title: A Review of Solutions for SDN-Exclusive Security Issues

abstract: Software Defined Networking is a paradigm still in its emergent stages in the realm of production-scale networks. Centralisation of network control introduces a new level of flexibility for network administrators and programmers. Security is a huge factor contributing to consumer resistance to implementation of SDN architecture. Without addressing the issues inherent from SDNs centralised nature, the benefits in performance and network configurative flexibility cannot be harnessed. This paper explores key threats posed to SDN environments and comparatively analyses some of the mechanisms proposed as mitigations against these threats – it also provides some insight into the future works which would enable a securer SDN architecture.


AGILE SOFTWARE DEVELOPMENT PRACTICES THAT INFLUENCE SOFTWARE QUALITY : A REVIEW 1


title: Data-dependent Initializations of Convolutional Neural Networks

abstract: Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while being roughly three orders of magnitude faster. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training.


title: Sequences, Items And Latent Links: Recommendation With Consumed Item Packs

abstract: Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). ::: In this paper, we introduce the notion of consumed item pack (CIP) which enables to link users (or items) based on their implicit analogous consumption behavior. Our proposal is generic, and we show that it captures three novel implicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a word embedding-based (DEEPCIP), as well as a state-of-the-art technique using implicit feedback (FISM). We show that our recommenders handle incremental updates incorporating freshly consumed items. We demonstrate that all three recommenders provide a recommendation quality that is competitive with state-of-the-art ones, including one incorporating both explicit and implicit feedback.


title: Ultra-wideband conformal apertures with digital beamforming for UHF to millimeter-wave applications

abstract: This paper presents a reconfigurable low profile ultra-wideband (UWB) phased array, having as much as 14.1:1 instantaneous bandwidth suitable for UHF to millimeter wave applications, with unprecedented integrated digital beam-forming capability across the entire bandwidth. To realize spectrum efficiency, aperture reconfiguration is also proposed. That is, the system is tuned at the feed network rather than the aperture of the antenna by introducing switches and LC loads. Further, the proposed UWB phased array significantly reduces cost due to its novel on-site encoding for tracking individual array element signals to eliminate hardware phased shifters, a primary cost component of the phased arrays.


title: Reinforcement-learning optimal control for type-1 diabetes

abstract: This paper proposes a reinforcement-learning based algorithm for optimal control of blood glucose in patients with type-1 diabetes. Specifically, the algorithm aims to suggest an optimal insulin injection policy. Its performance was assessed using simulations on a combination of the minimum model and part of the Hovorka model. The results show that the proposed methodology successfully regulates and significantly reduces the fluctuation of the blood glucose in both fasting and post-meal scenarios. A comparison between the proposed algorithm and an existing reinforcement learning algorithm also shows the superiority of our method and provide insights on how insulin doses should be chosen.


High-Precision Robot Odometry Using an Array of Optical Mice


title: Model-free Surface Visualization of Vascular Trees

abstract: Expressive and efficient visualizations of complex vascular structures are essential for medical applications, such as diagnosis and therapy planning. A variety of techniques has been developed which provide smooth high-quality visualizations of vascular structures based on rather simple model assumptions. For diagnostic applications, these model assumptions and the resulting deviations from the actual vessel surface are not acceptable. We present a model-free approach which employs the binary result of a prior vessel segmentation as input. Instead of directly converting the segmentation result into a surface, we compute a point cloud which is adaptively refined at thin structures, where aliasing effects are particularly obvious and artifacts may occur. The point cloud is transformed into a surface representation by means of MPU Implicits, which provide a smooth piecewise quadratic approximation. Our method has been applied to a variety of datasets including pathologic cases. The generated visualizations are considerably more accurate than model-based approaches. Compared to other model-free approaches, our method produces smoother results.


Formal Concept Lattices as Semantic Maps


title: A highly efficient Vivaldi antenna array design on thick substrate and fed by SIW structure with integrated GCPW feed

abstract: A compact Vivaldi antenna array printed on thick substrate and fed by a substrate integrated waveguide (SIW) structure is presented here. The array utilizes an SIW binary splitter to significantly minimize the feed structure insertion losses. Meanwhile, the splitter is fed with an integrated CPW to sustain a good input match and prevent higher order modes excitation over wide band operation. Extensive simulation and measurements were carried out and was presented here with a comparison to previously developed arrays.


title: Biohybrid robot powered by an antagonistic pair of skeletal muscle tissues

abstract: Biohybrid robots are attracting attention as promising candidates to enhance robot applicability to studies on biological designs and in vitro construction of biological dynamic systems. Rapid progress in biohybrid robots with skeletal muscle tissues formed on a flexible substrate has enabled various types of locomotion powered by muscle tissue. However, it has been difficult to achieve high levels of both large and long-term actuations of the skeletal muscle tissues because of their spontaneous shrinkage through the course of the tissue culture. To overcome this limitation, we adapted the concept of biological systems and developed a biohybrid robot actuated by an antagonistic pair of skeletal muscle tissues. Our robot achieved large actuation (~90° of rotation of a joint) by selective contractions of the skeletal muscle tissues and a long lifetime (~1 week) by balancing tensions of the antagonistic tissues to prevent the spontaneous shrinkage. As a demonstration, we showed that our biohybrid robots allowed a pick-and-place manipulation of objects. This research may provide a platform to exceed the limitations of design in conventional biohybrid robots and replicate various lifelike movements.


title: Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes

abstract: Decomposition of shapes into (approximate) convex parts is essential for applications such as part-based shape representation, shape matching, and collision detection. In this paper, we propose a novel convex decomposition using a parametric implicit shape model called Disjunctive Normal Shape Model (DNSM). The DNSM is formed as a union of polytopes which themselves are formed by intersections of halfspaces. The key idea is by deforming the polytopes, which naturally remain convex during the evolution, the polytopes capture convex parts without the need to compute convexity. The major contributions of this paper include a robust convex decomposition which also results in an efficient part-based shape representation, and a novel shape convexity measure. The experimental results show the potential of the proposed method.


title: Hourly irradiance forecasting in Malaysia using support vector machine

abstract: This paper investigates the use of support vector machine (SVM) to forecast hourly solar irradiance for a tropical country. The hourly irradiance data was obtained from Sepang Malaysia, recorded throughout 2011. The data is converted into corresponding clearness index values to facilitate model convergence. The forecast is tested against the standard multilayer perceptron (MLP) technique and persistence forecast. The evaluation metrics used to validate each model's performance are mean bias error, root mean square error, mean absolute error/average, and Kolmogorov- Smirnov integral test. Results show that the SVM performs significantly better than the conventional MLP technique.


title: Sociotechnical transitions for deep decarbonization

abstract: Rapid and deep reductions in greenhouse gas emission are needed to avoid dangerous climate change. This will necessitate low-carbon transitions across electricity, transport, heat, industrial, forestry, and agricultural systems. But despite recent rapid growth in renewable electricity generation, the rate of progress toward this wider goal of deep decarbonization remains slow. Moreover, many policy-oriented energy and climate researchers and models remain wedded to disciplinary approaches that focus on a single piece of the low-carbon transition puzzle, yet avoid many crucial real-world elements for accelerated transitions ( 1 ). We present a “sociotechnical” framework to address the multidimensionality of the deep decarbonization challenge and show how coevolutionary interactions between technologies and societal groups can accelerate low-carbon transitions.


title: Social Recommendation with Tag Side Information

abstract: Social recommendation is the recommendation process by using social information. Being created to annotate and categorize items, tags are shared by users to find, organize and understand online entities, and provide the inherent flexibility to predict users' preference. In this paper, tag information and social relations are combined into recommendation, and a new tag information aided social recommendation method is proposed. By means of matrix factorization and regularization technology, we calculate the feature vectors and predict the rating of users. Also a new similarity measuring method is put forward when exploiting tag information through link prediction. Experiments show that our method generates better performance than state-of-the-art methods.


title: Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree

abstract: In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures.


