small electric vehicle. Inside such vehicle appear frequent changes in the operating mode and in the load. The use of the model allowed the authors to compare various command modes, from the point of view of dynamic behavior and thermal losses on the inverter. This was important and it contributed to the development of a final solution of optimal command for driving the small electric vehicle equipped with BLDC motor.


title: Statistical Deobfuscation of Android Applications

abstract: This work presents a new approach for deobfuscating Android APKs based on probabilistic learning of large code bases (termed "Big Code"). The key idea is to learn a probabilistic model over thousands of non-obfuscated Android applications and to use this probabilistic model to deobfuscate new, unseen Android APKs. The concrete focus of the paper is on reversing layout obfuscation, a popular transformation which renames key program elements such as classes, packages, and methods, thus making it difficult to understand what the program does. Concretely, the paper: (i) phrases the layout deobfuscation problem of Android APKs as structured prediction in a probabilistic graphical model, (ii) instantiates this model with a rich set of features and constraints that capture the Android setting, ensuring both semantic equivalence and high prediction accuracy, and (iii) shows how to leverage powerful inference and learning algorithms to achieve overall precision and scalability of the probabilistic predictions. We implemented our approach in a tool called DeGuard and used it to: (i) reverse the layout obfuscation performed by the popular ProGuard system on benign, open-source applications, (ii) predict third-party libraries imported by benign APKs (also obfuscated by ProGuard), and (iii) rename obfuscated program elements of Android malware. The experimental results indicate that DeGuard is practically effective: it recovers 79.1% of the program element names obfuscated with ProGuard, it predicts third-party libraries with accuracy of 91.3%, and it reveals string decoders and classes that handle sensitive data in Android malware.


title: BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

abstract: In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.


title: An Overview of Feature-Oriented Software Development

abstract: Feature-oriented software development (FOSD) is a paradigm for the construction, customization, and synthesis of large-scale software systems. In this survey, we give an overview and a personal perspective on the roots of FOSD, connections to other software development paradigms, and recent developments in this field. Our aim is to point to connections between different lines of research and to identify open issues.


title: DIARC: A Testbed for Natural Human-Robot Interaction

abstract: DIARC, a distributed integrated affect, reflection, cognition architecture for robots, provides many features that are critical to successful natural human-robot interaction. As such, DIARC is an ideal platform for experimentation in HRI. In this paper we describe the architecture and and its implementation in ADE, paying particular attention to its interaction capabilities and features that allow robust operation. These features are evaluated in the context of the 2006 AAAI Robot Competition.


title: End-to-End Eye Movement Detection Using Convolutional Neural Networks

abstract: Common computational methods for automated eye movement detection - i.e. the task of detecting different types of eye movement in a continuous stream of gaze data - are limited in that they either involve thresholding on hand-crafted signal features, require individual detectors each only detecting a single movement, or require pre-segmented data. We propose a novel approach for eye movement detection that only involves learning a single detector end-to-end, i.e. directly from the continuous gaze data stream and simultaneously for different eye movements without any manual feature crafting or segmentation. Our method is based on convolutional neural networks (CNN) that recently demonstrated superior performance in a variety of tasks in computer vision, signal processing, and machine learning. We further introduce a novel multi-participant dataset that contains scripted and free-viewing sequences of ground-truth annotated saccades, fixations, and smooth pursuits. We show that our CNN-based method outperforms state-of-the-art baselines by a large margin on this challenging dataset, thereby underlining the significant potential of this approach for holistic, robust, and accurate eye movement protocol analysis.


title: Broadband High-Gain SIW Cavity-Backed Circular-Polarized Array Antenna

abstract: A circularly polarized (CP) $\text{4}\times\text{4}$ array antenna based on substrate integrated waveguide (SIW) technology is presented. Circular polarization is achieved by applying the sequential rotation technique (SRT) with a well-designed sequential feed network and linear-polarized array elements. The proposed $\text{4}\times\text{4}$ array has a wide axial ratio (AR) bandwidth of 14% from 18.3 to 21.1 GHz with gain $ > \!\!13\,{\rm dBic}$ . Then, the array is expanded to achieve higher gains. A $\text{16}\times\text{16}\hbox{-}{\rm element}$ array is designed, fabricated, and tested. Test results show that the $\text{16}\times\text{16}$ array has an AR bandwidth of 13.8% from 18.5 to 21.25 GHz and a peak gain of 25.9 dBic at 20.5 GHz.


ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning


title: Stomatal Development in Arabidopsis

abstract: Stomata consist of two guard cells around a pore and act as turgor-operated valves for gas exchange. Arabidopsis stomata develop from one or more asymmetric divisions followed by the symmetric division of the guard mother cell. Stomatal number is partly a function of the availability of smaller epidermal cells that are competent to divide asymmetrically. Stomata are spaced apart from each other by at least one neighbor cell. Pattern generation may involve cell-cell signaling that transmits spatial cues used to orient specific classes of asymmetric divisions. TOO MANY MOUTHS may function in receiving or transducing these cues to orient asymmetric divisions. TMM also is a negative or positive regulator of entry into the stomatal pathway, with the direction of the response dependent on organ and location. STOMATAL DENSITY AND DISTRIBUTION1 is a negative regulator of stomatal formation throughout the shoot and encodes a processing protease that may function in intercellular communication. FOUR LIPS apparently controls the number symmetric divisions at the guard mother cell stage. In some organs, such as the hypocotyl, the placement of stomata may be coordinated with internal features and involves genes that also regulate root hair and trichome formation. Other mutations affect guard cell morphogenesis, cytokinesis, and stomatal number in response to carbon dioxide concentration. The molecular analysis of stomatal development promises advances in understanding intercellular signaling, the control of the plane and polarity of asymmetric division, the specification of cell fate, and the regulation of cell differentiation and shape.


title: Assessing Vulnerabilities in Bluetooth Low Energy (BLE) Wireless Network Based IoT Systems

abstract: With the materialization of the internet of things (IoT), big data analytic and cloud computing services give rise to extra breadth in the assessment of more secure computing environments, better resource management and vulnerability analysis. In order to accurately assess the vulnerability of Bluetooth low energy (BLE) wireless network enabled IoT systems, we have proposed a novel approach to extend the calculation formula for Authentication which is one of variables used in the conventional base score equations of the Common Vulnerability Scoring System (CVSS) v2 proposed by the National Infrastructure Advisory Council. Through an example BLE wireless network based shopping cart IoT system we have demonstrated the weakness of the current CVSS v2 base score equations and how to overcome the weakness through our extension.


title: Modeling ITIL Business Motivation Model in ArchiMate

abstract: According to Enterprise Architecture (EA) approaches, organizations have motivational concepts that are used to model the motivations that underlie its design or change, which represent the organization’s Business Motivation Model (BMM). Likewise, this BMM is also present in the organizations who provide IT services. ITIL has become a reference for IT service providers, but is commonly modeled as a process-oriented approach to IT Service Management, often disregarding the remaining EA domains or its motivational elements. Conversely, we believe that like EA, ITIL has an important motivation model that should be formally represented.


title: Test of Memory Malingering (TOMM): Normative data for the Latin American Spanish speaking adult population.

abstract: The Test of Memory Malingering (TOMM) is an instrument used to assess purposeful embellishment or fabrication of memory difficulties for personal gain. Although the TOMM can be use in non-English speaking cultures, it has not been validated in Spanish-speaking Central and South American contexts.To generate normative data on TOMM across 7 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.The sample consisted of 2,266 healthy adults who were recruited from Argentina, Bolivia, Chile, Mexico, Paraguay, Peru, and Puerto Rico. Each subject was administered the TOMM as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.t-tests did not show significant differences in TOMM performance between men and women in any countries of the TOMM Trial 1 or 2. As a result, gender-adjusted norms were not generated.The results from this study will have a large impact on the practice of neuropsychology in Latin America, as this is the first normative multicenter study to create norms for the TOMM in this global region.


title: Real-time probabilistic fusion of sparse 3D LIDAR and dense stereo

abstract: Real-time 3D perception is critical for localisation, mapping, path planning and obstacle avoidance for mobile robots and autonomous vehicles. For outdoor operation in real-world environments, 3D perception is often provided by sparse 3D LIDAR scanners, which provide accurate but low-density depth maps, and dense stereo approaches, which require significant computational resources for accurate results. Here, taking advantage of the complementary error characteristics of LIDAR range sensing and dense stereo, we present a probabilistic method for fusing sparse 3D LIDAR data with stereo images to provide accurate dense depth maps and uncertainty estimates in real-time. We evaluate the method on data collected from a small urban autonomous vehicle and the KITTI dataset, providing accuracy results competitive with state-of-the-art stereo approaches and credible uncertainty estimates that do not misrepresent the true errors, and demonstrate real-time operation on a range of low-power GPU systems.