title: Viziometrics: Analyzing Visual Information in the Scientific Literature

abstract: Scientific results are communicated visually in the literature through diagrams, visualizations, and photographs. These information-dense objects have been largely ignored in bibliometrics and scientometrics studies when compared to citations and text. In this paper, we use techniques from computer vision and machine learning to classify more than 8 million figures from PubMed into 5 figure types and study the resulting patterns of visual information as they relate to impact. We find that the distribution of figures and figure types in the literature has remained relatively constant over time, but can vary widely across field and topic. Remarkably, we find a significant correlation between scientific impact and the use of visual information, where higher impact papers tend to include more diagrams, and to a lesser extent more plots and photographs. To explore these results and other ways of extracting this visual information, we have built a visual browser to illustrate the concept and explore design alternatives for supporting viziometric analysis and organizing visual information. We use these results to articulate a new research agenda -- viziometrics -- to study the organization and presentation of visual information in the scientific literature.


title: Outpatient Therapeutic Feeding Program Outcomes and Determinants in Treatment of Severe Acute Malnutrition in Tigray, Northern Ethiopia: A Retrospective Cohort Study

abstract: BACKGROUND ::: Outpatient Therapeutic feeding Program (OTP) brings the services for management of Severe Acute Malnutrition (SAM) closer to the community by making services available at decentralized treatment points within the primary health care settings, through the use of ready-to-use therapeutic foods, community outreach and mobilization. Little is known about the program outcomes. This study revealed the levels of program outcome indictors and determinant factors to recovery rate. ::: ::: ::: METHODS ::: A retrospective cohort study was conducted on 628 children who had been managed for SAM under OTP from April/2008 to January/2012. The children were selected using systematic random sampling from 12 health posts and 4 health centers. The study relied on information of demographic characteristics, anthropometries, Plumpy'Nut, medical problems and routine medications intakes. The results were estimated using Kaplan-Meier survival curves, log-rank test and Cox-regression. ::: ::: ::: RESULTS ::: The recovery, defaulter, mortality and weight gain rates were 61.78%, 13.85%, 3.02% and 5.23 gm/kg/day, respectively. Routine medications were administered partially and children with medical problems were managed inappropriately under the program. As a child consumed one more sachet of Plumpy'Nut, the recovery rate from SAM increased by 4% (HR = 1.04, 95%-CI = 1.03, 1.05, P<0.001). The adjusted hazard ratios to recovery of children with diarrhea, appetite loss with Plumpy'Nut and failure to gain weight were 2.20 (HR = 2.20, 95%-CI = 1.31, 3.41, P = 0.001), 4.49 (HR = 1.74, 95%-CI = 1.07, 2.83, P = 0.046) and 3.88 (HR = 1.95, 95%-CI = 1.17, 3.23, P<0.001), respectively. Children who took amoxicillin and de-worming had 95% (HR = 1.95, 95%-CI = 1.17, 3.23) and 74% (HR = 1.74, 95%-CI = 1.07, 2.83) more probability to recover from SAM as compared to those who didn't take them. ::: ::: ::: CONCLUSIONS ::: The OTP was partially successful. Management of children with comorbidities under the program and partial administration of routine drugs were major threats for the program effectiveness. The stakeholders should focus on creating the capacity of the OTP providers on proper management of SAM to achieve fully effective program.


title: Ripple-Based Control of Switching Regulators—An Overview

abstract: Switching regulators with ripple-based control (i.e., ?ripple regulators?) are conceptually simple, have fast transient responses to both line and load perturbations, and some versions operate with a switching frequency that is proportional to the load current under the discontinuous conduction mode. These characteristics make the ripple regulators well-suited, especially for power management applications in computers and portable electronic devices. Ripple regulators also have some drawbacks, including (in some versions) a poorly defined switching frequency, noise-induced jitter, inadequate dc regulation, and a tendency for fast-scale instability. This paper presents an overview of the various ripple-based control techniques, discusses their merits and limitations, and introduces techniques for reducing the noise sensitivity and the sensitivity to capacitor parameters, improving the frequency stability and the dc regulation, and avoiding fast-scale instability.


title: Aspect-level news browsing: understanding news events from multiple viewpoints

abstract: Aspect-level news browsing provides readers with a classified view of news articles with different viewpoints. It facilitates active interactions with which readers easily discover and compare diverse existing biased views over a news event. As such, it effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of articles. We first demonstrate the feasibility of aspect-level news browsing through user studies. We then deeply look into the news article production process and develop framing cycle-aware clustering. The evaluation results show that the developed method performs classification more accurately than other methods.


title: Contents lists available at ScienceDirect Intelligence Intelligence and physical attractiveness☆

abstract: Abstract This brief research note aims to estimate the magnitude of the association between general intelligence and physical attractiveness with large nationally representative samples from two nations. In the United Kingdom, attractive children are more intelligent by 12.4 IQ points ( r = .381), whereas in the United States, the correlation between intelligence and physical attractiveness is somewhat smaller ( r = .126). The association between intelligence and physical attractiveness is stronger among men than among women in both nations. The association remains significant net of a large number of control variables for social class, body size, and health.


title: Modeling the Invariance of Virtual Pointers in LLVM

abstract: Devirtualization is a compiler optimization that replaces indirect (virtual) function calls with direct calls. It is particularly effective in object-oriented languages, such as Java or C++, in which virtual methods are typically abundant. We present a novel abstract model to express the lifetimes of C++ dynamic objects and invariance of virtual table pointers in the LLVM intermediate representation. The model and the corresponding implementation in Clang and LLVM enable full devirtualization of virtual calls whenever the dynamic type is statically known and elimination of redundant virtual table loads in other cases. Due to the complexity of C++, this has not been achieved by any other C++ compiler so far. Although our model was designed for C++, it is also applicable to other languages that use virtual dispatch. Our benchmarks show an average of 0.8% performance improvement on real-world C++ programs, with more than 30% speedup in some cases. The implementation is already a part of the upstream LLVM/Clang and can be enabled with the -fstrict-vtable-pointers flag.


title: Improved Power-Quality-Based Welding Power Supply With Overcurrent Handling Capability

abstract: This paper proposes a power-factor-corrected canonical switching cell (CSC) converter-based switched-mode power supply for arc-welding applications. In the proposed arc-welding power supply (AWPS), CSC converter operating in discontinuous inductor current mode (DICM) is used to attain inherent power factor correction. The DICM operation substantially reduces the complexity of the control and effectively regulates the dc-link voltage. At the back end, a pulsewidth-modulated (PWM) isolated full bridge dc–dc converter is used to provide a high-frequency isolation, which is mandatory for the arc-welding process. A dual-loop control scheme is utilized to incorporate overcurrent protection and to regulate dc voltage at the output making it suitable for arc-welding applications. Test results are presented to confirm the viability of the proposed AWPS. The performance of the proposed AWPS is evaluated on the basis of total harmonic distortion (THD) of the supply current, power factor, dynamic response, and voltage regulation to prove its effectiveness.


FUGU: Elastic Data Stream Processing with Latency Constraints


title: A brief introduction to weakly supervised learning

abstract: Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus, it is desirable for machine-learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, where the training data are given with only coarse-grained labels; and inaccurate supervision, where the given labels are not always ground-truth.


title: Multi-level Semantic Labelling of Numerical Values

abstract: With the success of Open Data a huge amount of tabular data sources became available that could potentially be mapped and linked into the Web of (Linked) Data. Most existing approaches to “semantically label” such tabular data rely on mappings of textual information to classes, properties, or instances in RDF knowledge bases in order to link – and eventually transform – tabular data into RDF. However, as we will illustrate, Open Data tables typically contain a large portion of numerical columns and/or non-textual headers; therefore solutions that solely focus on textual “cues” are only partially applicable for mapping such data sources. We propose an approach to find and rank candidates of semantic labels and context descriptions for a given bag of numerical values. To this end, we apply a hierarchical clustering over information taken from DBpedia to build a background knowledge graph of possible “semantic contexts” for bags of numerical values, over which we perform a nearest neighbour search to rank the most likely candidates. Our evaluation shows that our approach can assign fine-grained semantic labels, when there is enough supporting evidence in the background knowledge graph. In other cases, our approach can nevertheless assign high level contexts to the data, which could potentially be used in combination with other approaches to narrow down the search space of possible labels.


title: The dawn of the liquid biopsy in the fight against cancer

abstract: // Irma G. Dominguez-Vigil 1 , Ana K. Moreno-Martinez 1, 2 , Julia Y. Wang 3 , Michael H. A. Roehrl 4 and Hugo A. Barrera-Saldana 1, 5 1 Departamento de Bioquimica y Medicina Molecular, Facultad de Medicina de la Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico 2 Genetics Laboratory, Vitagenesis, Monterrey, Nuevo Leon, Mexico 3 Curandis Laboratories, New York, NY, USA 4 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA 5 TecSalud, Tecnologico de Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico Correspondence to: Michael H. A. Roehrl, email: roehrlm@mskcc.org Hugo A. Barrera-Saldana, email: habarrera@gmail.com Keywords: liquid biopsy; cfDNA; ctDNA; early detection; diagnostics Received: October 17, 2017 Accepted: November 10, 2017 Published: December 08, 2017 ABSTRACT Cancer is a molecular disease associated with alterations in the genome, which, thanks to the highly improved sensitivity of mutation detection techniques, can be identified in cell-free DNA (cfDNA) circulating in blood, a method also called liquid biopsy. This is a non-invasive alternative to surgical biopsy and has the potential of revealing the molecular signature of tumors to aid in the individualization of treatments. In this review, we focus on cfDNA analysis, its advantages, and clinical applications employing genomic tools (NGS and dPCR) particularly in the field of oncology, and highlight its valuable contributions to early detection, prognosis, and prediction of treatment response.


title: Switching-Type H∞ Filter Design for T - S Fuzzy Systems With Unknown or Partially Unknown Membership Functions

abstract: This paper is concerned with the H∞ filter design problem for Takagi-Sugeno (T-S) fuzzy systems with unknown or partially unknown membership functions. If the membership functions are allowed to be unknown or partially unknown, then a fuzzy system may describe a wide class of nonlinear systems. However, in this case, the filter design of fuzzy systems, which is based on parallel distributed compensator strategy, is infeasible. To tackle this difficulty, a switching mechanism, which depends on the lower and upper bounds of the unknown membership functions, is introduced to construct the H∞ filter with varying gains. Some examples given in the simulation section show that the proposed method can achieve a better disturbance attenuation performance than a simple fixed-gain filter design approach.


title: Autonomic schemes for threat mitigation in Internet of Things

abstract: Internet of Things (IoT) refers to the expansion of Internet technologies to include wireless sensor networks (WSNs) and smart objects by extensive interfacing of exclusively identifiable, distributed communication devices. Due to the close connection with the physical world, it is an important requirement for IoT technology to be self-secure in terms of a standard information security model components. Autonomic security should be considered as a critical priority and careful provisions must be taken in the design of dynamic techniques, architectures and self-sufficient frameworks for future IoT. Over the years, many researchers have proposed threat mitigation approaches for IoT and WSNs. This survey considers specific approaches requiring minimal human intervention and discusses them in relation to self-security. This survey addresses and brings together a broad range of ideas linked together by IoT, autonomy and security. More particularly, this paper looks at threat mitigation approaches in IoT using an autonomic taxonomy and finally sets down future directions.


title: Surface tension-driven self-alignment.

abstract: Surface tension-driven self-alignment is a passive and highly-accurate positioning mechanism that can significantly simplify and enhance the construction of advanced microsystems. After years of research, demonstrations and developments, the surface engineering and manufacturing technology enabling capillary self-alignment has achieved a degree of maturity conducive to a successful transfer to industrial practice. In view of this transition, a broad and accessible review of the physics, material science and applications of capillary self-alignment is presented. Statics and dynamics of the self-aligning action of deformed liquid bridges are explained through simple models and experiments, and all fundamental aspects of surface patterning and conditioning, of choice, deposition and confinement of liquids, and of component feeding and interconnection to substrates are illustrated through relevant applications in micro- and nanotechnology. A final outline addresses remaining challenges and additional extensions envisioned to further spread the use and fully exploit the potential of the technique.


title: RC-RCD clamp circuit for ringing losses reduction in a flyback converter

abstract: An RCD clamp circuit is usually used in flyback converters, in order to limit the voltage spikes caused by leakage transformer inductance. Oscillation ringing appears due to the clamp diode, which deteriorates the converter's power rate. This brief describes this ringing phenomenon and the use of an RC-RCD clamp circuit for damping the clamp diode's oscillation. This clamp circuit is capable for improving a flyback converter's power ratio.


IoT Based Patient Health Monitoring System Using LabVIEW and Wireless Sensor Network


title: Image Captioning as Neural Machine Translation Task in SOCKEYE

abstract: Image captioning is an interdisciplinary research problem that stands between computer vision and natural language processing. The task is to generate a textual description of the content of an image. The typical model used for image captioning is an encoder-decoder deep network, where the encoder captures the essence of an image while the decoder is responsible for generating a sentence describing the image. Attention mechanisms can be used to automatically focus the decoder on parts of the image which are relevant to predict the next word. In this paper, we explore different decoders and attentional models popular in neural machine translation, namely attentional recurrent neural networks, self-attentional transformers, and fully-convolutional networks, which represent the current state of the art of neural machine translation. The image captioning module is available as part of SOCKEYE at https://github.com/awslabs/sockeye which tutorial can be found at https://awslabs.github.io/sockeye/image_captioning.html .


title: Creation and Staging of Android Theatre “Sayonara”towards Developing Highly Human-Like Robots

abstract: Even after long-term exposures, androids with a strikingly human-like appearance evoke unnatural feelings. The behavior that would induce human-like feelings after long exposures is difficult to determine, and it often depends on the cultural background of the observers. Therefore, in this study, we generate an acting performance system for the android, in which an android and a human interact in a stage play in the real world. We adopt the theatrical theory called Contemporary Colloquial Theatre Theory to give the android natural behaviors so that audiences can comfortably observe it even after long-minute exposure. A stage play is created and shown in various locations, and the audiences are requested to report their impressions of the stage and their cultural and psychological backgrounds in a self-evaluating questionnaire. Overall analysis indicates that the audience had positive feelings, in terms of attractiveness, towards the android on the stage even after 20 min of exposure. The singularly high acceptance of the android by Japanese audiences seems to be correlated with a high animism tendency, rather than to empathy. We also discuss how the stage play approach is limited and could be extended to contribute to realization of human–robot interaction in the real world.


title: The future of partial nephrectomy.

abstract: Innovation in recent times has accelerated due to factors such as the globalization of communication; but there are also more barriers/safeguards in place than ever before as we strive to streamline this process. From the first planned partial nephrectomy completed in 1887, it took over a century to become recommended practice for small renal tumours. At present, identified areas for improvement/innovation are 1) to preserve renal parenchyma, 2) to optimise pre-operative eGFR and 3) to reduce global warm ischaemia time. All 3 of these, are statistically significant predictors of post-operative renal function. Urologists, have a proud history of embracing innovation & have experimented with different clamping techniques of the renal vasculature, image guidance in robotics, renal hypothermia, lasers and new robots under development. The DaVinci model may soon no longer have a monopoly on this market, as it loses its stranglehold with novel technology emerging including added features, such as haptic feedback with reduced costs. As ever, our predictions of the future may well fall wide of the mark, but in order to progress, one must open the mind to the possibilities that already exist, as evolution of existing technology often appears to be a revolution in hindsight.


title: Large scale online kernel learning

abstract: In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nystrom Online Gradient Descent (NOGD) algorithm that applies the Nystrom method to approximate large kernel matrices. We explore these two approaches to tackle three online learning tasks: binary classification, multi-class classification, and regression. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches.