title: From Pose to Activity: Surveying Datasets and Introducing CONVERSE

abstract: We provide a comprehensive review of current appearance and pose modality action recognition datasets.Analysis shows pose based HAR requires data for the subtle interaction problem.The CONVERSE dataset of pose based conversational interactions is presented.CONVERSE presents interactions that are not semantically defined by pose.Benchmark results are provided for use of CONVERSE in pose HAR method evaluation. We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent progress of understanding person-person interaction modeling. We categorize datasets regarding several key properties for usage as a benchmark dataset; including the number of class labels, ground truths provided, and application domain they occupy. We also consider the level of abstraction of each dataset; grouping those that present actions, interactions and higher level semantic activities. The survey identifies key appearance and pose based datasets, noting a tendency for simplistic, emphasized, or scripted action classes that are often readily definable by a stable collection of sub-action gestures. There is a clear lack of datasets that provide closely related actions, those that are not implicitly identified via a series of poses and gestures, but rather a dynamic set of interactions. We therefore propose a novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. The intention is to provide events that are constructed from numerous primitive actions, interactions and motions, over a period of time; providing a set of subtle action classes that are more representative of the real world, and a challenge to currently developed recognition methodologies. We believe this is among one of the first datasets devoted to conversational interaction classification using 3D pose features and the attributed papers show this task is indeed possible. The full dataset is made publicly available to the research community at 1.


title: Acceptance of cosmetic surgery: Personality and individual difference predictors

abstract: This study examined the association between several attitudinal constructs related to acceptance of cosmetic surgery, and participant demographics, personality, and individual difference variables. A sample of 332 university students completed a battery of scales comprising the Acceptance of Cosmetic Surgery Scale (ACSS) and measures of the Big Five personality factors, self-esteem, conformity, self-assessed attractiveness, and demographics. Multiple regressions showed that the predictor variables explained a large proportion of the variance in ACSS factors (Adj. R2 ranging between .31 and .60). In addition, structural equation modelling revealed that distal factors (sex and age) were generally associated with acceptance of cosmetic surgery through the mediate influence of more proximate variables (in the first instance, the Big Five personality factors, followed by self-esteem and conformity, and finally self-assessed attractiveness). These results allow for the presentation of a preliminary model integrating personality and individual differences in predicting acceptance of cosmetic surgery.


title: Design and Analysis of Energy-Efficient Reconfigurable Pre-Emphasis Voltage-Mode Transmitters

abstract: This paper analyzes the signaling and digital power overhead of pre-emphasis voltage-mode transmitters. Utilizing a shunt branch in parallel with the differential channel to implement pre-emphasis is shown to have the best overall energy-efficiency. Leveraging this technique, an efficient pre-emphasis voltage mode transmitter architecture with output amplitude control, pre-emphasis coefficient control, and online impedance calibration is proposed and demonstrated. A 65 nm LP CMOS implementation of this architecture dissipates ~ 10 mW from a 1.2 V supply when transmitting 10 Gb/s 200 mV differential amplitude data with 2-tap pre-emphasis, achieving 1 pJ/bit energy efficiency.


title: A Testbed of Magnetic Induction-based Communication System for Underground Applications

abstract: Wireless underground sensor networks (WUSNs) can enable many important applications such as intelligent agriculture, pipeline fault diagnosis, mine disaster rescue, concealed border patrol, crude oil exploration, among others. The key challenge to realize WUSNs is the wireless communication in underground environments. Most existing wireless communication systems utilize the dipole antenna to transmit and receive propagating electromagnetic (EM) waves, which do not work well in underground environments due to the very high material absorption loss. The Magnetic Induction (MI) technique provides a promising alternative solution that could address the current problem in underground. Although the MI-based underground communication has been intensively investigated theoretically, to date, seldom effort has been made in developing a testbed for the MI-based underground communication that can validate the theoretical results. In this paper, a testbed of MI-based communication system is designed and implemented in an in-lab underground environment. The testbed realizes and tests not only the original MI mechanism that utilizes single coil but also recent developed techniques that use the MI waveguide and the 3-directional (3D) MI coils. The experiments are conducted in an in-lab underground environment with reconfigurable environmental parameters such as soil composition and water content. This paper provides the principles and guidelines for developing the MI underground communications testbed, which is very complicated and time-consuming due to the new communication mechanism and the new wireless transmission medium.