title: Robotic-assisted micro-surgery of the throat: The trans-nasal approach

abstract: Minimally Invasive Surgery of the throat is predominantly performed trans-orally. Although trans-oral (TO) access provides a scarless access into the airways, its outcomes are affected by complications, high cost, and long setup time. This paper investigates the clinical motivation for trans-nasal (TN) access to the throat and presents the design and hybrid position/compliant motion control of a rapidly deployable endo-nasal telerobotic system. The system exploits a unique O5 mm surgical slave with force sensing capabilities used to enable semi-automating the insertion process. Working channels allow the deployment of surgical tools such as a fiberscope, positioning sensors, grippers, suction tubes, cautery, and laser fibers. The treatment of vocal fold paralysis is chosen as a benchmark application and a feasibility study for collagen injection is conducted. Experiments on a realistic human intubation trainer demonstrated successful and safe TN deployment of the end-effector and the feasibility of robotic-assisted treatment of vocal nerve paralysis. We believe this system constitutes a first step toward low-cost office-based head and neck surgical procedures.


title: Review on UHF RFID antennas

abstract: This paper addresses the review of the ultra-high frequency (UHF) Radio Frequency Identification (RFID) antennas deployed in recent years including fixed reader antennas, handheld reader antennas. The development trends of UHF RFID antennas from application point of view and the technical challenges are presented and discussed.


title: An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring With Automated Multiobjective Power Scheduling

abstract: Nowadays, electricity energy demands requested from down-stream sectors in a smart grid constantly increase. One way to meet those demands is use of home energy management systems (HEMS). By effectively scheduling major household appliances in response to demand response (DR) schemes, residents can save their electricity bills. In this paper, an advanced HEMS facilitated by a nonintrusive load monitoring (NILM) technique with an automated nondominated sorting genetic algorithm-II (NSGA-II)-based multiobjective in-home power scheduling mechanism is proposed. The NILM as an electricity audit is able to nonintrusively estimate power consumed by each of monitored major household appliances at a certain period of time. Data identified by the NILM are very useful for DR implementation. For DR implementation, the NSGA-II-based multiobjective in-home power scheduling mechanism autonomously and meta-heuristically schedules monitored and enrolled major household appliances without user intervention. It is based on an analysis of the NILM with historical data with past trends. The experimental results reported in this paper reveal that the proposed advanced HEMS with the NILM assessed in a real-house environment with uncertainties is workable and feasible.


title: Multiscale models for biological systems

abstract: Life, amazingly rich in diversity of shapes and functions, explores the limits of extreme complexity in nature. In this review we shall discuss in general terms the use of multiscale mathematical and computer models to study the dynamics of biological systems. These models permit integration of the rapidly expanding knowledge concerning the molecular basis of biology and its complex, nonlinear relationship with the emerging shapes and functions of cells, tissues and organs in living organisms.


title: Male-to-Female Transsexuals Have Female Neuron Numbers in a Limbic Nucleus

abstract: Transsexuals experience themselves as being of the opposite sex, despite having the biological characteristics of one sex. A crucial question resulting from a previous brain study in male-to-female transsexuals was whether the reported difference according to gender identity in the central part of the bed nucleus of the stria terminalis (BSTc) was based on a neuronal difference in the BSTc itself or just a reflection of a difference in vasoactive intestinal polypeptide innervation from the amygdala, which was used as a marker. Therefore, we determined in 42 subjects the number of somatostatin-expressing neurons in the BSTc in relation to sex, sexual orientation, gender identity, and past or present hormonal status. Regardless of sexual orientation, men had almost twice as many somatostatin neurons as women (P < 0.006). The number of neurons in the BSTc of male-to-female transsexuals was similar to that of the females (P = 0.83). In contrast, the neuron number of a female-to-male transsexual was found to b...


title: Information systems success revisited

abstract: The DeLone & McLean IS Success Model (1992) has become a standard for the specification and justification of the measurement of the dependent variable in information systems research. Attempts to apply and test the model have resulted in both confirmation and challenges. This paper reviews and analyzes over 150 articles which have referenced the model over the past eight years in order to examine what more we have learned about measuring IS success. It highlights recent contributions to IS success measurement and proposes a Reformulated IS Success Model which recognizes and incorporates those contributions. The report concludes with recommendations for future IS success measurement.


title: Lexical Features in Coreference Resolution: To be Used With Caution

abstract: Lexical features are a major source of information in state-of-the-art coreference resolvers. Lexical features implicitly model some of the linguistic phenomena at a fine granularity level. They are especially useful for representing the context of mentions. In this paper we investigate a drawback of using many lexical features in state-of-the-art coreference resolvers. We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains. Furthermore, we show that the current coreference resolution evaluation is clearly flawed by only evaluating on a specific split of a specific dataset in which there is a notable overlap between the training, development and test sets.


title: Practical methods for constructing suffix trees

abstract: Sequence datasets are ubiquitous in modern life-science applications, and querying sequences is a common and critical operation in many of these applications. The suffix tree is a versatile data structure that can be used to evaluate a wide variety of queries on sequence datasets, including evaluating exact and approximate string matches, and finding repeat patterns. However, methods for constructing suffix trees are often very time-consuming, especially for suffix trees that are large and do not fit in the available main memory. Even when the suffix tree fits in memory, it turns out that the processor cache behavior of theoretically optimal suffix tree construction methods is poor, resulting in poor performance. Currently, there are a large number of algorithms for constructing suffix trees, but the practical tradeoffs in using these algorithms for different scenarios are not well characterized.In this paper, we explore suffix tree construction algorithms over a wide spectrum of data sources and sizes. First, we show that on modern processors, a cache-efficient algorithm with O(n2) worst-case complexity outperforms popular linear time algorithms like Ukkonen and McCreight, even for in-memory construction. For larger datasets, the disk I/O requirement quickly becomes the bottleneck in each algorithm's performance. To address this problem, we describe two approaches. First, we present a buffer management strategy for the O(n2) algorithm. The resulting new algorithm, which we call “Top Down Disk-based” (TDD), scales to sizes much larger than have been previously described in literature. This approach far outperforms the best known disk-based construction methods. Second, we present a new disk-based suffix tree construction algorithm that is based on a sort-merge paradigm, and show that for constructing very large suffix trees with very little resources, this algorithm is more efficient than TDD.


title: Deep fusion of hyperspectral and LiDAR data for thematic classification

abstract: Recently, the fusion of hyperspectral and light detection and ranging (LiDAR) data has obtained a great attention in the remote sensing community. In this paper, we propose a new feature fusion framework using deep neural network (DNN). The proposed framework employs a novel 3D convolutional neural network (CNN) to extract the spectral-spatial features of hyperspectral data, a deep 2D CNN to extract the elevation features of LiDAR data, and then a fully connected deep neural network to fuse the extracted features in the previous CNNs. Through the aforementioned three deep networks, one can extract the discriminant and invariant features of hyperspectral and LiDAR data. At last, logistic regression is used to produce the final classification results. The experimental results reveal that the proposed deep fusion model provides competitive results. Furthermore, the proposed deep fusion idea opens a new window for future research.


Modeling of Political Discourse Framing on Twitter.


title: Computer Analysis of Text Tonality Based on the JSM Method

abstract: The necessity of taking into account the coefficient of emotionality of lexemes to assess the emotionality of the text, which should be taken into account in voice control systems, is substantiated. The reduction of the JSM method was made, which made it possible to apply it to solving the problem of estimating the emotional load of the text. The core of the program module has been formed as part of a thesaurus classifier trained on collections of expert texts. The analysis of the quality of the proposed method of estimating the tonality of the text is presented. The results of the calculated values of the efficiency metrics showed that the use of a manual tonal dictionary allows to obtain a more accurate assessment of the text tonality and significantly reduce the operating time, since, compared to the automatic dictionary, the manual dictionary does not contain neutral words. The dependence of the accuracy of determining the tonality on the conflict resolution function used to classify texts by tonality and the number of training texts was discovered. The problem of limited emotive space was discovered: some of the words did not fall or partly fell under the binary classification. The algorithm for determining the tonality of the morphological composition of the text on the basis of the JSM method using tonal dictionaries and classification allows you to determine the tonality of the text on a binary scale.


title: Benchmarking Multimodal Sentiment Analysis

abstract: We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% over the state of the art by combining visual, text and audio features. We also discuss some major issues frequently ignored in multimodal sentiment analysis research: the role of speaker-independent models, importance of the modalities and generalizability. The paper thus serve as a new benchmark for further research in multimodal sentiment analysis and also demonstrates the different facets of analysis to be considered while performing such tasks.


title: Optical Absorption and Reflection Spectra of Paratellurite, TeO2

abstract: The absorption and the reflection spectra of the synthetic single crystal of TeO 2 were measured over the photon energy from 1.8 to 5.6 eV for linearly polarized light with the E -vector parallel and perpendicular to the optic axis. From absorption measurement from 80 to 500 K, the fundamental absorption tail was shown to obey the modified Urbach rule with the steepness constatat σ 0 =0.69. The optical constants n and k were determined from reflectivity through the Kramers-Kronig calculation. The strong dependence of the reflectivity on the direction of polarization of the incident light was observed, the origin of which was discussed under a model treating of optical transition from a p -like to an s -like state in a tetragonally deformed lattice. The temperature shift of the first reflection peak was estimated to be 2.5×10 -4 eV/K in the temperature range from 80 to 300 K.


title: Token-Level Metaphor Detection using Neural Networks

abstract: Automatic metaphor detection usually relies on various features, incorporating e.g. selectional preference violations or concreteness ratings to detect metaphors in text. These features rely on background corpora, hand-coded rules or additional, manually created resources, all speciﬁc to the language the system is being used on. We present a novel approach to metaphor detection using a neural network in combination with word embeddings, a method that has already proven to yield promising results for other natural language processing tasks. We show that foregoing manual feature engineering by solely relying on word embeddings trained on large corpora produces comparable results to other systems, while removing the need for additional resources.


title: Researching Ageism in Health-Care and Long Term Care

abstract: The literature across different fields defines ageism ambiguously and widely covers a span of intolerant knowledge, values, attitudes and behaviors towards older adults or more generally toward people of a certain age. In this chapter we provide an overview of how ageism is defined, measured, and assessed in health care and long-term care. In so doing, we aim to bridge the gap between the concept and measurement of ageism in these two contexts and to provide some general insights into the approaches, which researchers can apply to assess ageism in these settings. In this chapter, we therefore aim to answer the following questions namely (i) Why is it important to know how ageism in healthcare and long-term care has been empirically studied? (ii) What evidence for the existence of ageism among key stakeholders (e.g. health care professionals and long-term care workers, family members and older adults) is reported in empirical research covering these two contexts? and (iii) Which are the conceptual and methodological approaches used to measure and assess ageism involving these key stakeholders in the two contexts?


abstract: The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework. The framework encompasses many individual innovations in motion simulation algorithms, but the primary innovation is in the development of a comprehensive system for motion simulation and ergonomic analysis that is specifically designed to be independent of any particular human modeling system. The modules are developed as lightweight algorithms based on closed-form equations and simple numerical methods that can be communicated in written form and implemented in any computer language. The modules are independent of any particular figure model structure, requiring only basic forward-kinematics control and public-domain numerical algorithms. Key aspects of the module algorithms are “behavior-based,” meaning that the large amount of redundancy in the human kinematic linkage is resolved using empirical models based on laboratory data. The implementation of the HUMOSIM framework in human figure models will allow much faster and more accurate simulation of human interactions with products and workspaces using high-level, task-based control. INTRODUCTION Digital human figure models (DHM) are now widely used for ergonomic analysis of products and workplaces. In many organizations, DHM software is a tool of first resort for answering questions relating to physical interaction between people and objects. Yet any objective appraisal of the technology would conclude that the current reality of DHM software capability is far from the promise of a “digital human” that can interact realistically with products and environments. This paper is focused on efforts to improve the ability of DHM software to simulate physical posture and motion. Nearly every other aspect of DHM functionality also warrants improvement, including body shape representation, strength simulation, and cognitive function, but posture and motion are critical to the primary applications of DHM to the assessment of physical tasks. Posture simulation is as old as computerized manikins, because the manikin must be postured before an analysis can be conducted. Important early work was performed by Ryan for the U.S. Navy (Ryan 1970). Porter et al. (1993) summarized applications of digital human models in vehicle ergonomics during the early years of personal computers, at which time few of the current commercial DHM software tools were in use. Chaffin (2001) presented case studies of the expanding use of DHM for both product and workplace design and assessment. As evidence of the importance of posture and motion simulation, dozens of papers in the SAE literature and in other forums have presented a wide variety of methods for human simulating postures and motions, including multiple-regression (Snyder et al. 1972); analytic and numerical inverse kinematics (Jung et al. 1995; Tolani et al. 2000); optimization-based inverse kinematics (Wang and Verriest 1998); differential inverse kinematics (Zhang and Chaffin, 2000); functional regression on stretch-pivot parameters (Faraway 2000); scaling, warping, and blending of motion-capture data (Park et al. 2002; Faraway 2003; Monnier et al. 2003; Park et al. 2004; Dufour and Wang 2005); and many 2006-01-2365 The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis Matthew P. Reed, Julian Faraway, Don B. Chaffin and Bernard J. Martin University of Michigan


title: PERSONALITY BASED PSYCHOLOGICAL ANTECEDENTS OF CONSUMERS' TRUST IN E-COMMERCE

abstract: trust is one of the main determinants of the success of e%retailers and much research has dealt with website features triggering consumer trust to them. Another stream of research focuses on the psychological antecedents to online trust; i.e., what ''happens'' in the consumer's mind before or while a person decides to trust an e%retailer? So far, each effort has focused on only a few selected aspects of this trust formation process. Only few studies have attempted to identify major personality based psychological antecedents of trust. Our work identified the relative importance of the personality based psychological antecedents of trust in technology adoption and e%re tailing. We develop a model that incorporates the Big Five personality traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness and Openness to experience), the most widely accepted framework for personality research in psychology, as well as determinants of trust in our model. We empirically test our model using a survey%based approach sampling 140 university students . . Possible limitations of research are also discussed.


title: Deep learning for financial time series forecasting in A-Trader system

abstract: The paper presents aspects related to developing methods for financial time series forecasting using deep learning in relation to multi-agent stock trading system, called A-Trader. On the basis of this model, an investment strategies in A-Trader system can be build. The first part of the paper briefly discusses a problem of financial time series on FOREX market. Classical neural networks and deep learning models are outlined, their performances are analyzed. The final part presents deployment and evaluation of a deep learning model implemented using H20 library as an agent of A-Trader system.


title: Distributed agent-based air traffic flow management

abstract: Air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. The FAA estimates that in 2005 alone, there were over 322,000 hours of delays at a cost to the industry in excess of three billion dollars. Finding reliable and adaptive solutions to the flow management problem is of paramount importance if the Next Generation Air Transportation Systems are to achieve the stated goal of accommodating three times the current traffic volume. This problem is particularly complex as it requires the integration and/or coordination of many factors including: new data (e.g., changing weather info), potentially conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and very heavy traffic volume (e.g., over 40,000 flights over the US airspace). In this paper we use FACET -- an air traffic flow simulator developed at NASA and used extensively by the FAA and industry -- to test a multi-agent algorithm for traffic flow management. An agent is associated with a fix (a specific location in 2D space) and its action consists of setting the separation required among the airplanes going though that fix. Agents use reinforcement learning to set this separation and their actions speed up or slow down traffic to manage congestion. Our FACET based results show that agents receiving personalized rewards reduce congestion by up to 45% over agents receiving a global reward and by up to 67% over a current industry approach (Monte Carlo estimation).