title: Application of Internet of Things in Power-Line Monitoring

abstract: The Internet of Things (IoT) has been considered as the third revolution in the digital technology after the computer and the Internet, and it brings significant advantages to smart grid. Disaster prediction and prevention of power-lines is one of the most difficult problems for electricity transmission companies. Advanced sensing and communication technologies of IoT can effectively avoid or reduce the damage of natural disasters to the power-lines, and hence improve the reliability and stability of power transmission. In this paper, a three-layer architecture model of the Internet of things for smart grid (SG-IoT) is proposed. An overview of the key technologies in SG-IoT is given. An IoT based power-lines online monitoring system is presented.


title: Neural mechanisms of ageing and cognitive decline

abstract: During the past century, treatments for the diseases of youth and middle age have helped raise life expectancy significantly. However, cognitive decline has emerged as one of the greatest health threats of old age, with nearly 50% of adults over the age of 85 afflicted with Alzheimer's disease. Developing therapeutic interventions for such conditions demands a greater understanding of the processes underlying normal and pathological brain ageing. Recent advances in the biology of ageing in model organisms, together with molecular and systems-level studies of the brain, are beginning to shed light on these mechanisms and their potential roles in cognitive decline.


title: NestedNet: Learning Nested Sparse Structures in Deep Neural Networks

abstract: Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep architecture for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested sparse network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.


title: Ras, PI(3)K and mTOR signalling controls tumour cell growth

abstract: All eukaryotic cells coordinate cell growth with the availability of nutrients in their environment. The mTOR protein kinase has emerged as a critical growth-control node, receiving stimulatory signals from Ras and phosphatidylinositol-3-OH kinase (PI(3)K) downstream from growth factors, as well as nutrient inputs in the form of amino-acid, glucose and oxygen availability. Notably, components of the Ras and PI(3)K signalling pathways are mutated in most human cancers. The preponderance of mutations in these interconnected pathways suggests that the loss of growth-control checkpoints and promotion of cell survival in nutrient-limited conditions may be an obligate event in tumorigenesis.


1 A 2 . 9 TOPS / W Deep Convolutional Neural Network SoC in FD-SOI 28 nm for Intelligent Embedded Systems


title: Evidence for the activation of two different Ca2+ channels during the egg jelly-induced acrosome reaction of sea urchin sperm.

abstract: Abstract The influx of Ca2+ and its subsequent intracellular increase are required for the acrosome reaction of sea urchin sperm to occur. Spermatozoa must undergo this reaction, which is triggered by the egg jelly, in order to fertilize the egg. Here, the egg jelly-induced Ca2+ influx mechanisms have been studied in sperm loaded with FURA-2 using Mn2+ under the assumption that this divalent ion is an indicator of Ca2+ influx through Ca2+ channels. Egg jelly induced the immediate entry of Ca2+ (mixing time 1 s), however; we found that the influx of Mn2+ increased after a lag time of 5 s. Nisol-dipine (a Ca2+ channel blocker) did not block the Mn2+ influx which was inhibited by 40 mM of external [K+], low Na+, and 5 mM of tetraethylammonium (a K+ channel blocker). These conditions also inhibited the alkalinization and the acrosome reaction. The inhibition of the Mn2+ influx could be overcome by increasing internal pH (pHi) with ammonium (10 mM). On the contrary the influx of Ca2+ during the first 5 s was not inhibited by any of the conditions indicated before, except by nisoldipine. These data could be explained by the activation of two different Ca2+ channels by egg jelly. The first one being a receptor-operator Ca2+ channel that opens when the receptor for egg jelly is occupied independently of the ionic conditions. The other one could be considered as a second messenger-operated Ca2+ channel that requires at least an increase in pHi to open.


title: Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams

abstract: We propose in this paper two new models for modeling topic and word-topic dependencies between consecutive documents in document streams. The first model is a direct extension of Latent Dirichlet Allocation model (LDA) and makes use of a Dirichlet distribution to balance the influence of the LDA prior parameters wrt to topic and word-topic distribution of the previous document. The second extension makes use of copulas, which constitute a generic tools to model dependencies between random variables. We rely here on Archimedean copulas, and more precisely on Franck copulas, as they are symmetric and associative and are thus appropriate for exchangeable random variables. Our experiments, conducted on three standard collections that have been used in several studies on topic modeling, show that our proposals outperform previous ones (as dynamic topic models and temporal \LDA), both in terms of perplexity and for tracking similar topics in a document stream.


ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora


title: Computer simulation of the role of protein corona in cellular delivery of nanoparticles.

abstract: Understanding the role of serum protein in the process of nanoparticle delivery is of great importance in biomedicine. Here, by using dissipative particle dynamics simulations, we systematically investigate the interactions between the nanoparticle-protein corona complex and cell membranes of different types. It is found that the human serum albumin (HSA) will just adsorb onto charged (especially for positively charged) and hydrophobic nanoparticle surface. More importantly, we also provide specific insights into the effect of HSA adsorption on the in vivo transportation of nanoparticle (i.e., immune response and targeted cellular uptake). Our results show that the protein corona can change the interaction modes of hydrophobic nanoparticles and enhance the interaction of charged nanoparticles with macrophage cell membranes, while it may also cause the failure of insertion of hydrophobic nanoparticles and the loss of targeting specificity of charged nanoparticles with cancer cell membranes. These results can help better understand the biological significance of protein corona and may give some useful suggestions on better design of future nanoparticles in drug delivery.


title: A model-based method for the computation of fingerprints' orientation field

abstract: As a global feature of fingerprints, the orientation field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation field estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation field is proposed. First a combination model is established for the representation of the orientation field by considering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation field estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation estimation methods.


title: Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers

abstract: Abstract Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in conventional FCN based architectures. In order to process the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module’s parallel structures. We propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and Dice loss leading to qualitative improvements in segmentation. We demonstrate computational efficacy of incorporating conventional computer vision techniques for region of interest detection in an end-to-end deep learning based segmentation framework. From the segmentation maps we extract clinically relevant cardiac parameters and hand-craft features which reflect the clinical diagnostic analysis and train an ensemble system for cardiac disease classification. We validate our proposed network architecture on three publicly available datasets, namely: (i) Automated Cardiac Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular segmentation challenge (LV-2011), (iii) 2015 Kaggle Data Science Bowl cardiac challenge data. Our approach in ACDC-2017 challenge stood second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100% on a limited testing set (n=50). In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. In the Kaggle challenge our approach for LV volume gave a Continuous Ranked Probability Score (CRPS) of 0.0127, which would have placed us tenth in the original challenge. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computationally efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.


title: Vision for Mobile Robot Navigation: A Survey

abstract: Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.


title: A singularity-tolerant inverse kinematics including joint position and velocity limitations

abstract: Humanoid robots have many degrees of freedom which ideally enables them to accomplish different tasks. From a control viewpoint, however, the geometric complexity makes planning and control difficult. Favoring controllability properties, it is popular to operate with crouched knees, since the Center of Mass (CoM) height can be directly manipulated. In addition to a higher energy consumption however, this brings long-term damage to the actuator and series elastic elements. We propose a simple Inverse Kinematics (IK) formulation as a nonlinear optimization that can handle singularities and joint limits with inequality constraints. We also introduce safe regions for joint velocities and propose modifications that help the joint come out of singularities faster. The effectiveness of the proposed method is verified in simulations and on the real hardware for various balancing tasks.


title: Fall detection using machine learning algorithms

abstract: In this paper, the recognition of and the differentiation between fall activities and activities of daily living (ADL) was performed using the MobiFall dataset. A large database was constructed to train and validate the model. Feature selection methods were implemented to reduce dimensionality. Five different classification algorithms were implemented and evaluated based on their accuracy' sensitivity, and specificity achieved. The k-Nearest Neighbors' algorithm obtained an overall accuracy of 87.5% with a sensitivity of 90.70%, and a specificity of 83.78%.


title: Big Scholarly Data in CiteSeerX: Information Extraction from the Web

abstract: We examine CiteSeerX, an intelligent system designed with the goal of automatically acquiring and organizing large-scale collections of scholarly documents from the world wide web. From the perspective of automatic information extraction and modes of alternative search, we examine various functional aspects of this complex system with an eye towards ongoing and future research developments.