title: Secure Multi-Authority Data Access Control Scheme in Cloud Storage System Based on Attribute-Based Signcryption

abstract: Nowadays, secure data access control has become one of the major concerns in a cloud storage system. As a logical combination of attribute-based encryption and attribute-based signature, attribute-based signcryption (ABSC) can provide confidentiality and an anonymous authentication for sensitive data and is more efficient than traditional “encrypt-then-sign” or “sign-then-encrypt” strategies. Thus, ABSC is suitable for fine-grained access control in a semi-trusted cloud environment and is gaining more and more attention in recent years. However, in many previous ABSC schemes, user’s sensitive attributes can be disclosed to the authority, and only a single authority that is responsible for attribute management and key generation exists in the system. In this paper, we propose PMDAC-ABSC, a novel privacy-preserving data access control scheme based on Ciphertext-Policy ABSC, to provide a fine-grained control measure and attribute privacy protection simultaneously in a multi-authority cloud storage system. The attributes of both the signcryptor and the designcryptor can be protected to be known by the authorities and cloud server. Furthermore, the decryption overhead for user is significantly reduced by outsourcing the undesirable bilinear pairing operations to the cloud server without degrading the attribute privacy. The proposed scheme is proven to be secure in the standard model and has the ability to provide confidentiality, unforgeability, anonymous authentication, and public verifiability. The security analysis, asymptotic complexity comparison, and implementation results indicate that our construction can balance the security goals with practical efficiency in computation.


title: 6lo Technology for Smart Cities Development: Security Case Study

abstract: Excessive demand and the urgent need for the development of smart cities, evoke the need to provide the capacity for more low-resource devices to communicate and collaborate at a distance, in order to make the concept of the internet more real and practical. For this, IETF "IPv6 over Networks of Resource-constrained Nodes" (6lo) workgroup works on equipment of all resource constrained devices by IPv6 protocol to integrate the internet. From the security point of view, this integration is not without risks, the Internet carries many dangers and current security mechanisms are very greedy for such devices. This paper analyses potential security threats in 6lo as a particular case of mobile ad hoc networks, and provides some countermeasures ideas, in particular, the two principal security defense lines: the cryptography as a first line and the intrusion detection system as the second line. General Terms Internet, IPv6, Cryptography, IDS, Security.


title: Thin Images Reflected in the Water: Narcissism and Girls’ Vulnerability to the Thin-Ideal

abstract: The purpose of this research is to test how adolescent girls’ narcissistic traits—characterized by a need to impress others and avoid ego-threat—influence acute adverse effects of thin-ideal exposure. Participants (11–15 years; total N = 366; all female) reported their narcissistic traits. Next, in two experiments, they viewed images of either very thin or average-sized models, reported their wishful identification with the models (Experiment 2), and tasted high-calorie foods in an alleged taste test (both experiments). Narcissism kept girls from wishfully identifying with thin models, which is consistent with the view that narcissistic girls are prone to disengage from thin-ideal exposure. Moreover, narcissism protected vulnerable girls (those who experience low weight-esteem) from inhibiting their food intake, and led other girls (those who consider their appearance relatively unimportant) to increase their food intake. These effects did not generalize to conceptually related traits of self-esteem and perfectionism, and were not found for a low-calorie foods outcome, attesting to the specificity of findings. These experiments demonstrate the importance of narcissism at reducing girls’ thin-ideal vulnerability. Girls high in narcissism disengage self-protectively from threats to their self-image, a strategy that renders at least subsets of them less vulnerable to the thin-ideal.


title: Embedded Textual Content for Document Image Classification with Convolutional Neural Networks

abstract: In this paper we introduce a novel document image classification method based on combined visual and textual information. The proposed algorithm's pipeline is inspired to the ones of other recent state-of-the-art methods which perform document image classification using Convolutional Neural Networks. The main addition of our work is the introduction of a preprocessing step embedding additional textual information into the processed document images. To do so we combine Optical Character Recognition and Natural Language Processing algorithms to extract and manipulate relevant text concepts from document images. Such textual information is then visually embedded within each document image to improve the classification results of a Convolutional Neural Network. Our experiments prove that the overall document classification accuracy of a Convolutional Neural Network trained using these text-augmented document images is considerably higher than the one achieved by a similar model trained solely on classic document images, especially when different classes of documents share similar visual characteristics.


title: A high power factor symmetrical switched-mode power supply

abstract: This paper proposes a pre-regulator boost converter applied to a dc/dc converter in order to provide power factor correction. The combination of both stages results in a symmetrical switched power supply, which is composed of two symmetrical stages that operate at 100 kHz, as the individual output voltages are equal to +200 V/sub dc/ and -200 V/sub dc/, the total output voltage is 400 Vdc and the total output power is 500 W. The power factor correction IC UC3854 is employed in the control strategy of the boost stage.


title: A sticky HDP-HMM with application to speaker diarization

abstract: We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006) 1566--1581]. Although the basic HDP-HMM tends to over-segment the audio data---creating redundant states and rapidly switching among them---we describe an augmented HDP-HMM that provides effective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results.


title: Health is Wealth: Methods to Improve Attendance in a Lifestyle Intervention for a Largely Immigrant Filipino-American Sample

abstract: Diabetes prevention requires lifestyle changes, and traditional educational programs for lifestyle changes have had low attendance rates in ethnic populations. This article describes the development and implementation of an educational program, emphasizing retention strategies, cultural tailoring and community participation. Community-based participatory research approaches were used to adapt and test the feasibility of a culturally tailored lifestyle intervention (named Health is Wealth) for Filipino-American adults at risk for diabetes (n = 40) in order to increase program attendance. A unique feature of this program was the flexibility of scheduling the eight classes, and inclusion of activities, foods and proverbs consistent with Filipino culture. We found that with this approach, overall program attendance for the experimental and wait-listed control groups was 88% and participant satisfaction was high with 93% very satisfied. Flexible scheduling, a bilingual facilitator for the classes, and the community-academic partnership contributed to the high attendance for this lifestyle intervention.


title: Chapter 15 EARTH SCIENCE APPLICATIONS OF SENSOR DATA

abstract: Advances in earth observation technologies have led to the acquisition of vast volumes of accurate, timely and reliable environmental data which encompass a multitude of information about the land, ocean and atmosphere of the planet. Earth science sensor datasets capture multiple facets of information about natural processes and human activities that shape the physical landscape and environmental quality of our planet, and thus, offer an opportunity to monitor and understand the diverse phenomena affecting earth’s complex system. The monitoring, analysis and understanding of these rich sensor datasets is thus of prime importance for the efficient planning and management of critical resources, since the societal costs of mitigation or adaptation decisions for natural or human-induced adverse events are significant. Hence, a thorough understanding of earth science sensor datasets has a direct impact on a range of societally relevant issues. Moreover, earth science sensor datasets possess unique domain-specific properties that distinguish them from sensor datasets used in other domains, and thus demand the need for novel tools and techniques to be developed for their analysis, adhering to their characteristic issues and challenges.


title: Adaptive Road Crack Detection System by Pavement Classification

abstract: This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.


The Malevolent Side of Human Nature : A Meta-Analysis and Critical Review of the Literature on the Dark Triad ( Narcissism , Machiavellianism , and Psychopathy )


title: Limiting dilution assays. Experimental design and statistical analysis.

abstract: Two issues in limiting dilution analysis are considered. The first concerns the experimental design: a mathematical algorithm has been developed which calculates the number of replicate culture groups, and the (mean) number of cells per well to be used on the basis of the experimenter's a priori information about the unknown frequency. The procedure guarantees useful data if the a priori interval estimate of the frequency to be determined is correct and the cells are willing to grow. The second issue concerns the statistical method to be used for estimation of the unknown frequency. Several methods (minimum chi-square, maximum likelihood and the jackknife version of the maximum likelihood method) have been evaluated with artificial data from extensive Monte Carlo experiments. All three methods were useful in the statistical analysis of data. As a result of these experiments and theoretical considerations the jackknife version of the maximum likelihood estimation procedure is proposed as the statistical procedure of choice. The next best method is the maximum likelihood procedure.


A Fast Algorithm for the Minimum Covariance Determinant Estimator


title: CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach

abstract: With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card by accessing the modified device driver. Then a linear transformation is applied to extract the calibrated phase information, which we prove to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ the weights of the deep network to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a subnetwork between two consecutive layers forms a restricted Boltzmann machine. In the online stage, we use a probabilistic method based on the radial basis function for online location estimation. The proposed PhaseFi scheme is implemented and validated with extensive experiments in two representation indoor environments. It is shown to outperform three benchmark schemes based on CSI or received signal strength in both scenarios.


title: Batteries for Efficient Energy Extraction from a Water Salinity Difference

abstract: The salinity difference between seawater and river water is a renewable source of enormous entropic energy, but extracting it efficiently as a form of useful energy remains a challenge. Here we demonstrate a device called "mixing entropy battery", which can extract and store it as useful electrochemical energy. The battery, containing a Na(2-x)Mn(5)O(10) nanorod electrode, was shown to extract energy from real seawater and river water and can be applied to a variety of salt waters. We demonstrated energy extraction efficiencies of up to 74%. Considering the flow rate of river water into oceans as the limiting factor, the renewable energy production could potentially reach 2 TW, or ∼13% of the current world energy consumption. The mixing entropy battery is simple to fabricate and could contribute significantly to renewable energy in the future.


title: Roadmap methods vs. cell decomposition in robot motion planning

abstract: The task of planning trajectories plays an important role in transportation, robotics, information systems (sending messages), etc. In robot motion planning, the robot should pass around obstacles from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. Research on path planning has yielded many fundamentally different approaches to the solution of this problem that can be classified as roadmap methods (visibility graph method, Voronoi diagram) and methods based on cell decomposition. Assuming movements only in a restricted number of directions (eight directional or horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by decomposition methods using heuristic techniques. We present drawbacks of this approach (combinatorial explosion, limited granularity and generating infeasible solutions). Then, using the Voronoi diagrams, we need only polynomial time for finding a solution and, choosing a Euclidean or rectilinear metric, it can be adapted to tasks with general or directional-constrained movements.


title: The Pathologies of Big Data

abstract: Scale up your datasets enough and all your apps will come undone. What are the typical problems and where do the bottlenecks generally surface?


title: QoS-aware virtual SDN network planning

abstract: Software Defined Networking (SDN) technologies provide applications opportunities to manipulate underlying network flows and topologies via network controllers during runtime. In cloud environments, networked virtual machines can be enhanced by SDN by providing applications with controllable infrastructures to meet system-level quality requirements; however, customizing a suitable network topology with optimally placed controller(s) for given quality requirements and workload characteristics is often not an easy task. We call such problem virtual SDN network planning problem. In this paper, a Topology-Controller planner (TCPlanner) is proposed for customizing the network topology and placing the controllers. Experiments with different scales of network show that our approach can effectively plan virtual SDN networks to meet the various QoS requirements and reduce costs.


title: Design, Construction and Testing of a Voltage-based Maximum Power Point Tracker (VMPPT) for Small Satellite Power Supply

abstract: It is shown that at maximum power, the Photovoltaic (PV) voltage varies nonlinearily with temperature and isolation level, but is directly proportional to the PV cell open circuit voltage. The proportionality voltage-factor is fixed for a given PV generator regardless of temperature, isolation and panel configuration, but depends on cell material and manufacturing. This remarkable property is used to achieve temperature and insolation independent maximum power point tracking of satellite’s solar cells with a simple and reliable technique. The open circuit voltage is continuously measured by a microcontroller and is used to estimate the maximum power operating point of the system.


title: Improving Pronunciation Dictionary Coverage Of Names By Modelling Spelling Variation

abstract: This paper describes an attempt to improve the coverage of an existing name pronunciation dictionary by modelling variation in spelling. This is done by the derivation of string rewrite rules which operate on out-of-vocabulary words to map them to in-vocabulary words. These string rewrite rules are derived automatically, and are “pronunciation-neutral” in the sense that the mappings they perform on the existing dictionary do not result in a change of pronunciation. The approach is data-driven, and can be used online to make predictions for some (not all) OOV words, or offline to add significant numbers of new pronunciations to existing dictionaries. Offline the approach has been used to increase dictionary coverage for four domain-based dictionaries for forenames, surnames, streetnames and placenames. For surnames, a model trained on a 23,000-entry dictionary was subsequently able to add 5,000 new entries, improving both type coverage and token coverage of the dictionaries by about 1%. An informal evaluation suggests that the suggested pronunciations are good in 80% of cases.


title: Nucleocapsid formation and RNA synthesis of Marburg virus is dependent on two coiled coil motifs in the nucleoprotein

abstract: The nucleoprotein (NP) of Marburg virus (MARV) is responsible for the encapsidation of viral genomic RNA and the formation of the helical nucleocapsid precursors that accumulate in intracellular inclusions in infected cells. To form the large helical MARV nucleocapsid, NP needs to interact with itself and the viral proteins VP30, VP35 and L, which are also part of the MARV nucleocapsid. In the present study, a conserved coiled coil motif in the central part of MARV NP was shown to be an important element for the interactions of NP with itself and VP35, the viral polymerase cofactor. Additionally, the coiled coil motif was essential for the formation of NP-induced intracellular inclusions and for the function of NP in the process of transcription and replication of viral RNA in a minigenome system. Transfer of the coiled coil motif to a reporter protein was sufficient to mediate interaction of the constructed fusion protein with the N-terminus of NP. The coiled coil motif is bipartite, constituted by two coiled coils which are separated by a flexible linker.


title: Beyond Slideware: How a Free-form Presentation Medium Stimulates Free-form Thinking in the Classroom

abstract: We investigate how presentation in a free-form medium stimulates free-form thinking and discussion in the classroom. Most classroom presentations utilize slideware (e.g. PowerPoint). Yet, slides add intrusive segregations that obstruct the flow of information. In contrast, in a free-form medium of presentation, content is not separated into rigid slide compartments. Instead, it is visually arranged and transformed in a continuous space. We develop a case study that investigates student experiences authoring, presenting, viewing, and discussing free-form presentations in a graduate seminar class. We analyze interviews, present a sampling of student presentations, and develop findings: free-form presentation stimulates free-form thinking, spontaneous discussion, and emergent ideation.


title: DOOM Level Generation Using Generative Adversarial Networks

abstract: We applied Generative Adversarial Networks (GANs) to learn a model of DOOM levels from human-designed content. Initially, we analysed the levels and extracted several topological features. Then, for each level, we extracted a set of images identifying the occupied area, the height map, the walls, and the position of game objects. We trained two GANs: one using plain level images, one using both the images and some of the features extracted during the preliminary analysis. We used the two networks to generate new levels and compared the results to assess whether the network trained using also the topological features could generate levels more similar to human-designed ones. Our results show that GANs can capture intrinsic structure of DOOM levels and appears to be a promising approach to level generation in first person shooter games.


title: Racial bias in mock juror decision-making: a meta-analytic review of defendant treatment.

abstract: Common wisdom seems to suggest that racial bias, defined as disparate treatment of minority defendants, exists in jury decision-making, with Black defendants being treated more harshly by jurors than White defendants. The empirical research, however, is inconsistent—some studies show racial bias while others do not. Two previous meta-analyses have found conflicting results regarding the existence of racial bias in juror decision-making (Mazzella & Feingold, 1994, Journal of Applied Social Psychology, 24, 1315–1344; Sweeney & Haney, 1992, Behavioral Sciences and the Law, 10, 179–195). This research takes a meta-analytic approach to further investigate the inconsistencies within the empirical literature on racial bias in juror decision-making by defining racial bias as disparate treatment of racial out-groups (rather than focusing upon the minority group alone). Our results suggest that a small, yet significant, effect of racial bias in decision-making is present across studies, but that the effect becomes more pronounced when certain moderators are considered. The state of the research will be discussed in light of these findings.


title: Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

abstract: Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.


title: Parallelizing Linear Recurrent Neural Nets Over Sequence Length

abstract: Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences even with small minibatch size. We develop a parallel linear recurrence CUDA kernel and show that it can be applied to immediately speed up training and inference of several state of the art RNN architectures by up to 9x. We abstract recent work on linear RNNs into a new framework of linear surrogate RNNs and develop a linear surrogate model for the long short-term memory unit, the GILR-LSTM, that utilizes parallel linear recurrence. We extend sequence learning to new extremely long sequence regimes that were previously out of reach by successfully training a GILR-LSTM on a synthetic sequence classification task with a one million timestep dependency.


title: Polls Apart: South African Women and the 1999 General Election

abstract: More than one million more women than men registered to vote in South Africa’s second democratic election in 1999. The election saw 29.8 percent of seats in the national legislature (or 119 out of 400) going to women (up from 27 percent in the previous Parliament and in stark contrast to the less than 3 percent of women parliamentarians prior to 1994). Eight ministers and eight deputy ministers in South Africa’s new Parliament are women. The new cabinet has women in charge of nontraditional portfolios, such as foreign affairs, public service, mineral and energy affairs, communications and land affairs, in addition to health and housing. This doubles the number of female members compared to the previous executive.