title: Software defect prediction using ensemble learning on selected features

abstract: Abstract Context Several issues hinder software defect data including redundancy, correlation, feature irrelevance and missing samples. It is also hard to ensure balanced distribution between data pertaining to defective and non-defective software. In most experimental cases, data related to the latter software class is dominantly present in the dataset. Objective The objectives of this paper are to demonstrate the positive effects of combining feature selection and ensemble learning on the performance of defect classification. Along with efficient feature selection, a new two-variant (with and without feature selection) ensemble learning algorithm is proposed to provide robustness to both data imbalance and feature redundancy. Method We carefully combine selected ensemble learning models with efficient feature selection to address these issues and mitigate their effects on the defect classification performance. Results Forward selection showed that only few features contribute to high area under the receiver-operating curve (AUC). On the tested datasets, greedy forward selection (GFS) method outperformed other feature selection techniques such as Pearson’s correlation. This suggests that features are highly unstable. However, ensemble learners like random forests and the proposed algorithm, average probability ensemble (APE), are not as affected by poor features as in the case of weighted support vector machines (W-SVMs). Moreover, the APE model combined with greedy forward selection (enhanced APE) achieved AUC values of approximately 1.0 for the NASA datasets: PC2, PC4, and MC1. Conclusion This paper shows that features of a software dataset must be carefully selected for accurate classification of defective components. Furthermore, tackling the software data issues, mentioned above, with the proposed combined learning model resulted in remarkable classification performance paving the way for successful quality control.


title: Building functional networks of spiking model neurons

abstract: Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.


Design and optimisation of a voice coil motor (VCM) with a rotary actuator for an ultrasound scanner. IEEE Transactions on Industrial Electronics


title: Understanding video-ad consumption on YouTube: a measurement study on user behavior, popularity, and content properties

abstract: Faced with the challenge of attracting user attention and revenue, social media websites have turned to video advertisements (video-ads). While in traditional media the video-ad market is mostly based on an interaction between content providers and marketers, the use of video-ads in social media has enabled a more complex interaction, that also includes content creator and viewer preferences. To better understand this novel setting, we present the first data-driven analysis of video-ad exhibitions on YouTube.


title: Geometry-Based Localization for GPS Outage in Vehicular Cyber Physical Systems

abstract: Vehicular localization has witnessed significant attention due to the growing number of location-based services in vehicular cyber physical systems (VCPS). In vehicular localization, GPS outage is a challenging issue considering the growing urbanization including high rise buildings, multilevel flyovers and bridges. GPS-free and GPS-assisted cooperative localization techniques have been suggested in the literature for GPS outage. Due to the cost of infrastructure in GPS-free techniques, and the absence of location aware neighbors in cooperative techniques, efficient and scalable localization is a challenging task in VCPS. In this context, this paper proposes a geometry-based localization for GPS outage in VCPS (GeoLV). It is a GPS-assisted localization which reduces location-aware neighbor constraint of cooperative localization. GeoLV utilizes mathematical geometry to estimate vehicle location focusing on vehicular dynamics and road trajectory. The static and dynamic relocations are performed to reduce the impact of GPS outage on location-based services. A case study based comparative performance evaluation has been carried out to assess the efficiency and scalability of GeoLV. It is evident from the results that GeoLV handles both shorter and longer GPS outage problem better than the state-of-the-art techniques in VCPS.


title: A Survey on Security in Named Data Networking

abstract: Over the past three decades, since its invention, the Internet has evolved in both its sheer volume and usage. The Internet's core protocol, Internet Protocol (IP), has proven its usability and effectiveness to support a communication network. However, current Internet usage requires more than a communication network due to a shift in the nature of Internet applications from simple email application to large content producers such as NetFlix, Google, Amazon, etc. Named Data Networking (NDN) is one of the few initiatives/projects addressing the shortcomings of the current Internet architecture and intends to move the Internet toward a content distribution architecture. In this paper, we conduct a brief survey of security topics/problems inherent to the NDN architecture. Specifically, we describe current known problems and propose solutions to major security problems.