Cross-Domain Authorship Attribution Based on Compression: Notebook for PAN at CLEF 2018


title: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

abstract: The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept of end-to-end learning of optical flow and make it work really well. The large improvements in quality and speed are caused by three major contributions: first, we focus on the training data and show that the schedule of presenting data during training is very important. Second, we develop a stacked architecture that includes warping of the second image with intermediate optical flow. Third, we elaborate on small displacements by introducing a subnetwork specializing on small motions. FlowNet 2.0 is only marginally slower than the original FlowNet but decreases the estimation error by more than 50%. It performs on par with state-of-the-art methods, while running at interactive frame rates. Moreover, we present faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet.


title: A Comparative Study of Segmentation Techniques used for MR Brain Images.

abstract: In this paper we present a comparative study of MR brain image segmentation techniques. The aim of this study is to assess the robustness and accuracy of three most commonly used unsupervised segmentation methods k-means (KM), FCM and EM. KM is a well known hard segmentation method for quicker processing whereas FCM and EM are popularly used soft segmentation methods particularly for brain tissue models. Most of the neuroimage analyzing software employ one of these techniques as a preprocessing tool to enrich their subsequent processes. Based on the parameters similarity index and processing time, the performance of the methods on brain tissue segmentation are measured and compared. The similarity index calculation was done by using the 20 normal volumes and their gold standards available from IBSR. The rest of parameters are tested on the normal and abnormal brain volumes collected from scan centers and brain image pools. For normal and noiseless volumes all the three methods produced comparable results. FCM has an excellent performance over the anisotropic nature of volumes that are affected by partial volume effects (PVE) as well as diseased sets and EM was suitable for noisy sets, particularly volumes affected by intensity non-uniformity (INU) artifacts. KM, FCM and EM are rated in the given order in terms of processing time. This study is useful to choose a suitable method for computerizing brain diagnostic system.


title: Random Balance : Ensembles of variable priors classifiers for imbalanced data

abstract: Proportions of the classes for each ensemble member are chosen randomly.Member training data: sub-sample and over-sample through SMOTE.RB-Boost combines Random Balance with AdaBoost.M2.Experiments with 86 data sets demonstrate the advantage of Random Balance. In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Imbalanced data sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampled from the training set and augmented by artificial instances obtained using SMOTE. The novelty in the approach is that the proportions of the classes for each ensemble member are chosen randomly. The intuition behind the method is that the proposed diversity heuristic will ensure that the ensemble contains classifiers that are specialized for different operating points on the ROC space, thereby leading to larger AUC compared to other ensembles of classifiers. Experiments have been carried out to test the Random Balance approach by itself, and also in combination with standard ensemble methods. As a result, we propose a new ensemble creation method called RB-Boost which combines Random Balance with AdaBoost.M2. This combination involves enforcing random class proportions in addition to instance re-weighting. Experiments with 86 imbalanced data sets from two well known repositories demonstrate the advantage of the Random Balance approach.


title: Visual light landmarks for mobile devices

abstract: The omnipresence of indoor lighting makes it an ideal vehicle for pervasive communication with mobile devices. In this paper, we present a communication scheme that enables interior ambient LED lighting systems to send data to mobile devices using either cameras or light sensors. By exploiting rolling shutter camera sensors that are common on tablets, laptops and smartphones, it is possible to detect high-frequency changes in light intensity reflected off of surfaces and in direct line-of-sight of the camera. We present a demodulation approach that allows smartphones to accurately detect frequencies as high as 8kHz with 0.2kHz channel separation. In order to avoid humanly perceivable flicker in the lighting, our system operates at frequencies above 2kHz and compensates for the non-ideal frequency response of standard LED drivers by adjusting the light's duty-cycle. By modulating the PWM signal commonly used to drive LED lighting systems, we are able to encode data that can be used as localization landmarks. We show through experiments how a binary frequency shift keying modulation scheme can be used to transmit data at 1.25 bytes per second (fast enough to send an ID code) from up to 29 unique light sources simultaneously in a single collision domain. We also show how tags can demodulate the same signals using a light sensor instead of a camera for low-power applications.


title: Global Textual Relation Embedding for Relational Understanding

abstract: Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at this https URL


title: Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net

abstract: Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of two "Auto-Zoom Net" (AZNs), each employing fully convolutional networks that perform two tasks: (1) predict the locations and scales of object instances (the first AZN) or their parts (the second AZN); (2) estimate the part scores for predicted object instance or part regions. Our model can adaptively "zoom" (resize) predicted image regions into their proper scales to refine the parsing. ::: We conduct extensive experiments over the PASCAL part datasets on humans, horses, and cows. For humans, our approach significantly outperforms the state-of-the-arts by 5% mIOU and is especially better at segmenting small instances and small parts. We obtain similar improvements for parsing cows and horses over alternative methods. In summary, our strategy of first zooming into objects and then zooming into parts is very effective. It also enables us to process different regions of the image at different scales adaptively so that, for example, we do not need to waste computational resources scaling the entire image.


title: A Social Robot to Mitigate Stress, Anxiety, and Pain in Hospital Pediatric Care

abstract: Children and their parents may undergo challenging experiences when admitted for inpatient care at pediatric hospitals. While most hospitals make efforts to provide socio-emotional support for patients and their families during care, gaps still exist between human resource supply and demand. The Huggable project aims to close this gap by creating a social robot able to mitigate stress, anxiety, and pain in pediatric patients by engaging them in playful interactive activities. In this paper, we introduce a larger experimental design to compare the effects of the Huggable robot to a virtual character on a screen and a plush teddy bear, and provide initial qualitative analyses of patients' and parents' behaviors during intervention sessions collected thus far. We demonstrate preliminarily that children are more eager to emotionally connect with and be physically activated by a robot than a virtual character, illustrating the potential of social robots to provide socio-emotional support during inpatient pediatric care.


title: Assessing cellulase performance on pretreated lignocellulosic biomass using saccharification and fermentation-based protocols.

abstract: Cellulase enzyme is a key cost component in the production of fuels and chemicals from lignocellulosic biomass. Cellulolytic ability of the enzyme preparation is often measured by activity assays using model substrates such as filter paper. Using lignocellulosic biomass as the substrate to assess enzyme performance has the potential of being more process relevant. We describe two procedures that use washed pretreated cellulosic material to measure the efficacy of cellulase enzymes. First, a saccharification assay that measures glucose yield as a function of the amount of cellulase used in the process. And second, the simultaneous saccharification and fermentation (SSF) assay measures cellulase performance by the amount of ethanol produced from enzymatic hydrolysis of the cellulosic material. You can use both assays to screen cellulases under a variety of substrate types, loadings, and process conditions.


title: Towards a Unified Decentralized Swarm Management and Maintenance Coordination Based on MAVLink

abstract: Small and flexible micro-aerial vehicles (MAVs) are capable of flying autonomously following the instructions of an autopilot received from a controlling ground entity. Their utilization includes commercial applications and reconnaissance missions. Moreover, MAVs can be combined into swarms to be effectively used in missions that would be impossible or too time-consuming for single entities. This paper proposes a comprehensive set of coordination commands necessary for the communication between swarm MAVs and ground entities. Moreover, an implementation as an extension of the MAVLink micro air vehicle message library is presented.


title: INbreast: toward a full-field digital mammographic database.

abstract: Rationale and Objectives Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Materials and Methods Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. Joao [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. Results The new database—INbreast—has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. Conclusion The strengths of the actually presented database—INbreast—relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.