title: Gender Incongruence of Childhood: Clinical Utility and Stakeholder Agreement with the World Health Organization’s Proposed ICD-11 Criteria

abstract: The World Health Organization (WHO) is revising the tenth version of the International Classification of Diseases and Related Health Problems (ICD-10; WHO, 1992). This includes a reconceptualization of the definition and positioning of Gender Incongruence of Childhood (GIC). This study aimed to: 1) collect the views of transgender individuals and professionals regarding the retention of the diagnosis; 2) see if the proposed GIC criteria were acceptable to transgender individuals and health care providers 3) compare results between two countries with two different healthcare systems to see if these differences influence opinions regarding the GIC diagnosis; and 4) determine whether healthcare providers from high-income countries feel that the proposed criteria are clinically useful and easy to use. ::: A total of 628 participants were included in the study: 284 from the Netherlands (NL; 45.2 %), 8 from Flanders (Belgium; 1.3 %), and 336 (53.5%) from the United Kingdom (UK). Most participants were transgender people (or their partners/relatives; TG) (n = 522), 89 participants were healthcare providers (HCPs) and 17 were both healthcare providers and (partners/relatives of) transgender people. Participants completed an online survey developed for this study. ::: Overall, the majority response from transgender participants (42.9%) was that if the diagnosis would be removed from the mental health chapter it should also be removed from the ICD-11 completely, while 33.6% thought it should remain in the ICD-11. Participants were generally satisfied with other aspects of the proposed ICD-11 GIC diagnosis: most TG participants (58.4%) thought the term Gender Identity Disorder should change, and most thought Gender Incongruence was an improvement (63.0%). Furthermore, most participants (76.1%) did not consider GIC to be a psychiatric disorder and placement in a separate chapter dealing with Gender and Sexual Health (the majority response in the NL and selected by 37.5% of the TG participants overall) or as a Z-code (the majority response in the UK and selected by 26.7% of the TG participants overall) would be preferable. In the UK, the majority response (35.8%) was that narrowing the diagnosis for children was an improvement, while in the NL the majority response (49.5%) was that this was not an improvement. Although generally the results from healthcare providers were in line with the results from the transgender participants (and stakeholders) some differences were found. ::: This study suggests that, in an ideal world, a diagnosis is not welcomed. However, realistically - due to healthcare funding - including a GIC diagnosis in ICD-11 is seen as a requirement. The choice for positioning of a diagnosis of GIC within the ICD-11 is as a separate chapter dealing with symptoms and/or disorders regarding sexual and gender health. This was the overall first choice for NL participants and second choice for UK participants, after the use of a Z-code. The difference reflects the fact that in the UK, Z-codes carry no negative implications for reimbursement of treatment costs. These findings highlight the challenges faced by the WHO in their attempt to integrate research findings from different countries, with different cultures and healthcare systems in their quest to create a manual that is globally applicable.


title: Impedance-model-based stability analysis of DC microgrid

abstract: In DC microgrid, it is common that constant power loads (CPLs) exhibit negative impedance characteristics, which degrades the stability margin of the whole system. This paper focuses on the stability problem of isolated DC microgrid with multiple DC voltage control units and constant power loads. Taking controllers into account, the detailed impedance model of DC microgrid is established, and it shows the interaction between converters through analyzing the minor loop gain by using Nyquist criterion. Meanwhile, resonance is examined using impedance model. Impedances-based stability analysis reveals the influence of different factors on the stability, such as the size of the CPLs and the droop coefficient. To verify the analysis results, the simulation model is built based on PSCAD/EMTDC. The simulation results confirm the validity of the method. With the developed dc impedance and stability analysis method, the DC microgrid system stability characteristics can be evaluated, which can be used for system design to maintain stability.


title: Cortical Thickness in Untreated Transsexuals

abstract: Sex differences in cortical thickness (CTh) have been extensively investigated but as yet there are no reports on CTh in transsexuals. Our aim was to determine whether the CTh pattern in transsexuals before hormonal treatment follows their biological sex or their gender identity. We performed brain magnetic resonance imaging on 94 subjects: 24 untreated female-to-male transsexuals (FtMs), 18 untreated male-to-female transsexuals (MtFs), and 29 male and 23 female controls in a 3-T TIM-TRIO Siemens scanner. T1-weighted images were analyzed to obtain CTh and volumetric subcortical measurements with FreeSurfer software. CTh maps showed control females have thicker cortex than control males in the frontal and parietal regions. In contrast, males have greater right putamen volume. FtMs had a similar CTh to control females and greater CTh than males in the parietal and temporal cortices. FtMs had larger right putamen than females but did not differ from males. MtFs did not differ in CTh from female controls but had greater CTh than control males in the orbitofrontal, insular, and medial occipital regions. In conclusion, FtMs showed evidence of subcortical gray matter masculinization, while MtFs showed evidence of CTh feminization. In both types of transsexuals, the differences with respect to their biological sex are located in the right hemisphere.


title: Multiband PIFA vehicle telematics antennas

abstract: Compact, low-cost antennas for multiband telematics applications on vehicles are described. The multifrequency band antennas are variations on the printed inverted-F antenna (PIFA) principle. One type of PIFA is a compact variation of a conventional antenna while the other is a novel planar version customized for different mounting positions such as bumpers, window, and roof mount. In both derivations the dimensions are small and have been specifically designed for low-cost production. Each antenna was designed for mobile telephone band coverage from 890 to 2100 MHz. Results from free-space simulations are included but more work needs to be carried out as the vehicles have a significant effect on antenna performance. On-vehicle tests are described where the antennas were placed on glass and plastic areas and integrated into an existing multipurpose roof mount antenna.


title: GeoBrush: Interactive Mesh Geometry Cloning

abstract: We propose a method for interactive cloning of 3D surface geometry using a paintbrush interface, similar to the continuous cloning brush popular in image editing. Existing interactive mesh composition tools focus on atomic copy-and-paste of preselected feature areas, and are either limited to copying surface displacements, or require the solution of variational optimization problems, which is too expensive for an interactive brush interface. In contrast, our GeoBrush method supports real-time continuous copying of arbitrary high-resolution surface features between irregular meshes, including topological handles. We achieve this by first establishing a correspondence between the source and target geometries using a novel generalized discrete exponential map parameterization. Next we roughly align the source geometry with the target shape using Green Coordinates with automatically-constructed cages. Finally, we compute an offset membrane to smoothly blend the pasted patch with C continuity before stitching it into the target. The offset membrane is a solution of a bi-harmonic PDE, which is computed on the GPU in real time by exploiting the regular parametric domain. We demonstrate the effectiveness of GeoBrush with various editing scenarios, including detail enrichment and completion of scanned surfaces.


title: The Optimum Tapered Transmission Line Matching Section

abstract: The tapered transmission line matching section is analyzed by treating it as a high pass filter. The ideal reflection coefficient characteristic for the taper that gives the smallest pass band tolerance for a given cutoff frequency and vice versa is derived from the expression for the reflection coefficient of the optimum designed n section quarter-wave transformer by taking the limit as n tends to infinity. By neglecting the square of the reflection coefficient in the differential equation for the reflection coefficient on the taper, a synthesis procedure is derived for obtaining an optimum taper. The procedure is similar to that used in designing an optimum line source distribution in antenna theory. Conditions are derived for the maximum allowable pass band reflection and change in impedance level for which the theory remains accurate. For a maimum reflection coefficient of 0.1 in the pass band the theory remains accurate for all frequencies above the cutoff value provided the change in impedance level does not exceed 7.5. This optimum taper is compared with the well-known exponential and Gaussian tapers and is found to be 13.9 per cent and 27 per cent shorter respectively for the same cutoff frequency and pass band tolerance.


title: Assessing the origins, evolution and prospects of the literature on dynamic capabilities: A bibliometric analysis

abstract: The purpose of this study is to serve as orientation and guidance to academics that are starting or currently developing their research within the field of dynamic capabilities, in order to enhance their knowledge about which are the key scientific journals, authors and articles shaping this topic. This paper presents a bibliometric analysis on dynamic capabilities, making use of the Web of Science database to perform it. This analysis comprises fundamental issues such as (i) the number of studies published per year, (ii) the countries with the highest rate of productivity, (iii) the most prolific and influential authors, (iv) assessment of studies citing dynamic capabilities, and (v) the most productive journals on dynamic capabilities and recent studies on this topic. Results reveal an exponential growth in the number of publications on dynamic capabilities for the 2000–2012 period. Although, since 2012 this growth has decelerated, the number of publications on this topic remains noteworthy. This study brings useful information for those academics and practitioners attempting to analyze and deepen within this particular field of research, at the same time that provides some insights concerning the future development and progress of the dynamic capabilities topic in the management, business and economics academic literature.